From 3e8cd5bc9a254709abc71e1a4ff4cf9ad505e394 Mon Sep 17 00:00:00 2001 From: tlapusan Date: Tue, 22 Sep 2020 09:53:46 +0300 Subject: [PATCH 01/16] Start work for lightgbm --- dtreeviz/models/lightgbm_decision_tree.py | 137 + dtreeviz/models/spark_decision_tree.py | 4 +- .../dtreeviz_lightgbm_visualisations.ipynb | 1716 ++ .../dtreeviz_sklearn_visualisations.ipynb | 1703 +- notebooks/dtreeviz_spark_visualisations.ipynb | 1557 +- .../dtreeviz_xgboost_visualisations.ipynb | 21330 ++++++++++++---- 6 files changed, 19547 insertions(+), 6900 deletions(-) create mode 100644 dtreeviz/models/lightgbm_decision_tree.py create mode 100644 notebooks/dtreeviz_lightgbm_visualisations.ipynb diff --git a/dtreeviz/models/lightgbm_decision_tree.py b/dtreeviz/models/lightgbm_decision_tree.py new file mode 100644 index 00000000..4f6103c6 --- /dev/null +++ b/dtreeviz/models/lightgbm_decision_tree.py @@ -0,0 +1,137 @@ +from typing import List, Mapping + +import numpy as np +import pandas as pd +from lightgbm.basic import Booster + +from dtreeviz.models.shadow_decision_tree import ShadowDecTree + + +class ShadowLightGBMTree(ShadowDecTree): + + def __init__(self, + tree_model: Booster, + tree_index: int, + x_data: (pd.DataFrame, np.ndarray), + y_data: (pd.Series, np.ndarray), + feature_names: List[str] = None, + target_name: str = None, + class_names: (List[str], Mapping[int, str]) = None): + self.tree_model = tree_model + self.tree_index = tree_index + self.tree_nodes, self.children_left, self.children_right = self._get_nodes_info() + + super().__init__(tree_model, x_data, y_data, feature_names, target_name, class_names) + + def _get_nodes_info(self): + tree_nodes = {} + children_left = {} + children_right = {} + node_index = 0 + + def _walk_tree(node, node_id): + nonlocal node_index + tree_nodes[node_id] = node + if node.get("split_index") is None: + children_left[node_id] = -1 + children_right[node_id] = -1 + return + + node_index += 1 + children_left[node_id] = node_index + _walk_tree(node.get("left_child"), node_index) + + node_index += 1 + children_right[node_id] = node_index + _walk_tree(node.get("right_child"), node_index) + + def _convert_dict_to_list(my_dict): + my_list = [-1] * len(my_dict) + for key, value in my_dict.items(): + my_list[key] = value + return my_list + + tree_dump = self.tree_model.dump_model()["tree_info"][self.tree_index] + _walk_tree(tree_dump["tree_structure"], node_index) + + for node in tree_nodes.values(): + node.pop("left_child", None) + node.pop("right_child", None) + + children_left_list = _convert_dict_to_list(children_left) + children_right_list = _convert_dict_to_list(children_right) + tree_node_list = _convert_dict_to_list(tree_nodes) + + return tree_node_list, children_left_list, children_right_list + + def is_fit(self) -> bool: + return True + + def is_classifier(self) -> bool: + pass + + def get_class_weights(self): + pass + + def get_thresholds(self) -> np.ndarray: + pass + + def get_features(self) -> np.ndarray: + pass + + def criterion(self) -> str: + pass + + def get_class_weight(self): + pass + + def nclasses(self) -> int: + pass + + def classes(self) -> np.ndarray: + pass + + def get_node_samples(self): + pass + + def get_node_nsamples(self, id): + pass + + def get_children_left(self) -> np.ndarray: + return np.array(self.children_left, dtype=int) + + def get_children_right(self) -> np.ndarray: + return np.array(self.children_right, dtype=int) + + def get_node_split(self, id) -> (int, float): + pass + + def get_node_feature(self, id) -> int: + pass + + def get_node_nsamples_by_class(self, id): + pass + + def get_prediction(self, id): + pass + + def nnodes(self) -> int: + pass + + def get_node_criterion(self, id): + pass + + def get_feature_path_importance(self, node_list): + pass + + def get_max_depth(self) -> int: + pass + + def get_score(self) -> float: + pass + + def get_min_samples_leaf(self) -> (int, float): + pass + + def shouldGoLeftAtSplit(self, id, x): + pass diff --git a/dtreeviz/models/spark_decision_tree.py b/dtreeviz/models/spark_decision_tree.py index aea75bcb..840b3b7b 100644 --- a/dtreeviz/models/spark_decision_tree.py +++ b/dtreeviz/models/spark_decision_tree.py @@ -1,11 +1,11 @@ -from abc import ABC from collections import defaultdict from typing import List, Mapping import numpy as np +from pyspark.ml.classification import DecisionTreeClassificationModel +from pyspark.ml.regression import DecisionTreeRegressionModel from dtreeviz.models.shadow_decision_tree import ShadowDecTree -from pyspark.ml.classification import DecisionTreeClassificationModel, DecisionTreeRegressionModel class ShadowSparkTree(ShadowDecTree): diff --git a/notebooks/dtreeviz_lightgbm_visualisations.ipynb b/notebooks/dtreeviz_lightgbm_visualisations.ipynb new file mode 100644 index 00000000..ad198815 --- /dev/null +++ b/notebooks/dtreeviz_lightgbm_visualisations.ipynb @@ -0,0 +1,1716 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Imports" + ] + }, + { + "cell_type": "code", + "execution_count": 159, + "metadata": {}, + "outputs": [], + "source": [ + "%load_ext autoreload\n", + "%autoreload 2\n", + "\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "code", + "execution_count": 178, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "import lightgbm as lgb\n", + "from sklearn.model_selection import train_test_split\n", + "\n", + "import matplotlib.pyplot as plt\n", + "\n", + "\n", + "import sys\n", + "import os\n", + "# add library module to PYTHONPATH\n", + "sys.path.append(f\"{os.getcwd()}/../\")\n", + "\n", + "from dtreeviz.models.lightgbm_decision_tree import ShadowLightGBMTree\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Load and prepare dataset" + ] + }, + { + "cell_type": "code", + "execution_count": 162, + "metadata": {}, + "outputs": [], + "source": [ + "dataset = pd.read_csv(\"../data/titanic/titanic.csv\")" + ] + }, + { + "cell_type": "code", + "execution_count": 163, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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3411Futrelle, Mrs. Jacques Heath (Lily May Peel)female35.01011380353.1000C123S
4503Allen, Mr. William Henrymale35.0003734508.0500NaNS
.......................................
88688702Montvila, Rev. Juozasmale27.00021153613.0000NaNS
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88888903Johnston, Miss. Catherine Helen \"Carrie\"femaleNaN12W./C. 660723.4500NaNS
88989011Behr, Mr. Karl Howellmale26.00011136930.0000C148C
89089103Dooley, Mr. Patrickmale32.0003703767.7500NaNQ
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" + ], + "text/plain": [ + " PassengerId Survived Pclass \\\n", + "0 1 0 3 \n", + "1 2 1 1 \n", + "2 3 1 3 \n", + "3 4 1 1 \n", + "4 5 0 3 \n", + ".. ... ... ... \n", + "886 887 0 2 \n", + "887 888 1 1 \n", + "888 889 0 3 \n", + "889 890 1 1 \n", + "890 891 0 3 \n", + "\n", + " Name Sex Age SibSp \\\n", + "0 Braund, Mr. Owen Harris male 22.0 1 \n", + "1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.0 1 \n", + "2 Heikkinen, Miss. Laina female 26.0 0 \n", + "3 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.0 1 \n", + "4 Allen, Mr. William Henry male 35.0 0 \n", + ".. ... ... ... ... \n", + "886 Montvila, Rev. Juozas male 27.0 0 \n", + "887 Graham, Miss. Margaret Edith female 19.0 0 \n", + "888 Johnston, Miss. Catherine Helen \"Carrie\" female NaN 1 \n", + "889 Behr, Mr. Karl Howell male 26.0 0 \n", + "890 Dooley, Mr. Patrick male 32.0 0 \n", + "\n", + " Parch Ticket Fare Cabin Embarked \n", + "0 0 A/5 21171 7.2500 NaN S \n", + "1 0 PC 17599 71.2833 C85 C \n", + "2 0 STON/O2. 3101282 7.9250 NaN S \n", + "3 0 113803 53.1000 C123 S \n", + "4 0 373450 8.0500 NaN S \n", + ".. ... ... ... ... ... \n", + "886 0 211536 13.0000 NaN S \n", + "887 0 112053 30.0000 B42 S \n", + "888 2 W./C. 6607 23.4500 NaN S \n", + "889 0 111369 30.0000 C148 C \n", + "890 0 370376 7.7500 NaN Q \n", + "\n", + "[891 rows x 12 columns]" + ] + }, + "execution_count": 163, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dataset" + ] + }, + { + "cell_type": "code", + "execution_count": 164, + "metadata": {}, + "outputs": [], + "source": [ + "dataset.fillna({\"Age\":dataset.Age.mean()}, inplace=True)\n", + "# Encode categorical variables\n", + "dataset[\"Sex_label\"] = dataset.Sex.astype(\"category\").cat.codes\n", + "dataset[\"Cabin_label\"] = dataset.Cabin.astype(\"category\").cat.codes\n", + "dataset[\"Embarked_label\"] = dataset.Embarked.astype(\"category\").cat.codes" + ] + }, + { + "cell_type": "code", + "execution_count": 165, + "metadata": {}, + "outputs": [], + "source": [ + "features = [\"Pclass\", \"Age\", \"Fare\", \"Sex_label\", \"Cabin_label\", \"Embarked_label\"]\n", + "target = \"Survived\"" + ] + }, + { + "cell_type": "code", + "execution_count": 166, + "metadata": {}, + "outputs": [], + "source": [ + "X_train, X_test, y_train, y_test = train_test_split(dataset[features], dataset[target], test_size=0.2, random_state=42)" + ] + }, + { + "cell_type": "code", + "execution_count": 167, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " Pclass Age Fare Sex_label Cabin_label Embarked_label\n", + "331 1 45.500000 28.5000 1 56 2\n", + "733 2 23.000000 13.0000 1 -1 2\n", + "382 3 32.000000 7.9250 1 -1 2\n", + "704 3 26.000000 7.8542 1 -1 2\n", + "813 3 6.000000 31.2750 0 -1 2\n", + ".. ... ... ... ... ... ...\n", + "106 3 21.000000 7.6500 0 -1 2\n", + "270 1 29.699118 31.0000 1 -1 2\n", + "860 3 41.000000 14.1083 1 -1 2\n", + "435 1 14.000000 120.0000 0 47 2\n", + "102 1 21.000000 77.2875 1 98 2\n", + "\n", + "[712 rows x 6 columns]" + ] + }, + "execution_count": 167, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "X_train" + ] + }, + { + "cell_type": "code", + "execution_count": 168, + "metadata": {}, + "outputs": [], + "source": [ + "train_data = lgb.Dataset(data=X_train, label=y_train)\n", + "valid_data = lgb.Dataset(data=X_test, label=y_test)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Train the model" + ] + }, + { + "cell_type": "code", + "execution_count": 170, + "metadata": {}, + "outputs": [], + "source": [ + "lgbm_params = {\n", + " 'boosting': 'dart', # dart (drop out trees) often performs better\n", + " 'application': 'binary', # Binary classification\n", + " 'learning_rate': 0.05, # Learning rate, controls size of a gradient descent step\n", + " 'min_data_in_leaf': 20, # Data set is quite small so reduce this a bit\n", + " 'feature_fraction': 0.7, # Proportion of features in each boost, controls overfitting\n", + " 'num_leaves': 41, # Controls size of tree since LGBM uses leaf wise splits\n", + " 'metric': 'binary_error', # Area under ROC curve as the evaulation metric\n", + " 'drop_rate': 0.15,\n", + " 'max_depth':4,\n", + " \"seed\":1212\n", + " }" + ] + }, + { + "cell_type": "code", + "execution_count": 171, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[LightGBM] [Info] Number of positive: 268, number of negative: 444\n", + "[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000660 seconds.\n", + "You can set `force_row_wise=true` to remove the overhead.\n", + "And if memory is not enough, you can set `force_col_wise=true`.\n", + "[LightGBM] [Info] Total Bins 233\n", + "[LightGBM] [Info] Number of data points in the train set: 712, number of used features: 6\n", + "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.376404 -> initscore=-0.504838\n", + "[LightGBM] [Info] Start training from score -0.504838\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[1]\ttraining's binary_error: 0.376404\tvalid_1's binary_error: 0.413408\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[2]\ttraining's binary_error: 0.376404\tvalid_1's binary_error: 0.413408\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[3]\ttraining's binary_error: 0.376404\tvalid_1's binary_error: 0.413408\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[4]\ttraining's binary_error: 0.376404\tvalid_1's binary_error: 0.413408\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[5]\ttraining's binary_error: 0.376404\tvalid_1's binary_error: 0.413408\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[6]\ttraining's binary_error: 0.359551\tvalid_1's binary_error: 0.385475\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[7]\ttraining's binary_error: 0.330056\tvalid_1's binary_error: 0.351955\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[8]\ttraining's binary_error: 0.22191\tvalid_1's binary_error: 0.234637\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[9]\ttraining's binary_error: 0.223315\tvalid_1's binary_error: 0.234637\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[10]\ttraining's binary_error: 0.206461\tvalid_1's binary_error: 0.22905\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[11]\ttraining's binary_error: 0.210674\tvalid_1's binary_error: 0.234637\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[12]\ttraining's binary_error: 0.203652\tvalid_1's binary_error: 0.223464\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[13]\ttraining's binary_error: 0.206461\tvalid_1's binary_error: 0.223464\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[14]\ttraining's binary_error: 0.195225\tvalid_1's binary_error: 0.22905\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[15]\ttraining's binary_error: 0.188202\tvalid_1's binary_error: 0.22905\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[16]\ttraining's binary_error: 0.182584\tvalid_1's binary_error: 0.22905\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[17]\ttraining's binary_error: 0.182584\tvalid_1's binary_error: 0.234637\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[18]\ttraining's binary_error: 0.178371\tvalid_1's binary_error: 0.234637\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[19]\ttraining's binary_error: 0.183989\tvalid_1's binary_error: 0.240223\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[20]\ttraining's binary_error: 0.182584\tvalid_1's binary_error: 0.240223\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[21]\ttraining's binary_error: 0.186798\tvalid_1's binary_error: 0.234637\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[22]\ttraining's binary_error: 0.186798\tvalid_1's binary_error: 0.234637\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[23]\ttraining's binary_error: 0.176966\tvalid_1's binary_error: 0.223464\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[24]\ttraining's binary_error: 0.174157\tvalid_1's binary_error: 0.217877\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[25]\ttraining's binary_error: 0.167135\tvalid_1's binary_error: 0.223464\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[26]\ttraining's binary_error: 0.169944\tvalid_1's binary_error: 0.217877\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[27]\ttraining's binary_error: 0.169944\tvalid_1's binary_error: 0.206704\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[28]\ttraining's binary_error: 0.176966\tvalid_1's binary_error: 0.212291\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[29]\ttraining's binary_error: 0.175562\tvalid_1's binary_error: 0.212291\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[30]\ttraining's binary_error: 0.18118\tvalid_1's binary_error: 0.223464\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[31]\ttraining's binary_error: 0.172753\tvalid_1's binary_error: 0.206704\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[32]\ttraining's binary_error: 0.164326\tvalid_1's binary_error: 0.201117\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[33]\ttraining's binary_error: 0.171348\tvalid_1's binary_error: 0.217877\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[34]\ttraining's binary_error: 0.16573\tvalid_1's binary_error: 0.206704\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[35]\ttraining's binary_error: 0.164326\tvalid_1's binary_error: 0.189944\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[36]\ttraining's binary_error: 0.168539\tvalid_1's binary_error: 0.201117\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[37]\ttraining's binary_error: 0.171348\tvalid_1's binary_error: 0.201117\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[38]\ttraining's binary_error: 0.155899\tvalid_1's binary_error: 0.184358\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[39]\ttraining's binary_error: 0.155899\tvalid_1's binary_error: 0.178771\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[40]\ttraining's binary_error: 0.155899\tvalid_1's binary_error: 0.178771\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[41]\ttraining's binary_error: 0.158708\tvalid_1's binary_error: 0.173184\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[42]\ttraining's binary_error: 0.158708\tvalid_1's binary_error: 0.173184\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[43]\ttraining's binary_error: 0.151685\tvalid_1's binary_error: 0.178771\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[44]\ttraining's binary_error: 0.150281\tvalid_1's binary_error: 0.178771\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[45]\ttraining's binary_error: 0.150281\tvalid_1's binary_error: 0.173184\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[46]\ttraining's binary_error: 0.150281\tvalid_1's binary_error: 0.195531\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[47]\ttraining's binary_error: 0.154494\tvalid_1's binary_error: 0.195531\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[48]\ttraining's binary_error: 0.150281\tvalid_1's binary_error: 0.195531\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[49]\ttraining's binary_error: 0.146067\tvalid_1's binary_error: 0.195531\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[50]\ttraining's binary_error: 0.143258\tvalid_1's binary_error: 0.184358\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[51]\ttraining's binary_error: 0.143258\tvalid_1's binary_error: 0.189944\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[52]\ttraining's binary_error: 0.143258\tvalid_1's binary_error: 0.189944\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[53]\ttraining's binary_error: 0.143258\tvalid_1's binary_error: 0.178771\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[54]\ttraining's binary_error: 0.143258\tvalid_1's binary_error: 0.173184\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[55]\ttraining's binary_error: 0.143258\tvalid_1's binary_error: 0.173184\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[56]\ttraining's binary_error: 0.143258\tvalid_1's binary_error: 0.173184\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[57]\ttraining's binary_error: 0.143258\tvalid_1's binary_error: 0.173184\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[58]\ttraining's binary_error: 0.143258\tvalid_1's binary_error: 0.173184\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[59]\ttraining's binary_error: 0.143258\tvalid_1's binary_error: 0.173184\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[60]\ttraining's binary_error: 0.143258\tvalid_1's binary_error: 0.173184\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[61]\ttraining's binary_error: 0.143258\tvalid_1's binary_error: 0.184358\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[62]\ttraining's binary_error: 0.144663\tvalid_1's binary_error: 0.184358\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[63]\ttraining's binary_error: 0.144663\tvalid_1's binary_error: 0.178771\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[64]\ttraining's binary_error: 0.143258\tvalid_1's binary_error: 0.178771\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[65]\ttraining's binary_error: 0.144663\tvalid_1's binary_error: 0.189944\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[66]\ttraining's binary_error: 0.141854\tvalid_1's binary_error: 0.178771\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[67]\ttraining's binary_error: 0.140449\tvalid_1's binary_error: 0.184358\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[68]\ttraining's binary_error: 0.140449\tvalid_1's binary_error: 0.184358\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[69]\ttraining's binary_error: 0.140449\tvalid_1's binary_error: 0.184358\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[70]\ttraining's binary_error: 0.140449\tvalid_1's binary_error: 0.184358\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[71]\ttraining's binary_error: 0.140449\tvalid_1's binary_error: 0.178771\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[72]\ttraining's binary_error: 0.140449\tvalid_1's binary_error: 0.178771\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[73]\ttraining's binary_error: 0.143258\tvalid_1's binary_error: 0.178771\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[74]\ttraining's binary_error: 0.143258\tvalid_1's binary_error: 0.178771\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[75]\ttraining's binary_error: 0.143258\tvalid_1's binary_error: 0.178771\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[76]\ttraining's binary_error: 0.143258\tvalid_1's binary_error: 0.178771\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[77]\ttraining's binary_error: 0.144663\tvalid_1's binary_error: 0.178771\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[78]\ttraining's binary_error: 0.146067\tvalid_1's binary_error: 0.178771\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[79]\ttraining's binary_error: 0.144663\tvalid_1's binary_error: 0.178771\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[80]\ttraining's binary_error: 0.144663\tvalid_1's binary_error: 0.178771\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[81]\ttraining's binary_error: 0.144663\tvalid_1's binary_error: 0.178771\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[82]\ttraining's binary_error: 0.144663\tvalid_1's binary_error: 0.178771\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[83]\ttraining's binary_error: 0.144663\tvalid_1's binary_error: 0.178771\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[84]\ttraining's binary_error: 0.144663\tvalid_1's binary_error: 0.178771\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[85]\ttraining's binary_error: 0.144663\tvalid_1's binary_error: 0.178771\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[86]\ttraining's binary_error: 0.143258\tvalid_1's binary_error: 0.178771\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[87]\ttraining's binary_error: 0.146067\tvalid_1's binary_error: 0.178771\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[88]\ttraining's binary_error: 0.146067\tvalid_1's binary_error: 0.178771\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[89]\ttraining's binary_error: 0.146067\tvalid_1's binary_error: 0.178771\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[90]\ttraining's binary_error: 0.146067\tvalid_1's binary_error: 0.178771\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[91]\ttraining's binary_error: 0.146067\tvalid_1's binary_error: 0.178771\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[92]\ttraining's binary_error: 0.143258\tvalid_1's binary_error: 0.178771\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[93]\ttraining's binary_error: 0.141854\tvalid_1's binary_error: 0.178771\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[94]\ttraining's binary_error: 0.141854\tvalid_1's binary_error: 0.178771\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[95]\ttraining's binary_error: 0.141854\tvalid_1's binary_error: 0.178771\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[96]\ttraining's binary_error: 0.143258\tvalid_1's binary_error: 0.178771\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[97]\ttraining's binary_error: 0.141854\tvalid_1's binary_error: 0.178771\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[98]\ttraining's binary_error: 0.141854\tvalid_1's binary_error: 0.184358\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[99]\ttraining's binary_error: 0.141854\tvalid_1's binary_error: 0.178771\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[100]\ttraining's binary_error: 0.141854\tvalid_1's binary_error: 0.173184\n" + ] + } + ], + "source": [ + "lgbm_model = lgb.train(lgbm_params, train_data, valid_sets=[train_data, valid_data])" + ] + }, + { + "cell_type": "code", + "execution_count": 173, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'name': 'tree',\n", + " 'version': 'v3',\n", + " 'num_class': 1,\n", + " 'num_tree_per_iteration': 1,\n", + " 'label_index': 0,\n", + " 'max_feature_idx': 5,\n", + " 'objective': 'binary sigmoid:1',\n", + " 'average_output': False,\n", + " 'feature_names': ['Pclass',\n", + " 'Age',\n", + " 'Fare',\n", + " 'Sex_label',\n", + " 'Cabin_label',\n", + " 'Embarked_label'],\n", + " 'monotone_constraints': [],\n", + " 'feature_infos': {'Pclass': {'min_value': 1, 'max_value': 3, 'values': []},\n", + " 'Age': {'min_value': 0.42, 'max_value': 80, 'values': []},\n", + " 'Fare': {'min_value': 0, 'max_value': 512.3292, 'values': []},\n", + " 'Sex_label': {'min_value': 0, 'max_value': 1, 'values': []},\n", + " 'Cabin_label': {'min_value': -1, 'max_value': 146, 'values': []},\n", + " 'Embarked_label': {'min_value': -1, 'max_value': 2, 'values': []}},\n", + " 'tree_info': 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'decision_type': '<=',\n", + " 'default_left': True,\n", + " 'missing_type': 'None',\n", + " 'internal_value': -0.101345,\n", + " 'internal_weight': 12.2057,\n", + " 'internal_count': 52,\n", + " 'left_child': {'leaf_index': 1,\n", + " 'leaf_value': -0.10293094923638349,\n", + " 'leaf_weight': 5.633379578590395,\n", + " 'leaf_count': 24},\n", + " 'right_child': {'leaf_index': 12,\n", + " 'leaf_value': -0.09998577209757946,\n", + " 'leaf_weight': 6.572276175022125,\n", + " 'leaf_count': 28}},\n", + " 'right_child': {'split_index': 12,\n", + " 'split_feature': 2,\n", + " 'split_gain': 0.20806799829006195,\n", + " 'threshold': 87.80210000000001,\n", + " 'decision_type': '<=',\n", + " 'default_left': True,\n", + " 'missing_type': 'None',\n", + " 'internal_value': -0.0961427,\n", + " 'internal_weight': 9.62369,\n", + " 'internal_count': 41,\n", + " 'left_child': {'leaf_index': 7,\n", + " 'leaf_value': -0.09753145781524275,\n", + " 'leaf_weight': 4.929207131266592,\n", + " 'leaf_count': 21},\n", + " 'right_child': {'leaf_index': 13,\n", + " 'leaf_value': -0.09468445324773214,\n", + " 'leaf_weight': 4.694482982158661,\n", + " 'leaf_count': 20}}},\n", + " 'right_child': {'split_index': 5,\n", + " 'split_feature': 2,\n", + " 'split_gain': 2.0237700939178467,\n", + " 'threshold': 7.910400000000001,\n", + " 'decision_type': '<=',\n", + " 'default_left': True,\n", + " 'missing_type': 'None',\n", + " 'internal_value': -0.107175,\n", + " 'internal_weight': 87.7868,\n", + " 'internal_count': 374,\n", + " 'left_child': {'split_index': 8,\n", + " 'split_feature': 2,\n", + " 'split_gain': 0.8546940088272095,\n", + " 'threshold': 7.866700000000001,\n", + " 'decision_type': '<=',\n", + " 'default_left': True,\n", + " 'missing_type': 'None',\n", + " 'internal_value': -0.109086,\n", + " 'internal_weight': 32.6267,\n", + " 'internal_count': 139,\n", + " 'left_child': {'leaf_index': 4,\n", + " 'leaf_value': -0.108229515584232,\n", + " 'leaf_weight': 25.115483954548836,\n", + " 'leaf_count': 107},\n", + " 'right_child': {'leaf_index': 9,\n", + " 'leaf_value': -0.11195055422397104,\n", + " 'leaf_weight': 7.5111727714538565,\n", + " 'leaf_count': 32}},\n", + " 'right_child': {'split_index': 7,\n", + " 'split_feature': 2,\n", + " 'split_gain': 1.0632100105285645,\n", + " 'threshold': 31.33125,\n", + " 'decision_type': '<=',\n", + " 'default_left': True,\n", + " 'missing_type': 'None',\n", + " 'internal_value': -0.106045,\n", + " 'internal_weight': 55.1602,\n", + " 'internal_count': 235,\n", + " 'left_child': {'leaf_index': 6,\n", + " 'leaf_value': -0.10656910923137683,\n", + " 'leaf_weight': 47.88372641801834,\n", + " 'leaf_count': 204},\n", + " 'right_child': {'leaf_index': 8,\n", + " 'leaf_value': -0.10259842923684112,\n", + " 'leaf_weight': 7.2764486223459235,\n", + " 'leaf_count': 31}}}}}}],\n", + " 'feature_importances': {'Pclass': 2,\n", + " 'Fare': 9,\n", + " 'Sex_label': 1,\n", + " 'Embarked_label': 1},\n", + " 'pandas_categorical': []}" + ] + }, + "execution_count": 173, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "lgbm_model.dump_model(num_iteration=1)" + ] + }, + { + "cell_type": "code", + "execution_count": 174, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 174, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(20,20))\n", + "lgb.plot_tree(lgbm_model, tree_index=10)" + ] + }, + { + "cell_type": "code", + "execution_count": 176, + "metadata": {}, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "%3\n", + "\n", + "\n", + "\n", + "split0\n", + "\n", + "Sex_label\n", + " ≤ \n", + "0.000\n", + "count: 712\n", + "\n", + "\n", + "\n", + "split1\n", + "\n", + "Pclass\n", + " ≤ \n", + "2.500\n", + "count: 245\n", + "\n", + "\n", + "\n", + "split0->split1\n", + "\n", + "\n", + "yes\n", + "\n", + "\n", + "\n", + "split3\n", + "\n", + "Fare\n", + " ≤ \n", + "26.269\n", + "count: 467\n", + "\n", + "\n", + "\n", + "split0->split3\n", + "\n", + "\n", + "no\n", + "\n", + "\n", + "\n", + "split10\n", + "\n", + "Fare\n", + " ≤ \n", + "135.067\n", + "count: 130\n", + "\n", + "\n", + "\n", + "split1->split10\n", + "\n", + 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+ "Embarked_label\n", + " ≤ \n", + "1.500\n", + "count: 196\n", + "\n", + "\n", + "\n", + "split6->split8\n", + "\n", + "\n", + "no\n", + "\n", + "\n", + "\n", + "leaf1\n", + "\n", + "leaf 1: \n", + "-0.010\n", + "\n", + "\n", + "\n", + "split9->leaf1\n", + "\n", + "\n", + "yes\n", + "\n", + "\n", + "\n", + "leaf10\n", + "\n", + "leaf 10: \n", + "-0.014\n", + "\n", + "\n", + "\n", + "split9->leaf10\n", + "\n", + "\n", + "no\n", + "\n", + "\n", + "\n", + "leaf7\n", + "\n", + "leaf 7: \n", + "-0.003\n", + "\n", + "\n", + "\n", + "split8->leaf7\n", + "\n", + "\n", + "yes\n", + "\n", + "\n", + "\n", + "leaf9\n", + "\n", + "leaf 9: \n", + "-0.009\n", + "\n", + "\n", + "\n", + "split8->leaf9\n", + "\n", + "\n", + "no\n", + "\n", + "\n", + "\n", + "split7\n", + "\n", + "Fare\n", + " ≤ \n", + "30.750\n", + "count: 86\n", + "\n", + "\n", + "\n", + "split5->split7\n", + "\n", + "\n", + "yes\n", + "\n", + "\n", + "\n", + "leaf6\n", + "\n", + "leaf 6: \n", + "-0.007\n", + "\n", + "\n", + "\n", + "split5->leaf6\n", + "\n", + "\n", + "no\n", + "\n", + "\n", + "\n", + "leaf4\n", + "\n", + "leaf 4: \n", + "0.004\n", + "\n", + "\n", + "\n", + "split7->leaf4\n", + "\n", + "\n", + "yes\n", + "\n", + "\n", + "\n", + "leaf8\n", + "\n", + "leaf 8: \n", + "-0.002\n", + "\n", + "\n", + "\n", + "split7->leaf8\n", + "\n", + "\n", + "no\n", + "\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 176, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "lgb.create_tree_digraph(lgbm_model, tree_index=1, orientation = \"vertical\", show_info=['internal_count'])\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Model metadata interpretation" + ] + }, + { + "cell_type": "code", + "execution_count": 106, + "metadata": {}, + "outputs": [], + "source": [ + "import json" + ] + }, + { + "cell_type": "code", + "execution_count": 154, + "metadata": {}, + "outputs": [], + "source": [ + "tree_dump = lgbm_model.dump_model()[\"tree_info\"][1]" + ] + }, + { + "cell_type": "code", + "execution_count": 155, + "metadata": {}, + "outputs": [], + "source": [ + "def _get_nodes_info(node):\n", + " tree_nodes = {}\n", + " children_left = {}\n", + " children_right = {}\n", + " node_index = 0\n", + " \n", + " def _walk_tree(node, node_id):\n", + " nonlocal node_index\n", + " tree_nodes[node_id] = node\n", + " if node.get(\"split_index\") is None:\n", + " children_left[node_id] = -1\n", + " children_right[node_id] = -1\n", + " return\n", + "\n", + " node_index += 1\n", + " children_left[node_id] = node_index\n", + " _walk_tree(node.get(\"left_child\"), node_index)\n", + " \n", + " node_index += 1\n", + " children_right[node_id] = node_index\n", + " _walk_tree(node.get(\"right_child\"), node_index)\n", + " \n", + " def _convert_dict_to_list(my_dict):\n", + " my_list = [-1] * len(my_dict)\n", + " for key, value in my_dict.items():\n", + " my_list[key] = value\n", + " return my_list\n", + " \n", + " _walk_tree(node, node_index)\n", + "\n", + " for node in tree_nodes.values():\n", + " node.pop(\"left_child\", None)\n", + " node.pop(\"right_child\", None)\n", + " \n", + " children_left_list = _convert_dict_to_list(children_left)\n", + " children_right_list = _convert_dict_to_list(children_right)\n", + " tree_node_list = _convert_dict_to_list(tree_nodes)\n", + " \n", + " return tree_node_list, children_left_list, children_right_list\n" + ] + }, + { + "cell_type": "code", + "execution_count": 156, + "metadata": {}, + "outputs": [], + "source": [ + "tree_nodes, children_left, children_right = _get_nodes_info(tree_dump[\"tree_structure\"])" + ] + }, + { + "cell_type": "code", + "execution_count": 157, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[1, 2, 3, 4, -1, -1, -1, 8, 9, -1, -1, -1, 13, 14, 15, -1, -1, 18, -1, -1, 21, 22, -1, -1, -1]\n", + "[12, 7, 6, 5, -1, -1, -1, 11, 10, -1, -1, -1, 20, 17, 16, -1, -1, 19, -1, -1, 24, 23, -1, -1, -1]\n" + ] + } + ], + "source": [ + "print(children_left)\n", + "print(children_right)" + ] + }, + { + "cell_type": "code", + "execution_count": 153, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'split_index': 2,\n", + " 'split_feature': 2,\n", + " 'split_gain': 17.808000564575195,\n", + " 'threshold': 23.725000000000005,\n", + " 'decision_type': '<=',\n", + " 'default_left': True,\n", + " 'missing_type': 'None',\n", + " 'internal_value': 0.004239,\n", + " 'internal_weight': 27.1344,\n", + " 'internal_count': 115}" + ] + }, + "execution_count": 153, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tree_nodes[7]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# dtreeviz" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "ShadowLightGBMTree" + ] + }, + { + "cell_type": "code", + "execution_count": 184, + "metadata": {}, + "outputs": [], + "source": [ + "light_dtree = ShadowLightGBMTree(lgbm_model, tree_index=1, x_data=X_train, y_data=y_train, feature_names=features, target_name=target, class_names=[0,1] )\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.6" + }, + "toc": { + "base_numbering": 1, + "nav_menu": {}, + "number_sections": true, + "sideBar": true, + "skip_h1_title": false, + "title_cell": "Table of Contents", + "title_sidebar": "Contents", + "toc_cell": false, + "toc_position": {}, + "toc_section_display": true, + "toc_window_display": true + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/notebooks/dtreeviz_sklearn_visualisations.ipynb b/notebooks/dtreeviz_sklearn_visualisations.ipynb index 2c39bfa5..4a1c93d1 100644 --- a/notebooks/dtreeviz_sklearn_visualisations.ipynb +++ b/notebooks/dtreeviz_sklearn_visualisations.ipynb @@ -2,9 +2,18 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": 4, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The autoreload extension is already loaded. To reload it, use:\n", + " %reload_ext autoreload\n" + ] + } + ], "source": [ "%load_ext autoreload\n", "%autoreload 2\n", @@ -14,7 +23,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -26,7 +35,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -51,7 +60,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -65,7 +74,7 @@ " random_state=1234, splitter='best')" ] }, - "execution_count": 4, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -94,7 +103,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -117,7 +126,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 9, "metadata": {}, "outputs": [ { @@ -139,7 +148,29 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
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" \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -370,10 +401,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -389,7 +420,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -425,10 +456,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -444,7 +475,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -479,7 +510,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -502,118 +533,118 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -622,10 +653,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -644,7 +675,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -684,10 +715,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -700,7 +731,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -741,7 +772,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -959,118 +990,118 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1079,10 +1110,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1104,7 +1135,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1139,10 +1170,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1155,7 +1186,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1194,7 +1225,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1229,118 +1260,118 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1349,10 +1380,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1371,7 +1402,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1411,10 +1442,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1427,7 +1458,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1468,7 +1499,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1492,118 +1523,118 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1612,10 +1643,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1637,7 +1668,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1672,10 +1703,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1688,7 +1719,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1729,7 +1760,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1752,118 +1783,118 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1872,10 +1903,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1893,7 +1924,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1942,10 +1973,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1961,7 +1992,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2000,7 +2031,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2238,118 +2269,118 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2358,10 +2389,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2377,7 +2408,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2413,10 +2444,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2432,7 +2463,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2472,7 +2503,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2507,118 +2538,118 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2627,10 +2658,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2648,7 +2679,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2695,10 +2726,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2714,7 +2745,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2754,7 +2785,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2778,118 +2809,118 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2898,10 +2929,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2920,7 +2951,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2960,10 +2991,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2976,7 +3007,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3015,7 +3046,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3038,118 +3069,118 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3158,10 +3189,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3183,7 +3214,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3218,10 +3249,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3234,7 +3265,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3276,7 +3307,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3500,118 +3531,118 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3620,10 +3651,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3645,7 +3676,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3680,10 +3711,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3696,7 +3727,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3737,7 +3768,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3772,118 +3803,118 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3892,10 +3923,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3917,7 +3948,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3952,10 +3983,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3968,7 +3999,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4009,7 +4040,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4033,118 +4064,118 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4153,10 +4184,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4175,7 +4206,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4215,10 +4246,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4231,7 +4262,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4270,7 +4301,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4456,118 +4487,118 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4576,10 +4607,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4595,7 +4626,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4636,10 +4667,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4652,7 +4683,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4692,7 +4723,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4792,10 +4823,10 @@ "" ], "text/plain": [ - "" + "" ] }, - "execution_count": 10, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } @@ -4806,7 +4837,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 14, "metadata": {}, "outputs": [ { @@ -5797,10 +5828,10 @@ "" ], "text/plain": [ - "" + "" ] }, - "execution_count": 11, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" } @@ -5818,7 +5849,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 15, "metadata": {}, "outputs": [ { @@ -5853,118 +5884,118 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5976,10 +6007,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6001,7 +6032,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6036,10 +6067,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6052,7 +6083,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6103,7 +6134,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6177,118 +6208,118 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6300,10 +6331,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6322,7 +6353,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6362,10 +6393,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6378,7 +6409,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6429,7 +6460,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6459,118 +6490,118 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6582,10 +6613,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6601,7 +6632,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6637,10 +6668,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6656,7 +6687,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6705,7 +6736,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6735,118 +6766,118 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6858,10 +6889,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6877,7 +6908,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6918,10 +6949,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6934,7 +6965,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6983,7 +7014,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7104,10 +7135,10 @@ "" ], "text/plain": [ - "" + "" ] }, - "execution_count": 21, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" } @@ -7447,7 +7478,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 16, "metadata": {}, "outputs": [ { @@ -7462,7 +7493,7 @@ "Name: 10, dtype: float64" ] }, - "execution_count": 26, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" } @@ -7474,7 +7505,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 17, "metadata": {}, "outputs": [ { @@ -7495,16 +7526,16 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 28, + "execution_count": 18, "metadata": {}, "output_type": "execute_result" }, diff --git a/notebooks/dtreeviz_spark_visualisations.ipynb b/notebooks/dtreeviz_spark_visualisations.ipynb index 5abc1f03..995d3cbc 100644 --- a/notebooks/dtreeviz_spark_visualisations.ipynb +++ b/notebooks/dtreeviz_spark_visualisations.ipynb @@ -18,18 +18,9 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 1, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The autoreload extension is already loaded. To reload it, use:\n", - " %reload_ext autoreload\n" - ] - } - ], + "outputs": [], "source": [ "%load_ext autoreload\n", "%autoreload 2\n", @@ -39,7 +30,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -73,7 +64,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -85,7 +76,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -94,7 +85,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 5, "metadata": { "scrolled": true }, @@ -138,7 +129,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -292,7 +283,7 @@ "4 WE/P 5735 512.3292 T S " ] }, - "execution_count": 34, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -324,7 +315,7 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -333,7 +324,7 @@ "DataFrame[PassengerId: int, Survived: int, Pclass: int, Name: string, Sex: string, Age: double, SibSp: int, Parch: int, Ticket: string, Fare: double, Cabin: string, Embarked: string, Sex_label: double, Embarked_label: double]" ] }, - "execution_count": 35, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -345,7 +336,7 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -634,7 +625,7 @@ "[891 rows x 14 columns]" ] }, - "execution_count": 36, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -652,7 +643,7 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -668,7 +659,7 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ @@ -678,7 +669,7 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ @@ -694,7 +685,7 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ @@ -710,7 +701,7 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ @@ -720,7 +711,7 @@ }, { "cell_type": "code", - "execution_count": 44, + "execution_count": 14, "metadata": {}, "outputs": [ { @@ -757,7 +748,7 @@ }, { "cell_type": "code", - "execution_count": 45, + "execution_count": 15, "metadata": {}, "outputs": [ { @@ -900,7 +891,7 @@ "[891 rows x 4 columns]" ] }, - "execution_count": 45, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" } @@ -911,7 +902,7 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 16, "metadata": {}, "outputs": [ { @@ -920,7 +911,7 @@ "['Pclass', 'Sex_label', 'Embarked_label', 'Age_mean', 'SibSp', 'Parch', 'Fare']" ] }, - "execution_count": 46, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" } @@ -931,14 +922,14 @@ }, { "cell_type": "code", - "execution_count": 47, + "execution_count": 17, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "DecisionTreeClassificationModel: uid=DecisionTreeClassifier_8b5b440b9770, depth=4, numNodes=17, numClasses=2, numFeatures=7\n", + "DecisionTreeClassificationModel: uid=DecisionTreeClassifier_a52c10683bec, depth=4, numNodes=17, numClasses=2, numFeatures=7\n", " If (feature 1 in {0.0})\n", " If (feature 3 <= 3.5)\n", " If (feature 4 <= 2.5)\n", @@ -974,7 +965,7 @@ }, { "cell_type": "code", - "execution_count": 48, + "execution_count": 18, "metadata": {}, "outputs": [], "source": [ @@ -984,7 +975,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 19, "metadata": {}, "outputs": [ { @@ -1017,7 +1008,7 @@ " 'wait']" ] }, - "execution_count": 32, + "execution_count": 19, "metadata": {}, "output_type": "execute_result" } @@ -1028,7 +1019,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 20, "metadata": {}, "outputs": [ { @@ -1061,7 +1052,7 @@ " 'wait']" ] }, - "execution_count": 33, + "execution_count": 20, "metadata": {}, "output_type": "execute_result" } @@ -1079,7 +1070,7 @@ }, { "cell_type": "code", - "execution_count": 50, + "execution_count": 21, "metadata": {}, "outputs": [], "source": [ @@ -1088,7 +1079,7 @@ }, { "cell_type": "code", - "execution_count": 51, + "execution_count": 22, "metadata": {}, "outputs": [], "source": [ @@ -1097,7 +1088,7 @@ }, { "cell_type": "code", - "execution_count": 52, + "execution_count": 23, "metadata": {}, "outputs": [ { @@ -1129,7 +1120,7 @@ }, { "cell_type": "code", - "execution_count": 53, + "execution_count": 24, "metadata": {}, "outputs": [ { @@ -1413,7 +1404,7 @@ "16 -1 (-1, [], -1) " ] }, - "execution_count": 53, + "execution_count": 24, "metadata": {}, "output_type": "execute_result" } @@ -1424,7 +1415,7 @@ }, { "cell_type": "code", - "execution_count": 54, + "execution_count": 25, "metadata": {}, "outputs": [], "source": [ @@ -1436,7 +1427,7 @@ }, { "cell_type": "code", - "execution_count": 55, + "execution_count": 26, "metadata": {}, "outputs": [], "source": [ @@ -1452,7 +1443,7 @@ }, { "cell_type": "code", - "execution_count": 61, + "execution_count": 27, "metadata": {}, "outputs": [ { @@ -1483,118 +1474,118 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1603,10 +1594,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1622,7 +1613,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1656,10 +1647,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1672,7 +1663,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1707,7 +1698,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1874,118 +1865,118 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1994,10 +1985,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2019,7 +2010,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2063,10 +2054,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2079,7 +2070,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2119,7 +2110,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2148,118 +2139,118 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2268,10 +2259,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2287,7 +2278,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2323,10 +2314,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2342,7 +2333,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2382,7 +2373,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2412,118 +2403,118 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2532,10 +2523,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2551,7 +2542,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2587,10 +2578,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2606,7 +2597,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2644,7 +2635,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2807,118 +2798,118 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2927,10 +2918,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2952,7 +2943,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2996,10 +2987,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3012,7 +3003,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3051,7 +3042,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3080,118 +3071,118 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3200,10 +3191,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3222,7 +3213,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3262,10 +3253,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3278,7 +3269,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3320,7 +3311,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3350,127 +3341,127 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3486,7 +3477,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3535,10 +3526,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3551,7 +3542,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3600,7 +3591,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3796,127 +3787,127 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3932,7 +3923,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3973,10 +3964,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3989,7 +3980,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4031,7 +4022,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4126,10 +4117,10 @@ "" ], "text/plain": [ - "" + "" ] }, - "execution_count": 61, + "execution_count": 27, "metadata": {}, "output_type": "execute_result" } @@ -4147,7 +4138,7 @@ }, { "cell_type": "code", - "execution_count": 44, + "execution_count": 28, "metadata": { "scrolled": true }, @@ -4178,7 +4169,7 @@ }, { "cell_type": "code", - "execution_count": 45, + "execution_count": 29, "metadata": {}, "outputs": [ { @@ -4207,7 +4198,7 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 30, "metadata": {}, "outputs": [ { @@ -4236,7 +4227,7 @@ }, { "cell_type": "code", - "execution_count": 47, + "execution_count": 31, "metadata": { "scrolled": true }, @@ -4267,7 +4258,7 @@ }, { "cell_type": "code", - "execution_count": 48, + "execution_count": 32, "metadata": {}, "outputs": [ { @@ -4397,7 +4388,7 @@ "max 2.0 1.0 1.000000 3.000000 1.0 2.000000 41.579200" ] }, - "execution_count": 48, + "execution_count": 32, "metadata": {}, "output_type": "execute_result" } @@ -4415,7 +4406,7 @@ }, { "cell_type": "code", - "execution_count": 56, + "execution_count": 33, "metadata": {}, "outputs": [ { @@ -4431,7 +4422,7 @@ "Name: 10, dtype: float64" ] }, - "execution_count": 56, + "execution_count": 33, "metadata": {}, "output_type": "execute_result" } @@ -4443,7 +4434,7 @@ }, { "cell_type": "code", - "execution_count": 59, + "execution_count": 34, "metadata": {}, "outputs": [ { @@ -4464,7 +4455,7 @@ }, { "cell_type": "code", - "execution_count": 63, + "execution_count": 35, "metadata": {}, "outputs": [ { @@ -4475,7 +4466,7 @@ "Name: Sex_label, dtype: int64" ] }, - "execution_count": 63, + "execution_count": 35, "metadata": {}, "output_type": "execute_result" } @@ -4486,7 +4477,7 @@ }, { "cell_type": "code", - "execution_count": 62, + "execution_count": 38, "metadata": {}, "outputs": [ { @@ -4520,118 +4511,118 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4640,10 +4631,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4659,7 +4650,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4693,10 +4684,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4709,7 +4700,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4744,7 +4735,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4911,118 +4902,118 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5031,10 +5022,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5056,7 +5047,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5100,10 +5091,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5116,7 +5107,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5156,7 +5147,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5186,118 +5177,118 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5309,10 +5300,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5328,7 +5319,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5364,10 +5355,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5383,7 +5374,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5432,7 +5423,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5462,118 +5453,118 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5582,10 +5573,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5601,7 +5592,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5637,10 +5628,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5656,7 +5647,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5694,7 +5685,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5857,118 +5848,118 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5977,10 +5968,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6002,7 +5993,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6046,10 +6037,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6062,7 +6053,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6101,7 +6092,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6131,118 +6122,118 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6254,10 +6245,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6276,7 +6267,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6316,10 +6307,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6332,7 +6323,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6384,7 +6375,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6415,127 +6406,127 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6551,7 +6542,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6600,10 +6591,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6616,7 +6607,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6680,7 +6671,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6877,127 +6868,127 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7013,7 +7004,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7054,10 +7045,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7070,7 +7061,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7122,7 +7113,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7258,16 +7249,16 @@ "" ], "text/plain": [ - "" + "" ] }, - "execution_count": 62, + "execution_count": 38, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "trees.dtreeviz(spark_dtree, X=X)" + "trees.dtreeviz(spark_dtree, X=X, show_just_path=False)" ] }, { diff --git a/notebooks/dtreeviz_xgboost_visualisations.ipynb b/notebooks/dtreeviz_xgboost_visualisations.ipynb index 25caff92..243771e1 100644 --- a/notebooks/dtreeviz_xgboost_visualisations.ipynb +++ b/notebooks/dtreeviz_xgboost_visualisations.ipynb @@ -27,7 +27,7 @@ { "data": { "text/plain": [ - "'/Users/parrt/opt/anaconda3/bin/python'" + "'/Users/tudorl/Documents/workspace/personal/dtreeviz/venv/bin/python'" ] }, "execution_count": 2, @@ -113,17 +113,17 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 54, "metadata": {}, "outputs": [], "source": [ - "params = {\"max_depth\":3, \"eta\":0.05, \"objective\":\"binary:logistic\", \"subsample\":1}\n", - "xgb_model = xgb.train(params=params, dtrain=dtrain, num_boost_round=8)" + "params = {\"max_depth\":4, \"eta\":0.05, \"objective\":\"binary:logistic\", \"subsample\":1}\n", + "xgb_model = xgb.train(params=params, dtrain=dtrain, num_boost_round=1)" ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 46, "metadata": { "scrolled": true }, @@ -168,7 +168,7 @@ " 0\n", " 0-0\n", " Sex_label\n", - " 1.00\n", + " 1.000000\n", " 0-1\n", " 0-2\n", " 0-1\n", @@ -181,7 +181,7 @@ " 1\n", " 0-1\n", " Pclass\n", - " 3.00\n", + " 3.000000\n", " 0-3\n", " 0-4\n", " 0-3\n", @@ -194,7 +194,7 @@ " 2\n", " 0-2\n", " Cabin_label\n", - " 4.00\n", + " 4.000000\n", " 0-5\n", " 0-6\n", " 0-5\n", @@ -220,7 +220,7 @@ " 4\n", " 0-4\n", " Fare\n", - " 23.35\n", + " 23.350000\n", " 0-7\n", " 0-8\n", " 0-7\n", @@ -233,7 +233,7 @@ " 5\n", " 0-5\n", " Age\n", - " 3.50\n", + " 3.500000\n", " 0-9\n", " 0-10\n", " 0-9\n", @@ -246,7 +246,7 @@ " 6\n", " 0-6\n", " Age\n", - " 17.50\n", + " 17.500000\n", " 0-11\n", " 0-12\n", " 0-11\n", @@ -258,12 +258,12 @@ " 0\n", " 7\n", " 0-7\n", - " Leaf\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " 0.017355\n", + " Embarked_label\n", + " 2.000000\n", + " 0-13\n", + " 0-14\n", + " 0-13\n", + " 4.715125\n", " 29.25\n", " \n", " \n", @@ -284,12 +284,12 @@ " 0\n", " 9\n", " 0-9\n", - " Leaf\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " 0.022222\n", + " Fare\n", + " 20.825001\n", + " 0-15\n", + " 0-16\n", + " 0-15\n", + " 3.044444\n", " 3.50\n", " \n", " \n", @@ -297,12 +297,12 @@ " 0\n", " 10\n", " 0-10\n", - " Leaf\n", - " NaN\n", - " NaN\n", - " NaN\n", - " NaN\n", - " -0.075378\n", + " Age\n", + " 13.000000\n", + " 0-17\n", + " 0-18\n", + " 0-17\n", + " 1.927734\n", " 114.75\n", " \n", " \n", @@ -323,51 +323,171 @@ " 0\n", " 12\n", " 0-12\n", + " Age\n", + " 43.000000\n", + " 0-19\n", + " 0-20\n", + " 0-19\n", + " 3.702889\n", + " 24.00\n", + " \n", + " \n", + " 13\n", + " 0\n", + " 13\n", + " 0-13\n", " Leaf\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", - " -0.022000\n", - " 24.00\n", + " 0.037931\n", + " 13.50\n", + " \n", + " \n", + " 14\n", + " 0\n", + " 14\n", + " 0-14\n", + " Leaf\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " -0.001493\n", + " 15.75\n", + " \n", + " \n", + " 15\n", + " 0\n", + " 15\n", + " 0-15\n", + " Leaf\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " 0.060000\n", + " 1.50\n", + " \n", + " \n", + " 16\n", + " 0\n", + " 16\n", + " 0-16\n", + " Leaf\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " -0.016667\n", + " 2.00\n", + " \n", + " \n", + " 17\n", + " 0\n", + " 17\n", + " 0-17\n", + " Leaf\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " -0.030000\n", + " 4.00\n", + " \n", + " \n", + " 18\n", + " 0\n", + " 18\n", + " 0-18\n", + " Leaf\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " -0.076734\n", + " 110.75\n", + " \n", + " \n", + " 19\n", + " 0\n", + " 19\n", + " 0-19\n", + " Leaf\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " -0.004918\n", + " 14.25\n", + " \n", + " \n", + " 20\n", + " 0\n", + " 20\n", + " 0-20\n", + " Leaf\n", + " NaN\n", + " NaN\n", + " NaN\n", + " NaN\n", + " -0.044186\n", + " 9.75\n", " \n", " \n", "\n", "" ], "text/plain": [ - " Tree Node ID Feature Split Yes No Missing Gain \\\n", - "0 0 0 0-0 Sex_label 1.00 0-1 0-2 0-1 246.604279 \n", - "1 0 1 0-1 Pclass 3.00 0-3 0-4 0-3 60.127525 \n", - "2 0 2 0-2 Cabin_label 4.00 0-5 0-6 0-5 29.516968 \n", - "3 0 3 0-3 Leaf NaN NaN NaN NaN 0.087356 \n", - "4 0 4 0-4 Fare 23.35 0-7 0-8 0-7 17.870434 \n", - "5 0 5 0-5 Age 3.50 0-9 0-10 0-9 14.429703 \n", - "6 0 6 0-6 Age 17.50 0-11 0-12 0-11 8.358520 \n", - "7 0 7 0-7 Leaf NaN NaN NaN NaN 0.017355 \n", - "8 0 8 0-8 Leaf NaN NaN NaN NaN -0.067742 \n", - "9 0 9 0-9 Leaf NaN NaN NaN NaN 0.022222 \n", - "10 0 10 0-10 Leaf NaN NaN NaN NaN -0.075378 \n", - "11 0 11 0-11 Leaf NaN NaN NaN NaN 0.066667 \n", - "12 0 12 0-12 Leaf NaN NaN NaN NaN -0.022000 \n", - "\n", - " Cover \n", - "0 222.75 \n", - "1 78.50 \n", - "2 144.25 \n", - "3 42.50 \n", - "4 36.00 \n", - "5 118.25 \n", - "6 26.00 \n", - "7 29.25 \n", - "8 6.75 \n", - "9 3.50 \n", - "10 114.75 \n", - "11 2.00 \n", - "12 24.00 " + " Tree Node ID Feature Split Yes No Missing \\\n", + "0 0 0 0-0 Sex_label 1.000000 0-1 0-2 0-1 \n", + "1 0 1 0-1 Pclass 3.000000 0-3 0-4 0-3 \n", + "2 0 2 0-2 Cabin_label 4.000000 0-5 0-6 0-5 \n", + "3 0 3 0-3 Leaf NaN NaN NaN NaN \n", + "4 0 4 0-4 Fare 23.350000 0-7 0-8 0-7 \n", + "5 0 5 0-5 Age 3.500000 0-9 0-10 0-9 \n", + "6 0 6 0-6 Age 17.500000 0-11 0-12 0-11 \n", + "7 0 7 0-7 Embarked_label 2.000000 0-13 0-14 0-13 \n", + "8 0 8 0-8 Leaf NaN NaN NaN NaN \n", + "9 0 9 0-9 Fare 20.825001 0-15 0-16 0-15 \n", + "10 0 10 0-10 Age 13.000000 0-17 0-18 0-17 \n", + "11 0 11 0-11 Leaf NaN NaN NaN NaN \n", + "12 0 12 0-12 Age 43.000000 0-19 0-20 0-19 \n", + "13 0 13 0-13 Leaf NaN NaN NaN NaN \n", + "14 0 14 0-14 Leaf NaN NaN NaN NaN \n", + "15 0 15 0-15 Leaf NaN NaN NaN NaN \n", + "16 0 16 0-16 Leaf NaN NaN NaN NaN \n", + "17 0 17 0-17 Leaf NaN NaN NaN NaN \n", + "18 0 18 0-18 Leaf NaN NaN NaN NaN \n", + "19 0 19 0-19 Leaf NaN NaN NaN NaN \n", + "20 0 20 0-20 Leaf NaN NaN NaN NaN \n", + "\n", + " Gain Cover \n", + "0 246.604279 222.75 \n", + "1 60.127525 78.50 \n", + "2 29.516968 144.25 \n", + "3 0.087356 42.50 \n", + "4 17.870434 36.00 \n", + "5 14.429703 118.25 \n", + "6 8.358520 26.00 \n", + "7 4.715125 29.25 \n", + "8 -0.067742 6.75 \n", + "9 3.044444 3.50 \n", + "10 1.927734 114.75 \n", + "11 0.066667 2.00 \n", + "12 3.702889 24.00 \n", + "13 0.037931 13.50 \n", + "14 -0.001493 15.75 \n", + "15 0.060000 1.50 \n", + "16 -0.016667 2.00 \n", + "17 -0.030000 4.00 \n", + "18 -0.076734 110.75 \n", + "19 -0.004918 14.25 \n", + "20 -0.044186 9.75 " ] }, - "execution_count": 8, + "execution_count": 46, "metadata": {}, "output_type": "execute_result" } @@ -378,22 +498,22 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 47, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 9, + "execution_count": 47, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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06RJut5vjx49rl0I7DVmWKRQKfPPNN1QqFd566y3MGyo17DZIksTY2BgTExN0dnbS3d39xF4dO119QvU2WVhYYHR0FLfbzenTp5/buOnQUQOddHcKkiQxMzPDnTt38Pv99PX1EQjUYzQanmkhqx4Lq6urTExMkEqlOHLkiHZptxegeinMzMxw7949qtUqTU1NNDc34/f7NY+D57VRgXKpl89kWFxaYnF5mXw+TzgcZv/+/ZtOEjp0PEfopLuTUI7EMgsLUf7u76Ywmyt0dpapr6+nvr4ej8ezLkBi41FbJadisUgymSQejxOLxSiXywQCAXp6eqivr98zZLsVZFmmVCqxsrLC0tISiUQCURSx2+243W68Xq/WT1arFbPZrHk8bEWM6hyVJIlqtUq5XKZYLJJOp8lkMiSTSfL5PLIsEyqVqJubo6G7G9ehQwhNTQh6pjMdLxY66e4UZBmKRSV9wPIy9PXJdHXJlMsFjTzT6TSFQoFqtaqQhSxjXVmhFA4DaG5TNpsNr9dLfX09gUAAm822Lensdai2WzWCTCXLfD5PuVymXC6vC/MFiEQs1NdXMBq/zcGg9p3FYlEi3NxujcTVEGAAQZIgmYT795WyQgDNzdDeriRZ38Mbmo49AZ10nxWyrKRhHB5WcnUfPAgdHVtnKdzUn5KkZNn69V/f9L2vIsE+Kbabf7/4hZLMfRunjsfrO/W7i0WlnPzsLGSz4PMpA9jYqJbn0DVhHTuJh5Ku7j/zEMgyZDJKTu50Gg4fhrNnH64oPZQM9IW9Cdv1l/rnZ+oy9cN2u5JqsqtL2QTX1hQt+PZtJbVbczO0tYHHo3xGHycdzxE66W4BWVZI9uZNRcM9dgzCYf1UuuchCArJBoPKQ5ahUFC04Bs3lEzyfr+iBTc0KDWTdALWscPQSbcGsqyYAm/eVE6kx48rJ1B93b2iEASllltPj6IJiyLE44oWfOuWkg+4tVV5uN26FqxjR6CTLgrZrq0pZCuKCtkGg8pr+hp7TaAmXW9ogFBI+Vs+DwsL8PXXikYcCChacCikvFefHDqeAq816apkOziomPpOnlTW1eOspWg0is/n0/xPy+UyyWSSkLpgdbwwyLLMysoKwWBQ82AoFArk83kCgcCTf6E6AZxO6OuD3l5lN47FFC14cFC5gGtpUWzBTqeuBet4bLyW3gtbka3DkWdqagqv10uhUECWZQwGA62trTgcDnK5HHfu3Fnn8uVwOKhWq5qrVzqdJpfL4XK5sNvtJJNJThw9ysL/+l/MHz5MS0sLmUyGYDBIY2Pjy+6GXY2//Ms0+/bNU1fnJJfLYTabkWWZjo4OrFYrqVSKsbExDAaDFmXm8XjWhWBns1kymQx+vx9BECgUChw7duzZfKDV9ZLLwfy88iiVlN26sxPq6zdpwbIsMz4+Tjwep62tjUQiQWtrK36//xl7Sccuhe69oKLWjCBJcOLEt5pttWrh5s2btLS0sLq6SmtrK6Ojo3z00UdYrVZKpRL379+nUqlgMplwu91YLBai0Sgmk4m6ujpWV1epVCoAWikZwWBgYXaWSCjElStX+OCDD7h06RK/9Vu/9ZJ7Y3fDYrFx9epVenvbSafTNDQ0MDo6SiAQ0LK5LSwsaMl2rFYrLpeLVCpFsVgkHA4Ti8UQRZHp6WnMZjPBYPDZ3fTUz7tccOAA7N8P1SqsrsLMDFy/rvi5tbUptuAHeR4mJycplUp89dVXvPfee1y7do0PP/zw2dqiY0/itSDdWs1WlteTrQqDwUBvby8tLS3kcjl8Ph/Hjx8nnU4Tj8dpb2+nubmZSqWiJcq22WzU1dVhNBo17cpgMFCpVFheXtY04bpgEBobqa+vp1qtcu7cuZfXGXsERqOR7u4eDh3qZWBggEAgwPHjx4lGo6ytreH3+wmHw1pqTaPRiNvtJhAIYDAY1iVILxQKLC0tUa1WtUQ6OwZBULwcmpqUhyQpWvDcHFy5ApUKBIOEBYFiMEhDQwOiKHLixImda4OOPYVX2rwgy5BIwDffKP8+flw5/W2l7ORyOcbHxzl27Ng6bUgtirhV0pntkrSolRQaGxvpaGvD8LOfwa//+rrO1AMjHo7/9/+SdHTM8cYbh9f9XU2xuVWKxu3Go1gscuPGDdra2mhtbX1xfS/LihYcjSq24FgM2eFQtOCWFgSHQ7cDv5p4/SLSZBlSKYVsKxXFZrsd2X77GUXUnVqQ2vfVRqTpC+yx8YtfyLz7LthsOzweL2sMHoSEk8koWvDiojI5GxoUj4i6OsWHWJ8jrwJeH5uuGtQwOKj42Z448a33z6Pm8k4vRkEQvo0fzueVh67ZPBJqfotcTiCbVcyjO9FlL/1koXo3eL1w6JDyqFQgEoHxceVI5nAo+SGam5UoOvVzOl4p7GlNt7bp2axCtrmcQrYNDbtgvkoS/Kt/BX/xF/DP/zn8p/+kh7U9ArIM/+2/wR/+Ibz5JvzZn22ff+GVgaoFp9NKfojFRWXuNDYqWrDfv3WiDx27Fa+mpiuK8H//r6IUGI3KfD1xQpmnu4bXBEFJ1vA//gecO6cvmseAIMCZM8rB4OTJb/PRvNJQtWCfT3kcOQLlspIhbWRECZN0uxUtuKkJbDZ9Lu1hvFDS3UqrVnPMqg/49ihYW1V2/WcEfvxj+Jf/UvHa+bM/UxSCl+Wfvp1ckiTBm28inDmDfPo0PPBw2Eqml378fYnY2H+9vSIXLgh8//sysmwAvu2b16KfBEFR79vblUs3NT59dlYxRciykgykvV0h6VotWO3Lh/RTbX+r81StVr1xfr4W/f2CsePmBZU8y+UyuVyOVCpFLpcjk8lQKpUolUpartlakt04uOpr6mQAxY3IYrGwtNTKj35kwWwuc+aMmz/8QzdNTXbNFeh5VieoVqvk83mSySSZTIZ0Ok2xWKRUKmml1mvbbJBlglNTRHt6tM5UXxcEAZPJhMViwW634/F4cLvd+Hw+LRH685LnZULty1wuRyQSIRKJkMlkKJfLWt9MTwdpb1/DaKwCireCWpG5sbFxU6n31wayrARjLC8rJJxKKdnROjoUIv7FLxR7cVcXCILW18VikVgsRjQaJZlMUiwWNU+Q2mARNa+x0WjEbrfj9XppaGigvr5eK5762vX5k+P5ei/IsqxVZl1eXiaVSmk+kna7Hb/fj9PpxOPxYLVasVgsmM3mdUT7sEGs1YLV6rGFgkAulyWbjZPPZ8lmlYcsy7jdbhoaGmhsbMTr9WI0Gp9qkqi/mc1mWVxcZGVlhVwuhyAI2Gw2/H4/brcbj8eDzWbDarWuqxbxuDJVq1WtWGUqlSKbzWrPsizj8XgIh8NaCfJXoaLExMQEc3NzmEwmwuEwjY2NuN1uLbJsq2ob1WqVXC5HLBZjeXmZZDJJIBCgv79fizh77SDLiu1XTdh+/z78u38HlQryH/0RxQ8/ZHJmhvn5eQAtElLd1Leq1lG71lTlIhqNsrq6iiRJdHR00NXVpSXd17Eldp50ZVkmk8lo1WpNJhOhUIjGxkbq6urWFf97EQNTWyMrm82yurqqlfB2uVx0d3fT3Nz8WASsVvgdGxsjEongcDhoamoiHA7jdrufqza9lTzpdJrl5WUWFhYolUp7tuaXKIqMjIwwMzNDX18fnZ2dWCyWp94QJUkiGo0yPDyM2Wzm3LlzrzcRyDIsLiL/i39BpVhkMpWi8ru/i+0739FCp+Hp5626Yc7MzDAxMUE4HN6y+rMOYCdJVy2n/fXXXyNJkhbBpQYOqIuh1nFd1VLU6CC1CKNKXmoklyRJGAwG7YhuNBq1/AfVahWz2ayV6xZFEZPJRKVS0WLy1b8D2ner7Z2YmGB5eZne3l76+vq2jEiSZZl8Ps+1a9col8scPHiQpqYmjahFUUQURc0ksN1EU2ugqeXG1b5QPysIgvZ3td2qnGq/qM+1Zoja6rZ3797F6XRy+vTpPUE01WqVS5cuUVdXx5EjR7Q+rTXFPKw/a/tRDVZRx0CWZRYXF7l+/Trf+c538Hq9u74/nhskiUwiwWeXLnHw8GG6urvXzZ/H6Wv1PWrkpToP1TWovm9ycpKxsTHef/99nE7n69vnW2NnSFeWZebn57l16xbnzp2jvr5+U0dHo1FGRka4cOECRqORarVKoVBgYGCAs2fP4vF4mJqaYmVlhcbGRkKhEDdv3sRoNFIsFjUijUQinDhxgomJCbq7u5mYmODkyZNEIhHa29u5fPkyR44c4dq1a5w+fZqZmRnefPNNfv7zn3PkyBH279+/6chUrVYZHh4mHo/z3nvvbYpoSiaTfP7555w9e5ZwOLxJtrGxMYaGhujs7OTgwYNYrVZtwwDF5lgsFonH41y6dIm+vj7i8TgdHR309vZy69Yt4vE4NpuNfD6vaR5qWKr6rBJuZ2cn9+7d07T0dYPyYCy++eYbvvvd7+JyuR42hi8VsizzzTffYLVaOXTo0Lp+FUWRv/3bv+Wjjz5aJ7saAVgqlcjlcly9epX+/n46Ojr42c9+RjAY5PTp09p3ybJMMpnkypUrXLx4cWfDfPcQRFHk7//+77lw4QJ1dXWb+vrv/u7v+PDDDzGZTJv6ulwuU61WuXz5MocPH+b69euYTCY++ugjfvWrX1GpVHjvvfe0+S7LMrFYjIGBAS5evLinzV7PATvjMiZJEoODg3z44YcaYWyE0WhkaWmJn/3sZ9jtdi05TDKZ5Ouvv6ZYLLKwsMB3v/tdhoaG2LdvHzabjZGRETKZDG1tbVy8eJGBgQGampr48ssv2bdvH0tLS0QiEerq6ggEApRKJWw2G2+88QZWq5VcLqfF1a+srHDgwIF17VIzg504cYKbN29y7949ent7tddVYnj77bcJBAJb7tpGo5FYLEYoFOLatWukUilSqRR+vx9RFAkGg6TTac6dO8eZM2cQRZFCoUAsFqOvr4/6+npGRkbo7OxkYmICl8tFKBTi+vXrfPe73+UnP/kJv/M7v8Mf//Ef8/u///t89tlnWCyWLbORCYJAa2srJpOJmzdv8tZbbz3uML5wyLLM0tISH3300aZ+XVlZYWVlhdu3b5NIJFheXqa+vp5cLkc4HCaRSHD+/HksFgtra2u0t7cjCALz8/OcPn1a+x5BEPD5fLjdbs3W+zoiHo/j9/s3ES7A6uoq0WiUwcFBCoUCi4uL1NfXk81mtSRP58+fJ5/P43K5OHv2LNFoFFDm/tTU1DqvB0EQqK+vx+12s7a2Rn19/QuVdS9jR7cnURRJpVKk02ncbjcLCwvE43HsdsWzwGg00tbWRiwWo6WlRTPWh0Ih2tvbqVarRCIRGhoayOVydHd343A4OHr0KI2NjTQ1NbG4uMiBAwdYXV1lfHwck8mkVZQ9efIkDodjJ0XSUKlUaGho4PDhw8zOzrK8vIzf7ycWi9Hd3a0dw5LJJLdv30aSJBobGzGbzSwsLCDLsibD/v378Xg8WCwWuru7qVarHD16lGw2y5kzZ0gkEgSDQa1M+6sISZKYnJzk3Xff5csvv9RkXlxcXNef2WxWy5cwPz/P/v37CaoZ5reAfszdDEmSmJiY4O2332ZgYEDbmJaWlujq6tJOfarXkSRJLCws0NraysLCAk1NTXR2dr5kKV4dPJF5YW5ujlu3bnH+/PktzQvlcplsNkuhUMDj8ZDP5xEEAYvFgtfrBRRizmQyuFwuLW+teuQ2mUyYzWaMRiPpdBqHw0E2m8Vut2OxWMhms5ot1Gg0Eo/HCQQCFItF7HY72WyW+vr6TcfLxzUvfPHFF5w5c2ZL80Iul6NUKuH1eonH48iyjMVi0WzRPp+PUqmklRl3uVzIsozP5yObzVKpVPB4PCSTSbxer2abNBqNVCoVrFarJkc+n8fj8VAulzEajZuS7ciyzMLCAtevX+d73/venjQvSJKk9cXKygqXL1/G4/Fw7NgxrFYrdXV1lMtlisUi1WoVr9eruR2q6R03mhcuX76smSpeR2xnXpBlmUQigdfrJRKJcOXKFRwOB8ePH8dqtRIIBKhUKoiiSC6Xw+v1UiqVcDgcmkukz+dbd0Gumxceiud3kdbX10dTU9NT30I/T6iXAk9zkVapVOjv7yccDu+a29m9fpH2xRdfEAgE1l2kqVC9YYxGI6bcfBQAACAASURBVA6H44nk0S/S1iOdTvP5559z8OBBurq6NpGhenoQBOGpL8BEUWRqakq/SNsez9dlbHl5GUEQaGxsJBwO4/f7X6jLWG37q9Wq5jKm+nI6nU56enqe2GVsfHycSCSC1WqlpaXlpbiMVatVzWVseXmZfD6vmSf2qsvYvXv36OvrY9++fc/sMhaJRLhz5w4mk0l3GXsA1XtmcHCQSCRCX1/fM7uM1QZY3L9/n8nJSRobG3WXse3x/IIjagcjGo2yvLxMIpHQLtBcLpcWZeV0OnE4HJjNZkwm0xMFLaiLTBRFKpWKZsZQI95UkwaA0+kkFAoRDoefKWpJ1ZQzmQxLS0usrKxoGoLD4cDp9OH11uPzWTS5zGbzY/+e6jpXrVapVCrkcjmy2awW6ZbL5QBwu92Ew2Gampo0jWKvTvLa4IjZ2VnMZjPhcJiGhgYteGarY6pKJLlcjrVIhOW5OdaKRQKBAAcPHsTn8+3ZPnleUIOWJicnmZubw2AwUF9fT0NDA36/H5vNti1hqqeqYrFIIpEgEokQW13FPzGBu6mJ7osXse6xTf8F48Xm090YBqyGyuZyOfL5PNVqVfM0eJLvVH0GTSYTVqsVp9OJ0+nE6/VqNcmeNvrsSdqRzVa5fj3H+HieQ4cKmExR8vk8lUpFk2tdG2QZazxOacPtrmqXVm22TqcTl8ul3cI7HI5XWouQZVkLA56bizI4WKa3N70uhLr2vWoYcMjtJjwygvvMGQw9Pa9s/+wkVALeKgxY8eGFeNxKfX1Je7/RaNQiL0OhkBIGLAgIExMwPa3khOjv37ncm68WdlcS89rfq82vsB02milexiKTH6TFHR5WCsLu3w/79q3POb1RFk2m2iTmD7AbZNotkCT49FPo65Mf5HaRN82HdaHVsqzkof3qK6UA5JkzSrkcHU+E9eH1tXn2v52TW85LWVZS/N27B2NjEAzC4cPfVkTWAbstteNeymAkP6hAceuWkq/38GElU6OwRTazhxKowaDnQ90CsqwUCQ0EoK1NeNCvj+gjQVDyPb7zDkxMwM9/Dm+/rSQH1/v3sbGRVB97igqCstn19EB3t1IN+YsvlKQ7x44pz/o4PBR7Np/u84QsKxrtrVtKiatjx5Sk6LpXzM5BlpX1Go/DBx88xToVBOjtVTStX/5SOep2d+sL/kVB1Tza25WqxysrcPWqQshHjz66PtZrDJ10ayBJSsa84WFFmTp+fHPVYB3PDrmmrNKHHz7DZiYISm2xixeVBb+8rCSLN5n0QXuRMBiU5OrhsLKL3rqlmICOHt1lVQV2B1570lWz483Nwe3bSmWUCxeURP36un0+qFbh0iXFKrAjlSHMZnjrLZichJ/9TPlin08fwBcNQVA03PffV3bVW7eUnfXQIUUb1k1swGtMuup9wNSUkoy/oUGZK4+qHZlMJrFYLFq4sSiKWk6G3W6j3g2QJLhyBQ4eVDa4R3WZLMtEo1GCwaDmTpbNZpEkCY/H8+0bBUGxM4ZCirlh/37F/KCPyY5DlmUtXF+d84lEArvdjk0tJeT1KptfPq9oM0NDyph0db32VY/3dGHKp4F6+X33LszMQHNzFZNpgsZGv+YDbDKZCAQCuN1uKpUK4+PjmiuYGiBRrVaxWq1IkkQikdDcbHw+H7FYjP7+fiV8VxRZ+pM/YfnECZqamkin09TX17+WSVlkGe7cgfn5Nfr705hMJnK5HE6nk0qloiXxWVtbY35+XotQy+fzBAIBJEmiUqloyexzuZyWo0IQBLofpDKkUoFr1xSGP3cOzGZi8Tizs7M0NDRQKBS0ZPf6RqkoH3/yJ4tcvPhtcJDP56NSqdDW1gbA8vIyq6urGI1G/H4/a2tr+P1+jEYjhUJBqwaTzWZpamoikUjgcrmUvBmgVLsYHVWOlF1d0NennFAEQQucUddPLpejq6trL4dz7y7vhZcF+UFp7zt3lGKrvb3w0UdgNBr48Y+HSSRCLC0t0dLSwszMDG+//TZOpxNRFBkcHATAZrNRX19PqVQiEong8/lwOp2srKxQLpcxm81aRv7aTGf3pqdZ8nr5/PPPeffdd7l58ya/9Vu/9bK64qVAlhWT6/w8nD9v4i//8lPC4TDVahWXy8Xs7Cy/+Zu/idFoJBqNcu/ePSqVCqDkaXC73YCSmay9vZ14PI4oity9exdBEAiHw3R3dys/ZjYrpYSnpzVzw8rKClNTU3z++eecP3+eL7/8kt/5nd95Wd2x61Ctwscff4LHo/iIz8/Pk0gkCIfDmM1mpqenSSQSWi7jZDKJ1WrF7/czMzNDOBwmm81iMBiYnJxEFEX6+vpobW1VtFqbTbkkOXRIMQP9/OdKVdmDB5HNZq5fvw6gEa/T6dyU0vRVwStPurKsuHsNDcHamnLJfeLEt+YlWRa0RCyZTAaPx0N/fz+FQoEbN25w5MgRGhsbtbyjamket9uNzWajXC5TV1cHKG44qVRKS2BueWCw9Pp8VIJB3G43pVKJkydPvswueeFQ/ZyvXYPvfx+sVictLS0cPHiQq1ev0t3djdVqZWpqirq6Om1zK5fLiKKIJEmEQiEKhYKW2tPn82maciQS2ZQUCEFQNKoH3g0Os5lQMIjL5UIURU6dOvVyOmOXoqEhhNkc4MCBA9y+fZuenh7cbjdDQ0P09PTg8XgQBEGLDDUYDDQ0NFAqlbT1YjabsVgsZDIZVlZWNmuqqrtff79iapiZgU8+Aa+XJo8H2enEZrPhcrle6ZPgK2tekB8UUL11SzErHTmibKwbfWzL5TLXr1/n9OnTmypelMvlbXMHbwVJkrh69Sper5cDBw4o3yeKyBuCI2D3+yjvJKpVRbE5fVoxuUYiK6yurnLo0KF176utVvC4SKfTfPPNN3R3d2spILdqgDwwoJQ1v3BBC6Z4ncbgYRBF+OM/nubiRWFTCkf1BPckfbWyssLExAT79+9/9F2HJCEvLCh2X6dT8XjY+2Hduysi7XlDliEaVTRbWVbGsKFhe7u9Kv9ODfKm7xPF2nCfHfmNvQRZhi+/VDy7DhxQTxfPuc+3a8jMjOIP+NZbSoNew/HYCsoUlddFpD0Lnmp8JenbhSsIinN8MLhXx+jVt+nKslaXj+FhsNvhjTce73Z8p3fUjXkXKJe/fVgse3USPTHUMZmcVNaTSrjwnPt8+zdBZ6fi0vTLXyqmh/37X3sf0m+nqLBjU/SpxtdgUHx6GxogkVBCFUslRWsKh7cOA92j2JOabm2TJUmpPD06qrhmHjsGLtcuGR9RhH/zb+AnP4F/9s/ghz98LRa5LCtmnV/8Qrmk/sEPlHiFXQHVV/Drr5Wb1TNnIBJRCHlXTJoXC0mCP/gD+PGP4bd/G370o10yRWUZMhlF800mFR/D9vb1jdu94/VQTXc3dO8TQRSVBClra4rb109/qgTBvP++cmG9q4IaDAYlYcPSkrJj75qGPX/85Cfw7/+9olQ+fF9/wVBzB5w7pxDtj34Ev/EbiulhVzX0xUAQlPuOXTdFBUHJ4/Dmm8rijsWUxT4+DrOz8Nlnyo6xB/HCSffbzEaSdhOq3lBLkrRllikVogj/+3/Db/4m/Of/rLhjfv/7cOrUo4MaXiQ0+WQZ6d13kU6eRLpwQfn/Axn3Mh41htmszCefyPzgB/B7v7eLtNxaCIJifxocVBbyf/gPyjmbZ5ujewXfZhmTePttiZMnJd58U0KWd5l8gqAs7lOnlMVeKMC//bfwT/8p/NVfbUu8u3kMd2Q51FY6KBQKWhLudDpNqVSiWCxqBe/UjngYBEHAYDBgMBgwmUwP3LTsXL/ezX/8jzI2m0ChYKe/34zJpDLt82Hc2lSNlUqFQqFANpvVHlvJVzuggiQRuniRyMCAtiuoGZ5UGTe6ojmdTs0lzVITJ/u8bnRrZSyXy+TzebLZrDaOxWJRy79aO2m3w9qal9On4d13s8zPG4lEFPlsNhsej2edfKrHyIuoxKEGVagJ8LOZDLl//a8pvfUWlitXMP2f/0PkQcHURy3I2jE0Go1YrVasVit2u10bQ5fLhdVqfeEylkolTcZ0Ok0+n9fm6VakI0kCFy+G+PrriHZ630o+m82G3W7XxlCVT40UfK7yAVgsVLu6KEkSWYMBw+//PlI2y0xfH6VSiVKppM3RxyHVrXjGYrFo81PN2W2xWHZUxie26arvr1QqxGIxrVpEsVhELTLp8XhwuVzawlKFUQVclx91w3evz/MpaUReLJZZW7OythYln49SreaRZcWdpa6ujsbGRoLBoBae+7Sdo/52NpslGo0SiUTIZDKaO1PtonK73dpkfBz5NsqoLgC1AGOpVNKILpfLUSgUNJcdNZl0KBR65goS6u8XCgUikQgrKyuk02nK5TIGgwGHw4HX69XkVMfQZDJp8m1XIUORSwY2y1csFrWE9rVJ7Z1OJ8FgkIaGBgKBwBO7KG3VBlEUSSaTrKysEIvFtN8yGAy43W7t4XQ6vx1DoxEeJMJ/1BjWKhCSJGnjpyod6salblZ2u12r3BAIBJ6pfE5tG1KpFCsrigteLpdDFEVMJhNut1ur3OJwODQZ1TlaO0+3+u5a2VT5isWiVnhVHT913Xu9XhoaGrQqIE9bsUX9fYBSqUQsFiMSiXzLMZKE2WDA53BQ73RSHwhQfED+tfI9ao5uxTPqGKrFF9R5Wn5wAvJ4PNo89fl8DyuasDMuY2qm/+npaRYWFhAEgbq6OhoaGggGg1ok1ovyr1M7rFQqkUwmiUQiLC8vU6lUCIVC9PT04Pf7H6tKqVqeZGlpiXv37pHJZNYRgdfr1ep5vUj/QXVClMtlUqkUkUiESCRCoVDA5/Oxb98+wuHwY1fMkCSJWCzG5OQk8Xgcm81GMBiksbERv9//wmVUybFQKBCPx1leXiYWi2E0GmltbaWrqwu73f5Y7VE3kZmZGebn56lUKvh8Pm2jcrlcWj+96DFUZVxbWyMSiRCNRgEIhUJ0d3drZaUe57vU4qTT09Na1ehQKERjYyNut/uFaNW17QFFAVNrEy4tLWnVsLu6umhubn7sCihqSP309DSRSEQLx1c3KpvN9tyrw2yEyjNq/cVYLMbKyoqWg6WtrY2Ojo6N8/TZSFeWlYq6N27coFwu09PTQ2tr67qgAbWcTu3/1dLkSjkQpeFqozaWZFHNDbWJwNXPb3xWo2E2/qb6O9VqlUgkwsTEBPl8nmPHjtHc3LztjlepVLh9+zYLCwuEw2G6urrWLQK1rbWf3yhf7eBsJeNW5hRVho398DBZVRnVyTk5OUk0GqW9vZ2DBw8+tObV7Owsw8PDeDweent7CYVCWhDCRlk2yqj+v3aM1LZvN04bn7ebZ6pMtb9dKBSYm5tjenoau93OqVOnti3Eqc7Pb775hmKxSGdnJ+3t7dqJR33Pw+bnVuNXK7MkSRiNRi3/hvp9tf9XZVH7pLavtpKxUqmwvLzM5OQkxWKRI0eObBvcoc7r27dvMz8/T3NzM93d3VqU2FZjWHu8Vvt/K/lq27bV88b2rzOdbVgXta+nUimmpqZYXFykvb2dQ4cObY4afABJkpifn2d4eBiHw0FPTw+NjY3aiUftz41jKIriOhLebg1unLu1/fWk87f237lcjtnZWWZmZnA4HJw8eVIdk6cnXVmW5ZmZGe7cucO5c+eor6/fNCmWl5e5f/8+p06d0oiiUChoUV4ul4uZmRmWlpZob2/H4/Fw+/ZtXC4XkiTh9/sZGxsjkUhw5MgRZmdn6ezsZHJykgMHDjA8PMypU6e4fv063d3djIyM0NPTQyQS4dixY0xNTWmLckPbyefzDAwM4HA4OHPmzKYJl06n+eKLLzhw4ICWYGPjez799FPOnDmDw+HQBmF0dBSAw4cPa++dmpoiEomwb98+7HY7IyMjWsKcYDDIyMgIiUSCQCBALBbjH/2jf8Tly5c5f/48Q0ND+P1+kskkjY2N3L59m66uLsbGxvB4PHg8HrxerxauvHHijY+PMzU1xfvvv7+pHyRJ4ssvv0SWZd54441NmqMsy3z88ce8+eabmn1OkiStyu7BgwcRRZGBgQEEQeDMmTMMDw+Ty+Xw+Xx8/fXXvPHGG9y8eZPDhw8zPj5OZ2cni4uLBAIBcrkcBoMBi8VCOp0mGAwyPz9Pd3c3o6Oj9Pb2UqlUOHXqFHa7fV27ACKRCNeuXePkyZO0tLRsavvk5CTj4+OcOXOGYDCoTOqa96ytrTE0NMRbb72lzc9KpcLVq1c5ceIEPp8PUO4jBgYGMBqNnD59mlu3blEoFDAYDExPT3P48GHGxsY4e/YsQ0NDtLW1MT09jdls1o7gDQ0Nmt0/kUjQ2tqqbWyVSoXTp0+v2wxq5+mNGzcQBIE333xzk9aby+X49NNP6enpobe3d9M8LZfLfPzxx3z/+99fR3xXr15l3759NDc3k8lkGB4exuVycfjwYa5du4YgCFoCIL/fTzQapbm5mfv379Pd3c34+DgHDhxgdHSU/v5+RkdHaW5uZnBwUAvNttvtOJ1OTCYTPT09m2RT5+f09DQffPDBJvlFUeTy5csYjUZOnjy55clmdHQUQRDo6enRkk2pJ7azZ89qZJ5MJhkZGdFC+dU5azabGR0d5eLFi4yNjeF2u0mn05sUn42KhErq1WoVk8mELMtcuHBhE/kDRKNRBgYGOHjwIPv27csLgrAt6Rp/+MMfbvcalUrlh5cvX+ajjz7C7XZvuQvn83mGhoaYnZ1lcXGRiYkJlpaWSCQSJBIJxsfHGRwc5PDhwwwPD2s2yuHhYbxeL2azmWPHjpHNZtm3bx83btzA5/MxPj5OQ0MDIyMjnDp1itHRUY4ePaolwohGo5jNZlZWVmhra1NSytVAEAQsFgsdHR1MTExohv9afPHFF1y4cIGWlpZtjy2jo6PE43EGBwfJ5/PcvHmTqakpAoEAQ0NDrK2t4fV6+fjjj9m/fz+jo6NkMhlcLhefffYZ9+/fR5Zl3nnnHQqFAm+++SapVIpKpcLY2BiHDh1ieHiY1dVV7RIgmUzS09NDIBAgHo9TLBaZnp7m2LFjmzQLg8FAMBjE5/MxODhIZ2fnuvfMzc2RTqd58803ty15fufOnXUy3rp1i8nJSerr6xkaGmJmZoa1tTUymQzhcJgvvviClpYWisWiVrSwrq4Oq9VKe3s75XKZ/v5+UqkUJ06cIBqNcvbsWZaWljh//jxLS0ucOXOG1dVVMpkMIyMjm8KwVS3K5XLR0dHB5cuX6e3tXTfhi8UiAwMDXLx4UZufG+Url8sMDg6yuLjIzMwMMzMzLC4uaiXtx8bGcDgcxGIx7t+/Tzwep7W1lUuXLhEKhXA4HNqmd+vWLQ4cOEAsFsPv97Nv3z48Hg99fX0kEgmWl5eZn5/nBz/4AcvLy6ytrVEoFBgaGgIgEAhoJL9xnra3t2uJk9RcHiq++uorjhw5Qmdn57bz9MaNG8TjcW0sp6ammJiYwG63c/v2be7cuUMwGOT27du0t7czNDRELpfj448/xmAwcOXKFarVKteuXaNUKnHjxg0ymQx37txhdXWVyclJ5ufnsVgsnDhxgunpaVwuF5OTkwQCAQqFAk1NTZtkU+eny+ViZGSEtra2de2fnp5GFEXOnTu37fxUf//u3bvE43GGh4dZWloC0MbU7/dz69YtPB4PQ0NDtLS0cPfuXZLJJEePHiWdTtPS0sLIyAiRSIRqtcqvfvUrQqEQn3/+OQ0NDXz++ecEg0F++ctf4vV6GRgYwGKxcPv2bcrlMktLSxw5cmTbebpv3z6+/PJLOjo6KiaT6Y82CfIADzUkybKSrvBh9iZRFFldXSWbzWK1WpmbmyMej2sZuqrVKqFQiGQySSgUApSdu6GhgWq1iiiKRKNRQqEQlUqF5uZmGhsb2b9/P16vlxMnTrC2tkZvby+lUom1tTUcDgednZ1UKhUqlQqiKD5MDKxWK9Vqdcu2Pyq3QqVS4e7du7S2tjI/P6+Vs7ZYLFr59Pn5eTweD8lkUsv7mslkaG5upre3l3w+z9LSEk1NTVpKvLW1Na2se6VS4Xvf+x7FYpFwOExvby/lcplIJML777+P0+lcp1VvhCAI2Gy2LWWsVqubNqSNKJfLjI2N0d7ezuzsLLOzs1qV4mq1itlsxmQy4XA4KBQKeDwe7QIlHo9TKBSYnZ3FYDAwOjqK0+nk5s2b+P1+rl+/TigU4uuvvyYUCnHjxg3q6uoYHh7W5khTUxPZbHbb9qmTfOOpTM149bD5qZpiYrEYPp+Pubk5IpEIDodDSxWZyWRIJBLaJVs+n9dOKXV1dRQflHt/5513SCaTdHZ2Uq1WWVxc1ObFiRMnOHjwIJ2dncTjcdrb27Wq1f/kn/wTLUHMw2Cz2bTMarWoVCrYbLZtbZmqtjw1NUVrayv3799nfn5e0xqr1Sp+v59cLkcoFCKTyWgZ8VQzhd1u19rX1tZGqVSip6eHbDZLX18fqVSKAwcOaGk3Dx8+TLlcJhAIaOaPh52aHyZb7QlnK1SrVebn57FarRSLRebm5sjlcjgcDq26eCwWQxAEstksDQ0NZLNZjEYjTqeTe/fuaelDq9Uqv/Zrv0apVOL3fu/3KBaL/O7v/i6SJPHbv/3b2Gw2/vE//seEQiHee+89enp6OHHiBGfOnKG5uXmdN9FGqFz5SO+sR5kXbt++TSQS4fz58zgcjk0DXygUSKVSZLNZ6urqyGQy2i2/mimoUqmQSqXweDzaDavL5SKfz2M2mzUSi8fjuN1uUqkULpeLQqGgkaIkSfh8PrLZLGazWctLu7q6qmlZG9pOtVpleHiYRCLBd77znXWJVGRZSY791VdfcfbsWRobG5UO2XB8jUQiGAwGrS2VSgVZljWbk4pyuUw6ndYWrSpjoVDQSq2rnzcajVgsFlKpFHa7nXg8jtfrXXfbr/al0+kkk8lsedmijt3S0hIDAwO88847m7IzVatVPv1USaN48ODBLU0oakaoYrGoEa0sy4TDYY3wksmkZtc0GAxaP8RiMTweD6lUCq/Xq42zqu3ncjnsdjulUgmz2ayV/TYYDFSrVerq6iiXy4RCoS3ly2azfPnll7S3t7N///5NbR8cHCSZTHL27Nkt56e6UauXj5lMRttQVCVARTKZxGQyaV4Ooiji8Xg0z4dKpYLT6SSXy1FfX08mk8Hr9WoXR8lkEq/XS7FYxGq1ksvltPZYLJYtiVM9gt+5c4fl5WU++OCDTbbPRCLBpUuXOH36tKZN1n6PKIqsrKwgSRJWq5VSqaS93tTUpJkc4vG4tu5qXaXy+TxOp5NsNqsdvT0ez7pnt9u9Lvexy+XSEperc8G3RaIaWZa1+fnee+9t0vQrlQqffPIJLS0t9Pf3b6nJp1IpSqUSqVSKuro6LYWk6mFT+1sbZTQajSSTScrlMuFwWJMFFJIsl8vYbDYKhYK21jbOZ3WM1YvKrWTM5XJcvXqVYDDI0aNHH2peeKyLtMXFRQYHB/H5fOzfv59AIPBCPRUeF6osmUxGOw719PSwf//+LTNXqYv6+vXrFAoFent7tUvC3SabClXGYrHI7OwsU1NTuN1u3njjjS1JB9AW9czMDB0dHXR3d+N0KnNit8mpXnKtrq5y9+5dcrkcb7zxxrYJx9X5efPmTdxuN319fQSDwRd+y/0kUG2u6XSayclJFhcX2bdvH/39/euOrrXvz+fzfP311xQKBS1P7bO61j0vyLLi7z0/P8/4+DhOp5PTp09v64lSrVYZGRlhZmaGlpYWLa3ki/Y0eRKom2U0GmVsbIx8Ps/x48fVTW5nXMYkSSIajTI+Pq5pos3NzTQ0NKxzx3mRUBdooVAgFouxtLRELBbDarVqXhaP466i7lTqbStAfX09zc3Nmhb9sjYZVcZisUg8Htd8MkE5Bj6uW5XqqTE7O8u9e/e0o2FLS4vmjvMyZKx1x4lEIprLUSAQoK+vj/r6+ke6U6kkFovFmJiYIJFIYLFYCIfDhMNhPB6PRlAvQz5QNj7VrUr1bXc4HHR3d9PS0vLY8zSbzTI5OcnS0hJGo5HGxkaamprw+XwvRUa17yuVimbXjkQilMtlWltb6enp0fzKH/U9qhnh3r17WnWP5uZmzSb8sjbSWhlVv+hIJEI+n6e+vp7e3l5NEX2AnU3tqJJAKpXSOlg9RqnRKj6fD5fLpTllq8frJ+0w9bdEUdQipfL5PMlkknQ6TTabRRRFLel1U1MTgUDgsf0Ct/vNUqm0bnGUSiVMJpMWEOH1enE6nTgcDiwWi2bLeRxfy42oDVNUneuz2SypVEpz0FZtz4FAgHA4TH19vWZbeho51QkUj8dZXFwkHo9rMqoO9T6fD4fDgcPhwGw2azI+6e/VylcsFrWggVQqRSqVolAoAOByuWhoaKCpqQm32/3UG4C6QHK5HCsrK1rghyiKWCwW3G635hGiymcymZ5aPlVGNcy0UChoAQTpdJpMJqPJqPp+NzU14fV6n5pE1DWbz+c1//RkMqnNE1U+VUa73b5OxqeVTw1UyufzpFIpTT517vh8PsLhMA0NDZqd9mnlU4Nb1DJBKseowVcejwe3243dbsdut2sc8zTy1fKM6n2iypjJZMhkMpTLZYxGI16vl8bGRhoaGh4WpPR88+mqn69Wq+TzeTKZjNZYNfxQFEVtMdRO7K1sI+pzrc+dagN1OBw4nU68Xq8Wivi8w2TVNqk1uVT51IgctbpBrXxqW7Y7Dm+UT5XRarWuk9HtdmvE/rxkrB1/NUQ2nU6vk1G9rKxt7+OMoerHqspns9lwOBzriE8lhOchW2171PBYdRGp4bGFQuGJ5FO/c+MY1oaS1sqoEoNq3noRMqqEXytjtVrVouhqZXxS+ex2uxaxqMr3vEOBazlGjWqs3dCKxeK28m3VpofxjHrBWBuV+RThzs9EuquP+vbHQe2k2Dig6xqzYTI8bGLsJmwnX+1rsN5ZIg3IMQAAAHZJREFUXn28iLj1nUCtXOrt7OOM4V6U71FzVH1W5dsLcxTWh/c+7hx91eSDF8IzBaBtuxcfRbo6dOjQoWMHsefy6erQoUPHXoZOujp06NDxAqGTrg4dOnS8QOikq0OHDh0vEDrp6tChQ8cLhE66OnTo0PEC8f8Bsn3GBJs8ilMAAAAASUVORK5CYII=\n", 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\n", 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" ] @@ -473,7 +593,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", "text/plain": [ "
" ] @@ -578,17 +698,17 @@ { "data": { "image/svg+xml": [ - "\n", - "\n", + "\n", + "\n", "G\n", - "\n", - "\n", + "\n", + "\n", "cluster_legend\n", "\n", "\n", - "\n", - "node4\n", - "\n", + "\n", + "node7\n", + "\n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -734,50 +854,48 @@ " \n", " \n", " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -785,15 +903,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -801,57 +919,57 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", " \n", + " \n", " \n", " \n", + " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", "\n", "\n", - "\n", - "leaf7\n", - "\n", - "\n", + "\n", + "leaf13\n", + "\n", + "\n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -874,17 +992,17 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -892,16 +1010,66 @@ " \n", "\n", "\n", - "\n", - "node4->leaf7\n", - "\n", - "\n", + "\n", + "node7->leaf13\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "leaf14\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n", + "\n", + "\n", + "node7->leaf14\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "leaf8\n", - "\n", - "\n", + "\n", + "\n", " \n", " \n", "\n", "\n", - "\n", - "node4->leaf8\n", - "\n", - "\n", - "\n", "\n", - "\n", - "node1\n", - "\n", + "\n", + "node4\n", + "\n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", " \n", - " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -1142,18 +1312,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1161,7 +1328,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1171,50 +1338,58 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", "\n", "\n", - "\n", - "node1->node4\n", - "\n", - "\n", + "\n", + "node4->node7\n", + "\n", + "\n", "\n", "\n", - "\n", - "node2\n", - "\n", + "\n", + "node4->leaf8\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "node1\n", + "\n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -1417,10 +1579,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1428,7 +1590,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1436,37 +1598,37 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -1475,67 +1637,21 @@ " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", "\n", "\n", - "\n", - "\n", - "leaf3\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "\n", - "\n", - "\n", - "node1->leaf3\n", - "\n", - "\n", + "\n", + "node1->node4\n", + "\n", + "\n", "\n", "\n", - "\n", - "node5\n", - "\n", + "\n", + "node2\n", + "\n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -1730,15 +1854,18 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -1746,56 +1873,106 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", "\n", "\n", - "\n", - "node6\n", - "\n", + "\n", + "\n", + "leaf3\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n", + "\n", + "\n", + "node1->leaf3\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "node9\n", + "\n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1941,48 +2118,50 @@ " \n", " \n", " \n", - " \n", - " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -1990,15 +2169,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2006,58 +2185,55 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", "\n", "\n", - "\n", - "\n", - "leaf9\n", - "\n", - "\n", + "\n", + "node10\n", + "\n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n", + "\n", + "\n", + "\n", + "leaf15\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", "\n", "\n", - "\n", - "node5->leaf9\n", - "\n", - "\n", + "\n", + "node9->leaf15\n", + "\n", + "\n", "\n", "\n", - "\n", - "leaf10\n", - "\n", - "\n", + "\n", + "leaf16\n", + "\n", + "\n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2153,16 +2585,16 @@ " \n", "\n", "\n", - "\n", - "node5->leaf10\n", - "\n", - "\n", + "\n", + "node9->leaf16\n", + "\n", + "\n", "\n", "\n", - "\n", - "leaf11\n", - "\n", - "\n", + "\n", + "leaf17\n", + "\n", + "\n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", "\n", "\n", - "\n", - "node6->leaf11\n", - "\n", - "\n", + "\n", + "node10->leaf17\n", + "\n", + "\n", "\n", "\n", - "\n", - "leaf12\n", - "\n", - "\n", + "\n", + "leaf18\n", + "\n", + "\n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2252,27 +2691,15 @@ " \n", "\n", "\n", - "\n", - "node6->leaf12\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "node2->node5\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "node2->node6\n", - "\n", - "\n", + "\n", + "node10->leaf18\n", + "\n", + "\n", "\n", "\n", - "\n", - "node0\n", - "\n", + "\n", + "node5\n", + "\n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -2467,15 +2894,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2483,74 +2910,68 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", + " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", "\n", "\n", - "\n", - "node0->node1\n", - "\n", - 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" \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2796,7 +13772,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2806,7 +13782,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2817,7 +13793,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2834,7 +13810,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2846,15 +13822,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2862,57 +13838,58 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", "\n", "\n", - "\n", - "leaf7\n", - "\n", - "\n", + "\n", + "\n", + "leaf15\n", + "\n", + "\n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -2953,16 +13927,16 @@ " \n", "\n", "\n", - "\n", - "node4->leaf7\n", - "\n", - "\n", + "\n", + "node9->leaf15\n", + "\n", + "\n", "\n", "\n", - "\n", - "leaf8\n", - "\n", - "\n", + "\n", + "leaf16\n", + "\n", + "\n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3005,15 +13977,16 @@ " \n", "\n", "\n", - "\n", - "node4->leaf8\n", - "\n", - "\n", + "\n", + "node9->leaf16\n", + "\n", + "\n", "\n", "\n", - "\n", - "node1\n", - "\n", + "\n", + "leaf17\n", + "\n", + "\n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + "\n", + "\n", + "\n", + "node10->leaf17\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "leaf18\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", - " \n", - " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", "\n", "\n", - "\n", - "node1->node4\n", - "\n", - "\n", + "\n", + "node10->leaf18\n", + "\n", + "\n", "\n", "\n", - "\n", - "node2\n", - "\n", + "\n", + "node5\n", + "\n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -3478,18 +14286,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3497,106 +14302,68 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", + " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", "\n", "\n", + "\n", + "node5->node9\n", + "\n", + "\n", + "\n", "\n", - "\n", - "leaf3\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "\n", - "\n", - "\n", - "node1->leaf3\n", - "\n", - "\n", + "\n", + "node5->node10\n", + "\n", + "\n", "\n", "\n", - "\n", - "node5\n", - "\n", + "\n", + "node6\n", + "\n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3745,7 +14512,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3756,7 +14523,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3764,7 +14531,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3780,7 +14547,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3791,15 +14558,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3807,56 +14574,57 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", "\n", "\n", - "\n", - "node6\n", - "\n", + "\n", + "\n", + "node12\n", + "\n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4005,7 +14773,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4016,7 +14784,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4024,7 +14792,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4040,7 +14808,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4051,15 +14819,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4067,7 +14835,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4083,42 +14851,40 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", "\n", "\n", - "\n", - "\n", - "leaf9\n", - "\n", - "\n", + "\n", + "leaf19\n", + "\n", + "\n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4161,16 +14927,16 @@ " \n", "\n", "\n", - "\n", - "node5->leaf9\n", - "\n", - "\n", + "\n", + "node12->leaf19\n", + "\n", + "\n", "\n", "\n", - "\n", - "leaf10\n", - "\n", - "\n", + "\n", + "leaf20\n", + "\n", + "\n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4214,16 +14979,22 @@ " \n", "\n", "\n", - "\n", - "node5->leaf10\n", - "\n", - "\n", + "\n", + "node12->leaf20\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", + "node6->node12\n", + "\n", + "\n", + "\n", + "\n", + "\n", "leaf11\n", - "\n", - "\n", + "\n", + "\n", " \n", " \n", "\n", "\n", - "\n", + "\n", "node6->leaf11\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "leaf12\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "\n", - "\n", - "\n", - "node6->leaf12\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "node2->node5\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "node2->node6\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "node0\n", - "\n", + "\n", + "\n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4487,7 +15210,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4528,10 +15251,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4544,7 +15267,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4561,10 +15284,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4581,37 +15304,85 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", "\n", "\n", - "\n", + "\n", "node0->node1\n", - "\n", - "\n", - "<\n", + "\n", + "\n", + "<\n", "\n", "\n", - "\n", + "\n", "node0->node2\n", - "\n", - "\n", - "≥\n", + "\n", + "\n", + "≥\n", + "\n", + "\n", + "\n", + "\n", + "X_y\n", + "\n", + "\n", + "Pclass\n", + "\n", + "Age\n", + "\n", + "Fare\n", + "\n", + "Sex_label\n", + "\n", + "Cabin_label\n", + "\n", + "Embarked_label\n", + "\n", + "3.00\n", + "\n", + "4.00\n", + "\n", + "16.70\n", + "\n", + "0.00\n", + "\n", + "145.00\n", + "\n", + "2.00\n", + "\n", + "\n", + "\n", + "leaf13->X_y\n", + "\n", + "\n", + "  Prediction\n", + " 1\n", "\n", "\n", "\n", "\n", "\n", "\n", - "\n", + "\n", + "\n", "legend\n", - "\n", - "\n", + "\n", + "\n", " \n", " " ], "text/plain": [ - "" + "" ] }, - "execution_count": 18, + "execution_count": 46, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "trees.dtreeviz(xgb_shadow)" + "trees.dtreeviz(xgb_shadow, show_just_path=False, X=x)" ] }, { @@ -5039,7 +15810,7 @@ "3.0 <= Pclass \n", "Fare < 23.35\n", "Sex_label < 1.0\n", - "None\n" + "\n" ] } ], @@ -5049,7 +15820,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 48, "metadata": {}, "outputs": [ { @@ -5057,9 +15828,10 @@ "output_type": "stream", "text": [ "3.0 <= Pclass \n", + "Age < 36.5\n", "Fare < 23.35\n", "Sex_label < 1.0\n", - "None\n" + "\n" ] } ], @@ -5126,14 +15898,14 @@ { "data": { "image/svg+xml": [ - "\n", - "\n", + "\n", + "\n", "G\n", - "\n", + "\n", "\n", - "\n", + "\n", "node4\n", - "\n", + "\n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -5235,10 +16007,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5257,7 +16029,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5280,7 +16052,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5316,10 +16088,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5333,7 +16105,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5347,7 +16119,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5363,13 +16135,13 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5380,16 +16152,16 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", "\n", "\n", - "\n", + "\n", "leaf7\n", - "\n", - "\n", + "\n", + "\n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -5416,10 +16188,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5437,7 +16209,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5454,7 +16226,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5470,7 +16242,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5510,22 +16282,22 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", "\n", "\n", - "\n", + "\n", "node4->leaf7\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "leaf8\n", - "\n", - "\n", + "\n", + "\n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -5624,10 +16396,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5645,7 +16417,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5662,7 +16434,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5678,7 +16450,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5722,21 +16494,21 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", "\n", "\n", - "\n", + "\n", "node4->leaf8\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "node1\n", - "\n", + "\n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -5980,10 +16752,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6002,7 +16774,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6025,7 +16797,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6062,10 +16834,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6079,7 +16851,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6093,7 +16865,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6109,13 +16881,13 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6126,21 +16898,21 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", "\n", "\n", - "\n", + "\n", "node1->node4\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "node2\n", - "\n", + "\n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - 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\n", " \n", " \n", " \n", @@ -6865,7 +17637,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6905,10 +17677,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6922,7 +17694,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6936,7 +17708,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6952,13 +17724,13 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6969,17 +17741,17 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", "\n", "\n", "\n", - "\n", + "\n", "leaf3\n", - "\n", - "\n", + "\n", + "\n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", 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" \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -7147,10 +17919,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7168,7 +17940,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7185,7 +17957,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7201,7 +17973,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7247,21 +18019,21 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", "\n", "\n", - "\n", + "\n", "node1->leaf3\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "node5\n", - "\n", + "\n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - 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\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -7885,10 +18657,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7904,7 +18676,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7944,10 +18716,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7965,7 +18737,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7979,7 +18751,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7995,13 +18767,13 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8012,15 +18784,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", "\n", "\n", - "\n", + "\n", "node6\n", - "\n", + "\n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " 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\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -8127,10 +18899,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8149,7 +18921,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8172,7 +18944,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8208,10 +18980,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8225,7 +18997,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8239,7 +19011,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8252,13 +19024,13 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8269,17 +19041,17 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", "\n", "\n", "\n", - "\n", + "\n", "leaf9\n", - "\n", - "\n", + "\n", + "\n", " \n", " \n", " \n", " \n", - " 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\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -8719,10 +19491,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8740,7 +19512,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8757,7 +19529,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8773,7 +19545,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8818,22 +19590,22 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", "\n", "\n", - "\n", + "\n", "node5->leaf9\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "leaf10\n", - "\n", - "\n", + "\n", + "\n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " 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\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -9041,10 +19813,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9062,7 +19834,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9079,7 +19851,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9095,7 +19867,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9141,22 +19913,22 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", "\n", "\n", - "\n", + "\n", "node5->leaf10\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "leaf11\n", - "\n", - "\n", + "\n", + "\n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -9240,10 +20012,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9261,7 +20033,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9278,7 +20050,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9294,7 +20066,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9338,22 +20110,22 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", "\n", "\n", - "\n", + "\n", "node6->leaf11\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "leaf12\n", - "\n", - "\n", + "\n", + "\n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -9400,10 +20172,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9421,7 +20193,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9438,7 +20210,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9454,7 +20226,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9497,33 +20269,33 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", "\n", "\n", - "\n", + "\n", "node6->leaf12\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "node2->node5\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "node2->node6\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "node0\n", - "\n", + "\n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " 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+21368,24 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", "\n", "\n", - "\n", + "\n", "node0->node1\n", - "\n", - "\n", - "<\n", + "\n", + "\n", + "<\n", "\n", "\n", - "\n", + "\n", "node0->node2\n", - "\n", - "\n", - "≥\n", + "\n", + "\n", + "≥\n", "\n", "\n", "\n", @@ -10623,7 +21395,7 @@ "" ], "text/plain": [ - "" + "" ] }, "execution_count": 28, @@ -10649,7 +21421,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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\n", 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\n", 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" ] @@ -11023,7 +21795,7 @@ "2.0 <= Pclass \n", "1.0 <= Survived \n", "Fare < 27.83\n", - "None\n" + "\n" ] } ], @@ -11045,7 +21817,7 @@ "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] @@ -11067,7 +21839,7 @@ "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] @@ -11329,7 +22101,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.3" + "version": "3.7.6" }, "toc": { "base_numbering": 1, From 6ea3bd38caaaccea5bbd2d015c24b446b5e13c04 Mon Sep 17 00:00:00 2001 From: tlapusan Date: Fri, 25 Sep 2020 16:39:55 +0300 Subject: [PATCH 02/16] Implemented is_fit, is_classifier, get_left_children, get_right_children methods --- dtreeviz/models/lightgbm_decision_tree.py | 18 +- dtreeviz/models/shadow_decision_tree.py | 8 +- dtreeviz/models/sklearn_decision_trees.py | 11 +- .../dtreeviz_lightgbm_visualisations.ipynb | 312 +-- setup.py | 3 +- testing/dtreeviz/models/conftest.py | 4 + .../models/fixtures/dataset_lightgbm.csv | 713 ++++++ .../fixtures/lightgbm_model_classifier.txt | 1931 +++++++++++++++++ .../test_decision_tree_lightgbm_classifier.py | 34 + .../test_decision_tree_spark_classifier.py | 4 +- 10 files changed, 2886 insertions(+), 152 deletions(-) create mode 100644 testing/dtreeviz/models/fixtures/dataset_lightgbm.csv create mode 100644 testing/dtreeviz/models/fixtures/lightgbm_model_classifier.txt create mode 100644 testing/dtreeviz/models/test_decision_tree_lightgbm_classifier.py diff --git a/dtreeviz/models/lightgbm_decision_tree.py b/dtreeviz/models/lightgbm_decision_tree.py index 4f6103c6..10b1e590 100644 --- a/dtreeviz/models/lightgbm_decision_tree.py +++ b/dtreeviz/models/lightgbm_decision_tree.py @@ -10,18 +10,18 @@ class ShadowLightGBMTree(ShadowDecTree): def __init__(self, - tree_model: Booster, + booster: Booster, tree_index: int, x_data: (pd.DataFrame, np.ndarray), y_data: (pd.Series, np.ndarray), feature_names: List[str] = None, target_name: str = None, class_names: (List[str], Mapping[int, str]) = None): - self.tree_model = tree_model + self.booster = booster self.tree_index = tree_index self.tree_nodes, self.children_left, self.children_right = self._get_nodes_info() - super().__init__(tree_model, x_data, y_data, feature_names, target_name, class_names) + super().__init__(booster, x_data, y_data, feature_names, target_name, class_names) def _get_nodes_info(self): tree_nodes = {} @@ -51,7 +51,7 @@ def _convert_dict_to_list(my_dict): my_list[key] = value return my_list - tree_dump = self.tree_model.dump_model()["tree_info"][self.tree_index] + tree_dump = self.booster.dump_model()["tree_info"][self.tree_index] _walk_tree(tree_dump["tree_structure"], node_index) for node in tree_nodes.values(): @@ -65,10 +65,16 @@ def _convert_dict_to_list(my_dict): return tree_node_list, children_left_list, children_right_list def is_fit(self) -> bool: - return True + return isinstance(self.booster, Booster) def is_classifier(self) -> bool: - pass + objective = self.booster.dump_model(num_iteration=1)["objective"] + if "binary" in objective: + return True + elif objective in ["regression", "regression_l1", "huber", "fair", "poisson", "quantile", "mape", "gamma", + "tweedie"]: + return False + raise Exception(f"objective {objective} is not yet supported by dtreeviz's lightgbm implementation") def get_class_weights(self): pass diff --git a/dtreeviz/models/shadow_decision_tree.py b/dtreeviz/models/shadow_decision_tree.py index a23b8f82..6120e8b3 100644 --- a/dtreeviz/models/shadow_decision_tree.py +++ b/dtreeviz/models/shadow_decision_tree.py @@ -47,9 +47,9 @@ def __init__(self, Target's name :param class_names: List[str], Mapping[int, str] Class' names (in case of a classifier) - """ + # TODO check if we need the self.tree_model self.tree_model = tree_model if not self.is_fit(): raise Exception(f"Model {tree_model} is not fit.") @@ -57,10 +57,8 @@ def __init__(self, self.feature_names = feature_names self.target_name = target_name self.class_names = class_names - # self.class_weight = self.get_class_weight() self.x_data = ShadowDecTree._get_x_data(x_data) self.y_data = ShadowDecTree._get_y_data(y_data) - # self.node_to_samples = self.get_node_samples() self.root, self.leaves, self.internal = self._get_tree_nodes() if class_names: self.class_names = self._get_class_names() @@ -448,7 +446,9 @@ def get_shadow_tree(tree_model, x_data, y_data, feature_names, target_name, clas from dtreeviz.models import xgb_decision_tree return xgb_decision_tree.ShadowXGBDTree(tree_model, tree_index, x_data, y_data, feature_names, target_name, class_names) - else: raise ValueError(f"Tree model must be in (DecisionTreeRegressor, DecisionTreeClassifier, xgboost.core.Booster, but was {tree_model.__class__.__name__}") + else: + raise ValueError( + f"Tree model must be in (DecisionTreeRegressor, DecisionTreeClassifier, xgboost.core.Booster, but was {tree_model.__class__.__name__}") class ShadowDecTreeNode(): diff --git a/dtreeviz/models/sklearn_decision_trees.py b/dtreeviz/models/sklearn_decision_trees.py index 95ef25b8..8ed9fa94 100644 --- a/dtreeviz/models/sklearn_decision_trees.py +++ b/dtreeviz/models/sklearn_decision_trees.py @@ -15,6 +15,7 @@ def __init__(self, tree_model, target_name: str = None, class_names: (List[str], Mapping[int, str]) = None): + self.tree_model = tree_model self.node_to_samples = None super().__init__(tree_model, x_data, y_data, feature_names, target_name, class_names) @@ -105,13 +106,13 @@ def get_feature_path_importance(self, node_list): gini_importance[self.tree_model.tree_.feature[node]] += self.tree_model.tree_.weighted_n_node_samples[ node] * \ - self.tree_model.tree_.impurity[node] \ - - self.tree_model.tree_.weighted_n_node_samples[ + self.tree_model.tree_.impurity[node] \ + - self.tree_model.tree_.weighted_n_node_samples[ node_left] * \ - self.tree_model.tree_.impurity[node_left] \ - - self.tree_model.tree_.weighted_n_node_samples[ + self.tree_model.tree_.impurity[node_left] \ + - self.tree_model.tree_.weighted_n_node_samples[ node_right] * \ - self.tree_model.tree_.impurity[node_right] + self.tree_model.tree_.impurity[node_right] normalizer = np.sum(gini_importance) if normalizer > 0.0: gini_importance /= normalizer diff --git a/notebooks/dtreeviz_lightgbm_visualisations.ipynb b/notebooks/dtreeviz_lightgbm_visualisations.ipynb index ad198815..1035f9cb 100644 --- a/notebooks/dtreeviz_lightgbm_visualisations.ipynb +++ b/notebooks/dtreeviz_lightgbm_visualisations.ipynb @@ -9,7 +9,7 @@ }, { "cell_type": "code", - "execution_count": 159, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -21,7 +21,7 @@ }, { "cell_type": "code", - "execution_count": 178, + "execution_count": 42, "metadata": {}, "outputs": [], "source": [ @@ -50,7 +50,7 @@ }, { "cell_type": "code", - "execution_count": 162, + "execution_count": 43, "metadata": {}, "outputs": [], "source": [ @@ -59,7 +59,7 @@ }, { "cell_type": "code", - "execution_count": 163, + "execution_count": 44, "metadata": {}, "outputs": [ { @@ -311,7 +311,7 @@ "[891 rows x 12 columns]" ] }, - "execution_count": 163, + "execution_count": 44, "metadata": {}, "output_type": "execute_result" } @@ -322,7 +322,7 @@ }, { "cell_type": "code", - "execution_count": 164, + "execution_count": 45, "metadata": {}, "outputs": [], "source": [ @@ -335,7 +335,7 @@ }, { "cell_type": "code", - "execution_count": 165, + "execution_count": 124, "metadata": {}, "outputs": [], "source": [ @@ -345,7 +345,7 @@ }, { "cell_type": "code", - "execution_count": 166, + "execution_count": 125, "metadata": {}, "outputs": [], "source": [ @@ -354,7 +354,7 @@ }, { "cell_type": "code", - "execution_count": 167, + "execution_count": 126, "metadata": {}, "outputs": [ { @@ -508,7 +508,7 @@ "[712 rows x 6 columns]" ] }, - "execution_count": 167, + "execution_count": 126, "metadata": {}, "output_type": "execute_result" } @@ -519,7 +519,7 @@ }, { "cell_type": "code", - "execution_count": 168, + "execution_count": 127, "metadata": {}, "outputs": [], "source": [ @@ -536,7 +536,7 @@ }, { "cell_type": "code", - "execution_count": 170, + "execution_count": 128, "metadata": {}, "outputs": [], "source": [ @@ -547,7 +547,6 @@ " 'min_data_in_leaf': 20, # Data set is quite small so reduce this a bit\n", " 'feature_fraction': 0.7, # Proportion of features in each boost, controls overfitting\n", " 'num_leaves': 41, # Controls size of tree since LGBM uses leaf wise splits\n", - " 'metric': 'binary_error', # Area under ROC curve as the evaulation metric\n", " 'drop_rate': 0.15,\n", " 'max_depth':4,\n", " \"seed\":1212\n", @@ -556,7 +555,7 @@ }, { "cell_type": "code", - "execution_count": 171, + "execution_count": 129, "metadata": {}, "outputs": [ { @@ -564,7 +563,7 @@ "output_type": "stream", "text": [ "[LightGBM] [Info] Number of positive: 268, number of negative: 444\n", - "[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000660 seconds.\n", + "[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000661 seconds.\n", "You can set `force_row_wise=true` to remove the overhead.\n", "And if memory is not enough, you can set `force_col_wise=true`.\n", "[LightGBM] [Info] Total Bins 233\n", @@ -572,205 +571,205 @@ "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.376404 -> initscore=-0.504838\n", "[LightGBM] [Info] Start training from score -0.504838\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", - "[1]\ttraining's binary_error: 0.376404\tvalid_1's binary_error: 0.413408\n", + "[1]\ttraining's binary_logloss: 0.640939\tvalid_1's binary_logloss: 0.658567\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", - "[2]\ttraining's binary_error: 0.376404\tvalid_1's binary_error: 0.413408\n", + "[2]\ttraining's binary_logloss: 0.621711\tvalid_1's binary_logloss: 0.637865\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", - "[3]\ttraining's binary_error: 0.376404\tvalid_1's binary_error: 0.413408\n", + "[3]\ttraining's binary_logloss: 0.613448\tvalid_1's binary_logloss: 0.628601\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", - "[4]\ttraining's binary_error: 0.376404\tvalid_1's binary_error: 0.413408\n", + "[4]\ttraining's binary_logloss: 0.618758\tvalid_1's binary_logloss: 0.634066\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", - "[5]\ttraining's binary_error: 0.376404\tvalid_1's binary_error: 0.413408\n", + "[5]\ttraining's binary_logloss: 0.611054\tvalid_1's binary_logloss: 0.626046\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", - "[6]\ttraining's binary_error: 0.359551\tvalid_1's binary_error: 0.385475\n", + "[6]\ttraining's binary_logloss: 0.5955\tvalid_1's binary_logloss: 0.611146\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", - "[7]\ttraining's binary_error: 0.330056\tvalid_1's binary_error: 0.351955\n", + "[7]\ttraining's binary_logloss: 0.58875\tvalid_1's binary_logloss: 0.603778\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", - "[8]\ttraining's binary_error: 0.22191\tvalid_1's binary_error: 0.234637\n", + "[8]\ttraining's binary_logloss: 0.597049\tvalid_1's binary_logloss: 0.605336\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", - "[9]\ttraining's binary_error: 0.223315\tvalid_1's binary_error: 0.234637\n", + "[9]\ttraining's binary_logloss: 0.593235\tvalid_1's binary_logloss: 0.602332\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", - "[10]\ttraining's binary_error: 0.206461\tvalid_1's binary_error: 0.22905\n", + "[10]\ttraining's binary_logloss: 0.592783\tvalid_1's binary_logloss: 0.602075\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", - "[11]\ttraining's binary_error: 0.210674\tvalid_1's binary_error: 0.234637\n", + "[11]\ttraining's binary_logloss: 0.585948\tvalid_1's binary_logloss: 0.595022\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", - "[12]\ttraining's binary_error: 0.203652\tvalid_1's binary_error: 0.223464\n", + "[12]\ttraining's binary_logloss: 0.579765\tvalid_1's binary_logloss: 0.58828\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", - "[13]\ttraining's binary_error: 0.206461\tvalid_1's binary_error: 0.223464\n", + "[13]\ttraining's binary_logloss: 0.580939\tvalid_1's binary_logloss: 0.589965\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", - "[14]\ttraining's binary_error: 0.195225\tvalid_1's binary_error: 0.22905\n", + "[14]\ttraining's binary_logloss: 0.580072\tvalid_1's binary_logloss: 0.589449\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", - "[15]\ttraining's binary_error: 0.188202\tvalid_1's binary_error: 0.22905\n", + "[15]\ttraining's binary_logloss: 0.565899\tvalid_1's binary_logloss: 0.576587\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", - "[16]\ttraining's binary_error: 0.182584\tvalid_1's binary_error: 0.22905\n", + "[16]\ttraining's binary_logloss: 0.552591\tvalid_1's binary_logloss: 0.565089\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", - "[17]\ttraining's binary_error: 0.182584\tvalid_1's binary_error: 0.234637\n", + "[17]\ttraining's binary_logloss: 0.557147\tvalid_1's binary_logloss: 0.568919\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", - "[18]\ttraining's binary_error: 0.178371\tvalid_1's binary_error: 0.234637\n", + "[18]\ttraining's binary_logloss: 0.556348\tvalid_1's binary_logloss: 0.568146\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", - "[19]\ttraining's binary_error: 0.183989\tvalid_1's binary_error: 0.240223\n", + "[19]\ttraining's binary_logloss: 0.558855\tvalid_1's binary_logloss: 0.570841\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", - "[20]\ttraining's binary_error: 0.182584\tvalid_1's binary_error: 0.240223\n", + "[20]\ttraining's binary_logloss: 0.548215\tvalid_1's binary_logloss: 0.561395\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", - "[21]\ttraining's binary_error: 0.186798\tvalid_1's binary_error: 0.234637\n", + "[21]\ttraining's binary_logloss: 0.550811\tvalid_1's binary_logloss: 0.563784\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", - "[22]\ttraining's binary_error: 0.186798\tvalid_1's binary_error: 0.234637\n", + "[22]\ttraining's binary_logloss: 0.549959\tvalid_1's binary_logloss: 0.563336\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", - "[23]\ttraining's binary_error: 0.176966\tvalid_1's binary_error: 0.223464\n", + "[23]\ttraining's binary_logloss: 0.537481\tvalid_1's binary_logloss: 0.552264\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", - "[24]\ttraining's binary_error: 0.174157\tvalid_1's binary_error: 0.217877\n", + "[24]\ttraining's binary_logloss: 0.541707\tvalid_1's binary_logloss: 0.555525\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", - "[25]\ttraining's binary_error: 0.167135\tvalid_1's binary_error: 0.223464\n", + "[25]\ttraining's binary_logloss: 0.536407\tvalid_1's binary_logloss: 0.549906\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", - "[26]\ttraining's binary_error: 0.169944\tvalid_1's binary_error: 0.217877\n", + "[26]\ttraining's binary_logloss: 0.537403\tvalid_1's binary_logloss: 0.550955\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", - "[27]\ttraining's binary_error: 0.169944\tvalid_1's binary_error: 0.206704\n", + "[27]\ttraining's binary_logloss: 0.532393\tvalid_1's binary_logloss: 0.545874\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", - "[28]\ttraining's binary_error: 0.176966\tvalid_1's binary_error: 0.212291\n", + "[28]\ttraining's binary_logloss: 0.534353\tvalid_1's binary_logloss: 0.547562\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", - "[29]\ttraining's binary_error: 0.175562\tvalid_1's binary_error: 0.212291\n", + "[29]\ttraining's binary_logloss: 0.53476\tvalid_1's binary_logloss: 0.547888\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", - "[30]\ttraining's binary_error: 0.18118\tvalid_1's binary_error: 0.223464\n", + "[30]\ttraining's binary_logloss: 0.536781\tvalid_1's binary_logloss: 0.550096\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", - "[31]\ttraining's binary_error: 0.172753\tvalid_1's binary_error: 0.206704\n", + "[31]\ttraining's binary_logloss: 0.525707\tvalid_1's binary_logloss: 0.540535\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", - "[32]\ttraining's binary_error: 0.164326\tvalid_1's binary_error: 0.201117\n", + "[32]\ttraining's binary_logloss: 0.520982\tvalid_1's binary_logloss: 0.535947\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", - "[33]\ttraining's binary_error: 0.171348\tvalid_1's binary_error: 0.217877\n", + "[33]\ttraining's binary_logloss: 0.511546\tvalid_1's binary_logloss: 0.527521\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", - "[34]\ttraining's binary_error: 0.16573\tvalid_1's binary_error: 0.206704\n", + "[34]\ttraining's binary_logloss: 0.515536\tvalid_1's binary_logloss: 0.531158\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", - "[35]\ttraining's binary_error: 0.164326\tvalid_1's binary_error: 0.189944\n", + "[35]\ttraining's binary_logloss: 0.51139\tvalid_1's binary_logloss: 0.526897\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", - "[36]\ttraining's binary_error: 0.168539\tvalid_1's binary_error: 0.201117\n", + "[36]\ttraining's binary_logloss: 0.502231\tvalid_1's binary_logloss: 0.519538\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", - "[37]\ttraining's binary_error: 0.171348\tvalid_1's binary_error: 0.201117\n", + "[37]\ttraining's binary_logloss: 0.506038\tvalid_1's binary_logloss: 0.522807\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", - "[38]\ttraining's binary_error: 0.155899\tvalid_1's binary_error: 0.184358\n", + "[38]\ttraining's binary_logloss: 0.502896\tvalid_1's binary_logloss: 0.518761\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", - "[39]\ttraining's binary_error: 0.155899\tvalid_1's binary_error: 0.178771\n", + "[39]\ttraining's binary_logloss: 0.50794\tvalid_1's binary_logloss: 0.522753\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", - "[40]\ttraining's binary_error: 0.155899\tvalid_1's binary_error: 0.178771\n", + "[40]\ttraining's binary_logloss: 0.51014\tvalid_1's binary_logloss: 0.524781\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", - "[41]\ttraining's binary_error: 0.158708\tvalid_1's binary_error: 0.173184\n", + "[41]\ttraining's binary_logloss: 0.502009\tvalid_1's binary_logloss: 0.518216\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", - "[42]\ttraining's binary_error: 0.158708\tvalid_1's binary_error: 0.173184\n", + "[42]\ttraining's binary_logloss: 0.503493\tvalid_1's binary_logloss: 0.519647\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", - "[43]\ttraining's binary_error: 0.151685\tvalid_1's binary_error: 0.178771\n", + "[43]\ttraining's binary_logloss: 0.505827\tvalid_1's binary_logloss: 0.52115\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", - "[44]\ttraining's binary_error: 0.150281\tvalid_1's binary_error: 0.178771\n", + "[44]\ttraining's binary_logloss: 0.507748\tvalid_1's binary_logloss: 0.523087\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", - "[45]\ttraining's binary_error: 0.150281\tvalid_1's binary_error: 0.173184\n", + "[45]\ttraining's binary_logloss: 0.499966\tvalid_1's binary_logloss: 0.516922\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", - "[46]\ttraining's binary_error: 0.150281\tvalid_1's binary_error: 0.195531\n", + "[46]\ttraining's binary_logloss: 0.492749\tvalid_1's binary_logloss: 0.511242\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", - "[47]\ttraining's binary_error: 0.154494\tvalid_1's binary_error: 0.195531\n", + "[47]\ttraining's binary_logloss: 0.486069\tvalid_1's binary_logloss: 0.506232\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", - "[48]\ttraining's binary_error: 0.150281\tvalid_1's binary_error: 0.195531\n", + "[48]\ttraining's binary_logloss: 0.48192\tvalid_1's binary_logloss: 0.502655\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", - "[49]\ttraining's binary_error: 0.146067\tvalid_1's binary_error: 0.195531\n", + "[49]\ttraining's binary_logloss: 0.478171\tvalid_1's binary_logloss: 0.499608\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", - "[50]\ttraining's binary_error: 0.143258\tvalid_1's binary_error: 0.184358\n", + "[50]\ttraining's binary_logloss: 0.472476\tvalid_1's binary_logloss: 0.495484\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", - "[51]\ttraining's binary_error: 0.143258\tvalid_1's binary_error: 0.189944\n", + "[51]\ttraining's binary_logloss: 0.470073\tvalid_1's binary_logloss: 0.49259\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", - "[52]\ttraining's binary_error: 0.143258\tvalid_1's binary_error: 0.189944\n", + "[52]\ttraining's binary_logloss: 0.472979\tvalid_1's binary_logloss: 0.494933\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", - "[53]\ttraining's binary_error: 0.143258\tvalid_1's binary_error: 0.178771\n", + "[53]\ttraining's binary_logloss: 0.475346\tvalid_1's binary_logloss: 0.49683\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", - "[54]\ttraining's binary_error: 0.143258\tvalid_1's binary_error: 0.173184\n", + "[54]\ttraining's binary_logloss: 0.468404\tvalid_1's binary_logloss: 0.49058\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", - "[55]\ttraining's binary_error: 0.143258\tvalid_1's binary_error: 0.173184\n", + "[55]\ttraining's binary_logloss: 0.461846\tvalid_1's binary_logloss: 0.485623\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", - "[56]\ttraining's binary_error: 0.143258\tvalid_1's binary_error: 0.173184\n", + "[56]\ttraining's binary_logloss: 0.464086\tvalid_1's binary_logloss: 0.487428\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", - "[57]\ttraining's binary_error: 0.143258\tvalid_1's binary_error: 0.173184\n", + "[57]\ttraining's binary_logloss: 0.466253\tvalid_1's binary_logloss: 0.489323\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", - "[58]\ttraining's binary_error: 0.143258\tvalid_1's binary_error: 0.173184\n", + "[58]\ttraining's binary_logloss: 0.469335\tvalid_1's binary_logloss: 0.491784\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", - "[59]\ttraining's binary_error: 0.143258\tvalid_1's binary_error: 0.173184\n", + "[59]\ttraining's binary_logloss: 0.471966\tvalid_1's binary_logloss: 0.493935\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", - "[60]\ttraining's binary_error: 0.143258\tvalid_1's binary_error: 0.173184\n", + "[60]\ttraining's binary_logloss: 0.464868\tvalid_1's binary_logloss: 0.488109\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", - "[61]\ttraining's binary_error: 0.143258\tvalid_1's binary_error: 0.184358\n", + "[61]\ttraining's binary_logloss: 0.459943\tvalid_1's binary_logloss: 0.484701\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", - "[62]\ttraining's binary_error: 0.144663\tvalid_1's binary_error: 0.184358\n", + "[62]\ttraining's binary_logloss: 0.461884\tvalid_1's binary_logloss: 0.48634\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", - "[63]\ttraining's binary_error: 0.144663\tvalid_1's binary_error: 0.178771\n", + "[63]\ttraining's binary_logloss: 0.455884\tvalid_1's binary_logloss: 0.481261\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", - "[64]\ttraining's binary_error: 0.143258\tvalid_1's binary_error: 0.178771\n", + "[64]\ttraining's binary_logloss: 0.458999\tvalid_1's binary_logloss: 0.483648\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", - "[65]\ttraining's binary_error: 0.144663\tvalid_1's binary_error: 0.189944\n", + "[65]\ttraining's binary_logloss: 0.453018\tvalid_1's binary_logloss: 0.479658\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", - "[66]\ttraining's binary_error: 0.141854\tvalid_1's binary_error: 0.178771\n", + "[66]\ttraining's binary_logloss: 0.450743\tvalid_1's binary_logloss: 0.477097\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", - "[67]\ttraining's binary_error: 0.140449\tvalid_1's binary_error: 0.184358\n", + "[67]\ttraining's binary_logloss: 0.445282\tvalid_1's binary_logloss: 0.47333\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", - "[68]\ttraining's binary_error: 0.140449\tvalid_1's binary_error: 0.184358\n", + "[68]\ttraining's binary_logloss: 0.446671\tvalid_1's binary_logloss: 0.474649\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", - "[69]\ttraining's binary_error: 0.140449\tvalid_1's binary_error: 0.184358\n", + "[69]\ttraining's binary_logloss: 0.449065\tvalid_1's binary_logloss: 0.476274\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", - "[70]\ttraining's binary_error: 0.140449\tvalid_1's binary_error: 0.184358\n", + "[70]\ttraining's binary_logloss: 0.45185\tvalid_1's binary_logloss: 0.47838\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", - "[71]\ttraining's binary_error: 0.140449\tvalid_1's binary_error: 0.178771\n", + "[71]\ttraining's binary_logloss: 0.449139\tvalid_1's binary_logloss: 0.476315\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", - "[72]\ttraining's binary_error: 0.140449\tvalid_1's binary_error: 0.178771\n", + "[72]\ttraining's binary_logloss: 0.451161\tvalid_1's binary_logloss: 0.477907\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", - "[73]\ttraining's binary_error: 0.143258\tvalid_1's binary_error: 0.178771\n", + "[73]\ttraining's binary_logloss: 0.44564\tvalid_1's binary_logloss: 0.473323\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", - "[74]\ttraining's binary_error: 0.143258\tvalid_1's binary_error: 0.178771\n", + "[74]\ttraining's binary_logloss: 0.441195\tvalid_1's binary_logloss: 0.470672\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", - "[75]\ttraining's binary_error: 0.143258\tvalid_1's binary_error: 0.178771\n", + "[75]\ttraining's binary_logloss: 0.443346\tvalid_1's binary_logloss: 0.47225\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", - "[76]\ttraining's binary_error: 0.143258\tvalid_1's binary_error: 0.178771\n", + "[76]\ttraining's binary_logloss: 0.445156\tvalid_1's binary_logloss: 0.473473\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", - "[77]\ttraining's binary_error: 0.144663\tvalid_1's binary_error: 0.178771\n", + "[77]\ttraining's binary_logloss: 0.442355\tvalid_1's binary_logloss: 0.471216\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", - "[78]\ttraining's binary_error: 0.146067\tvalid_1's binary_error: 0.178771\n", + "[78]\ttraining's binary_logloss: 0.437473\tvalid_1's binary_logloss: 0.467788\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", - "[79]\ttraining's binary_error: 0.144663\tvalid_1's binary_error: 0.178771\n", + "[79]\ttraining's binary_logloss: 0.43288\tvalid_1's binary_logloss: 0.463857\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", - "[80]\ttraining's binary_error: 0.144663\tvalid_1's binary_error: 0.178771\n", + "[80]\ttraining's binary_logloss: 0.430735\tvalid_1's binary_logloss: 0.462257\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", - "[81]\ttraining's binary_error: 0.144663\tvalid_1's binary_error: 0.178771\n", + "[81]\ttraining's binary_logloss: 0.426096\tvalid_1's binary_logloss: 0.459126\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", - "[82]\ttraining's binary_error: 0.144663\tvalid_1's binary_error: 0.178771\n", + "[82]\ttraining's binary_logloss: 0.421839\tvalid_1's binary_logloss: 0.456389\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", - "[83]\ttraining's binary_error: 0.144663\tvalid_1's binary_error: 0.178771\n", + "[83]\ttraining's binary_logloss: 0.424049\tvalid_1's binary_logloss: 0.457922\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", - "[84]\ttraining's binary_error: 0.144663\tvalid_1's binary_error: 0.178771\n", + "[84]\ttraining's binary_logloss: 0.425955\tvalid_1's binary_logloss: 0.459369\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", - "[85]\ttraining's binary_error: 0.144663\tvalid_1's binary_error: 0.178771\n", + "[85]\ttraining's binary_logloss: 0.428071\tvalid_1's binary_logloss: 0.460796\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", - "[86]\ttraining's binary_error: 0.143258\tvalid_1's binary_error: 0.178771\n", + "[86]\ttraining's binary_logloss: 0.423691\tvalid_1's binary_logloss: 0.457797\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", - "[87]\ttraining's binary_error: 0.146067\tvalid_1's binary_error: 0.178771\n", + "[87]\ttraining's binary_logloss: 0.425278\tvalid_1's binary_logloss: 0.458791\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", - "[88]\ttraining's binary_error: 0.146067\tvalid_1's binary_error: 0.178771\n", + "[88]\ttraining's binary_logloss: 0.421503\tvalid_1's binary_logloss: 0.456482\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", - "[89]\ttraining's binary_error: 0.146067\tvalid_1's binary_error: 0.178771\n", + "[89]\ttraining's binary_logloss: 0.423744\tvalid_1's binary_logloss: 0.45801\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", - "[90]\ttraining's binary_error: 0.146067\tvalid_1's binary_error: 0.178771\n", + "[90]\ttraining's binary_logloss: 0.421604\tvalid_1's binary_logloss: 0.456472\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", - "[91]\ttraining's binary_error: 0.146067\tvalid_1's binary_error: 0.178771\n", + "[91]\ttraining's binary_logloss: 0.423473\tvalid_1's binary_logloss: 0.457807\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", - "[92]\ttraining's binary_error: 0.143258\tvalid_1's binary_error: 0.178771\n", + "[92]\ttraining's binary_logloss: 0.419479\tvalid_1's binary_logloss: 0.455986\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", - "[93]\ttraining's binary_error: 0.141854\tvalid_1's binary_error: 0.178771\n", + "[93]\ttraining's binary_logloss: 0.422009\tvalid_1's binary_logloss: 0.457592\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", - "[94]\ttraining's binary_error: 0.141854\tvalid_1's binary_error: 0.178771\n", + "[94]\ttraining's binary_logloss: 0.423807\tvalid_1's binary_logloss: 0.458851\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", - "[95]\ttraining's binary_error: 0.141854\tvalid_1's binary_error: 0.178771\n", + "[95]\ttraining's binary_logloss: 0.425966\tvalid_1's binary_logloss: 0.460372\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", - "[96]\ttraining's binary_error: 0.143258\tvalid_1's binary_error: 0.178771\n", + "[96]\ttraining's binary_logloss: 0.428078\tvalid_1's binary_logloss: 0.461829\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", - "[97]\ttraining's binary_error: 0.141854\tvalid_1's binary_error: 0.178771\n", + "[97]\ttraining's binary_logloss: 0.424218\tvalid_1's binary_logloss: 0.458384\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", - "[98]\ttraining's binary_error: 0.141854\tvalid_1's binary_error: 0.184358\n", + "[98]\ttraining's binary_logloss: 0.425655\tvalid_1's binary_logloss: 0.459594\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", - "[99]\ttraining's binary_error: 0.141854\tvalid_1's binary_error: 0.178771\n", + "[99]\ttraining's binary_logloss: 0.42746\tvalid_1's binary_logloss: 0.460839\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", - "[100]\ttraining's binary_error: 0.141854\tvalid_1's binary_error: 0.173184\n" + "[100]\ttraining's binary_logloss: 0.423387\tvalid_1's binary_logloss: 0.457639\n" ] } ], @@ -780,8 +779,10 @@ }, { "cell_type": "code", - "execution_count": 173, - "metadata": {}, + "execution_count": 130, + "metadata": { + "scrolled": true + }, "outputs": [ { "data": { @@ -1004,7 +1005,7 @@ " 'pandas_categorical': []}" ] }, - "execution_count": 173, + "execution_count": 130, "metadata": {}, "output_type": "execute_result" } @@ -1015,16 +1016,36 @@ }, { "cell_type": "code", - "execution_count": 174, + "execution_count": 118, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "'tweedie'" ] }, - "execution_count": 174, + "execution_count": 118, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "lgbm_model.dump_model(num_iteration=1)[\"objective\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 69, "metadata": {}, "output_type": "execute_result" }, @@ -1047,16 +1068,16 @@ }, { "cell_type": "code", - "execution_count": 175, + "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 175, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" }, @@ -1089,7 +1110,7 @@ }, { "cell_type": "code", - "execution_count": 176, + "execution_count": 27, "metadata": {}, "outputs": [ { @@ -1477,10 +1498,10 @@ "
\n" ], "text/plain": [ - "" + "" ] }, - "execution_count": 176, + "execution_count": 27, "metadata": {}, "output_type": "execute_result" } @@ -1592,7 +1613,9 @@ { "cell_type": "code", "execution_count": 153, - "metadata": {}, + "metadata": { + "scrolled": true + }, "outputs": [ { "data": { @@ -1625,18 +1648,26 @@ "# dtreeviz" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Save the model" + ] + }, { "cell_type": "code", - "execution_count": null, + "execution_count": 34, "metadata": {}, "outputs": [], "source": [ - "ShadowLightGBMTree" + "# lgbm_model.save_model(\"../testing/dtreeviz/models/fixtures/lightgbm_model_classifier.txt\")\n", + "# lgbm = lgb.Booster(model_file=\"../testing/dtreeviz/models/fixtures/lightgbm_model_classifier.txt\")" ] }, { "cell_type": "code", - "execution_count": 184, + "execution_count": 54, "metadata": {}, "outputs": [], "source": [ @@ -1645,10 +1676,23 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 56, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "data": { + "text/plain": [ + "numpy.ndarray" + ] + }, + "execution_count": 56, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "type(light_dtree.get_children_left())" + ] }, { "cell_type": "code", diff --git a/setup.py b/setup.py index ab73ccf5..c7284e0d 100644 --- a/setup.py +++ b/setup.py @@ -8,7 +8,8 @@ url='https://github.com/parrt/dtreeviz', license='MIT', packages=find_packages(), - install_requires=['graphviz>=0.9','pandas','numpy','scikit-learn','matplotlib','colour', 'xgboost', 'pytest', 'pyspark'], + install_requires=['graphviz>=0.9','pandas','numpy','scikit-learn','matplotlib','colour', 'xgboost', 'pytest', 'pyspark', + 'lightgbm'], python_requires='>=3.6', author='Terence Parr, Tudor Lapusan, and Prince Grover', author_email='parrt@cs.usfca.edu', diff --git a/testing/dtreeviz/models/conftest.py b/testing/dtreeviz/models/conftest.py index 3ecc62bd..3717a288 100644 --- a/testing/dtreeviz/models/conftest.py +++ b/testing/dtreeviz/models/conftest.py @@ -35,3 +35,7 @@ def y_dataset_regressor(dataset) -> pd.Series: @pytest.fixture() def dataset_spark() -> pd.DataFrame: return pd.read_csv("fixtures/dataset_spark.csv") + +@pytest.fixture() +def dataset_lightgbm() -> pd.DataFrame: + return pd.read_csv("fixtures/dataset_lightgbm.csv") \ No newline at end of file diff --git a/testing/dtreeviz/models/fixtures/dataset_lightgbm.csv b/testing/dtreeviz/models/fixtures/dataset_lightgbm.csv new file mode 100644 index 00000000..69db6748 --- /dev/null +++ b/testing/dtreeviz/models/fixtures/dataset_lightgbm.csv @@ -0,0 +1,713 @@ +,Pclass,Age,Fare,Sex_label,Cabin_label,Embarked_label,Survived +331,1,45.5,28.5,1,56,2,0 +733,2,23.0,13.0,1,-1,2,0 +382,3,32.0,7.925,1,-1,2,0 +704,3,26.0,7.8542,1,-1,2,0 +813,3,6.0,31.275,0,-1,2,0 +118,1,24.0,247.5208,1,36,0,0 +536,1,45.0,26.55,1,26,2,0 +361,2,29.0,27.7208,1,-1,0,0 +29,3,29.69911764705882,7.8958,1,-1,2,0 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+leaf_weight=4.8207417502999323 5.245101034641265 9.2136362940073031 11.879214555025102 10.36711858212948 47.58261613547802 14.619299396872519 7.3729167133569709 3.2931643277406684 3.4529967904090881 9.3338668644428235 9.4889924153685552 +leaf_count=31 22 39 51 44 264 90 32 22 18 40 59 +internal_value=0 0.0231294 -0.0241641 0.000445269 -0.027439 -0.0316447 -0.0116985 0.0521673 0.0410964 -0.0151069 0.0593335 +internal_weight=0 48.0096 88.6601 26.9537 83.415 62.2019 17.74 21.0559 8.27374 21.2131 12.7822 +internal_count=712 245 467 115 445 354 76 130 49 91 81 +shrinkage=0.05 + + +end of trees + +feature_importances: +Age=308 +Fare=308 +Cabin_label=165 +Embarked_label=123 +Pclass=98 +Sex_label=69 + +parameters: +[boosting: dart] +[objective: binary] +[metric: binary_error] +[tree_learner: serial] +[device_type: cpu] +[data: ] +[valid: ] +[num_iterations: 100] +[learning_rate: 0.05] +[num_leaves: 16] +[num_threads: 0] +[force_col_wise: 0] +[force_row_wise: 0] +[histogram_pool_size: -1] +[max_depth: 4] +[min_data_in_leaf: 20] +[min_sum_hessian_in_leaf: 0.001] +[bagging_fraction: 1] +[pos_bagging_fraction: 1] +[neg_bagging_fraction: 1] +[bagging_freq: 0] +[bagging_seed: 25892] +[feature_fraction: 0.7] +[feature_fraction_bynode: 1] +[feature_fraction_seed: 3653] +[extra_trees: 0] +[extra_seed: 19633] +[early_stopping_round: 0] +[first_metric_only: 0] +[max_delta_step: 0] +[lambda_l1: 0] +[lambda_l2: 0] +[min_gain_to_split: 0] +[drop_rate: 0.15] +[max_drop: 50] +[skip_drop: 0.5] +[xgboost_dart_mode: 0] +[uniform_drop: 0] +[drop_seed: 12966] +[top_rate: 0.2] +[other_rate: 0.1] +[min_data_per_group: 100] +[max_cat_threshold: 32] +[cat_l2: 10] +[cat_smooth: 10] +[max_cat_to_onehot: 4] +[top_k: 20] +[monotone_constraints: ] +[monotone_constraints_method: basic] +[monotone_penalty: 0] +[feature_contri: ] +[forcedsplits_filename: ] +[refit_decay_rate: 0.9] +[cegb_tradeoff: 1] +[cegb_penalty_split: 0] +[cegb_penalty_feature_lazy: ] +[cegb_penalty_feature_coupled: ] +[path_smooth: 0] +[interaction_constraints: ] +[verbosity: 1] +[saved_feature_importance_type: 0] +[max_bin: 255] +[max_bin_by_feature: ] +[min_data_in_bin: 3] +[bin_construct_sample_cnt: 200000] +[data_random_seed: 3996] +[is_enable_sparse: 1] +[enable_bundle: 1] +[use_missing: 1] +[zero_as_missing: 0] +[feature_pre_filter: 1] +[pre_partition: 0] +[two_round: 0] +[header: 0] +[label_column: ] +[weight_column: ] +[group_column: ] +[ignore_column: ] +[categorical_feature: ] +[forcedbins_filename: ] +[objective_seed: 25511] +[num_class: 1] +[is_unbalance: 0] +[scale_pos_weight: 1] +[sigmoid: 1] +[boost_from_average: 1] +[reg_sqrt: 0] +[alpha: 0.9] +[fair_c: 1] +[poisson_max_delta_step: 0.7] +[tweedie_variance_power: 1.5] +[lambdarank_truncation_level: 20] +[lambdarank_norm: 1] +[label_gain: ] +[eval_at: ] +[multi_error_top_k: 1] +[auc_mu_weights: ] +[num_machines: 1] +[local_listen_port: 12400] +[time_out: 120] +[machine_list_filename: ] +[machines: ] +[gpu_platform_id: -1] +[gpu_device_id: -1] +[gpu_use_dp: 0] + +end of parameters + +pandas_categorical:[] diff --git a/testing/dtreeviz/models/test_decision_tree_lightgbm_classifier.py b/testing/dtreeviz/models/test_decision_tree_lightgbm_classifier.py new file mode 100644 index 00000000..c844b648 --- /dev/null +++ b/testing/dtreeviz/models/test_decision_tree_lightgbm_classifier.py @@ -0,0 +1,34 @@ +import lightgbm as lgb +import pytest +from dtreeviz.models.lightgbm_decision_tree import ShadowLightGBMTree +import numpy as np + + +@pytest.fixture() +def lgb_dec_tree(): + return lgb.Booster(model_file="fixtures/lightgbm_model_classifier.txt") + + +@pytest.fixture() +def shadow_dec_tree(lgb_dec_tree, dataset_lightgbm) -> ShadowLightGBMTree: + features = ["Pclass", "Age", "Fare", "Sex_label", "Cabin_label", "Embarked_label"] + target = "Survived" + return ShadowLightGBMTree(lgb_dec_tree, 1, dataset_lightgbm[features], dataset_lightgbm[target], features, target) + + +def test_is_fit(shadow_dec_tree: ShadowLightGBMTree): + assert shadow_dec_tree.is_fit() is True + + +def test_is_classifier(shadow_dec_tree: ShadowLightGBMTree): + assert shadow_dec_tree.is_classifier() is True, "Should be a classifier decision tree" + + +def test_get_children_left(shadow_dec_tree: ShadowLightGBMTree): + assert np.array_equal(shadow_dec_tree.get_children_left(), np.array( + [1, 2, 3, 4, -1, -1, -1, 8, 9, -1, -1, -1, 13, 14, 15, -1, -1, 18, -1, -1, 21, 22, -1, -1, -1])) + + +def test_get_children_right(shadow_dec_tree): + assert np.array_equal(shadow_dec_tree.get_children_right(), np.array( + [12, 7, 6, 5, -1, -1, -1, 11, 10, -1, -1, -1, 20, 17, 16, -1, -1, 19, -1, -1, 24, 23, -1, -1, -1])) diff --git a/testing/dtreeviz/models/test_decision_tree_spark_classifier.py b/testing/dtreeviz/models/test_decision_tree_spark_classifier.py index ef3142ec..574f1692 100644 --- a/testing/dtreeviz/models/test_decision_tree_spark_classifier.py +++ b/testing/dtreeviz/models/test_decision_tree_spark_classifier.py @@ -105,7 +105,8 @@ def test_get_min_samples_leaf(spark_dtree): def test_get_thresholds(spark_dtree): assert np.array_equal(spark_dtree.get_thresholds(), - np.array([(list([0.0]), list([1.0, 2.0])), 3.5, 2.5, -1, -1, -1, 2.5, 3.5, 1.5, -1, -1, -1, 24.808349999999997, + np.array([(list([0.0]), list([1.0, 2.0])), 3.5, 2.5, -1, -1, -1, 2.5, 3.5, 1.5, -1, -1, -1, + 24.808349999999997, (list([1.0, 2.0]), list([0.0, 3.0])), -1, -1, -1])) # assert np.array_equal(spark_dtree.get_thresholds(), # np.array([list([0.0]), 3.5, 2.5, -1, -1, -1, 2.5, 3.5, 1.5, -1, -1, -1, 24.808349999999997, @@ -148,4 +149,3 @@ def test_is_categorical_split(spark_dtree): assert spark_dtree.is_categorical_split(5) is False assert spark_dtree.is_categorical_split(12) is False assert spark_dtree.is_categorical_split(13) is True - From cb56dac3579cd2c842d1b43bd887a84b94af1da2 Mon Sep 17 00:00:00 2001 From: tlapusan Date: Thu, 22 Oct 2020 13:03:00 +0300 Subject: [PATCH 03/16] Add viz_leaf_samples viz and some unit tests --- dtreeviz/models/lightgbm_decision_tree.py | 11 +- dtreeviz/models/shadow_decision_tree.py | 3 +- .../dtreeviz_lightgbm_visualisations.ipynb | 170 ++++++++++++------ testing/dtreeviz/models/conftest.py | 2 + .../test_decision_tree_lightgbm_classifier.py | 9 + 5 files changed, 131 insertions(+), 64 deletions(-) diff --git a/dtreeviz/models/lightgbm_decision_tree.py b/dtreeviz/models/lightgbm_decision_tree.py index 10b1e590..e50f6f59 100644 --- a/dtreeviz/models/lightgbm_decision_tree.py +++ b/dtreeviz/models/lightgbm_decision_tree.py @@ -4,7 +4,7 @@ import pandas as pd from lightgbm.basic import Booster -from dtreeviz.models.shadow_decision_tree import ShadowDecTree +from dtreeviz.models.shadow_decision_tree import ShadowDecTree, VisualisationNotYetSupportedError class ShadowLightGBMTree(ShadowDecTree): @@ -86,7 +86,7 @@ def get_features(self) -> np.ndarray: pass def criterion(self) -> str: - pass + raise VisualisationNotYetSupportedError("criterion()", "LightGBM") def get_class_weight(self): pass @@ -101,7 +101,10 @@ def get_node_samples(self): pass def get_node_nsamples(self, id): - pass + if self.children_right[id] == -1 and self.children_left[id] == -1: + return self.tree_nodes[id]["leaf_count"] + else: + return self.tree_nodes[id]["internal_count"] def get_children_left(self) -> np.ndarray: return np.array(self.children_left, dtype=int) @@ -125,7 +128,7 @@ def nnodes(self) -> int: pass def get_node_criterion(self, id): - pass + raise VisualisationNotYetSupportedError("get_node_criterion()", "LightGBM") def get_feature_path_importance(self, node_list): pass diff --git a/dtreeviz/models/shadow_decision_tree.py b/dtreeviz/models/shadow_decision_tree.py index 6120e8b3..a6d653bf 100644 --- a/dtreeviz/models/shadow_decision_tree.py +++ b/dtreeviz/models/shadow_decision_tree.py @@ -446,6 +446,7 @@ def get_shadow_tree(tree_model, x_data, y_data, feature_names, target_name, clas from dtreeviz.models import xgb_decision_tree return xgb_decision_tree.ShadowXGBDTree(tree_model, tree_index, x_data, y_data, feature_names, target_name, class_names) + # TODO - add logic for LightGBM else: raise ValueError( f"Tree model must be in (DecisionTreeRegressor, DecisionTreeClassifier, xgboost.core.Booster, but was {tree_model.__class__.__name__}") @@ -596,4 +597,4 @@ def __str__(self): class VisualisationNotYetSupportedError(Exception): def __init__(self, method_name, model_name): - super().__init__(f"{method_name} is not implemented yet for {model_name}") + super().__init__(f"{method_name} is not implemented for {model_name} yet") diff --git a/notebooks/dtreeviz_lightgbm_visualisations.ipynb b/notebooks/dtreeviz_lightgbm_visualisations.ipynb index 1035f9cb..36e2dfa7 100644 --- a/notebooks/dtreeviz_lightgbm_visualisations.ipynb +++ b/notebooks/dtreeviz_lightgbm_visualisations.ipynb @@ -21,7 +21,7 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -38,6 +38,7 @@ "# add library module to PYTHONPATH\n", "sys.path.append(f\"{os.getcwd()}/../\")\n", "\n", + "from dtreeviz import trees\n", "from dtreeviz.models.lightgbm_decision_tree import ShadowLightGBMTree\n" ] }, @@ -50,7 +51,7 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -59,7 +60,7 @@ }, { "cell_type": "code", - "execution_count": 44, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -311,7 +312,7 @@ "[891 rows x 12 columns]" ] }, - "execution_count": 44, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -322,7 +323,7 @@ }, { "cell_type": "code", - "execution_count": 45, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -335,7 +336,7 @@ }, { "cell_type": "code", - "execution_count": 124, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -345,7 +346,7 @@ }, { "cell_type": "code", - "execution_count": 125, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -354,7 +355,7 @@ }, { "cell_type": "code", - "execution_count": 126, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -508,7 +509,7 @@ "[712 rows x 6 columns]" ] }, - "execution_count": 126, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -519,7 +520,7 @@ }, { "cell_type": "code", - "execution_count": 127, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -536,7 +537,7 @@ }, { "cell_type": "code", - "execution_count": 128, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ @@ -555,7 +556,7 @@ }, { "cell_type": "code", - "execution_count": 129, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -563,7 +564,7 @@ "output_type": "stream", "text": [ "[LightGBM] [Info] Number of positive: 268, number of negative: 444\n", - "[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000661 seconds.\n", + "[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000871 seconds.\n", "You can set `force_row_wise=true` to remove the overhead.\n", "And if memory is not enough, you can set `force_col_wise=true`.\n", "[LightGBM] [Info] Total Bins 233\n", @@ -779,7 +780,7 @@ }, { "cell_type": "code", - "execution_count": 130, + "execution_count": 12, "metadata": { "scrolled": true }, @@ -1005,7 +1006,7 @@ " 'pandas_categorical': []}" ] }, - "execution_count": 130, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } @@ -1016,16 +1017,16 @@ }, { "cell_type": "code", - "execution_count": 118, + "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "'tweedie'" + "'binary sigmoid:1'" ] }, - "execution_count": 118, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } @@ -1036,16 +1037,16 @@ }, { "cell_type": "code", - "execution_count": 69, + "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 69, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" }, @@ -1068,16 +1069,16 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 14, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" }, @@ -1110,7 +1111,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 16, "metadata": {}, "outputs": [ { @@ -1498,10 +1499,10 @@ "\n" ], "text/plain": [ - "" + "" ] }, - "execution_count": 27, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" } @@ -1519,7 +1520,7 @@ }, { "cell_type": "code", - "execution_count": 106, + "execution_count": 17, "metadata": {}, "outputs": [], "source": [ @@ -1528,7 +1529,7 @@ }, { "cell_type": "code", - "execution_count": 154, + "execution_count": 18, "metadata": {}, "outputs": [], "source": [ @@ -1537,7 +1538,7 @@ }, { "cell_type": "code", - "execution_count": 155, + "execution_count": 19, "metadata": {}, "outputs": [], "source": [ @@ -1584,7 +1585,7 @@ }, { "cell_type": "code", - "execution_count": 156, + "execution_count": 20, "metadata": {}, "outputs": [], "source": [ @@ -1593,7 +1594,7 @@ }, { "cell_type": "code", - "execution_count": 157, + "execution_count": 21, "metadata": {}, "outputs": [ { @@ -1612,7 +1613,7 @@ }, { "cell_type": "code", - "execution_count": 153, + "execution_count": 22, "metadata": { "scrolled": true }, @@ -1620,32 +1621,26 @@ { "data": { "text/plain": [ - "{'split_index': 2,\n", - " 'split_feature': 2,\n", - " 'split_gain': 17.808000564575195,\n", - " 'threshold': 23.725000000000005,\n", - " 'decision_type': '<=',\n", - " 'default_left': True,\n", - " 'missing_type': 'None',\n", - " 'internal_value': 0.004239,\n", - " 'internal_weight': 27.1344,\n", - " 'internal_count': 115}" + "{'leaf_index': 9,\n", + " 'leaf_value': -0.008689234807276692,\n", + " 'leaf_weight': 40.59755164384842,\n", + " 'leaf_count': 175}" ] }, - "execution_count": 153, + "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "tree_nodes[7]" + "tree_nodes[19]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "# dtreeviz" + "# dtreeviz clasification" ] }, { @@ -1657,7 +1652,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 23, "metadata": {}, "outputs": [], "source": [ @@ -1667,7 +1662,7 @@ }, { "cell_type": "code", - "execution_count": 54, + "execution_count": 24, "metadata": {}, "outputs": [], "source": [ @@ -1676,7 +1671,7 @@ }, { "cell_type": "code", - "execution_count": 56, + "execution_count": 25, "metadata": {}, "outputs": [ { @@ -1685,7 +1680,7 @@ "numpy.ndarray" ] }, - "execution_count": 56, + "execution_count": 25, "metadata": {}, "output_type": "execute_result" } @@ -1694,26 +1689,83 @@ "type(light_dtree.get_children_left())" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Visualizations" + ] + }, { "cell_type": "code", - "execution_count": null, + "execution_count": 26, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "data": { + "text/plain": [ + "{'leaf_index': 6,\n", + " 'leaf_value': -0.007128848419371046,\n", + " 'leaf_weight': 9.549900010228155,\n", + " 'leaf_count': 41}" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tree_nodes[24]" + ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 27, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "trees.viz_leaf_samples(light_dtree)" + ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 23, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "ename": "VisualisationNotYetSupportedError", + "evalue": "get_node_criterion() is not implemented for LightGBM yet", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mVisualisationNotYetSupportedError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtrees\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mviz_leaf_criterion\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlight_dtree\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;32m~/Documents/workspace/personal/dtreeviz/notebooks/../dtreeviz/trees.py\u001b[0m in \u001b[0;36mviz_leaf_criterion\u001b[0;34m(tree_model, tree_index, figsize, display_type, colors, fontsize, fontname, grid, bins)\u001b[0m\n\u001b[1;32m 1487\u001b[0m shadow_tree = ShadowDecTree.get_shadow_tree(tree_model, None, None, None, None, None,\n\u001b[1;32m 1488\u001b[0m tree_index)\n\u001b[0;32m-> 1489\u001b[0;31m \u001b[0mleaf_id\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mleaf_criteria\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mshadow_tree\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_leaf_criterion\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1490\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1491\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mdisplay_type\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"plot\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/Documents/workspace/personal/dtreeviz/notebooks/../dtreeviz/models/shadow_decision_tree.py\u001b[0m in \u001b[0;36mget_leaf_criterion\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 376\u001b[0m \"\"\"\n\u001b[1;32m 377\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 378\u001b[0;31m \u001b[0mleaf_criterion\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnode\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mid\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnode\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcriterion\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mnode\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mleaves\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 379\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m 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\u001b[0;36mget_node_criterion\u001b[0;34m(self, id)\u001b[0m\n\u001b[1;32m 129\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 130\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mget_node_criterion\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mid\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 131\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mVisualisationNotYetSupportedError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"get_node_criterion()\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"LightGBM\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 132\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 133\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mget_feature_path_importance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m 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pd.read_csv("fixtures/dataset_spark.csv") diff --git a/testing/dtreeviz/models/test_decision_tree_lightgbm_classifier.py b/testing/dtreeviz/models/test_decision_tree_lightgbm_classifier.py index c844b648..5bc59a4d 100644 --- a/testing/dtreeviz/models/test_decision_tree_lightgbm_classifier.py +++ b/testing/dtreeviz/models/test_decision_tree_lightgbm_classifier.py @@ -32,3 +32,12 @@ def test_get_children_left(shadow_dec_tree: ShadowLightGBMTree): def test_get_children_right(shadow_dec_tree): assert np.array_equal(shadow_dec_tree.get_children_right(), np.array( [12, 7, 6, 5, -1, -1, -1, 11, 10, -1, -1, -1, 20, 17, 16, -1, -1, 19, -1, -1, 24, 23, -1, -1, -1])) + +def test_get_node_nsamples(shadow_dec_tree): + assert shadow_dec_tree.get_node_nsamples(0) == 712 + assert shadow_dec_tree.get_node_nsamples(3) == 110 + assert shadow_dec_tree.get_node_nsamples(4) == 56 + assert shadow_dec_tree.get_node_nsamples(7) == 115 + assert shadow_dec_tree.get_node_nsamples(13) == 340 + assert shadow_dec_tree.get_node_nsamples(19) == 175 + assert shadow_dec_tree.get_node_nsamples(21) == 86 From 42c57a0da0e4f23a997dc3024d089652aa58bb0d Mon Sep 17 00:00:00 2001 From: tlapusan Date: Tue, 10 Nov 2020 15:22:59 +0200 Subject: [PATCH 04/16] Implement more methods --- dtreeviz/models/lightgbm_decision_tree.py | 70 +- dtreeviz/models/shadow_decision_tree.py | 6 +- dtreeviz/models/spark_decision_tree.py | 5 + .../dtreeviz_lightgbm_visualisations.ipynb | 6392 ++++++++++++++++- .../test_decision_tree_lightgbm_classifier.py | 40 + 5 files changed, 6451 insertions(+), 62 deletions(-) diff --git a/dtreeviz/models/lightgbm_decision_tree.py b/dtreeviz/models/lightgbm_decision_tree.py index e50f6f59..a4a5108b 100644 --- a/dtreeviz/models/lightgbm_decision_tree.py +++ b/dtreeviz/models/lightgbm_decision_tree.py @@ -1,3 +1,4 @@ +from collections import defaultdict from typing import List, Mapping import numpy as np @@ -20,6 +21,9 @@ def __init__(self, self.booster = booster self.tree_index = tree_index self.tree_nodes, self.children_left, self.children_right = self._get_nodes_info() + self.thresholds = None # lazy evaluation + self.features = None # lazy evaluation + self.node_to_samples = None super().__init__(booster, x_data, y_data, feature_names, target_name, class_names) @@ -80,25 +84,56 @@ def get_class_weights(self): pass def get_thresholds(self) -> np.ndarray: - pass + if self.thresholds is not None: + return self.thresholds + + node_thresholds = [-1] * self.nnodes() + for i in range(self.nnodes()): + if self.children_left[i] != -1 and self.children_right[i] != -1: + node_thresholds[i] = round(self.tree_nodes[i]["threshold"], 2) + + self.thresholds = np.array(node_thresholds) + return self.thresholds def get_features(self) -> np.ndarray: - pass + if self.features is not None: + return self.features + + self.features = [-1] * self.nnodes() + for i, node in enumerate(self.tree_nodes): + self.features[i] = node.get("split_feature", -1) + + self.features = np.array(self.features) + return self.features def criterion(self) -> str: raise VisualisationNotYetSupportedError("criterion()", "LightGBM") def get_class_weight(self): - pass + return None def nclasses(self) -> int: - pass + if self.booster._Booster__num_class == 1: + return 2 + else: + return self.booster._Booster__num_class def classes(self) -> np.ndarray: - pass + if self.is_classifier(): + return np.unique(self.y_data) def get_node_samples(self): - pass + if self.node_to_samples is not None: + return self.node_to_samples + + node_to_samples = defaultdict(list) + for i in range(self.x_data.shape[0]): + path = self.predict(self.x_data[i], path_only=True) + for node in path: + node_to_samples[node.id].append(i) + + self.node_to_samples = node_to_samples + return self.node_to_samples def get_node_nsamples(self, id): if self.children_right[id] == -1 and self.children_left[id] == -1: @@ -113,19 +148,28 @@ def get_children_right(self) -> np.ndarray: return np.array(self.children_right, dtype=int) def get_node_split(self, id) -> (int, float): - pass + return self.get_thresholds()[id] def get_node_feature(self, id) -> int: - pass + return self.get_features()[id] def get_node_nsamples_by_class(self, id): - pass + all_nodes = self.internal + self.leaves + if self.is_classifier(): + node_value = [node.n_sample_classes() for node in all_nodes if node.id == id] + return node_value[0][0], node_value[0][1] def get_prediction(self, id): - pass + all_nodes = self.internal + self.leaves + if self.is_classifier(): + node_value = [node.n_sample_classes() for node in all_nodes if node.id == id] + return np.argmax((node_value[0][0], node_value[0][1])) + elif not self.is_classifier(): + node_samples = [node.samples() for node in all_nodes if node.id == id][0] + return np.mean(self.y_data[node_samples]) def nnodes(self) -> int: - pass + return len(self.tree_nodes) def get_node_criterion(self, id): raise VisualisationNotYetSupportedError("get_node_criterion()", "LightGBM") @@ -134,6 +178,8 @@ def get_feature_path_importance(self, node_list): pass def get_max_depth(self) -> int: + # max_depth can be found in lgbm_model.params, but only if the max_depth is specified + # otherwise the max depth is -1, from lgbm_model.model_to_string() (to double check) pass def get_score(self) -> float: @@ -143,4 +189,4 @@ def get_min_samples_leaf(self) -> (int, float): pass def shouldGoLeftAtSplit(self, id, x): - pass + return x <= self.get_node_split(id) diff --git a/dtreeviz/models/shadow_decision_tree.py b/dtreeviz/models/shadow_decision_tree.py index a6d653bf..6b010569 100644 --- a/dtreeviz/models/shadow_decision_tree.py +++ b/dtreeviz/models/shadow_decision_tree.py @@ -242,7 +242,7 @@ def get_score(self) -> float: @abstractmethod def get_min_samples_leaf(self) -> (int, float): - """Returns the minimum number of samples required to be at a leaf node.""" + """Returns the minimum number of samples required to be at a leaf node, during node splitting""" pass @abstractmethod @@ -286,7 +286,7 @@ def get_split_node_heights(self, X_train, y_train, nbins) -> Mapping[int, int]: # print(f"\tmax={np.max(height_of_bins):2.0f}, heights={list(height_of_bins)}, {len(height_of_bins)} bins") return node_heights - def predict(self, x: np.ndarray) -> Tuple[Number, List]: + def predict(self, x: np.ndarray, path_only: bool = False) -> Tuple[Number, List]: """ Given an x - vector of features, return predicted class or value based upon this tree. Also return path from root to leaf as 2nd value in return tuple. @@ -312,6 +312,8 @@ def walk(t, x, path): path = [] leaf = walk(self.root, x, path) + if path_only : + return path return leaf.prediction(), path def tesselation(self): diff --git a/dtreeviz/models/spark_decision_tree.py b/dtreeviz/models/spark_decision_tree.py index 840b3b7b..42a2e8a5 100644 --- a/dtreeviz/models/spark_decision_tree.py +++ b/dtreeviz/models/spark_decision_tree.py @@ -101,6 +101,9 @@ def criterion(self) -> str: def nclasses(self) -> int: if not self.is_classifier(): return 1 + + # TODO for multiclass decision tree, the numClasses is the max of classe values + 1 + # ex. [15, 25, 35, 40, 45, 55, 65, 70] -> numClasses = 71 return self.tree_model.numClasses # TODO @@ -110,6 +113,8 @@ def classes(self) -> np.ndarray: return np.unique(self.y_data) def get_node_samples(self): + # TODO in case dataset is large, it will take a while to compute all node samples + # maybe to include a message for waiting... or loading bar if self.node_to_samples is not None: return self.node_to_samples diff --git a/notebooks/dtreeviz_lightgbm_visualisations.ipynb b/notebooks/dtreeviz_lightgbm_visualisations.ipynb index 36e2dfa7..c24e5279 100644 --- a/notebooks/dtreeviz_lightgbm_visualisations.ipynb +++ b/notebooks/dtreeviz_lightgbm_visualisations.ipynb @@ -550,8 +550,7 @@ " 'num_leaves': 41, # Controls size of tree since LGBM uses leaf wise splits\n", " 'drop_rate': 0.15,\n", " 'max_depth':4,\n", - " \"seed\":1212\n", - " }" + " \"seed\":1212}" ] }, { @@ -564,7 +563,7 @@ "output_type": "stream", "text": [ "[LightGBM] [Info] Number of positive: 268, number of negative: 444\n", - "[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000871 seconds.\n", + "[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000721 seconds.\n", "You can set `force_row_wise=true` to remove the overhead.\n", "And if memory is not enough, you can set `force_col_wise=true`.\n", "[LightGBM] [Info] Total Bins 233\n", @@ -781,6 +780,2119 @@ { "cell_type": "code", "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "lgbm_model.attr(\"num_classes\")" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "lgbm_model._Booster__num_class" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "['_Booster__attr',\n", + " '_Booster__boost',\n", + " '_Booster__get_eval_info',\n", + " '_Booster__higher_better_inner_eval',\n", + " '_Booster__init_predictor',\n", + " '_Booster__inner_eval',\n", + " '_Booster__inner_predict',\n", + " '_Booster__inner_predict_buffer',\n", + " '_Booster__is_predicted_cur_iter',\n", + " '_Booster__name_inner_eval',\n", + " '_Booster__need_reload_eval_info',\n", + " '_Booster__num_class',\n", + " '_Booster__num_dataset',\n", + " '_Booster__num_inner_eval',\n", + " '_Booster__set_objective_to_none',\n", + " '__class__',\n", + " '__copy__',\n", + " '__deepcopy__',\n", + " '__del__',\n", + " '__delattr__',\n", + " '__dict__',\n", + " '__dir__',\n", + " '__doc__',\n", + " '__eq__',\n", + " '__format__',\n", + " '__ge__',\n", + " '__getattribute__',\n", + " '__getstate__',\n", + " '__gt__',\n", + " '__hash__',\n", + " '__init__',\n", + " '__init_subclass__',\n", + " 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5.217264920473097 45.016617834568024 4.9943994134664527 6.2529186606407157 12.987390145659445 13.257303223013878 7.791682168841362\n", + "leaf_count=29 38 160 30 28 210 23 28 67 60 39\n", + "internal_value=0 7.203e-05 -0.000279912 -0.0018511 -0.00155218 -0.00200342 -0.000880114 0.000969269 -0.000521356 -0.00350328\n", + "internal_weight=0 65.186 59.547 84.9124 71.9034 50.011 40.2215 19.3255 21.8924 13.0089\n", + "internal_count=712 314 285 398 331 233 188 97 98 67\n", + "shrinkage=0.00357143\n", + "\n", + "\n", + "Tree=95\n", + "num_leaves=11\n", + "num_cat=0\n", + "split_feature=3 1 4 1 1 2 2 2 1 1\n", + "split_gain=54.3125 9.82978 4.63324 3.38065 2.24181 1.94627 2.4416 1.5536 1.577 1.21495\n", + "threshold=1.0000000180025095e-35 6.5000000000000009 4.0000000000000009 29.849558823529414 36.250000000000007 7.7625000000000011 10.508350000000002 50.247900000000008 36.250000000000007 32.250000000000007\n", + "decision_type=2 2 2 2 2 2 2 2 2 2\n", + "left_child=3 -2 9 5 -4 -1 -7 8 -5 -3\n", + "right_child=1 2 4 7 -6 6 -8 -9 -10 -11\n", + "leaf_value=0.0030518124320246844 0.0020956814678364261 -0.0023814654445537958 0.00021963162161823331 0.0035652731078044003 -0.0019408922638953629 -0.00061373257752549289 0.0015074900292047226 0.0041211224684653668 0.0011190555316605833 -0.0033030567359648593\n", + "leaf_weight=5.4571755528450003 5.343170002102851 56.280791118741035 9.2689581811428088 4.7300155609846133 9.4941098392009717 6.8661845922470119 23.218076691031456 7.2322221696376792 5.6450975984334946 18.581763982772827\n", + "leaf_count=22 22 273 38 21 39 28 108 40 26 95\n", + "internal_value=0 -0.00202691 -0.00226219 0.00188955 -0.000873593 0.00133482 0.00102336 0.00300931 0.00223429 -0.00261022\n", + "internal_weight=0 98.9688 93.6256 53.1488 18.7631 35.5414 30.0843 17.6073 10.3751 74.8626\n", + "internal_count=712 467 445 245 77 158 136 87 47 368\n", + "shrinkage=0.003125\n", + "\n", + "\n", + "Tree=96\n", + "num_leaves=12\n", + "num_cat=0\n", + "split_feature=3 1 2 1 5 2 5 2 2 1 1\n", + "split_gain=28.6809 6.32464 2.51564 2.49505 2.32945 3.45665 1.63254 1.60812 1.42917 1.57284 1.20597\n", + "threshold=1.0000000180025095e-35 9.5000000000000018 26.268750000000004 29.849558823529414 1.5000000000000002 15.372900000000001 1.0000000180025095e-35 30.750000000000004 50.247900000000008 36.250000000000007 14.250000000000002\n", + "decision_type=2 2 2 2 2 2 2 2 2 2 2\n", + "left_child=3 -2 6 4 5 -1 -3 -4 9 -5 -6\n", + "right_child=1 2 7 8 10 -7 -8 -9 -10 -11 -12\n", + "leaf_value=0.012462156933558476 0.02104370698199403 -0.010788702505577917 0.0045066805632135052 0.047424869730141976 -0.016202028227753535 0.06443369937692231 -0.034607045104047697 -0.021548611856724759 0.05647547791847659 0.0089002543906712538 0.011262468398255\n", + "leaf_weight=7.7210800349712354 6.6488148123025885 7.1594160795211783 7.3234995603561428 4.2801250070333463 4.4045531153678921 4.3612445443868637 50.271086007356644 17.45170858502388 5.6141573786735526 5.0087134689092636 16.620411902666092\n", + "leaf_count=33 29 38 32 21 23 24 290 78 40 26 78\n", + "internal_value=0 -0.0227351 -0.0262759 0.0220303 0.0148927 0.0312218 -0.0316378 -0.0138467 0.0378867 0.0266517 0.00550889\n", + "internal_weight=0 88.8545 82.2057 48.0103 33.1073 12.0823 57.4305 24.7752 14.903 9.28884 21.025\n", + "internal_count=712 467 438 245 158 57 328 110 87 47 101\n", + "shrinkage=0.0466667\n", + "\n", + "\n", + "Tree=97\n", + "num_leaves=13\n", + "num_cat=0\n", + "split_feature=3 0 1 4 1 1 4 4 1 1 1 1\n", + "split_gain=40.1222 16.7835 8.62268 4.29517 2.35133 1.19978 0.635269 0.97978 0.610262 0.310457 0.301635 0.221059\n", + "threshold=1.0000000180025095e-35 2.5000000000000004 6.5000000000000009 4.0000000000000009 38.500000000000007 27.500000000000004 60.000000000000007 89.500000000000014 21.500000000000004 27.500000000000004 28.250000000000004 14.250000000000002\n", + "decision_type=2 2 2 2 2 2 2 2 2 2 2 2\n", + "left_child=1 6 -2 5 -5 -4 9 -8 11 -1 -10 -3\n", + "right_child=2 8 3 4 -6 -7 7 -9 10 -11 -12 -13\n", + "leaf_value=0.0035435190502354323 0.002172268766763089 -0.00021295912203113417 -0.0019676801893398484 0.0002984985015640311 -0.0020891715969200944 -0.002870459029423309 0.0017453561501000457 0.0042168601941481631 0.00098318988111309067 0.0044800861034467359 3.0462165390396312e-05 -0.0012366358060957442\n", + "leaf_weight=6.3321614563465181 5.3664612025022498 4.5217098593711871 25.395481631159782 10.428248703479769 8.1753753721714002 45.956646963953972 3.6957817673683158 3.4421452730894089 5.1833545714616802 10.377738550305365 12.835960075259207 4.8665683716535568\n", + "leaf_count=33 22 20 129 43 34 239 20 21 21 56 54 20\n", + "internal_value=0 0.00172475 -0.00193236 -0.00217722 -0.000750765 -0.00254914 0.0037696 0.0029372 -5.4506e-05 0.00412518 0.00030452 -0.000743599\n", + "internal_weight=0 51.2554 95.3222 89.9558 18.6036 71.3521 23.8478 7.13793 27.4076 16.7099 18.0193 9.38828\n", + "internal_count=712 245 467 445 77 368 130 41 115 89 75 40\n", + "shrinkage=0.00333333\n", + "\n", + "\n", + "Tree=98\n", + "num_leaves=11\n", + "num_cat=0\n", + "split_feature=0 1 2 1 4 2 1 2 1 1\n", + "split_gain=12.4401 6.22081 4.23128 3.3344 3.0382 2.66267 6.0324 1.3343 1.24798 1.23019\n", + "threshold=2.5000000000000004 17.500000000000004 52.277100000000011 32.250000000000007 69.500000000000014 23.725000000000005 14.250000000000002 7.910400000000001 43.500000000000007 23.500000000000004\n", + "decision_type=2 2 2 2 2 2 2 2 2 2\n", + "left_child=1 -1 4 5 -3 6 -2 -5 -4 -7\n", + "right_child=3 2 8 7 -6 9 -8 -9 -10 -11\n", + "leaf_value=0.0032331754826656245 0.0021833654998130911 -0.00080816468508835724 0.0016565407180112156 -0.0039208234474561333 0.001556281919046441 -0.003476782901287188 -0.0011687841766839999 -0.0019750102384718869 7.0180467782142905e-05 -0.0013509883882421941\n", + "leaf_weight=5.8589716404676428 5.0600014030933371 34.911294341087341 13.811217397451403 5.0838542580604535 5.4326450973749152 5.4528673589229566 56.476582780480385 7.6147711575031281 6.2227551788091651 4.144930899143219\n", + "leaf_count=29 21 164 68 28 24 25 265 39 29 20\n", + "internal_value=0 0.000339679 5.88992e-05 -0.00136571 -0.000489772 -0.00111787 -0.000893145 -0.00275401 0.0011638 -0.00255873\n", + "internal_weight=0 66.2369 60.3779 83.833 40.3439 71.1344 61.5366 12.6986 20.034 9.5978\n", + "internal_count=712 314 285 398 188 331 286 67 97 45\n", + "shrinkage=0.00294118\n", + "\n", + "\n", + "Tree=99\n", + "num_leaves=12\n", + "num_cat=0\n", + "split_feature=3 0 1 5 0 1 1 1 1 1 5\n", + "split_gain=27.8642 12.6496 6.04878 3.0613 1.73053 1.7149 1.25481 0.668194 0.601694 0.474375 0.287565\n", + "threshold=1.0000000180025095e-35 2.5000000000000004 6.5000000000000009 1.5000000000000002 1.5000000000000002 32.250000000000007 27.500000000000004 27.500000000000004 23.500000000000004 38.500000000000007 1.0000000180025095e-35\n", + "decision_type=2 2 2 2 2 2 2 2 2 2 2\n", + "left_child=1 7 -2 -3 9 -6 -5 8 -1 -4 -9\n", + "right_child=2 3 4 6 5 -7 -8 10 -10 -11 -12\n", + "leaf_value=0.052508021645799298 0.02791754712508699 0.023826949577689282 -0.0084791494774024197 -0.00048415211934626779 -0.027042905514187039 -0.046622487210136808 -0.027467007919845438 0.046603130797200805 0.025164537152241397 -0.023542032048196437 0.063751521178106188\n", + "leaf_weight=4.8207417502999323 5.245101034641265 9.2136362940073031 11.879214555025102 10.36711858212948 47.58261613547802 14.619299396872519 7.3729167133569709 3.2931643277406684 3.4529967904090881 9.3338668644428235 9.4889924153685552\n", + "leaf_count=31 22 39 51 44 264 90 32 22 18 40 59\n", + "internal_value=0 0.0231294 -0.0241641 0.000445269 -0.027439 -0.0316447 -0.0116985 0.0521673 0.0410964 -0.0151069 0.0593335\n", + "internal_weight=0 48.0096 88.6601 26.9537 83.415 62.2019 17.74 21.0559 8.27374 21.2131 12.7822\n", + "internal_count=712 245 467 115 445 354 76 130 49 91 81\n", + "shrinkage=0.05\n", + "\n", + "\n", + "end of trees\n", + "\n", + "feature_importances:\n", + "Age=308\n", + "Fare=308\n", + "Cabin_label=165\n", + "Embarked_label=123\n", + "Pclass=98\n", + "Sex_label=69\n", + "\n", + "parameters:\n", + "[boosting: dart]\n", + "[objective: binary]\n", + "[metric: binary_logloss]\n", + "[tree_learner: serial]\n", + "[device_type: cpu]\n", + "[data: ]\n", + "[valid: ]\n", + "[num_iterations: 100]\n", + "[learning_rate: 0.05]\n", + "[num_leaves: 16]\n", + "[num_threads: 0]\n", + "[force_col_wise: 0]\n", + "[force_row_wise: 0]\n", + "[histogram_pool_size: -1]\n", + "[max_depth: 4]\n", + "[min_data_in_leaf: 20]\n", + "[min_sum_hessian_in_leaf: 0.001]\n", + "[bagging_fraction: 1]\n", + "[pos_bagging_fraction: 1]\n", + "[neg_bagging_fraction: 1]\n", + "[bagging_freq: 0]\n", + "[bagging_seed: 25892]\n", + "[feature_fraction: 0.7]\n", + "[feature_fraction_bynode: 1]\n", + "[feature_fraction_seed: 3653]\n", + "[extra_trees: 0]\n", + "[extra_seed: 19633]\n", + "[early_stopping_round: 0]\n", + "[first_metric_only: 0]\n", + "[max_delta_step: 0]\n", + "[lambda_l1: 0]\n", + "[lambda_l2: 0]\n", + "[min_gain_to_split: 0]\n", + "[drop_rate: 0.15]\n", + "[max_drop: 50]\n", + "[skip_drop: 0.5]\n", + "[xgboost_dart_mode: 0]\n", + "[uniform_drop: 0]\n", + "[drop_seed: 12966]\n", + "[top_rate: 0.2]\n", + "[other_rate: 0.1]\n", + "[min_data_per_group: 100]\n", + "[max_cat_threshold: 32]\n", + "[cat_l2: 10]\n", + "[cat_smooth: 10]\n", + "[max_cat_to_onehot: 4]\n", + "[top_k: 20]\n", + "[monotone_constraints: ]\n", + "[monotone_constraints_method: basic]\n", + "[monotone_penalty: 0]\n", + "[feature_contri: ]\n", + "[forcedsplits_filename: ]\n", + "[refit_decay_rate: 0.9]\n", + "[cegb_tradeoff: 1]\n", + "[cegb_penalty_split: 0]\n", + "[cegb_penalty_feature_lazy: ]\n", + "[cegb_penalty_feature_coupled: ]\n", + "[path_smooth: 0]\n", + "[interaction_constraints: ]\n", + "[verbosity: 1]\n", + "[saved_feature_importance_type: 0]\n", + "[max_bin: 255]\n", + "[max_bin_by_feature: ]\n", + "[min_data_in_bin: 3]\n", + "[bin_construct_sample_cnt: 200000]\n", + "[data_random_seed: 3996]\n", + "[is_enable_sparse: 1]\n", + "[enable_bundle: 1]\n", + "[use_missing: 1]\n", + "[zero_as_missing: 0]\n", + "[feature_pre_filter: 1]\n", + "[pre_partition: 0]\n", + "[two_round: 0]\n", + "[header: 0]\n", + "[label_column: ]\n", + "[weight_column: ]\n", + "[group_column: ]\n", + "[ignore_column: ]\n", + "[categorical_feature: ]\n", + "[forcedbins_filename: ]\n", + "[objective_seed: 25511]\n", + "[num_class: 1]\n", + "[is_unbalance: 0]\n", + "[scale_pos_weight: 1]\n", + "[sigmoid: 1]\n", + "[boost_from_average: 1]\n", + "[reg_sqrt: 0]\n", + "[alpha: 0.9]\n", + "[fair_c: 1]\n", + "[poisson_max_delta_step: 0.7]\n", + "[tweedie_variance_power: 1.5]\n", + "[lambdarank_truncation_level: 20]\n", + "[lambdarank_norm: 1]\n", + "[label_gain: ]\n", + "[eval_at: ]\n", + "[multi_error_top_k: 1]\n", + "[auc_mu_weights: ]\n", + "[num_machines: 1]\n", + "[local_listen_port: 12400]\n", + "[time_out: 120]\n", + "[machine_list_filename: ]\n", + "[machines: ]\n", + "[gpu_platform_id: -1]\n", + "[gpu_device_id: -1]\n", + "[gpu_use_dp: 0]\n", + "\n", + "end of parameters\n", + "\n", + "pandas_categorical:[]\n", + "\n" + ] + } + ], + "source": [ + "print(lgbm_model.model_to_string())" + ] + }, + { + "cell_type": "code", + "execution_count": 17, "metadata": { "scrolled": true }, @@ -1006,7 +3118,7 @@ " 'pandas_categorical': []}" ] }, - "execution_count": 12, + "execution_count": 17, "metadata": {}, "output_type": "execute_result" } @@ -1017,7 +3129,21 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 18, "metadata": {}, "outputs": [ { @@ -1026,7 +3152,7 @@ "'binary sigmoid:1'" ] }, - "execution_count": 13, + "execution_count": 18, "metadata": {}, "output_type": "execute_result" } @@ -1037,16 +3163,16 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 14, + "execution_count": 19, "metadata": {}, "output_type": "execute_result" }, @@ -1069,16 +3195,16 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 15, + "execution_count": 20, "metadata": {}, "output_type": "execute_result" }, @@ -1111,7 +3237,27 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['Pclass', 'Age', 'Fare', 'Sex_label', 'Cabin_label', 'Embarked_label']" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "features" + ] + }, + { + "cell_type": "code", + "execution_count": 22, "metadata": {}, "outputs": [ { @@ -1499,10 +3645,10 @@ "
\n" ], "text/plain": [ - "" + "" ] }, - "execution_count": 16, + "execution_count": 22, "metadata": {}, "output_type": "execute_result" } @@ -1511,6 +3657,20 @@ "lgb.create_tree_digraph(lgbm_model, tree_index=1, orientation = \"vertical\", show_info=['internal_count'])\n" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, { "cell_type": "markdown", "metadata": {}, @@ -1520,7 +3680,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 23, "metadata": {}, "outputs": [], "source": [ @@ -1529,7 +3689,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 24, "metadata": {}, "outputs": [], "source": [ @@ -1538,7 +3698,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 25, "metadata": {}, "outputs": [], "source": [ @@ -1585,7 +3745,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 26, "metadata": {}, "outputs": [], "source": [ @@ -1594,7 +3754,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 27, "metadata": {}, "outputs": [ { @@ -1613,7 +3773,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 28, "metadata": { "scrolled": true }, @@ -1627,7 +3787,59 @@ " 'leaf_count': 175}" ] }, - "execution_count": 22, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tree_nodes[19]" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'split_index': 0,\n", + " 'split_feature': 3,\n", + " 'split_gain': 187.06399536132812,\n", + " 'threshold': 1.0000000180025095e-35,\n", + " 'decision_type': '<=',\n", + " 'default_left': True,\n", + " 'missing_type': 'None',\n", + " 'internal_value': 0,\n", + " 'internal_weight': 0,\n", + " 'internal_count': 712}" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tree_nodes[0]" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'leaf_index': 9,\n", + " 'leaf_value': -0.008689234807276692,\n", + " 'leaf_weight': 40.59755164384842,\n", + " 'leaf_count': 175}" + ] + }, + "execution_count": 30, "metadata": {}, "output_type": "execute_result" } @@ -1652,7 +3864,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 44, "metadata": {}, "outputs": [], "source": [ @@ -1662,66 +3874,4134 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 45, "metadata": {}, "outputs": [], "source": [ "light_dtree = ShadowLightGBMTree(lgbm_model, tree_index=1, x_data=X_train, y_data=y_train, feature_names=features, target_name=target, class_names=[0,1] )\n" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Visualizations" + ] + }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 47, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "numpy.ndarray" + "Pclass 3.000\n", + "Age 32.000\n", + "Fare 7.925\n", + "Sex_label 1.000\n", + "Cabin_label -1.000\n", + "Embarked_label 2.000\n", + "Name: 382, dtype: float64" ] }, - "execution_count": 25, + "execution_count": 47, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "type(light_dtree.get_children_left())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Visualizations" + "x = X_train[features].iloc[2]\n", + "x" ] }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 49, "metadata": {}, "outputs": [ { "data": { + "image/svg+xml": [ + "\n", + "\n", + "G\n", + "\n", + "\n", + "cluster_legend\n", + "\n", + "\n", + "cluster_instance\n", + "\n", + "\n", + "\n", + "node3\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " 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\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n", + "\n", + "" + ], "text/plain": [ - "{'leaf_index': 6,\n", - " 'leaf_value': -0.007128848419371046,\n", - " 'leaf_weight': 9.549900010228155,\n", - " 'leaf_count': 41}" + "" ] }, - "execution_count": 26, + "execution_count": 49, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "tree_nodes[24]" + "trees.dtreeviz(light_dtree, X=x, show_just_path=False)" ] }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 69, "metadata": {}, "outputs": [ { @@ -1743,8 +8023,10 @@ }, { "cell_type": "code", - "execution_count": 23, - "metadata": {}, + "execution_count": 70, + "metadata": { + "scrolled": true + }, "outputs": [ { "ename": "VisualisationNotYetSupportedError", @@ -1753,12 +8035,12 @@ "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mVisualisationNotYetSupportedError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtrees\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mviz_leaf_criterion\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlight_dtree\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtrees\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mviz_leaf_criterion\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlight_dtree\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;32m~/Documents/workspace/personal/dtreeviz/notebooks/../dtreeviz/trees.py\u001b[0m in \u001b[0;36mviz_leaf_criterion\u001b[0;34m(tree_model, tree_index, figsize, display_type, colors, fontsize, fontname, grid, bins)\u001b[0m\n\u001b[1;32m 1487\u001b[0m shadow_tree = ShadowDecTree.get_shadow_tree(tree_model, None, None, None, None, None,\n\u001b[1;32m 1488\u001b[0m tree_index)\n\u001b[0;32m-> 1489\u001b[0;31m \u001b[0mleaf_id\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mleaf_criteria\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mshadow_tree\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_leaf_criterion\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1490\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1491\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mdisplay_type\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"plot\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m~/Documents/workspace/personal/dtreeviz/notebooks/../dtreeviz/models/shadow_decision_tree.py\u001b[0m in \u001b[0;36mget_leaf_criterion\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 376\u001b[0m \"\"\"\n\u001b[1;32m 377\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 378\u001b[0;31m \u001b[0mleaf_criterion\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnode\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mid\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnode\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcriterion\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mnode\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mleaves\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 379\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mleaf_criterion\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 380\u001b[0m \u001b[0;32mreturn\u001b[0m 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\u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mleaves\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 379\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mleaf_criterion\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 380\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/Documents/workspace/personal/dtreeviz/notebooks/../dtreeviz/models/shadow_decision_tree.py\u001b[0m in \u001b[0;36mcriterion\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 521\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 522\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mcriterion\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 523\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshadow_tree\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_node_criterion\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mid\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 524\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 525\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0msplit_samples\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m 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\u001b[0mVisualisationNotYetSupportedError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"get_node_criterion()\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"LightGBM\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 132\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 133\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mget_feature_path_importance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnode_list\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/Documents/workspace/personal/dtreeviz/notebooks/../dtreeviz/models/shadow_decision_tree.py\u001b[0m in \u001b[0;36mcriterion\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 522\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 523\u001b[0m \u001b[0;32mdef\u001b[0m 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b/testing/dtreeviz/models/test_decision_tree_lightgbm_classifier.py index 5bc59a4d..a2773774 100644 --- a/testing/dtreeviz/models/test_decision_tree_lightgbm_classifier.py +++ b/testing/dtreeviz/models/test_decision_tree_lightgbm_classifier.py @@ -33,6 +33,7 @@ def test_get_children_right(shadow_dec_tree): assert np.array_equal(shadow_dec_tree.get_children_right(), np.array( [12, 7, 6, 5, -1, -1, -1, 11, 10, -1, -1, -1, 20, 17, 16, -1, -1, 19, -1, -1, 24, 23, -1, -1, -1])) + def test_get_node_nsamples(shadow_dec_tree): assert shadow_dec_tree.get_node_nsamples(0) == 712 assert shadow_dec_tree.get_node_nsamples(3) == 110 @@ -41,3 +42,42 @@ def test_get_node_nsamples(shadow_dec_tree): assert shadow_dec_tree.get_node_nsamples(13) == 340 assert shadow_dec_tree.get_node_nsamples(19) == 175 assert shadow_dec_tree.get_node_nsamples(21) == 86 + + +def test_get_thresholds(shadow_dec_tree: ShadowLightGBMTree): + thresholds = shadow_dec_tree.get_thresholds() + assert thresholds[0] == 0 + assert thresholds[1] == 2.5 + assert thresholds[6] == -1 + assert thresholds[8] == 1.5 + assert thresholds[13] == 7.91 + assert thresholds[17] == 1.5 + assert thresholds[22] == -1 + + +def test_nnodes(shadow_dec_tree): + assert shadow_dec_tree.nnodes() == 25 + + +def test_get_features(shadow_dec_tree: ShadowLightGBMTree): + assert np.array_equal(shadow_dec_tree.get_features(), np.array( + [3, 0, 2, 2, -1, -1, -1, 2, 5, -1, -1, -1, 2, 2, 2, -1, -1, 5, -1, -1, 0, 2, -1, -1, -1])) + + +def test_nclasses(shadow_dec_tree: ShadowLightGBMTree): + assert shadow_dec_tree.nclasses() == 2 + + +def test_classes(shadow_dec_tree: ShadowLightGBMTree): + assert np.array_equal(shadow_dec_tree.classes(), np.array([0, 1])) + + +def test_get_node_samples(shadow_dec_tree: ShadowLightGBMTree): + node_samples = shadow_dec_tree.get_node_samples(); + assert len(node_samples[0]) == 712 + assert len(node_samples[1]) == 245 + assert len(node_samples[8]) == 95 + assert len(node_samples[3]) == 110 + assert len(node_samples[13]) == 340 + assert len(node_samples[17]) == 196 + assert len(node_samples[21]) == 86 From 1a63a765ee8ea985d1506ff317cbc1d1d9e60428 Mon Sep 17 00:00:00 2001 From: tlapusan Date: Tue, 24 Nov 2020 10:47:20 +0200 Subject: [PATCH 05/16] Implement get_min_samples_leaf method --- dtreeviz/models/lightgbm_decision_tree.py | 9 +- .../dtreeviz_lightgbm_visualisations.ipynb | 17730 ++++++++++++---- .../test_decision_tree_lightgbm_classifier.py | 3 + 3 files changed, 13634 insertions(+), 4108 deletions(-) diff --git a/dtreeviz/models/lightgbm_decision_tree.py b/dtreeviz/models/lightgbm_decision_tree.py index a4a5108b..19f8424c 100644 --- a/dtreeviz/models/lightgbm_decision_tree.py +++ b/dtreeviz/models/lightgbm_decision_tree.py @@ -175,18 +175,19 @@ def get_node_criterion(self, id): raise VisualisationNotYetSupportedError("get_node_criterion()", "LightGBM") def get_feature_path_importance(self, node_list): - pass + raise VisualisationNotYetSupportedError("get_feature_path_importance()", "LightGBM") def get_max_depth(self) -> int: # max_depth can be found in lgbm_model.params, but only if the max_depth is specified # otherwise the max depth is -1, from lgbm_model.model_to_string() (to double check) - pass + raise VisualisationNotYetSupportedError("get_max_depth()", "LightGBM") def get_score(self) -> float: - pass + raise VisualisationNotYetSupportedError("get_score()", "LightGBM") def get_min_samples_leaf(self) -> (int, float): - pass + default_value = 20 + return self.booster.params.get("min_data_in_leaf", default_value) def shouldGoLeftAtSplit(self, id, x): return x <= self.get_node_split(id) diff --git a/notebooks/dtreeviz_lightgbm_visualisations.ipynb b/notebooks/dtreeviz_lightgbm_visualisations.ipynb index c24e5279..c362a8d6 100644 --- a/notebooks/dtreeviz_lightgbm_visualisations.ipynb +++ b/notebooks/dtreeviz_lightgbm_visualisations.ipynb @@ -335,195 +335,30 @@ ] }, { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "features = [\"Pclass\", \"Age\", \"Fare\", \"Sex_label\", \"Cabin_label\", \"Embarked_label\"]\n", - "target = \"Survived\"" - ] - }, - { - "cell_type": "code", - "execution_count": 7, + "cell_type": "markdown", "metadata": {}, - "outputs": [], "source": [ - "X_train, X_test, y_train, y_test = train_test_split(dataset[features], dataset[target], test_size=0.2, random_state=42)" + "# Classification" ] }, { - "cell_type": "code", - "execution_count": 8, + "cell_type": "markdown", "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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PclassAgeFareSex_labelCabin_labelEmbarked_label
331145.50000028.50001562
733223.00000013.00001-12
382332.0000007.92501-12
704326.0000007.85421-12
81336.00000031.27500-12
.....................
106321.0000007.65000-12
270129.69911831.00001-12
860341.00000014.10831-12
435114.000000120.00000472
102121.00000077.28751982
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712 rows × 6 columns

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" - ], - "text/plain": [ - " Pclass Age Fare Sex_label Cabin_label Embarked_label\n", - "331 1 45.500000 28.5000 1 56 2\n", - "733 2 23.000000 13.0000 1 -1 2\n", - "382 3 32.000000 7.9250 1 -1 2\n", - "704 3 26.000000 7.8542 1 -1 2\n", - "813 3 6.000000 31.2750 0 -1 2\n", - ".. ... ... ... ... ... ...\n", - "106 3 21.000000 7.6500 0 -1 2\n", - "270 1 29.699118 31.0000 1 -1 2\n", - "860 3 41.000000 14.1083 1 -1 2\n", - "435 1 14.000000 120.0000 0 47 2\n", - "102 1 21.000000 77.2875 1 98 2\n", - "\n", - "[712 rows x 6 columns]" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], "source": [ - "X_train" + "## Datasets" ] }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ + "features = [\"Pclass\", \"Age\", \"Fare\", \"Sex_label\", \"Cabin_label\", \"Embarked_label\"]\n", + "target = \"Survived\"\n", + "\n", + "X_train, X_test, y_train, y_test = train_test_split(dataset[features], dataset[target], test_size=0.2, random_state=42)\n", + "\n", "train_data = lgb.Dataset(data=X_train, label=y_train)\n", "valid_data = lgb.Dataset(data=X_test, label=y_test)" ] @@ -532,30 +367,12 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Train the model" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "lgbm_params = {\n", - " 'boosting': 'dart', # dart (drop out trees) often performs better\n", - " 'application': 'binary', # Binary classification\n", - " 'learning_rate': 0.05, # Learning rate, controls size of a gradient descent step\n", - " 'min_data_in_leaf': 20, # Data set is quite small so reduce this a bit\n", - " 'feature_fraction': 0.7, # Proportion of features in each boost, controls overfitting\n", - " 'num_leaves': 41, # Controls size of tree since LGBM uses leaf wise splits\n", - " 'drop_rate': 0.15,\n", - " 'max_depth':4,\n", - " \"seed\":1212}" + "## Model training" ] }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -563,7 +380,7 @@ "output_type": "stream", "text": [ "[LightGBM] [Info] Number of positive: 268, number of negative: 444\n", - "[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000721 seconds.\n", + "[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000900 seconds.\n", "You can set `force_row_wise=true` to remove the overhead.\n", "And if memory is not enough, you can set `force_col_wise=true`.\n", "[LightGBM] [Info] Total Bins 233\n", @@ -571,2693 +388,265 @@ "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.376404 -> initscore=-0.504838\n", "[LightGBM] [Info] Start training from score -0.504838\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", - "[1]\ttraining's binary_logloss: 0.640939\tvalid_1's binary_logloss: 0.658567\n", + "[1]\ttraining's binary_logloss: 0.641119\tvalid_1's binary_logloss: 0.658611\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", - "[2]\ttraining's binary_logloss: 0.621711\tvalid_1's binary_logloss: 0.637865\n", + "[2]\ttraining's binary_logloss: 0.622109\tvalid_1's binary_logloss: 0.638123\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", - "[3]\ttraining's binary_logloss: 0.613448\tvalid_1's binary_logloss: 0.628601\n", + "[3]\ttraining's binary_logloss: 0.613812\tvalid_1's binary_logloss: 0.628821\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", - "[4]\ttraining's binary_logloss: 0.618758\tvalid_1's binary_logloss: 0.634066\n", + "[4]\ttraining's binary_logloss: 0.619015\tvalid_1's binary_logloss: 0.634182\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", - "[5]\ttraining's binary_logloss: 0.611054\tvalid_1's binary_logloss: 0.626046\n", + "[5]\ttraining's binary_logloss: 0.61131\tvalid_1's binary_logloss: 0.626159\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", - "[6]\ttraining's binary_logloss: 0.5955\tvalid_1's binary_logloss: 0.611146\n", + "[6]\ttraining's binary_logloss: 0.595817\tvalid_1's binary_logloss: 0.610791\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", - "[7]\ttraining's binary_logloss: 0.58875\tvalid_1's binary_logloss: 0.603778\n", + "[7]\ttraining's binary_logloss: 0.589086\tvalid_1's binary_logloss: 0.603435\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", - "[8]\ttraining's binary_logloss: 0.597049\tvalid_1's binary_logloss: 0.605336\n", + "[8]\ttraining's binary_logloss: 0.597361\tvalid_1's binary_logloss: 0.605012\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", - "[9]\ttraining's binary_logloss: 0.593235\tvalid_1's binary_logloss: 0.602332\n", + "[9]\ttraining's binary_logloss: 0.593551\tvalid_1's 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binary_logloss: 0.457798\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", - "[98]\ttraining's binary_logloss: 0.425655\tvalid_1's binary_logloss: 0.459594\n", + "[98]\ttraining's binary_logloss: 0.4279\tvalid_1's binary_logloss: 0.459013\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", - "[99]\ttraining's binary_logloss: 0.42746\tvalid_1's binary_logloss: 0.460839\n", + "[99]\ttraining's binary_logloss: 0.429667\tvalid_1's binary_logloss: 0.460296\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", - "[100]\ttraining's binary_logloss: 0.423387\tvalid_1's binary_logloss: 0.457639\n" + "[100]\ttraining's binary_logloss: 0.425572\tvalid_1's binary_logloss: 0.45739\n" ] } ], "source": [ + "lgbm_params = {\n", + " 'boosting': 'dart', # dart (drop out trees) often performs better\n", + " 'application': 'binary', # Binary classification\n", + " 'learning_rate': 0.05, # Learning rate, controls size of a gradient descent step\n", + " 'min_data_in_leaf': 22, # Data set is quite small so reduce this a bit\n", + " 'feature_fraction': 0.7, # Proportion of features in each boost, controls overfitting\n", + " 'num_leaves': 41, # Controls size of tree since LGBM uses leaf wise splits\n", + " 'drop_rate': 0.15,\n", + " 'max_depth':4,\n", + " \"seed\":1212}\n", + "\n", "lgbm_model = lgb.train(lgbm_params, train_data, valid_sets=[train_data, valid_data])" ] }, { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "lgbm_model.attr(\"num_classes\")" - ] - }, - { - "cell_type": "code", - "execution_count": 13, + "cell_type": "markdown", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "1" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], "source": [ - "lgbm_model._Booster__num_class" + "## LightGBM default visualisations" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": { - "scrolled": true - }, "outputs": [ { "data": { "text/plain": [ - "['_Booster__attr',\n", - " '_Booster__boost',\n", - " '_Booster__get_eval_info',\n", - " '_Booster__higher_better_inner_eval',\n", - " '_Booster__init_predictor',\n", - " '_Booster__inner_eval',\n", - " '_Booster__inner_predict',\n", - " '_Booster__inner_predict_buffer',\n", - " '_Booster__is_predicted_cur_iter',\n", - " '_Booster__name_inner_eval',\n", - " '_Booster__need_reload_eval_info',\n", - " '_Booster__num_class',\n", - " '_Booster__num_dataset',\n", - " '_Booster__num_inner_eval',\n", - " '_Booster__set_objective_to_none',\n", - " '__class__',\n", - " '__copy__',\n", - " '__deepcopy__',\n", - " '__del__',\n", - " '__delattr__',\n", - " '__dict__',\n", - " '__dir__',\n", - " '__doc__',\n", - " '__eq__',\n", - " '__format__',\n", - " '__ge__',\n", - " '__getattribute__',\n", - " '__getstate__',\n", - " '__gt__',\n", - " '__hash__',\n", - " '__init__',\n", - " '__init_subclass__',\n", - " '__le__',\n", - " '__lt__',\n", - " '__module__',\n", - " '__ne__',\n", - " '__new__',\n", - " '__reduce__',\n", - " '__reduce_ex__',\n", - " '__repr__',\n", - " '__setattr__',\n", - " '__setstate__',\n", - " '__sizeof__',\n", - " '__str__',\n", - " '__subclasshook__',\n", - " '__weakref__',\n", - " '_free_buffer',\n", - " '_to_predictor',\n", - " '_train_data_name',\n", - " 'add_valid',\n", - " 'attr',\n", - " 'best_iteration',\n", - " 'best_score',\n", - " 'current_iteration',\n", - " 'dump_model',\n", - " 'eval',\n", - " 'eval_train',\n", - " 'eval_valid',\n", - " 'feature_importance',\n", - " 'feature_name',\n", - " 'free_dataset',\n", - " 'free_network',\n", - " 'get_leaf_output',\n", - " 'get_split_value_histogram',\n", - " 'handle',\n", - " 'lower_bound',\n", - " 'model_from_string',\n", - " 'model_to_string',\n", - " 'name_valid_sets',\n", - " 'network',\n", - " 'num_feature',\n", - " 'num_model_per_iteration',\n", - " 'num_trees',\n", - " 'pandas_categorical',\n", - " 'params',\n", - " 'predict',\n", - " 'refit',\n", - " 'reset_parameter',\n", - " 'rollback_one_iter',\n", - " 'save_model',\n", - " 'set_attr',\n", - " 'set_network',\n", - " 'set_train_data_name',\n", - " 'shuffle_models',\n", - " 'train_set_version',\n", - " 'trees_to_dataframe',\n", - " 'update',\n", - " 'upper_bound']" + "" ] }, - "execution_count": 14, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" - } - ], - "source": [ - "dir(lgbm_model)" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ + }, { "data": { + "image/png": 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4.0000000000000009 38.500000000000007 27.500000000000004 60.000000000000007 89.500000000000014 21.500000000000004 27.500000000000004 28.250000000000004 14.250000000000002\n", - "decision_type=2 2 2 2 2 2 2 2 2 2 2 2\n", - "left_child=1 6 -2 5 -5 -4 9 -8 11 -1 -10 -3\n", - "right_child=2 8 3 4 -6 -7 7 -9 10 -11 -12 -13\n", - "leaf_value=0.0035435190502354323 0.002172268766763089 -0.00021295912203113417 -0.0019676801893398484 0.0002984985015640311 -0.0020891715969200944 -0.002870459029423309 0.0017453561501000457 0.0042168601941481631 0.00098318988111309067 0.0044800861034467359 3.0462165390396312e-05 -0.0012366358060957442\n", - "leaf_weight=6.3321614563465181 5.3664612025022498 4.5217098593711871 25.395481631159782 10.428248703479769 8.1753753721714002 45.956646963953972 3.6957817673683158 3.4421452730894089 5.1833545714616802 10.377738550305365 12.835960075259207 4.8665683716535568\n", - "leaf_count=33 22 20 129 43 34 239 20 21 21 56 54 20\n", - "internal_value=0 0.00172475 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0.001556281919046441 -0.003476782901287188 -0.0011687841766839999 -0.0019750102384718869 7.0180467782142905e-05 -0.0013509883882421941\n", - "leaf_weight=5.8589716404676428 5.0600014030933371 34.911294341087341 13.811217397451403 5.0838542580604535 5.4326450973749152 5.4528673589229566 56.476582780480385 7.6147711575031281 6.2227551788091651 4.144930899143219\n", - "leaf_count=29 21 164 68 28 24 25 265 39 29 20\n", - "internal_value=0 0.000339679 5.88992e-05 -0.00136571 -0.000489772 -0.00111787 -0.000893145 -0.00275401 0.0011638 -0.00255873\n", - "internal_weight=0 66.2369 60.3779 83.833 40.3439 71.1344 61.5366 12.6986 20.034 9.5978\n", - "internal_count=712 314 285 398 188 331 286 67 97 45\n", - "shrinkage=0.00294118\n", - "\n", - "\n", - "Tree=99\n", - "num_leaves=12\n", - "num_cat=0\n", - "split_feature=3 0 1 5 0 1 1 1 1 1 5\n", - "split_gain=27.8642 12.6496 6.04878 3.0613 1.73053 1.7149 1.25481 0.668194 0.601694 0.474375 0.287565\n", - "threshold=1.0000000180025095e-35 2.5000000000000004 6.5000000000000009 1.5000000000000002 1.5000000000000002 32.250000000000007 27.500000000000004 27.500000000000004 23.500000000000004 38.500000000000007 1.0000000180025095e-35\n", - "decision_type=2 2 2 2 2 2 2 2 2 2 2\n", - "left_child=1 7 -2 -3 9 -6 -5 8 -1 -4 -9\n", - "right_child=2 3 4 6 5 -7 -8 10 -10 -11 -12\n", - "leaf_value=0.052508021645799298 0.02791754712508699 0.023826949577689282 -0.0084791494774024197 -0.00048415211934626779 -0.027042905514187039 -0.046622487210136808 -0.027467007919845438 0.046603130797200805 0.025164537152241397 -0.023542032048196437 0.063751521178106188\n", - "leaf_weight=4.8207417502999323 5.245101034641265 9.2136362940073031 11.879214555025102 10.36711858212948 47.58261613547802 14.619299396872519 7.3729167133569709 3.2931643277406684 3.4529967904090881 9.3338668644428235 9.4889924153685552\n", - "leaf_count=31 22 39 51 44 264 90 32 22 18 40 59\n", - "internal_value=0 0.0231294 -0.0241641 0.000445269 -0.027439 -0.0316447 -0.0116985 0.0521673 0.0410964 -0.0151069 0.0593335\n", - "internal_weight=0 48.0096 88.6601 26.9537 83.415 62.2019 17.74 21.0559 8.27374 21.2131 12.7822\n", - "internal_count=712 245 467 115 445 354 76 130 49 91 81\n", - "shrinkage=0.05\n", - "\n", - "\n", - "end of trees\n", - "\n", - "feature_importances:\n", - "Age=308\n", - "Fare=308\n", - "Cabin_label=165\n", - "Embarked_label=123\n", - "Pclass=98\n", - "Sex_label=69\n", - "\n", - "parameters:\n", - "[boosting: dart]\n", - "[objective: binary]\n", - "[metric: binary_logloss]\n", - "[tree_learner: serial]\n", - "[device_type: cpu]\n", - "[data: ]\n", - "[valid: ]\n", - "[num_iterations: 100]\n", - "[learning_rate: 0.05]\n", - "[num_leaves: 16]\n", - "[num_threads: 0]\n", - "[force_col_wise: 0]\n", - "[force_row_wise: 0]\n", - "[histogram_pool_size: -1]\n", - "[max_depth: 4]\n", - "[min_data_in_leaf: 20]\n", - "[min_sum_hessian_in_leaf: 0.001]\n", - "[bagging_fraction: 1]\n", - "[pos_bagging_fraction: 1]\n", - "[neg_bagging_fraction: 1]\n", - "[bagging_freq: 0]\n", - "[bagging_seed: 25892]\n", - "[feature_fraction: 0.7]\n", - "[feature_fraction_bynode: 1]\n", - "[feature_fraction_seed: 3653]\n", - "[extra_trees: 0]\n", - "[extra_seed: 19633]\n", - "[early_stopping_round: 0]\n", - "[first_metric_only: 0]\n", - "[max_delta_step: 0]\n", - "[lambda_l1: 0]\n", - "[lambda_l2: 0]\n", - "[min_gain_to_split: 0]\n", - "[drop_rate: 0.15]\n", - "[max_drop: 50]\n", - "[skip_drop: 0.5]\n", - "[xgboost_dart_mode: 0]\n", - "[uniform_drop: 0]\n", - "[drop_seed: 12966]\n", - "[top_rate: 0.2]\n", - "[other_rate: 0.1]\n", - "[min_data_per_group: 100]\n", - "[max_cat_threshold: 32]\n", - "[cat_l2: 10]\n", - "[cat_smooth: 10]\n", - "[max_cat_to_onehot: 4]\n", - "[top_k: 20]\n", - "[monotone_constraints: ]\n", - "[monotone_constraints_method: basic]\n", - "[monotone_penalty: 0]\n", - "[feature_contri: ]\n", - "[forcedsplits_filename: ]\n", - "[refit_decay_rate: 0.9]\n", - "[cegb_tradeoff: 1]\n", - "[cegb_penalty_split: 0]\n", - "[cegb_penalty_feature_lazy: ]\n", - "[cegb_penalty_feature_coupled: ]\n", - "[path_smooth: 0]\n", - "[interaction_constraints: ]\n", - "[verbosity: 1]\n", - "[saved_feature_importance_type: 0]\n", - "[max_bin: 255]\n", - "[max_bin_by_feature: ]\n", - "[min_data_in_bin: 3]\n", - "[bin_construct_sample_cnt: 200000]\n", - "[data_random_seed: 3996]\n", - "[is_enable_sparse: 1]\n", - "[enable_bundle: 1]\n", - "[use_missing: 1]\n", - "[zero_as_missing: 0]\n", - "[feature_pre_filter: 1]\n", - "[pre_partition: 0]\n", - "[two_round: 0]\n", - "[header: 0]\n", - "[label_column: ]\n", - "[weight_column: ]\n", - "[group_column: ]\n", - "[ignore_column: ]\n", - "[categorical_feature: ]\n", - "[forcedbins_filename: ]\n", - "[objective_seed: 25511]\n", - "[num_class: 1]\n", - "[is_unbalance: 0]\n", - "[scale_pos_weight: 1]\n", - "[sigmoid: 1]\n", - "[boost_from_average: 1]\n", - "[reg_sqrt: 0]\n", - "[alpha: 0.9]\n", - "[fair_c: 1]\n", - "[poisson_max_delta_step: 0.7]\n", - "[tweedie_variance_power: 1.5]\n", - "[lambdarank_truncation_level: 20]\n", - "[lambdarank_norm: 1]\n", - "[label_gain: ]\n", - "[eval_at: ]\n", - "[multi_error_top_k: 1]\n", - "[auc_mu_weights: ]\n", - "[num_machines: 1]\n", - "[local_listen_port: 12400]\n", - "[time_out: 120]\n", - "[machine_list_filename: ]\n", - "[machines: ]\n", - "[gpu_platform_id: -1]\n", - "[gpu_device_id: -1]\n", - "[gpu_use_dp: 0]\n", - "\n", - "end of parameters\n", - "\n", - "pandas_categorical:[]\n", - "\n" - ] - } - ], - "source": [ - "print(lgbm_model.model_to_string())" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "text/plain": [ - "{'name': 'tree',\n", - " 'version': 'v3',\n", - " 'num_class': 1,\n", - " 'num_tree_per_iteration': 1,\n", - " 'label_index': 0,\n", - " 'max_feature_idx': 5,\n", - " 'objective': 'binary sigmoid:1',\n", - " 'average_output': False,\n", - " 'feature_names': ['Pclass',\n", - " 'Age',\n", - " 'Fare',\n", - " 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\n", 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5zJo1a9j+iv/96KOPEASBZcuWcfDgQXbv3k1XVxdPPPEEEyZMAE7dhNatWye12bnhhhsAeP/991GpVBgMBjQajdSdYvXq1ZjNZl5++WWefPLJM8zWxfBHTEwML7zwAg899JAUbhgLuXOEzAXh8/nYvn07TqeT22+/HaPReMaJ5/F4eOWVVzhx4sSw18XY6+DgIB988AG7du1iYGCA3bt3849//IPXX38dh8MxLLb25ptv8sUXX/DFF19IK9Br1qzhiy++YOvWrezYsYM//elPvPjiixQWFvK///u//OIXv6CysvKMsatUKrKysrjrrrv4+OOPqa+vvyzyZc1mMydPnuSLL77A6/Vis9nYvn07H374IQ0NDWzbto1t27YxODgo3bBKS0t5+eWX2bNnDwApKSksWbIEgL1795KWlkZISAj79+8HThmDHz9+nIKCAmprazEajeTl5fHBBx/Q0tLCsmXLKCwsxOPxjDlej8eDxWKhsbERp9PJ7Nmz+fLLL1EoFOTk5ACnSn5vvvlmnnjiCQoLC+nt7aWnp4eysjJuu+22UWexarUao9GI0+lkz549fPbZZ/T397Nz5042bdpEXV0dmzdvZuLEiZLw/+Y3v6GgoIDw8HC+/PJL6fuPHTvGokWL+Oabb/B4PPT29lJRUcHy5cv56quv2LRpE9dffz1NTU20trayd+9e+vr6pFzf01EoFERERPDQQw+xefPmcZ878kxX5oJob2+nubmZBx54QHokOx21Wk1YWBhut5uioiLq6+tZtGgRx44dY3BwkPT0dHbt2iXNKHbt2sXq1av59NNPKS8vl0xTuru7qa+v58knn+SPf/wj1157LR0dHXR2dnLnnXfyP//zPzz00ENMmTKF//u//2PmzJksXryYv/3tb8TFxY04NoVCgcFg4Cc/+Qn//Oc/+elPf/qdznYFQeDIkSMsWLCADz74AJPJRFVVFW1tbRw7dgyFQsHJkycxm82S0Lz00kvs37+fxx57jDlz5qBQKNDpdFRWVlJVVcXKlSspKirCZrOxYMECAHJzc3n66ad57rnnyM7OJjs7G4PBQGlpKbm5uQQHB2MymaTHbQCr1UphYeEZTSIFQWDGjBm43W4WL15MQEAAXq8Xf39/Dh06BJzq3lBaWkphYSFvvPEGGRkZ5Obmctddd53hpzAS9fX1lJeXAxAeHk57ezuHDx8mLS2NiIgIMjIyUCqVUtudwMBAQkJCiI6OBqC/vx+Px4NGo2FgYACn00lvby8+n4+AgAC6u7txu93SWAoLC6W2PQ6HQ3pyGglxhmuxWEY09jkdWXRlLoimpiaysrJGFdyhdHZ2smPHDkJDQ9Hr9TgcDoqKisjLyyM2NpaMjAwUCgXd3d1oNBoCAwPp6+uTHvvNZjOCIODv74/VasXj8WCz2fB6veh0Oik8UVNTw5VXXonRaMRkMqFUKkdtLAinhDcuLg6Hw4HH48Hf3/9iHqJzQuyIMGHCBJKTk/nyyy+ZMmUKW7ZsYdmyZQwODpKSksL999+PWq3Gz8+P++67j4yMDLZt24YgCCxcuBCv18ttt91GXV0dU6ZMISoqSqoc6+/vl2b5U6ZMISgoCKvVSn9/P5MnT8bpdGKxWIiOjpZinXAqRjqk4+2w/NexfnY6nVJ5bmRkJHl5eeTn50vx17MhCAJOpxODwcB9991HS0sLTU1NqNVqqfR3aEVbeXk5WVlZ+Pv7SzfbkJAQlEolTqcTvV6Pn5+fFJ5yOBxER0dLebsqlQqPx0NFRQVlZWWcOHGCiIiIs45PjMGPB1l0ZS4IjUYjieFYdfNerxetVstPf/pTqqqqKC8vR6VS4fV6AaRFm+joaCwWi1RCKvbSCg8Pl1LBIiIihoUd+vr6iIuLkzIU5syZg9VqpaysTPI6PRsejwdBEC5JWehY8dihVFRUoFQqpZLa3bt3ExYWRkdHB0VFRVx//fXs2LEDt9vNHXfcgcPhYP369XR0dKBSqXC5XABs2bKF3t5eli5dikKhoLKykvvuu4+oqCg++ugj/P39aW1tlRY9P/vsMxobG/nZz37G119/TWFhIbfccsuwbgkajYbs7OwzFtLGg9Pp5MYbb2T27NnMnz+f+Ph4XC4Xr776KrNnzyYsLGzE4+PxeLDb7ZJPwwcffMCSJUvo6+sjMDCQiooKFAoFbW1tJCYmSusHCxYsYGBggI0bN3LPPfeg0+lYsmQJu3fvZsWKFfT09HD48GHmz5/P9u3bufXWW1EqlWzZsoVp06ZxzTXX4PF4iIyMZOrUqaPulyAIlJaWEhwcTFBQ0LiOhZy9IHNBWK1WXn31Ve6///5RL5zBwUFee+01srOzKS0txev1smDBAjZt2kRAQABZWVkcPXqUxx57jKioKGpqajhw4ADh4eFMmzaNwsJCVq1ahUKhYNu2bVgsFrKzswkJCeH48ePSwltBQQEHDx6kpKSEsLAwHn30Ub755hsmT54sPWaOhCAIHDhwgObmZm655ZbzCi94vV6OHTtGXFwcFouFiIgITpw4QUJCAlqtltraWlJSUggPDx/2udOzF0a6Ht955x2cTifBwcHMmjWL5OTkMW8OQ4VejMuKAurz+VAoFHi93mGiOvTGObTsWmS07IXxcPp+ibPgiooKtm7dyn333TditsvpdotDP3v6e8XXvV7vsKyF0xd1T9/Hoa/5fD6p6GKs3GNBEKisrOTDDz/kP/7jP4aOX04Zk7m0lJSU8PXXX3PHHXeMuJA2lKG18UM5/cI420UlzkhPr7Mfybd16EU0El6vl+LiYr766iseeeSRMVuCj4bX6+Wll14iISFBqpLr7u7GYrGQl5eHx+Nh9uzZ0sKSyHjydEVfV6PRSERExHcWc74Q0R0NUbh27NjB/Pnzyc/Pv2heDZcKcfFy586dNDc3c+utt55+3sspYzKXBkEQqK+vR6fTMXXqVF599VUWLVrE1KlTRzUMGU8hwtneM/R3p///SO8dLc4mdgLYsWMHdrudBx988LwFF06FRq6//nqeffZZbrzxRqqrq1m+fDkhISH09vaydu1aoqKiaG9vlzxgg4ODsdlsZyxMnU5ISMi425V/3xAzHGJiYigsLGT//v3MmjWL7Ozsy847w+fz0d/fz5EjR6isrKSgoIB77733nM1xZNH9EXMuscaR8Hq9PPXUU+zYsUOyzHv33XdZsWIFq1atIicnh5CQkMvqwvF4PHR1dXHgwAFaWlqYO3cu+fn5F2wtqFAoSExMJDY2lry8PHw+H2vWrOHqq68GYO7cuVRVVfH8888PW/yJj4/n2WefvRi79r1FzCC5+eab6enp4ZtvvmHv3r1EREQwceJE4uLiCAoKulQ2jKMiLpD19/dTX19PSUkJbrebgoICfvKTn5x3LzdZdH/g2Gw23n//fa655hoqKipISkri4MGDTJ8+nZ6eHjo6OlixYgVarXZc2xPbmIjpW06nk+7ubgDS0tJ48sknWb58OcXFxbz77rvo9Xry8/NJTEwkNDR03Cu8Fwux8qq7u5vq6moqKyvx9/dn+vTpLFu2bKT+VueFz+dj9+7dZGZmEh0dTVxcHHPnzsXf35+uri5qa2ux2WzAqXJkvV7P3XffzYwZMy5Y8H8oKJVKIiIiWLFiBS6Xi+bmZkpKSti5cycKhYKkpCQSExMJDw8nODgYrVZ7UW/oYk60xWKhq6uLhoYG2tra0Gg0JCYmSpWLFxr+kGO6P3B8Ph/PPfccEyZMkHwREhIS8Hq9tLS0MH36dFasWDFqrqT4GN7W1kZ9fT2NjY3AqUyClJQU2traWL16NStXruTXv/416enpUrzV6/XS2tpKaWkpra2tuN1uEhISiI+PJzIyEqPReNb8x/PB4/FIdpFiDrHJZMJgMJCenk5ubq7kEnUxv9fn8/HNN9+QlpaGUqmkqamJmpoaOjo6CAkJITExkfT0dP7rv/6L4uJinnnmGZYsWcLevXux2WxMnjz5oo3lUrFu3TruvvvuixrTHQvxPLLZbNTX19PU1ITZbMZisSAIAqGhoRgMBkJCQiQvXNGkfKjfsmjp6PF4cLvduN1u7HY7NptNas9jtVrRaDQEBwcTGRlJcnIyCQkJUjn0OZwv8kLajxlxoeK3v/0tTz31FO+99x4/+9nPcLlcNDY2smHDBp566qlhxQNi19OGhgbJvzQuLo7k5GRSUlKGeYv29fVRXFzMnDlzRp01igtedrud5uZmWlpa6Orqore3l8HBQYKDg9Hr9eh0OrRaLVqtFo1Gg0ajkS4c0VJQrJV3uVy4XC4GBgaw2+1YrVYsFgsOh4Pg4GDCwsKIjo4mISGBmJgYaZX+YjuawakMjtraWurq6mhpaZFKaNPT00lMTBw2k62qqiIkJITY2FgUCgXt7e0UFhZeFpVwY6FWq7nlllu+0zzmoeW/LpdLattjsViw2Ww4nU5cLhcej0cyw4d/dY4Qc5vFPPCgoCD0ej1GoxG9Xi+VKIufOU9k0f0xI660vvbaazz88MPs2rWL3bt3s3LlSsrLywkMDGTmzJmYTCbq6+vp7OxEpVKRlJREcnIy8fHxwxZxRkvpOZcTdOg5Nzg4SH9/PxaLRRJQu92Oy+XC7XZLF46YpSDGQv39/SVPXq1WK108p8f+LoVtpMvloqGhgZMnT9LW1obH4yE5OZm0tDQSExOlBbnRbkCnZ2p8n7hc4vNDxVf85/V6sdvtkvAODg5KZvdDW/WIghsQECDNYIf+uwjIonsh2O12Tpw48b24OPR6PSkpKcNEZ2BggDfeeIOJEydyxRVXSHXydXV1VFdXYzabJQOY1NRUoqKiRnTJb29vp62t7bK56L5NVCoVOp2OkydP0traisPhIC4ujszMTOLi4ggNDT3vRR6n00lVVdWo9f2XEzqd7nQ3rW8N0T3M4XDQ1tZGa2sr3d3dOJ1OKVzg8/mkVjuBgYHDWjqJ/dLcbjdOp1MSZj8/P2nmq9VqiY6OJj4+nqioKPz9/c83fiuL7oWwY8cOqqqqyM7O/q6Hcla8Xi979+7lV7/6lTQzFcsn6+rqJKEVS0BTU1PJyMiQ4qpjicYzzzxDQUHBqMYkP2Sqqqqw2Wxcc801xMfHExoaetFySYuKijh8+DB5eXkXYaSXlq+//prHH3+csLCwS/5dohGS2WymqqqK2tpayZ4yNjaW2NhYIiMjpRmrRqM5o2nmWIgiLLrU2Ww2Ojo6JEFXKpWEhISQk5NDamoqwcHB443tynm6F4JCoWDOnDlnLQW8HPB6vTQ3N0sr9S0tLVRUVNDX1yf1e5o/fz5Go1Fa9T2XEzQoKIjFixePO8vhh4Rer79ki12CIDBz5szvhYl5U1OTVLJ9qRC7NBw7doza2lqp9HjZsmWEhIRIIYGLgejRGxAQgF6vJyoqirS0NClU4XA46OnpobKykgMHDqBUKpk4ceIFp0LKovsDwmw2s3btWoxGI3FxccyZM4fo6Gip1fSPMTQg8/3A5/PR3NzMzp07cblcTJo0idWrV0v90L7Nc1ehONUpODg4mODgYJKSkvB6vfT19XH48GHeffddaRJzPkb9suj+gAgJCWHlypWEhYWdcaKKiw1w+SyG/BC50F5h3zcGBwepr68nJiaG/v5+9Ho9ra2tkmubxWIhKSnprLnIdrudrVu30t3dzdKlS0lKSrqsJgmiCIeHh3P11VezaNEiiouLeeedd5g5cyYzZsw4p/xzWXTPk4shYOPZxmhGIUN/FlGpVFKa1VCcTicvvfQS/v7+REdHk5+fz6FDh/B4PNx5553j8jMd79hGen2ksY60nZE+P9pK/3iO2dBjNdJ2zmZFeL6mN++9955kTXnjjTeyc+dObDYbt9122znFQi/2+TWeY3r6e8fz3T6fj1dffZXbbruN0tJSBEGgvb0djUaDz+cjMzMTvV4/ojWiIAgMDAzwzjvvkJ+fzw033HCG2Hq9XkpLS4mJiRnRtEgQBNra2qipqWH69OmoVCoOHTpEWFgY2dnZw84pk8kkuZGJvrddXV20t7dLqZD19fVERETg8XgoKSkBTnkPi7nJCoVCKq7Jzc1l48aNklHSeMMecueI80AQBGprayUbvfPB6/VSUlKC3W6npaWFzz//nA0bNkjVXWycINIAACAASURBVF6vl3379vHPf/6T3t5eSktLOXbsGG63m7q6Og4ePIjVah1XVoVaraanp4dp06Zx7Ngx3n//febNm0dtbS1lZWUj7t9Y2xV7RO3atYtt27ZhNpulz9bV1UmG06KDl9vtHnE7Pp+PgwcPSkUXYjHG3r17z1jRdzgcklfCaAiCQGNjIy0tLQiCQHFxMVu3bsXpdErjLisr4+jRo1Ku8oEDB+jv76enp4f9+/dLnz1XlEoldrud1NRUampqWL9+Penp6fj7+7Njx45xHdeh+yC2GjpfbDYbFRUVUoxSdHgTv2Pfvn18/vnnmM1m6ZiIM9f9+/fT398/5ndoNBquv/563nvvPXJzc6mrq2PFihVcc801xMbG8tVXX0nFMi6XS0r/g1N/+/Xr1zNlyhTmz58/LEdWRKFQsHv3bqqrq/F6vZIFp8fjYXBwEI/Hw7vvviv1dmtpaWFgYIC//vWvUsqh+P41a9bQ19fH22+/LWVD/O1vf8NsNvPee+9hs9n461//SmNjI5WVlbS0tLB582ZaWlrO2G+F4pRR/K233kpnZyd79uwZ9zkji+554HK5ePHFF6UD7fP5pDQUMQAvtlKBf1XVtLa2Shf0wMAAr7/+OiaTidraWlQqFd988w09PT3AqRStDRs2SAJfVFREWVkZO3bs4P3338dut7N+/fpxj1mpVBIYGCiduCaTibCwsDMS3cXv2717N1arddhFMvQ9u3fv5qOPPiI5OZnGxkapJNjtdtPV1cX27dtxOBz4fD60Wq2USWG324eJqUKh4Pjx45w4cUJK5/F4PHzyySdYrVZpvGJzwi1btkjltPAvE5LKykqpeGLr1q3s378fq9WK0+lk3759VFdXA6fa1OzYsYOamhq2bNnC22+/jcfj4f3332ft2rUolUreeustHA7HuZ0Upx1nEbEiTavV4nA42LRpEx0dHZIYjITX6+X5559n9+7dklA7nU4cDgder1cqDDn9/Oro6JBuXl6vlw0bNvDJJ58AUFxczEsvvSRNFNra2ti4cSNwqivDzp07OXHiBJs3b+add97B4/Gwbt26cQnJjBkzEASB9PR04uLi+OSTT6itrUWtVpOWlkZfXx8Oh4NbbrmF3/72txQVFUntfVQqFTNnzhx1Vq1UKtHr9QwMDPDuu+/yzDPPUFNTw9q1a3nhhReoqKjg0KFDaDQagoKCSExMxN/fH61Wi9Vq5b333gNOFbD09PSQm5tLQ0MDLpeLzs5OrFYr06dPp7q6WjJYF/fptttuk/KvR8PPz4/777+fI0eOjMuQHeTwwnnR3NxMTk4OmzZt4sorr+TIkSMUFxdz4sQJrrvuOo4cOYJSqeTxxx9HoVBw7Ngxtm7dis/n49ZbbwVOZQOIj5vz5s2TOgakpKQAp2YpVquVnTt3kpGRQWpqKnq9nrfeeou0tDQmTJgg9dAa6pNqt9uHeaQCUvmtyWQiMTGRWbNmcfz4cZKTk0lMTDxjRlVUVMQvf/lLMjIyWLFiBcuWLRs2q/d6vWzevJnly5eTnJzM3XffTVtbG2+++SZWq5Urr7ySgwcPUlNTw+23305hYSHLli3jzTffJCgoiNtvv52pU6dKGRRBQUE4HA7eeustent7ufHGG2ltbeWZZ55h8uTJKJVKKisrWbx4sSRogiDQ2trKxo0baWtrY968eWRlZaFWq4mLi5MaVcbGxtLZ2SllXZSWlpKQkEBaWhqvvPIK8fHx5ObmSoL7+OOP8+mnn9Ld3U1SUpK0z2L129nw+XyEhITg8XhISUlhwYIFHD16lMDAQGbMmEFHRwf3338/ISEhLF26lGXLluHz+YY9eovObbm5uWzfvp3FixfT3t7O1q1bKSsrY9WqVRQXF9PT08OvfvUrdDodpaWlbNmyhcHBQW666SYEQaC6uhqFQoFWq8VsNtPY2DisfFfsDrFz504mTZpEbGwsaWlpvPTSS8THxzNp0iTWr1+P0+mUjvng4CBNTU1YLBap6MDtdlNfX098fDwtLS1MmjSJqKgoDAaDtBjV0NBAVVUVBw8eZPPmzbz00kukp6eTl5fHPffcM67H8pqaGpqbm5kwYQIOh4OQkBBKSkqkQp78/Hwp9pqamorVamVgYEC63sRZ9lBLUPG1oZahIn5+fnR1daFSqcY0JxeLc2w2G0ajccx9kUX3HPF6vezatQuVSkV1dTU1NTVUVFQQFBREVFQUBw4cICkpCY1Gg9vtxs/Pj+LiYurq6liyZMmIcSmVSkVjYyPh4eHSzDMrK4vnn3+eZ555hvr6epKSklCpVDidzhG9ZuFUSe5HH300YoxWpVLR0NCARqPh2LFj+Hw+TCYTH3/88RnvPXjwIBaLhcOHD1NSUsK6devIzMzk0UcfBf7lUyuOITAwEKPRiE6n4+jRo8yZM4eZM2dSUFDAkSNHcLlchIaGotVqmTVrFs3NzWek4KnVagwGA5WVlXR1dZGUlMTDDz/M008/TXBwMNdcc80ZF2d9fT1Hjx4lLy9vxE6+CoWCyMhIpk2bxsGDB0lLS8Plckkr4m63e5gfr2j2LZYciwiCwK5du2hqajrzhBgBsevtoUOH8Pl8eDweNm7cSH9/Pzabje7uburq6ti4cSMpKSn893//97DPi6GV6upqqYDF4XAQExNDU1MTgiCQl5cntQcvLS2lpqaGhQsXEhcXh8vlYsOGDQQEBHD8+HHef/99lEolNTU11NXVkZeXR2pqKi+++CK/+93vpEmEWq3G5XJJJuCnC5Hdbqe4uFgqBhHf09nZSU5ODr29vSiVSiIjI1GpVISHhw+rBFOr1cNyX6dOnUpra6skhiMhlo/7+/tjs9lISkqitbWVo0ePotFoMJlMUs8znU5He3s7Pp+P/Px8VCoVlZWVTJs2TbLSbG5uJikpCZfLRU9PD4GBgdTX15OSkoKfnx8DAwNSM9SSkhJyc3PHXCQTc3zHaw0qiy7ntkDT3t6Oy+Xi3nvvxefz8fHHHzNt2jT279/PihUrqKqqorGxkSlTpkj9tiZNmkRMTAz79u0jJCSEZcuWSZVhAwMDCIIgtZWBUxddYGAgarWamJgYcnNzKS8vJzo6mlWrVnH06FFqamooKCgYdrIaDAZuuummMf1ZxyIwMJDPP/+cgoICVq5cyZIlS9i0aZP0e7VazdKlS9m8eTMJCQlYLBZpNiOGTsTGf8nJyZw4cYL+/n5cLhd2u33YmAXhVGtus9lMSUkJoaGhUvuf9vZ28vLyaGxsxOv14vV6pe0AxMTEcMcdd1BWVsa6dev4z//8T5RKJf39/bjdbiwWCzU1NcTFxWEwGKiqqiIiIoKKigr8/f1ZuXIlJSUlnDx5khkzZmCxWCgtLcVgMAybFSoUChYvXsyVV155Qce1s7OTF198kYyMDJYvX84111wjtZoXaW9vx2w287Of/QyNRsPHH3/MLbfcQnNzM9dddx1qtZri4mKysrLweDwoFAry8/OJiIjgwIEDfPXVV9x4440sX76cmpoa6uvrufLKK+nv76e4uBidTsf+/fsJCAhApVKRkJBAbm4uxcXFqFQqbrnlFo4dO8bx48eZNGnSsGIYvV4/oon5eBbenE4ny5YtY8qUKVx11VUkJCSgUqlYs2YNR48elZ58Tsfn85GXlyeZBjkcDmbMmCHdcPz9/aXjqNPpsNlsVFVVsXTpUoxGoxRu0Wg0Un+1O+64Q9r+vffeS1tbG3feeSeCcKrBpjizjYmJITEx8ax/08HBQdavX09+fv64PSl+NBVpLpeLN954g/nz51NbW0t6errkChUaGkpFRQWzZ88mPT192OcKCwsxGAxMnToVQRBobm6mu7ubSZMmUVtbS0tLC+Xl5VJS9+rVq6msrCQ7O5uMjAy8Xi+7d++WHk3z8vJISUnB6XRy4MABUlJSSExMpKqqiuTkZAICAjhy5AgqlYrOzk7y8/OJioqirKxMaibY3NxMX18fmZmZkkuX1+vlzTffZNWqVRckuuJje3d3NxMmTJBOpJdeeomf/vSn0mO6uLrb3d1NfHw8MTExHD16lICAAGJjY2ltbSUwMJDMzEyOHDlCXFycFN90OBzDDGg8Hg9arZbe3l7J5cnhcOB2u8nPz6e1tZWGhgamTp1KRUUFeXl5hIeHU1JSIs0+IyIimDlzJoJwqmeVz+cjPT2d/fv3ExsbS05ODg0NDQQEBEiinpWVRXt7OyaTiYyMDDweD9XV1SQmJhITEyOJwMGDB7HZbCxatOi8jyucEp5Dhw6Rn58vJdcXFRUN6xzR0tJCe3s7kydPprGxkcbGRsxmM9XV1TgcDq677jrMZjNhYWFMmjRJWnC1WCwAZGdnk5mZCZwqAW9rayM9PR2fz8fJkydJT0/n+PHjKJVKWltbmThxIjExMdKCW05ODi0tLZhMJrKzswkODpbGf7bOEWNNXMSno6HZCeINd+3atcydO5dp06adNdQwnqyY8WSmjLWN8e6Py+Xiiy++wG63c+eddw4N68llwHDqIK1btw6r1UpYWBhVVVUYDAapvbLFYpEESyyLVSgUFBYWYjQaR61IEwSBDz74gJ6eHvLy8pg/f740czlfE5jTOT2NZ6SZxcUS3dH485//PEx0xxqzyEjHwGq1UlNTA5x6lM/Ozkaj0Yy4vbOlO42H0S7C018bKgSnf8fFEt2RGKtdjyAIFBYWcuzYMXJycliwYIEkhOebTjbS8Rzr/ILRRVe8WQ0MDEjubtXV1Wi1WtLS0sZ8ehwYGODzzz/H5XKxdOlSyfHucsnTHYq4aFlRUcGuXbvIz88fKfPiwsqAbTYbDQ0NF2fE3xJiMH3odF+hULBo0SIeffRR/t//+3+cOHGCCRMmEBoaitPpZMeOHbz99tts2LCBwMBA4uPjycnJweVycfPNN4/6XQqFgjvvvFP6//NlrM8O/f3lcjKe7ziCg4NHLKkdbXsX67iOtJ2R4sCXEwqFgqVLl7JkyRLp54uxzbO9dj7f8dprr5GTk0NNTQ05OTnExsZSWFjIww8/fMbT4+nfGxQUxG233UZNTQ2bN2/Gz8+PqVOnSm3UL6L713khxrZtNpsUeomIiOC22247r551Y4ruF198gcViGea3erlTVVXFrFmzmDdvnvSaIJwyPL7iiivIysoiICCATz/9lMWLF9PT00NkZCRdXV2SOYxSqSQnJ4eFCxeekyDKyFwKLodzzOVyYTab0ev1w7yTVSoVYWFhzJ07l4qKCkpKSpg3bx4xMTG0tLScVXRFlEolmZmZpKWlSQtlX3/9NaGhoeTk5JCWloZOpztnU5vzQQyFjGS4k5uby+233z5mA9azMaboqlQqli5dSmpq6nl9wXeBwWA4w5jD5XKxZs0aEhIScDgcuFwusrOzKS8vx+PxEBQUxNy5c9mwYQMNDQ2sXr2axx57jJMnT14WJ7yMzHeNy+Vi06ZNBAQEEBoayoQJE0hOTkaj0dDf309TUxMOh4MFCxbw5ZdfApyzO59KpSIxMZGEhAScTiddXV1UVVVx+PBhBEFAo9EQHx9PXFwcERER0mKa2CniXF3GxCKLwcFB7HY77e3ttLS00NnZicfjISAggKysLFasWIHRaLwo7nI/6OwFl8uFxWKRChDEvkubNm0iNjaWqVOnEhERQWBgoBSHffTRR0lNTWX27Nn4+/tLsUcZmR87ISEhXHfddQQFBdHd3U15eTkHDx7E7XYzadIk4uPjeeqpp4iMjMRisUiLoueDQqEgMDCQpKQkyXBGTM1qaWmhtraWgwcPMjg4KHnlinnJonOYWq0e0U/X4XBIxSZiVxKxhD4qKoqMjAzmz58v2UZe7EnXD1J0BUFgy5YtHDt2TCpCSElJYd68eWi1WuluNdKK5erVq4f9TqFQSP3tL2e8Xi8NDQ2XbFbudDqleNuPjZqaGqZNm3ZJtu3n58fRo0fp6uq6JNu/mNTV1eHn54dOp0On05GUlITP58PhcNDQ0CDlWIuhAnFd5VxblI+EaCSv0+mIiopiypQpAFIqoViNaLfbJVEdrXNEQECAJM6iKA/tp3apGTN7QcxDHU944VKawJyLMcfevXvp6+vjqquukh45zndMYmuW86nF/7YJDg6Wem9dbHp7e78XwnApEKueLkVvsMHBQRoaGi65T+3FQKvVkpCQMGoKFfyrLLuqqoq6ujq6u7sxGo2kp6dLnUmGfr6jowOtVjsuf9qh16CY9WCxWLBarVKbJzHV8PRWT+KMVq1WD2vZo9Vq0el0hISESH69QznPa+nCUsZOF12z2UxxcTEBAQHExcVJfwSfzycllg8tnzwXfD4fZWVl9PX1MXPmTCmNqLy8nJaWFqZOnSod5JycHMxmM62trVKurcjevXvxer1cccUV5zUOGRmZC0PUFa/XK7mA1dXVYbPZiI2NJT09nbS0NH75y1/S1dXFH/7wBwoKCoZNkMSsgcHBQdra2mhubpYamoqde0WxFBuail0k/P39h81exVQvsRuwy+XC4XBgt9sl8R7aYdhoNBITE0NCQgIRERFSVdo4Rfjido7weDy8+OKLPPHEE/zud7/jv/7rv4iMjEShUHDkyBGio6OJi4vD5/Ph5+cnlVaqVCo8Ho9UCigeVKfTSWtrK6mpqbS1tXH06FGKi4txu90sWbKEvr4+PvzwQ3Jzc+nu7mbjxo1kZWVx8uRJqqqqWLhwIW+99Ra/+MUvLpqjvIyMzIUhipNarSYxMZHExEQWLlwouerV1NSwfft2tm7dSnt7O8ePH+fXv/41d999NxqNho6ODsrLy2lubsZmsxEVFUV8fDwFBQVS596L1TJJRNSjvr4+enp6aG9vp6ysDKvVisFgkDxPQkJCLigUcc6iq9Vq0ev1BAcH4/F42LdvH/39/fj5+REUFITL5eKdd96ht7eXG264gZ07d6LT6TAYDLS1tTFr1izy8vIwm81s2bKFyspK8vPzSUlJITIykjvuuEO6c8Gpeu/e3l727t0rbWfy5Mn84Q9/IDg4mOnTp/PRRx9hs9mGda2VkZG5vBDtELOyssjMzMRoNPLHP/5RqhJ88skn2bNnD7NmzSIoKIgJEyYwefJkyTznUsdbxcW7wMBAYmJiyMvLk4TYZDJRXV3N3//+d9RqNQUFBRQUFBAYGHjx83RHwuFw0NLSwlNPPcXrr7/O448/LgmwUqkkNDSU8vJyWltbqaioYMqUKTgcDsrLy1m6dClwanFix44d5ObmMn36dCnm0tfXh9PpZOLEiQiCQGxsLH/+85/505/+REVFBQaDAT8/PxwOhxQIFx8dZGRkvj/ExsbywgsvAHD8+HECAgK49tprmTRp0ogdqb8LRCFOSEggPj6eBQsW0Nvby5EjR3jjjTfIzc1lzpw555TloHr66afP9vuny8vLiY2NxWAwAKcq1D777DMefPBBEhIS2LZtm1T33d7ejsVi4cCBA9JsNTc3l/3795ORkYFGo6G3t5fc3FyUSiWpqamSYfW8efNwuVz87W9/w2AwYLVacblc1NfXMzAwQG9vL5MmTaKmpgatVkt4eDh2u53o6Gj6+vqYP3++FF5obm5GEITzji3LyMhcWhQKBSEhIdJC9cqVK7nllltISUkZJmDiZGo0QRNzbYfGbs+2oD/S70fzaDj9/xUKBUqlEp1OR1paGvn5+TQ3N7Nt2zZiYmLQ6/XiNq3AC6Pt+znPdL1eL3fffbdk2Pv444+zZ88eyc4QkFZ6xfSi1atXIwgC4eHhkiGwyWSipaUFg8FAamoqSqUSl8slpaEYjUbJHLyxsZErrriC9PR0DAYDTqeTVatW0dnZSUtLC3fdddcZHrIyMjKXLz6fj+3bt9PU1MQDDzww4mO62+1m7dq1XHPNNZLP9FC8Xi9ffvklbW1t3HLLLQQFBWEymdi1axc33XTTGZogCILk0Cf6SsMpb42mpiaSk5MJDw+XJo2zZs3i448/ZmBggAcffHCYr64YKlm8eDF5eXm8+eabrFy5clzFIOesVBEREcO8COLi4vi3f/s3AGlmOVZ6lUKhkGIiQzEYDKxatUr6eaQ0MfEzCoWC5ORkkpOTz2n8F2KeIiMjc3E4efIkTU1NrF69WpqsnY6fnx9ut5v+/n4prS4xMVHqvqJWq9myZQs///nPCQwMxOPxsHPnTr755huWLVtGa2sriYmJ0nXucrn49NNPeeSRR/jLX/7ClClTUCgUfPbZZzz88MO8/vrr6HQ6VqxYwYYNG1Cr1cyfP59169ZRVVU1Yq62QqEgKiqKBx54gPfff5/Y2Ngx15YueHo4HhORS7Xtc/0er9dLTU0NFouFoKAg0tLSqKqqQq1Wk5WVdU4dPWVkZM6fo0ePsmTJklEFV0ShUNDQ0MDRo0dpb2/n3nvvpby8nOrqau68807JPFypVHL06FGioqLQarXY7XaOHz9OQkICgFTE0dvbi9FoxOFw0NXVRUBAAHa7nZCQEEwmE62trYSFheHn54deryczM5PY2NizTu4UCgURERFkZWVRW1s7opnTUH50z+Tr1q2TrB1nz56NVquluLiY6667junTp3/Xw5OR+VHgdrvH3YW6oaFBMvBXq9UMDg5K3TmMRiPh4eEAbN++HbfbzeHDhzGZTFx77bUoFAqcTidr166V8v69Xi9+fn5SBxE4NSETS4cHBwelkuLi4mLy8/MJCgoatWOLiFarHVez2h+k6AqCQHd3NwMDA8MOrEqlwmg0MmPGDGpqaigpKeGmm24iLS2NxsZGWXRlZL4lUlNTOXr0KMuWLRtVyDweD93d3WRmZrJt2zYAUlJSOH78OIODg5SVlUmL90ajkUceeYT+/n48Hg/BwcGsX7+eVatWERAQwEMPPYTP50OlUrFjxw4KCgoQBIHt27czYcIE9uzZw4wZM9Dr9Xz99deEhITg9Xp55ZVXJPOdxYsXj7o/Ho+HqqoqbrjhhjH3/aKWAV8u7Nmzhx07dhAZGUlAQAC5ubmkp6cTFBTEc889x4QJE9iyZQvXXnst9fX1+Pn5cf31148YrJeRkbn4OJ1O3nzzTaZMmcKMGTNGLGzy+Xz09fXh7+8vtXnS6/X09vaiUqlQq9W43W4pjVT8jDjZstlsQzMKgFNxXavVSnBwMCqVCovFglarZWBggKCgIFQqFX19fQQGBqJSqTCbzQCEhoaOOjN3u928//77xMTEcPXVV6NUKi+sDPiTTz7B5XJJKWPfByorK5kxYwYzZszAbDZTVlZGXV0dbreb6OhocnNzCQsLk1LTxK6l8sKajMy3g+id8PHHHxMWFsbChQu/d9eg2Mdv06ZNxMfHc/XVV4vrQhcmujabjbq6uos83EuLWq0mLS1NCtKLpsQOh4MTJ05QVVWFyWQiPDycnJwcUlJSCA0NHZa3ZzKZMBqN0uKaWJ99OSKaSP8QFgJF+z232z3MJUqMp4mmJX5+flJa4vfpQpX5F2J89cCBAxw+fJjc3FymTp067Fq8HPH5fLS1tbFv3z5MJhNXX301KSkpQ30j5B5ppyO2225ubqaiooLGxkZUKhUZGRlkZWWh1Wq5/fbbWb58Offffz9arZbnnntOMjG+3GhtbeW6665j4sSJ3/VQRmXoeTY4OIjJZKK7uxuTyURfX59kZORyufDz85P+DXWJE2+eQ42nfT4fOp2O4OBg9Hq9tLASEREx7OK9nC/iHzuCIOBwODh06BClpaXo9Xry8vLIyMg4Izf2uxib+F+TyURVVRXl5eWo1WpmzZpFTk7OSObpsuiOxtAD2tPTw8mTJzl58iQlJSW8/vrreDweVq1axe9+9zv+8Y9/8Nhjj12WfrJff/01/v7+zJ49+7seyjDE6h+Xy0VdXR21tbV0dHRgs9nOEEfRz+Nca9k9Hg8DAwNYrVb6+vowm810d3fT1dWF0+kkLCyM+Ph4MjIyiImJOVe3KJlvEfGm2tTURFlZGfX19VJ36ZSUFOLj46Xu15fiZjpUD3w+H729vVJH5vb2dvz9/cnMzJS6UZ/l+2XRPRcEQeCZZ57ht7/9LXDq0T0/P58pU6bwl7/85ZJ4ql4ou3fvvqxEV5y5VFRUcPz4cSwWC4mJiaSlpRETE4PBYDjn1irnMwbRqKS5uZnq6mr6+/uJjY1l2rRpxMfHX/IxyFwYPp8Pk8lEW1sbjY2NtLS0SE82QUFBGAwGwsLCMBqNhISEDMtUGs1DW5wIiKENMWzY09NDT08PfX19DAwMSAtrokNaTEzMuDx//39k0T0XBEHgs88+o7y8nJCQEAwGAyEhIXzzzTf8/ve//0GJ7sUwnT99e2azmaKiIhobG0lLS2PSpElERER858dNXLhpamri0KFDDAwMMH36dAoKCoY1WZS5fBFv5jabDZvNRm9vL729vZjNZiwWC06nU4r7D82GGOqhIM5ixZ9F10Sj0UhYWBihoaEEBQURFBR0IesFsuheDJ5//nn+/d///TsXj5E4m+gODAzw+eefM3/+fKqrq0lMTOTw4cNMnToVs9lMe3s7V1111RmO+eeCaDS9d+9eysrKmDNnDhMmTLgk/aUuBj6fD7PZzJ49e2htbeXqq68mNTX1shyrzPgQxVRcgB3aO03sHKFQKIYtwl7Cm+3FNTGX+RdiLDgsLOy8/3higF4QBCIiIgDo7++nu7tbyj+02WyEh4dL3xcSEjLuhn8BAQHU1NRgNBrp7++nsLCQsLAwqePpjBkzLrgVkdvtZv369ej1eu6///4z4rJinC4sLGzEcYsz5O7ubtLS0qT8SLGX1dD32Ww2+vv7CQ8Pl24Uout/ZGQkPp+Pnp4e9Ho9Op0Ot9uNyWTCYDBI21IqlYSFhXH99dfT3t7Oxo0bmTx5MjNnzpSF9zJGFFabzYbVaqW/vx+TyTQsLCCGFUaK3YthhaF904KDg6UwRVhYmLS2oNVqL9m5IIvuBdDd3c1vfvMbnnvuOfR6/bge10WBCQ4ORq1WtsBHIQAAIABJREFUU1RUREVFBStXrgROCdRbb72Fw+Fg2bJlbNu2jYyMDFwuF3a7nbCwMOrr63nkkUfGrFuHUwJz/fXX8+yzz/KrX/2Kw4cPc9NNN+F2uzlx4gSff/45c+fOHVdJptj8MjY2Vnq/z+djw4YNREdHs2jRolGT3N955x2uv/76YXXp4vFyu9188MEHpKamEhcXh1Kp5C9/+QtLly5l5syZ0jH1eDy89tprpKenY7Vaueuuu3A6nbz66qtkZGTgdrux2+0YjUYaGhp44IEHeP3115k+fbq0naEoFApiYmK45557ePnll9HpdOTl5cnCexnh8Xjo7OykqamJhoYGOjs7CQgIQKfTERoaSnh4OHl5eVJY4PSeiKeLrvhfQRBwu91YrVbMZjMmk4ny8nIsFgs2mw2A+Ph4aQHPaDRetPNCFt3zRBAEKisr8fl87Nu3j6uvvpo9e/Zw7NgxwsLCyMjI4MCBA8ydO5epU6cyODhIRUUFX331FR6Ph4ceegi1Ws3zzz/PwoUL6ejoICIiAqvVSkVFBYGBgQiCgNVqZfbs2Tz77LP4fD5+85vfsH///v+vvTMPjuq88vbTq3rT0q1935GQACEJLHYM2GBjYzBB2CaAkziLnUomycxkZmo8VZM4mThJOV8mtise2xiviYMDtsHsxjZGgACBQEIbCO1ba2stLfW+fH9Q946EJBA2BiVznyqVpO7b9763l9PvPe85vx+Dg4PizFgYz0Qz1mnTpjFz5kymTZvGXXfdxfPPP8/69evp6Ohg6dKlqFSqSc12e3t7efTRRzGZTKxbt47ly5ejUCiw2WwUFhZOaJekUCgICwvD5XKxb98+rly5wn333TfKvruoqIglS5ag1+sBiIyMxOv1MjAwwOHDh9mwYQNdXV309PTw3e9+l2eeeYbCwkI6OjoYGhrinnvu4emnn8br9fKrX/2K3/zmN+zatYsTJ04QFRVFXl7euKkhocf+iSee4L333iM7O1sKuncQYSba1NREWVkZTU1NBAcHEx8fT0FBATExMaPSAl/mtRIs4mNiYsRjC7+HhoZoaWmhvr6ekydP4vP5yMjIICcn50aVCzdECrpfEIfDwenTp5k7dy47d+5kyZIl7N27l4KCAjo7O3n33XeZMWMG1dXV5OXlcfnyZX72s5+Rl5fHt7/9bQwGA+3t7aIWxPbt23nuuecIDg7m+eef5+WXX2bv3r3I5XJUKhV9fX1iPkpYmRcQLOdramqA0QsHHo+H8+fPo9Vq2bFjBwBZWVnU1taKbZR79uwZdW4TCTkPDAzQ3t7O2bNnOXLkiKi+9Mwzz0zKn85sNvPJJ5+QmJhIY2MjKpWK48ePs3jxYpKTk5k+fbp4POENbTAYuPvuu5HJZFitVnw+H0qlEofDgdfrxW63i2IlVqsVl8slWrsUFxezYcMG2traOHnypOhaci0ymYywsDAxFziZKwiJW4sw8zx79iylpaUEBwcze/ZsVq1aJV5V3Q67HuF3UFAQWVlZZGVlAVcNeaurq9m5cycqlYpFixaRnp7+hbzSpKD7BfD7/aIQcl5eHkePHqW2tpbY2FisViurV6/mypUrzJkzR/zmTklJ4dlnn+XUqVP88Y9/5Cc/+QnBwcGEhoYSGRlJQEAAg4ODDAwMoNfrSUtLw+l0cvbsWfr6+pg+fbpY2iJcWgnIZDLmz59Pfn7+mLH6fD6WLFlCUFDQl+5Y6+rq4qWXXiIoKIi77rqL1atXk5CQwMDAwA0VmASDUqVSyerVq7l8+TKXLl1CrVbjdDpxuVz4fD7x+R25EGKz2YCrs1+5XE5vby8xMTFik4Tf78dsNpOeno7VaqW7uxutVsvChQtRKBTExcWJx58I4Uvs76Gr728Nr9dLdXU1n376KWlpaXz961/HaDTe8SuOkcc3mUwsXLiQ+fPn09raSlFREcePH+fBBx8cYyt/I6Sg+wUQLn+mTZtGUlISK1eupL6+HrPZTGdnJ4ODgzz44IN8/PHHrFu3DrlcTl1dHUeOHMHn8xEbG4tSqUSv17Np0ybOnTvHpk2b6Onp4eLFi2IAWbFiBZGRkVy6dIlNmzaJPnNr1qwZ1akDVwXgIyMjxx1vdHT0LTnvwMBA/umf/om5c+eK3UIOh4Pnn3+elpYW4uPjx33zeTwesREiLy+PoqIiFi9eTFNTEzExMbS0tJCYmMjg4CAajQa3201wcLDYul1RUUFCQgIhISGsX7+esrIyHnvsMYaGhujr6+OBBx6grKyMrVu3YrfbKS8vZ+3ataSlpXHkyBHcbjeLFi2a8Lx8Ph/Hjh0jPT1dCrq3GY/Hw8GDB+nt7eXrX//6mNyp3+9nYGAAtVo9alF1JHa7nf7+fiIiIpDL5QwNDeF2u9Hr9aOkANxuNw6HA61WK14xut1u7HY7er0emUzG0NAQGo0GlUrF4OAgCoVCbMiAq2skCQkJPPbYY9TX1/Pee++xZMkScnJyJh14pZKxSXKjkjGLxcLvfvc7MjIyiI2NZdmyZQA3vOwWcrHXvtFGtr6Ox8jtJ1On+1U6ZjQ0NLB7927WrVtHYmLiDRcSx2Oix0zGv2q8+8fbfjzcbjeff/45tbW1fOtb35JSC7cRv9/PoUOH6O/vZ/369eN+tjweD7///e9ZuHAhCxYsGPVY4ff27dsJDAzknnvuQaVS8etf/5rAwECeeuopgoODgf9doA4MDMTn8/HII4/g9/t59dVXCQoKEkvIBgYGGBwcZOnSpXzwwQe0tLTw61//GpPJNO74BwYGeO6553j88cdJS0ublPbCjRNxEpPCaDTy9NNP87WvfU3MQU4mzylsJwTZkf8L94/3czM4nU7eeust3nrrLT755BOam5v561//yp49e3C5XF/ofEeSlJTExo0b2b9/PwcOHGBwcPC6wfVmzufabcb7e6Lbrt1+JEIZ2xtvvIHVauWb3/ymFHBvMz09PdTX1/Pwww9POJlRKpWEhYXhdDr5+OOPeeedd+jt7WX//v3s2LGD2tpascLHZDKJrfzCVdfevXsBsNls1NTUcPfdd1NaWorb7aa3t5f6+npWr15NUVERhw8fZs2aNXR2dmK32/nRj35EYmIiAwMD445NJpMREhLCD37wAw4ePCimx26EFHRvEcIquF6vvyPtpUJuczzkcjnl5eVER0fz8ccf8/rrr5Odnc2xY8eorq7+0scWSq+EBcLXX3+dffv20d7ePuk34u3C6XRSWVnJm2++ycGDB1mxYgVr1679Us0hEl+MxsZGMjIyJvVlV1dXx8GDB6mqqqKmpobBwUGKi4vxer3ExsaKVSe5ubn8+te/5rPPPsNisYgLYYODg3g8HtRqNXa7HafTSX9/Pz6fD7VaLdaJBwQEIJPJ0Gg0eDweYmJixOqGiYiMjEStVk9ahVDK6f4d4Pf7ef/996mrq2PatGlkZGQQGhoK/O9MT6PREBERgUwmw2Kx4Ha7iY6OviUzXeE4arWaxYsXU1BQQHl5OXv37sXj8ZCamkpmZiYxMTGjVNq+au0FgeHhYRoaGqipqaGtrY2YmBiWL18uzobu9ILN/1U0Gg0dHR3XXYQV0m8ej4egoCCeeOIJmpubqaurExdhhY4zoU04Li6OmTNnolAoxBLE4OBgsbzRaDSiVCrFK9GhoSFiY2PFQKxQKAgMDOTAgQPk5+djsViIioqacIw+nw+n0znpblUp6P4dIJPJKCwsJDExkcuXL/PXv/4Vn89HQkIC2dnZhIeHExQURGdnJwaDgQcffJDTp08TGRlJdnb2LR9LQEAAc+bMYc6cOVgsFmprazl27JjYGRYWFkZcXNwY8ZuRb+rJBMJri92FsqOuri7a29tpa2ujt7cXp9NJYmIiM2bMYM2aNeJsRuLOkp6ezqFDhzCbzRMGNa/Xi9frJSoqip6eHnbt2sW9996Lx+MhNDSU2tpaDAYDHR0dxMfHc+bMGSorK5k9ezZBQUHs27ePxx57DJ1Ox4MPPsiJEyd4+OGH6evro7y8nJUrV3L06FEee+wxZDIZn3zyCQsXLqS3t5dTp05RVVXFhg0biIqKGvcc/H4/JSUlhIaGTrjQdy3SQtokeeaZZ7jvvvumpJ7u+fPnycrKEhfS/H4//f39NDc3U1lZKQqyZ2ZmMm3atFHtsJPJO98KhIDY29tLd3c3bW1tmM1mrFYrKpVKXDFWq9Xo9Xq0Wq1oFCgEZZ/Ph9frxeVy4XA4sNls2Gw23G43LpdLnPWEhYURExNDdHQ0YWFhhISE3LbzlJg8fr+furo6PvjgA7Zu3SpeiU2EkKq6dptrW86Fme+121y7aC38PXJ7n8836asfn8/HhQsX+Oijj/jJT34y0vlCEry5FVRWVtLW1nanhzEucrmcu+66i6CgoDH3CY0UXV1dVFVV0dDQgEKhICkpiRkzZhAaGnrHZn6CvJ7D4cDhcIiBc3h4WMy7CfW6wgdDoVCgVqtFXQadTodarSYgIACNRkNAQIAUYG8jghaCVqvF7XajUqmw2WzodDrxC9JgMIz7mgjpgKamJg4ePMjcuXOZM2fOlL8SEaoWjhw5gtVqZf369dfKPkpBV+IqQpAbHBykrq6OyspKhoaGCA0NZdasWcTFxREYGDjqA+L1esUFiKn8QZC4M9hsNp5++mm2bt3K2bNnCQ0NpaqqipiYGBwOB3q9ntWrV49qWRfo6urixz/+MXA1v1tRUUF6ejrf+ta3yM/PJygoaEp9gQruxGfOnKGlpYWCggJmz54tdkCOQFIZk7iKIG1nMpkwGo3k5+fjdDppb2+nvLyczz//HIVCwfTp00lNTSUiIoJz587x9ttv8y//8i8kJCRIgVdiFFqtlsWLF7Nnzx4WLVrEwYMHWbZsGUqlkrKyMsxm87jVCU6nk4GBAcrKyqiqqkImkxEREcH999+Pw+HgnXfewWAwMGPGDJKSksTc/+1EaJ7o7u6mtraWqqoqVCoV+fn5rFq16gvPyKWZrgTwv7Pg/v5+KisrqaurY3BwkKKiIj788ENmzpzJr371K1auXDkl89oSd47e3l7+4R/+gWeffZa3336bkJAQYmNjASgrK+PRRx8lNTWVzs5OmpqaqKurExdVDxw4wPvvv8+SJUv4+c9/zvz585HL5Xi9XsxmMxcvXqS5uRmHw0FsbCxxcXFERkYSGho6Ydrii+LxeOjv78disdDe3k5ra6sosZqamsqMGTNES/cbBFspvSBxcwjqZpcvX+Y73/kOFy5cAK42gHz/+99n8+bNU1LM/XYhl8uJi4u7JV8+Ho+HtrY2vF7vLRjZV4tOpxujM+D1emlra+PTTz9ly5YtYit7VlYW1dXVNDc3i2WKRqORhIQE0tLSxPLBAwcOcPbsWX7wgx+Mq7cgxCe73U5LSwstLS10d3fT29sr5ouDgoIwGAxijj8gIICAgAAUCsWoRVifz4fb7cbpdIprBzabDavVyuDgIHa7HYPBQGhoKFFRUcTHx48qc7yJWa0UdCVuDp/PxwsvvIBSqeT48eN4vV5UKhUBAQGo1WrmzZs3Kf3dv1dqa2tZuXIld91115feV3FxsSj0MtUpKyvjpz/9KUajUbxtaGiI119/nVWrVhEWFsbly5dpaGgQRYdSUlJITk4mJiZGTDNcG7RvRqnrWldpQcBeCKA2m00Mqh6PZ1Q1g6DYJyy6CkHaYDAQHBw8Zub8JVJpUk5X4ubw+/3odDoKCwv5/ve/D0juuSMpKirC7Xbfkn253W6WLVs2SldgqiIIyQjNCkNDQ9TV1REaGsrHH3+MQqEgLS2NgoICIiMjJ+W+cLN52pHlXgqFgqCgIPR6vWjVY7PZcDgcYtAdWfkil8vFShetVotOpxObJEZ2kX7V73Up6EpcFynYSgh4PB4qKiro6uqir68PgJSUFObNm0d4eDh6vf4rqTYQOr5sNhudnZ20tbWJ+ghCUBV0lnU6HRqNBo1GI6YXhByxkF4QShRtNtso/zShEy0mJobY2FhMJhNarfaWV+5IQVdC4ivmVrsu3ymsVqvoqxcREYFOp/tCIt43Qqgt7+npoba2lrq6OjweD0qlkoiICGJjY8nKykKv16NSqVAqleKM9WbGIiweC7rMbreb/v5+Ojo6KCkpob+/H6/XS2BgIBkZGSQlJWEymb50Ll8KuhIStwC3282f/vQn8vLyMJvNJCYmcurUKZKTk4mIiKCsrIzc3FymTZt2p4f6hTEajaxYsYKIiIgx990K6VCPx0NzczOlpaW0t7cTEhJCeno6Dz/8MIGBgaIl+q0K8kIJpVKpFAWPTCYTKSkpo9yFe3p6qKmpYffu3TgcDjIzM5k1axbh4eFfaGYvBV2JSdHe3o7ZbEan0+H3+0lPTx/1je/3+2lubiYgIGDCPvXBwUFaW1vJzMwc983a0dGBy+US64H9fj8NDQ2oVCri4+OBqw4PgrCPSqUSH+vz+aiuriYyMlL0sBqP4eFhent7RbEbYdydnZ2kpKTgcrkYGhoiKSmJ4eFhurq6iI6OHrfbbyTCB/f06dOYTCZ27dpFYGAgn3/+OfHx8VgsFgoKCm7osCE8lz09PQQGBn5h9bOhoSGqq6tJSEjAZDJRX1+PWq0mMTERuVyOy+WiqqoKg8FAamoqHR0dopi8XC7HYrGQmJg4qQVTp9PJm2++id1uJyYmhry8PE6ePIlMJmPDhg3XPQdhtnnx4kVOnjxJYGAg+fn5rFmz5pYH2ZtBSDdotVri4+OJi4vD7/czPDxMeXk577//Pnq9nqVLl950/frUafeQmNLU1tby7LPPYrfbOXfuHHa7XSzDEXrajx07RnFxsXjbyPt9Ph99fX289tprYh5OuF34u7q6mo8++kg8pt/v58yZMxQVFYkfTpfLxcsvvyzm40bu4+DBg6JPnPB4j8dDe3s7Ho8Hv9/PhQsXeOmll8Rt7HY727dv5/z583R2dvLaa6+JCmmvvPIKTU1NvPHGG5Mq6brnnns4evQoJpMJuVxOfn4+GzduJDs7m4GBAU6dOkVpaSnDw8PXNRL1+/389re/pbi4eIxt+Ehxn5GPF8wUu7u78fl8HDlyhBMnTvCLX/yCjz/+mKKiIv7yl7+Iz09bWxstLS384Q9/wGKx8Pbbb3P69GksFgv/8z//w5UrV9i5c+ekDEvVajWNjY3k5uZSVFTE66+/TkFBAVVVVWK54UTnaTab2bZtG7W1tWzcuJEtW7aQlZU1qp37emO49rm43vM63vY32sdIvQbBsn3BggV873vfY/78+Rw8eJCdO3eK78fJIM10JSZFeHg4SqWS4eFhdDodR44cobq6msHBQRITE/H5fERHR3P48GFOnTrFN77xDU6cOIHdbicvL48PPviA5ORkFAoFO3fuJCEhgd7eXqqrq1m5ciVVVVXU19ePKkeSy+WEhobS09PDgQMHuHLlCnl5eQwODvKb3/yGmTNnEh0dTUlJCQUFBZhMJvGNPzQ0xPHjxzl16hTx8fFs3rwZpVJJfHz8qBl6X18fra2tDAwMkJqaisFgIDc3lxdeeAGZTEZeXh779+/H5XKNmvUJ4jsjCQwMZN68eWRlZREUFMQ777zD/fffj8fjISsrC4PBwLp164iPj2ft2rXce++9opHmSFpaWggPD+fAgQOi4tX+/fu5fPkyGzZsoKysDKvVypNPPolGo6GlpYUjR45QV1fHmjVrCA0NZcWKFfj9fn73u9/R0NBAXFwcKpVKrKGNi4ujq6sLvV6Px+OhqakJt9tNbm4uHo+HxYsX8/Of/5zCwsJJzbbVajXBwcFiF5fFYiE8PByZTIbL5aKyspLMzEw0Go14hVFbW8vu3bvZsGEDSUlJY2aLHo+HoqIiZsyYMW4bsd/v5/LlyzQ1NbFkyRLcbje1tbVoNJpxr6b8fj9dXV3U19eTmJgo2liZzWaamppISkpCr9dTWVmJyWQiKSmJCxcuIJPJmD17tnhlJcy+09PTSU5O5vTp07z66qs8/vjjo7wLJ0IKuhKTRriEVigUuFwuUlNTqaysZN68ebzzzjuEhoayZMkSent7KS8vx2AwcPbsWbKzs9HpdDzwwAM8+eSTpKenk5KSwo4dO8jLy+PEiRO43W5WrlzJuXPnxj22RqPB6/Vy/vx5TCYTmzdv5re//S16vZ6FCxfS3d09avuKigq2bdvG/Pnzue+++yZs5oiJieGFF17gv//7vzl58iQhISGoVCr6+/tFM09BXlDA5/Px6aefjhKA9/v9nDt3DpVKxQcffIBcLicjI4PGxkZxttTY2IjFYqG1tZXTp0/zwgsvkJqayi9+8YtR+zl58iRGo5HPPvuMpqYmWlpacDqdqFQqqqurRXNSq9WKQqFg9+7dHD9+nC1btjBr1izkcjkGg4ETJ06wcuVKwsLC2LFjB42NjXz9618XX8vQ0FBR+vL3v/8927Zt49ixY3i9XlGrduR5u91uzGYzfr9fXMCSy+W43W6CgoJEreKlS5dSVlZGZGQk06dPp76+nk2bNhEZGcnGjRu5//77UavV7Nmzhy1btkyYjpLJZJw6dQqTyURQUBB+v5+AgACcTicALpeLXbt2cd9994myjH6/n0uXLhEXFyfWlQvB3O12s337dpYvX8727dv5t3/7NwC2bdvGypUreeONNzCZTKSmpnLkyBGysrLo7OwErqbGli9fPuaLQalUsmDBAlQqFe+++y5PPPHEDRuHpKArMSkcDgdKpZKUlBT6+/upr68nPj5eLMsR6jcVCgUajYb+/n46OzuRy+XY7XaUSiUBAQFERkZSUlJCXl4eHo+HjIwMOjo6KCoqIjMzk+HhYfGYfr9fLBX65JNPiI2NZWBgAIVCgc/nIz4+HqvVSkxMDBqNhs7OTnEc6enp/Pu//ztnz55l+/bt/OM//iM6nU6UhfR6vXR2dtLf3y+WCUVFRVFSUkJHRwcLFiygsbERs9lMbGzsqNmeXC5n0aJFY5ojHnroIbETajza29t57rnnMJlMLFu2jAceeACFQjFm5t3T08OGDRtwOp18+OGHPPDAAxw6dIg1a9bgdDppamri7rvvxufzoVAoWLNmDTNmzODYsWN4PB7Wrl3LxYsXOXfuHDNnzsRoNPK1r32Nv/zlL8yZM4fq6moCAwPR6XTk5+czPDxMe3s7MTExJCYmUlxcTGNjozgzFbDZbBw/fhytVovH4xFTNnBVg6GpqYmQkBDKyspEJa53332Xjo4OWltbqamp4cSJEzz33HPk5eXxH//xHxMGXOF51uv1WCwWXn75Zbq6unj00Uc5duwYAwMDrFixgvLyctFfLT4+nmeeeYYVK1Zgt9vZs2cPmzZtEgOlw+Ggvb2d7OxsduzYQU9PDxqNhp6eHtLS0nj77bdpamrigQceoKSkhKqqKoxGI9HR0Vy6dInly5ePO06ZTMbcuXOpr6+nvr6ezMzMCc8JpKArMUlksqs278IbPjs7m4CAAGbOnInb7WbBggXMnDmTS5cukZ6eTmZmJocOHSIrKwuFQkF2djZOp5P777+fsLAwLBYL69evF7u7FAoFFouFzMxM6urqxA+KXq8nMTGR+Ph4hoaGiIuLQ61W09DQwHe+8x0sFgtnzpxh5cqVpKSkiEaEjY2NnD9/HrlcTnZ2thjYPB4PeXl5OJ1O+vr6aGtro6uri1mzZjFz5kyCg4Ox2Wxs3LiRzs5OWltb2bRp05hAqtPpxGMJXPv/tSgUCl588UXmz59PVFQUKpWKoqKiUdtYLBbS09MJDw9n0aJFVFVVUVtbC8BHH33E1772NRISErBarYSGhuJ2uyktLcVisRAbG0t8fLy4EKdWq2lvbyc1NRWz2cyPf/xjDAaDqGNcX1/P0qVLCQoK4siRI2RkZJCfn09sbCxtbW08+uijo847ODiYNWvWiNUL1+aXJ/q5cOECf/zjHzEajcybN497772X7u7uG9rgCJSXlzM8PMysWbNQKpUYDAZKS0vFtERqaipwNagWFBRw8eJFtm7dSmFhITKZDIfDwV/+8hcUCgV2u10sLRNy/oKGrpAaEV6rvLw8qqurOXDgAGvWrLnhOGNiYrBYLDfcTgq6EpMiNzeX3NzcCe/Pz8/H7/eLnlQymYwtW7aI9wtBNCkpSbxtZP3qQw89BFzNxZaWluLxeFAoFOTm5hISEjLhIkV8fDw5OTlj9p2fn09+fv6Y7TMzM8WZSHZ29ijnDJlMxrx588S/g4ODxRKvW7GCbjAYeOSRR667TVpamtgSPHv2bHJycvjwww/Fy97U1FTmzp0rjkmlUrF+/fox+1m+fLk4M/P7/aNW2O+++24A5s6dKwabrVu3ivucPXv2pM55spUFmZmZvPLKK+Tm5hIdHY1KpWLv3r00NTWNW34m4PP5xCuZmpoacnJyqKmpoby8HJlMhtlsxmKx4HQ6USqVdHR0MHv2bBQKBT09PZjNZhYuXIhareahhx4SzUgrKirEq6OLFy8SHh7OlStXSEtLQ6fTUV1djVwuF+VOh4aGWLp06Q3Ptbm5mYKCghs+H1LQlbhlXE/R/0bbC38HBgaydOnSG+77VjDePm/WMuirRiaTsXbtWlGj4Is0I9zodbnZ1+1mMRqNrF27dtR+Fy5cyKuvvioaP453TL/fz/LlywkLCyMpKQmNRsP06dMxGAxoNBoMBgMbN24UHSVWrVpFTU0N69atIyoqiqGhIeBqmkJYZP3ud79LQ0ODKNoUGBjId77zHVpbW9m8eTNarZaqqirWr1+PTqejubmZH/7whwQGBk54fn6/X1w0HvnFPxFS0JWQmOLcTlulr4LxAqrRaOSxxx7j3Xff5f7772f69OljZs5KpVK0oBopCLRixYpx9x0YGMicOXPE/4UroJHbhoWFjarjFmb1Qm5ZyM8KCFcD4yGkI44ePUpVVRXf+973RtWOT8Tf7ispISHxN4tMJiM+Pp5vfvOblJSU8Pbbb9Pa2ipZJMlwAAAV70lEQVTWcI+3/Xg/k9luMvsZue2NjiEE2/Lycl555RU8Hg9PPfXUpBtZpJmuhMQtQqi28Pl8yOVyAgICcDgcyGSyKe/7dSeQyWSEhoayZcsW6urqOHz4MF6vl5ycHKZPny4K6EyF501YdOvv76e0tJTa2lqioqIoLCy8oaHmtUhBV2JcrFYrx48fv24u6/8KQm5QcCcuKysbd5HO5/Px0ksv0dfXh9vtZuPGjZw9exaz2czmzZtJTk4e8xiZTEZNTc0tk4r8KmlqavpK0hwKhYJp06aRmppKT08PFy5c4J133kGhUJCSksK0adNExa+REoxfJX6/X5SKNJvN1NTU0NHRgUqlYtasWWzZsuVaM8pJI4mYS4zB7/fT1tYmqv7/X6evr4+amhqxlC07O5sHH3yQ6OjoMfoTr776KnFxcXz88cdiZ1hDQwNut5vHH398zL5tNhuVlZV4PJ7beUpfCL1ez4wZM77y/LIwq7RarVy5coXa2lqsVisejweTySTWVAcGBqLVasUmiHEMIm94HMFJQpB7tFgsmM1m2trasNlsqNVqwsLCyMzMJD4+Hp1ONxkNYMk5QkLiyyJosZrNZiorK2ltbcXhcJCcnEx6ejpxcXHodDr+8Ic/EB8fz6FDh8QFGUHce7yqDIkbIwRht9tNT0+PKL5ktVpxOp24XC5cLhcymQyNRjPGrmeknq7H4xFFzh0Oxyhhc41GQ0hICNHR0cTExBAcHIxKpfoiKQ4p6EpI3EqEon+bzSbOxNrb29FqtQwPDzN9+nQCAgLIzs6mtrYWtVpNTk4OarV6jATiZEVSpgpT5crnWkEaITDb7XYcDgcul0tsfBBy7EL3nxBgtVqtGFCvXUj7kkhBV0Liq2Lkh7+rq0sU3e7r6yMqKoq0tDTS09PF/F9RURHd3d089NBDKJVK2tvb2bt377g25VMNpVJJYWHhHRvryOfa5/MxODhIX18fAwMDWK1W0R9NCLgj1ewElTCFQoFKpUKlUqHRaNDr9QQFBRESEoLRaBQthr5kAJaCroTE7URwPmhra6Ouro4rV64gl8uJjIzkz3/+M4cPH+aJJ57gpz/9KZcvX6a/v39SnUx3mnfffZfNmzdft4vsViLIeTocDpqbm2lqaqKnp4ehoSEcDgc6nQ6j0UhISAgGgwG9Xk9AQIAYVEc2kwiymF6vV3SJsNvtDA8PY7Va6evro7+/H7fbjV6vx2AwEBUVRVJSkthFdxNpBinoSkjcSXw+H/39/VRXV7Nx40ba29tRKBTk5+fz4IMPsnz5chYuXHinh3lDXnvttVHaC18FI7+wysvL6erqwuPxEBcXR2JiIuHh4QQGBqLX6295JYNQsTA8PMzg4CAdHR00NDTQ29uLRqMhMTGRmTNnEhoaKgqsT4DkBiwhcScR2lAjIiIICQnBZDKJ/5eXl7NkyZI7PcQ7jtfrxWw2c+rUKdrb2wkPD2fWrFmsWLFCtEb/qvPJQj11QECAqKc7b9480eq9vr6e/fv343A4mD59Onl5eQQHB9/0uKSgKyFxm0hJSeHkyZNiblGhUHDy5MlJtY5OVTweD93d3YSEhGCz2dDpdFgsFkwmEy6XC7vdTlhY2IRmjn6/n/b2do4cOYLL5WLBggWsXr1aFDu/08hkMrFsLDQ0lDlz5jA0NERZWRnvvPMOiYmJLF26lMDAwEmPVwq6EhK3CYVCMUb+caKaT0Fu8MsEHkEkR9iHsLCkVCpHLTAJ9wsLVHK5HL/fP6l6XLfbzbPPPsuWLVsoLS1Fp9NRX19PSEgIbrebmJgY7r333nGdH7xeL0ePHqWmpoZVq1aRnJw8Zkbr8XhEv7LxEMrAhMt9IW870WMmOu+Rt41UvxOOMbLCISgoSNRTPnfuHK+99hoPPPAA6enpk3q9FD/72c+ud/9175SQkPhyCF1egvEmXA0C+/btG6XZe20guJZr12YuXbrEtm3bMJvNZGVlUVJSQmlpqSjI3tLSwosvvsj58+eZNWsWe/fu5c033yQ8PJwzZ85QUlKCUqkULXcAzp8/T0ZGBnq9XjyO0JCwd+9eFixYwPHjx7nvvvuIi4ujp6eHiooKli1bJuoSjAxke/bsobu7m61bt4rOuiPPz+Vy8eKLLxIREYHJZBpzrj6fj3379lFcXCwapX722WejBPZH7k9w5SgqKsJisYiKYJ9//jnFxcUMDAwQHR3Nn//8Z/R6PaGhodjtdl555RVycnJGzdZlsqtOwnFxcWRkZPD2228THBwstARbgf830WsuCd5ISEwxbDYbe/bsYd++fWLXVGVlJSUlJfT19VFRUUF1dfUoo8re3l52797NiRMnAAgKCmL9+vU0NTVx5coVXnrpJdLT08W2bovFwtatW7ly5QqNjY3s3btXzFGWlJSwbNky3n//fVE2cSJkMhkLFiygt7eX9PR0wsLC+Pzzz2lvbycsLIzw8HA++eQTnnrqKQ4cOEBvby8+n4+qqir6+/vZtGnThLoUKpUKhUKB1WqlpqaGiooKXC4XNTU1VFVV0dTUxOHDh1m6dCl6vZ7i4mLef/99MjMzRf+0kV9GDoeDjz76iHvuuYdDhw6J9bz79+9nyZIlHDhwAI/HQ1tbG319ffh8Pk6dOsWpU6cmNCaVyWQYjUaefPJJPvnkEwYGBm74+krpBQmJKYTf7+fixYusW7eO9957j8cee0wU7j59+jT3338/5eXltLe381//9V8YjUa2b99OUVERDz/8MNnZ2aKE4c6dO+nq6uLYsWMYDAbeeustHnnkEXJzc8nJycFutzN9+nRiYmLYunUr27ZtY3BwEICwsDDa2tqw2+0YDAbgatCqqqqio6NDHK/P56Onp4eUlBQ6OzuZN28eDQ0NhIeH09/fT05ODi0tLbzxxhu88cYbpKens2rVKjQaDd/+9rcnlc+uq6vjwoULdHV1sWXLFmpra6msrOQb3/gGXq+XkJAQZDIZBw8eJCoqit/97nc89dRTtLW1MW3aNFGIaGhoiL6+PoKDg3E4HHR3d6PRaHA4HKLnnNPpFM+3tbUVq9VKbGysmJqZSNksJCRE9IPLy8u77vlIQVdCYgrh8/koKSkhODgYn89HcXEx0dHRNDQ0sHjxYnw+H+np6WzZsgWlUolCoWDx4sUMDw9z/vx5kpOTCQkJQaFQ8O1vf5uf//zn2Gw2li1bhkKhoLq6mhkzZiCTyTh69Ch33303KpWKRYsWMTw8TE1NDT6fD6fTKbbBCng8HsxmM3a7XbxNmIWnp6djNpsBiIiIEHULAAYGBsSOsfb2dqqqqoiKirqhgaNAW1sbsbGxPP7447hcLoaGhrDZbAQEBBAUFCSmYNRqNWvWrOHEiROYzWaxKsThcPDWW2+JJpUulwu1Wj0qTeLxeNBoNKPkGUtLSzl37hxnz56luLiYlStXXnecarV6UhoaUtCVkJhCVFZW4nA4ePLJJ9FqtXz00UcUFhbS0dFBVFQUGRkZvPTSSwwNDfHII4/gdDo5c+aMWPDf2NhIbm4uBw4cYHh4mPnz55Obm8vu3bsBuPfee9m5cydarZZdu3YRExPD3LlzRTv2DRs28OGHH3Lo0KExLsoGg4Hly5ePqdMV/MMmyjcbjUYWLFjAqlWrWL16NampqeLMfe3atRMu2Hk8Hnp6esjIyODQoUN4PB6mT58uplbKysro6uqiv7+fsLAw1q5dS3FxMQ6Hg6ioKHbs2MGmTZvEWbXP5yMgIICDBw8yZ84cfD4fhw8fJi8vj88//1wUTO/s7KSjo4P77ruPxYsXExAQMMaE9FpcLhdVVVUUFhbe8DWWmiMkJO4gx44dE228AbGFVavVikIuH3zwATKZDK/Xy5w5c4iLi0OlUqHT6fD7/QwPD4u5V6HO1Gaz4fV6RVUsm82Gz+dDr9djs9lQKBS4XC7gqmTl4OAggYGBBAQEiMe9VlHrizZHeDweXC4XGo1GDLAul4u33nqLlJQUli5dOmGlgc1mQ6lU4nQ6kclk6HQ6hoaGRgnZCOP0+/0MDQ0hl8vRaDQ4nU60Wu2Yagi73S6OxW63i7rHgnSk8PwI7c5Op/O6esgOh4PXXnuNrKws7r77bmQymdSRJiExVbk26F6L3++ntbWV8vJykpKSyMzMnIy04FfCREHX6/XS0dGB3W4nKCgIo9FIW1sbGo2GqKioCd0XHA4H77//PnK5nFWrVmE0GqdEbe5k8fl8NDY2sn//fmbMmMGSJUuELxWpI01C4m8VwdYmLi5O/H+q4fP5+O1vf0tiYiLd3d3k5OSgUqk4ffo0P/jBD0hMTBzzGEGG8dFHHxUbDYQOMKGmdyqeq1DL3NDQwIkTJ3C5XKxbt47Y2FipOUJC4u+JqRCA7HY7bW1tGI1GsWZVqFcNCwtj5cqVvPrqq5w8eZIf/ehH1NfXU19fP27QFR6rUCjIzc0lOzub8vJydu3ahVKpJDMzk6ysLEJCQsSUxJ14DoRMgDCbr6io4MqVK4SEhLBw4UJSUlJuuolFCroSEhKTwuv1cuLECYqKijCZTEybNo2MjAwCAgKwWCy0tbVhtVqZN28en376KX6/f5SL70QImgdz584lPz+fzs5OampqeO+99wAwmUykpKSQmJhIcHCw2EItPPZWIdQ8e71esb25oaGB5uZm+vv7CQwMJDs7m/nz538hzQXxfKWcroTEnePYsWP4fL6/CWnHN998k/Xr12MwGOju7qampob6+nqxtXju3LkYDAZiYmLo7u5Gq9XetGnjSATN3J6eHjH4ORwOVCoVarUao9FIaGgooaGhBAUFiQtq4wmTX6vFK7QPDw0NMTAwQG9vLz09PQwODuJ2u/F4PISEhJCcnExCQgImk0nU2p0E0kKahMRU5dKlSxw6dGjSNat3kqGhIZ566qlR9a1erxer1UpzczMXL16kv78fnU5HVlYWKSkphISE3LR32UQI0otOpxO73U5fXx89PT1YLBYGBwex2+3ibHXkYwQ9hZFjEETNDQYDwcHBYvAODg4W63W/hHSkFHQlJKYqQiD5W0Aul183gArnYrFYqKiooLGxEYfDQUJCAtOnTyc6OhqDwTBGD0EQrPkyCGkBodzN7XaLM9qRzhFKpVKcKavV6q/KZFMKuhISErcX4TLe6XRSV1dHdXU1nZ2d6HQ6MjIySElJITIykubmZp577jm+//3vk5WVdd2Zpd/vx2q1YrFY6O/vF38Ldj1utxulUin+jEwxCDNgIa0g2PlotVoCAwMJDg7GaDRiMplEzeMvIS8pBV0JCYk7x0hVsM7OTi5dukR9fT1Wq5WWlhaef/55kpKS+M///E8KCwvFVIvX66W9vZ3GxkZaW1vp6upCqVSKwdFoNI6y67nZIOnz+bDZbKPsenp7e+nv76evrw+VSkVUVBQJCQkkJydjMpkmW6kgBV0JCYmpgxBzhoeH2bp1Kx988AEAOp2OLVu2sGnTJtFmXXBwiI+PJzIyEq1WO2pft7p6YSQDAwOYzWaamppobGzE7XaTkJDA7NmziY2NvZ6bhRR0JSQkph4ej4enn35aVDOrqakhKSmJwsJCFi1aRExMzJg23juF3+9nYGCAxsZGLly4gM1mIy8vj9mzZ4/XIiwFXQkJiamHoJVQVFREXV0dBQUFZGVlodfrp0SgnQi/34/FYuH06dNcuXKFpUuXMmPGjJEzXynoSkhITC2EmeOf//xnMjIyWLBgwZicrM/no6OjA6PRiE6nG3cfg4ODWCwWEhIS8Pl89Pb2otVqCQoKGjdwO51OBgYGCAwMFGUcnU4ng4ODBAUFoVQqMZvNYilZV1cXfr+fyMjIMfvz+/309fWxb98+goKCWL16tVCFIQVdCQmJqcXw8DCvvPIKS5YsIS8vb9wA6Xa7+eUvf8mGDRuYOXOmeLsQs1wuF9u2bSM2NlZ0g7Db7TQ0NPDDH/5wTNeY1+vljTfeIDg4mO7ubr73ve/h9/t59dVXiYiIwGKxkJycTHFxMWazmX/+538W7X1++ctfEhoaOu65eL1e3nvvPVQqFevXr0cul1836Ep2PRISEredU6dOkZOTM2HAhav+a6GhodhsNnbt2sXrr79OR0cHO3bs4E9/+hMVFRUUFRWRlpaGTqfjzJkz5ObmolAoKC4u5sMPPxxlN2S326moqODee++lrq6OgYEBHA4Hly9fZtGiRdTU1JCcnMy//uu/ijPhhQsXEhISct06YoVCwcaNG+nr66Otre2G5y4FXQkJidtOc3OzaC10Iy5cuEBpaSmNjY20tbUhk8k4fvw4QUFBJCYmkpGRgUwmY8mSJezevZvy8nKioqIoKChAJpPhdrs5deoU58+fx2q1olar8fl8WK1WhoaGcLvdqNVqXC4XRqORhoYG5syZQ0REBE6nk56eHurr6687RrlczrRp02htbb3h+UhBV0JC4raj1WonZeIo2J8bDAY2b97MwMAAtbW1KJVKUSNBSDesWrWKe+65h9TUVFJSUkaVgPl8PpRKJcHBwVgsFjFnK9j+DA4OYjKZGB4e5ujRo2IAjYmJYeXKlbjd7huOdWBgYFSL9ERIQVdCQuK2c9ddd3Hw4MFRfmvX4na7MRgMpKWlIZfLOXnyJPHx8ajVatLT06mrqyMsLEwM3p2dnbS1tfGjH/0Iv99PSUkJfr8flUrFggULmDdvHps3b6a4uJiHH34Yr9fLhQsXKCws5Ny5c6xfv57KykoaGxvZs2cPra2t/OlPf8JkMpGTkzPhOP1+Px0dHVy5coXU1NQbnru0kCYhIXHb8fv9nDhxgoqKCh599NEbSiVOFKdGqoh5vV7kcjlyuXzU9tdqPVy732uFcEbeJ9w/0dh8Ph9NTU289dZbfOtb3yI+Ph6k6gUJCYmpiM/no6Kigs8++4y5c+eSm5v7ZfQObitCudixY8cwm82sW7duZFmZFHQlJCSmJkKDxLFjx6irqyM7O5uZM2cSFhb2VSmAfWEERbS2tjbOnj1LR0cHBQUF5ObmXqu+JgVdCQmJqY3g/FtaWkplZSV+v5/09HTS0tKIjo4WRXBu1yx4ZFwcHh6mpaWFy5cv09jYSEhICPn5+WRkZEwkdSkFXQkJib8NhHhksVi4dOkSdXV1dHd3ExISQkREBDExMURERBAWFoZarR4T8G4mKI+X3x0eHqa7uxuz2YzZbKazsxOXy0VsbCzp6emkp6eLojvXOZYUdCUkJP72GGkK2dPTQ2dnpxgILRYLcrkcrVaLTqcjICAArVaLVqtFo9EQEBAgeqnJZDJ8Pp/ofeZwOHA6nQwPD+N0OsW/HQ6HaLIZGRlJVFQUUVFRhISEiGOS7HokJCT+T+L3+3E4HNhsNmw2m2jhI/ztdDrxeDyic4RQ1aBSqQgICECj0aDT6URrHq1Wi16vR6VS3YoUhhR0JSQkJG4j1w26N7Jgn/q1GxISEhJ/Q0ytmgwJCQmJv3OkoCshISFxG5GCroSEhMRtRAq6EhISErcRKehKSEhI3EakoCshISFxG/n/viRfOiTomnEAAAAASUVORK5CYII=\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "plt.figure(figsize=(20,20))\n", - "lgb.plot_tree(lgbm_model, tree_index=10)" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['Pclass', 'Age', 'Fare', 'Sex_label', 'Cabin_label', 'Embarked_label']" - ] - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "features" - ] - }, - { - "cell_type": "code", - "execution_count": 22, + "execution_count": 10, "metadata": {}, "outputs": [ { @@ -3269,674 +658,464 @@ "\n", "\n", - "\n", - "\n", + "\n", + "\n", "%3\n", - "\n", + "\n", "\n", "\n", "split0\n", - "\n", - "Sex_label\n", - " ≤ \n", - "0.000\n", - "count: 712\n", + "\n", + "Sex_label\n", + " ≤ \n", + "0.000\n", + "0.000 value\n", + "count: 712\n", "\n", "\n", "\n", "split1\n", - "\n", - "Pclass\n", - " ≤ \n", - "2.500\n", - "count: 245\n", + "\n", + "Pclass\n", + " ≤ \n", + "2.500\n", + "0.014 value\n", + "count: 245\n", "\n", "\n", "\n", "split0->split1\n", - 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"\n", - "leaf 10: \n", - "-0.014\n", + "leaf9\n", + "\n", + "leaf 9: \n", + "-0.014\n", + "count: 32\n", "\n", - "\n", + "\n", "\n", - "split9->leaf10\n", - "\n", - "\n", - "no\n", + "split8->leaf9\n", + "\n", + "\n", + "no\n", "\n", "\n", "\n", "leaf7\n", - "\n", - "leaf 7: \n", - "-0.003\n", + "\n", + "leaf 7: \n", + "-0.008\n", + "count: 156\n", "\n", - "\n", + "\n", "\n", - "split8->leaf7\n", - "\n", - "\n", - "yes\n", + "split9->leaf7\n", + "\n", + "\n", + "yes\n", "\n", - "\n", + "\n", "\n", - "leaf9\n", - "\n", - "leaf 9: \n", - "-0.009\n", + "leaf10\n", + "\n", + "leaf 10: \n", + "-0.010\n", + "count: 40\n", "\n", - "\n", + "\n", "\n", - "split8->leaf9\n", - "\n", - "\n", - "no\n", + "split9->leaf10\n", + "\n", + "\n", + "no\n", "\n", "\n", "\n", "split7\n", - "\n", - "Fare\n", - " ≤ \n", - "30.750\n", - "count: 86\n", + "\n", + "Fare\n", + " ≤ \n", + "30.750\n", + "-0.000 value\n", + "count: 86\n", "\n", "\n", "\n", "split5->split7\n", - "\n", - "\n", - "yes\n", + "\n", + "\n", + "yes\n", "\n", "\n", "\n", "leaf6\n", - "\n", - "leaf 6: \n", - "-0.007\n", + "\n", + "leaf 6: \n", + "-0.007\n", + "count: 41\n", "\n", "\n", "\n", "split5->leaf6\n", - "\n", - "\n", - "no\n", + "\n", + "\n", + "no\n", "\n", "\n", "\n", "leaf4\n", - "\n", - "leaf 4: \n", - "0.004\n", + "\n", + "leaf 4: \n", + "0.004\n", + "count: 28\n", "\n", "\n", "\n", "split7->leaf4\n", - "\n", - "\n", - "yes\n", + "\n", + "\n", + "yes\n", "\n", "\n", "\n", "leaf8\n", - "\n", - "leaf 8: \n", - "-0.002\n", + "\n", + "leaf 8: \n", + "-0.002\n", + "count: 58\n", "\n", "\n", "\n", "split7->leaf8\n", - "\n", - "\n", - "no\n", + "\n", + "\n", + "no\n", "\n", "\n", "\n" ], "text/plain": [ - "" + "" ] }, - "execution_count": 22, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "lgb.create_tree_digraph(lgbm_model, tree_index=1, orientation = \"vertical\", show_info=['internal_count'])\n" + "lgb.create_tree_digraph(lgbm_model, tree_index=1, orientation = \"vertical\", show_info=['internal_count', 'leaf_count', 'internal_value'])\n" ] }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## Model metadata interpretation" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [], - "source": [ - "import json" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [], - "source": [ - "tree_dump = lgbm_model.dump_model()[\"tree_info\"][1]" + "## dtreeviz visualizations" ] }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ - "def _get_nodes_info(node):\n", - " tree_nodes = {}\n", - " children_left = {}\n", - " children_right = {}\n", - " node_index = 0\n", - " \n", - " def _walk_tree(node, node_id):\n", - " nonlocal node_index\n", - " tree_nodes[node_id] = node\n", - " if node.get(\"split_index\") is None:\n", - " children_left[node_id] = -1\n", - " children_right[node_id] = -1\n", - " return\n", - "\n", - " node_index += 1\n", - " children_left[node_id] = node_index\n", - " _walk_tree(node.get(\"left_child\"), node_index)\n", - " \n", - " node_index += 1\n", - " children_right[node_id] = node_index\n", - " _walk_tree(node.get(\"right_child\"), node_index)\n", - " \n", - " def _convert_dict_to_list(my_dict):\n", - " my_list = [-1] * len(my_dict)\n", - " for key, value in my_dict.items():\n", - " my_list[key] = value\n", - " return my_list\n", - " \n", - " _walk_tree(node, node_index)\n", - "\n", - " for node in tree_nodes.values():\n", - " node.pop(\"left_child\", None)\n", - " node.pop(\"right_child\", None)\n", - " \n", - " children_left_list = _convert_dict_to_list(children_left)\n", - " children_right_list = _convert_dict_to_list(children_right)\n", - " tree_node_list = _convert_dict_to_list(tree_nodes)\n", - " \n", - " return tree_node_list, children_left_list, children_right_list\n" + "# lgbm_model.save_model(\"../testing/dtreeviz/models/fixtures/lightgbm_model_classifier.txt\")\n", + "# lgbm = lgb.Booster(model_file=\"../testing/dtreeviz/models/fixtures/lightgbm_model_classifier.txt\")" ] }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ - "tree_nodes, children_left, children_right = _get_nodes_info(tree_dump[\"tree_structure\"])" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[1, 2, 3, 4, -1, -1, -1, 8, 9, -1, -1, -1, 13, 14, 15, -1, -1, 18, -1, -1, 21, 22, -1, -1, -1]\n", - "[12, 7, 6, 5, -1, -1, -1, 11, 10, -1, -1, -1, 20, 17, 16, -1, -1, 19, -1, -1, 24, 23, -1, -1, -1]\n" - ] - } - ], - "source": [ - "print(children_left)\n", - "print(children_right)" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "text/plain": [ - "{'leaf_index': 9,\n", - " 'leaf_value': -0.008689234807276692,\n", - " 'leaf_weight': 40.59755164384842,\n", - " 'leaf_count': 175}" - ] - }, - "execution_count": 28, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "tree_nodes[19]" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'split_index': 0,\n", - " 'split_feature': 3,\n", - " 'split_gain': 187.06399536132812,\n", - " 'threshold': 1.0000000180025095e-35,\n", - " 'decision_type': '<=',\n", - " 'default_left': True,\n", - " 'missing_type': 'None',\n", - " 'internal_value': 0,\n", - " 'internal_weight': 0,\n", - " 'internal_count': 712}" - ] - }, - "execution_count": 29, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "tree_nodes[0]" + "light_dtree = ShadowLightGBMTree(lgbm_model, tree_index=1, x_data=X_train, y_data=y_train, feature_names=features, target_name=target, class_names=[0,1] )\n" ] }, { "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'leaf_index': 9,\n", - " 'leaf_value': -0.008689234807276692,\n", - " 'leaf_weight': 40.59755164384842,\n", - " 'leaf_count': 175}" - ] - }, - "execution_count": 30, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "tree_nodes[19]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# dtreeviz clasification" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Save the model" - ] - }, - { - "cell_type": "code", - "execution_count": 44, - "metadata": {}, - "outputs": [], - "source": [ - "# lgbm_model.save_model(\"../testing/dtreeviz/models/fixtures/lightgbm_model_classifier.txt\")\n", - "# lgbm = lgb.Booster(model_file=\"../testing/dtreeviz/models/fixtures/lightgbm_model_classifier.txt\")" - ] - }, - { - "cell_type": "code", - "execution_count": 45, - "metadata": {}, - "outputs": [], - "source": [ - "light_dtree = ShadowLightGBMTree(lgbm_model, tree_index=1, x_data=X_train, y_data=y_train, feature_names=features, target_name=target, class_names=[0,1] )\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Visualizations" - ] - }, - { - "cell_type": "code", - "execution_count": 47, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Pclass 3.000\n", - "Age 32.000\n", - "Fare 7.925\n", - "Sex_label 1.000\n", - "Cabin_label -1.000\n", - "Embarked_label 2.000\n", - "Name: 382, dtype: float64" - ] - }, - "execution_count": 47, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "x = X_train[features].iloc[2]\n", - "x" - ] - }, - { - "cell_type": "code", - "execution_count": 49, + "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "image/svg+xml": [ - "\n", - "\n", + "\n", + "\n", "G\n", - "\n", + "\n", "\n", "cluster_legend\n", "\n", - "\n", - "cluster_instance\n", - "\n", "\n", "\n", "node3\n", - "\n", + "\n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4071,10 +1250,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4093,7 +1272,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4133,10 +1312,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4149,7 +1328,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4187,7 +1366,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4195,8 +1374,8 @@ "\n", "\n", "leaf4\n", - "\n", - "\n", + "\n", + "\n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4290,14 +1469,14 @@ "\n", "\n", "node3->leaf5\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "leaf6\n", - "\n", - "\n", + "\n", + "\n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4320,19 +1499,18 @@ " \n", " \n", " \n", - " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4343,7 +1521,7 @@ "\n", "\n", "node2\n", - "\n", + "\n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4500,7 +1678,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4540,10 +1718,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4556,7 +1734,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4578,25 +1756,25 @@ " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4604,13 +1782,13 @@ "\n", "\n", "node2->node3\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "node7\n", - "\n", + "\n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4767,7 +1945,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4807,10 +1985,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4823,7 +2001,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4850,20 +2028,20 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4872,13 +2050,13 @@ "\n", "\n", "node2->leaf6\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "node8\n", - "\n", + "\n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5025,7 +2203,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5034,7 +2212,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5042,7 +2220,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5061,7 +2239,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5083,10 +2261,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5094,7 +2272,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5102,27 +2280,26 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5132,7 +2309,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5142,16 +2319,16 @@ " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", "\n", "\n", "\n", "leaf9\n", - "\n", - "\n", + "\n", + "\n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5174,19 +2351,19 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5196,14 +2373,14 @@ "\n", "\n", "node8->leaf9\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "leaf10\n", - "\n", - "\n", + "\n", + "\n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5226,19 +2403,18 @@ " \n", " \n", " \n", - " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5248,14 +2424,14 @@ "\n", "\n", "node8->leaf10\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "leaf11\n", - "\n", - "\n", + "\n", + "\n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5278,16 +2454,18 @@ " \n", " \n", " \n", - " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -5298,19 +2476,19 @@ "\n", "\n", "node7->node8\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "node7->leaf11\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "node1\n", - "\n", + "\n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5445,10 +2623,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5464,7 +2642,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5500,10 +2678,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5519,7 +2697,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5559,7 +2737,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5567,20 +2745,19 @@ "\n", "\n", "node1->node2\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "node1->node7\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "node12\n", - "\n", - "\n", + "\n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5740,7 +2914,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5780,10 +2954,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5796,7 +2970,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5809,10 +2983,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5831,20 +3005,10 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5853,7 +3017,7 @@ "\n", "\n", "node14\n", - "\n", + "\n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5988,10 +3152,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6010,7 +3174,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6050,10 +3214,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6066,7 +3230,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6105,7 +3269,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6113,8 +3277,7 @@ "\n", "\n", "node17\n", - "\n", - "\n", + "\n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", " \n", " \n", - " \n", " \n", - " \n", - " \n", - " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -6322,18 +3474,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6341,58 +3490,51 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", "\n", @@ -6400,8 +3542,8 @@ "\n", "\n", "leaf15\n", - "\n", - "\n", + "\n", + "\n", " \n", "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
PclassAgeFareSex_labelCabin_labelEmbarked_label
count52.00000052.00000052.00000052.052.00000052.000000
mean1.15384632.38648869.9922290.053.9423081.057692
std0.36432113.85153423.2790590.045.2870641.055582
min1.0000003.00000030.0000000.0-1.000000-1.000000
25%1.00000022.75000053.1000000.017.0000000.000000
50%1.00000031.50000070.1500000.047.0000002.000000
75%1.00000039.00000083.2374750.088.2500002.000000
max2.00000062.000000120.0000000.0144.0000002.000000
\n", + "
" + ], + "text/plain": [ + " Pclass Age Fare Sex_label Cabin_label \\\n", + "count 52.000000 52.000000 52.000000 52.0 52.000000 \n", + "mean 1.153846 32.386488 69.992229 0.0 53.942308 \n", + "std 0.364321 13.851534 23.279059 0.0 45.287064 \n", + "min 1.000000 3.000000 30.000000 0.0 -1.000000 \n", + "25% 1.000000 22.750000 53.100000 0.0 17.000000 \n", + "50% 1.000000 31.500000 70.150000 0.0 47.000000 \n", + "75% 1.000000 39.000000 83.237475 0.0 88.250000 \n", + "max 2.000000 62.000000 120.000000 0.0 144.000000 \n", + "\n", + " Embarked_label \n", + "count 52.000000 \n", + "mean 1.057692 \n", + "std 1.055582 \n", + "min -1.000000 \n", + "25% 0.000000 \n", + "50% 2.000000 \n", + "75% 2.000000 \n", + "max 2.000000 " + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "trees.describe_node_sample(light_dtree, node_id=5)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Pclass 2.0\n", + "Age 42.0\n", + "Fare 13.0\n", + "Sex_label 0.0\n", + "Cabin_label -1.0\n", + "Embarked_label 2.0\n", + "Name: 865, dtype: float64" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "x = X_train[features].iloc[10]\n", + "x" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Pclass < 2.5\n", + "Fare < 29.85\n", + "0.0 <= Sex_label \n", + "\n" + ] + } + ], + "source": [ + "print(trees.explain_prediction_path(light_dtree, x=x))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Regression" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Datasets" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "features_reg = [\"Pclass\", \"Survived\", \"Fare\", \"Sex_label\", \"Cabin_label\", \"Embarked_label\"]\n", + "target_reg = \"Age\"\n", + "\n", + "X_train_reg, X_test_reg, y_train_reg, y_test_reg = train_test_split(dataset[features_reg], dataset[target_reg], test_size=0.2, random_state=42)\n", + "\n", + "train_data_reg = lgb.Dataset(data=X_train_reg, label=y_train_reg)\n", + "valid_data_reg = lgb.Dataset(data=X_test_reg, label=y_test_reg)\n", + "\n", + "lgbm_params = {\n", + " 'num_tree':10,\n", + " 'boosting': 'dart', # dart (drop out trees) often performs better\n", + " 'application': 'regression_l1', \n", + " 'learning_rate': 0.05, # Learning rate, controls size of a gradient descent step\n", + " 'min_data_in_leaf': 20, # Data set is quite small so reduce this a bit\n", + " 'feature_fraction': 0.7, # Proportion of features in each boost, controls overfitting\n", + " 'num_leaves': 41, # Controls size of tree since LGBM uses leaf wise splits\n", + " 'drop_rate': 0.15,\n", + " 'max_depth':4,\n", + " \"seed\":1212}\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Model training" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000612 seconds.\n", + "You can set `force_row_wise=true` to remove the overhead.\n", + "And if memory is not enough, you can set `force_col_wise=true`.\n", + "[LightGBM] [Info] Total Bins 175\n", + "[LightGBM] [Info] Number of data points in the train set: 712, number of used features: 6\n", + "[LightGBM] [Info] Start training from score 29.699118\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[1]\ttraining's l1: 9.03156\tvalid_1's l1: 9.05193\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[2]\ttraining's l1: 9.00275\tvalid_1's l1: 9.03197\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[3]\ttraining's l1: 8.96288\tvalid_1's l1: 9.01286\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[4]\ttraining's l1: 8.96182\tvalid_1's l1: 9.01891\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[5]\ttraining's l1: 8.92147\tvalid_1's l1: 9.01404\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[6]\ttraining's l1: 8.90348\tvalid_1's l1: 8.99877\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[7]\ttraining's l1: 8.86911\tvalid_1's l1: 8.97774\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n", + "[8]\ttraining's l1: 29.4816\tvalid_1's l1: 8.97774\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n", + "[9]\ttraining's l1: 29.4939\tvalid_1's l1: 8.97774\n", + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", + "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n", + "[10]\ttraining's l1: 29.5271\tvalid_1's l1: 8.97774\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/tudorl/Documents/workspace/personal/dtreeviz/venv/lib/python3.7/site-packages/lightgbm/engine.py:151: UserWarning: Found `num_tree` in params. Will use it instead of argument\n", + " warnings.warn(\"Found `{}` in params. Will use it instead of argument\".format(alias))\n" + ] + } + ], + "source": [ + "lgbm_reg_model = lgb.train(lgbm_params, train_data_reg, valid_sets=[train_data_reg, valid_data_reg])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## LightGBM default visualisations" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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"/Users/tudorl/Documents/workspace/personal/dtreeviz/venv/lib/python3.7/site-packages/numpy/core/fromnumeric.py:3335: RuntimeWarning: Mean of empty slice.\n", + " out=out, **kwargs)\n", + "/Users/tudorl/Documents/workspace/personal/dtreeviz/venv/lib/python3.7/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars\n", + " ret = ret.dtype.type(ret / rcount)\n" + ] + }, + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "G\n", + "\n", + "\n", + "\n", + "node4\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", 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PclassSurvivedFareSex_labelCabin_labelEmbarked_label
count226.000000226.000000226.000000226.000000226.000000226.0
mean2.8849560.2035408.1254020.8097354.8761062.0
std0.3197840.4035242.0531440.39338227.4959820.0
min2.0000000.0000000.0000000.000000-1.0000002.0
25%3.0000000.0000007.7750001.000000-1.0000002.0
50%3.0000000.0000007.9250001.000000-1.0000002.0
75%3.0000000.0000008.6625001.000000-1.0000002.0
max3.0000001.00000012.4750001.000000145.0000002.0
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" + ], + "text/plain": [ + " Pclass Survived Fare Sex_label Cabin_label \\\n", + "count 226.000000 226.000000 226.000000 226.000000 226.000000 \n", + "mean 2.884956 0.203540 8.125402 0.809735 4.876106 \n", + "std 0.319784 0.403524 2.053144 0.393382 27.495982 \n", + "min 2.000000 0.000000 0.000000 0.000000 -1.000000 \n", + "25% 3.000000 0.000000 7.775000 1.000000 -1.000000 \n", + "50% 3.000000 0.000000 7.925000 1.000000 -1.000000 \n", + "75% 3.000000 0.000000 8.662500 1.000000 -1.000000 \n", + "max 3.000000 1.000000 12.475000 1.000000 145.000000 \n", + "\n", + " Embarked_label \n", + "count 226.0 \n", + "mean 2.0 \n", + "std 0.0 \n", + "min 2.0 \n", + "25% 2.0 \n", + "50% 2.0 \n", + "75% 2.0 \n", + "max 2.0 " + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "trees.describe_node_sample(light_dtree_reg, node_id=19)" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "data": { + 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"\n", + "3.00\n", + "\n", + "0.00\n", + "\n", + "31.27\n", + "\n", + "0.00\n", + "\n", + "-1.00\n", + "\n", + "2.00\n", + "\n", + "\n", + "\n", + "leaf20->X_y\n", + "\n", + "\n", + "  Prediction\n", + " 27.07\n", + "\n", + "\n", + "" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "trees.dtreeviz(light_dtree_reg, X=x, show_just_path=True)" + ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 31, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1.5 <= Pclass \n", + "12.5 <= Fare \n", + "1.5 <= Embarked_label \n", + "\n" + ] + } + ], + "source": [ + "print(trees.explain_prediction_path(light_dtree_reg, x))" + ] }, { "cell_type": "code", diff --git a/testing/dtreeviz/models/test_decision_tree_lightgbm_classifier.py b/testing/dtreeviz/models/test_decision_tree_lightgbm_classifier.py index a2773774..f533bd10 100644 --- a/testing/dtreeviz/models/test_decision_tree_lightgbm_classifier.py +++ b/testing/dtreeviz/models/test_decision_tree_lightgbm_classifier.py @@ -81,3 +81,6 @@ def test_get_node_samples(shadow_dec_tree: ShadowLightGBMTree): assert len(node_samples[13]) == 340 assert len(node_samples[17]) == 196 assert len(node_samples[21]) == 86 + +def test_get_min_samples_leaf(shadow_dec_tree: ShadowLightGBMTree): + assert shadow_dec_tree.get_min_samples_leaf() == 20 From 5b8f4fc5c34f4709418323df3105c97f1845ede2 Mon Sep 17 00:00:00 2001 From: tlapusan Date: Fri, 27 Nov 2020 14:19:19 +0200 Subject: [PATCH 06/16] adjust visualisations for lightgbm categorical variable --- dtreeviz/models/lightgbm_decision_tree.py | 14 +- dtreeviz/models/shadow_decision_tree.py | 18 +- dtreeviz/trees.py | 27 +- .../dtreeviz_lightgbm_visualisations.ipynb | 20230 ++++++++-------- notebooks/dtreeviz_spark_visualisations.ipynb | 17729 +++++++------- 5 files changed, 18326 insertions(+), 19692 deletions(-) diff --git a/dtreeviz/models/lightgbm_decision_tree.py b/dtreeviz/models/lightgbm_decision_tree.py index 19f8424c..61ed80c8 100644 --- a/dtreeviz/models/lightgbm_decision_tree.py +++ b/dtreeviz/models/lightgbm_decision_tree.py @@ -80,6 +80,13 @@ def is_classifier(self) -> bool: return False raise Exception(f"objective {objective} is not yet supported by dtreeviz's lightgbm implementation") + def is_categorical_split(self, id) -> bool: + node = self.tree_nodes[id] + if 'split_index' in node: + if node["decision_type"] == "==": + return True + return False + def get_class_weights(self): pass @@ -90,7 +97,10 @@ def get_thresholds(self) -> np.ndarray: node_thresholds = [-1] * self.nnodes() for i in range(self.nnodes()): if self.children_left[i] != -1 and self.children_right[i] != -1: - node_thresholds[i] = round(self.tree_nodes[i]["threshold"], 2) + if self.is_categorical_split(i): + node_thresholds[i] = (list(map(int, self.tree_nodes[i]["threshold"].split("||"))), []) + else: + node_thresholds[i] = round(self.tree_nodes[i]["threshold"], 2) self.thresholds = np.array(node_thresholds) return self.thresholds @@ -190,4 +200,6 @@ def get_min_samples_leaf(self) -> (int, float): return self.booster.params.get("min_data_in_leaf", default_value) def shouldGoLeftAtSplit(self, id, x): + if self.is_categorical_split(id): + return x in self.get_node_split(id)[0] return x <= self.get_node_split(id) diff --git a/dtreeviz/models/shadow_decision_tree.py b/dtreeviz/models/shadow_decision_tree.py index 6b010569..6a3fa92e 100644 --- a/dtreeviz/models/shadow_decision_tree.py +++ b/dtreeviz/models/shadow_decision_tree.py @@ -3,8 +3,10 @@ from numbers import Number from typing import List, Tuple, Mapping +import lightgbm import numpy as np import pandas as pd +import pyspark.ml import sklearn import xgboost @@ -312,7 +314,7 @@ def walk(t, x, path): path = [] leaf = walk(self.root, x, path) - if path_only : + if path_only: return path return leaf.prediction(), path @@ -448,10 +450,20 @@ def get_shadow_tree(tree_model, x_data, y_data, feature_names, target_name, clas from dtreeviz.models import xgb_decision_tree return xgb_decision_tree.ShadowXGBDTree(tree_model, tree_index, x_data, y_data, feature_names, target_name, class_names) - # TODO - add logic for LightGBM + elif isinstance(tree_model, lightgbm.basic.Booster): + from dtreeviz.models import lightgbm_decision_tree + return lightgbm_decision_tree.ShadowLightGBMTree(tree_model, tree_index, x_data, y_data, feature_names, + target_name, class_names) + + elif isinstance(tree_model, pyspark.ml.classification.DecisionTreeClassificationModel): + from dtreeviz.models import spark_decision_tree + return spark_decision_tree.ShadowSparkTree(tree_model, x_data, y_data, feature_names, target_name, + class_names) else: raise ValueError( - f"Tree model must be in (DecisionTreeRegressor, DecisionTreeClassifier, xgboost.core.Booster, but was {tree_model.__class__.__name__}") + f"Tree model must be in (DecisionTreeRegressor, DecisionTreeClassifier, xgboost.core.Booster, " + f"lightgbm.basic.Booster, pyspark.ml.classification.DecisionTreeClassificationModel but was " + f"{tree_model.__class__.__name__}") class ShadowDecTreeNode(): diff --git a/dtreeviz/trees.py b/dtreeviz/trees.py index 4933d006..292a372a 100644 --- a/dtreeviz/trees.py +++ b/dtreeviz/trees.py @@ -1014,12 +1014,13 @@ def wedge(ax, x, color): fontname=fontname, color=colors['text_wedge']) else: - ax.text(xr/2, -2 * th, - f"{node.split()[0]}", - horizontalalignment='center', - fontsize=ticks_fontsize, - fontname=fontname, - color=colors['text_wedge']) + left_split_values = node.split()[0] + for split_value in left_split_values: + tria = np.array( + [[split_value, tipy], [split_value - tw, -th], [split_value + tw, -th]]) + t = patches.Polygon(tria, facecolor=color) + t.set_clip_on(False) + ax.add_patch(t) wedge(ax, node.split(), color=colors['wedge']) if highlight_node: @@ -1130,14 +1131,14 @@ def wedge(ax, x, color): if highlight_node: wedge(ax, X[node.feature()], color=colors['highlight']) else: - # TODO -125 is hard coded... - ax.text(xr / 2, -125, - f"{node.split()[0]}", - horizontalalignment='center', - fontsize=ticks_fontsize, - fontname=fontname, - color=colors['text_wedge']) + xticks = list(overall_feature_range) + node.split()[0] + ax.set_xticks(xticks) ax.scatter(X_feature, y_train, s=5, c=colors['scatter_marker'], alpha=colors['scatter_marker_alpha'], lw=.3) + + left_split_values = node.split()[0] + for split_value in left_split_values: + wedge(ax, split_value, color=colors['wedge']) + if highlight_node: wedge(ax, X[node.feature()], color=colors['highlight']) diff --git a/notebooks/dtreeviz_lightgbm_visualisations.ipynb b/notebooks/dtreeviz_lightgbm_visualisations.ipynb index c362a8d6..e105d274 100644 --- a/notebooks/dtreeviz_lightgbm_visualisations.ipynb +++ b/notebooks/dtreeviz_lightgbm_visualisations.ipynb @@ -350,17 +350,17 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ - "features = [\"Pclass\", \"Age\", \"Fare\", \"Sex_label\", \"Cabin_label\", \"Embarked_label\"]\n", + "features = [\"Pclass\", \"Sex_label\", \"Embarked_label\", \"Age\", \"Cabin_label\", \"Fare\"]\n", "target = \"Survived\"\n", "\n", "X_train, X_test, y_train, y_test = train_test_split(dataset[features], dataset[target], test_size=0.2, random_state=42)\n", "\n", - "train_data = lgb.Dataset(data=X_train, label=y_train)\n", - "valid_data = lgb.Dataset(data=X_test, label=y_test)" + "train_data = lgb.Dataset(data=X_train, label=y_train, feature_name=features, categorical_feature=[\"Sex_label\", \"Pclass\", \"Embarked_label\", \"Cabin_label\"])\n", + "valid_data = lgb.Dataset(data=X_test, label=y_test, feature_name=features, categorical_feature=[\"Sex_label\", \"Pclass\", \"Embarked_label\", \"Cabin_label\"])\n" ] }, { @@ -372,221 +372,239 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 7, "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/tudorl/Documents/workspace/personal/dtreeviz/venv/lib/python3.7/site-packages/lightgbm/basic.py:1551: UserWarning: Using categorical_feature in Dataset.\n", + " warnings.warn('Using categorical_feature in Dataset.')\n", + "/Users/tudorl/Documents/workspace/personal/dtreeviz/venv/lib/python3.7/site-packages/lightgbm/basic.py:1286: UserWarning: Overriding the parameters from Reference Dataset.\n", + " warnings.warn('Overriding the parameters from Reference Dataset.')\n", + "/Users/tudorl/Documents/workspace/personal/dtreeviz/venv/lib/python3.7/site-packages/lightgbm/basic.py:1098: UserWarning: categorical_column in param dict is overridden.\n", + " warnings.warn('{} in param dict is overridden.'.format(cat_alias))\n" + ] + }, { "name": "stdout", "output_type": "stream", "text": [ + "[LightGBM] [Warning] Met negative value in categorical features, will convert it to NaN\n", "[LightGBM] [Info] Number of positive: 268, number of negative: 444\n", - "[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000900 seconds.\n", - "You can set `force_row_wise=true` to remove the overhead.\n", - "And if memory is not enough, you can set `force_col_wise=true`.\n", - "[LightGBM] [Info] Total Bins 233\n", + "[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001117 seconds.\n", + "You can set `force_col_wise=true` to remove the overhead.\n", + "[LightGBM] [Info] Total Bins 190\n", "[LightGBM] [Info] Number of data points in the train set: 712, number of used features: 6\n", "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.376404 -> initscore=-0.504838\n", "[LightGBM] [Info] Start training from score -0.504838\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", - "[1]\ttraining's binary_logloss: 0.641119\tvalid_1's binary_logloss: 0.658611\n", + "[1]\ttraining's binary_logloss: 0.65258\tvalid_1's binary_logloss: 0.669903\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", - "[2]\ttraining's binary_logloss: 0.622109\tvalid_1's binary_logloss: 0.638123\n", + "[2]\ttraining's binary_logloss: 0.643893\tvalid_1's binary_logloss: 0.659912\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", - "[3]\ttraining's binary_logloss: 0.613812\tvalid_1's binary_logloss: 0.628821\n", + "[3]\ttraining's binary_logloss: 0.636543\tvalid_1's binary_logloss: 0.652435\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", - "[4]\ttraining's binary_logloss: 0.619015\tvalid_1's binary_logloss: 0.634182\n", + "[4]\ttraining's binary_logloss: 0.636963\tvalid_1's binary_logloss: 0.653424\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", - "[5]\ttraining's binary_logloss: 0.61131\tvalid_1's binary_logloss: 0.626159\n", + "[5]\ttraining's 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0.426028\tvalid_1's binary_logloss: 0.457223\n", + "[89]\ttraining's binary_logloss: 0.433548\tvalid_1's binary_logloss: 0.461066\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", - "[90]\ttraining's binary_logloss: 0.423828\tvalid_1's binary_logloss: 0.455572\n", + "[90]\ttraining's binary_logloss: 0.429495\tvalid_1's binary_logloss: 0.458119\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", - "[91]\ttraining's binary_logloss: 0.425653\tvalid_1's binary_logloss: 0.456987\n", + "[91]\ttraining's binary_logloss: 0.431409\tvalid_1's binary_logloss: 0.459657\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", - "[92]\ttraining's binary_logloss: 0.421828\tvalid_1's binary_logloss: 0.454857\n", + "[92]\ttraining's binary_logloss: 0.429346\tvalid_1's binary_logloss: 0.458242\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", - "[93]\ttraining's binary_logloss: 0.424315\tvalid_1's binary_logloss: 0.456486\n", + "[93]\ttraining's binary_logloss: 0.431565\tvalid_1's binary_logloss: 0.460056\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", - "[94]\ttraining's binary_logloss: 0.426098\tvalid_1's binary_logloss: 0.457841\n", + "[94]\ttraining's binary_logloss: 0.433494\tvalid_1's binary_logloss: 0.461576\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", - "[95]\ttraining's binary_logloss: 0.428235\tvalid_1's binary_logloss: 0.459423\n", + "[95]\ttraining's binary_logloss: 0.435539\tvalid_1's binary_logloss: 0.463143\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", - "[96]\ttraining's binary_logloss: 0.430317\tvalid_1's binary_logloss: 0.460902\n", + "[96]\ttraining's binary_logloss: 0.43755\tvalid_1's binary_logloss: 0.464826\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", - "[97]\ttraining's binary_logloss: 0.426465\tvalid_1's binary_logloss: 0.457798\n", + "[97]\ttraining's binary_logloss: 0.434015\tvalid_1's binary_logloss: 0.462054\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", - "[98]\ttraining's binary_logloss: 0.4279\tvalid_1's binary_logloss: 0.459013\n", + "[98]\ttraining's binary_logloss: 0.435753\tvalid_1's binary_logloss: 0.463473\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", - "[99]\ttraining's binary_logloss: 0.429667\tvalid_1's binary_logloss: 0.460296\n", + "[99]\ttraining's binary_logloss: 0.437382\tvalid_1's binary_logloss: 0.464767\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", - "[100]\ttraining's binary_logloss: 0.425572\tvalid_1's binary_logloss: 0.45739\n" + "[100]\ttraining's binary_logloss: 0.432906\tvalid_1's binary_logloss: 0.461309\n" ] } ], @@ -614,22 +632,22 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 9, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] @@ -646,7 +664,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 9, "metadata": {}, "outputs": [ { @@ -658,411 +676,347 @@ "\n", "\n", - "\n", - "\n", + "\n", + "\n", "%3\n", - "\n", + "\n", "\n", "\n", "split0\n", - "\n", - "Sex_label\n", - " ≤ \n", - "0.000\n", - "0.000 value\n", - "count: 712\n", - "\n", - "\n", - "\n", - "split1\n", - "\n", - "Pclass\n", - " ≤ \n", - "2.500\n", - "0.014 value\n", - "count: 245\n", - "\n", - "\n", - "\n", - "split0->split1\n", - "\n", - "\n", - "yes\n", + "\n", + "Pclass\n", + " = \n", + "3\n", + "0.000 value\n", + "count: 712\n", "\n", "\n", - "\n", + "\n", "split3\n", - "\n", - "Fare\n", - " ≤ \n", - "26.269\n", - "-0.007 value\n", - "count: 467\n", + "\n", + "Age\n", + " ≤ \n", + "6.500\n", + "-0.005 value\n", + "count: 398\n", "\n", "\n", - "\n", + "\n", "split0->split3\n", - "\n", - "\n", - "no\n", + "\n", + "\n", + "yes\n", "\n", - "\n", - "\n", - "split10\n", - "\n", - "Fare\n", - " ≤ \n", - "126.825\n", - 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"split6->leaf7\n", + "\n", + "\n", + "no\n", "\n", - "\n", - "\n", - "leaf4\n", - "\n", - "leaf 4: \n", - "0.004\n", - "count: 28\n", + "\n", + "\n", + "leaf2\n", + "\n", + "leaf 2: \n", + "0.012\n", + "count: 44\n", "\n", - "\n", - "\n", - "split7->leaf4\n", - "\n", - "\n", - "yes\n", + "\n", + "\n", + "split7->leaf2\n", + "\n", + "\n", + "yes\n", "\n", "\n", - "\n", + "\n", "leaf8\n", - "\n", - "leaf 8: \n", - "-0.002\n", - "count: 58\n", + "\n", + "leaf 8: \n", + "0.020\n", + "count: 34\n", "\n", "\n", - "\n", + "\n", "split7->leaf8\n", - "\n", - "\n", - "no\n", + "\n", + "\n", + "no\n", "\n", "\n", "\n" ], "text/plain": [ - "" + "" ] }, - "execution_count": 10, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -1080,7 +1034,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ @@ -1090,7 +1044,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ @@ -1099,23 +1053,23 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "image/svg+xml": [ - "\n", - "\n", + "\n", + "\n", "G\n", - "\n", - "\n", + "\n", + "\n", "cluster_legend\n", "\n", "\n", "\n", - "node3\n", - "\n", + "node4\n", + "\n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1261,50 +1215,48 @@ " \n", " \n", " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -1312,15 +1264,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1328,54 +1280,53 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", " \n", + " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", "\n", "\n", - "\n", - "leaf4\n", - "\n", - "\n", + "\n", + "node7\n", + "\n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "\n", - "\n", - "\n", - "node3->leaf4\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "leaf5\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "\n", - "\n", - "\n", - "node3->leaf5\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "leaf6\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "\n", - "\n", - "\n", - "\n", - "node2\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1668,7 +1473,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1678,7 +1483,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1689,7 +1494,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1706,7 +1511,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1718,15 +1523,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1734,61 +1539,59 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", " \n", + " \n", " \n", - " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", - " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", "\n", "\n", - "\n", - "node2->node3\n", - "\n", - "\n", - "\n", "\n", - "\n", - "node7\n", - "\n", + "\n", + "leaf5\n", + "\n", + "\n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", + "\n", + "\n", + "\n", + "node4->leaf5\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "leaf6\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", + "\n", + "\n", + "\n", + "node4->leaf6\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "leaf8\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n", + "\n", + "\n", + "node7->leaf8\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "leaf9\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n", + "\n", + "\n", + "node7->leaf9\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "node3\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", " \n", - " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", + " \n", " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -1985,15 +2004,18 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -2001,62 +2023,52 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", "\n", "\n", + "\n", + "node3->node4\n", + "\n", + "\n", + "\n", "\n", - "\n", - "node2->leaf6\n", - "\n", - "\n", + "\n", + "node3->node7\n", + "\n", + "\n", "\n", "\n", - "\n", - "node8\n", - "\n", + "\n", + "node1\n", + "\n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -2261,18 +2263,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2280,37 +2279,39 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -2319,16 +2320,21 @@ " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", "\n", "\n", - "\n", - "leaf9\n", - "\n", - "\n", + "\n", + "node1->node3\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "node10\n", + "\n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "\n", - "\n", - "\n", - "node8->leaf9\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "leaf10\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "\n", - "\n", - "\n", - "node8->leaf10\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "leaf11\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "\n", - "\n", - "\n", - "\n", - "node7->node8\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "node7->leaf11\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "node1\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", " \n", - " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -2678,18 +2531,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2697,67 +2547,110 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", " \n", " \n", - " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", "\n", "\n", - "\n", - "node1->node2\n", - "\n", - "\n", - "\n", "\n", - "\n", - "node1->node7\n", - "\n", - "\n", + "\n", + "leaf2\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n", + "\n", + "\n", + "node1->leaf2\n", + "\n", + "\n", "\n", "\n", - "\n", - "node12\n", - "\n", + "\n", + "node13\n", + "\n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2904,7 +2797,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2914,7 +2807,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2925,7 +2818,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2942,7 +2835,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2954,15 +2847,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2970,54 +2863,57 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", " \n", - " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", "\n", "\n", - "\n", - "\n", - "node14\n", - "\n", + "\n", + "leaf14\n", + "\n", + "\n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", + "\n", + "\n", + "\n", + "node13->leaf14\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "leaf15\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n", + "\n", + "\n", + "node13->leaf15\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "node11\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3163,50 +3161,48 @@ " \n", " \n", " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -3214,15 +3210,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3230,54 +3226,58 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", "\n", "\n", - "\n", - "node17\n", - "\n", + "\n", + "node11->node13\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "node16\n", + "\n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3423,50 +3423,48 @@ " \n", " \n", " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -3474,15 +3472,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3490,60 +3488,58 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", " \n", - " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", "\n", "\n", "\n", - "\n", - "leaf15\n", - "\n", - "\n", + "\n", + "leaf12\n", + "\n", + "\n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -3588,15 +3582,14 @@ "\n", "\n", "\n", - "node14->leaf15\n", - "\n", - "\n", + "node11->leaf12\n", + "\n", + "\n", "\n", "\n", - "\n", - "leaf16\n", - "\n", - "\n", + "\n", + "node17\n", + "\n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", - " \n", - "\n", - "\n", - "\n", - "node14->leaf16\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "leaf18\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "\n", - "\n", - "\n", - "node17->leaf18\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "leaf19\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "\n", - "\n", - "\n", - "node17->leaf19\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "node13\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3896,50 +3735,48 @@ " \n", " \n", " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -3947,15 +3784,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3963,67 +3800,230 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", " \n", - " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", "\n", "\n", + "\n", + "leaf18\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n", + "\n", + "\n", + "node17->leaf18\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "leaf19\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n", + "\n", + "\n", + "node17->leaf19\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "leaf20\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n", + "\n", + "\n", "\n", - "node13->node14\n", - "\n", - "\n", + "node16->node17\n", + "\n", + "\n", "\n", "\n", "\n", - "node13->node17\n", - "\n", - "\n", + "node16->leaf20\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "node10->node11\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "node10->node16\n", + "\n", + "\n", "\n", "\n", "\n", - "node20\n", - "\n", + "node0\n", + "\n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4169,7 +4169,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4177,7 +4177,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4185,7 +4185,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4199,7 +4199,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4213,10 +4213,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4224,7 +4224,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4232,56 +4232,178 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", + " \n", " \n", - " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", "\n", "\n", + "\n", + "node0->node1\n", + "\n", + "\n", + "<\n", + "\n", "\n", - "\n", - "node21\n", - "\n", + "\n", + "node0->node10\n", + "\n", + "\n", + "≥\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "legend\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n", + "\n", + "" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "trees.dtreeviz(light_dtree)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Pclass 3.000\n", + "Sex_label 1.000\n", + "Embarked_label 2.000\n", + "Age 32.000\n", + "Cabin_label -1.000\n", + "Fare 7.925\n", + "Name: 382, dtype: float64\n" + ] + }, + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "G\n", + "\n", + "\n", + "cluster_legend\n", + "\n", + "\n", + "cluster_instance\n", + "\n", + "\n", + "\n", + "node4\n", + "\n", + "\n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4427,50 +4552,48 @@ " \n", " \n", " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -4478,15 +4601,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4494,54 +4617,64 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", " \n", + " \n", " \n", + " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", "\n", "\n", - "\n", - "leaf22\n", - "\n", - "\n", + "\n", + "leaf6\n", + "\n", + "\n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4584,16 +4718,16 @@ " \n", "\n", "\n", - "\n", - "node21->leaf22\n", - "\n", - "\n", + "\n", + "node4->leaf6\n", + "\n", + "\n", "\n", "\n", - "\n", - "leaf23\n", - "\n", - "\n", + "\n", + "node3\n", + "\n", + "\n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", - " \n", - "\n", - "\n", - "\n", - "node21->leaf23\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "leaf24\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "\n", - "\n", - "\n", - "\n", - "node20->node21\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "node20->leaf24\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "node12->node13\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "node12->node20\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "node0\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", - " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -4910,15 +4935,18 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -4926,191 +4954,47 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", "\n", "\n", - "\n", - "node0->node1\n", - "\n", - "\n", - "<\n", - "\n", - "\n", - "\n", - "node0->node12\n", - "\n", - "\n", - "≥\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "legend\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "\n", - "\n", - "" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "trees.dtreeviz(light_dtree)" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Pclass 3.000\n", - "Age 32.000\n", - "Fare 7.925\n", - "Sex_label 1.000\n", - "Cabin_label -1.000\n", - "Embarked_label 2.000\n", - "Name: 382, dtype: float64\n" - ] - }, - { - "data": { - "image/svg+xml": [ - "\n", - "\n", - "G\n", - "\n", - "\n", - "cluster_legend\n", - "\n", - "\n", - "cluster_instance\n", + "\n", + "node3->node4\n", + "\n", + "\n", "\n", "\n", - "\n", - "node17\n", - "\n", - "\n", + "\n", + "node1\n", + "\n", + "\n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5259,50 +5143,48 @@ " \n", " \n", " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -5310,15 +5192,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5326,123 +5208,72 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", "\n", "\n", - "\n", - "leaf18\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "\n", - "\n", - "\n", - "node17->leaf18\n", - "\n", - "\n", + "\n", + "node1->node3\n", + "\n", + "\n", "\n", "\n", - "\n", - "node13\n", - "\n", - "\n", + "\n", + "node0\n", + "\n", + "\n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -5642,15 +5466,18 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -5658,71 +5485,85 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", "\n", "\n", - "\n", - "node13->node17\n", - "\n", - "\n", + "\n", + "node0->node1\n", + "\n", + "\n", "\n", "\n", - "\n", - "node12\n", - "\n", - "\n", + "\n", + "X_y\n", + "\n", + "\n", + "Pclass\n", + "\n", + "Sex_label\n", + "\n", + "Embarked_label\n", + "\n", + "Age\n", + "\n", + "Cabin_label\n", + "\n", + "Fare\n", + "\n", + "3.00\n", + "\n", + "1.00\n", + "\n", + "2.00\n", + "\n", + "32.00\n", + "\n", + "-1.00\n", + "\n", + "7.92\n", + "\n", + "\n", + "\n", + "leaf6->X_y\n", + "\n", + "\n", + "  Prediction\n", + " 0\n", + "\n", + "\n", + "\n", + "legend\n", + "\n", + "\n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", 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\n", 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\n", 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" ] @@ -6423,14 +5670,14 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 17, "metadata": { "scrolled": true }, "outputs": [ { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "
" ] @@ -6447,7 +5694,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 18, "metadata": {}, "outputs": [ { @@ -6472,113 +5719,103 @@ "
\n", " \n", " Pclass\n", - " Age\n", - " Fare\n", " Sex_label\n", - " Cabin_label\n", " Embarked_label\n", + " Age\n", + " Cabin_label\n", + " Fare\n", "
\n", " \n", " \n", "
\n", " count\n", - " 52.000000\n", - " 52.000000\n", - " 52.000000\n", - " 52.0\n", - " 52.000000\n", - " 52.000000\n", + " 121.0\n", + " 121.000000\n", + " 121.0\n", + " 121.000000\n", + " 121.000000\n", + " 121.000000\n", "
\n", "
\n", " mean\n", - " 1.153846\n", - " 32.386488\n", - " 69.992229\n", - " 0.0\n", - " 53.942308\n", - " 1.057692\n", + " 3.0\n", + " 0.710744\n", + " 2.0\n", + " 20.417355\n", + " 3.520661\n", + " 12.499723\n", "
\n", "
\n", " std\n", - " 0.364321\n", - " 13.851534\n", - " 23.279059\n", " 0.0\n", - " 45.287064\n", - " 1.055582\n", + " 0.455302\n", + " 0.0\n", + " 4.735657\n", + " 24.624886\n", + " 10.342666\n", "
\n", "
\n", " min\n", - " 1.000000\n", - " 3.000000\n", - " 30.000000\n", - " 0.0\n", - " -1.000000\n", + " 3.0\n", + " 0.000000\n", + " 2.0\n", + " 7.000000\n", " -1.000000\n", + " 0.000000\n", "
\n", "
\n", " 25%\n", - " 1.000000\n", - " 22.750000\n", - " 53.100000\n", - " 0.0\n", - " 17.000000\n", + " 3.0\n", " 0.000000\n", + " 2.0\n", + " 18.000000\n", + " -1.000000\n", + " 7.795800\n", "
\n", "
\n", " 50%\n", + " 3.0\n", " 1.000000\n", - " 31.500000\n", - " 70.150000\n", - " 0.0\n", - " 47.000000\n", - " 2.000000\n", + " 2.0\n", + " 21.000000\n", + " -1.000000\n", + " 8.050000\n", "
\n", "
\n", " 75%\n", + " 3.0\n", " 1.000000\n", - " 39.000000\n", - " 83.237475\n", - " 0.0\n", - " 88.250000\n", - " 2.000000\n", + " 2.0\n", + " 24.000000\n", + " -1.000000\n", + " 10.170800\n", "
\n", "
\n", " max\n", - " 2.000000\n", - " 62.000000\n", - " 120.000000\n", - " 0.0\n", - " 144.000000\n", - " 2.000000\n", + " 3.0\n", + " 1.000000\n", + " 2.0\n", + " 27.000000\n", + " 145.000000\n", + " 56.495800\n", "
\n", " \n", "\n", "" ], "text/plain": [ - " Pclass Age Fare Sex_label Cabin_label \\\n", - "count 52.000000 52.000000 52.000000 52.0 52.000000 \n", - "mean 1.153846 32.386488 69.992229 0.0 53.942308 \n", - "std 0.364321 13.851534 23.279059 0.0 45.287064 \n", - "min 1.000000 3.000000 30.000000 0.0 -1.000000 \n", - "25% 1.000000 22.750000 53.100000 0.0 17.000000 \n", - "50% 1.000000 31.500000 70.150000 0.0 47.000000 \n", - "75% 1.000000 39.000000 83.237475 0.0 88.250000 \n", - "max 2.000000 62.000000 120.000000 0.0 144.000000 \n", - "\n", - " Embarked_label \n", - "count 52.000000 \n", - "mean 1.057692 \n", - "std 1.055582 \n", - "min -1.000000 \n", - "25% 0.000000 \n", - "50% 2.000000 \n", - "75% 2.000000 \n", - "max 2.000000 " + " Pclass Sex_label Embarked_label Age Cabin_label Fare\n", + "count 121.0 121.000000 121.0 121.000000 121.000000 121.000000\n", + "mean 3.0 0.710744 2.0 20.417355 3.520661 12.499723\n", + "std 0.0 0.455302 0.0 4.735657 24.624886 10.342666\n", + "min 3.0 0.000000 2.0 7.000000 -1.000000 0.000000\n", + "25% 3.0 0.000000 2.0 18.000000 -1.000000 7.795800\n", + "50% 3.0 1.000000 2.0 21.000000 -1.000000 8.050000\n", + "75% 3.0 1.000000 2.0 24.000000 -1.000000 10.170800\n", + "max 3.0 1.000000 2.0 27.000000 145.000000 56.495800" ] }, - "execution_count": 17, + "execution_count": 18, "metadata": {}, "output_type": "execute_result" } @@ -6589,22 +5826,22 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Pclass 2.0\n", - "Age 42.0\n", - "Fare 13.0\n", "Sex_label 0.0\n", - "Cabin_label -1.0\n", "Embarked_label 2.0\n", + "Age 42.0\n", + "Cabin_label -1.0\n", + "Fare 13.0\n", "Name: 865, dtype: float64" ] }, - "execution_count": 18, + "execution_count": 19, "metadata": {}, "output_type": "execute_result" } @@ -6616,16 +5853,16 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 20, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Pclass < 2.5\n", - "Fare < 29.85\n", - "0.0 <= Sex_label \n", + "22.5 <= Age \n", + "12.32 <= Fare < 52.28\n", + "Pclass in [] \n", "\n" ] } @@ -6650,17 +5887,17 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 21, "metadata": {}, "outputs": [], "source": [ - "features_reg = [\"Pclass\", \"Survived\", \"Fare\", \"Sex_label\", \"Cabin_label\", \"Embarked_label\"]\n", + "features_reg = [\"Pclass\", \"Survived\", \"Sex_label\", \"Cabin_label\", \"Embarked_label\"]\n", "target_reg = \"Age\"\n", "\n", "X_train_reg, X_test_reg, y_train_reg, y_test_reg = train_test_split(dataset[features_reg], dataset[target_reg], test_size=0.2, random_state=42)\n", "\n", - "train_data_reg = lgb.Dataset(data=X_train_reg, label=y_train_reg)\n", - "valid_data_reg = lgb.Dataset(data=X_test_reg, label=y_test_reg)\n", + "train_data_reg = lgb.Dataset(data=X_train_reg, label=y_train_reg, categorical_feature=[\"Pclass\", \"Survived\", \"Sex_label\", \"Embarked_label\", \"Cabin_label\"])\n", + "valid_data_reg = lgb.Dataset(data=X_test_reg, label=y_test_reg, categorical_feature=[\"Pclass\", \"Survived\", \"Sex_label\", \"Embarked_label\", \"Cabin_label\"])\n", "\n", "lgbm_params = {\n", " 'num_tree':10,\n", @@ -6684,42 +5921,39 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 22, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000612 seconds.\n", + "[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000597 seconds.\n", "You can set `force_row_wise=true` to remove the overhead.\n", "And if memory is not enough, you can set `force_col_wise=true`.\n", - "[LightGBM] [Info] Total Bins 175\n", - "[LightGBM] [Info] Number of data points in the train set: 712, number of used features: 6\n", + "[LightGBM] [Info] Total Bins 21\n", + "[LightGBM] [Info] Number of data points in the train set: 712, number of used features: 5\n", "[LightGBM] [Info] Start training from score 29.699118\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", - "[1]\ttraining's l1: 9.03156\tvalid_1's l1: 9.05193\n", + "[1]\ttraining's l1: 9.05112\tvalid_1's l1: 9.06394\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", - "[2]\ttraining's l1: 9.00275\tvalid_1's l1: 9.03197\n", + "[2]\ttraining's l1: 9.027\tvalid_1's l1: 9.05478\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", - "[3]\ttraining's l1: 8.96288\tvalid_1's l1: 9.01286\n", + "[3]\ttraining's l1: 9.00439\tvalid_1's l1: 9.04684\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", - "[4]\ttraining's l1: 8.96182\tvalid_1's l1: 9.01891\n", + "[4]\ttraining's l1: 9.00495\tvalid_1's l1: 9.04704\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", - "[5]\ttraining's l1: 8.92147\tvalid_1's l1: 9.01404\n", + "[5]\ttraining's l1: 9.00016\tvalid_1's l1: 9.05159\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", - "[6]\ttraining's l1: 8.90348\tvalid_1's l1: 8.99877\n", + "[6]\ttraining's l1: 8.97761\tvalid_1's l1: 9.03943\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", - "[7]\ttraining's l1: 8.86911\tvalid_1's l1: 8.97774\n", + "[7]\ttraining's l1: 8.96441\tvalid_1's l1: 9.03242\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", - "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n", - "[8]\ttraining's l1: 29.4816\tvalid_1's l1: 8.97774\n", + "[8]\ttraining's l1: 15.6318\tvalid_1's l1: 15.6257\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", - "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n", - "[9]\ttraining's l1: 29.4939\tvalid_1's l1: 8.97774\n", + "[9]\ttraining's l1: 15.3672\tvalid_1's l1: 15.359\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", - "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n", - "[10]\ttraining's l1: 29.5271\tvalid_1's l1: 8.97774\n" + "[10]\ttraining's l1: 15.1814\tvalid_1's l1: 15.1653\n" ] }, { @@ -6727,7 +5961,13 @@ "output_type": "stream", "text": [ "/Users/tudorl/Documents/workspace/personal/dtreeviz/venv/lib/python3.7/site-packages/lightgbm/engine.py:151: UserWarning: Found `num_tree` in params. Will use it instead of argument\n", - " warnings.warn(\"Found `{}` in params. Will use it instead of argument\".format(alias))\n" + " warnings.warn(\"Found `{}` in params. Will use it instead of argument\".format(alias))\n", + "/Users/tudorl/Documents/workspace/personal/dtreeviz/venv/lib/python3.7/site-packages/lightgbm/basic.py:1551: UserWarning: Using categorical_feature in Dataset.\n", + " warnings.warn('Using categorical_feature in Dataset.')\n", + "/Users/tudorl/Documents/workspace/personal/dtreeviz/venv/lib/python3.7/site-packages/lightgbm/basic.py:1286: UserWarning: Overriding the parameters from Reference Dataset.\n", + " warnings.warn('Overriding the parameters from Reference Dataset.')\n", + "/Users/tudorl/Documents/workspace/personal/dtreeviz/venv/lib/python3.7/site-packages/lightgbm/basic.py:1098: UserWarning: categorical_column in param dict is overridden.\n", + " warnings.warn('{} in param dict is overridden.'.format(cat_alias))\n" ] } ], @@ -6744,22 +5984,22 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 22, + "execution_count": 23, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", + "image/png": 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cc845zJ49m0WLFnHGGWcAsHDhQs4880yeeOIJNtlkkw5OXrn6/GXVlvzV6kq9K/2eDfSKiPeB9yV9KKlPWvZERLwGIGkSsDdFYTwuFbiuwECKYlZfiOoLb71/A26JiEvT54OAYdn9w97A9sA+wKSIWAn8WdKDrTjP5vJMyn5fmaa/AuyU3U/YUFKvVhzPzEomIjj11FPZcccdOeeccwAYOnQob7/99sfrDBo0iKeeesqjUjuBigbfSNo2tbDqB8aclRWwtvgw/V6VTdd/ri/W0WCbkLQ1cB5wQEQMA+6haGnWW9pgm0eA/STVryPgzIgYnn62joj723oSFeSJRqbXA76cZdgsIpa0NYOZdX7Tp09n4sSJPPjggwwfPpzhw4dz7733VjuWNaHSUam3AyslbUfRpbcFcFOHpSrsIWnrdI/weOBPwIYUxW+xpAEU9ymbcwNwL3CLpK4U3Zb/JKkbgKQvSOoJ/BE4XlIXSQOB/SrM2FKe47Pfj6bp+4Ez61eQVJUBSma25uy9995EBLNmzWLGjBnMmDGDQw455BPr1NXVubXYSVTalboqIlZI+hpwdURcLenZFrZpeI9xakRU/JUN4EngGlYPvvldGnzzLPASxSCZZgetAETEzyX1BiYCI4FBwDNpwM07wFHA74D9Ke4t/jeri1hL+57ZQp6NJM2iaBWfmOadBfwyze9KUZRPr+R4AD26dWFOhV8q7mxqa2sruifTWZU5f5mzQ/nzW7lUWhiXSzoROBk4PM3r1twGEdHoVyYiYlA2PZ5i8E3DZbUU9/0a235US/tNn2uy6Xyk6HfTT0NnNLbfJo6V77ulPBc0mL+Q1S3JfP54smthZmbVUWlX6inACODSiHg93Vub2HGxzMzMqqOiFmNEvCDpAtJ37yLideCnHRmsM5D0O2DrBrMviIj7qpHHzMw6XkWFUdLhwOXA54Ct04CRH0bEER0Zrtoi4mvVzmBmZmtWpV2pYym+cP8eQETMALbpoExmZmZVU2lhXB4RixvMW9XeYczMzKqt0lGpz0v6OtBF0vYUXzl4pONimZmZVUelLcYzgZ0pvo93E7AY+HZHhTIzM6uWFluM6eHY90TEfsD3Oj6SmZlZ9bTYYkwP1l6Vnh5jZma2Vqv0HuMSYLakaWQP6o6IszoklZmZWZVUWhjvSD9mZmZrtUqffDOho4OYmZl1BpU++eZ1Pv1+RCLCX/I3M7O1SqVdqbtn092BY4G+7R/HzMysuir6HmNELMp+5kXEL4ByvhTQzMysGZV2pe6afVyPogVZaWvTzMysNCotbldk0yuA14Hj2j+OmZlZdVVaGE+NiNfyGellxWZmZmuVSp+VeluF88zMzEqt2RajpB0oHh7eW9LR2aINKUanmpmZrVVa6kodDBwG9AEOz+a/D3yzo0KZmZlVS7OFMSKmAFMkjYiIR9dQJjMzs6qpdPDNs5K+RdGt+nEXakSM7pBUZmZmVVLp4JuJwCbA3wJ/ADan6E41MzNbq1RaGLeLiAuBpemB4ocCe3ZcLDMzs+qotDAuT7/fkzQE6A1s3DGRzMzMqqfSe4zXSdoIuBC4C+gFXNRhqczMzKqk0vcxXp8m/wD4VVNmZrbWqqgrVdIASTdI+n/p806STu3YaGZmZmtepfcYxwP3AZumzy8D3+6IQGZmZtVUaWHsFxG3AKsAImIFsLLDUpmZmVVJpYVxqaTPAwEg6cvA4g5LZWZmViWVjko9h2I06raSpgP9gWM6LJU1adnylQwac0+1Y7TJuUNXMKqk2aHc+SvNXjfu0DWQxqxza7bFKGlLgIh4BtgX2Av4R2DniJjV8fHMrLMaPXo0G2+8MUOGDPnE/KuvvpoddtiBnXfemfPPP79K6czarqWu1Duz6ckR8XxEPBcRy5vcYi0iaaWkGZKek3SrpL9qZt2xks5bk/nMqmnUqFFMnTr1E/MeeughpkyZwsyZM3n++ec57zz/L2Hl01JhVDa9Ln5/cVlEDI+IIcBHwOnVDmTWWeyzzz707dv3E/OuvfZaxowZw/rrrw/Axhv7AVlWPi0Vxmhiel30MLAdgKSTJM2SNFPSxIYrSvqmpCfT8tvrW5qSjk2tz5mS/pjm7SzpidQynSVp+zV6Vmbt6OWXX+bhhx9mzz33ZN999+XJJ5+sdiSzVmtp8M0ukv5C0XLskaZJnyMiNuzQdJ2EpK7AV4GpknYGvg/sFRELJfVtZJM7IuLXadtLgFOBqykeo/e3ETFPUp+07unAVRHxW0mfA7o0cvzTgNMA+vXrz0VDV7TzGa4ZA3oUg0DKqsz5K81eW1vbqv2+9dZbLF269OPtFi9ezOzZsxk3bhwvvfQSRxxxBDfddBOSmt9RC5YsWdLqbJ1FmbPDupm/pRcVf+qP9Dqmh6QZafph4AaKwUe3RsRCgIh4t5HthqSC2IfiubL3pfnTgfGSbgHuSPMeBb4naXOKgvpKw51FxHXAdQBbbrNdXDG70sHEncu5Q1dQ1uxQ7vyVZq8bWdOq/dbV1dGzZ09qaortBg8ezJlnnsl+++3Hfvvtx+WXX86QIUPo379/G1KvVltb+/ExyqbM2WHdzF/p9xjXVfX3GIdHxJkR8VGF240HzoiIocAPSC93jojTKVqbWwBPS/p8RNwEHAEsA+6VtH+7n4XZGnLUUUfx0EMPAUW36kcffUS/fv2qnMqsdVwYW+9B4Nj0wAOa6ErdAJgvqRswsn6mpG0j4vGIuAh4B9hC0jbAaxHxf4EpwLAOPwOzdnDiiScyYsQI5syZw+abb84NN9zA6NGjee211xgyZAgnnHACEyZM+MzdqGZrWjn7haooIp6XdCnwB0krgWeBUQ1WuxB4nKL4PU5RKAF+lgbXCHgAmAlcAPyDpOXAW8CPmzt+j25dmFPSL2HX1ta2uquuMylz/o7IPmnSpEbn33jjje16HLM1zYWxGRHRq4n5E4AJDeaNzaavBa5tZLujG9nduPRjZmadgLtSzczMMi6MZmZmGRdGMzOzjAujmZlZxoXRzMws48JoZmaWcWE0MzPLuDCamZllXBjNzMwyLoxmZmYZF0YzM7OMC6OZmVnGhdHMzCzjwmhmZpZxYTQzM8u4MJqZmWVcGM3MzDIujGZmZhkXRjMzs4wLo5mZWcaF0czMLOPCaGZmlnFhNDMzy7gwmpmZZVwYzczMMi6MZmZmGRdGMzOzjAujmZlZxoXRzMws48JoZmaWcWE0MzPLuDCamZllXBjNzMwyXasdwFpn2fKVDBpzT7VjtMm5Q1cwqqTZAcYf3LOi9UaPHs3dd9/NxhtvzHPPPQfAu+++y/HHH09dXR2DBg3illtuYaONNurIuGbWRutMi1HS9yQ9L2mWpBmS9myHfR4haUw75VvSHvux6hs1ahRTp079xLxx48ZxwAEH8Morr3DAAQcwbty4KqUzs5asE4VR0gjgMGDXiBgGfAWYW+G2TbaqI+KuiPBfOPuEffbZh759+35i3pQpUzj55JMBOPnkk7nzzjurEc3MKrBOFEZgILAwIj4EiIiFEfFnSXWS+gFI2l1SbZoeK2mipOnAREmPSdq5fmeSatP6oyRdI6m3pDckrZeW95Q0V1I3SdtKmirpaUkPS9ohrbO1pEclzZZ0yRq+HraGLViwgIEDBwKwySabsGDBgionMrOmrCv3GO8HLpL0MvB7YHJE/KGFbXYC9o6IZZK+AxwHXCxpIDAwIp6SNAQgIhZLmgHsCzxE0Tq9LyKWS7oOOD0iXkndt/8K7A9cBVwbEf8h6VvNBZF0GnAaQL9+/blo6Iq2XYUqG9CjuM9YVkuWLKG2traidd966y2WLl368forVqz4xLYrV66seF/toTXZO6My5y9zdlg3868ThTEilkjaDfgbYD9gcgX3Bu+KiGVp+haK4noxRYG8rZH1JwPHUxTGE4B/ldQL2Au4VVL9euun338N/F2angj8tJn81wHXAWy5zXZxxexy/mc7d+gKypodisE3NTU1Fa1bV1dHz56r199ss80YPHgwAwcOZP78+Wy66aYV76s91NbWrtHjtbcy5y9zdlg3868rXalExMqIqI2Ii4EzKIrSClZfg+4NNlmabTsPWCRpGEXxm9zIIe4CDpbUF9gNeDDt+72IGJ797JjHao9zs87viCOOYMKECQBMmDCBI488ssqJzKwp60RhlDRY0vbZrOHAG0AdRRGD1a23pkwGzgd6R8SshgsjYgnwJEUX6d2pEP8FeF3SsSmHJO2SNplO0bIEGNn6s7LO6sQTT2TEiBHMmTOHzTffnBtuuIExY8Ywbdo0tt9+e37/+98zZky7DGY2sw5Q3n6t1ukFXC2pD0Ur8VWKe3Y7AjdI+hFQ28I+bqMoej9qZp3JwK1ATTZvJHCtpO8D3YCbgZnA2cBNki4AprTyfKwTmzRpUqPzH3jggTWcxMzaYp0ojBHxNMW9voYeBr7QyPpjG5m3gAbXKyLGA+Ozz7cBarDO68DBjezvdWBENuv7TZ/Baj26dWHOuEMrWbXTqa2tpW5kTbVjtFmZByCYWeXWia5UMzOzSrkwmpmZZVwYzczMMi6MZmZmGRdGMzOzjAujmZlZxoXRzMws48JoZmaWcWE0MzPLuDCamZllXBjNzMwyLoxmZmYZF0YzM7OMC6OZmVnGhdHMzCzjwmhmZpZxYTQzM8u4MJqZmWVcGM3MzDIujGZmZhkXRjMzs4wLo5mZWcaF0czMLOPCaGZmlnFhNDMzy7gwmpmZZVwYzczMMi6MZmZmGRdGMzOzjAujmZlZxoXRzMws48JoZmaWcWE0MzPLuDCamZllXBjNzMwyLoxmZmYZF0YzM7OMC6OZmVlGEVHtDNYKkt4H5lQ7Rxv1AxZWO8RnUOb8Zc4O5c5f5uyw9uTfKiL6V7JB147NYx1gTkTsXu0QbSHpqbJmh3LnL3N2KHf+MmeHdTO/u1LNzMwyLoxmZmYZF8byua7aAT6DMmeHcucvc3Yod/4yZ4d1ML8H35iZmWXcYjQzM8u4MJqZmWVcGEtC0sGS5kh6VdKYaudpLUl1kmZLmiHpqWrnaYmk30h6W9Jz2by+kqZJeiX93qiaGZvSRPaxkual6z9D0iHVzNgUSVtIekjSC5Kel3R2ml+Wa99U/rJc/+6SnpA0M+X/QZq/taTH09+fyZI+V+2sDTWTfbyk17NrP7zFffkeY+cnqQvwMnAg8CbwJHBiRLxQ1WCtIKkO2D0iSvFFYUn7AEuA/4iIIWneZcC7ETEu/eNko4i4oJo5G9NE9rHAkoi4vJrZWiJpIDAwIp6RtAHwNHAUMIpyXPum8h9HOa6/gJ4RsURSN+BPwNnAOcAdEXGzpF8BMyPi2mpmbaiZ7KcDd0fEbZXuyy3GctgDeDUiXouIj4CbgSOrnGmtFhF/BN5tMPtIYEKankDxB6/TaSJ7KUTE/Ih4Jk2/D7wIbEZ5rn1T+UshCkvSx27pJ4D9gfrC0imvfzPZW82FsRw2A+Zmn9+kRP+zJQHcL+lpSadVO0wbDYiI+Wn6LWBANcO0wRmSZqWu1k7ZFZmTNAj4IvA4Jbz2DfJDSa6/pC6SZgBvA9OA/wLei4gVaZVO+/enYfaIqL/2l6Zrf6Wk9VvajwujrSl7R8SuwFeBb6XuvtKK4h5Eme5DXAtsCwwH5gNXVDdO8yT1Am4Hvh0Rf8mXleHaN5K/NNc/IlZGxHBgc4reqh2qHKliDbNLGgL8H4pz+BLQF2ixC96FsRzmAVtknzdP80ojIual328Dv6P4H65sFqR7SPX3kt6ucp6KRcSC9EdjFfBrOvH1T/eHbgd+GxF3pNmlufaN5S/T9a8XEe8BDwEjgD6S6p+t3en//mTZD07d2xERHwL/TgXX3oWxHJ4Etk8jwz4HnADcVeVMFZPUMw1EQFJP4CDguea36pTuAk5O0ycDU6qYpVXqi0ryNTrp9U8DKG4AXoyIn2eLSnHtm8pfouvfX1KfNN2DYsDfixRF5pi0Wqe8/k1kfyn7B5Uo7o22eO09KrUk0vDuXwBdgN9ExKVVjlQxSdtQtBKheKPLTZ09v6RJQA3FK2sWABcDdwK3AFsCbwDHRUSnG+TSRPYaim68AOqAf8zu2XUakvYGHgZmA6vS7O9S3Kcrw7VvKhJ7JR8AAAPsSURBVP+JlOP6D6MYXNOFouF0S0T8MP0/fDNFV+SzwN+nFlin0Uz2B4H+gIAZwOnZIJ3G9+XCaGZmtpq7Us3MzDIujGZmZhkXRjMzs4wLo5mZWcaF0czMLOPCaNZJSFqZvQFgRnqkWGv3cZSkndo/HUjaVFLFD2Jup2MO76xvorC1V9eWVzGzNWRZepzVZ3EUcDdQ8ZtXJHXNnoPZpIj4M6u/5N3h0pNWhgO7A/euqeOaucVo1olJ2k3SH9LD1+/LnuLxTUlPpnfP3S7pryTtBRwB/Cy1OLeVVCtp97RNv/T6LySNknRX+vLzA+npRL9J77N7VtKn3t4iaZDSOx7T9neqeDdinaQzJJ2Ttn1MUt+0Xq2kq1Ke5yTtkeb3TdvPSusPS/PHSpooaTowEfghcHza/nhJe0h6NB3nEUmDszx3SJqq4p2Nl2W5D5b0TLpWD6R5LZ6vrcMiwj/+8U8n+AFWUjyZYwbFk4K6AY8A/dPy4ymeegTw+Wy7S4Az0/R44JhsWS3FezCheBJOXZoeRfGWhL7p848pnmYC0Ifi/Z89G+QbBDyXbf8qsAHFU0UWUzxRBOBKiodn1x//12l6n2z7q4GL0/T+wIw0PZbiHYY9suNck2XYEOiapr8C3J6t9xrQG+hO8XScLVK2ucDWab2Kz9c/6+6Pu1LNOo9PdKWmNwMMAaYVj3mkC8WbGQCGSLqE4o96L+C+NhxvWqx+rNpBwBGSzkufu1M8fu3FZrZ/KIp3Dr4vaTHwn2n+bGBYtt4kKN4TKWnD9DzLvYG/S/MflPR5SRum9e+KiGVNHLM3MEHS9hSPV+uWLXsgIhYDSHoB2ArYCPhjRLyejvVZztfWES6MZp2XgOcjYkQjy8YDR0XETEmjKJ6F2pgVrL5l0r3BsqUNjvV3ETGnFfnyZ2Wuyj6v4pN/Wxo+d7Kl51AubWbZjygK8tfS4KTaJvKspPm/b205X1tH+B6jWec1B+gvaQQUrzOStHNatgEwX8UrjkZm27yfltWrA3ZL080NnLkPODO9gQBJX/zs8T92fNrn3sDi1Kp7mJRbUg2wMBq8dzFpeD69Wf3Ko1EVHPsxYB9JW6dj9U3zO/J8reRcGM06qYj4iKKY/VTSTIp7j3ulxRdSvHFiOvBSttnNwL+kASXbApcD/yTpWYp7jE35EUW35CxJz6fP7eWDdPxfAaemeWOB3STNAsax+pVSDT0E7FQ/+Aa4DPhJ2l+LPV4R8Q5wGnBHuoaT06KOPF8rOb9dw8w6jKRa4LyIeKraWcwq5RajmZlZxi1GMzOzjFuMZmZmGRdGMzOzjAujmZlZxoXRzMws48JoZmaW+f8atN6Mb+ShIAAAAABJRU5ErkJggg==\n", 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" ] @@ -6776,7 +6016,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 24, "metadata": {}, "outputs": [ { @@ -6788,347 +6028,315 @@ "\n", "\n", - "\n", - "\n", + "\n", + "\n", "%3\n", - "\n", + "\n", "\n", "\n", "split0\n", - "\n", - "Pclass\n", - " ≤ \n", - "1.500\n", - "0.000 value\n", - "count: 712\n", + "\n", + "Pclass\n", + " = \n", + "1\n", + "0.000 value\n", + "count: 712\n", "\n", - "\n", + "\n", "\n", + "split4\n", + "\n", + "Embarked_label\n", + " = \n", + "0\n", + "0.005 value\n", + "count: 163\n", + "\n", + "\n", + "\n", + "split0->split4\n", + "\n", + "\n", + "yes\n", + "\n", + "\n", + "\n", "split1\n", - "\n", - "Fare\n", - " ≤ \n", - "26.775\n", - "0.005 value\n", - "count: 163\n", + "\n", + "Embarked_label\n", + " = \n", + "1\n", + "-0.003 value\n", + "count: 549\n", "\n", "\n", - "\n", + "\n", "split0->split1\n", - "\n", - "\n", - "yes\n", + "\n", + "\n", + "no\n", "\n", - "\n", - "\n", - "split2\n", - "\n", - "Embarked_label\n", - " ≤ \n", - "0.000\n", - "-0.003 value\n", - "count: 549\n", + "\n", + "\n", + "split5\n", + "\n", + "Sex_label\n", + " = \n", + "1\n", + "0.003 value\n", + "count: 57\n", + "\n", + "\n", + "\n", + "split4->split5\n", + "\n", + "\n", + "yes\n", "\n", - "\n", - "\n", - "split0->split2\n", - "\n", - "\n", - "no\n", + "\n", + "\n", + "split8\n", + "\n", + "Sex_label\n", + " = \n", + "1\n", + "0.007 value\n", + "count: 106\n", + "\n", + "\n", + "\n", + "split4->split8\n", + "\n", + "\n", + "no\n", "\n", "\n", - "\n", + "\n", "leaf0\n", - "\n", - "leaf 0: \n", - "0.364\n", - "count: 22\n", + "\n", + "leaf 0: \n", + "0.024\n", + "count: 29\n", "\n", - "\n", + "\n", "\n", - "split1->leaf0\n", - "\n", - "\n", - "yes\n", - "\n", - "\n", - "\n", - "split7\n", - "\n", - "Fare\n", - " ≤ \n", - "90.540\n", - "0.003 value\n", - "count: 141\n", - "\n", - "\n", - "\n", - "split1->split7\n", - "\n", - "\n", - "no\n", + "split5->leaf0\n", + "\n", + "\n", + "yes\n", "\n", - "\n", + "\n", "\n", - "split8\n", - "\n", 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"\n", "leaf4\n", - "\n", - "leaf 4: \n", - "-0.042\n", - "count: 226\n", + "\n", + "leaf 4: \n", + "-0.000\n", + "count: 90\n", "\n", - "\n", - "\n", - "split4->leaf4\n", - "\n", - "\n", - "yes\n", + "\n", + "\n", + "split6->leaf4\n", + "\n", + "\n", + "yes\n", "\n", - "\n", - "\n", - "leaf5\n", - "\n", - "leaf 5: \n", - "0.001\n", - "count: 197\n", + "\n", + "\n", + "leaf7\n", + "\n", + "leaf 7: \n", + "-0.040\n", + "count: 58\n", "\n", - "\n", - "\n", - "split4->leaf5\n", - "\n", - "\n", - "no\n", + "\n", + "\n", + "split6->leaf7\n", + "\n", + "\n", + "no\n", "\n", "\n", "\n" ], "text/plain": [ - "" + "" ] }, - "execution_count": 23, + "execution_count": 24, "metadata": {}, "output_type": "execute_result" } @@ -7146,7 +6354,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 32, "metadata": {}, "outputs": [], "source": [ @@ -7155,303 +6363,20 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 33, "metadata": {}, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/tudorl/Documents/workspace/personal/dtreeviz/venv/lib/python3.7/site-packages/numpy/core/fromnumeric.py:3335: RuntimeWarning: Mean of empty slice.\n", - " out=out, **kwargs)\n", - "/Users/tudorl/Documents/workspace/personal/dtreeviz/venv/lib/python3.7/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars\n", - " ret = ret.dtype.type(ret / rcount)\n" - ] - }, { "data": { "image/svg+xml": [ - "\n", + "\n", "\n", "G\n", - "\n", + "\n", "\n", "\n", - "node4\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - 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- "node7\n", - "\n", + "node2\n", + "\n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7467,136 +6392,141 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", " \n", - " \n", - " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -7604,14 +6534,18 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -7621,7 +6555,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7635,7 +6569,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7650,15 +6584,6 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -7668,17 +6593,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", "\n", "\n", - "\n", - "\n", - "leaf5\n", - "\n", - "\n", + "\n", + "node5\n", + "\n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - 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- " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", "\n", "\n", - "\n", - "node4->leaf6\n", - "\n", - "\n", - "\n", "\n", - "\n", - "leaf8\n", - "\n", - "\n", + "\n", + "leaf3\n", + "\n", + "\n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -8100,10 +6932,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8121,7 +6953,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8138,7 +6970,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8154,7 +6986,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8166,53 +6998,55 @@ " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", "\n", "\n", - "\n", - "node7->leaf8\n", - "\n", - "\n", + "\n", + "node2->leaf3\n", + "\n", + "\n", "\n", "\n", - "\n", - "leaf9\n", - "\n", - "\n", + "\n", + "leaf4\n", + "\n", + "\n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -8260,10 +7100,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8281,7 +7121,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8298,7 +7138,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8314,7 +7154,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8327,9 +7167,10 @@ " \n", " \n", " \n", + " \n", " \n", - " \n", " \n", + " \n", " \n", " \n", " \n", @@ -8337,41 +7178,42 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", "\n", "\n", - "\n", - "node7->leaf9\n", - "\n", - "\n", + "\n", + "node2->leaf4\n", + "\n", + "\n", "\n", "\n", - "\n", - "node3\n", - "\n", + "\n", + "leaf6\n", + "\n", + "\n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " 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\n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9634,7 +7808,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9650,7 +7824,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9699,10 +7873,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9719,7 +7893,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9733,7 +7907,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9748,15 +7922,6 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -9766,17 +7931,27 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", "\n", "\n", + "\n", + "node1->node2\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "node1->node5\n", + "\n", + "\n", + "\n", "\n", - "\n", - "leaf2\n", - "\n", - "\n", + "\n", + "node8\n", + "\n", " \n", " \n", " \n", " \n", - " \n", + " 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\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", "\n", "\n", - "\n", - "node1->leaf2\n", - "\n", - "\n", - "\n", "\n", - "\n", - "node11\n", - "\n", + "\n", + "node9\n", + "\n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9951,156 +8697,161 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", " \n", - " \n", - " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -10108,12 +8859,12 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -10122,12 +8873,12 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -10139,15 +8890,6 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", 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\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", " \n", - " \n", - " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -10768,14 +9494,18 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -10785,11 +9515,14 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -10799,7 +9532,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -10814,15 +9547,6 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -10832,17 +9556,17 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", "\n", "\n", "\n", - "\n", - "leaf12\n", - "\n", - "\n", + "\n", + "leaf10\n", + "\n", + "\n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -10900,10 +9626,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -10921,7 +9647,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -10938,7 +9664,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -10954,7 +9680,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -10967,10 +9693,8 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -10978,42 +9702,43 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", "\n", "\n", - "\n", - "node11->leaf12\n", - "\n", - "\n", + "\n", + "node9->leaf10\n", + "\n", + "\n", "\n", "\n", - "\n", - "leaf13\n", - "\n", - "\n", + "\n", + "leaf11\n", + "\n", + "\n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -11075,10 +9788,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -11096,7 +9809,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -11113,7 +9826,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -11129,7 +9842,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -11142,10 +9855,10 @@ " \n", " \n", " \n", - " \n", - " \n", " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -11153,42 +9866,41 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", "\n", "\n", - "\n", - "node11->leaf13\n", - "\n", - "\n", + "\n", + "node9->leaf11\n", + "\n", + "\n", "\n", "\n", - "\n", - "node15\n", - "\n", + "\n", + "node13\n", + "\n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -11293,7 +10290,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -11306,7 +10303,20 @@ " \n", " \n", " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -11332,15 +10342,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -11353,12 +10363,12 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -11367,12 +10377,12 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -11384,15 +10394,6 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -11402,15 +10403,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", "\n", "\n", - "\n", - "node18\n", - "\n", + "\n", + "node16\n", + "\n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -11426,511 +10427,251 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", " \n", - " \n", - " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -11938,12 +10679,12 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -11952,12 +10693,12 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -11969,15 +10710,6 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -11987,17 +10719,17 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", "\n", "\n", "\n", - "\n", - "leaf16\n", - "\n", - "\n", + "\n", + "leaf14\n", + "\n", + "\n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -12047,10 +11005,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -12068,7 +11026,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -12085,7 +11043,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -12101,7 +11059,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -12116,8 +11074,8 @@ " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -12127,40 +11085,42 @@ " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", "\n", "\n", - "\n", - "node15->leaf16\n", - "\n", - "\n", + "\n", + "node13->leaf14\n", + "\n", + "\n", "\n", "\n", - "\n", - "leaf17\n", - "\n", - "\n", + "\n", + "leaf15\n", + "\n", + "\n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -12220,10 +11239,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -12241,7 +11260,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -12258,7 +11277,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -12274,7 +11293,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -12287,8 +11306,9 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", + " \n", " \n", " \n", " \n", @@ -12296,43 +11316,42 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", "\n", "\n", - "\n", - "node15->leaf17\n", - "\n", - "\n", + "\n", + "node13->leaf15\n", + "\n", + "\n", "\n", "\n", - "\n", - "leaf19\n", - "\n", - "\n", + "\n", + "leaf17\n", + "\n", + "\n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -12584,10 +11467,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -12605,7 +11488,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -12622,7 +11505,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -12638,7 +11521,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -12650,55 +11533,54 @@ " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", "\n", "\n", - "\n", - "node18->leaf19\n", - "\n", - "\n", + "\n", + "node16->leaf17\n", + "\n", + "\n", "\n", "\n", - "\n", - "leaf20\n", - "\n", - "\n", + "\n", + "leaf18\n", + "\n", + "\n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -12921,10 +11664,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -12942,7 +11685,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -12959,7 +11702,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -12975,7 +11718,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -12988,9 +11731,9 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -12998,67 +11741,65 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", "\n", "\n", - "\n", - "node18->leaf20\n", - "\n", - "\n", + "\n", + "node16->leaf18\n", + "\n", + "\n", "\n", "\n", - "\n", - "node14->node15\n", - "\n", - "\n", + "\n", + "node12->node13\n", + "\n", + "\n", "\n", "\n", - "\n", - "node14->node18\n", - "\n", - "\n", + "\n", + "node12->node16\n", + "\n", + "\n", "\n", "\n", - "\n", - "node10->node11\n", - "\n", - "\n", + "\n", + "node8->node9\n", + "\n", + "\n", "\n", "\n", - "\n", - "node10->node14\n", - "\n", - "\n", + "\n", + "node8->node12\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "node0\n", - "\n", + "\n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -13074,754 +11815,750 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " 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\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -13829,28 +12566,21 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -13877,16 +12607,17 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", + " \n", " \n", " \n", " \n", @@ -13897,11 +12628,14 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -13911,7 +12645,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -13939,15 +12673,6 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -13957,39 +12682,38 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", "\n", "\n", - "\n", + "\n", "node0->node1\n", - "\n", - "\n", - "<\n", + "\n", + "\n", + "<\n", "\n", "\n", - "\n", - "node0->node10\n", - "\n", - "\n", - "≥\n", + "\n", + "node0->node8\n", + "\n", + "\n", + "≥\n", "\n", "\n", "\n", "\n", "\n", "\n", - "\n", "\n", "" ], "text/plain": [ - "" + "" ] }, - "execution_count": 25, + "execution_count": 33, "metadata": {}, "output_type": "execute_result" } @@ -14000,12 +12724,12 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 34, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "
" ] @@ -14022,7 +12746,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 35, "metadata": {}, "outputs": [ { @@ -14048,7 +12772,6 @@ " \n", " Pclass\n", " Survived\n", - " Fare\n", " Sex_label\n", " Cabin_label\n", " Embarked_label\n", @@ -14057,121 +12780,103 @@ " \n", "
\n", " count\n", - " 226.000000\n", - " 226.000000\n", - " 226.000000\n", - " 226.000000\n", - " 226.000000\n", - " 226.0\n", + " 250.0\n", + " 250.000000\n", + " 250.0\n", + " 250.00000\n", + " 250.000000\n", "
\n", "
\n", " mean\n", - " 2.884956\n", - " 0.203540\n", - " 8.125402\n", - " 0.809735\n", - " 4.876106\n", - " 2.0\n", + " 3.0\n", + " 0.148000\n", + " 1.0\n", + " 1.62800\n", + " 1.720000\n", "
\n", "
\n", " std\n", - " 0.319784\n", - " 0.403524\n", - " 2.053144\n", - " 0.393382\n", - " 27.495982\n", " 0.0\n", + " 0.355812\n", + " 0.0\n", + " 18.50349\n", + " 0.695366\n", "
\n", "
\n", " min\n", - " 2.000000\n", - " 0.000000\n", + " 3.0\n", " 0.000000\n", + " 1.0\n", + " -1.00000\n", " 0.000000\n", - " -1.000000\n", - " 2.0\n", "
\n", "
\n", " 25%\n", - " 3.000000\n", + " 3.0\n", " 0.000000\n", - " 7.775000\n", - " 1.000000\n", - " -1.000000\n", - " 2.0\n", + " 1.0\n", + " -1.00000\n", + " 2.000000\n", "
\n", "
\n", " 50%\n", - " 3.000000\n", + " 3.0\n", " 0.000000\n", - " 7.925000\n", - " 1.000000\n", - " -1.000000\n", - " 2.0\n", + " 1.0\n", + " -1.00000\n", + " 2.000000\n", "
\n", "
\n", " 75%\n", - " 3.000000\n", + " 3.0\n", " 0.000000\n", - " 8.662500\n", - " 1.000000\n", - " -1.000000\n", - " 2.0\n", + " 1.0\n", + " -1.00000\n", + " 2.000000\n", "
\n", "
\n", " max\n", - " 3.000000\n", + " 3.0\n", " 1.000000\n", - " 12.475000\n", - " 1.000000\n", - " 145.000000\n", - " 2.0\n", + " 1.0\n", + " 140.00000\n", + " 2.000000\n", "
\n", " \n", "\n", "" ], "text/plain": [ - " Pclass Survived Fare Sex_label Cabin_label \\\n", - "count 226.000000 226.000000 226.000000 226.000000 226.000000 \n", - "mean 2.884956 0.203540 8.125402 0.809735 4.876106 \n", - "std 0.319784 0.403524 2.053144 0.393382 27.495982 \n", - "min 2.000000 0.000000 0.000000 0.000000 -1.000000 \n", - "25% 3.000000 0.000000 7.775000 1.000000 -1.000000 \n", - "50% 3.000000 0.000000 7.925000 1.000000 -1.000000 \n", - "75% 3.000000 0.000000 8.662500 1.000000 -1.000000 \n", - "max 3.000000 1.000000 12.475000 1.000000 145.000000 \n", - "\n", - " Embarked_label \n", - "count 226.0 \n", - "mean 2.0 \n", - "std 0.0 \n", - "min 2.0 \n", - "25% 2.0 \n", - "50% 2.0 \n", - "75% 2.0 \n", - "max 2.0 " + " Pclass Survived Sex_label Cabin_label Embarked_label\n", + "count 250.0 250.000000 250.0 250.00000 250.000000\n", + "mean 3.0 0.148000 1.0 1.62800 1.720000\n", + "std 0.0 0.355812 0.0 18.50349 0.695366\n", + "min 3.0 0.000000 1.0 -1.00000 0.000000\n", + "25% 3.0 0.000000 1.0 -1.00000 2.000000\n", + "50% 3.0 0.000000 1.0 -1.00000 2.000000\n", + "75% 3.0 0.000000 1.0 -1.00000 2.000000\n", + "max 3.0 1.000000 1.0 140.00000 2.000000" ] }, - "execution_count": 27, + "execution_count": 35, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "trees.describe_node_sample(light_dtree_reg, node_id=19)" + "trees.describe_node_sample(light_dtree_reg, node_id=14)" ] }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 30, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -14694,10 +13399,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -14716,7 +13421,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -14756,10 +13461,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -14773,7 +13478,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -14787,7 +13492,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -14803,13 +13508,13 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -14820,7 +13525,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -14845,206 +13550,206 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", 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\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -15052,10 +13757,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -15073,7 +13778,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -15090,7 +13795,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -15106,7 +13811,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -15151,7 +13856,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -15182,490 +13887,490 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " 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\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -15678,10 +14383,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -15703,7 +14408,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -15721,7 +14426,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -15772,10 +14477,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -15789,7 +14494,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -15803,7 +14508,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -15819,13 +14524,13 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " 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-17365,7 +16070,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -17383,7 +16088,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -17421,10 +16126,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -17441,7 +16146,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -17455,7 +16160,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -17484,13 +16189,13 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -17501,7 +16206,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -17553,10 +16258,10 @@ "" ], "text/plain": [ - "" + "" ] }, - "execution_count": 30, + "execution_count": 138, "metadata": {}, "output_type": "execute_result" } @@ -17567,7 +16272,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 139, "metadata": {}, "outputs": [ { @@ -17585,6 +16290,13 @@ "print(trees.explain_prediction_path(light_dtree_reg, x))" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, { "cell_type": "code", "execution_count": null, diff --git a/notebooks/dtreeviz_spark_visualisations.ipynb b/notebooks/dtreeviz_spark_visualisations.ipynb index c0fea438..67ba7849 100644 --- a/notebooks/dtreeviz_spark_visualisations.ipynb +++ b/notebooks/dtreeviz_spark_visualisations.ipynb @@ -18,7 +18,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -30,7 +30,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -65,16 +65,7 @@ }, { "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "spark_version = int(pyspark.__version__.split(\".\")[0])" - ] - }, - { - "cell_type": "code", - "execution_count": 5, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -83,18 +74,19 @@ "3" ] }, - "execution_count": 5, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ + "spark_version = int(pyspark.__version__.split(\".\")[0])\n", "spark_version" ] }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -106,7 +98,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -115,7 +107,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 6, "metadata": { "scrolled": true }, @@ -173,7 +165,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -190,9 +182,17 @@ "model = pipeline.fit(data)\n" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## mllib internal\n", + "development purpose" + ] + }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -201,14 +201,14 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "DecisionTreeClassificationModel: uid=DecisionTreeClassifier_0d107b9a930d, depth=4, numNodes=17, numClasses=2, numFeatures=7\n", + "DecisionTreeClassificationModel: uid=DecisionTreeClassifier_a2395fca1824, depth=4, numNodes=17, numClasses=2, numFeatures=7\n", " If (feature 1 in {0.0})\n", " If (feature 3 <= 3.5)\n", " If (feature 4 <= 2.5)\n", @@ -244,7 +244,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ @@ -254,7 +254,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -263,7 +263,7 @@ "'InternalNode(prediction = 0.0, impurity = 0.4730129578614428, split = org.apache.spark.ml.tree.CategoricalSplit@ca78837e)'" ] }, - "execution_count": 13, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } @@ -272,16 +272,9 @@ "tree_model_classifier._call_java('rootNode').toString()" ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## dtreeviz visualisations" - ] - }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ @@ -290,7 +283,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 13, "metadata": {}, "outputs": [ { @@ -322,7 +315,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 14, "metadata": {}, "outputs": [ { @@ -606,7 +599,7 @@ "16 -1 (-1, [], -1) " ] }, - "execution_count": 16, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" } @@ -616,23 +609,25 @@ ] }, { - "cell_type": "code", - "execution_count": 17, + "cell_type": "markdown", "metadata": {}, - "outputs": [], "source": [ - "dataset = Pipeline(stages=[sex_label_indexer, embarked_label_indexer, age_imputer]) \\\n", - " .fit(data) \\\n", - " .transform(data) \\\n", - " .toPandas()[features + [target]]" + "## dtreeviz visualisations" ] }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 15, "metadata": {}, "outputs": [], "source": [ + "tree_model_classifier = model.stages[4]\n", + "\n", + "dataset = Pipeline(stages=[sex_label_indexer, embarked_label_indexer, age_imputer]) \\\n", + " .fit(data) \\\n", + " .transform(data) \\\n", + " .toPandas()[features + [target]]\n", + "\n", "spark_dtree = ShadowSparkTree(tree_model_classifier, dataset[features], dataset[target], feature_names=features, target_name=target, class_names=[0, 1])\n" ] }, @@ -645,23 +640,23 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "image/svg+xml": [ - "\n", - "\n", + "\n", + "\n", "G\n", - "\n", - "\n", + "\n", + "\n", "cluster_legend\n", "\n", "\n", - "\n", + "\n", "node2\n", - "\n", + "\n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -796,10 +791,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -815,7 +810,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -849,10 +844,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -865,7 +860,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -900,16 +895,16 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", "\n", "\n", - "\n", + "\n", "leaf3\n", - "\n", - "\n", + "\n", + "\n", " \n", " \n", "\n", "\n", - "\n", + "\n", "node2->leaf3\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "leaf4\n", - "\n", - "\n", + "\n", + "\n", " \n", " \n", "\n", "\n", - "\n", + "\n", "node2->leaf4\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "leaf5\n", - "\n", - "\n", + "\n", + "\n", " \n", " \n", "\n", "\n", - "\n", + "\n", "node1\n", - "\n", + "\n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1187,10 +1182,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1212,7 +1207,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1256,10 +1251,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1272,7 +1267,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1312,21 +1307,21 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", "\n", "\n", - "\n", + "\n", "node1->node2\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "node6\n", - "\n", + "\n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1461,10 +1456,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1480,7 +1475,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1516,10 +1511,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1535,7 +1530,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1575,22 +1570,22 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", "\n", "\n", "\n", - "\n", + "\n", "node1->leaf5\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "node8\n", - "\n", + "\n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1725,10 +1720,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1744,7 +1739,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1780,10 +1775,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1799,7 +1794,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1837,16 +1832,16 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", "\n", "\n", - "\n", + "\n", "leaf9\n", - "\n", - "\n", + "\n", + "\n", " \n", " \n", "\n", "\n", - "\n", + "\n", "node8->leaf9\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "leaf10\n", - "\n", - "\n", + "\n", + "\n", " \n", " \n", "\n", "\n", - "\n", + "\n", "node8->leaf10\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "leaf11\n", - "\n", - "\n", + "\n", + "\n", " \n", " \n", "\n", "\n", - "\n", + "\n", "node7\n", - "\n", + "\n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2120,10 +2115,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2145,7 +2140,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2189,10 +2184,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2205,7 +2200,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2244,21 +2239,21 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", "
\n", "\n", - "\n", + "\n", "node7->node8\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "node12\n", - "\n", + "\n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2393,10 +2388,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2415,7 +2410,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2455,10 +2450,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2471,7 +2466,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2513,22 +2508,22 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", "\n", "\n", "\n", - "\n", + "\n", "node7->leaf11\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "node13\n", - "\n", + "\n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2679,7 +2680,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2728,10 +2729,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2744,7 +2745,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2759,50 +2760,25 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", "\n", "\n", - "\n", + "\n", "leaf14\n", - "\n", - "\n", + "\n", + "\n", " \n", " \n", "\n", "\n", - "\n", + "\n", "node13->leaf14\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "leaf15\n", - "\n", - "\n", + "\n", + "\n", " \n", " \n", "\n", "\n", - "\n", + "\n", "node13->leaf15\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "leaf16\n", - "\n", - "\n", + "\n", + "\n", " \n", " \n", "\n", "\n", - "\n", + "\n", "node12->node13\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "node12->leaf16\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "node6->node7\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "node6->node12\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "node0\n", - "\n", + "\n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3125,7 +3104,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3166,10 +3145,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3182,7 +3161,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3199,58 +3178,42 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", "\n", "\n", - "\n", + "\n", "node0->node1\n", - "\n", - "\n", - "<\n", + "\n", + "\n", + "<\n", "\n", "\n", - "\n", + "\n", "node0->node6\n", - "\n", - "\n", - "≥\n", + "\n", + "\n", + "≥\n", "\n", "\n", "\n", "\n", "\n", - "\n", + "\n", "legend\n", - "\n", - "\n", + "\n", + "\n", " \n", " " ], "text/plain": [ - "" + "" ] }, - "execution_count": 19, + "execution_count": 17, "metadata": {}, "output_type": "execute_result" } @@ -3340,14 +3303,14 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 18, "metadata": { "scrolled": true }, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -3371,12 +3334,12 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 19, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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" ] @@ -3400,12 +3363,12 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 20, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -3429,14 +3392,14 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 21, "metadata": { "scrolled": true }, "outputs": [ { "data": { - "image/png": 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\n", 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" ] @@ -3460,7 +3423,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 22, "metadata": {}, "outputs": [ { @@ -3590,7 +3553,7 @@ "max 2.0 1.0 1.000000 3.000000 1.0 2.000000 41.579200" ] }, - "execution_count": 24, + "execution_count": 22, "metadata": {}, "output_type": "execute_result" } @@ -3608,7 +3571,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 22, "metadata": {}, "outputs": [ { @@ -3624,7 +3587,7 @@ "Name: 10, dtype: float64" ] }, - "execution_count": 25, + "execution_count": 22, "metadata": {}, "output_type": "execute_result" } @@ -3636,13 +3599,14 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 23, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ + "tree type \n", "2.5 <= Pclass \n", "Fare < 24.81\n", "Sex_label in [1.0, 2.0] \n", @@ -3657,26 +3621,33 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 24, "metadata": {}, "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "tree type \n" + ] + }, { "data": { "image/svg+xml": [ - "\n", - "\n", + "\n", + "\n", "G\n", - "\n", - "\n", + "\n", + "\n", "cluster_legend\n", "\n", - "\n", + "\n", "cluster_instance\n", "\n", "\n", - "\n", + "\n", "node2\n", - "\n", + "\n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3811,10 +3782,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3830,7 +3801,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3864,10 +3835,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3880,7 +3851,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3915,16 +3886,16 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", "\n", "\n", - "\n", + "\n", "leaf3\n", - "\n", - "\n", + "\n", + "\n", " \n", " \n", "
\n", "\n", - "\n", + "\n", "node2->leaf3\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "leaf4\n", - "\n", - "\n", + "\n", + "\n", " \n", " \n", "\n", "\n", - "\n", + "\n", "node2->leaf4\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "leaf5\n", - "\n", - "\n", + "\n", + "\n", " \n", " \n", "\n", "\n", - "\n", + "\n", "node1\n", - "\n", + "\n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4202,10 +4173,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4227,7 +4198,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4271,10 +4242,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4287,7 +4258,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4327,22 +4298,22 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", "\n", "\n", - "\n", + "\n", "node1->node2\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "node6\n", - "\n", - "\n", + "\n", + "\n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4480,10 +4451,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4499,7 +4470,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4535,10 +4506,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4554,7 +4525,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4603,22 +4574,22 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", "\n", "\n", "\n", - "\n", + "\n", "node1->leaf5\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "node8\n", - "\n", + "\n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4753,10 +4724,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4772,7 +4743,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4808,10 +4779,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4827,7 +4798,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4865,16 +4836,16 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", "\n", "\n", - "\n", + "\n", "leaf9\n", - "\n", - "\n", + "\n", + "\n", " \n", " \n", "\n", "\n", - "\n", + "\n", "node8->leaf9\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "leaf10\n", - "\n", - "\n", + "\n", + "\n", " \n", " \n", "\n", "\n", - "\n", + "\n", "node8->leaf10\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "leaf11\n", - "\n", - "\n", + "\n", + "\n", " \n", " \n", "\n", "\n", - "\n", + "\n", "node7\n", - "\n", + "\n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5148,10 +5119,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5173,7 +5144,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5217,10 +5188,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5233,7 +5204,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5272,22 +5243,22 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", "\n", "\n", - "\n", + "\n", "node7->node8\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "node12\n", - "\n", - "\n", + "\n", + "\n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5425,10 +5396,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5447,7 +5418,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5487,10 +5458,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5503,7 +5474,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5555,23 +5526,23 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", "\n", "\n", "\n", - "\n", + "\n", "node7->leaf11\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "node13\n", - "\n", - "\n", + "\n", + "\n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5722,7 +5705,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5771,10 +5754,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5787,7 +5770,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5802,10 +5785,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5851,16 +5834,16 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", "\n", "\n", - "\n", + "\n", "leaf14\n", - "\n", - "\n", + "\n", + "\n", " \n", " \n", "\n", "\n", - "\n", + "\n", "node13->leaf14\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "leaf15\n", - "\n", - "\n", + "\n", + "\n", " \n", " \n", "\n", "\n", - "\n", + "\n", "node13->leaf15\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "leaf16\n", - "\n", - "\n", + "\n", + "\n", " \n", " \n", "\n", "\n", - "\n", + "\n", "node12->node13\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "node12->leaf16\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "node6->node7\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "node6->node12\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "node0\n", - "\n", - "\n", + "\n", + "\n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6184,7 +6173,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6225,10 +6214,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6241,7 +6230,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6258,10 +6247,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6293,74 +6282,74 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", "\n", "\n", - "\n", + "\n", "node0->node1\n", - "\n", - "\n", - "<\n", + "\n", + "\n", + "<\n", "\n", "\n", - "\n", + "\n", "node0->node6\n", - "\n", - "\n", - "≥\n", + "\n", + "\n", + "≥\n", "\n", "\n", "\n", "\n", "\n", - "\n", + "\n", "X_y\n", - "\n", - "\n", - "Pclass\n", - "\n", - "Sex_label\n", - "\n", - "Embarked_label\n", - "\n", - "Age_mean\n", - "\n", - "SibSp\n", - "\n", - "Parch\n", - "\n", - "Fare\n", - "\n", - "3.00\n", - "\n", - "1.00\n", - "\n", - "0.00\n", - "\n", - "4.00\n", - "\n", - "1.00\n", - "\n", - "1.00\n", - "\n", - "16.70\n", + "\n", + "\n", + "Pclass\n", + "\n", + "Sex_label\n", + "\n", + "Embarked_label\n", + "\n", + "Age_mean\n", + "\n", + "SibSp\n", + "\n", + "Parch\n", + "\n", + "Fare\n", + "\n", + "3.00\n", + "\n", + "1.00\n", + "\n", + "0.00\n", + "\n", + "4.00\n", + "\n", + "1.00\n", + "\n", + "1.00\n", + "\n", + "16.70\n", "\n", "\n", - "\n", + "\n", "leaf15->X_y\n", - "\n", - "\n", - "  Prediction\n", - " 0\n", + "\n", + "\n", + "  Prediction\n", + " 0\n", "\n", "\n", - "\n", + "\n", "legend\n", - "\n", - "\n", + "\n", + "\n", " \n", " " ], "text/plain": [ - "" + "" ] }, - "execution_count": 27, + "execution_count": 24, "metadata": {}, "output_type": "execute_result" } @@ -6457,7 +6446,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 25, "metadata": {}, "outputs": [], "source": [ @@ -6466,7 +6455,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 26, "metadata": {}, "outputs": [], "source": [ @@ -6476,7 +6465,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 27, "metadata": {}, "outputs": [], "source": [ @@ -6488,7 +6477,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 28, "metadata": {}, "outputs": [], "source": [ @@ -6500,7 +6489,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 29, "metadata": {}, "outputs": [ { @@ -6691,7 +6680,7 @@ "[891 rows x 8 columns]" ] }, - "execution_count": 32, + "execution_count": 29, "metadata": {}, "output_type": "execute_result" } @@ -6709,7 +6698,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 30, "metadata": {}, "outputs": [], "source": [ @@ -6726,20 +6715,27 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 31, "metadata": {}, "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "tree type \n" + ] + }, { "data": { "image/svg+xml": [ - "\n", - "\n", + "\n", + "\n", "G\n", - "\n", + "\n", "\n", - "\n", + "\n", "node2\n", - "\n", + "\n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6755,288 +6751,227 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7062,15 +6997,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7078,13 +7013,16 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -7093,13 +7031,16 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -7108,40 +7049,24 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", "\n", "\n", - "\n", + "\n", "node5\n", - "\n", + "\n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -7222,10 +7147,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7244,7 +7169,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7262,7 +7187,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7299,10 +7224,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7315,7 +7240,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7333,7 +7258,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7350,13 +7275,13 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7367,17 +7292,17 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", "\n", "\n", "\n", - "\n", + "\n", "leaf3\n", - "\n", - "\n", + "\n", + "\n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -7502,10 +7427,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7521,7 +7446,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7538,7 +7463,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7584,22 +7509,22 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", "\n", "\n", - "\n", + "\n", "node2->leaf3\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "leaf4\n", - "\n", - "\n", + "\n", + "\n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -7689,10 +7614,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7708,7 +7633,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7725,7 +7650,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7772,22 +7697,22 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", "\n", "\n", - "\n", + "\n", "node2->leaf4\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "leaf6\n", - "\n", - "\n", + "\n", + "\n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -7861,10 +7786,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7880,7 +7805,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7897,7 +7822,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7943,22 +7868,22 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", "\n", "\n", - "\n", + "\n", "node5->leaf6\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "leaf7\n", - "\n", - "\n", + "\n", + "\n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -7989,10 +7914,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8008,7 +7933,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8025,7 +7950,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8070,21 +7995,21 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", "\n", "\n", - "\n", + "\n", "node5->leaf7\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "node1\n", - "\n", + "\n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -8328,10 +8253,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8347,7 +8272,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8382,10 +8307,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8398,7 +8323,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8416,7 +8341,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8433,13 +8358,13 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8450,27 +8375,27 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", "\n", "\n", - "\n", + "\n", "node1->node2\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "node1->node5\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "node8\n", - "\n", + "\n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " 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\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -9173,10 +9098,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9192,7 +9117,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9226,10 +9151,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9242,7 +9167,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9260,7 +9185,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9277,13 +9202,13 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9294,16 +9219,16 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", "\n", "\n", "\n", - "\n", + "\n", "node9\n", - "\n", + "\n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " 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\n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -9802,10 +9727,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9821,7 +9746,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9856,10 +9781,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9872,7 +9797,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9890,7 +9815,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9907,13 +9832,13 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9924,15 +9849,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", "\n", "\n", - "\n", + "\n", "node12\n", - "\n", + "\n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - 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\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -10164,10 +10089,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -10186,7 +10111,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -10204,7 +10129,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -10241,10 +10166,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -10257,7 +10182,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -10275,7 +10200,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -10289,13 +10214,13 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -10306,17 +10231,17 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", "\n", "\n", "\n", - "\n", + "\n", "leaf10\n", - "\n", - "\n", + "\n", + "\n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " 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\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -10770,10 +10695,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -10789,7 +10714,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -10806,7 +10731,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -10853,22 +10778,22 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", "\n", "\n", - "\n", + "\n", "node9->leaf10\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "leaf11\n", - "\n", - "\n", + "\n", + "\n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -10937,10 +10862,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -10956,7 +10881,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -10973,7 +10898,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -11020,22 +10945,22 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", "\n", "\n", - "\n", + "\n", "node9->leaf11\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "leaf13\n", - "\n", - "\n", + "\n", + "\n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -11253,10 +11178,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -11272,7 +11197,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -11289,7 +11214,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -11335,22 +11260,22 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", "\n", "\n", - "\n", + "\n", "node12->leaf13\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "leaf14\n", - "\n", - "\n", + "\n", + "\n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -11388,10 +11313,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -11407,7 +11332,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -11424,7 +11349,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -11469,33 +11394,33 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", "\n", "\n", - "\n", + "\n", "node12->leaf14\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "node8->node9\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "node8->node12\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "node0\n", - "\n", + "\n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", 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\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -12414,10 +12339,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -12437,7 +12362,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -12455,7 +12380,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -12493,10 +12418,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -12509,7 +12434,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -12527,7 +12452,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -12558,13 +12483,13 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -12575,24 +12500,24 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", "\n", "\n", - "\n", + "\n", "node0->node1\n", - "\n", - "\n", - "<\n", + "\n", + "\n", + "<\n", "\n", "\n", - "\n", + "\n", "node0->node8\n", - "\n", - "\n", - "≥\n", + "\n", + "\n", + "≥\n", "\n", "\n", "\n", @@ -12602,10 +12527,10 @@ "" ], "text/plain": [ - "" + "" ] }, - "execution_count": 34, + "execution_count": 31, "metadata": {}, "output_type": "execute_result" } @@ -12623,12 +12548,19 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 32, "metadata": {}, "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "tree type \n" + ] + }, { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -12652,12 +12584,19 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 33, "metadata": {}, "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "tree type \n" + ] + }, { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] @@ -12681,12 +12620,19 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 34, "metadata": {}, "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "tree type \n" + ] + }, { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -12710,7 +12656,7 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 35, "metadata": {}, "outputs": [ { @@ -12726,7 +12672,7 @@ "Name: 3, dtype: float64" ] }, - "execution_count": 38, + "execution_count": 35, "metadata": {}, "output_type": "execute_result" } @@ -12738,13 +12684,14 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 36, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ + "tree type \n", "Pclass < 1.5\n", "Parch < 0.5\n", "Sex_label in [1.0, 2.0] \n", @@ -12758,24 +12705,31 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 37, "metadata": {}, "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "tree type \n" + ] + }, { "data": { "image/svg+xml": [ - "\n", - "\n", + "\n", + "\n", "G\n", - "\n", - "\n", + "\n", + "\n", "cluster_instance\n", "\n", "\n", - "\n", + "\n", "node2\n", - "\n", - "\n", + "\n", + "\n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -12791,291 +12745,230 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", " \n", - " \n", - " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -13101,15 +12994,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -13117,13 +13010,16 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -13132,13 +13028,16 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -13147,40 +13046,24 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", "\n", "\n", - "\n", + "\n", "node5\n", - "\n", + "\n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -13261,10 +13144,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -13283,7 +13166,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -13301,7 +13184,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -13338,10 +13221,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -13354,7 +13237,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -13372,7 +13255,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -13389,13 +13272,13 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -13406,17 +13289,17 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", "\n", "\n", "\n", - "\n", + "\n", "leaf3\n", - "\n", - "\n", + "\n", + "\n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -13541,10 +13424,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -13560,7 +13443,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -13577,7 +13460,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -13623,22 +13506,22 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", "\n", "\n", - "\n", + "\n", "node2->leaf3\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "leaf4\n", - "\n", - "\n", + "\n", + "\n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -13728,10 +13611,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -13747,7 +13630,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -13764,7 +13647,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -13811,22 +13694,22 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", "\n", "\n", - "\n", + "\n", "node2->leaf4\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "leaf6\n", - "\n", - "\n", + "\n", + "\n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -13900,10 +13783,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -13919,7 +13802,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -13936,7 +13819,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -13982,22 +13865,22 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", "\n", "\n", - "\n", + "\n", "node5->leaf6\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "leaf7\n", - "\n", - "\n", + "\n", + "\n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -14028,10 +13911,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -14047,7 +13930,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -14064,7 +13947,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -14109,22 +13992,22 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", "\n", "\n", - "\n", + "\n", "node5->leaf7\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "node1\n", - "\n", - "\n", + "\n", + "\n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -14371,10 +14254,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -14390,7 +14273,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -14425,10 +14308,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -14441,7 +14324,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -14459,7 +14342,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -14476,13 +14359,13 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -14493,27 +14376,27 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", "\n", "\n", - "\n", + "\n", "node1->node2\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "node1->node5\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "node8\n", - "\n", + "\n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " 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\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -15845,10 +15728,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -15864,7 +15747,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -15899,10 +15782,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -15915,7 +15798,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -15933,7 +15816,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -15950,13 +15833,13 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -15967,15 +15850,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", "\n", "\n", - "\n", + "\n", "node12\n", - "\n", + "\n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " 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\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -16207,10 +16090,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -16229,7 +16112,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -16247,7 +16130,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -16284,10 +16167,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -16300,7 +16183,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -16318,7 +16201,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -16332,13 +16215,13 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -16349,17 +16232,17 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", "\n", "\n", "\n", - "\n", + "\n", "leaf10\n", - "\n", - "\n", + "\n", + "\n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", 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\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -16813,10 +16696,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -16832,7 +16715,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -16849,7 +16732,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -16896,22 +16779,22 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", "\n", "\n", - "\n", + "\n", "node9->leaf10\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "leaf11\n", - "\n", - "\n", + "\n", + "\n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -16980,10 +16863,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -16999,7 +16882,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -17016,7 +16899,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -17063,22 +16946,22 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", "\n", "\n", - "\n", + "\n", "node9->leaf11\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "leaf13\n", - "\n", - "\n", + "\n", + "\n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -17296,10 +17179,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -17315,7 +17198,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -17332,7 +17215,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -17378,22 +17261,22 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", "\n", "\n", - "\n", + "\n", "node12->leaf13\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "leaf14\n", - "\n", - "\n", + "\n", + "\n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -17431,10 +17314,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -17450,7 +17333,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -17467,7 +17350,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -17512,34 +17395,34 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", "\n", "\n", - "\n", + "\n", "node12->leaf14\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "node8->node9\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", 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-18622,66 +18505,66 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", "\n", "\n", - "\n", + "\n", "node0->node1\n", - "\n", - "\n", - "<\n", + "\n", + "\n", + "<\n", "\n", "\n", - "\n", + "\n", "node0->node8\n", - "\n", - "\n", - "≥\n", + "\n", + "\n", + "≥\n", "\n", "\n", "\n", - "\n", + "\n", "X_y\n", - "\n", - "\n", - "Pclass\n", - "\n", - "Sex_label\n", - "\n", - "Embarked_label\n", - "\n", - "Age_mean\n", - "\n", - "SibSp\n", - "\n", - "Parch\n", - "\n", - "Survived\n", - "\n", - "1.00\n", - "\n", - "1.00\n", - "\n", - "0.00\n", - "\n", - "35.00\n", - "\n", - "1.00\n", - "\n", - "0.00\n", - "\n", - "1.00\n", + "\n", + "\n", + "Pclass\n", + "\n", + "Sex_label\n", + "\n", + "Embarked_label\n", + "\n", + "Age_mean\n", + "\n", + "SibSp\n", + "\n", + "Parch\n", + "\n", + "Survived\n", + "\n", + "1.00\n", + "\n", + "1.00\n", + "\n", + "0.00\n", + "\n", + "35.00\n", + "\n", + "1.00\n", + "\n", + "0.00\n", + "\n", + "1.00\n", "\n", "\n", - "\n", + "\n", "leaf4->X_y\n", - "\n", - "\n", - "  Prediction\n", - " 95.30\n", + "\n", + "\n", + "  Prediction\n", + " 95.30\n", "\n", "\n", "\n", @@ -18690,10 +18573,10 @@ "" ], "text/plain": [ - "" + "" ] }, - "execution_count": 40, + "execution_count": 37, "metadata": {}, "output_type": "execute_result" } @@ -18709,6 +18592,20 @@ "outputs": [], "source": [] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, { "cell_type": "code", "execution_count": null, @@ -18733,7 +18630,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.3" + "version": "3.7.6" }, "toc": { "base_numbering": 1, From 175813df55eab1f46a4ea58b3be33b5a785a4c7a Mon Sep 17 00:00:00 2001 From: tlapusan Date: Tue, 8 Dec 2020 10:25:58 +0200 Subject: [PATCH 07/16] Get left,right node samples for categorical feature split --- dtreeviz/models/shadow_decision_tree.py | 9 +- dtreeviz/trees.py | 50 +- .../dtreeviz_lightgbm_visualisations.ipynb | 9415 +++++++++-------- 3 files changed, 5198 insertions(+), 4276 deletions(-) diff --git a/dtreeviz/models/shadow_decision_tree.py b/dtreeviz/models/shadow_decision_tree.py index 6a3fa92e..ccbeb199 100644 --- a/dtreeviz/models/shadow_decision_tree.py +++ b/dtreeviz/models/shadow_decision_tree.py @@ -543,8 +543,13 @@ def split_samples(self) -> Tuple[np.ndarray, np.ndarray]: samples = np.array(self.samples()) node_X_data = self.shadow_tree.x_data[samples, self.feature()] split = self.split() - left = np.nonzero(node_X_data < split)[0] - right = np.nonzero(node_X_data >= split)[0] + if self.is_categorical_split(): + indices = np.sum([node_X_data == split_value for split_value in self.split()[0]], axis=0) + left = np.nonzero(indices == 1)[0] + right = np.nonzero(indices == 0)[0] + else: + left = np.nonzero(node_X_data < split)[0] + right = np.nonzero(node_X_data >= split)[0] return left, right def isleaf(self) -> bool: diff --git a/dtreeviz/trees.py b/dtreeviz/trees.py index 8546ec0a..a38c097e 100644 --- a/dtreeviz/trees.py +++ b/dtreeviz/trees.py @@ -960,10 +960,8 @@ def class_split_viz(node: ShadowDecTreeNode, ax.spines['bottom'].set_linewidth(.3) class_names = node.shadow_tree.class_names - class_values = node.shadow_tree.classes() X_hist = [X_feature[y_train == cl] for cl in class_values] - if histtype == 'strip': ax.yaxis.set_visible(False) ax.spines['left'].set_visible(False) @@ -1130,6 +1128,9 @@ def wedge(ax, x, color): right = y_train[right] split = node.split() + # ax.scatter(X_feature, y_train, s=5, c=colors['scatter_marker'], alpha=colors['scatter_marker_alpha'], lw=.3) + + ax.plot([overall_feature_range[0], split], [np.mean(left), np.mean(left)], '--', color=colors['split_line'], linewidth=1) ax.plot([split, split], [*y_range], '--', color=colors['split_line'], linewidth=1) @@ -1140,16 +1141,45 @@ def wedge(ax, x, color): if highlight_node: wedge(ax, X[node.feature()], color=colors['highlight']) else: - xticks = list(overall_feature_range) + node.split()[0] - ax.set_xticks(xticks) - ax.scatter(X_feature, y_train, s=5, c=colors['scatter_marker'], alpha=colors['scatter_marker_alpha'], lw=.3) + left_index, right_index = node.split_samples() - left_split_values = node.split()[0] - for split_value in left_split_values: - wedge(ax, split_value, color=colors['wedge']) - if highlight_node: - wedge(ax, X[node.feature()], color=colors['highlight']) + print(f"id {node.id}, split_left {node.split()[0]}, {overall_feature_range }") + ax.set_xlim(overall_feature_range[0] - 0.05, overall_feature_range[1] + 0.05) + ax.scatter(X_feature[left_index], y_train[left_index], s=5, c=colors["classes"][2][0], alpha=colors['scatter_marker_alpha'], lw=.3) + ax.scatter(X_feature[right_index], y_train[right_index], s=5, c=colors["classes"][2][1], alpha=colors['scatter_marker_alpha'], lw=.3) + + + # indices = np.sum([X_feature == split_value for split_value in node.split()[0]], axis=0) + # X_hist = [X_feature[indices == 1], X_feature[indices == 0]] + + # bins = np.linspace(start=overall_feature_range[0], stop=overall_feature_range[1], num=overall_feature_range[1] * 5, endpoint=True) + + # print(f"\nrange: {overall_feature_range}, r={r}, nbins={nbins}, len(bins)={len(bins)}, binwidth={binwidth}\n{bins}") + # bins[-1] = overall_feature_range[1] # make sure rounding doesn't kill last value on right + # hist, bins, barcontainers = ax.hist(X_hist, + # align='mid', + # bins=bins) + # Alter appearance of each bar + # for patch in barcontainers: + # for rect in patch.patches: + # rect.set_linewidth(.5) + # rect.set_edgecolor(colors['rect_edge']) + # ax.set_yticks([0, max([max(h) for h in hist])]) + + + + # xticks = list(overall_feature_range) + node.split()[0] + # print(f"overall feature range {overall_feature_range}") + # ax.set_xticks(xticks) + # ax.scatter(X_feature, y_train, s=5, c=colors['scatter_marker'], alpha=colors['scatter_marker_alpha'], lw=.3) + # + # left_split_values = node.split()[0] + # for split_value in left_split_values: + # wedge(ax, split_value, color=colors['wedge']) + # + # if highlight_node: + # wedge(ax, X[node.feature()], color=colors['highlight']) # plt.tight_layout() if filename is not None: diff --git a/notebooks/dtreeviz_lightgbm_visualisations.ipynb b/notebooks/dtreeviz_lightgbm_visualisations.ipynb index e105d274..51a2f8b2 100644 --- a/notebooks/dtreeviz_lightgbm_visualisations.ipynb +++ b/notebooks/dtreeviz_lightgbm_visualisations.ipynb @@ -375,25 +375,13 @@ "execution_count": 7, "metadata": {}, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/tudorl/Documents/workspace/personal/dtreeviz/venv/lib/python3.7/site-packages/lightgbm/basic.py:1551: UserWarning: Using categorical_feature in Dataset.\n", - " warnings.warn('Using categorical_feature in Dataset.')\n", - "/Users/tudorl/Documents/workspace/personal/dtreeviz/venv/lib/python3.7/site-packages/lightgbm/basic.py:1286: UserWarning: Overriding the parameters from Reference Dataset.\n", - " warnings.warn('Overriding the parameters from Reference Dataset.')\n", - "/Users/tudorl/Documents/workspace/personal/dtreeviz/venv/lib/python3.7/site-packages/lightgbm/basic.py:1098: UserWarning: categorical_column in param dict is overridden.\n", - " warnings.warn('{} in param dict is overridden.'.format(cat_alias))\n" - ] - }, { "name": "stdout", "output_type": "stream", "text": [ "[LightGBM] [Warning] Met negative value in categorical features, will convert it to NaN\n", "[LightGBM] [Info] Number of positive: 268, number of negative: 444\n", - "[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001117 seconds.\n", + "[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000723 seconds.\n", "You can set `force_col_wise=true` to remove the overhead.\n", "[LightGBM] [Info] Total Bins 190\n", "[LightGBM] [Info] Number of data points in the train set: 712, number of used features: 6\n", @@ -550,13 +538,7 @@ "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[75]\ttraining's binary_logloss: 0.452551\tvalid_1's binary_logloss: 0.476973\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", - "[76]\ttraining's binary_logloss: 0.453432\tvalid_1's binary_logloss: 0.477883\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + "[76]\ttraining's binary_logloss: 0.453432\tvalid_1's binary_logloss: 0.477883\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[77]\ttraining's binary_logloss: 0.448871\tvalid_1's binary_logloss: 0.473901\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", @@ -606,6 +588,18 @@ "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[100]\ttraining's binary_logloss: 0.432906\tvalid_1's binary_logloss: 0.461309\n" ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/tudorl/Documents/workspace/personal/dtreeviz/venv/lib/python3.7/site-packages/lightgbm/basic.py:1551: UserWarning: Using categorical_feature in Dataset.\n", + " warnings.warn('Using categorical_feature in Dataset.')\n", + "/Users/tudorl/Documents/workspace/personal/dtreeviz/venv/lib/python3.7/site-packages/lightgbm/basic.py:1286: UserWarning: Overriding the parameters from Reference Dataset.\n", + " warnings.warn('Overriding the parameters from Reference Dataset.')\n", + "/Users/tudorl/Documents/workspace/personal/dtreeviz/venv/lib/python3.7/site-packages/lightgbm/basic.py:1098: UserWarning: categorical_column in param dict is overridden.\n", + " warnings.warn('{} in param dict is overridden.'.format(cat_alias))\n" + ] } ], "source": [ @@ -1044,7 +1038,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 16, "metadata": {}, "outputs": [], "source": [ @@ -1053,7 +1047,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 18, "metadata": {}, "outputs": [ { @@ -1084,118 +1078,118 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1204,10 +1198,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1229,7 +1223,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1264,10 +1258,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1280,7 +1274,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1318,7 +1312,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1341,118 +1335,118 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1461,10 +1455,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1483,7 +1477,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1523,10 +1517,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1539,7 +1533,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1581,7 +1575,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1814,118 +1808,118 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1934,10 +1928,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1955,7 +1949,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2004,10 +1998,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2023,7 +2017,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2048,7 +2042,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2083,118 +2077,118 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2203,10 +2197,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2228,7 +2222,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2263,10 +2257,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2279,7 +2273,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2320,7 +2314,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2349,118 +2343,118 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2469,10 +2463,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2491,7 +2485,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2531,10 +2525,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2547,7 +2541,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2589,7 +2583,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2665,118 +2659,118 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2785,10 +2779,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2807,7 +2801,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2847,10 +2841,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2863,7 +2857,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2904,7 +2898,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3030,118 +3024,118 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3150,10 +3144,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3175,7 +3169,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3210,10 +3204,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3226,7 +3220,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3263,7 +3257,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3292,118 +3286,118 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3412,10 +3406,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3437,7 +3431,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3472,10 +3466,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3488,7 +3482,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3529,7 +3523,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3604,118 +3598,118 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3724,10 +3718,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3749,7 +3743,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3784,10 +3778,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3800,7 +3794,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3841,7 +3835,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4038,118 +4032,118 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4158,10 +4152,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4177,7 +4171,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4213,10 +4207,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4232,7 +4226,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4258,7 +4252,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4356,10 +4350,10 @@ "" ], "text/plain": [ - "" + "" ] }, - "execution_count": 14, + "execution_count": 18, "metadata": {}, "output_type": "execute_result" } @@ -4368,6 +4362,13 @@ "trees.dtreeviz(light_dtree)" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, { "cell_type": "code", "execution_count": 15, @@ -5887,7 +5888,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -5896,6 +5897,7 @@ "\n", "X_train_reg, X_test_reg, y_train_reg, y_test_reg = train_test_split(dataset[features_reg], dataset[target_reg], test_size=0.2, random_state=42)\n", "\n", + "# categorical_feature=[\"Pclass\", \"Survived\", \"Sex_label\", \"Embarked_label\", \"Cabin_label\"]\n", "train_data_reg = lgb.Dataset(data=X_train_reg, label=y_train_reg, categorical_feature=[\"Pclass\", \"Survived\", \"Sex_label\", \"Embarked_label\", \"Cabin_label\"])\n", "valid_data_reg = lgb.Dataset(data=X_test_reg, label=y_test_reg, categorical_feature=[\"Pclass\", \"Survived\", \"Sex_label\", \"Embarked_label\", \"Cabin_label\"])\n", "\n", @@ -5921,14 +5923,14 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000597 seconds.\n", + "[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000594 seconds.\n", "You can set `force_row_wise=true` to remove the overhead.\n", "And if memory is not enough, you can set `force_col_wise=true`.\n", "[LightGBM] [Info] Total Bins 21\n", @@ -5961,13 +5963,7 @@ "output_type": "stream", "text": [ "/Users/tudorl/Documents/workspace/personal/dtreeviz/venv/lib/python3.7/site-packages/lightgbm/engine.py:151: UserWarning: Found `num_tree` in params. Will use it instead of argument\n", - " warnings.warn(\"Found `{}` in params. Will use it instead of argument\".format(alias))\n", - "/Users/tudorl/Documents/workspace/personal/dtreeviz/venv/lib/python3.7/site-packages/lightgbm/basic.py:1551: UserWarning: Using categorical_feature in Dataset.\n", - " warnings.warn('Using categorical_feature in Dataset.')\n", - "/Users/tudorl/Documents/workspace/personal/dtreeviz/venv/lib/python3.7/site-packages/lightgbm/basic.py:1286: UserWarning: Overriding the parameters from Reference Dataset.\n", - " warnings.warn('Overriding the parameters from Reference Dataset.')\n", - "/Users/tudorl/Documents/workspace/personal/dtreeviz/venv/lib/python3.7/site-packages/lightgbm/basic.py:1098: UserWarning: categorical_column in param dict is overridden.\n", - " warnings.warn('{} in param dict is overridden.'.format(cat_alias))\n" + " warnings.warn(\"Found `{}` in params. Will use it instead of argument\".format(alias))\n" ] } ], @@ -5984,16 +5980,16 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 23, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" }, @@ -6016,7 +6012,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -6333,10 +6329,10 @@ "\n" ], "text/plain": [ - "" + "" ] }, - "execution_count": 24, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } @@ -6354,7 +6350,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ @@ -6363,9 +6359,26 @@ }, { "cell_type": "code", - "execution_count": 33, - "metadata": {}, + "execution_count": 26, + "metadata": { + "scrolled": false + }, "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "id 2, split_left [1], (0, 1)\n", + "id 5, split_left [1], (0, 1)\n", + "id 1, split_left [0], (-1, 2)\n", + "id 9, split_left [1], (0, 1)\n", + "id 13, split_left [1], (0, 1)\n", + "id 16, split_left [1], (0, 1)\n", + "id 12, split_left [3], (1, 3)\n", + "id 8, split_left [1], (-1, 2)\n", + "id 0, split_left [1], (1, 3)\n" + ] + }, { "data": { "image/svg+xml": [ @@ -6376,7 +6389,7 @@ "\n", "\n", "node2\n", - "\n", + "\n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6392,121 +6405,174 @@ " \n", " \n", " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", + " \n", " \n", - " \n", + " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6532,20 +6598,16 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -6553,12 +6615,12 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6567,16 +6629,13 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -6584,16 +6643,16 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6601,7 +6660,7 @@ "\n", "\n", "node5\n", - "\n", + "\n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6617,170 +6676,223 @@ " \n", " \n", " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", + " \n", " \n", - " \n", + " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6806,20 +6918,16 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -6827,12 +6935,12 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6841,16 +6949,13 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -6858,16 +6963,16 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6893,38 +6998,38 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -6932,10 +7037,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6953,7 +7058,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6970,7 +7075,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6986,7 +7091,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7031,7 +7136,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7039,8 +7144,8 @@ "\n", "\n", "node2->leaf3\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", @@ -7062,37 +7167,37 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -7100,10 +7205,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7121,7 +7226,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7138,7 +7243,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7154,7 +7259,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7198,7 +7303,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7206,8 +7311,8 @@ "\n", "\n", "node2->leaf4\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", @@ -7229,73 +7334,73 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -7303,10 +7408,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7324,7 +7429,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7341,7 +7446,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7357,7 +7462,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7401,7 +7506,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7409,8 +7514,8 @@ "\n", "\n", "node5->leaf6\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", @@ -7432,51 +7537,51 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -7484,10 +7589,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7505,7 +7610,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7522,7 +7627,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7538,7 +7643,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7582,7 +7687,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7590,13 +7695,13 @@ "\n", "\n", "node5->leaf7\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "node1\n", - "\n", + "\n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7612,185 +7717,189 @@ " \n", " \n", " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7799,7 +7908,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7808,36 +7917,49 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7871,15 +7993,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7891,12 +8013,12 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7905,12 +8027,12 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7922,16 +8044,16 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7939,19 +8061,19 @@ "\n", "\n", "node1->node2\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "node1->node5\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "node8\n", - "\n", + "\n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7967,571 +8089,575 @@ " \n", " \n", " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8540,7 +8666,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8549,35 +8675,51 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -8609,15 +8751,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8629,16 +8771,13 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -8646,12 +8785,12 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8663,16 +8802,16 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8681,7 +8820,7 @@ "\n", "\n", "node9\n", - "\n", + "\n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8697,122 +8836,175 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", + " \n", " \n", - " \n", + " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8838,20 +9030,16 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -8859,12 +9047,12 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8873,16 +9061,13 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -8890,16 +9075,16 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8907,7 +9092,7 @@ "\n", "\n", "node12\n", - "\n", + "\n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8923,555 +9108,603 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", 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" \n", " \n", " \n", @@ -9492,20 +9725,16 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -9513,16 +9742,13 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -9530,12 +9756,12 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9547,16 +9773,16 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9582,43 +9808,43 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -9626,10 +9852,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9647,7 +9873,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9664,7 +9890,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9680,7 +9906,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9723,7 +9949,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9754,33 +9980,33 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -9788,10 +10014,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9809,7 +10035,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9826,7 +10052,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9842,7 +10068,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9886,7 +10112,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9900,7 +10126,7 @@ "\n", "\n", "node13\n", - "\n", + "\n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9916,407 +10142,460 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " 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\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", + " \n", " \n", - " \n", + " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -10342,20 +10621,16 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -10363,12 +10638,12 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -10377,16 +10652,13 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -10394,16 +10666,16 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -10411,7 +10683,7 @@ "\n", "\n", "node16\n", - "\n", + "\n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -10427,212 +10699,265 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", + " \n", " \n", - " \n", + " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -10658,20 +10983,16 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -10679,12 +11000,12 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -10693,16 +11014,13 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -10710,16 +11028,16 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -10745,259 +11063,259 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -11005,10 +11323,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -11026,7 +11344,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -11043,7 +11361,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -11059,7 +11377,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -11105,7 +11423,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -11136,102 +11454,102 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -11239,10 +11557,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -11260,7 +11578,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -11277,7 +11595,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -11293,7 +11611,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -11336,7 +11654,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -11367,99 +11685,99 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -11467,10 +11785,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -11488,7 +11806,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -11505,7 +11823,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -11521,7 +11839,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -11565,7 +11883,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -11596,67 +11914,67 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -11664,10 +11982,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -11685,7 +12003,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -11702,7 +12020,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -11718,7 +12036,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -11761,7 +12079,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -11799,7 +12117,7 @@ "\n", "\n", "node0\n", - "\n", + "\n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -11815,776 +12133,824 @@ " \n", " \n", " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(1, 1, figsize=(10, 5))\n", + "ax.spines['top'].set_visible(False)\n", + "ax.spines['right'].set_visible(False)\n", + "ax.spines['left'].set_linewidth(.3)\n", + "ax.spines['bottom'].set_linewidth(.3)\n", + " \n", + " \n", + "bins = np.linspace(start=1, stop=3+1, num=3, endpoint=True)\n", + "\n", + "ax.hist(X_hist, bins=bins, align='mid');\n" + ] + }, + { + "cell_type": "code", + "execution_count": 204, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([1. , 2.5, 4. ])" + ] + }, + "execution_count": 204, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "bins" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, { "cell_type": "code", "execution_count": 34, @@ -12744,6 +13605,27 @@ "trees.viz_leaf_samples(light_dtree_reg)" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, { "cell_type": "code", "execution_count": 35, @@ -16332,7 +17214,12 @@ "title_cell": "Table of Contents", "title_sidebar": "Contents", "toc_cell": false, - "toc_position": {}, + "toc_position": { + "height": "calc(100% - 180px)", + "left": "10px", + "top": "150px", + "width": "262px" + }, "toc_section_display": true, "toc_window_display": true } From f65ce04a7de2eec2b693c5228cff9815cd3f8370 Mon Sep 17 00:00:00 2001 From: tlapusan Date: Thu, 10 Dec 2020 10:13:57 +0200 Subject: [PATCH 08/16] Add xticks for categorical split --- dtreeviz/trees.py | 6 ++---- 1 file changed, 2 insertions(+), 4 deletions(-) diff --git a/dtreeviz/trees.py b/dtreeviz/trees.py index a38c097e..da29901d 100644 --- a/dtreeviz/trees.py +++ b/dtreeviz/trees.py @@ -1129,8 +1129,6 @@ def wedge(ax, x, color): split = node.split() # ax.scatter(X_feature, y_train, s=5, c=colors['scatter_marker'], alpha=colors['scatter_marker_alpha'], lw=.3) - - ax.plot([overall_feature_range[0], split], [np.mean(left), np.mean(left)], '--', color=colors['split_line'], linewidth=1) ax.plot([split, split], [*y_range], '--', color=colors['split_line'], linewidth=1) @@ -1143,11 +1141,11 @@ def wedge(ax, x, color): else: left_index, right_index = node.split_samples() - - print(f"id {node.id}, split_left {node.split()[0]}, {overall_feature_range }") + print(f"id {node.id}, split_left {node.split()[0]}, {overall_feature_range }, { np.unique(X_feature)}") ax.set_xlim(overall_feature_range[0] - 0.05, overall_feature_range[1] + 0.05) ax.scatter(X_feature[left_index], y_train[left_index], s=5, c=colors["classes"][2][0], alpha=colors['scatter_marker_alpha'], lw=.3) ax.scatter(X_feature[right_index], y_train[right_index], s=5, c=colors["classes"][2][1], alpha=colors['scatter_marker_alpha'], lw=.3) + ax.set_xticks(np.unique(np.concatenate((X_feature, np.asarray(overall_feature_range))))) # indices = np.sum([X_feature == split_value for split_value in node.split()[0]], axis=0) From fb5b2a8e719ab299f0c606348769f9ba49190060 Mon Sep 17 00:00:00 2001 From: tlapusan Date: Mon, 14 Dec 2020 09:52:12 +0200 Subject: [PATCH 09/16] Add basic implementation for categorical split --- dtreeviz/colors.py | 6 +- dtreeviz/trees.py | 49 +- .../dtreeviz_lightgbm_visualisations.ipynb | 9798 ++++++++--------- notebooks/dtreeviz_spark_visualisations.ipynb | 7756 ++++++------- 4 files changed, 8428 insertions(+), 9181 deletions(-) diff --git a/dtreeviz/colors.py b/dtreeviz/colors.py index 2a97cbf7..f49eccd4 100644 --- a/dtreeviz/colors.py +++ b/dtreeviz/colors.py @@ -7,6 +7,8 @@ LIGHTBLUE = '#a6bddb' GREY = '#444443' WEDGE_COLOR = GREY +CATEGORICAL_SPLIT_LEFT= '#FE5733' +CATEGORICAL_SPLIT_RIGHT = '#a1dab4' HIGHLIGHT_COLOR = '#D67C03' @@ -49,7 +51,9 @@ 'tick_label': GREY, 'leaf_label': GREY, 'pie': GREY, - 'hist_bar': LIGHTBLUE + 'hist_bar': LIGHTBLUE, + 'categorical_split_left': CATEGORICAL_SPLIT_LEFT, + 'categorical_split_right': CATEGORICAL_SPLIT_RIGHT } diff --git a/dtreeviz/trees.py b/dtreeviz/trees.py index da29901d..eba2bfcc 100644 --- a/dtreeviz/trees.py +++ b/dtreeviz/trees.py @@ -869,7 +869,11 @@ def get_leaves(): llabel = all_llabel rlabel = all_rlabel - lcolor = rcolor = colors['arrow'] + if node.is_categorical_split(): + lcolor, rcolor = colors["categorical_split_left"], colors["categorical_split_right"] + else: + lcolor = rcolor = colors['arrow'] + lpw = rpw = "0.3" if node.left.id in highlight_path: lcolor = colors['highlight'] @@ -1140,44 +1144,25 @@ def wedge(ax, x, color): wedge(ax, X[node.feature()], color=colors['highlight']) else: left_index, right_index = node.split_samples() + tw = (xmax - xmin) * .018 - print(f"id {node.id}, split_left {node.split()[0]}, {overall_feature_range }, { np.unique(X_feature)}") - ax.set_xlim(overall_feature_range[0] - 0.05, overall_feature_range[1] + 0.05) - ax.scatter(X_feature[left_index], y_train[left_index], s=5, c=colors["classes"][2][0], alpha=colors['scatter_marker_alpha'], lw=.3) - ax.scatter(X_feature[right_index], y_train[right_index], s=5, c=colors["classes"][2][1], alpha=colors['scatter_marker_alpha'], lw=.3) - ax.set_xticks(np.unique(np.concatenate((X_feature, np.asarray(overall_feature_range))))) - - - # indices = np.sum([X_feature == split_value for split_value in node.split()[0]], axis=0) - # X_hist = [X_feature[indices == 1], X_feature[indices == 0]] - - # bins = np.linspace(start=overall_feature_range[0], stop=overall_feature_range[1], num=overall_feature_range[1] * 5, endpoint=True) - - # print(f"\nrange: {overall_feature_range}, r={r}, nbins={nbins}, len(bins)={len(bins)}, binwidth={binwidth}\n{bins}") - # bins[-1] = overall_feature_range[1] # make sure rounding doesn't kill last value on right - # hist, bins, barcontainers = ax.hist(X_hist, - # align='mid', - # bins=bins) - # Alter appearance of each bar - # for patch in barcontainers: - # for rect in patch.patches: - # rect.set_linewidth(.5) - # rect.set_edgecolor(colors['rect_edge']) - # ax.set_yticks([0, max([max(h) for h in hist])]) - + # print(f"id {node.id}, split_left {node.split()[0]}, {overall_feature_range }, { np.unique(X_feature)}") + ax.set_xlim(overall_feature_range[0] - tw, overall_feature_range[1] + tw) + ax.scatter(X_feature[left_index], y_train[left_index], s=5, c=colors["categorical_split_left"], alpha=colors['scatter_marker_alpha'], lw=.3) + ax.scatter(X_feature[right_index], y_train[right_index], s=5, c=colors["categorical_split_right"], alpha=colors['scatter_marker_alpha'], lw=.3) + ax.plot([xmin - tw, xmax + tw], [np.mean(y_train[left_index]), np.mean(y_train[left_index])], '--', color=colors["categorical_split_left"], + linewidth=1) + ax.plot([xmin - tw, xmax + tw], [np.mean(y_train[right_index]), np.mean(y_train[right_index])], '--', color=colors["categorical_split_right"], + linewidth=1) + ax.set_xticks(np.unique(np.concatenate((X_feature, np.asarray(overall_feature_range))))) - # xticks = list(overall_feature_range) + node.split()[0] - # print(f"overall feature range {overall_feature_range}") - # ax.set_xticks(xticks) - # ax.scatter(X_feature, y_train, s=5, c=colors['scatter_marker'], alpha=colors['scatter_marker_alpha'], lw=.3) - # # left_split_values = node.split()[0] # for split_value in left_split_values: # wedge(ax, split_value, color=colors['wedge']) # - # if highlight_node: - # wedge(ax, X[node.feature()], color=colors['highlight']) + if highlight_node: + wedge(ax, X[node.feature()], color=colors['highlight']) # plt.tight_layout() if filename is not None: diff --git a/notebooks/dtreeviz_lightgbm_visualisations.ipynb b/notebooks/dtreeviz_lightgbm_visualisations.ipynb index 51a2f8b2..91a49936 100644 --- a/notebooks/dtreeviz_lightgbm_visualisations.ipynb +++ b/notebooks/dtreeviz_lightgbm_visualisations.ipynb @@ -51,7 +51,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -60,7 +60,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 10, "metadata": {}, "outputs": [ { @@ -312,7 +312,7 @@ "[891 rows x 12 columns]" ] }, - "execution_count": 4, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } @@ -323,7 +323,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ @@ -350,7 +350,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ @@ -372,16 +372,15 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "[LightGBM] [Warning] Met negative value in categorical features, will convert it to NaN\n", "[LightGBM] [Info] Number of positive: 268, number of negative: 444\n", - "[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000723 seconds.\n", + "[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000604 seconds.\n", "You can set `force_col_wise=true` to remove the overhead.\n", "[LightGBM] [Info] Total Bins 190\n", "[LightGBM] [Info] Number of data points in the train set: 712, number of used features: 6\n", @@ -626,16 +625,16 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 8, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" }, @@ -658,7 +657,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 15, "metadata": {}, "outputs": [ { @@ -1007,10 +1006,10 @@ "\n" ], "text/plain": [ - "" + "" ] }, - "execution_count": 9, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" } @@ -1028,7 +1027,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 16, "metadata": {}, "outputs": [], "source": [ @@ -1038,7 +1037,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 17, "metadata": {}, "outputs": [], "source": [ @@ -1078,118 +1077,118 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1198,10 +1197,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1223,7 +1222,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1258,10 +1257,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1274,7 +1273,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1312,7 +1311,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1335,118 +1334,118 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1455,10 +1454,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1477,7 +1476,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1517,10 +1516,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1533,7 +1532,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1575,7 +1574,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1808,118 +1807,118 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1928,10 +1927,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1949,7 +1948,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1998,10 +1997,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2017,7 +2016,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2042,7 +2041,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2050,14 +2049,14 @@ "\n", "\n", "node3->node4\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "node3->node7\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", @@ -2077,118 +2076,118 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2197,10 +2196,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2222,7 +2221,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2257,10 +2256,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2273,7 +2272,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2314,7 +2313,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2343,118 +2342,118 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2463,10 +2462,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2485,7 +2484,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2525,10 +2524,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2541,7 +2540,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2583,7 +2582,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2659,118 +2658,118 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2779,10 +2778,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2801,7 +2800,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2841,10 +2840,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2857,7 +2856,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2898,7 +2897,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3024,118 +3023,118 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3144,10 +3143,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3169,7 +3168,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3204,10 +3203,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3220,7 +3219,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3257,7 +3256,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3286,118 +3285,118 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3406,10 +3405,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3431,7 +3430,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3466,10 +3465,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3482,7 +3481,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3523,7 +3522,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3598,118 +3597,118 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3718,10 +3717,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3743,7 +3742,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3778,10 +3777,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3794,7 +3793,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3835,7 +3834,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4032,118 +4031,118 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4152,10 +4151,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4171,7 +4170,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4207,10 +4206,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4226,7 +4225,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4252,7 +4251,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4260,15 +4259,15 @@ "\n", "\n", "node0->node1\n", - "\n", - "\n", + "\n", + "\n", "<\n", "\n", "\n", "\n", "node0->node10\n", - "\n", - "\n", + "\n", + "\n", "≥\n", "\n", "\n", @@ -4350,7 +4349,7 @@ "" ], "text/plain": [ - "" + "" ] }, "execution_count": 18, @@ -4371,7 +4370,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 19, "metadata": {}, "outputs": [ { @@ -4419,118 +4418,118 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4542,10 +4541,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4567,7 +4566,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4602,10 +4601,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4618,7 +4617,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4666,7 +4665,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4743,118 +4742,118 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4866,10 +4865,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4887,7 +4886,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4936,10 +4935,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4955,7 +4954,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4980,7 +4979,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5010,118 +5009,118 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5133,10 +5132,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5158,7 +5157,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5193,10 +5192,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5209,7 +5208,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5259,7 +5258,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5289,118 +5288,118 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5412,10 +5411,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5431,7 +5430,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5467,10 +5466,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5486,7 +5485,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5512,7 +5511,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5633,10 +5632,10 @@ "" ], "text/plain": [ - "" + "" ] }, - "execution_count": 15, + "execution_count": 19, "metadata": {}, "output_type": "execute_result" } @@ -5888,7 +5887,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 37, "metadata": {}, "outputs": [], "source": [ @@ -5923,14 +5922,14 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 38, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000594 seconds.\n", + "[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000998 seconds.\n", "You can set `force_row_wise=true` to remove the overhead.\n", "And if memory is not enough, you can set `force_col_wise=true`.\n", "[LightGBM] [Info] Total Bins 21\n", @@ -5963,7 +5962,13 @@ "output_type": "stream", "text": [ "/Users/tudorl/Documents/workspace/personal/dtreeviz/venv/lib/python3.7/site-packages/lightgbm/engine.py:151: UserWarning: Found `num_tree` in params. Will use it instead of argument\n", - " warnings.warn(\"Found `{}` in params. Will use it instead of argument\".format(alias))\n" + " warnings.warn(\"Found `{}` in params. Will use it instead of argument\".format(alias))\n", + "/Users/tudorl/Documents/workspace/personal/dtreeviz/venv/lib/python3.7/site-packages/lightgbm/basic.py:1551: UserWarning: Using categorical_feature in Dataset.\n", + " warnings.warn('Using categorical_feature in Dataset.')\n", + "/Users/tudorl/Documents/workspace/personal/dtreeviz/venv/lib/python3.7/site-packages/lightgbm/basic.py:1286: UserWarning: Overriding the parameters from Reference Dataset.\n", + " warnings.warn('Overriding the parameters from Reference Dataset.')\n", + "/Users/tudorl/Documents/workspace/personal/dtreeviz/venv/lib/python3.7/site-packages/lightgbm/basic.py:1098: UserWarning: categorical_column in param dict is overridden.\n", + " warnings.warn('{} in param dict is overridden.'.format(cat_alias))\n" ] } ], @@ -5980,16 +5985,16 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 39, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 10, + "execution_count": 39, "metadata": {}, "output_type": "execute_result" }, @@ -6012,7 +6017,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 40, "metadata": {}, "outputs": [ { @@ -6329,10 +6334,10 @@ "\n" ], "text/plain": [ - "" + "" ] }, - "execution_count": 11, + "execution_count": 40, "metadata": {}, "output_type": "execute_result" } @@ -6350,7 +6355,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 41, "metadata": {}, "outputs": [], "source": [ @@ -6359,37 +6364,22 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 45, "metadata": { "scrolled": false }, "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "id 2, split_left [1], (0, 1)\n", - "id 5, split_left [1], (0, 1)\n", - "id 1, split_left [0], (-1, 2)\n", - "id 9, split_left [1], (0, 1)\n", - "id 13, split_left [1], (0, 1)\n", - "id 16, split_left [1], (0, 1)\n", - "id 12, split_left [3], (1, 3)\n", - "id 8, split_left [1], (-1, 2)\n", - "id 0, split_left [1], (1, 3)\n" - ] - }, { "data": { "image/svg+xml": [ - "\n", + "\n", "\n", "G\n", - "\n", + "\n", "\n", "\n", "node2\n", - "\n", + "\n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6405,174 +6395,112 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6598,16 +6526,20 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -6615,12 +6547,12 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6629,13 +6561,16 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -6643,6 +6578,12 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -6652,7 +6593,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6660,7 +6601,7 @@ "\n", "\n", "node5\n", - "\n", + "\n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6676,223 +6617,161 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6918,16 +6797,20 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -6935,12 +6818,12 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6949,13 +6832,16 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -6963,6 +6849,12 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -6972,7 +6864,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6998,38 +6890,38 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -7037,10 +6929,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7058,7 +6950,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7075,7 +6967,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7091,7 +6983,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7136,7 +7028,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7144,8 +7036,8 @@ "\n", "\n", "node2->leaf3\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", @@ -7167,37 +7059,37 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -7205,10 +7097,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7226,7 +7118,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7243,7 +7135,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7259,7 +7151,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7303,7 +7195,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7311,14 +7203,14 @@ "\n", "\n", "node2->leaf4\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "leaf6\n", - "\n", - "\n", + "\n", + "\n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -7408,10 +7300,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7429,7 +7321,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7446,7 +7338,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7462,7 +7354,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7506,7 +7398,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7514,14 +7406,14 @@ "\n", "\n", "node5->leaf6\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "leaf7\n", - "\n", - "\n", + "\n", + "\n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -7589,10 +7481,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7610,7 +7502,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7627,7 +7519,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7643,7 +7535,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7687,7 +7579,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7695,13 +7587,13 @@ "\n", "\n", "node5->leaf7\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "node1\n", - "\n", + "\n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7717,189 +7609,189 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7908,7 +7800,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7917,7 +7809,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7925,7 +7817,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7933,12 +7825,12 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7946,7 +7838,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7954,7 +7846,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7995,10 +7887,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8015,7 +7907,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8029,7 +7921,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8044,6 +7936,12 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -8053,7 +7951,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8061,19 +7959,19 @@ "\n", "\n", "node1->node2\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "node1->node5\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "node8\n", - "\n", + "\n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8089,575 +7987,575 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + 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\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8666,7 +8564,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8675,7 +8573,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8683,7 +8581,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8691,12 +8589,12 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8704,7 +8602,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8712,7 +8610,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8753,10 +8651,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8773,7 +8671,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8787,7 +8685,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8802,6 +8700,12 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -8811,7 +8715,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8820,7 +8724,7 @@ "\n", "\n", "node9\n", - "\n", + "\n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8836,175 +8740,113 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9030,16 +8872,20 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -9047,12 +8893,12 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9061,20 +8907,29 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -9084,7 +8939,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9092,7 +8947,7 @@ "\n", "\n", "node12\n", - "\n", + "\n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9108,603 +8963,562 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " 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" \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9725,16 +9539,19 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -9742,13 +9559,16 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -9756,12 +9576,12 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9773,6 +9593,12 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -9782,7 +9608,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9791,8 +9617,8 @@ "\n", "\n", "leaf10\n", - "\n", - "\n", + "\n", + "\n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -9852,10 +9678,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9873,7 +9699,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9890,7 +9716,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9906,7 +9732,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9949,7 +9775,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9957,14 +9783,14 @@ "\n", "\n", "node9->leaf10\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "leaf11\n", - "\n", - "\n", + "\n", + "\n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -10014,10 +9840,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -10035,7 +9861,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -10052,7 +9878,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -10068,7 +9894,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -10112,7 +9938,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -10120,13 +9946,13 @@ "\n", "\n", "node9->leaf11\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "node13\n", - "\n", + "\n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -10142,460 +9968,398 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " 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+ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -10621,16 +10385,20 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -10638,12 +10406,12 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -10652,13 +10420,16 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -10666,6 +10437,12 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -10675,7 +10452,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -10683,7 +10460,7 @@ "\n", "\n", "node16\n", - "\n", + "\n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -10699,265 +10476,203 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -10983,16 +10698,20 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -11000,12 +10719,12 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -11014,13 +10733,16 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -11028,6 +10750,12 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -11037,7 +10765,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -11046,8 +10774,8 @@ "\n", "\n", "leaf14\n", - "\n", - "\n", + "\n", + "\n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " 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\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -11323,10 +11051,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -11344,7 +11072,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -11361,7 +11089,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -11377,7 +11105,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -11423,7 +11151,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -11431,14 +11159,14 @@ "\n", "\n", "node13->leaf14\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "leaf15\n", - "\n", - "\n", + "\n", + "\n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -11557,10 +11285,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -11578,7 +11306,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -11595,7 +11323,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -11611,7 +11339,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -11654,7 +11382,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -11662,14 +11390,14 @@ "\n", "\n", "node13->leaf15\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "leaf17\n", - "\n", - "\n", + "\n", + "\n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -11785,10 +11513,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -11806,7 +11534,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -11823,7 +11551,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -11839,7 +11567,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -11883,7 +11611,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -11891,14 +11619,14 @@ "\n", "\n", "node16->leaf17\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "leaf18\n", - "\n", - "\n", + "\n", + "\n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -11982,10 +11710,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -12003,7 +11731,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -12020,7 +11748,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -12036,7 +11764,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -12079,7 +11807,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -12087,37 +11815,37 @@ "\n", "\n", "node16->leaf18\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "node12->node13\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "node12->node16\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "node8->node9\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "node8->node12\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "node0\n", - "\n", + "\n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -12133,854 +11861,816 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " 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\n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -13032,6 +12725,12 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -13041,7 +12740,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -13049,16 +12748,16 @@ "\n", "\n", "node0->node1\n", - "\n", - "\n", - "<\n", + "\n", + "\n", + "<\n", "\n", "\n", "\n", "node0->node8\n", - "\n", - "\n", - "≥\n", + "\n", + "\n", + "≥\n", "\n", "\n", "\n", @@ -13069,523 +12768,21 @@ "" ], "text/plain": [ - "" + "" ] }, - "execution_count": 26, + "execution_count": 45, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "trees.dtreeviz(light_dtree_reg)" + "trees.dtreeviz(light_dtree_reg, show_node_labels=False)" ] }, { "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[2, 5, 1, 9, 13, 16, 12, 8, 0]" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "[node.id for node in light_dtree_reg.internal]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 257, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(array([ 5, 9, 11, 12, 13, 15, 16, 19, 21, 27, 28, 29, 32, 33, 35, 38, 44,\n", - " 45, 46, 59, 61, 64, 66, 67, 74, 75, 77, 79, 80, 84, 85]),\n", - " array([ 0, 1, 2, 3, 4, 6, 7, 8, 10, 14, 17, 18, 20, 22, 23, 24, 25,\n", - " 26, 30, 31, 34, 36, 37, 39, 40, 41, 42, 43, 47, 48, 49, 50, 51, 52,\n", - " 53, 54, 55, 56, 57, 58, 60, 62, 63, 65, 68, 69, 70, 71, 72, 73, 76,\n", - " 78, 81, 82, 83, 86]))" - ] - }, - "execution_count": 257, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "light_dtree_reg.internal[0].split_samples()" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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"cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 34, + "execution_count": 46, "metadata": {}, "outputs": [ { @@ -13605,27 +12802,6 @@ "trees.viz_leaf_samples(light_dtree_reg)" ] }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, { "cell_type": "code", "execution_count": 35, diff --git a/notebooks/dtreeviz_spark_visualisations.ipynb b/notebooks/dtreeviz_spark_visualisations.ipynb index 67ba7849..353e681c 100644 --- a/notebooks/dtreeviz_spark_visualisations.ipynb +++ b/notebooks/dtreeviz_spark_visualisations.ipynb @@ -18,7 +18,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -30,7 +30,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -65,7 +65,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -74,7 +74,7 @@ "3" ] }, - "execution_count": 3, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -86,7 +86,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -98,7 +98,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -107,7 +107,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 7, "metadata": { "scrolled": true }, @@ -165,7 +165,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -192,7 +192,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -201,14 +201,14 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "DecisionTreeClassificationModel: uid=DecisionTreeClassifier_a2395fca1824, depth=4, numNodes=17, numClasses=2, numFeatures=7\n", + "DecisionTreeClassificationModel: uid=DecisionTreeClassifier_acc27a9d0539, depth=4, numNodes=17, numClasses=2, numFeatures=7\n", " If (feature 1 in {0.0})\n", " If (feature 3 <= 3.5)\n", " If (feature 4 <= 2.5)\n", @@ -6446,7 +6446,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ @@ -6455,7 +6455,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ @@ -6465,7 +6465,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ @@ -6477,7 +6477,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 14, "metadata": {}, "outputs": [], "source": [ @@ -6489,7 +6489,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 15, "metadata": {}, "outputs": [ { @@ -6680,7 +6680,7 @@ "[891 rows x 8 columns]" ] }, - "execution_count": 29, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" } @@ -6698,7 +6698,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 16, "metadata": {}, "outputs": [], "source": [ @@ -6715,27 +6715,52 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 23, "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "tree type \n" - ] - }, + "data": { + "text/plain": [ + "Pclass 3.0\n", + "Sex_label 1.0\n", + "Embarked_label 0.0\n", + "Age_mean 4.0\n", + "SibSp 1.0\n", + "Parch 1.0\n", + "Survived 1.0\n", + "Name: 10, dtype: float64" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "X=dataset_reg[features_reg].iloc[10]\n", + "X" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ { "data": { "image/svg+xml": [ - "\n", - "\n", + "\n", + "\n", "G\n", - "\n", + "\n", + "\n", + "cluster_instance\n", + "\n", "\n", "\n", "node2\n", - "\n", + "Node 2\n", + "\n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6751,185 +6776,189 @@ " \n", " \n", " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + 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\n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6937,7 +6966,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6945,7 +6974,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6953,25 +6982,12 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6997,15 +7013,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7013,12 +7029,12 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7031,12 +7047,12 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7049,16 +7065,22 @@ " \n", " \n", " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7066,7 +7088,8 @@ "\n", "\n", "node5\n", - "\n", + "Node 5\n", + "\n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -7147,10 +7170,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7169,7 +7192,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7187,7 +7210,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7224,10 +7247,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7240,7 +7263,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7258,7 +7281,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7275,13 +7298,13 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7292,7 +7315,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7301,8 +7324,9 @@ "\n", "\n", "leaf3\n", - "\n", - "\n", + "\n", + "Node 3\n", + "\n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -7427,10 +7451,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7446,7 +7470,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7463,7 +7487,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7509,7 +7533,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7517,14 +7541,15 @@ "\n", "\n", "node2->leaf3\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "leaf4\n", - "\n", - "\n", + "\n", + "Node 4\n", + "\n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -7614,10 +7639,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7633,7 +7658,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7650,7 +7675,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7697,7 +7722,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7705,14 +7730,15 @@ "\n", "\n", "node2->leaf4\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "leaf6\n", - "\n", - "\n", + "\n", + "Node 6\n", + "\n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -7786,10 +7812,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7805,7 +7831,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7822,7 +7848,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7868,7 +7894,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7876,14 +7902,15 @@ "\n", "\n", "node5->leaf6\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "leaf7\n", - "\n", - "\n", + "\n", + "Node 7\n", + "\n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -7914,10 +7941,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7933,7 +7960,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7950,7 +7977,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7995,7 +8022,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8003,13 +8030,14 @@ "\n", "\n", "node5->leaf7\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "node1\n", - "\n", + "Node 1\n", + "\n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - 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\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -8253,10 +8281,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8272,7 +8300,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8307,10 +8335,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8323,7 +8351,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8341,7 +8369,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8358,13 +8386,13 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8375,7 +8403,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8383,19 +8411,21 @@ "\n", "\n", "node1->node2\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", 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\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -10111,7 +10150,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -10129,7 +10168,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -10166,10 +10205,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -10182,7 +10221,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -10200,7 +10239,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -10214,24 +10253,24 @@ " \n", " \n", " \n", - " \n", + " \n", 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\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -10695,10 +10735,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -10714,7 +10754,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -10731,7 +10771,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -10778,7 +10818,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -10786,14 +10826,15 @@ "\n", "\n", "node9->leaf10\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "leaf11\n", - "\n", - "\n", + "\n", + "Node 11\n", + "\n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -10862,10 +10903,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -10881,7 +10922,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -10898,7 +10939,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -10945,7 +10986,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -10953,14 +10994,15 @@ "\n", "\n", "node9->leaf11\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "leaf13\n", - "\n", - "\n", + "\n", + "Node 13\n", + "\n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -11178,10 +11220,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -11197,7 +11239,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -11214,7 +11256,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -11260,7 +11302,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -11268,14 +11310,15 @@ "\n", "\n", "node12->leaf13\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "leaf14\n", - "\n", - "\n", + "\n", + "Node 14\n", + "\n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -11313,10 +11356,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -11332,7 +11375,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -11349,7 +11392,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -11394,7 +11437,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -11402,25 +11445,27 @@ "\n", "\n", "node12->leaf14\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "node8->node9\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", 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\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -12362,7 +12410,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -12380,7 +12428,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -12418,10 +12466,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -12434,7 +12482,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -12452,7 +12500,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -12483,24 +12531,24 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -12508,35 +12556,76 @@ "\n", "\n", "node0->node1\n", - "\n", - "\n", - "<\n", + "\n", + "\n", + "<\n", "\n", "\n", "\n", "node0->node8\n", - "\n", - "\n", - "≥\n", + "\n", + "\n", + "≥\n", "\n", "\n", "\n", "\n", "\n", + "\n", + "\n", + "X_y\n", + "\n", + "\n", + "Pclass\n", + "\n", + "Sex_label\n", + "\n", + "Embarked_label\n", + "\n", + "Age_mean\n", + "\n", + "SibSp\n", + "\n", + "Parch\n", + "\n", + "Survived\n", + "\n", + "3.00\n", + "\n", + "1.00\n", + "\n", + "0.00\n", + "\n", + "4.00\n", + "\n", + "1.00\n", + "\n", + "1.00\n", + "\n", + "1.00\n", + "\n", + "\n", + "\n", + "leaf13->X_y\n", + "\n", + "\n", + "  Prediction\n", + " 23.33\n", + "\n", "\n", "" ], "text/plain": [ - "" + "" ] }, - "execution_count": 31, + "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "trees.dtreeviz(spark_dtree_reg)" + "trees.dtreeviz(spark_dtree_reg, show_node_labels=True, X=X)" ] }, { @@ -12548,16 +12637,9 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 18, "metadata": {}, "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "tree type \n" - ] - }, { "data": { "image/png": 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\n", From fb023e11985fdeb3cb30c10c1eedba40e90b5878 Mon Sep 17 00:00:00 2001 From: tlapusan Date: Thu, 21 Jan 2021 17:01:06 +0200 Subject: [PATCH 10/16] Center wedges and recalculate nbins for categorical splits (classifier trees) --- dtreeviz/trees.py | 27 +- .../dtreeviz_lightgbm_visualisations.ipynb | 8126 ++++++++-------- notebooks/dtreeviz_spark_visualisations.ipynb | 8401 ++++++++--------- 3 files changed, 8241 insertions(+), 8313 deletions(-) diff --git a/dtreeviz/trees.py b/dtreeviz/trees.py index eba2bfcc..1397dc20 100644 --- a/dtreeviz/trees.py +++ b/dtreeviz/trees.py @@ -869,7 +869,7 @@ def get_leaves(): llabel = all_llabel rlabel = all_rlabel - if node.is_categorical_split(): + if node.is_categorical_split() and not shadow_tree.is_classifier(): lcolor, rcolor = colors["categorical_split_left"], colors["categorical_split_right"] else: lcolor = rcolor = colors['arrow'] @@ -939,6 +939,10 @@ def class_split_viz(node: ShadowDecTreeNode, X: np.array = None, highlight_node: bool = False ): + + def _get_bins(overall_range, nbins_): + return np.linspace(start=overall_range[0], stop=overall_range[1], num=nbins_, endpoint=True) + height_range = (.5, 1.5) h = prop_size(n=node_heights[node.id], counts=node_heights.values(), output_range=height_range) figsize = (3.3, h) @@ -948,10 +952,17 @@ def class_split_viz(node: ShadowDecTreeNode, # Get X, y data for all samples associated with this node. X_feature = X_train[:, node.feature()] - X_feature, y_train = X_feature[node.samples()], y_train[node.samples()] + X_node_feature, y_train = X_feature[node.samples()], y_train[node.samples()] n_classes = node.shadow_tree.nclasses() nbins = get_num_bins(histtype, n_classes) + + # for categorical splits, we need to ensure that each vertical bar will represent only one categorical feature value + if node.is_categorical_split(): + feature_unique_size = np.unique(X_feature).size + # keep the bar widths as uniform as possible for all node visualisations + nbins = nbins if nbins > feature_unique_size else feature_unique_size + 1 + overall_feature_range = (np.min(X_train[:, node.feature()]), np.max(X_train[:, node.feature()])) overall_feature_range_wide = (overall_feature_range[0] - overall_feature_range[0] * .08, @@ -965,7 +976,7 @@ def class_split_viz(node: ShadowDecTreeNode, class_names = node.shadow_tree.class_names class_values = node.shadow_tree.classes() - X_hist = [X_feature[y_train == cl] for cl in class_values] + X_hist = [X_node_feature[y_train == cl] for cl in class_values] if histtype == 'strip': ax.yaxis.set_visible(False) ax.spines['left'].set_visible(False) @@ -982,9 +993,7 @@ def class_split_viz(node: ShadowDecTreeNode, else: X_colors = [colors[cl] for cl in class_values] - bins = np.linspace(start=overall_feature_range[0], stop=overall_feature_range[1], num=nbins, endpoint=True) - # print(f"\nrange: {overall_feature_range}, r={r}, nbins={nbins}, len(bins)={len(bins)}, binwidth={binwidth}\n{bins}") - # bins[-1] = overall_feature_range[1] # make sure rounding doesn't kill last value on right + bins = _get_bins(overall_feature_range, nbins) hist, bins, barcontainers = ax.hist(X_hist, color=X_colors, align='mid', @@ -1026,7 +1035,13 @@ def wedge(ax, x, color): color=colors['text_wedge']) else: left_split_values = node.split()[0] + bins = _get_bins(overall_feature_range, nbins) for split_value in left_split_values: + # to display the wedge exactly in the middle of the vertical bar + for bin_index in range(len(bins) - 1): + if bins[bin_index] <= split_value <= bins[bin_index + 1]: + split_value = (bins[bin_index] + bins[bin_index + 1]) / 2 + break tria = np.array( [[split_value, tipy], [split_value - tw, -th], [split_value + tw, -th]]) t = patches.Polygon(tria, facecolor=color) diff --git a/notebooks/dtreeviz_lightgbm_visualisations.ipynb b/notebooks/dtreeviz_lightgbm_visualisations.ipynb index 91a49936..a4cbbfe2 100644 --- a/notebooks/dtreeviz_lightgbm_visualisations.ipynb +++ b/notebooks/dtreeviz_lightgbm_visualisations.ipynb @@ -51,7 +51,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -60,7 +60,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -312,7 +312,7 @@ "[891 rows x 12 columns]" ] }, - "execution_count": 10, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -323,7 +323,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -350,7 +350,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -372,15 +372,27 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 7, "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/tudorl/Documents/workspace/personal/dtreeviz/venv/lib/python3.7/site-packages/lightgbm/basic.py:1551: UserWarning: Using categorical_feature in Dataset.\n", + " warnings.warn('Using categorical_feature in Dataset.')\n", + "/Users/tudorl/Documents/workspace/personal/dtreeviz/venv/lib/python3.7/site-packages/lightgbm/basic.py:1286: UserWarning: Overriding the parameters from Reference Dataset.\n", + " warnings.warn('Overriding the parameters from Reference Dataset.')\n", + "/Users/tudorl/Documents/workspace/personal/dtreeviz/venv/lib/python3.7/site-packages/lightgbm/basic.py:1098: UserWarning: categorical_column in param dict is overridden.\n", + " warnings.warn('{} in param dict is overridden.'.format(cat_alias))\n" + ] + }, { "name": "stdout", "output_type": "stream", "text": [ "[LightGBM] [Info] Number of positive: 268, number of negative: 444\n", - "[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000604 seconds.\n", + "[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001330 seconds.\n", "You can set `force_col_wise=true` to remove the overhead.\n", "[LightGBM] [Info] Total Bins 190\n", "[LightGBM] [Info] Number of data points in the train set: 712, number of used features: 6\n", @@ -587,18 +599,6 @@ "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[100]\ttraining's binary_logloss: 0.432906\tvalid_1's binary_logloss: 0.461309\n" ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/tudorl/Documents/workspace/personal/dtreeviz/venv/lib/python3.7/site-packages/lightgbm/basic.py:1551: UserWarning: Using categorical_feature in Dataset.\n", - " warnings.warn('Using categorical_feature in Dataset.')\n", - "/Users/tudorl/Documents/workspace/personal/dtreeviz/venv/lib/python3.7/site-packages/lightgbm/basic.py:1286: UserWarning: Overriding the parameters from Reference Dataset.\n", - " warnings.warn('Overriding the parameters from Reference Dataset.')\n", - "/Users/tudorl/Documents/workspace/personal/dtreeviz/venv/lib/python3.7/site-packages/lightgbm/basic.py:1098: UserWarning: categorical_column in param dict is overridden.\n", - " warnings.warn('{} in param dict is overridden.'.format(cat_alias))\n" - ] } ], "source": [ @@ -625,16 +625,16 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 14, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" }, @@ -657,7 +657,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 9, "metadata": {}, "outputs": [ { @@ -1006,10 +1006,10 @@ "\n" ], "text/plain": [ - "" + "" ] }, - "execution_count": 15, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -1027,17 +1027,7 @@ }, { "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [], - "source": [ - "# lgbm_model.save_model(\"../testing/dtreeviz/models/fixtures/lightgbm_model_classifier.txt\")\n", - "# lgbm = lgb.Booster(model_file=\"../testing/dtreeviz/models/fixtures/lightgbm_model_classifier.txt\")" - ] - }, - { - "cell_type": "code", - "execution_count": 17, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ @@ -1046,7 +1036,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 53, "metadata": {}, "outputs": [ { @@ -1077,118 +1067,118 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1197,10 +1187,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1222,7 +1212,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1257,10 +1247,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1273,7 +1263,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1311,7 +1301,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1334,118 +1324,118 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1454,10 +1444,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1476,7 +1466,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1516,10 +1506,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1532,7 +1522,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1574,7 +1564,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1807,130 +1797,130 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1948,7 +1938,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1997,10 +1987,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2016,7 +2006,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2041,7 +2031,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2049,14 +2039,14 @@ "\n", "\n", "node3->node4\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "node3->node7\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", @@ -2076,118 +2066,118 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2196,10 +2186,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2221,7 +2211,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2256,10 +2246,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2272,7 +2262,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2313,7 +2303,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2342,118 +2332,118 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2462,10 +2452,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2484,7 +2474,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2524,10 +2514,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2540,7 +2530,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2582,7 +2572,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2658,118 +2648,118 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2778,10 +2768,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2800,7 +2790,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2840,10 +2830,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2856,7 +2846,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2897,7 +2887,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3023,118 +3013,118 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3143,10 +3133,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3168,7 +3158,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3203,10 +3193,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3219,7 +3209,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3256,7 +3246,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3285,118 +3275,118 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3405,10 +3395,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3430,7 +3420,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3465,10 +3455,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3481,7 +3471,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3522,7 +3512,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3597,118 +3587,118 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3717,10 +3707,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3742,7 +3732,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3777,10 +3767,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3793,7 +3783,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3834,7 +3824,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4031,130 +4021,130 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4170,7 +4160,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4206,10 +4196,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4225,7 +4215,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4251,7 +4241,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4259,15 +4249,15 @@ "\n", "\n", "node0->node1\n", - "\n", - "\n", + "\n", + "\n", "<\n", "\n", "\n", "\n", "node0->node10\n", - "\n", - "\n", + "\n", + "\n", "≥\n", "\n", "\n", @@ -4349,10 +4339,10 @@ "" ], "text/plain": [ - "" + "" ] }, - "execution_count": 18, + "execution_count": 53, "metadata": {}, "output_type": "execute_result" } @@ -4361,13 +4351,6 @@ "trees.dtreeviz(light_dtree)" ] }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, { "cell_type": "code", "execution_count": 19, @@ -5887,7 +5870,7 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 14, "metadata": {}, "outputs": [], "source": [ @@ -5922,14 +5905,15 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000998 seconds.\n", + "[LightGBM] [Warning] Met negative value in categorical features, will convert it to NaN\n", + "[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000679 seconds.\n", "You can set `force_row_wise=true` to remove the overhead.\n", "And if memory is not enough, you can set `force_col_wise=true`.\n", "[LightGBM] [Info] Total Bins 21\n", @@ -5985,16 +5969,16 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 39, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" }, @@ -6017,7 +6001,7 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 17, "metadata": {}, "outputs": [ { @@ -6334,10 +6318,10 @@ "\n" ], "text/plain": [ - "" + "" ] }, - "execution_count": 40, + "execution_count": 17, "metadata": {}, "output_type": "execute_result" } @@ -6355,7 +6339,7 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 18, "metadata": {}, "outputs": [], "source": [ @@ -6364,7 +6348,7 @@ }, { "cell_type": "code", - "execution_count": 45, + "execution_count": 54, "metadata": { "scrolled": false }, @@ -6395,83 +6379,83 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6487,7 +6471,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6528,10 +6512,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6549,7 +6533,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6563,7 +6547,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6579,10 +6563,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6593,7 +6577,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6617,132 +6601,132 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6758,7 +6742,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6799,10 +6783,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6820,7 +6804,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6834,7 +6818,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6850,10 +6834,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6864,7 +6848,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6890,38 +6874,38 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -6929,10 +6913,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6950,7 +6934,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6967,7 +6951,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6983,7 +6967,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7028,7 +7012,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7059,37 +7043,37 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -7097,10 +7081,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7118,7 +7102,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7135,7 +7119,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7151,7 +7135,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7195,7 +7179,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7226,73 +7210,73 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -7300,10 +7284,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7321,7 +7305,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7338,7 +7322,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7354,7 +7338,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7398,7 +7382,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7429,51 +7413,51 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -7481,10 +7465,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7502,7 +7486,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7519,7 +7503,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7535,7 +7519,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7579,7 +7563,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7609,189 +7593,189 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7809,7 +7793,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7825,7 +7809,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7838,7 +7822,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7887,10 +7871,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7907,7 +7891,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7921,7 +7905,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7937,10 +7921,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7951,7 +7935,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7987,575 +7971,575 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " 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\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8573,7 +8557,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8589,7 +8573,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8602,7 +8586,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8651,10 +8635,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8671,7 +8655,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8685,7 +8669,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8701,10 +8685,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8715,7 +8699,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8740,84 +8724,84 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8833,7 +8817,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8874,10 +8858,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8895,7 +8879,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8909,7 +8893,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8925,10 +8909,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8939,7 +8923,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8963,517 +8947,517 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - 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\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9489,7 +9473,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9505,7 +9489,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9541,10 +9525,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9561,7 +9545,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9578,7 +9562,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9594,10 +9578,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9608,7 +9592,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9634,43 +9618,43 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -9678,10 +9662,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9699,7 +9683,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9716,7 +9700,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9732,7 +9716,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9775,7 +9759,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9806,33 +9790,33 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -9840,10 +9824,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9861,7 +9845,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9878,7 +9862,7 @@ " 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\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -10346,7 +10330,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -10387,10 +10371,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -10408,7 +10392,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -10422,7 +10406,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -10438,10 +10422,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -10452,7 +10436,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -10476,174 +10460,174 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " 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\n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -10659,7 +10643,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -10700,10 +10684,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -10721,7 +10705,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -10735,7 +10719,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -10751,10 +10735,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -10765,7 +10749,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -10791,259 +10775,259 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " 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\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -11051,10 +11035,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -11072,7 +11056,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -11089,7 +11073,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -11105,7 +11089,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -11151,7 +11135,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -11182,102 +11166,102 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -11285,10 +11269,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -11306,7 +11290,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -11323,7 +11307,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -11339,7 +11323,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -11382,7 +11366,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -11413,99 +11397,99 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -11513,10 +11497,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -11534,7 +11518,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -11551,7 +11535,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -11567,7 +11551,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -11611,7 +11595,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -11642,67 +11626,67 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -11710,10 +11694,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -11731,7 +11715,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -11748,7 +11732,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -11764,7 +11748,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -11807,7 +11791,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -11861,738 +11845,738 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " 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+12592,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -12624,7 +12608,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -12660,10 +12644,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -12680,7 +12664,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -12697,7 +12681,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -12726,10 +12710,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -12740,7 +12724,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -12768,10 +12752,10 @@ "" ], "text/plain": [ - "" + "" ] }, - "execution_count": 45, + "execution_count": 54, "metadata": {}, "output_type": "execute_result" } @@ -12782,7 +12766,7 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 36, "metadata": {}, "outputs": [ { diff --git a/notebooks/dtreeviz_spark_visualisations.ipynb b/notebooks/dtreeviz_spark_visualisations.ipynb index 353e681c..93be08ea 100644 --- a/notebooks/dtreeviz_spark_visualisations.ipynb +++ b/notebooks/dtreeviz_spark_visualisations.ipynb @@ -208,7 +208,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "DecisionTreeClassificationModel: uid=DecisionTreeClassifier_acc27a9d0539, depth=4, numNodes=17, numClasses=2, numFeatures=7\n", + "DecisionTreeClassificationModel: uid=DecisionTreeClassifier_fe721bfdec86, depth=4, numNodes=17, numClasses=2, numFeatures=7\n", " If (feature 1 in {0.0})\n", " If (feature 3 <= 3.5)\n", " If (feature 4 <= 2.5)\n", @@ -244,7 +244,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ @@ -254,7 +254,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 12, "metadata": {}, "outputs": [ { @@ -263,7 +263,7 @@ "'InternalNode(prediction = 0.0, impurity = 0.4730129578614428, split = org.apache.spark.ml.tree.CategoricalSplit@ca78837e)'" ] }, - "execution_count": 11, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } @@ -274,7 +274,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ @@ -283,7 +283,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 14, "metadata": {}, "outputs": [ { @@ -315,7 +315,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 15, "metadata": {}, "outputs": [ { @@ -599,7 +599,7 @@ "16 -1 (-1, [], -1) " ] }, - "execution_count": 14, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" } @@ -617,7 +617,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 16, "metadata": {}, "outputs": [], "source": [ @@ -640,7 +640,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 18, "metadata": {}, "outputs": [ { @@ -671,118 +671,118 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -791,10 +791,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -810,7 +810,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -844,10 +844,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -860,7 +860,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -895,7 +895,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1062,118 +1062,118 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1182,10 +1182,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1207,7 +1207,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1251,10 +1251,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1267,7 +1267,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1307,7 +1307,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1336,118 +1336,118 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1456,10 +1456,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1475,7 +1475,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1511,10 +1511,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1530,7 +1530,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1570,7 +1570,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1600,118 +1600,118 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1720,10 +1720,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1739,7 +1739,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1775,10 +1775,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1794,7 +1794,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1832,7 +1832,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1995,118 +1995,118 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2115,10 +2115,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2140,7 +2140,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2184,10 +2184,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2200,7 +2200,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2239,7 +2239,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2268,118 +2268,118 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2388,10 +2388,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2410,7 +2410,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2450,10 +2450,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2466,7 +2466,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2508,7 +2508,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2538,133 +2538,133 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2680,7 +2680,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2729,10 +2729,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2745,7 +2745,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2769,7 +2769,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2965,130 +2965,130 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3104,7 +3104,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3145,10 +3145,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3161,7 +3161,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3187,7 +3187,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3282,10 +3282,10 @@ "" ], "text/plain": [ - "" + "" ] }, - "execution_count": 17, + "execution_count": 18, "metadata": {}, "output_type": "execute_result" } @@ -6446,7 +6446,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 19, "metadata": {}, "outputs": [], "source": [ @@ -6455,7 +6455,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 20, "metadata": {}, "outputs": [], "source": [ @@ -6465,7 +6465,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 21, "metadata": {}, "outputs": [], "source": [ @@ -6477,7 +6477,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 22, "metadata": {}, "outputs": [], "source": [ @@ -6489,7 +6489,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 23, "metadata": {}, "outputs": [ { @@ -6680,7 +6680,7 @@ "[891 rows x 8 columns]" ] }, - "execution_count": 15, + "execution_count": 23, "metadata": {}, "output_type": "execute_result" } @@ -6698,7 +6698,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 24, "metadata": {}, "outputs": [], "source": [ @@ -6715,7 +6715,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 25, "metadata": {}, "outputs": [ { @@ -6731,7 +6731,7 @@ "Name: 10, dtype: float64" ] }, - "execution_count": 23, + "execution_count": 25, "metadata": {}, "output_type": "execute_result" } @@ -6743,24 +6743,20 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 28, "metadata": {}, "outputs": [ { "data": { "image/svg+xml": [ - "\n", - "\n", + "\n", + "\n", "G\n", - "\n", - "\n", - "cluster_instance\n", - "\n", + "\n", "\n", "\n", "node2\n", - "Node 2\n", - "\n", + "\n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - 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\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6974,7 +6970,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7015,10 +7011,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7031,7 +7027,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7049,7 +7045,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7066,10 +7062,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7080,7 +7076,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7088,8 +7084,7 @@ "\n", "\n", "node5\n", - "Node 5\n", - "\n", + "\n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -7170,10 +7165,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7192,7 +7187,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7210,7 +7205,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7247,10 +7242,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7263,7 +7258,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7281,7 +7276,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7298,13 +7293,13 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7315,7 +7310,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7324,9 +7319,8 @@ "\n", "\n", "leaf3\n", - "\n", - "Node 3\n", - "\n", + "\n", + "\n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -7451,10 +7445,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7470,7 +7464,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7487,7 +7481,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7533,7 +7527,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7541,15 +7535,14 @@ "\n", "\n", "node2->leaf3\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "leaf4\n", - "\n", - "Node 4\n", - "\n", + "\n", + "\n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -7639,10 +7632,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7658,7 +7651,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7675,7 +7668,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7722,7 +7715,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7730,15 +7723,14 @@ "\n", "\n", "node2->leaf4\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "leaf6\n", - "\n", - "Node 6\n", - "\n", + "\n", + "\n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -7812,10 +7804,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7831,7 +7823,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7848,7 +7840,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7894,7 +7886,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7902,15 +7894,14 @@ "\n", "\n", "node5->leaf6\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "leaf7\n", - "\n", - "Node 7\n", - "\n", + "\n", + "\n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -7941,10 +7932,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7960,7 +7951,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7977,7 +7968,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8022,7 +8013,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8030,14 +8021,13 @@ "\n", "\n", "node5->leaf7\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "node1\n", - "Node 1\n", - "\n", + "\n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " 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\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -8281,10 +8271,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8300,7 +8290,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ 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" \n", " \n", " \n", @@ -9883,7 +9867,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9891,9 +9875,7 @@ "\n", "\n", "node12\n", - "\n", - "Node 12\n", - "\n", + "\n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " 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" \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -10150,7 +10129,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -10168,7 +10147,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -10205,10 +10184,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -10221,7 +10200,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -10239,7 +10218,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -10253,24 +10232,24 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -10279,9 +10258,8 @@ "\n", "\n", "leaf10\n", - "\n", - "Node 10\n", - "\n", + "\n", + "\n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - 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\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -10735,10 +10713,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -10754,7 +10732,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -10771,7 +10749,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -10818,7 +10796,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -10826,15 +10804,14 @@ "\n", "\n", "node9->leaf10\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "leaf11\n", - "\n", - "Node 11\n", - "\n", + "\n", + "\n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -10903,10 +10880,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -10922,7 +10899,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -10939,7 +10916,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -10986,7 +10963,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -10994,15 +10971,14 @@ "\n", "\n", "node9->leaf11\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "leaf13\n", - "\n", - "Node 13\n", - "\n", + "\n", + "\n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " 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\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -11220,10 +11196,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -11239,7 +11215,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -11256,7 +11232,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -11302,7 +11278,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -11310,15 +11286,14 @@ "\n", "\n", "node12->leaf13\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "leaf14\n", - "\n", - "Node 14\n", - "\n", + "\n", + "\n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -11356,10 +11331,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -11375,7 +11350,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -11392,7 +11367,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -11437,7 +11412,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -11445,27 +11420,25 @@ "\n", "\n", "node12->leaf14\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "node8->node9\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "node8->node12\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "node0\n", - "\n", - "Node 0\n", - "\n", + "\n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " 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-12410,7 +12380,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -12428,7 +12398,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -12466,10 +12436,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -12482,7 +12452,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -12500,7 +12470,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -12531,24 +12501,24 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -12556,76 +12526,35 @@ "\n", "\n", "node0->node1\n", - "\n", - "\n", - "<\n", + "\n", + "\n", + "<\n", "\n", "\n", "\n", "node0->node8\n", - "\n", - "\n", - "≥\n", + "\n", + "\n", + "≥\n", "\n", "\n", "\n", "\n", "\n", - "\n", - "\n", - "X_y\n", - "\n", - "\n", - "Pclass\n", - "\n", - "Sex_label\n", - "\n", - "Embarked_label\n", - "\n", - "Age_mean\n", - "\n", - "SibSp\n", - "\n", - "Parch\n", - "\n", - "Survived\n", - "\n", - "3.00\n", - "\n", - "1.00\n", - "\n", - "0.00\n", - "\n", - "4.00\n", - "\n", - "1.00\n", - "\n", - "1.00\n", - "\n", - "1.00\n", - "\n", - "\n", - "\n", - "leaf13->X_y\n", - "\n", - "\n", - "  Prediction\n", - " 23.33\n", - "\n", "\n", "" ], "text/plain": [ - "" + "" ] }, - "execution_count": 26, + "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "trees.dtreeviz(spark_dtree_reg, show_node_labels=True, X=X)" + "trees.dtreeviz(spark_dtree_reg)" ] }, { From 3faea37dd273669b28879282b51ae29a9d8e9ddd Mon Sep 17 00:00:00 2001 From: tlapusan Date: Thu, 21 Jan 2021 17:30:30 +0200 Subject: [PATCH 11/16] Colors for left, right split --- dtreeviz/colors.py | 4 +- dtreeviz/trees.py | 5 - .../dtreeviz_lightgbm_visualisations.ipynb | 7184 ++++++++-------- notebooks/dtreeviz_spark_visualisations.ipynb | 7462 ++++++++--------- 4 files changed, 7325 insertions(+), 7330 deletions(-) diff --git a/dtreeviz/colors.py b/dtreeviz/colors.py index f49eccd4..fe64ecf6 100644 --- a/dtreeviz/colors.py +++ b/dtreeviz/colors.py @@ -7,8 +7,8 @@ LIGHTBLUE = '#a6bddb' GREY = '#444443' WEDGE_COLOR = GREY -CATEGORICAL_SPLIT_LEFT= '#FE5733' -CATEGORICAL_SPLIT_RIGHT = '#a1dab4' +CATEGORICAL_SPLIT_LEFT= '#FFC300' +CATEGORICAL_SPLIT_RIGHT = BLUE HIGHLIGHT_COLOR = '#D67C03' diff --git a/dtreeviz/trees.py b/dtreeviz/trees.py index 1397dc20..b5c141ab 100644 --- a/dtreeviz/trees.py +++ b/dtreeviz/trees.py @@ -1161,7 +1161,6 @@ def wedge(ax, x, color): left_index, right_index = node.split_samples() tw = (xmax - xmin) * .018 - # print(f"id {node.id}, split_left {node.split()[0]}, {overall_feature_range }, { np.unique(X_feature)}") ax.set_xlim(overall_feature_range[0] - tw, overall_feature_range[1] + tw) ax.scatter(X_feature[left_index], y_train[left_index], s=5, c=colors["categorical_split_left"], alpha=colors['scatter_marker_alpha'], lw=.3) ax.scatter(X_feature[right_index], y_train[right_index], s=5, c=colors["categorical_split_right"], alpha=colors['scatter_marker_alpha'], lw=.3) @@ -1172,10 +1171,6 @@ def wedge(ax, x, color): linewidth=1) ax.set_xticks(np.unique(np.concatenate((X_feature, np.asarray(overall_feature_range))))) - # left_split_values = node.split()[0] - # for split_value in left_split_values: - # wedge(ax, split_value, color=colors['wedge']) - # if highlight_node: wedge(ax, X[node.feature()], color=colors['highlight']) diff --git a/notebooks/dtreeviz_lightgbm_visualisations.ipynb b/notebooks/dtreeviz_lightgbm_visualisations.ipynb index a4cbbfe2..f1473d94 100644 --- a/notebooks/dtreeviz_lightgbm_visualisations.ipynb +++ b/notebooks/dtreeviz_lightgbm_visualisations.ipynb @@ -5870,7 +5870,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 69, "metadata": {}, "outputs": [], "source": [ @@ -6348,7 +6348,7 @@ }, { "cell_type": "code", - "execution_count": 54, + "execution_count": 71, "metadata": { "scrolled": false }, @@ -6379,83 +6379,83 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6471,7 +6471,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6512,10 +6512,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6533,7 +6533,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6547,7 +6547,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6563,10 +6563,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6577,7 +6577,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6601,132 +6601,132 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6742,7 +6742,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6783,10 +6783,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6804,7 +6804,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6818,7 +6818,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6834,10 +6834,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6848,7 +6848,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6874,38 +6874,38 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -6913,10 +6913,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6934,7 +6934,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6951,7 +6951,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6967,7 +6967,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7012,7 +7012,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7020,8 +7020,8 @@ "\n", "\n", "node2->leaf3\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", @@ -7043,37 +7043,37 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -7081,10 +7081,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7102,7 +7102,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7119,7 +7119,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7135,7 +7135,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7179,7 +7179,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7187,8 +7187,8 @@ "\n", "\n", "node2->leaf4\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", @@ -7210,73 +7210,73 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -7284,10 +7284,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7305,7 +7305,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7322,7 +7322,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7338,7 +7338,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7382,7 +7382,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7390,8 +7390,8 @@ "\n", "\n", "node5->leaf6\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", @@ -7413,51 +7413,51 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -7465,10 +7465,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7486,7 +7486,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7503,7 +7503,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7519,7 +7519,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7563,7 +7563,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7571,8 +7571,8 @@ "\n", "\n", "node5->leaf7\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", @@ -7593,189 +7593,189 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7793,7 +7793,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7809,7 +7809,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7822,7 +7822,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7871,10 +7871,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7891,7 +7891,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7905,7 +7905,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7921,10 +7921,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7935,7 +7935,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7943,14 +7943,14 @@ "\n", "\n", "node1->node2\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "node1->node5\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", @@ -7971,575 +7971,575 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " 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\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8557,7 +8557,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8573,7 +8573,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8586,7 +8586,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8635,10 +8635,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8655,7 +8655,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8669,7 +8669,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8685,10 +8685,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8699,7 +8699,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8724,84 +8724,84 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8817,7 +8817,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8858,10 +8858,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8879,7 +8879,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8893,7 +8893,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8909,10 +8909,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", 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\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9473,7 +9473,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9489,7 +9489,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9525,10 +9525,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9545,7 +9545,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9562,7 +9562,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9578,10 +9578,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9592,7 +9592,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9618,43 +9618,43 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -9662,10 +9662,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9683,7 +9683,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9700,7 +9700,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9716,7 +9716,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9759,7 +9759,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9767,8 +9767,8 @@ "\n", "\n", "node9->leaf10\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", @@ -9790,33 +9790,33 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -9824,10 +9824,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9845,7 +9845,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9862,7 +9862,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9878,7 +9878,7 @@ " 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\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -10330,7 +10330,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -10371,10 +10371,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -10392,7 +10392,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -10406,7 +10406,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -10422,10 +10422,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -10436,7 +10436,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -10460,174 +10460,174 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " 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\n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -10643,7 +10643,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -10684,10 +10684,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -10705,7 +10705,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -10719,7 +10719,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -10735,10 +10735,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -10749,7 +10749,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -10775,259 +10775,259 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " 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\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -11035,10 +11035,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -11056,7 +11056,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -11073,7 +11073,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -11089,7 +11089,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -11135,7 +11135,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -11143,8 +11143,8 @@ "\n", "\n", "node13->leaf14\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", @@ -11166,102 +11166,102 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -11269,10 +11269,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -11290,7 +11290,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -11307,7 +11307,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -11323,7 +11323,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -11366,7 +11366,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -11374,8 +11374,8 @@ "\n", "\n", "node13->leaf15\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", @@ -11397,99 +11397,99 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -11497,10 +11497,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -11518,7 +11518,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -11535,7 +11535,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -11551,7 +11551,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -11595,7 +11595,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -11603,8 +11603,8 @@ "\n", "\n", "node16->leaf17\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", @@ -11626,67 +11626,67 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -11694,10 +11694,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -11715,7 +11715,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -11732,7 +11732,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -11748,7 +11748,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -11791,7 +11791,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -11799,32 +11799,32 @@ "\n", "\n", "node16->leaf18\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "node12->node13\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "node12->node16\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "node8->node9\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", "node8->node12\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", @@ -11845,738 +11845,738 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " 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+12664,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -12681,7 +12681,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -12710,10 +12710,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -12724,7 +12724,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -12732,15 +12732,15 @@ "\n", "\n", "node0->node1\n", - "\n", - "\n", + "\n", + "\n", "<\n", "\n", "\n", "\n", "node0->node8\n", - "\n", - "\n", + "\n", + "\n", "≥\n", "\n", "\n", @@ -12752,16 +12752,16 @@ "" ], "text/plain": [ - "" + "" ] }, - "execution_count": 54, + "execution_count": 71, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "trees.dtreeviz(light_dtree_reg, show_node_labels=False)" + "trees.dtreeviz(light_dtree_reg)" ] }, { diff --git a/notebooks/dtreeviz_spark_visualisations.ipynb b/notebooks/dtreeviz_spark_visualisations.ipynb index 93be08ea..02f89aba 100644 --- a/notebooks/dtreeviz_spark_visualisations.ipynb +++ b/notebooks/dtreeviz_spark_visualisations.ipynb @@ -6446,7 +6446,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 29, "metadata": {}, "outputs": [], "source": [ @@ -6455,7 +6455,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 30, "metadata": {}, "outputs": [], "source": [ @@ -6465,7 +6465,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 31, "metadata": {}, "outputs": [], "source": [ @@ -6477,7 +6477,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 32, "metadata": {}, "outputs": [], "source": [ @@ -6489,7 +6489,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 33, "metadata": {}, "outputs": [ { @@ -6680,7 +6680,7 @@ "[891 rows x 8 columns]" ] }, - "execution_count": 23, + "execution_count": 33, "metadata": {}, "output_type": "execute_result" } @@ -6698,7 +6698,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 34, "metadata": {}, "outputs": [], "source": [ @@ -6715,7 +6715,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 35, "metadata": {}, "outputs": [ { @@ -6731,7 +6731,7 @@ "Name: 10, dtype: float64" ] }, - "execution_count": 25, + "execution_count": 35, "metadata": {}, "output_type": "execute_result" } @@ -6743,7 +6743,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 40, "metadata": {}, "outputs": [ { @@ -6772,189 +6772,189 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - 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\n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6970,7 +6970,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7011,10 +7011,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7027,7 +7027,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7045,7 +7045,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7062,10 +7062,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7076,7 +7076,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7100,62 +7100,62 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -7165,10 +7165,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7187,7 +7187,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7205,7 +7205,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7242,10 +7242,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7258,7 +7258,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7276,7 +7276,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7293,13 +7293,13 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7310,7 +7310,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7336,108 +7336,108 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -7445,10 +7445,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7464,7 +7464,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7481,7 +7481,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7527,7 +7527,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7535,8 +7535,8 @@ "\n", "\n", "node2->leaf3\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", @@ -7558,73 +7558,73 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -7632,10 +7632,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7651,7 +7651,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7668,7 +7668,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7715,7 +7715,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7723,8 +7723,8 @@ "\n", "\n", "node2->leaf4\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", "\n", @@ -7746,57 +7746,57 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -7804,10 +7804,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7823,7 +7823,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7840,7 +7840,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7886,7 +7886,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7917,14 +7917,14 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -7932,10 +7932,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7951,7 +7951,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7968,7 +7968,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8013,7 +8013,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8043,225 +8043,225 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " 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\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -8271,10 +8271,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8290,7 +8290,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8325,10 +8325,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8341,7 +8341,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8359,7 +8359,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8376,13 +8376,13 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8393,7 +8393,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8429,684 +8429,684 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " 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\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -9116,10 +9116,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9135,7 +9135,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9169,10 +9169,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9185,7 +9185,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9203,7 +9203,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9220,13 +9220,13 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9237,7 +9237,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9262,480 +9262,480 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " 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\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -9745,10 +9745,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9764,7 +9764,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9799,10 +9799,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9815,7 +9815,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9833,7 +9833,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9850,13 +9850,13 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9867,7 +9867,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9891,213 +9891,213 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " 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\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -10107,10 +10107,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -10129,7 +10129,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -10147,7 +10147,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -10184,10 +10184,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -10200,7 +10200,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -10218,7 +10218,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -10232,13 +10232,13 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -10249,7 +10249,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -10275,437 +10275,437 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " 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\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -10713,10 +10713,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -10732,7 +10732,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -10749,7 +10749,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -10796,7 +10796,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -10827,52 +10827,52 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -10880,10 +10880,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -10899,7 +10899,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -10916,7 +10916,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -10963,7 +10963,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -10994,201 +10994,201 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -11196,10 +11196,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -11215,7 +11215,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -11232,7 +11232,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -11278,7 +11278,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -11309,21 +11309,21 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -11331,10 +11331,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -11350,7 +11350,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -11367,7 +11367,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -11412,7 +11412,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -11454,900 +11454,900 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " 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+ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -12380,7 +12380,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -12398,7 +12398,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -12436,10 +12436,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -12452,7 +12452,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -12470,7 +12470,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -12501,13 +12501,13 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -12518,7 +12518,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -12545,10 +12545,10 @@ "" ], "text/plain": [ - "" + "" ] }, - "execution_count": 28, + "execution_count": 40, "metadata": {}, "output_type": "execute_result" } From b10aaac9997e9cb1ab72d3ed5943d6142c6c8377 Mon Sep 17 00:00:00 2001 From: tlapusan Date: Wed, 3 Feb 2021 14:24:50 +0200 Subject: [PATCH 12/16] Add split_samples in shadow_decision_tree for each library --- dtreeviz/models/lightgbm_decision_tree.py | 15 + dtreeviz/models/shadow_decision_tree.py | 29 +- dtreeviz/models/sklearn_decision_trees.py | 10 + dtreeviz/models/spark_decision_tree.py | 14 + dtreeviz/models/xgb_decision_tree.py | 10 + .../dtreeviz_lightgbm_visualisations.ipynb | 19068 ++++++--------- .../dtreeviz_sklearn_visualisations.ipynb | 19235 ++++++++++------ notebooks/dtreeviz_spark_visualisations.ipynb | 10116 +++++--- .../dtreeviz_xgboost_visualisations.ipynb | 11225 +++++---- .../test_decision_tree_lightgbm_classifier.py | 21 + .../test_decision_trees_sk_classifier.py | 5 + 11 files changed, 31150 insertions(+), 28598 deletions(-) diff --git a/dtreeviz/models/lightgbm_decision_tree.py b/dtreeviz/models/lightgbm_decision_tree.py index 61ed80c8..ea2ab3ff 100644 --- a/dtreeviz/models/lightgbm_decision_tree.py +++ b/dtreeviz/models/lightgbm_decision_tree.py @@ -98,6 +98,7 @@ def get_thresholds(self) -> np.ndarray: for i in range(self.nnodes()): if self.children_left[i] != -1 and self.children_right[i] != -1: if self.is_categorical_split(i): + # TODO find out the right split values, we need them for prediction path node_thresholds[i] = (list(map(int, self.tree_nodes[i]["threshold"].split("||"))), []) else: node_thresholds[i] = round(self.tree_nodes[i]["threshold"], 2) @@ -145,6 +146,20 @@ def get_node_samples(self): self.node_to_samples = node_to_samples return self.node_to_samples + def get_split_samples(self, id): + samples = np.array(self.get_node_samples()[id]) + node_X_data = self.x_data[samples, self.get_node_feature(id)] + split = self.get_node_split(id) + + if self.is_categorical_split(id): + indices = np.sum([node_X_data == split_value for split_value in self.get_node_split(id)[0]], axis=0) + left = np.nonzero(indices == 1)[0] + right = np.nonzero(indices == 0)[0] + else: + left = np.nonzero(node_X_data <= split)[0] + right = np.nonzero(node_X_data > split)[0] + return left, right + def get_node_nsamples(self, id): if self.children_right[id] == -1 and self.children_left[id] == -1: return self.tree_nodes[id]["leaf_count"] diff --git a/dtreeviz/models/shadow_decision_tree.py b/dtreeviz/models/shadow_decision_tree.py index 86c0e344..f612aba9 100644 --- a/dtreeviz/models/shadow_decision_tree.py +++ b/dtreeviz/models/shadow_decision_tree.py @@ -131,6 +131,11 @@ def get_node_samples(self): """ pass + @abstractmethod + def get_split_samples(self, id): + """Returns left and right split indexes from a node""" + pass + @abstractmethod def get_node_nsamples(self, id): """Returns number of samples for a specific node id.""" @@ -393,7 +398,7 @@ def get_leaf_sample_counts_by_class(self): def _normalize_class_names(self, class_names): if self.is_classifier(): if class_names is None: - return {i : f"class {i}" for i in range(self.nclasses())} + return {i: f"class {i}" for i in range(self.nclasses())} if isinstance(class_names, dict): return class_names elif isinstance(class_names, Sequence): @@ -454,14 +459,18 @@ def get_shadow_tree(tree_model, x_data, y_data, feature_names, target_name, clas return xgb_decision_tree.ShadowXGBDTree(tree_model, tree_index, x_data, y_data, feature_names, target_name, class_names) elif (str(type(tree_model)).endswith("pyspark.ml.classification.DecisionTreeClassificationModel'>") or - str(type(tree_model)).endswith("pyspark.ml.classification.DecisionTreeClassificationModel'>")): + str(type(tree_model)).endswith("pyspark.ml.classification.DecisionTreeClassificationModel'>")): from dtreeviz.models import spark_decision_tree return spark_decision_tree.ShadowSparkTree(tree_model, tree_index, x_data, y_data, - feature_names, target_name, class_names) + feature_names, target_name, class_names) + elif "lightgbm.basic.Booster" in str(type(tree_model)): + from dtreeviz.models import lightgbm_decision_tree + return lightgbm_decision_tree.ShadowLightGBMTree(tree_model, tree_index, x_data, y_data, + feature_names, target_name, class_names) else: raise ValueError( f"Tree model must be in (DecisionTreeRegressor, DecisionTreeClassifier, " - "xgboost.core.Booster, pyspark DecisionTreeClassificationModel or " + "xgboost.core.Booster, lightgbm.basic.Booster, pyspark DecisionTreeClassificationModel or " f"pyspark DecisionTreeClassificationModel) but you passed a {tree_model.__class__.__name__}!") @@ -539,17 +548,7 @@ def criterion(self): def split_samples(self) -> Tuple[np.ndarray, np.ndarray]: """Returns the list of indexes to the left and the right of the split value.""" - samples = np.array(self.samples()) - node_X_data = self.shadow_tree.x_data[samples, self.feature()] - split = self.split() - if self.is_categorical_split(): - indices = np.sum([node_X_data == split_value for split_value in self.split()[0]], axis=0) - left = np.nonzero(indices == 1)[0] - right = np.nonzero(indices == 0)[0] - else: - left = np.nonzero(node_X_data < split)[0] - right = np.nonzero(node_X_data >= split)[0] - return left, right + return self.shadow_tree.get_split_samples(self.id) def isleaf(self) -> bool: return self.left is None and self.right is None diff --git a/dtreeviz/models/sklearn_decision_trees.py b/dtreeviz/models/sklearn_decision_trees.py index 8ed9fa94..4374cb8f 100644 --- a/dtreeviz/models/sklearn_decision_trees.py +++ b/dtreeviz/models/sklearn_decision_trees.py @@ -65,6 +65,16 @@ def get_node_samples(self): self.node_to_samples = node_to_samples return node_to_samples + def get_split_samples(self, id): + samples = np.array(self.get_node_samples()[id]) + node_X_data = self.x_data[samples, self.get_node_feature(id)] + split = self.get_node_split(id) + + left = np.nonzero(node_X_data <= split)[0] + right = np.nonzero(node_X_data > split)[0] + + return left, right + def get_node_nsamples(self, id): return len(self.get_node_samples()[id]) diff --git a/dtreeviz/models/spark_decision_tree.py b/dtreeviz/models/spark_decision_tree.py index cf893386..18ebcd92 100644 --- a/dtreeviz/models/spark_decision_tree.py +++ b/dtreeviz/models/spark_decision_tree.py @@ -130,6 +130,20 @@ def get_node_samples(self): self.node_to_samples = node_to_samples return self.node_to_samples + def get_split_samples(self, id): + samples = np.array(self.get_node_samples()[id]) + node_X_data = self.x_data[samples, self.get_node_feature(id)] + split = self.get_node_split(id) + + if self.is_categorical_split(id): + indices = np.sum([node_X_data == split_value for split_value in self.get_node_split(id)[0]], axis=0) + left = np.nonzero(indices == 1)[0] + right = np.nonzero(indices == 0)[0] + else: + left = np.nonzero(node_X_data <= split)[0] + right = np.nonzero(node_X_data > split)[0] + return left, right + def get_node_nsamples(self, id): def _get_nsamples(spark_version): if spark_version >= 3: diff --git a/dtreeviz/models/xgb_decision_tree.py b/dtreeviz/models/xgb_decision_tree.py index 3f8f8795..10672fff 100644 --- a/dtreeviz/models/xgb_decision_tree.py +++ b/dtreeviz/models/xgb_decision_tree.py @@ -108,6 +108,16 @@ def get_node_samples(self): self.node_to_samples = node_to_samples return node_to_samples + def get_split_samples(self, id): + samples = np.array(self.get_node_samples()[id]) + node_X_data = self.x_data[samples, self.get_node_feature(id)] + split = self.get_node_split(id) + + left = np.nonzero(node_X_data < split)[0] + right = np.nonzero(node_X_data >= split)[0] + + return left, right + def get_node_nsamples(self, id): return len(self.get_node_samples()[id]) diff --git a/notebooks/dtreeviz_lightgbm_visualisations.ipynb b/notebooks/dtreeviz_lightgbm_visualisations.ipynb index f1473d94..81b008c2 100644 --- a/notebooks/dtreeviz_lightgbm_visualisations.ipynb +++ b/notebooks/dtreeviz_lightgbm_visualisations.ipynb @@ -51,7 +51,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -60,7 +60,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -312,7 +312,7 @@ "[891 rows x 12 columns]" ] }, - "execution_count": 4, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -323,7 +323,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -350,7 +350,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -359,8 +359,8 @@ "\n", "X_train, X_test, y_train, y_test = train_test_split(dataset[features], dataset[target], test_size=0.2, random_state=42)\n", "\n", - "train_data = lgb.Dataset(data=X_train, label=y_train, feature_name=features, categorical_feature=[\"Sex_label\", \"Pclass\", \"Embarked_label\", \"Cabin_label\"])\n", - "valid_data = lgb.Dataset(data=X_test, label=y_test, feature_name=features, categorical_feature=[\"Sex_label\", \"Pclass\", \"Embarked_label\", \"Cabin_label\"])\n" + "train_data = lgb.Dataset(data=X_train, label=y_train, feature_name=features, categorical_feature=[\"Sex_label\", \"Pclass\"])\n", + "valid_data = lgb.Dataset(data=X_test, label=y_test, feature_name=features, categorical_feature=[\"Sex_label\", \"Pclass\"])\n" ] }, { @@ -372,7 +372,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 10, "metadata": {}, "outputs": [ { @@ -392,9 +392,10 @@ "output_type": "stream", "text": [ "[LightGBM] [Info] Number of positive: 268, number of negative: 444\n", - "[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001330 seconds.\n", - "You can set `force_col_wise=true` to remove the overhead.\n", - "[LightGBM] [Info] Total Bins 190\n", + "[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000696 seconds.\n", + "You can set `force_row_wise=true` to remove the overhead.\n", + "And if memory is not enough, you can set `force_col_wise=true`.\n", + "[LightGBM] [Info] Total Bins 234\n", "[LightGBM] [Info] Number of data points in the train set: 712, number of used features: 6\n", "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.376404 -> initscore=-0.504838\n", "[LightGBM] [Info] Start training from score -0.504838\n", @@ -403,201 +404,207 @@ "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[2]\ttraining's binary_logloss: 0.643893\tvalid_1's binary_logloss: 0.659912\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", - "[3]\ttraining's binary_logloss: 0.636543\tvalid_1's binary_logloss: 0.652435\n", + "[3]\ttraining's binary_logloss: 0.635571\tvalid_1's binary_logloss: 0.65038\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", - "[4]\ttraining's binary_logloss: 0.636963\tvalid_1's binary_logloss: 0.653424\n", + "[4]\ttraining's binary_logloss: 0.635661\tvalid_1's binary_logloss: 0.650539\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", - "[5]\ttraining's binary_logloss: 0.618963\tvalid_1's binary_logloss: 0.634379\n", + "[5]\ttraining's binary_logloss: 0.616822\tvalid_1's binary_logloss: 0.631508\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", - "[6]\ttraining's binary_logloss: 0.603176\tvalid_1's binary_logloss: 0.619948\n", + "[6]\ttraining's binary_logloss: 0.600971\tvalid_1's binary_logloss: 0.616676\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", - "[7]\ttraining's binary_logloss: 0.58736\tvalid_1's binary_logloss: 0.603529\n", + "[7]\ttraining's binary_logloss: 0.584723\tvalid_1's binary_logloss: 0.601243\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", - "[8]\ttraining's binary_logloss: 0.595014\tvalid_1's binary_logloss: 0.604151\n", + "[8]\ttraining's binary_logloss: 0.592342\tvalid_1's binary_logloss: 0.602174\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", - "[9]\ttraining's binary_logloss: 0.595934\tvalid_1's binary_logloss: 0.605973\n", + "[9]\ttraining's binary_logloss: 0.593155\tvalid_1's binary_logloss: 0.60277\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", - "[10]\ttraining's binary_logloss: 0.601026\tvalid_1's binary_logloss: 0.611071\n", + "[10]\ttraining's binary_logloss: 0.598793\tvalid_1's binary_logloss: 0.608303\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", - "[11]\ttraining's binary_logloss: 0.58601\tvalid_1's binary_logloss: 0.596881\n", + "[11]\ttraining's binary_logloss: 0.58313\tvalid_1's binary_logloss: 0.594522\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", - "[12]\ttraining's binary_logloss: 0.572235\tvalid_1's binary_logloss: 0.583693\n", + "[12]\ttraining's binary_logloss: 0.569181\tvalid_1's binary_logloss: 0.580411\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", - "[13]\ttraining's binary_logloss: 0.578231\tvalid_1's binary_logloss: 0.589832\n", + "[13]\ttraining's binary_logloss: 0.57544\tvalid_1's binary_logloss: 0.586339\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", - "[14]\ttraining's binary_logloss: 0.577778\tvalid_1's binary_logloss: 0.589108\n", + "[14]\ttraining's binary_logloss: 0.575021\tvalid_1's binary_logloss: 0.585831\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", - "[15]\ttraining's binary_logloss: 0.564439\tvalid_1's binary_logloss: 0.576758\n", + "[15]\ttraining's binary_logloss: 0.561416\tvalid_1's binary_logloss: 0.573668\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", - "[16]\ttraining's binary_logloss: 0.559598\tvalid_1's binary_logloss: 0.570926\n", + "[16]\ttraining's binary_logloss: 0.555723\tvalid_1's binary_logloss: 0.567806\n", 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"[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", - "[33]\ttraining's binary_logloss: 0.52441\tvalid_1's binary_logloss: 0.53968\n", + "[33]\ttraining's binary_logloss: 0.520105\tvalid_1's binary_logloss: 0.535745\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", - "[34]\ttraining's binary_logloss: 0.525454\tvalid_1's binary_logloss: 0.540781\n", + "[34]\ttraining's binary_logloss: 0.521289\tvalid_1's binary_logloss: 0.537066\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", - "[35]\ttraining's binary_logloss: 0.521757\tvalid_1's binary_logloss: 0.537036\n", + "[35]\ttraining's binary_logloss: 0.517493\tvalid_1's binary_logloss: 0.533434\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", - "[36]\ttraining's binary_logloss: 0.518098\tvalid_1's binary_logloss: 0.533918\n", + "[36]\ttraining's binary_logloss: 0.513594\tvalid_1's binary_logloss: 0.530056\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", - "[37]\ttraining's binary_logloss: 0.520783\tvalid_1's binary_logloss: 0.536465\n", + "[37]\ttraining's binary_logloss: 0.516442\tvalid_1's binary_logloss: 0.532601\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", - "[38]\ttraining's binary_logloss: 0.512808\tvalid_1's binary_logloss: 0.529452\n", + "[38]\ttraining's binary_logloss: 0.508339\tvalid_1's binary_logloss: 0.525514\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", - "[39]\ttraining's binary_logloss: 0.515993\tvalid_1's binary_logloss: 0.532086\n", + "[39]\ttraining's binary_logloss: 0.511694\tvalid_1's binary_logloss: 0.528106\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", - "[40]\ttraining's binary_logloss: 0.518484\tvalid_1's binary_logloss: 0.534383\n", + "[40]\ttraining's binary_logloss: 0.514235\tvalid_1's binary_logloss: 0.530497\n", 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] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", - "[97]\ttraining's binary_logloss: 0.434015\tvalid_1's binary_logloss: 0.462054\n", + "[97]\ttraining's binary_logloss: 0.429157\tvalid_1's binary_logloss: 0.460482\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", - "[98]\ttraining's binary_logloss: 0.435753\tvalid_1's binary_logloss: 0.463473\n", + "[98]\ttraining's binary_logloss: 0.430924\tvalid_1's binary_logloss: 0.461824\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", - "[99]\ttraining's binary_logloss: 0.437382\tvalid_1's binary_logloss: 0.464767\n", + "[99]\ttraining's binary_logloss: 0.432548\tvalid_1's binary_logloss: 0.463037\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", - "[100]\ttraining's binary_logloss: 0.432906\tvalid_1's binary_logloss: 0.461309\n" + "[100]\ttraining's binary_logloss: 0.428043\tvalid_1's binary_logloss: 0.459459\n" ] } ], @@ -625,22 +632,22 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 8, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "
" ] @@ -657,7 +664,27 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "lightgbm.basic.Booster" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "type(lgbm_model)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, "metadata": {}, "outputs": [ { @@ -677,29 +704,29 @@ "\n", "\n", "split0\n", - "\n", - "Pclass\n", - " = \n", - "3\n", - "0.000 value\n", - "count: 712\n", + "\n", + "Pclass\n", + " = \n", + "3\n", + "0.000 value\n", + "count: 712\n", "\n", "\n", "\n", "split3\n", - "\n", - "Age\n", - " ≤ \n", - "6.500\n", - "-0.005 value\n", - "count: 398\n", + "\n", + "Age\n", + " ≤ \n", + "6.500\n", + "-0.005 value\n", + "count: 398\n", "\n", "\n", "\n", "split0->split3\n", - "\n", - "\n", - "yes\n", + "\n", + "\n", + "yes\n", "\n", "\n", "\n", @@ -714,135 +741,135 @@ "\n", "\n", "split0->split1\n", - "\n", - "\n", - "no\n", + "\n", + "\n", + "no\n", "\n", "\n", "\n", "leaf0\n", - "\n", - "leaf 0: \n", - "0.006\n", - "count: 26\n", + "\n", + "leaf 0: \n", + "0.006\n", + "count: 26\n", "\n", "\n", "\n", "split3->leaf0\n", - "\n", - "\n", - "yes\n", + "\n", + "\n", + "yes\n", "\n", "\n", "\n", "split4\n", - "\n", - "Embarked_label\n", - " = \n", - "2\n", - "-0.006 value\n", - "count: 372\n", + "\n", + "Embarked_label\n", + " ≤ \n", + "1.500\n", + "-0.006 value\n", + "count: 372\n", "\n", "\n", "\n", "split3->split4\n", - "\n", - "\n", - "no\n", + "\n", + "\n", + "no\n", "\n", - "\n", + "\n", "\n", - "split9\n", - "\n", - "Age\n", - " ≤ \n", - "27.500\n", - "-0.008 value\n", - "count: 272\n", + "split8\n", + "\n", + "Fare\n", + " ≤ \n", + "15.075\n", + "-0.001 value\n", + "count: 100\n", "\n", - "\n", + "\n", "\n", - "split4->split9\n", - "\n", - "\n", - "yes\n", + "split4->split8\n", + "\n", + "\n", + "yes\n", "\n", - "\n", + "\n", "\n", - "split8\n", - "\n", - "Fare\n", - " ≤ \n", - "15.075\n", - "-0.001 value\n", - "count: 100\n", + "split9\n", + "\n", + "Age\n", + " ≤ \n", + "27.500\n", + "-0.008 value\n", + "count: 272\n", "\n", - "\n", + "\n", "\n", - "split4->split8\n", - "\n", - "\n", - "no\n", + "split4->split9\n", + "\n", + "\n", + "no\n", "\n", "\n", "\n", "leaf4\n", "\n", "leaf 4: \n", - "-0.006\n", - "count: 121\n", + "-0.003\n", + "count: 78\n", "\n", - "\n", + "\n", "\n", - "split9->leaf4\n", - "\n", - "\n", - "yes\n", + "split8->leaf4\n", + "\n", + "\n", + "yes\n", "\n", - "\n", + "\n", "\n", - "leaf10\n", - "\n", - "leaf 10: \n", - "-0.009\n", - "count: 151\n", + "leaf9\n", + "\n", + "leaf 9: \n", + "0.005\n", + "count: 22\n", "\n", - "\n", + "\n", "\n", - "split9->leaf10\n", - "\n", - "\n", - "no\n", + "split8->leaf9\n", + "\n", + "\n", + "no\n", "\n", "\n", "\n", "leaf5\n", - "\n", - "leaf 5: \n", - "-0.003\n", - "count: 78\n", + "\n", + "leaf 5: \n", + "-0.006\n", + "count: 121\n", "\n", - "\n", + "\n", "\n", - "split8->leaf5\n", - "\n", - "\n", - "yes\n", + "split9->leaf5\n", + "\n", + "\n", + "yes\n", "\n", - "\n", + "\n", "\n", - "leaf9\n", - "\n", - "leaf 9: \n", - "0.005\n", - "count: 22\n", + "leaf10\n", + "\n", + "leaf 10: \n", + "-0.009\n", + "count: 151\n", "\n", - "\n", + "\n", "\n", - "split8->leaf9\n", - "\n", - "\n", - "no\n", + "split9->leaf10\n", + "\n", + "\n", + "no\n", "\n", "\n", "\n", @@ -1006,10 +1033,10 @@ "\n" ], "text/plain": [ - "" + "" ] }, - "execution_count": 9, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } @@ -1027,7 +1054,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 41, "metadata": {}, "outputs": [], "source": [ @@ -1036,4658 +1063,93 @@ }, { "cell_type": "code", - "execution_count": 53, + "execution_count": 42, + "metadata": {}, + "outputs": [ + { + "ename": "ValueError", + "evalue": "Tree model must be in (DecisionTreeRegressor, DecisionTreeClassifier, xgboost.core.Booster, pyspark DecisionTreeClassificationModel or pyspark DecisionTreeClassificationModel) but you passed a ShadowLightGBMTree!", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtrees\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtreeviz\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlight_dtree\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;32m~/Documents/workspace/personal/dtreeviz/notebooks/../dtreeviz/trees.py\u001b[0m in \u001b[0;36mdtreeviz\u001b[0;34m(tree_model, x_data, y_data, feature_names, target_name, class_names, tree_index, precision, orientation, instance_orientation, show_root_edge_labels, show_node_labels, show_just_path, fancy, histtype, highlight_path, X, max_X_features_LR, max_X_features_TD, label_fontsize, ticks_fontsize, fontname, title, title_fontsize, colors, scale)\u001b[0m\n\u001b[1;32m 737\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 738\u001b[0m shadow_tree = ShadowDecTree.get_shadow_tree(tree_model, x_data, y_data, feature_names, target_name, class_names,\n\u001b[0;32m--> 739\u001b[0;31m tree_index)\n\u001b[0m\u001b[1;32m 740\u001b[0m \u001b[0mcolors\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0madjust_colors\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcolors\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 741\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/Documents/workspace/personal/dtreeviz/notebooks/../dtreeviz/models/shadow_decision_tree.py\u001b[0m in \u001b[0;36mget_shadow_tree\u001b[0;34m(tree_model, x_data, y_data, feature_names, target_name, class_names, tree_index)\u001b[0m\n\u001b[1;32m 470\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 471\u001b[0m raise ValueError(\n\u001b[0;32m--> 472\u001b[0;31m \u001b[0;34mf\"Tree model must be in (DecisionTreeRegressor, DecisionTreeClassifier, \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 473\u001b[0m \u001b[0;34m\"xgboost.core.Booster, pyspark DecisionTreeClassificationModel or \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 474\u001b[0m f\"pyspark DecisionTreeClassificationModel) but you passed a {tree_model.__class__.__name__}!\")\n", + "\u001b[0;31mValueError\u001b[0m: Tree model must be in (DecisionTreeRegressor, DecisionTreeClassifier, xgboost.core.Booster, pyspark DecisionTreeClassificationModel or pyspark DecisionTreeClassificationModel) but you passed a ShadowLightGBMTree!" + ] + } + ], + "source": [ + "trees.dtreeviz(light_dtree)" + ] + }, + { + "cell_type": "code", + "execution_count": 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\n", 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\n", + " \n", " \n", " \n", " \n", @@ -6601,195 +2657,219 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6802,12 +2882,12 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6816,12 +2896,12 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6833,32 +2913,40 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", "\n", "\n", + "\n", + "node2->node3\n", + "\n", + "\n", + "\n", "\n", - "\n", - "leaf3\n", - "\n", - "\n", + "\n", + "node7\n", + "Node 7\n", + "\n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "\n", - "\n", - "\n", - "node2->leaf3\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "leaf4\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", "\n", "\n", - "\n", - "node2->leaf4\n", - "\n", - "\n", + "\n", + "\n", + "node2->leaf6\n", + "\n", + "\n", "\n", "\n", - "\n", - "leaf6\n", - "\n", - "\n", + "\n", + "leaf8\n", + "\n", + "Node 8\n", + "\n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -7284,10 +3247,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7305,7 +3268,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7322,7 +3285,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7338,7 +3301,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7353,7 +3316,8 @@ " \n", " \n", " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -7363,41 +3327,41 @@ " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", "\n", "\n", - "\n", - "node5->leaf6\n", - "\n", - "\n", + "\n", + "node7->leaf8\n", + "\n", + "\n", "\n", "\n", - "\n", - "leaf7\n", - "\n", - "\n", + "\n", + "leaf9\n", + "\n", + "Node 9\n", + "\n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -7465,10 +3421,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7486,7 +3442,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7503,7 +3459,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7519,7 +3475,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7532,9 +3488,9 @@ " \n", " \n", " \n", + " \n", " \n", - " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7542,42 +3498,42 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", "\n", "\n", - "\n", - "node5->leaf7\n", - "\n", - "\n", + "\n", + "node7->leaf9\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "node1\n", - "\n", + "Node 1\n", + "\n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7593,189 +3549,185 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7783,104 +3735,99 @@ " \n", " \n", " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", - " \n", + " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7889,12 +3836,12 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7903,16 +3850,13 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -7920,42 +3864,46 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", "\n", "\n", - "\n", + "\n", "node1->node2\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", - "node1->node5\n", - "\n", - "\n", + "\n", + "node1->node7\n", + "\n", + "\n", "\n", "\n", - "\n", - "node8\n", - "\n", + "\n", + "node10\n", + "Node 10\n", + "\n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7971,575 +3919,571 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " 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\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8548,7 +4492,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8557,49 +4501,36 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8633,15 +4564,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8653,12 +4584,12 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8667,12 +4598,12 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8684,31 +4615,35 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", "\n", "\n", "\n", - "\n", - "node9\n", - "\n", + "\n", + "node11\n", + "Node 11\n", + "\n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8817,7 +4762,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8858,10 +4803,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8879,7 +4824,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8893,7 +4838,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8909,10 +4854,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8923,15 +4868,16 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", "\n", "\n", - "\n", - "node12\n", - "\n", + "\n", + "node14\n", + "Node 14\n", + "\n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8947,533 +4893,525 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " 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- " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", + " \n", " \n", + " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9481,43 +5419,60 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -9525,17 +5480,14 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -9545,14 +5497,11 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -9562,7 +5511,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9578,31 +5527,35 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", "\n", "\n", "\n", - "\n", - "leaf10\n", - "\n", - "\n", + "\n", + "leaf12\n", + "\n", + "Node 12\n", + "\n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -9662,10 +5625,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9683,7 +5646,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9700,7 +5663,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9716,7 +5679,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9729,8 +5692,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -9738,43 +5703,43 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", "\n", "\n", - "\n", - "node9->leaf10\n", - "\n", - "\n", + "\n", + "node11->leaf12\n", + "\n", + "\n", "\n", "\n", - "\n", - "leaf11\n", - "\n", - "\n", + "\n", + "leaf13\n", + "\n", + "Node 13\n", + "\n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -9824,10 +5789,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9845,7 +5810,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9862,7 +5827,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9878,7 +5843,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9890,22 +5855,22 @@ " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -9922,21 +5887,22 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", "\n", "\n", - "\n", - "node9->leaf11\n", - "\n", - "\n", + "\n", + "node11->leaf13\n", + "\n", + "\n", "\n", "\n", - "\n", - "node13\n", - "\n", + "\n", + "node15\n", + "Node 15\n", + "\n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -10330,7 +6011,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -10371,10 +6052,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -10392,7 +6073,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -10406,7 +6087,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -10422,10 +6103,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -10436,15 +6117,16 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", "\n", "\n", - "\n", - "node16\n", - "\n", + "\n", + "node18\n", + "Node 18\n", + "\n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -10460,240 +6142,525 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", 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\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", " \n", " \n", " \n", @@ -10703,13 +6670,16 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -10717,12 +6687,12 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -10734,11 +6704,11 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -10749,17 +6719,18 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", "\n", "\n", "\n", - "\n", - "leaf14\n", - "\n", - "\n", + "\n", + "leaf16\n", + "\n", + "Node 16\n", + "\n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -11035,10 +6790,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -11056,7 +6811,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -11073,7 +6828,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -11089,7 +6844,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -11102,10 +6857,8 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -11113,44 +6866,44 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", "\n", "\n", - "\n", - "node13->leaf14\n", - "\n", - "\n", + "\n", + "node15->leaf16\n", + "\n", + "\n", "\n", "\n", - "\n", - "leaf15\n", - "\n", - "\n", + "\n", + "leaf17\n", + "\n", + "Node 17\n", + "\n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -11269,10 +6953,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -11290,7 +6974,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -11307,7 +6991,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -11323,7 +7007,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -11336,9 +7020,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -11346,42 +7031,43 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", "\n", "\n", - "\n", - "node13->leaf15\n", - "\n", - "\n", + "\n", + "node15->leaf17\n", + "\n", + "\n", "\n", "\n", - "\n", - "leaf17\n", - "\n", - "\n", + "\n", + "leaf19\n", + "\n", + "Node 19\n", + "\n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -11497,10 +7384,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -11518,7 +7405,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -11535,7 +7422,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -11551,7 +7438,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -11563,54 +7450,57 @@ " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", "\n", "\n", - "\n", - "node16->leaf17\n", - "\n", - "\n", + "\n", + "node18->leaf19\n", + "\n", + "\n", "\n", "\n", - "\n", - "leaf18\n", - "\n", - "\n", + "\n", + "leaf20\n", + "\n", + "Node 20\n", + "\n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -11694,10 +7658,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -11715,7 +7679,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -11732,7 +7696,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -11748,7 +7712,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -11760,76 +7724,80 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", "\n", "\n", - "\n", - "node16->leaf18\n", - "\n", - 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@@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -12710,10 +8678,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -12724,24 +8692,24 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", "\n", "\n", - "\n", + "\n", "node0->node1\n", - "\n", - "\n", - "<\n", + "\n", + "\n", + "<\n", "\n", "\n", - "\n", - "node0->node8\n", - "\n", - "\n", - "≥\n", + "\n", + "node0->node10\n", + "\n", + "\n", + "≥\n", "\n", "\n", "\n", @@ -12752,26 +8720,26 @@ "" ], "text/plain": [ - "" + "" ] }, - "execution_count": 71, + "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "trees.dtreeviz(light_dtree_reg)" + "trees.dtreeviz(light_dtree_reg, show_node_labels=True)" ] }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 27, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", "text/plain": [ "
" ] @@ -12788,7 +8756,7 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 28, "metadata": {}, "outputs": [ { @@ -12822,66 +8790,66 @@ " \n", "
\n", " count\n", - " 250.0\n", - " 250.000000\n", - " 250.0\n", - " 250.00000\n", - " 250.000000\n", + " 481.000000\n", + " 481.000000\n", + " 481.000000\n", + " 481.000000\n", + " 481.000000\n", "
\n", "
\n", " mean\n", - " 3.0\n", - " 0.148000\n", - " 1.0\n", - " 1.62800\n", - " 1.720000\n", + " 2.719335\n", + " 0.291060\n", + " 0.686071\n", + " 4.869023\n", + " 1.879418\n", "
\n", "
\n", " std\n", - " 0.0\n", - " 0.355812\n", - " 0.0\n", - " 18.50349\n", - " 0.695366\n", + " 0.449792\n", + " 0.454724\n", + " 0.464571\n", + " 27.624972\n", + " 0.325980\n", "
\n", "
\n", " min\n", - " 3.0\n", + " 2.000000\n", " 0.000000\n", - " 1.0\n", - " -1.00000\n", " 0.000000\n", + " -1.000000\n", + " 1.000000\n", "
\n", "
\n", " 25%\n", - " 3.0\n", + " 2.000000\n", " 0.000000\n", - " 1.0\n", - " -1.00000\n", + " 0.000000\n", + " -1.000000\n", " 2.000000\n", "
\n", "
\n", " 50%\n", - " 3.0\n", + " 3.000000\n", " 0.000000\n", - " 1.0\n", - " -1.00000\n", + " 1.000000\n", + " -1.000000\n", " 2.000000\n", "
\n", "
\n", " 75%\n", - " 3.0\n", - " 0.000000\n", - " 1.0\n", - " -1.00000\n", + " 3.000000\n", + " 1.000000\n", + " 1.000000\n", + " -1.000000\n", " 2.000000\n", "
\n", "
\n", " max\n", - " 3.0\n", + " 3.000000\n", + " 1.000000\n", " 1.000000\n", - " 1.0\n", - " 140.00000\n", + " 145.000000\n", " 2.000000\n", "
\n", " \n", @@ -12889,18 +8857,18 @@ "
" ], "text/plain": [ - " Pclass Survived Sex_label Cabin_label Embarked_label\n", - "count 250.0 250.000000 250.0 250.00000 250.000000\n", - "mean 3.0 0.148000 1.0 1.62800 1.720000\n", - "std 0.0 0.355812 0.0 18.50349 0.695366\n", - "min 3.0 0.000000 1.0 -1.00000 0.000000\n", - "25% 3.0 0.000000 1.0 -1.00000 2.000000\n", - "50% 3.0 0.000000 1.0 -1.00000 2.000000\n", - "75% 3.0 0.000000 1.0 -1.00000 2.000000\n", - "max 3.0 1.000000 1.0 140.00000 2.000000" + " Pclass Survived Sex_label Cabin_label Embarked_label\n", + "count 481.000000 481.000000 481.000000 481.000000 481.000000\n", + "mean 2.719335 0.291060 0.686071 4.869023 1.879418\n", + "std 0.449792 0.454724 0.464571 27.624972 0.325980\n", + "min 2.000000 0.000000 0.000000 -1.000000 1.000000\n", + "25% 2.000000 0.000000 0.000000 -1.000000 2.000000\n", + "50% 3.000000 0.000000 1.000000 -1.000000 2.000000\n", + "75% 3.000000 1.000000 1.000000 -1.000000 2.000000\n", + "max 3.000000 1.000000 1.000000 145.000000 2.000000" ] }, - "execution_count": 35, + "execution_count": 28, "metadata": {}, "output_type": "execute_result" } @@ -12911,14 +8879,14 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 29, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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" ] }, "metadata": { @@ -12933,61 +8901,50 @@ }, { "cell_type": "code", - "execution_count": 137, + "execution_count": 45, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "Pclass 3.000\n", - "Survived 0.000\n", - "Fare 31.275\n", - "Sex_label 0.000\n", - "Cabin_label -1.000\n", - "Embarked_label 2.000\n", - "Name: 542, dtype: float64" + "Pclass 3\n", + "Survived 1\n", + "Sex_label 1\n", + "Cabin_label -1\n", + "Embarked_label 0\n", + "Name: 762, dtype: int64" ] }, - "execution_count": 137, + "execution_count": 45, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "x = X_train_reg[features_reg].iloc[20]\n", + "x = X_train_reg[features_reg].iloc[25]\n", "x" ] }, { "cell_type": "code", - "execution_count": 138, + "execution_count": 51, "metadata": {}, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/tudorl/Documents/workspace/personal/dtreeviz/venv/lib/python3.7/site-packages/numpy/core/fromnumeric.py:3335: RuntimeWarning: Mean of empty slice.\n", - " out=out, **kwargs)\n", - "/Users/tudorl/Documents/workspace/personal/dtreeviz/venv/lib/python3.7/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars\n", - " ret = ret.dtype.type(ret / rcount)\n" - ] - }, { "data": { "image/svg+xml": [ - "\n", - "\n", + "\n", + "\n", "G\n", - "\n", + "\n", "\n", "cluster_instance\n", "\n", "\n", "\n", - "node18\n", - "\n", - "\n", + "node11\n", + "\n", + "\n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -13003,499 +8960,146 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " 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+ " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", " \n", - " \n", - " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -13503,14 +9107,18 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -13520,7 +9128,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -13534,7 +9142,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -13550,33 +9158,30 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", "\n", "\n", - "\n", - "leaf20\n", - "\n", - "\n", + "\n", + "leaf12\n", + "\n", + "\n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " 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\n", " \n", " \n", " \n", @@ -13837,7 +9289,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -13853,7 +9305,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -13866,8 +9318,9 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", + " \n", " \n", " \n", " \n", @@ -13876,729 +9329,42 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", "\n", "\n", "\n", - "node18->leaf20\n", - "\n", - "\n", + "node11->leaf12\n", + "\n", + "\n", "\n", "\n", "\n", - "node14\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " 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" \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -15199,7 +9965,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -15215,7 +9981,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -15264,10 +10030,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -15284,7 +10050,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -15298,7 +10064,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -15314,13 +10080,13 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -15331,22 +10097,22 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", "\n", "\n", - "\n", - "node10->node14\n", - "\n", - "\n", + "\n", + "node10->node11\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "node0\n", - "\n", - "\n", + "\n", + "\n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -15362,786 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\n", - " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -16168,16 +10930,16 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -16188,11 +10950,14 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -16202,7 +10967,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -16231,79 +10996,72 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", "\n", "\n", - "\n", + "\n", "node0->node10\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "X_y\n", - "\n", - "\n", - "Pclass\n", - "\n", - "Survived\n", - "\n", - "Fare\n", - "\n", - "Sex_label\n", - "\n", - "Cabin_label\n", - "\n", - "Embarked_label\n", - "\n", - "3.00\n", - "\n", - "0.00\n", - "\n", - "31.27\n", - "\n", - "0.00\n", - "\n", - "-1.00\n", - "\n", - "2.00\n", + "\n", + "\n", + "Pclass\n", + "\n", + "Survived\n", + "\n", + "Sex_label\n", + "\n", + "Cabin_label\n", + "\n", + "Embarked_label\n", + "\n", + "3\n", + "\n", + "1\n", + "\n", + "1\n", + "\n", + "-1\n", + "\n", + "0\n", "\n", "\n", - "\n", - "leaf20->X_y\n", - "\n", - "\n", - "  Prediction\n", - " 27.07\n", + "\n", + "leaf12->X_y\n", + "\n", + "\n", + "  Prediction\n", + " 26.70\n", "\n", "\n", "" ], "text/plain": [ - "" + "" ] }, - "execution_count": 138, + "execution_count": 51, "metadata": {}, "output_type": "execute_result" } @@ -16314,16 +11072,16 @@ }, { "cell_type": "code", - "execution_count": 139, + "execution_count": 52, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "1.5 <= Pclass \n", - "12.5 <= Fare \n", - "1.5 <= Embarked_label \n", + "0.0 <= Embarked_label \n", + "Pclass in [] \n", + "Sex_label in [1] \n", "\n" ] } diff --git a/notebooks/dtreeviz_sklearn_visualisations.ipynb b/notebooks/dtreeviz_sklearn_visualisations.ipynb index 51729c18..0f2053fa 100644 --- a/notebooks/dtreeviz_sklearn_visualisations.ipynb +++ b/notebooks/dtreeviz_sklearn_visualisations.ipynb @@ -57,7 +57,12 @@ { "data": { "text/plain": [ - "DecisionTreeClassifier(max_depth=4, random_state=1234)" + "DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n", + " max_depth=4, max_features=None, max_leaf_nodes=None,\n", + " min_impurity_decrease=0.0, min_impurity_split=None,\n", + " min_samples_leaf=1, min_samples_split=2,\n", + " min_weight_fraction_leaf=0.0, presort='deprecated',\n", + " random_state=1234, splitter='best')" ] }, "execution_count": 4, @@ -117,7 +122,7 @@ "outputs": [ { "data": { - "image/png": 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+ "image/png": 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\n", 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" ] @@ -139,7 +144,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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" ] @@ -168,7 +173,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -190,7 +195,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -214,181 +219,169 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "image/svg+xml": [ - "\n", - "\n", + "\n", + "\n", "G\n", - "\n", - "\n", + "\n", + "\n", "cluster_legend\n", "\n", "\n", - "\n", + "\n", "node3\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " 2021-01-26T16:27:47.631929\n", - " image/svg+xml\n", - " \n", - " \n", - " Matplotlib v3.3.2, https://matplotlib.org/\n", - " \n", - " \n", - " \n", - " \n", - " \n", + "\n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " 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- " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -396,29 +389,29 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -432,18 +425,18 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -451,7 +444,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -463,20 +456,20 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -486,153 +479,141 @@ " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", "\n", "\n", - "\n", + "\n", "node6\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " 2021-01-26T16:27:47.748843\n", - " image/svg+xml\n", - " \n", - " \n", - " Matplotlib v3.3.2, https://matplotlib.org/\n", - " \n", - " \n", - " \n", - " \n", - " \n", + "\n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -641,19 +622,19 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -663,18 +644,18 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -685,13 +666,13 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -703,10 +684,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -719,16 +700,16 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -743,13 +724,13 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -760,37 +741,25 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", "\n", "\n", "\n", - "\n", + "\n", "leaf4\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " 2021-01-26T16:27:49.435564\n", - " image/svg+xml\n", - " \n", - " \n", - " Matplotlib v3.3.2, https://matplotlib.org/\n", - " \n", - " \n", - " \n", - " \n", - " \n", + "\n", + "\n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -798,21 +767,21 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -820,36 +789,24 @@ " \n", "\n", "\n", - "\n", + "\n", "node3->leaf4\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "leaf5\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " 2021-01-26T16:27:49.478007\n", - " image/svg+xml\n", - " \n", - " \n", - " Matplotlib v3.3.2, https://matplotlib.org/\n", - " \n", - " \n", - " \n", - " \n", - " \n", + "\n", + "\n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -857,12 +814,12 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -876,63 +833,51 @@ " \n", "\n", "\n", - "\n", + "\n", "node3->leaf5\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "leaf7\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " 2021-01-26T16:27:49.521316\n", - " image/svg+xml\n", - " \n", - " \n", - " Matplotlib v3.3.2, https://matplotlib.org/\n", - " \n", - " \n", - " \n", - " \n", - " \n", + "\n", + "\n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -940,62 +885,50 @@ " \n", "\n", "\n", - "\n", + "\n", "node6->leaf7\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "leaf8\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " 2021-01-26T16:27:49.565569\n", - " image/svg+xml\n", - " \n", - " \n", - " Matplotlib v3.3.2, https://matplotlib.org/\n", - " \n", - " \n", - " \n", - " \n", - " \n", + "\n", + "\n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -1003,153 +936,141 @@ " \n", "\n", "\n", - "\n", + "\n", "node6->leaf8\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "node2\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " 2021-01-26T16:27:47.927879\n", - " image/svg+xml\n", - " \n", - " \n", - " Matplotlib v3.3.2, https://matplotlib.org/\n", - " \n", - " \n", - " \n", - " \n", - " \n", + "\n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1158,21 +1079,21 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -1183,15 +1104,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -1202,12 +1123,12 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -1218,10 +1139,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1234,15 +1155,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -1257,13 +1178,13 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -1273,165 +1194,153 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", "\n", "\n", - "\n", + "\n", "node2->node3\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "node2->node6\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "node9\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " 2021-01-26T16:27:48.279844\n", - " image/svg+xml\n", - " \n", - " \n", - " Matplotlib v3.3.2, https://matplotlib.org/\n", - " \n", - " \n", - " \n", - " \n", - " \n", + "\n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1440,19 +1349,19 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -1462,18 +1371,18 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -1484,13 +1393,13 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -1502,10 +1411,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1518,15 +1427,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -1542,13 +1451,13 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -1559,154 +1468,142 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", "\n", "\n", "\n", - "\n", + "\n", "node10\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " 2021-01-26T16:27:48.042613\n", - " image/svg+xml\n", - " \n", - " \n", - " Matplotlib v3.3.2, https://matplotlib.org/\n", - " \n", - " \n", - " \n", - " \n", - " \n", + "\n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1715,21 +1612,21 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -1740,15 +1637,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -1759,12 +1656,12 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -1775,10 +1672,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1791,15 +1688,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -1814,14 +1711,14 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -1832,153 +1729,141 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", "\n", "\n", - "\n", + "\n", "node13\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " 2021-01-26T16:27:48.159936\n", - " image/svg+xml\n", - " \n", - " \n", - " Matplotlib v3.3.2, https://matplotlib.org/\n", - " \n", - " \n", - " \n", - " \n", - " \n", + "\n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1987,19 +1872,19 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -2008,34 +1893,34 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -2057,18 +1942,18 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -2076,15 +1961,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -2099,13 +1984,13 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -2115,54 +2000,42 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", "\n", "\n", "\n", - "\n", + "\n", "leaf11\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " 2021-01-26T16:27:49.610298\n", - " image/svg+xml\n", - " \n", - " \n", - " Matplotlib v3.3.2, https://matplotlib.org/\n", - " \n", - " \n", - " \n", - " \n", - " \n", + "\n", + "\n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -2170,7 +2043,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2178,61 +2051,49 @@ " \n", "\n", "\n", - "\n", + "\n", "node10->leaf11\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "leaf12\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " 2021-01-26T16:27:49.655731\n", - " image/svg+xml\n", - " \n", - " \n", - " Matplotlib v3.3.2, https://matplotlib.org/\n", - " \n", - " \n", - " \n", - " \n", - " \n", + "\n", + "\n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -2240,36 +2101,24 @@ " \n", "\n", "\n", - "\n", + "\n", "node10->leaf12\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "leaf14\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " 2021-01-26T16:27:49.690469\n", - " image/svg+xml\n", - " \n", - " \n", - " Matplotlib v3.3.2, https://matplotlib.org/\n", - " \n", - " \n", - " \n", - " \n", - " \n", + "\n", + "\n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2280,21 +2129,21 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -2302,63 +2151,51 @@ " \n", "\n", "\n", - "\n", + "\n", "node13->leaf14\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "leaf15\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " 2021-01-26T16:27:49.734939\n", - " image/svg+xml\n", - " \n", - " \n", - " Matplotlib v3.3.2, https://matplotlib.org/\n", - " \n", - " \n", - " \n", - " \n", - " \n", + "\n", + "\n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -2366,165 +2203,153 @@ " \n", "\n", "\n", - "\n", + "\n", "node13->leaf15\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "node9->node10\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "node9->node13\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "node1\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " 2021-01-26T16:27:48.395714\n", - " image/svg+xml\n", - " \n", - " \n", - " Matplotlib v3.3.2, https://matplotlib.org/\n", - " \n", - " \n", - " \n", - " \n", - " \n", + "\n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2533,18 +2358,18 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -2552,29 +2377,29 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -2588,18 +2413,18 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -2607,15 +2432,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -2631,13 +2456,13 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -2647,165 +2472,153 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", "\n", "\n", - "\n", + "\n", "node1->node2\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "node1->node9\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "node16\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " 2021-01-26T16:27:49.142575\n", - " image/svg+xml\n", - " \n", - " \n", - " Matplotlib v3.3.2, https://matplotlib.org/\n", - " \n", - " \n", - " \n", - " \n", - " \n", + "\n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2814,19 +2627,19 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -2835,16 +2648,16 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -2853,17 +2666,17 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -2882,18 +2695,18 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -2901,15 +2714,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -2925,13 +2738,13 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -2941,154 +2754,142 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", "\n", "\n", "\n", - "\n", + "\n", "node18\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " 2021-01-26T16:27:48.510652\n", - " image/svg+xml\n", - " \n", - " \n", - " Matplotlib v3.3.2, https://matplotlib.org/\n", - " \n", - " \n", - " \n", - " \n", - " \n", + "\n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3097,19 +2898,19 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -3119,18 +2920,18 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -3141,13 +2942,13 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -3159,10 +2960,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3175,15 +2976,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -3197,13 +2998,13 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -3214,153 +3015,141 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", "\n", "\n", - "\n", + "\n", "node21\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " 2021-01-26T16:27:48.648064\n", - " image/svg+xml\n", - " \n", - " \n", - " Matplotlib v3.3.2, https://matplotlib.org/\n", - " \n", - " \n", - " \n", - " \n", - " \n", + "\n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3369,21 +3158,21 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -3394,15 +3183,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -3413,12 +3202,12 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -3429,10 +3218,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3445,16 +3234,16 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -3470,13 +3259,13 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -3487,60 +3276,48 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", "\n", "\n", "\n", - "\n", + "\n", "leaf19\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " 2021-01-26T16:27:49.779585\n", - " image/svg+xml\n", - " \n", - " \n", - " Matplotlib v3.3.2, https://matplotlib.org/\n", - " \n", - " \n", - " \n", - " \n", - " \n", + "\n", + "\n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3548,36 +3325,24 @@ " \n", "\n", "\n", - "\n", + "\n", "node18->leaf19\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "leaf20\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " 2021-01-26T16:27:49.819891\n", - " image/svg+xml\n", - " \n", - " \n", - " Matplotlib v3.3.2, https://matplotlib.org/\n", - " \n", - " \n", - " \n", - " \n", - " \n", + "\n", + "\n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3585,21 +3350,21 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -3607,63 +3372,51 @@ " \n", "\n", "\n", - "\n", + "\n", "node18->leaf20\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "leaf22\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " 2021-01-26T16:27:49.864637\n", - " image/svg+xml\n", - " \n", - " \n", - " Matplotlib v3.3.2, https://matplotlib.org/\n", - " \n", - " \n", - " \n", - " \n", - " \n", + "\n", + "\n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -3671,53 +3424,41 @@ " \n", "\n", "\n", - "\n", + "\n", "node21->leaf22\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "leaf23\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " 2021-01-26T16:27:49.964508\n", - " image/svg+xml\n", - " \n", - " \n", - " Matplotlib v3.3.2, https://matplotlib.org/\n", - " \n", - " \n", - " \n", - " \n", - " \n", + "\n", + "\n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -3725,10 +3466,10 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -3736,153 +3477,141 @@ " \n", "\n", "\n", - "\n", + "\n", "node21->leaf23\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "node17\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " 2021-01-26T16:27:48.773045\n", - " image/svg+xml\n", - " \n", - " \n", - " Matplotlib v3.3.2, https://matplotlib.org/\n", - " \n", - " \n", - " \n", - " \n", - " \n", + "\n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3891,21 +3620,21 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -3916,15 +3645,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -3935,12 +3664,12 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -3951,10 +3680,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3967,16 +3696,16 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -3992,13 +3721,13 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -4008,165 +3737,153 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", "\n", "\n", - "\n", + "\n", "node17->node18\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "node17->node21\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "node24\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " 2021-01-26T16:27:49.018595\n", - " image/svg+xml\n", - " \n", - " \n", - " Matplotlib v3.3.2, https://matplotlib.org/\n", - " \n", - " \n", - " \n", - " \n", - " \n", + "\n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4175,21 +3892,21 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -4200,15 +3917,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -4219,12 +3936,12 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -4235,10 +3952,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4251,15 +3968,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -4274,14 +3991,14 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -4292,154 +4009,142 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", "\n", "\n", "\n", - "\n", + "\n", "node26\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " 2021-01-26T16:27:48.898788\n", - " image/svg+xml\n", - " \n", - " \n", - " Matplotlib v3.3.2, https://matplotlib.org/\n", - " \n", - " \n", - " \n", - " \n", - " \n", + "\n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4448,19 +4153,19 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -4470,18 +4175,18 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -4492,13 +4197,13 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -4510,10 +4215,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4526,15 +4231,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -4549,13 +4254,13 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -4565,36 +4270,24 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", "\n", "\n", - "\n", + "\n", "leaf27\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " 2021-01-26T16:27:50.132009\n", - " image/svg+xml\n", - " \n", - " \n", - " Matplotlib v3.3.2, https://matplotlib.org/\n", - " \n", - " \n", - " \n", - " \n", - " \n", + "\n", + "\n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4602,21 +4295,21 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -4624,62 +4317,50 @@ " \n", "\n", "\n", - "\n", + "\n", "node26->leaf27\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "leaf28\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " 2021-01-26T16:27:50.177630\n", - " image/svg+xml\n", - " \n", - " \n", - " Matplotlib v3.3.2, https://matplotlib.org/\n", - " \n", - " \n", - " \n", - " \n", - " \n", + "\n", + "\n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -4687,42 +4368,30 @@ " \n", "\n", "\n", - "\n", + "\n", "node26->leaf28\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "node24->node26\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "leaf25\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " 2021-01-26T16:27:50.059290\n", - " image/svg+xml\n", - " \n", - " \n", - " Matplotlib v3.3.2, https://matplotlib.org/\n", - " \n", - " \n", - " \n", - " \n", - " \n", + "\n", + "\n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4730,21 +4399,21 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -4752,165 +4421,153 @@ " \n", "\n", "\n", - "\n", + "\n", "node24->leaf25\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "node16->node17\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "node16->node24\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "node0\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " 2021-01-26T16:27:49.350603\n", - " image/svg+xml\n", - " \n", - " \n", - " Matplotlib v3.3.2, https://matplotlib.org/\n", - " \n", - " \n", - " \n", - " \n", - " \n", + "\n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4919,18 +4576,18 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -4938,31 +4595,31 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -4979,10 +4636,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4995,16 +4652,16 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -5020,12 +4677,12 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -5035,24 +4692,24 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", "\n", "\n", - "\n", + "\n", "node0->node1\n", - "\n", - "\n", - "<\n", + "\n", + "\n", + "<\n", "\n", "\n", - "\n", + "\n", "node0->node16\n", - "\n", - "\n", - "≥\n", + "\n", + "\n", + "≥\n", "\n", "\n", "\n", @@ -5063,30 +4720,18 @@ "\n", "\n", "\n", - "\n", + "\n", "legend\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " 2021-01-26T16:27:47.407868\n", - " image/svg+xml\n", - " \n", - " \n", - " Matplotlib v3.3.2, https://matplotlib.org/\n", - " \n", - " \n", - " \n", - " \n", - " \n", + "\n", + "\n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5095,16 +4740,16 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -5120,10 +4765,10 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -5132,10 +4777,10 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -5147,10 +4792,10 @@ "" ], "text/plain": [ - "" + "" ] }, - "execution_count": 10, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } @@ -5167,49 +4812,37 @@ { "data": { "image/svg+xml": [ - "\n", - "\n", + "\n", + "\n", "G\n", - "\n", - "\n", + "\n", + "\n", "cluster_legend\n", "\n", "\n", - "\n", + "\n", "node3\n", - "Pclass@1.50\n", + "Pclass@1.50\n", "\n", "\n", - "\n", + "\n", "node6\n", - "Fare@28.86\n", + "Fare@28.86\n", "\n", "\n", "\n", - "\n", + "\n", "leaf4\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " 2021-01-26T16:27:50.903690\n", - " image/svg+xml\n", - " \n", - " \n", - " Matplotlib v3.3.2, https://matplotlib.org/\n", - " \n", - " \n", - " \n", - " \n", - " \n", + "\n", + "\n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5217,21 +4850,21 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -5239,36 +4872,24 @@ " \n", "\n", "\n", - "\n", + "\n", "node3->leaf4\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "leaf5\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " 2021-01-26T16:27:50.946279\n", - " image/svg+xml\n", - " \n", - " \n", - " Matplotlib v3.3.2, https://matplotlib.org/\n", - " \n", - " \n", - " \n", - " \n", - " \n", + "\n", + "\n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5276,12 +4897,12 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -5295,63 +4916,51 @@ " \n", "\n", "\n", - "\n", + "\n", "node3->leaf5\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "leaf7\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " 2021-01-26T16:27:50.994564\n", - " image/svg+xml\n", - " \n", - " \n", - " Matplotlib v3.3.2, https://matplotlib.org/\n", - " \n", - " \n", - " \n", - " \n", - " \n", + "\n", + "\n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -5359,62 +4968,50 @@ " \n", "\n", "\n", - "\n", + "\n", "node6->leaf7\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "leaf8\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " 2021-01-26T16:27:51.042827\n", - " image/svg+xml\n", - " \n", - " \n", - " Matplotlib v3.3.2, https://matplotlib.org/\n", - " \n", - " \n", - " \n", - " \n", - " \n", + "\n", + "\n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -5422,87 +5019,75 @@ " \n", "\n", "\n", - "\n", + "\n", "node6->leaf8\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "node2\n", - "Age@2.50\n", + "Age@2.50\n", "\n", "\n", - "\n", + "\n", "node2->node3\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "node2->node6\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "node9\n", - "Fare@23.35\n", + "Fare@23.35\n", "\n", "\n", "\n", - "\n", + "\n", "node10\n", - "Age@36.50\n", + "Age@36.50\n", "\n", "\n", - "\n", + "\n", "node13\n", - "Embarked_label@1.50\n", + "Embarked_label@1.50\n", "\n", "\n", "\n", - "\n", + "\n", "leaf11\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " 2021-01-26T16:27:51.084699\n", - " image/svg+xml\n", - " \n", - " \n", - " Matplotlib v3.3.2, https://matplotlib.org/\n", - " \n", - " \n", - " \n", - " \n", - " \n", + "\n", + "\n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -5510,7 +5095,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5518,61 +5103,49 @@ " \n", "\n", "\n", - "\n", + "\n", "node10->leaf11\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "leaf12\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " 2021-01-26T16:27:51.132657\n", - " image/svg+xml\n", - " \n", - " \n", - " Matplotlib v3.3.2, https://matplotlib.org/\n", - " \n", - " \n", - " \n", - " \n", - " \n", + "\n", + "\n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -5580,36 +5153,24 @@ " \n", "\n", "\n", - "\n", + "\n", "node10->leaf12\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "leaf14\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " 2021-01-26T16:27:51.170610\n", - " image/svg+xml\n", - " \n", - " \n", - " Matplotlib v3.3.2, https://matplotlib.org/\n", - " \n", - " \n", - " \n", - " \n", - " \n", + "\n", + "\n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5620,21 +5181,21 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -5642,63 +5203,51 @@ " \n", "\n", "\n", - "\n", + "\n", "node13->leaf14\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "leaf15\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " 2021-01-26T16:27:51.219604\n", - " image/svg+xml\n", - " \n", - " \n", - " Matplotlib v3.3.2, https://matplotlib.org/\n", - " \n", - " \n", - " \n", - " \n", - " \n", + "\n", + "\n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -5706,105 +5255,93 @@ " \n", "\n", "\n", - "\n", + "\n", "node13->leaf15\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "node9->node10\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "node9->node13\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "node1\n", - "Pclass@2.50\n", + "Pclass@2.50\n", "\n", "\n", - "\n", + "\n", "node1->node2\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "node1->node9\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "node16\n", - "Cabin_label@3.50\n", + "Cabin_label@3.50\n", "\n", "\n", "\n", - "\n", + "\n", "node18\n", - "Fare@38.35\n", + "Fare@38.35\n", "\n", "\n", - "\n", + "\n", "node21\n", - "Age@13.00\n", + "Age@13.00\n", "\n", "\n", "\n", - "\n", + "\n", "leaf19\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " 2021-01-26T16:27:51.270082\n", - " image/svg+xml\n", - " \n", - " \n", - " Matplotlib v3.3.2, https://matplotlib.org/\n", - " \n", - " \n", - " \n", - " \n", - " \n", + "\n", + "\n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5812,36 +5349,24 @@ " \n", "\n", "\n", - "\n", + "\n", "node18->leaf19\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "leaf20\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " 2021-01-26T16:27:51.315140\n", - " image/svg+xml\n", - " \n", - " \n", - " Matplotlib v3.3.2, https://matplotlib.org/\n", - " \n", - " \n", - " \n", - " \n", - " \n", + "\n", + "\n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5849,21 +5374,21 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -5871,63 +5396,51 @@ " \n", "\n", "\n", - "\n", + "\n", "node18->leaf20\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "leaf22\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " 2021-01-26T16:27:51.358094\n", - " image/svg+xml\n", - " \n", - " \n", - " Matplotlib v3.3.2, https://matplotlib.org/\n", - " \n", - " \n", - " \n", - " \n", - " \n", + "\n", + "\n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -5935,53 +5448,41 @@ " \n", "\n", "\n", - "\n", + "\n", "node21->leaf22\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "leaf23\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " 2021-01-26T16:27:51.409170\n", - " image/svg+xml\n", - " \n", - " \n", - " Matplotlib v3.3.2, https://matplotlib.org/\n", - " \n", - " \n", - " \n", - " \n", - " \n", + "\n", + "\n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -5989,10 +5490,10 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -6000,64 +5501,52 @@ " \n", "\n", "\n", - "\n", + "\n", "node21->leaf23\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "node17\n", - "Age@3.50\n", + "Age@3.50\n", "\n", "\n", - "\n", + "\n", "node17->node18\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "node17->node21\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "node24\n", - "Age@17.50\n", + "Age@17.50\n", "\n", "\n", "\n", - "\n", + "\n", "node26\n", - "Fare@7.90\n", + "Fare@7.90\n", "\n", "\n", - "\n", + "\n", "leaf27\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " 2021-01-26T16:27:51.495907\n", - " image/svg+xml\n", - " \n", - " \n", - " Matplotlib v3.3.2, https://matplotlib.org/\n", - " \n", - " \n", - " \n", - " \n", - " \n", + "\n", + "\n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6065,21 +5554,21 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -6087,62 +5576,50 @@ " \n", "\n", "\n", - "\n", + "\n", "node26->leaf27\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "leaf28\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " 2021-01-26T16:27:51.534500\n", - " image/svg+xml\n", - " \n", - " \n", - " Matplotlib v3.3.2, https://matplotlib.org/\n", - " \n", - " \n", - " \n", - " \n", - " \n", + "\n", + "\n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -6150,42 +5627,30 @@ " \n", "\n", "\n", - "\n", + "\n", "node26->leaf28\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "node24->node26\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "leaf25\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " 2021-01-26T16:27:51.451926\n", - " image/svg+xml\n", - " \n", - " \n", - " Matplotlib v3.3.2, https://matplotlib.org/\n", - " \n", - " \n", - " \n", - " \n", - " \n", + "\n", + "\n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6193,21 +5658,21 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -6215,41 +5680,41 @@ " \n", "\n", "\n", - "\n", + "\n", "node24->leaf25\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "node16->node17\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "node16->node24\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "node0\n", - "Sex_label@0.50\n", + "Sex_label@0.50\n", "\n", "\n", - "\n", + "\n", "node0->node1\n", - "\n", - "\n", - "<\n", + "\n", + "\n", + "<\n", "\n", "\n", - "\n", + "\n", "node0->node16\n", - "\n", - "\n", - "≥\n", + "\n", + "\n", + "≥\n", "\n", "\n", "\n", @@ -6260,30 +5725,18 @@ "\n", "\n", "\n", - "\n", + "\n", "legend\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " 2021-01-26T16:27:50.681304\n", - " image/svg+xml\n", - " \n", - " \n", - " Matplotlib v3.3.2, https://matplotlib.org/\n", - " \n", - " \n", - " \n", - " \n", - " \n", + "\n", + "\n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6292,16 +5745,16 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -6317,10 +5770,10 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -6329,10 +5782,10 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -6344,7 +5797,7 @@ "" ], "text/plain": [ - "" + "" ] }, "execution_count": 11, @@ -8153,16 +7606,21 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "DecisionTreeRegressor(criterion='mae', max_depth=3, random_state=1234)" + "DecisionTreeRegressor(ccp_alpha=0.0, criterion='mae', max_depth=3,\n", + " max_features=None, max_leaf_nodes=None,\n", + " min_impurity_decrease=0.0, min_impurity_split=None,\n", + " min_samples_leaf=1, min_samples_split=2,\n", + " min_weight_fraction_leaf=0.0, presort='deprecated',\n", + " random_state=1234, splitter='best')" ] }, - "execution_count": 20, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" } @@ -8176,7 +7634,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 16, "metadata": {}, "outputs": [], "source": [ @@ -8199,40 +7657,295 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "Tree\n", + "\n", + "\n", + "\n", + "0\n", + "\n", + "node #0\n", + "Pclass <= 1.5\n", + "mae = 9.074\n", + "samples = 891\n", + "value = 29.699\n", + "\n", + "\n", + "\n", + "1\n", + "\n", + "node #1\n", + "Cabin_label <= 79.5\n", + "mae = 11.234\n", + "samples = 216\n", + "value = 35.0\n", + "\n", + "\n", + "\n", + "0->1\n", + "\n", + "\n", + "True\n", + "\n", + "\n", + "\n", + "8\n", + "\n", + "node #8\n", + "Fare <= 27.825\n", + "mae = 8.214\n", + "samples = 675\n", + "value = 29.699\n", + "\n", + "\n", + "\n", + "0->8\n", + "\n", + "\n", + "False\n", + "\n", + "\n", + "\n", + "2\n", + "\n", + "node #2\n", + "Fare <= 64.29\n", + "mae = 11.217\n", + "samples = 148\n", + "value = 31.0\n", + "\n", + "\n", + "\n", + "1->2\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "5\n", + "\n", + "node #5\n", + "Survived <= 0.5\n", + "mae = 10.36\n", + "samples = 68\n", + "value = 39.0\n", + "\n", + "\n", + "\n", + "1->5\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "3\n", + "\n", + "node #3\n", + "mae = 10.99\n", + "samples = 76\n", + "value = 35.0\n", + "\n", + "\n", + "\n", + "2->3\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "4\n", + "\n", + "node #4\n", + "mae = 10.912\n", + "samples = 72\n", + "value = 29.699\n", + "\n", + "\n", + "\n", + "2->4\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "6\n", + "\n", + "node #6\n", + "mae = 9.315\n", + "samples = 20\n", + "value = 47.0\n", + "\n", + "\n", + "\n", + "5->6\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "7\n", + "\n", + "node #7\n", + "mae = 9.046\n", + "samples = 48\n", + "value = 36.0\n", + "\n", + "\n", + "\n", + "5->7\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "9\n", + "\n", + "node #9\n", + "Cabin_label <= 139.5\n", + "mae = 7.472\n", + "samples = 596\n", + "value = 29.699\n", + "\n", + "\n", + "\n", + "8->9\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "12\n", + "\n", + "node #12\n", + "Fare <= 51.698\n", + "mae = 12.384\n", + "samples = 79\n", + "value = 21.0\n", + "\n", + "\n", + "\n", + "8->12\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "10\n", + "\n", + "node #10\n", + "mae = 7.386\n", + "samples = 583\n", + "value = 29.699\n", + "\n", + "\n", + "\n", + "9->10\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "11\n", + "\n", + "node #11\n", + "mae = 9.938\n", + "samples = 13\n", + "value = 24.0\n", + "\n", + "\n", + "\n", + "9->11\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "13\n", + "\n", + "node #13\n", + "mae = 12.317\n", + "samples = 58\n", + "value = 11.0\n", + "\n", + "\n", + "\n", + "12->13\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "14\n", + "\n", + "node #14\n", + "mae = 3.386\n", + "samples = 21\n", + "value = 29.699\n", + "\n", + "\n", + "\n", + "12->14\n", + "\n", + "\n", + "\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn import tree as sklearn_tree\n", + "\n", + "dot_data = sklearn_tree.export_graphviz(tree_regressor, out_file=None, feature_names=features_reg,\n", + " filled=True, node_ids=True)\n", + "graphviz.Source(dot_data)" + ] + }, + { + "cell_type": "code", + "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "image/svg+xml": [ - "\n", - "\n", + "\n", + "\n", "G\n", - "\n", + "\n", "\n", - "\n", + "\n", "node2\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " 2021-01-26T16:27:55.059314\n", - " image/svg+xml\n", - " \n", - " \n", - " Matplotlib v3.3.2, https://matplotlib.org/\n", - " \n", - " \n", - " \n", - " \n", - " \n", + "\n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8240,157 +7953,157 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -8400,19 +8113,19 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -8422,18 +8135,6040 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + 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\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -8445,16 +14180,16 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -8464,13 +14199,13 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -8482,10 +14217,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8499,7 +14234,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8513,15 +14248,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -8529,13 +14264,13 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8546,35 +14281,23 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", "\n", "\n", - "\n", + "\n", "node5\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " 2021-01-26T16:27:55.159003\n", - " image/svg+xml\n", - " \n", - " \n", - " Matplotlib v3.3.2, https://matplotlib.org/\n", - " \n", - " \n", - " \n", - " \n", - " \n", + "\n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8582,77 +14305,77 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -8662,19 +14385,19 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -8684,15 +14407,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -8702,15 +14425,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -8719,16 +14442,16 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -8744,18 +14467,18 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -8764,7 +14487,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8778,15 +14501,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -8794,13 +14517,13 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8811,37 +14534,25 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", "\n", "\n", "\n", - "\n", + "\n", "leaf3\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " 2021-01-26T16:27:55.926136\n", - " image/svg+xml\n", - " \n", - " \n", - " Matplotlib v3.3.2, https://matplotlib.org/\n", - " \n", - " \n", - " \n", - " \n", - " \n", + "\n", + "\n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8849,85 +14560,85 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -8935,19 +14646,19 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -8956,15 +14667,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -8973,15 +14684,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -8989,25 +14700,25 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -9019,11 +14730,11 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -9033,42 +14744,30 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", "\n", "\n", - "\n", + "\n", "node2->leaf3\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "leaf4\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " 2021-01-26T16:27:56.000939\n", - " image/svg+xml\n", - " \n", - " \n", - " Matplotlib v3.3.2, https://matplotlib.org/\n", - " \n", - " \n", - " \n", - " \n", - " \n", + "\n", + "\n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9076,81 +14775,81 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -9158,19 +14857,19 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -9179,15 +14878,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -9196,15 +14895,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -9212,24 +14911,24 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -9241,11 +14940,11 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -9255,42 +14954,30 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", "\n", "\n", - "\n", + "\n", "node2->leaf4\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "leaf6\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " 2021-01-26T16:27:56.071784\n", - " image/svg+xml\n", - " \n", - " \n", - " Matplotlib v3.3.2, https://matplotlib.org/\n", - " \n", - " \n", - " \n", - " \n", - " \n", + "\n", + "\n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9298,29 +14985,29 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -9328,19 +15015,19 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -9349,15 +15036,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -9366,15 +15053,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -9382,25 +15069,25 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -9412,12 +15099,12 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -9427,42 +15114,30 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", "\n", "\n", - "\n", + "\n", "node5->leaf6\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "leaf7\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " 2021-01-26T16:27:56.155806\n", - " image/svg+xml\n", - " \n", - " \n", - " Matplotlib v3.3.2, https://matplotlib.org/\n", - " \n", - " \n", - " \n", - " \n", - " \n", + "\n", + "\n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9470,57 +15145,57 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -9528,19 +15203,19 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -9549,15 +15224,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -9566,15 +15241,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -9582,25 +15257,25 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -9612,12 +15287,12 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -9627,41 +15302,29 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", "\n", "\n", - "\n", + "\n", "node5->leaf7\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "node1\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " 2021-01-26T16:27:55.281777\n", - " image/svg+xml\n", - " \n", - " \n", - " Matplotlib v3.3.2, https://matplotlib.org/\n", - " \n", - " \n", - " \n", - " \n", - " \n", + "\n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9669,225 +15332,225 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -9897,21 +15560,21 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -9922,16 +15585,16 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -9943,17 +15606,17 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -9963,17 +15626,17 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -9992,18 +15655,18 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -10012,7 +15675,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -10026,7 +15689,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -10039,13 +15702,13 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -10056,47 +15719,35 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", "\n", "\n", - "\n", + "\n", "node1->node2\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "node1->node5\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "node8\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " 2021-01-26T16:27:55.686423\n", - " image/svg+xml\n", - " \n", - " \n", - " Matplotlib v3.3.2, https://matplotlib.org/\n", - " \n", - " \n", - " \n", - " \n", - " \n", + "\n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -10104,684 +15755,684 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", 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\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -10791,19 +16442,19 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -10813,18 +16464,18 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -10835,13 +16486,13 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -10853,10 +16504,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -10870,7 +16521,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -10884,15 +16535,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -10900,13 +16551,13 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -10917,36 +16568,24 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", "\n", "\n", "\n", - "\n", + "\n", "node9\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " 2021-01-26T16:27:55.469934\n", - " image/svg+xml\n", - " \n", - " \n", - " Matplotlib v3.3.2, https://matplotlib.org/\n", - " \n", - " \n", - " \n", - " \n", - " \n", + "\n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -10954,605 +16593,605 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", 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\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -11562,19 +17201,19 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -11583,16 +17222,16 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -11601,17 +17240,17 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -11630,19 +17269,19 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -11651,15 +17290,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -11668,15 +17307,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -11684,13 +17323,13 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -11701,35 +17340,23 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", "\n", "\n", - "\n", + "\n", "node12\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " 2021-01-26T16:27:55.583633\n", - " image/svg+xml\n", - " \n", - " \n", - " Matplotlib v3.3.2, https://matplotlib.org/\n", - " \n", - " \n", - " \n", - " \n", - " \n", + "\n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -11737,88 +17364,88 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -11828,19 +17455,19 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -11850,18 +17477,18 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -11873,15 +17500,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -11891,13 +17518,13 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -11909,10 +17536,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -11926,7 +17553,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -11940,7 +17567,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -11953,13 +17580,13 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -11970,37 +17597,25 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", "\n", "\n", - "\n", - "\n", - "leaf10\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " 2021-01-26T16:27:56.239425\n", - " image/svg+xml\n", - " \n", - " \n", - " Matplotlib v3.3.2, https://matplotlib.org/\n", - " \n", - " \n", - " \n", - " \n", - " \n", + "\n", + "\n", + "leaf10\n", + "\n", + "\n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -12008,592 +17623,592 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " 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\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -12601,19 +18216,19 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -12622,15 +18237,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -12639,15 +18254,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -12655,25 +18270,25 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -12685,11 +18300,11 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -12700,42 +18315,30 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", "\n", "\n", - "\n", + "\n", "node9->leaf10\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "leaf11\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " 2021-01-26T16:27:56.349229\n", - " image/svg+xml\n", - " \n", - " \n", - " Matplotlib v3.3.2, https://matplotlib.org/\n", - " \n", - " \n", - " \n", - " \n", - " \n", + "\n", + "\n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -12743,22 +18346,22 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -12766,19 +18369,19 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -12787,15 +18390,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -12804,15 +18407,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -12820,24 +18423,24 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -12849,12 +18452,12 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -12864,42 +18467,30 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", "\n", "\n", - "\n", + "\n", "node9->leaf11\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "leaf13\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " 2021-01-26T16:27:56.426168\n", - " image/svg+xml\n", - " \n", - " \n", - " Matplotlib v3.3.2, https://matplotlib.org/\n", - " \n", - " \n", - " \n", - " \n", - " \n", + "\n", + "\n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -12907,67 +18498,67 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -12975,19 +18566,19 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -12996,15 +18587,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -13013,15 +18604,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -13029,24 +18620,24 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -13058,12 +18649,12 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -13073,42 +18664,30 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", "\n", "\n", - "\n", + "\n", "node12->leaf13\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "leaf14\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " 2021-01-26T16:27:56.501177\n", - " image/svg+xml\n", - " \n", - " \n", - " Matplotlib v3.3.2, https://matplotlib.org/\n", - " \n", - " \n", - " \n", - " \n", - " \n", + "\n", + "\n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -13116,30 +18695,30 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -13147,19 +18726,19 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -13168,15 +18747,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -13185,15 +18764,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -13201,25 +18780,25 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -13231,10 +18810,10 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -13244,53 +18823,41 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", "\n", "\n", - "\n", + "\n", "node12->leaf14\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "node8->node9\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "node8->node12\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "node0\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " 2021-01-26T16:27:55.798389\n", - " image/svg+xml\n", - " \n", - " \n", - " Matplotlib v3.3.2, https://matplotlib.org/\n", - " \n", - " \n", - " \n", - " \n", - " \n", + "\n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -13298,900 +18865,900 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " 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\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -14201,20 +19768,20 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -14224,15 +19791,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -14242,15 +19809,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -14259,14 +19826,14 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -14280,18 +19847,18 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -14300,7 +19867,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -14314,15 +19881,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -14330,12 +19897,12 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -14343,13 +19910,13 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -14360,24 +19927,24 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", "\n", "\n", - "\n", + "\n", "node0->node1\n", - "\n", - "\n", - "<\n", + "\n", + "\n", + "<\n", "\n", "\n", - "\n", + "\n", "node0->node8\n", - "\n", - "\n", - "≥\n", + "\n", + "\n", + "≥\n", "\n", "\n", "\n", @@ -14387,7 +19954,7 @@ "" ], "text/plain": [ - "" + "" ] }, "execution_count": 22, @@ -18250,7 +23817,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.6" + "version": "3.7.6" }, "toc": { "base_numbering": 1, @@ -18268,4 +23835,4 @@ }, "nbformat": 4, "nbformat_minor": 4 -} \ No newline at end of file +} diff --git a/notebooks/dtreeviz_spark_visualisations.ipynb b/notebooks/dtreeviz_spark_visualisations.ipynb index 02f89aba..430851c9 100644 --- a/notebooks/dtreeviz_spark_visualisations.ipynb +++ b/notebooks/dtreeviz_spark_visualisations.ipynb @@ -208,7 +208,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "DecisionTreeClassificationModel: uid=DecisionTreeClassifier_fe721bfdec86, depth=4, numNodes=17, numClasses=2, numFeatures=7\n", + "DecisionTreeClassificationModel: uid=DecisionTreeClassifier_b9c1e8ba2f3b, depth=4, numNodes=17, numClasses=2, numFeatures=7\n", " If (feature 1 in {0.0})\n", " If (feature 3 <= 3.5)\n", " If (feature 4 <= 2.5)\n", @@ -617,7 +617,7 @@ }, { 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+ "trees.dtreeviz(spark_dtree, fancy=True)" ] }, { @@ -6446,7 +9102,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 15, "metadata": {}, "outputs": [], "source": [ @@ -6455,7 +9111,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 16, "metadata": {}, "outputs": [], "source": [ @@ -6465,7 +9121,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 17, "metadata": {}, "outputs": [], "source": [ @@ -6477,7 +9133,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 18, "metadata": {}, "outputs": [], "source": [ @@ -6489,7 +9145,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 19, "metadata": {}, "outputs": [ { @@ -6680,7 +9336,7 @@ "[891 rows x 8 columns]" ] }, - "execution_count": 33, + "execution_count": 19, "metadata": {}, "output_type": "execute_result" } @@ -6698,7 +9354,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 20, "metadata": {}, "outputs": [], "source": [ @@ -6715,7 +9371,7 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 21, "metadata": {}, "outputs": [ { @@ -6731,7 +9387,7 @@ "Name: 10, dtype: float64" ] }, - "execution_count": 35, + "execution_count": 21, "metadata": {}, "output_type": "execute_result" } @@ -6743,7 +9399,7 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 22, "metadata": {}, "outputs": [ { @@ -6772,189 +9428,189 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " 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\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6970,7 +9626,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7011,10 +9667,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7027,7 +9683,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7045,7 +9701,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7062,10 +9718,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7076,7 +9732,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7100,62 +9756,62 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -7165,10 +9821,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7187,7 +9843,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7205,7 +9861,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7242,10 +9898,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7258,7 +9914,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7276,7 +9932,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7293,13 +9949,13 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7310,7 +9966,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7336,108 +9992,108 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -7445,10 +10101,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7464,7 +10120,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7481,7 +10137,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7527,7 +10183,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7558,73 +10214,73 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -7632,10 +10288,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7651,7 +10307,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7668,7 +10324,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7715,7 +10371,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7746,57 +10402,57 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -7804,10 +10460,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7823,7 +10479,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7840,7 +10496,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7886,7 +10542,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7917,14 +10573,14 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -7932,10 +10588,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7951,7 +10607,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7968,7 +10624,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8013,7 +10669,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8043,225 +10699,225 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " 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\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -8271,10 +10927,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8290,7 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\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -9745,10 +12401,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9764,7 +12420,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9799,10 +12455,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9815,7 +12471,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9833,7 +12489,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9850,13 +12506,13 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9867,7 +12523,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9891,213 +12547,213 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " 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\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -10107,10 +12763,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -10129,7 +12785,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -10147,7 +12803,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -10184,10 +12840,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -10200,7 +12856,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -10218,7 +12874,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -10232,13 +12888,13 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -10249,7 +12905,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -10275,437 +12931,437 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " 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\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -10713,10 +13369,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -10732,7 +13388,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -10749,7 +13405,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -10796,7 +13452,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -10827,52 +13483,52 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -10880,10 +13536,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -10899,7 +13555,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -10916,7 +13572,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -10963,7 +13619,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -10994,201 +13650,201 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " 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\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -11196,10 +13852,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -11215,7 +13871,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -11232,7 +13888,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -11278,7 +13934,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -11309,21 +13965,21 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -11331,10 +13987,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -11350,7 +14006,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -11367,7 +14023,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -11412,7 +14068,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -11454,900 +14110,900 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " 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\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -12357,10 +15013,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -12380,7 +15036,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -12398,7 +15054,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -12436,10 +15092,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -12452,7 +15108,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -12470,7 +15126,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -12501,13 +15157,13 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -12518,7 +15174,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -12545,10 +15201,10 @@ "" ], "text/plain": [ - "" + "" ] }, - "execution_count": 40, + "execution_count": 22, "metadata": {}, "output_type": "execute_result" } diff --git a/notebooks/dtreeviz_xgboost_visualisations.ipynb b/notebooks/dtreeviz_xgboost_visualisations.ipynb index ade793e2..9649b94c 100644 --- a/notebooks/dtreeviz_xgboost_visualisations.ipynb +++ b/notebooks/dtreeviz_xgboost_visualisations.ipynb @@ -2,9 +2,18 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": 39, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The autoreload extension is already loaded. To reload it, use:\n", + " %reload_ext autoreload\n" + ] + } + ], "source": [ "%load_ext autoreload\n", "%autoreload 2\n", @@ -21,16 +30,16 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 40, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "'/Users/parrt/opt/anaconda3/bin/python'" + "'/Users/tudorl/Documents/workspace/personal/dtreeviz/venv/bin/python'" ] }, - "execution_count": 2, + "execution_count": 40, "metadata": {}, "output_type": "execute_result" } @@ -42,7 +51,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 41, "metadata": {}, "outputs": [], "source": [ @@ -70,7 +79,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 42, "metadata": {}, "outputs": [], "source": [ @@ -86,7 +95,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 43, "metadata": {}, "outputs": [], "source": [ @@ -104,7 +113,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 44, "metadata": {}, "outputs": [], "source": [ @@ -113,7 +122,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 45, "metadata": {}, "outputs": [], "source": [ @@ -123,7 +132,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 46, "metadata": { "scrolled": true }, @@ -367,7 +376,7 @@ "12 24.00 " ] }, - "execution_count": 8, + "execution_count": 46, "metadata": {}, "output_type": "execute_result" } @@ -378,22 +387,22 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 47, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 9, + "execution_count": 47, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] @@ -418,7 +427,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 48, "metadata": {}, "outputs": [], "source": [ @@ -435,7 +444,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 49, "metadata": {}, "outputs": [], "source": [ @@ -445,7 +454,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 50, "metadata": {}, "outputs": [], "source": [ @@ -468,12 +477,12 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 51, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -490,12 +499,12 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 52, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -519,12 +528,12 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 53, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -541,14 +550,14 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 54, "metadata": { "scrolled": true }, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -572,161 +581,149 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 55, "metadata": {}, "outputs": [ { "data": { "image/svg+xml": [ - "\n", - "\n", + "\n", + "\n", "G\n", - "\n", - "\n", + "\n", + "\n", "cluster_legend\n", "\n", "\n", - "\n", + "\n", "node4\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " 2021-01-26T16:26:50.766844\n", - " image/svg+xml\n", - " \n", - " \n", - " Matplotlib v3.3.2, https://matplotlib.org/\n", - " \n", - " \n", - " \n", - " \n", - " \n", + "\n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -735,19 +732,19 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -757,18 +754,18 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -779,13 +776,13 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -797,10 +794,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -813,15 +810,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -837,13 +834,13 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -854,53 +851,41 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", "\n", "\n", - "\n", + "\n", "leaf7\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " 2021-01-26T16:26:51.822282\n", - " image/svg+xml\n", - " \n", - " \n", - " Matplotlib v3.3.2, https://matplotlib.org/\n", - " \n", - " \n", - " \n", - " \n", - " \n", + "\n", + "\n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -908,7 +893,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -916,63 +901,51 @@ " \n", "\n", "\n", - "\n", + "\n", "node4->leaf7\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "leaf8\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " 2021-01-26T16:26:51.865951\n", - " image/svg+xml\n", - " \n", - " \n", - " Matplotlib v3.3.2, https://matplotlib.org/\n", - " \n", - " \n", - " \n", - " \n", - " \n", + "\n", + "\n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -980,153 +953,141 @@ " \n", "\n", "\n", - "\n", + "\n", "node4->leaf8\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "node1\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " 2021-01-26T16:26:50.940697\n", - " image/svg+xml\n", - " \n", - " \n", - " Matplotlib v3.3.2, https://matplotlib.org/\n", - " \n", - " \n", - " \n", - " \n", - " \n", + "\n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1135,18 +1096,18 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -1154,29 +1115,29 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -1190,18 +1151,18 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -1209,15 +1170,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -1233,12 +1194,12 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -1248,159 +1209,147 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", "\n", "\n", - "\n", + "\n", "node1->node4\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "node2\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " 2021-01-26T16:26:51.436153\n", - " image/svg+xml\n", - " \n", - " \n", - " Matplotlib v3.3.2, https://matplotlib.org/\n", - " \n", - " \n", - " \n", - " \n", - " \n", + "\n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1409,19 +1358,19 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -1430,16 +1379,16 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -1448,17 +1397,17 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -1477,18 +1426,18 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -1496,15 +1445,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -1520,12 +1469,12 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -1535,55 +1484,43 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", "\n", "\n", "\n", - "\n", + "\n", "leaf3\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " 2021-01-26T16:26:51.781764\n", - " image/svg+xml\n", - " \n", - " \n", - " Matplotlib v3.3.2, https://matplotlib.org/\n", - " \n", - " \n", - " \n", - " \n", - " \n", + "\n", + "\n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -1591,7 +1528,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1599,153 +1536,141 @@ " \n", "\n", "\n", - "\n", + "\n", "node1->leaf3\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "node5\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " 2021-01-26T16:26:51.110022\n", - " image/svg+xml\n", - " \n", - " \n", - " Matplotlib v3.3.2, https://matplotlib.org/\n", - " \n", - " \n", - " \n", - " \n", - " \n", + "\n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1754,21 +1679,21 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -1779,15 +1704,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -1798,12 +1723,12 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -1814,10 +1739,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1830,16 +1755,16 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -1855,13 +1780,13 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -1871,153 +1796,141 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", "\n", "\n", - "\n", + "\n", "node6\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " 2021-01-26T16:26:51.271520\n", - " image/svg+xml\n", - " \n", - " \n", - " Matplotlib v3.3.2, https://matplotlib.org/\n", - " \n", - " \n", - " \n", - " \n", - " \n", + "\n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2026,21 +1939,21 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -2051,15 +1964,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -2070,12 +1983,12 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -2086,10 +1999,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2102,15 +2015,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -2125,14 +2038,14 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -2143,37 +2056,25 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", "\n", "\n", "\n", - "\n", + "\n", "leaf9\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " 2021-01-26T16:26:51.899804\n", - " image/svg+xml\n", - " \n", - " \n", - " Matplotlib v3.3.2, https://matplotlib.org/\n", - " \n", - " \n", - " \n", - " \n", - " \n", + "\n", + "\n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2184,23 +2085,23 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -2208,53 +2109,41 @@ " \n", "\n", "\n", - "\n", + "\n", "node5->leaf9\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "leaf10\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " 2021-01-26T16:26:51.943473\n", - " image/svg+xml\n", - " \n", - " \n", - " Matplotlib v3.3.2, https://matplotlib.org/\n", - " \n", - " \n", - " \n", - " \n", - " \n", + "\n", + "\n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -2262,10 +2151,10 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -2273,36 +2162,24 @@ " \n", "\n", "\n", - "\n", + "\n", "node5->leaf10\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "leaf11\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " 2021-01-26T16:26:51.982772\n", - " image/svg+xml\n", - " \n", - " \n", - " Matplotlib v3.3.2, https://matplotlib.org/\n", - " \n", - " \n", - " \n", - " \n", - " \n", + "\n", + "\n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2310,21 +2187,21 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -2332,63 +2209,51 @@ " \n", "\n", "\n", - "\n", + "\n", "node6->leaf11\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "leaf12\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " 2021-01-26T16:26:52.021045\n", - " image/svg+xml\n", - " \n", - " \n", - " Matplotlib v3.3.2, https://matplotlib.org/\n", - " \n", - " \n", - " \n", - " \n", - " \n", + "\n", + "\n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -2396,165 +2261,153 @@ " \n", "\n", "\n", - "\n", + "\n", "node6->leaf12\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "node2->node5\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "node2->node6\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "node0\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " 2021-01-26T16:26:51.599150\n", - " image/svg+xml\n", - " \n", - " \n", - " Matplotlib v3.3.2, https://matplotlib.org/\n", - " \n", - " \n", - " \n", - " \n", - " \n", + "\n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2563,18 +2416,18 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -2582,31 +2435,31 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -2623,10 +2476,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2639,16 +2492,16 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -2664,12 +2517,12 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -2679,54 +2532,42 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", "\n", "\n", - "\n", + "\n", "node0->node1\n", - "\n", - "\n", - "<\n", + "\n", + "\n", + "<\n", "\n", "\n", - "\n", + "\n", "node0->node2\n", - "\n", - "\n", - "≥\n", + "\n", + "\n", + "≥\n", "\n", "\n", "\n", "\n", "\n", "\n", - "\n", + "\n", "legend\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " 2021-01-26T16:26:50.480618\n", - " image/svg+xml\n", - " \n", - " \n", - " Matplotlib v3.3.2, https://matplotlib.org/\n", - " \n", - " \n", - " \n", - " \n", - " \n", + "\n", + "\n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2735,16 +2576,16 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -2760,10 +2601,10 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -2772,10 +2613,10 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -2787,10 +2628,10 @@ "" ], "text/plain": [ - "" + "" ] }, - "execution_count": 17, + "execution_count": 55, "metadata": {}, "output_type": "execute_result" } @@ -2801,161 +2642,149 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 56, "metadata": {}, "outputs": [ { "data": { "image/svg+xml": [ - "\n", - "\n", + "\n", + "\n", "G\n", - "\n", - "\n", + "\n", + "\n", "cluster_legend\n", "\n", "\n", - "\n", + "\n", "node4\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " 2021-01-26T16:26:52.653679\n", - " image/svg+xml\n", - " \n", - " \n", - " Matplotlib v3.3.2, https://matplotlib.org/\n", - " \n", - " \n", - " \n", - " \n", - " \n", + "\n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2964,19 +2793,19 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -2986,18 +2815,18 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -3008,13 +2837,13 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -3026,10 +2855,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3042,15 +2871,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -3066,13 +2895,13 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -3083,53 +2912,41 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", "\n", "\n", - "\n", + "\n", "leaf7\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " 2021-01-26T16:26:53.926815\n", - " image/svg+xml\n", - " \n", - " \n", - " Matplotlib v3.3.2, https://matplotlib.org/\n", - " \n", - " \n", - " \n", - " \n", - " \n", + "\n", + "\n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -3137,7 +2954,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3145,63 +2962,51 @@ " \n", "\n", "\n", - "\n", + "\n", "node4->leaf7\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "leaf8\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " 2021-01-26T16:26:53.979892\n", - " image/svg+xml\n", - " \n", - " \n", - " Matplotlib v3.3.2, https://matplotlib.org/\n", - " \n", - " \n", - " \n", - " \n", - " \n", + "\n", + "\n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -3209,153 +3014,141 @@ " \n", "\n", "\n", - "\n", + "\n", "node4->leaf8\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "node1\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " 2021-01-26T16:26:52.899948\n", - " image/svg+xml\n", - " \n", - " \n", - " Matplotlib v3.3.2, https://matplotlib.org/\n", - " \n", - " \n", - " \n", - " \n", - " \n", + "\n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3364,18 +3157,18 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -3383,29 +3176,29 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -3419,18 +3212,18 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -3438,15 +3231,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -3462,12 +3255,12 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -3477,159 +3270,147 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", "\n", "\n", - "\n", + "\n", "node1->node4\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "node2\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " 2021-01-26T16:26:53.436992\n", - " image/svg+xml\n", - " \n", - " \n", - " Matplotlib v3.3.2, https://matplotlib.org/\n", - " \n", - " \n", - " \n", - " \n", - " \n", + "\n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3638,19 +3419,19 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -3659,16 +3440,16 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -3677,17 +3458,17 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -3706,18 +3487,18 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -3725,15 +3506,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -3749,12 +3530,12 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -3764,55 +3545,43 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", "\n", "\n", "\n", - "\n", + "\n", "leaf3\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " 2021-01-26T16:26:53.752889\n", - " image/svg+xml\n", - " \n", - " \n", - " Matplotlib v3.3.2, https://matplotlib.org/\n", - " \n", - " \n", - " \n", - " \n", - " \n", + "\n", + "\n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -3820,7 +3589,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3828,153 +3597,141 @@ " \n", "\n", "\n", - "\n", + "\n", "node1->leaf3\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "node5\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " 2021-01-26T16:26:53.099647\n", - " image/svg+xml\n", - " \n", - " \n", - " Matplotlib v3.3.2, https://matplotlib.org/\n", - " \n", - " \n", - " \n", - " \n", - " \n", + "\n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3983,21 +3740,21 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -4008,15 +3765,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -4027,12 +3784,12 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -4043,10 +3800,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4059,16 +3816,16 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -4084,13 +3841,13 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -4100,153 +3857,141 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", "\n", "\n", - "\n", + "\n", "node6\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " 2021-01-26T16:26:53.273778\n", - " image/svg+xml\n", - " \n", - " \n", - " Matplotlib v3.3.2, https://matplotlib.org/\n", - " \n", - " \n", - " \n", - " \n", - " \n", + "\n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4255,21 +4000,21 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -4280,15 +4025,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -4299,12 +4044,12 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -4315,10 +4060,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4331,15 +4076,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -4354,14 +4099,14 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -4372,37 +4117,25 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", "\n", "\n", "\n", - "\n", + "\n", "leaf9\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " 2021-01-26T16:26:54.016492\n", - " image/svg+xml\n", - " \n", - " \n", - " Matplotlib v3.3.2, https://matplotlib.org/\n", - " \n", - " \n", - " \n", - " \n", - " \n", + "\n", + "\n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4413,23 +4146,23 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -4437,53 +4170,41 @@ " \n", "\n", "\n", - "\n", + "\n", "node5->leaf9\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "leaf10\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " 2021-01-26T16:26:54.059528\n", - " image/svg+xml\n", - " \n", - " \n", - " Matplotlib v3.3.2, https://matplotlib.org/\n", - " \n", - " \n", - " \n", - " \n", - " \n", + "\n", + "\n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -4491,10 +4212,10 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -4502,36 +4223,24 @@ " \n", "\n", "\n", - "\n", + "\n", "node5->leaf10\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "leaf11\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " 2021-01-26T16:26:54.100996\n", - " image/svg+xml\n", - " \n", - " \n", - " Matplotlib v3.3.2, https://matplotlib.org/\n", - " \n", - " \n", - " \n", - " \n", - " \n", + "\n", + "\n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4539,21 +4248,21 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -4561,63 +4270,51 @@ " \n", "\n", "\n", - "\n", + "\n", "node6->leaf11\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "leaf12\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " 2021-01-26T16:26:54.145639\n", - " image/svg+xml\n", - " \n", - " \n", - " Matplotlib v3.3.2, https://matplotlib.org/\n", - " \n", - " \n", - " \n", - " \n", - " \n", + "\n", + "\n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -4625,165 +4322,153 @@ " \n", "\n", "\n", - "\n", + "\n", "node6->leaf12\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "node2->node5\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "node2->node6\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "node0\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " 2021-01-26T16:26:53.642044\n", - " image/svg+xml\n", - " \n", - " \n", - " Matplotlib v3.3.2, https://matplotlib.org/\n", - " \n", - " \n", - " \n", - " \n", - " \n", + "\n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4792,18 +4477,18 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -4811,31 +4496,31 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -4852,10 +4537,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4868,16 +4553,16 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -4893,12 +4578,12 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -4908,54 +4593,42 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", "\n", "\n", - "\n", + "\n", "node0->node1\n", - "\n", - "\n", - "<\n", + "\n", + "\n", + "<\n", "\n", "\n", - "\n", + "\n", "node0->node2\n", - "\n", - "\n", - "≥\n", + "\n", + "\n", + "≥\n", "\n", "\n", "\n", "\n", "\n", "\n", - "\n", + "\n", "legend\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " 2021-01-26T16:26:52.426672\n", - " image/svg+xml\n", - " \n", - " \n", - " Matplotlib v3.3.2, https://matplotlib.org/\n", - " \n", - " \n", - " \n", - " \n", - " \n", + "\n", + "\n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4964,16 +4637,16 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -4989,10 +4662,10 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -5001,10 +4674,10 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -5016,10 +4689,10 @@ "" ], "text/plain": [ - "" + "" ] }, - "execution_count": 18, + "execution_count": 56, "metadata": {}, "output_type": "execute_result" } @@ -5037,7 +4710,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 57, "metadata": {}, "outputs": [ { @@ -5168,7 +4841,7 @@ "max 2.000000 " ] }, - "execution_count": 19, + "execution_count": 57, "metadata": {}, "output_type": "execute_result" } @@ -5179,7 +4852,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 58, "metadata": {}, "outputs": [ { @@ -5310,7 +4983,7 @@ "max 2.000000 " ] }, - "execution_count": 20, + "execution_count": 58, "metadata": {}, "output_type": "execute_result" } @@ -5328,7 +5001,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 59, "metadata": { "scrolled": true }, @@ -5339,7 +5012,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 60, "metadata": {}, "outputs": [ { @@ -5354,7 +5027,7 @@ "Name: 10, dtype: float64" ] }, - "execution_count": 22, + "execution_count": 60, "metadata": {}, "output_type": "execute_result" } @@ -5365,7 +5038,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 61, "metadata": {}, "outputs": [ { @@ -5385,7 +5058,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 62, "metadata": {}, "outputs": [ { @@ -5419,7 +5092,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 63, "metadata": {}, "outputs": [], "source": [ @@ -5429,7 +5102,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 64, "metadata": {}, "outputs": [], "source": [ @@ -5440,7 +5113,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 65, "metadata": {}, "outputs": [], "source": [ @@ -5456,40 +5129,28 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 66, "metadata": {}, "outputs": [ { "data": { "image/svg+xml": [ - "\n", - "\n", + "\n", + "\n", "G\n", - "\n", + "\n", "\n", - "\n", + "\n", "node4\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " 2021-01-26T16:26:56.260685\n", - " image/svg+xml\n", - " \n", - " \n", - " Matplotlib v3.3.2, https://matplotlib.org/\n", - " \n", - " \n", - " \n", - " \n", - " \n", + "\n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5497,83 +5158,83 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -5583,19 +5244,19 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -5605,18 +5266,18 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -5628,15 +5289,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -5646,13 +5307,13 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -5664,10 +5325,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5681,7 +5342,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5695,15 +5356,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -5711,13 +5372,13 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5728,36 +5389,24 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", "\n", "\n", - "\n", + "\n", "leaf7\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " 2021-01-26T16:26:57.418049\n", - " image/svg+xml\n", - " \n", - " \n", - " Matplotlib v3.3.2, https://matplotlib.org/\n", - " \n", - " \n", - " \n", - " \n", - " \n", + "\n", + "\n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5765,10 +5414,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -5776,19 +5425,19 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -5797,15 +5446,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -5814,15 +5463,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -5830,23 +5479,23 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -5857,11 +5506,11 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -5870,42 +5519,30 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", "\n", "\n", - "\n", + "\n", "node4->leaf7\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "leaf8\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " 2021-01-26T16:26:57.505538\n", - " image/svg+xml\n", - " \n", - " \n", - " Matplotlib v3.3.2, https://matplotlib.org/\n", - " \n", - " \n", - " \n", - " \n", - " \n", + "\n", + "\n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5913,82 +5550,82 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -5996,19 +5633,19 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -6017,15 +5654,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -6034,15 +5671,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -6050,25 +5687,25 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -6080,11 +5717,11 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -6094,41 +5731,29 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", "\n", "\n", - "\n", + "\n", "node4->leaf8\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "node1\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " 2021-01-26T16:26:56.440866\n", - " image/svg+xml\n", - " \n", - " \n", - " Matplotlib v3.3.2, https://matplotlib.org/\n", - " \n", - " \n", - " \n", - " \n", - " \n", + "\n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6136,225 +5761,225 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -6364,19 +5989,19 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -6386,18 +6011,18 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -6409,16 +6034,16 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -6428,13 +6053,13 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -6446,10 +6071,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6463,7 +6088,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6477,15 +6102,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -6493,13 +6118,13 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6510,41 +6135,29 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", "\n", "\n", - "\n", + "\n", "node1->node4\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "node2\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " 2021-01-26T16:26:56.995009\n", - " image/svg+xml\n", - " \n", - " \n", - " Matplotlib v3.3.2, https://matplotlib.org/\n", - " \n", - " \n", - " \n", - " \n", - " \n", + "\n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6552,684 +6165,684 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " 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\n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -7239,19 +6852,19 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -7261,18 +6874,18 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -7283,13 +6896,13 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -7301,10 +6914,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7318,7 +6931,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7332,15 +6945,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -7348,13 +6961,13 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7365,37 +6978,25 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", "\n", "\n", "\n", - "\n", + "\n", "leaf3\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " 2021-01-26T16:26:57.317962\n", - " image/svg+xml\n", - " \n", - " \n", - " Matplotlib v3.3.2, https://matplotlib.org/\n", - " \n", - " \n", - " \n", - " \n", - " \n", + "\n", + "\n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7403,151 +7004,151 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -7555,19 +7156,19 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -7576,15 +7177,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -7593,15 +7194,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -7609,25 +7210,25 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -7639,12 +7240,12 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -7655,41 +7256,29 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", "\n", "\n", - "\n", + "\n", "node1->leaf3\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "node5\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " 2021-01-26T16:26:56.602103\n", - " image/svg+xml\n", - " \n", - " \n", - " Matplotlib v3.3.2, https://matplotlib.org/\n", - " \n", - " \n", - " \n", - " \n", - " \n", + "\n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7697,605 +7286,605 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " 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\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -8305,18 +7894,18 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -8324,31 +7913,31 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -8364,19 +7953,19 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -8385,7 +7974,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8399,15 +7988,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -8415,13 +8004,13 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8432,35 +8021,23 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", "\n", "\n", - "\n", + "\n", "node6\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " 2021-01-26T16:26:56.789662\n", - " image/svg+xml\n", - " \n", - " \n", - " Matplotlib v3.3.2, https://matplotlib.org/\n", - " \n", - " \n", - " \n", - " \n", - " \n", + "\n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8468,88 +8045,88 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -8559,19 +8136,19 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -8581,18 +8158,18 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -8604,15 +8181,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -8622,13 +8199,13 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -8640,10 +8217,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8657,7 +8234,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8671,7 +8248,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8684,13 +8261,13 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8701,37 +8278,25 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", "\n", "\n", "\n", - "\n", + "\n", "leaf9\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " 2021-01-26T16:26:57.589123\n", - " image/svg+xml\n", - " \n", - " \n", - " Matplotlib v3.3.2, https://matplotlib.org/\n", - " \n", - " \n", - " \n", - " \n", - " \n", + "\n", + "\n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8739,423 +8304,423 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - 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\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -9163,19 +8728,19 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -9184,15 +8749,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -9201,15 +8766,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -9217,24 +8782,24 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -9246,12 +8811,12 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -9262,42 +8827,30 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", "\n", "\n", - "\n", + "\n", "node5->leaf9\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "leaf10\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " 2021-01-26T16:26:57.666231\n", - " image/svg+xml\n", - " \n", - " \n", - " Matplotlib v3.3.2, https://matplotlib.org/\n", - " \n", - " \n", - " \n", - " \n", - " \n", + "\n", + "\n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9305,191 +8858,191 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -9497,19 +9050,19 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -9518,15 +9071,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -9535,15 +9088,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -9551,25 +9104,25 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -9581,12 +9134,12 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -9597,42 +9150,30 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", "\n", "\n", - "\n", + "\n", "node5->leaf10\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "leaf11\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " 2021-01-26T16:26:57.746043\n", - " image/svg+xml\n", - " \n", - " \n", - " Matplotlib v3.3.2, https://matplotlib.org/\n", - " \n", - " \n", - " \n", - " \n", - " \n", + "\n", + "\n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9640,67 +9181,67 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -9708,19 +9249,19 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -9729,15 +9270,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -9746,15 +9287,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -9762,24 +9303,24 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -9791,12 +9332,12 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -9806,42 +9347,30 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", "\n", "\n", - "\n", + "\n", "node6->leaf11\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "leaf12\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " 2021-01-26T16:26:57.824263\n", - " image/svg+xml\n", - " \n", - " \n", - " Matplotlib v3.3.2, https://matplotlib.org/\n", - " \n", - " \n", - " \n", - " \n", - " \n", + "\n", + "\n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9849,30 +9378,30 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -9880,19 +9409,19 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -9901,15 +9430,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -9918,15 +9447,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -9934,25 +9463,25 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -9964,10 +9493,10 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -9977,53 +9506,41 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", "\n", "\n", - "\n", + "\n", "node6->leaf12\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "node2->node5\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "node2->node6\n", - "\n", - "\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "node0\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " 2021-01-26T16:26:57.191349\n", - " image/svg+xml\n", - " \n", - " \n", - " Matplotlib v3.3.2, https://matplotlib.org/\n", - " \n", - " \n", - " \n", - " \n", - " \n", + "\n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -10031,900 +9548,900 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " 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\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -10934,18 +10451,18 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -10953,15 +10470,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -10969,29 +10486,29 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -11005,18 +10522,18 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -11025,15 +10542,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -11042,15 +10559,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -11058,12 +10575,12 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -11071,13 +10588,13 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -11088,24 +10605,24 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", "\n", "\n", - "\n", + "\n", "node0->node1\n", - "\n", - "\n", - "<\n", + "\n", + "\n", + "<\n", "\n", "\n", - "\n", + "\n", "node0->node2\n", - "\n", - "\n", - "≥\n", + "\n", + "\n", + "≥\n", "\n", "\n", "\n", @@ -11115,10 +10632,10 @@ "" ], "text/plain": [ - "" + "" ] }, - "execution_count": 28, + "execution_count": 66, "metadata": {}, "output_type": "execute_result" } @@ -11136,12 +10653,12 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 67, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -11158,12 +10675,12 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 68, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -11187,7 +10704,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 69, "metadata": {}, "outputs": [ { @@ -11318,7 +10835,7 @@ "max 2.000000 " ] }, - "execution_count": 31, + "execution_count": 69, "metadata": {}, "output_type": "execute_result" } @@ -11329,7 +10846,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 70, "metadata": {}, "outputs": [ { @@ -11460,7 +10977,7 @@ "max 2.000000 " ] }, - "execution_count": 32, + "execution_count": 70, "metadata": {}, "output_type": "execute_result" } @@ -11478,7 +10995,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 71, "metadata": {}, "outputs": [ { @@ -11493,7 +11010,7 @@ "Name: 10, dtype: float64" ] }, - "execution_count": 33, + "execution_count": 71, "metadata": {}, "output_type": "execute_result" } @@ -11505,7 +11022,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 72, "metadata": {}, "outputs": [ { @@ -11532,12 +11049,12 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 73, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] @@ -11554,12 +11071,12 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 74, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", "text/plain": [ "
" ] @@ -11585,7 +11102,7 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 75, "metadata": {}, "outputs": [], "source": [ @@ -11783,26 +11300,6 @@ "node_data = dataset_reg_test.query(\"Cabin_label < 137 and Pclass >= 2.5 and Fare >= 19.5375004\")\n", "node_data" ] - }, - { - "cell_type": "code", - "execution_count": 46, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "14.333333333333334" - ] - }, - "execution_count": 46, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "np.mean(node_data[\"Age\"])" - ] } ], "metadata": { @@ -11821,7 +11318,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.6" + "version": "3.7.6" }, "toc": { "base_numbering": 1, diff --git a/testing/testlib/models/test_decision_tree_lightgbm_classifier.py b/testing/testlib/models/test_decision_tree_lightgbm_classifier.py index f533bd10..e820da4d 100644 --- a/testing/testlib/models/test_decision_tree_lightgbm_classifier.py +++ b/testing/testlib/models/test_decision_tree_lightgbm_classifier.py @@ -82,5 +82,26 @@ def test_get_node_samples(shadow_dec_tree: ShadowLightGBMTree): assert len(node_samples[17]) == 196 assert len(node_samples[21]) == 86 + +def test_get_split_samples(shadow_dec_tree: ShadowLightGBMTree): + left_0, rigth_0 = shadow_dec_tree.get_split_samples(0) + assert len(left_0) == 245 and len(rigth_0) == 467 + + left_1, right_1 = shadow_dec_tree.get_split_samples(1) + assert len(left_1) == 130 and len(right_1) == 115 + + left_3, right_3 = shadow_dec_tree.get_split_samples(3) + assert len(left_3) == 56 and len(right_3) == 54 + + left_7, right_7 = shadow_dec_tree.get_split_samples(7) + assert len(left_7) == 95 and len(right_7) == 20 + + left_12, right_12 = shadow_dec_tree.get_split_samples(12) + assert len(left_12) == 340 and len(right_12) == 127 + + left_17, right_17 = shadow_dec_tree.get_split_samples(17) + assert len(left_17) == 21 and len(right_17) == 175 + + def test_get_min_samples_leaf(shadow_dec_tree: ShadowLightGBMTree): assert shadow_dec_tree.get_min_samples_leaf() == 20 diff --git a/testing/testlib/models/test_decision_trees_sk_classifier.py b/testing/testlib/models/test_decision_trees_sk_classifier.py index 8f45c7d0..03917410 100644 --- a/testing/testlib/models/test_decision_trees_sk_classifier.py +++ b/testing/testlib/models/test_decision_trees_sk_classifier.py @@ -169,3 +169,8 @@ def test_get_node_nsamples_by_class(shadow_dec_tree): assert np.array_equal(shadow_dec_tree.get_node_nsamples_by_class(5), np.array([72, 72])) assert np.array_equal(shadow_dec_tree.get_node_nsamples_by_class(10), np.array([5, 9])) assert np.array_equal(shadow_dec_tree.get_node_nsamples_by_class(11), np.array([404, 55])) + +# def test_get_split_samples(shadow_dec_tree): +# hard to test because dataset is not the same with dataset the decision tree model was trained +# left_0, right_0 = shadow_dec_tree.get_split_samples(0) +# assert len(left_0) == 314 and len(right_0) == 577 From 5a9364cac1ba9ecef24137bcdb65d912e41c84a1 Mon Sep 17 00:00:00 2001 From: tlapusan Date: Wed, 3 Feb 2021 14:26:30 +0200 Subject: [PATCH 13/16] Move test for sklearn pipeline to the new path, testlib --- .../models/test_decision_trees_sk_pipeline.py | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename testing/{dtreeviz => testlib}/models/test_decision_trees_sk_pipeline.py (100%) diff --git a/testing/dtreeviz/models/test_decision_trees_sk_pipeline.py b/testing/testlib/models/test_decision_trees_sk_pipeline.py similarity index 100% rename from testing/dtreeviz/models/test_decision_trees_sk_pipeline.py rename to testing/testlib/models/test_decision_trees_sk_pipeline.py From f03826238d2c1ab48719cd43dc8817b28ee0f4c6 Mon Sep 17 00:00:00 2001 From: tlapusan Date: Wed, 3 Feb 2021 18:54:27 +0200 Subject: [PATCH 14/16] Adapt the root node labels for each library --- dtreeviz/models/lightgbm_decision_tree.py | 3 + dtreeviz/models/shadow_decision_tree.py | 3 + dtreeviz/models/sklearn_decision_trees.py | 4 + dtreeviz/models/spark_decision_tree.py | 3 + dtreeviz/models/xgb_decision_tree.py | 3 + dtreeviz/trees.py | 6 +- .../dtreeviz_lightgbm_visualisations.ipynb | 11152 +++++++---- .../dtreeviz_sklearn_visualisations.ipynb | 16461 ++++++++-------- notebooks/dtreeviz_spark_visualisations.ipynb | 11346 ++++------- .../dtreeviz_xgboost_visualisations.ipynb | 7725 ++++---- 10 files changed, 23560 insertions(+), 23146 deletions(-) diff --git a/dtreeviz/models/lightgbm_decision_tree.py b/dtreeviz/models/lightgbm_decision_tree.py index ea2ab3ff..aeabf14f 100644 --- a/dtreeviz/models/lightgbm_decision_tree.py +++ b/dtreeviz/models/lightgbm_decision_tree.py @@ -160,6 +160,9 @@ def get_split_samples(self, id): right = np.nonzero(node_X_data > split)[0] return left, right + def get_root_node_labels(self): + return ["≤", ">"] + def get_node_nsamples(self, id): if self.children_right[id] == -1 and self.children_left[id] == -1: return self.tree_nodes[id]["leaf_count"] diff --git a/dtreeviz/models/shadow_decision_tree.py b/dtreeviz/models/shadow_decision_tree.py index f612aba9..cb79672b 100644 --- a/dtreeviz/models/shadow_decision_tree.py +++ b/dtreeviz/models/shadow_decision_tree.py @@ -253,6 +253,9 @@ def shouldGoLeftAtSplit(self, id, x): """Return true if it should go to the left node child based on node split criterion and x value""" pass + def get_root_node_labels(self): + pass + def is_categorical_split(self, id) -> bool: """Checks if the node split is a categorical one. diff --git a/dtreeviz/models/sklearn_decision_trees.py b/dtreeviz/models/sklearn_decision_trees.py index 4374cb8f..e9a69788 100644 --- a/dtreeviz/models/sklearn_decision_trees.py +++ b/dtreeviz/models/sklearn_decision_trees.py @@ -75,6 +75,9 @@ def get_split_samples(self, id): return left, right + def get_root_node_labels(self): + return ["≤", ">"] + def get_node_nsamples(self, id): return len(self.get_node_samples()[id]) @@ -140,3 +143,4 @@ def get_min_samples_leaf(self): def shouldGoLeftAtSplit(self, id, x): return x < self.get_node_split(id) + diff --git a/dtreeviz/models/spark_decision_tree.py b/dtreeviz/models/spark_decision_tree.py index 18ebcd92..884ac6d5 100644 --- a/dtreeviz/models/spark_decision_tree.py +++ b/dtreeviz/models/spark_decision_tree.py @@ -144,6 +144,9 @@ def get_split_samples(self, id): right = np.nonzero(node_X_data > split)[0] return left, right + def get_root_node_labels(self): + return ["≤", ">"] + def get_node_nsamples(self, id): def _get_nsamples(spark_version): if spark_version >= 3: diff --git a/dtreeviz/models/xgb_decision_tree.py b/dtreeviz/models/xgb_decision_tree.py index 10672fff..69db2bd7 100644 --- a/dtreeviz/models/xgb_decision_tree.py +++ b/dtreeviz/models/xgb_decision_tree.py @@ -118,6 +118,9 @@ def get_split_samples(self, id): return left, right + def get_root_node_labels(self): + return ["<", "≥"] + def get_node_nsamples(self, id): return len(self.get_node_samples()[id]) diff --git a/dtreeviz/trees.py b/dtreeviz/trees.py index b5c141ab..eb789f6f 100644 --- a/dtreeviz/trees.py +++ b/dtreeviz/trees.py @@ -843,11 +843,13 @@ def get_leaves(): if show_just_path: show_root_edge_labels = False + + # TODO do we need show_edge_labels ? show_edge_labels = False all_llabel = '<' if show_edge_labels else '' all_rlabel = '≥' if show_edge_labels else '' - root_llabel = '<' if show_root_edge_labels else '' - root_rlabel = '≥' if show_root_edge_labels else '' + root_llabel = shadow_tree.get_root_node_labels()[0] if show_root_edge_labels else '' + root_rlabel = shadow_tree.get_root_node_labels()[1] if show_root_edge_labels else '' edges = [] # non leaf edges with > and <= diff --git a/notebooks/dtreeviz_lightgbm_visualisations.ipynb b/notebooks/dtreeviz_lightgbm_visualisations.ipynb index 81b008c2..b2395a5a 100644 --- a/notebooks/dtreeviz_lightgbm_visualisations.ipynb +++ b/notebooks/dtreeviz_lightgbm_visualisations.ipynb @@ -9,9 +9,18 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 29, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The autoreload extension is already loaded. To reload it, use:\n", + " %reload_ext autoreload\n" + ] + } + ], "source": [ "%load_ext autoreload\n", "%autoreload 2\n", @@ -21,7 +30,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 30, "metadata": {}, "outputs": [], "source": [ @@ -51,7 +60,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 31, "metadata": {}, "outputs": [], "source": [ @@ -60,7 +69,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 32, "metadata": {}, "outputs": [ { @@ -312,7 +321,7 @@ "[891 rows x 12 columns]" ] }, - "execution_count": 7, + "execution_count": 32, "metadata": {}, "output_type": "execute_result" } @@ -323,7 +332,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 33, "metadata": {}, "outputs": [], "source": [ @@ -350,7 +359,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 34, "metadata": {}, "outputs": [], "source": [ @@ -372,27 +381,15 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 35, "metadata": {}, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/tudorl/Documents/workspace/personal/dtreeviz/venv/lib/python3.7/site-packages/lightgbm/basic.py:1551: UserWarning: Using categorical_feature in Dataset.\n", - " warnings.warn('Using categorical_feature in Dataset.')\n", - "/Users/tudorl/Documents/workspace/personal/dtreeviz/venv/lib/python3.7/site-packages/lightgbm/basic.py:1286: UserWarning: Overriding the parameters from Reference Dataset.\n", - " warnings.warn('Overriding the parameters from Reference Dataset.')\n", - "/Users/tudorl/Documents/workspace/personal/dtreeviz/venv/lib/python3.7/site-packages/lightgbm/basic.py:1098: UserWarning: categorical_column in param dict is overridden.\n", - " warnings.warn('{} in param dict is overridden.'.format(cat_alias))\n" - ] - }, { "name": "stdout", "output_type": "stream", "text": [ "[LightGBM] [Info] Number of positive: 268, number of negative: 444\n", - "[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000696 seconds.\n", + "[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000974 seconds.\n", "You can set `force_row_wise=true` to remove the overhead.\n", "And if memory is not enough, you can set `force_col_wise=true`.\n", "[LightGBM] [Info] Total Bins 234\n", @@ -590,13 +587,7 @@ "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[95]\ttraining's binary_logloss: 0.430696\tvalid_1's binary_logloss: 0.461634\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", - "[96]\ttraining's binary_logloss: 0.432746\tvalid_1's binary_logloss: 0.463187\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + "[96]\ttraining's binary_logloss: 0.432746\tvalid_1's binary_logloss: 0.463187\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[97]\ttraining's binary_logloss: 0.429157\tvalid_1's binary_logloss: 0.460482\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", @@ -606,6 +597,18 @@ "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[100]\ttraining's binary_logloss: 0.428043\tvalid_1's binary_logloss: 0.459459\n" ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/tudorl/Documents/workspace/personal/dtreeviz/venv/lib/python3.7/site-packages/lightgbm/basic.py:1551: UserWarning: Using categorical_feature in Dataset.\n", + " warnings.warn('Using categorical_feature in Dataset.')\n", + "/Users/tudorl/Documents/workspace/personal/dtreeviz/venv/lib/python3.7/site-packages/lightgbm/basic.py:1286: UserWarning: Overriding the parameters from Reference Dataset.\n", + " warnings.warn('Overriding the parameters from Reference Dataset.')\n", + "/Users/tudorl/Documents/workspace/personal/dtreeviz/venv/lib/python3.7/site-packages/lightgbm/basic.py:1098: UserWarning: categorical_column in param dict is overridden.\n", + " warnings.warn('{} in param dict is overridden.'.format(cat_alias))\n" + ] } ], "source": [ @@ -632,16 +635,16 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 36, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 11, + "execution_count": 36, "metadata": {}, "output_type": "execute_result" }, @@ -664,7 +667,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 37, "metadata": {}, "outputs": [ { @@ -673,7 +676,7 @@ "lightgbm.basic.Booster" ] }, - "execution_count": 13, + "execution_count": 37, "metadata": {}, "output_type": "execute_result" } @@ -684,7 +687,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 38, "metadata": {}, "outputs": [ { @@ -1033,10 +1036,10 @@ "
\n" ], "text/plain": [ - "" + "" ] }, - "execution_count": 12, + "execution_count": 38, "metadata": {}, "output_type": "execute_result" } @@ -1054,7 +1057,7 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 39, "metadata": {}, "outputs": [], "source": [ @@ -1063,233 +1066,3542 @@ }, { "cell_type": "code", - "execution_count": 42, - "metadata": {}, - "outputs": [ - { - "ename": "ValueError", - "evalue": "Tree model must be in (DecisionTreeRegressor, DecisionTreeClassifier, xgboost.core.Booster, pyspark DecisionTreeClassificationModel or pyspark DecisionTreeClassificationModel) but you passed a ShadowLightGBMTree!", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtrees\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtreeviz\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlight_dtree\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;32m~/Documents/workspace/personal/dtreeviz/notebooks/../dtreeviz/trees.py\u001b[0m in \u001b[0;36mdtreeviz\u001b[0;34m(tree_model, x_data, y_data, feature_names, target_name, class_names, tree_index, precision, orientation, instance_orientation, show_root_edge_labels, show_node_labels, show_just_path, fancy, histtype, highlight_path, X, max_X_features_LR, max_X_features_TD, label_fontsize, ticks_fontsize, fontname, title, title_fontsize, colors, scale)\u001b[0m\n\u001b[1;32m 737\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 738\u001b[0m shadow_tree = ShadowDecTree.get_shadow_tree(tree_model, x_data, y_data, feature_names, target_name, class_names,\n\u001b[0;32m--> 739\u001b[0;31m tree_index)\n\u001b[0m\u001b[1;32m 740\u001b[0m \u001b[0mcolors\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0madjust_colors\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcolors\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 741\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/Documents/workspace/personal/dtreeviz/notebooks/../dtreeviz/models/shadow_decision_tree.py\u001b[0m in \u001b[0;36mget_shadow_tree\u001b[0;34m(tree_model, x_data, y_data, feature_names, target_name, class_names, tree_index)\u001b[0m\n\u001b[1;32m 470\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 471\u001b[0m raise ValueError(\n\u001b[0;32m--> 472\u001b[0;31m \u001b[0;34mf\"Tree model must be in (DecisionTreeRegressor, DecisionTreeClassifier, \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 473\u001b[0m \u001b[0;34m\"xgboost.core.Booster, pyspark DecisionTreeClassificationModel or \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 474\u001b[0m f\"pyspark DecisionTreeClassificationModel) but you passed a {tree_model.__class__.__name__}!\")\n", - "\u001b[0;31mValueError\u001b[0m: Tree model must be in (DecisionTreeRegressor, DecisionTreeClassifier, xgboost.core.Booster, pyspark DecisionTreeClassificationModel or pyspark DecisionTreeClassificationModel) but you passed a ShadowLightGBMTree!" - ] - } - ], - "source": [ - "trees.dtreeviz(light_dtree)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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SZoOmBe2U+mtQTgS+m5lvjohNgc2AY4ELM/P4iDgaOBo4aoAZJEmSOqHpWpxnTratKHrrL/20USLiGcArgUMAMvMB4IGI2A94Vb3bmcD3saBJkqQR0HSpp62AjwAv4pEpNYJqXc4dgc2nkWERcCdwekTsClwOHAFsmZmr631uB7acxjEkSZJmjaYPCZwG9ICLgb2AHwGrgZdQXYqcjnn19/lSZu4O/CfV5cyHZWZS3Zv2GBGxJCIuA84bGxubZhRJkqT2NS1ofwgcWpb9Y4GrgG+XZf8AqrNq+04zwypgVWZeWr8/l6qw3RERWwHUf66Z6MOZuTQz9wD2X7x48TSjSJIkta9pQQvgtvr1dVQFCuAcqqc7pywzbwdujYgd6qG962NcABxcjx0MnD+d40iSJM0WTQva5cBB9esrgdfWr7edoRzvA74aET8BdgP+FjgeKCLip1SXV4+foWNJkiR1WtNpNo4Cvl0Uvfupnqj8UFH0rge2Br4y3RCZeSWwxwSb9p7u95YkSZptGp1BK8v+xcDzgK+WZf8XVGXq74ElVGe/JEmSNEOaXuJct+899etnUU0mu6Ys+w/NeCpJkqQR1qigFUXvDcDPgFcURW8R8EPgncA/FUXv3QPMJ0mSNHKankH7JNWN+xcCh1FNHLsj8DbgQ4OJJkmSNJqaFrQdgK+UZT+BNwLfql//GPi9QYWTJEkaRU0L2s+AXYui92JgZ+Db9fhrgRUDyCVJkjSymk6z8VngPOAh4JKy7P+oKHrHAR/lkclkJUmSNAOaTrPxD8CewIE8MjdZH3hZWfbPHlA2SZKkkdT0DBpl2b+SahWBde8vGUgiSZKkEbcx86BJkiRpCCxokiRJHWNBkyRJ6hgLmiRJUsdY0CRJkjrGgiZJktQxFjRJkqSOsaBJkiR1jAVNkiSpYyxokiRJHWNBkyRJ6hgLmiRJUsdY0CRJkjpmXtsBJEkalpUrb+GEE7/cdowpmb/5Zrzr0IPajqEhsaBJkkZGPOGJLNil13aMKVl1db/tCBoiL3FKkiR1TGcKWkRsEhE/johv1+8XRcSlEbE8IsYiYtO2M0qSJA1DZwoacARw/bj3nwY+n5kvAO4GDmsllSRJ0pB1oqBFxALgDcAp9fsAXg2cW+9yJvCmdtJJkiQNVycKGvB3wIeBh+r3zwbWZuaD9ftVwNZtBJMkSRq21gtaROwLrMnMy6f4+SURcRlw3tjY2MyGkyRJakEXptn4A+CNEfF64MnA5sCJwPyImFefRVsA3DbRhzNzKbA0IrZfvHjxsmGFliRJGpTWz6Bl5jGZuSAzFwJvBf41M98GXAS8ud7tYOD8liJKkiQNVesFbQOOAj4YEcup7kk7teU8kiRJQ9GFS5wPy8zvA9+vX98EvLzNPJIkSW3o8hk0SZKkkWRBkyRJ6hgLmiRJUsdY0CRJkjrGgiZJktQxFjRJkqSO6dQ0G5IkaWIrV97CCSd+ue0YG23+5pvxrkMPajvGrGNBkzQn+MtLc1084Yks2KXXdoyNturqftsRZiULmqQ5wV9ekuYS70GTJEnqGAuaJElSx1jQJEmSOsaCJkmS1DEWNEmSpI7xKU5JkjQwToEzNRY0SZI0ME6BMzVe4pQkSeoYC5okSVLHWNAkSZI6xnvQpuDk089i7b33tx1jo62+/Q4W7NJ2CkmS9HgsaFOw9t77Z+UNj7euOqvtCJIkqQEvcUqSJHWMBU2SJKljvMQpSS2arZN4ek+rNFgWNElq0WydxNN7WqXB8hKnJElSx7R+Bi0itgHOArYEEliamSdGxLOAMWAhsAI4IDPvbiun2uMlIEnSqGm9oAEPAkdm5hUR8XTg8ogogUOACzPz+Ig4GjgaOKrFnGqJl4AkSaOm9Uucmbk6M6+oX98HXA9sDewHnFnvdibwpnYSSpIkDVfrBW28iFgI7A5cCmyZmavrTbdTXQKVJEma8zpT0CLiacB5wPsz897x2zIzqe5Pm+hzSyLiMuC8sbGxwQeVJEkasE4UtIh4IlU5+2pmfqMeviMitqq3bwWsmeizmbk0M/cA9l+8ePFQ8kqSJA1S6wUtIgI4Fbg+Mz83btMFwMH164OB84edTZIkqQ1deIrzD4C3A1dHxJX12LHA8cA5EXEYcAtwQEv5JEmShqr1gpaZ/w7EJJv3HmYWSZKkLmj9EqckSZIezYImSZLUMRY0SZKkjrGgSZIkdYwFTZIkqWMsaJIkSR1jQZMkSeoYC5okSVLHWNAkSZI6xoImSZLUMRY0SZKkjrGgSZIkdYwFTZIkqWMsaJIkSR1jQZMkSeoYC5okSVLHWNAkSZI6xoImSZLUMRY0SZKkjrGgSZIkdYwFTZIkqWMsaJIkSR1jQZMkSeoYC5okSVLHWNAkSZI6pvMFLSL2iYhlEbE8Io5uO48kSdKgdbqgRcQmwBeB1wE7AQdGxE7tppIkSRqsThc04OXA8sy8KTMfAL4O7NdyJkmSpIGa13aAx7E1cOu496uAPSfZ94k333zz4BMBd665g01uHc6xZtLdd/2C1eYeGnMPl7mHy9zDN1uzz9bcd665gxtvvHGgx9hhhx02rU9APUZk5kAPPh0R8WZgn8x8Z/3+7cCemfnecfssAZZQnQ08Hzi7jawz6ADgnLZDTIG5h8vcw2Xu4ZqtuWH2Zjd3O1bM1oL2+8DHM/O19ftjADLzU60GG6CIuCwz92g7x8Yy93CZe7jMPVyzNTfM3uzm7p6u34P2f4HtImJRRGwKvBW4oOVMkiRJA9Xpe9Ay88GIeC/wv4FNgNMy89qWY0mSJA1UpwsaQGZ+B/hO2zmGaGnbAabI3MNl7uEy93DN1twwe7Obu2M6fQ+aJEnSKOr6PWiSJEkjx4LWARGxTURcFBHXRcS1EXFE25maiogVEXF1RFwZEZe1naepiJgfEedGxA0RcX39xHDnRcQH6r8j10TE2RHx5LYzNRURR9S5r42I97edZzIRcVpErImIa8aNnVD/XflJRHwzIua3mXEiE+Uet+3IiMiI2KKNbBsyyf/fH4+I2+qfK1dGxOvbzDiRSXLvFhGXrPt5GBEvbzPjRCbJvWtEXFz/LP+niNi8zYwTmez3ZEQ8KyLKiPhp/ecz2846Uyxo3fAgcGRm7gTsBbxnli1p9ceZudsse9T5ROC7mbkjsCtwfct5HldEbA38JbBHZu5M9eDMW9tN1UxE7Ay8i2p1kF2BfSPiBe2mmtQZwD7rjZXAzpn5YuBG4Jhhh2rgDB6bm4jYBngNsHLYgRo6gwlyA5+vf67sVt+L3DVn8Njc/wP4RGbuBny0ft81Z/DY3KcAR2fmLsA3gQ8NO1QDk/2ePBq4MDO3Ay6s388JFrQOyMzVmXlF/fo+qrKwdbup5q6IeAbwSuBUgMx8IDPXtpuqsXnAUyJiHrAZ8LOW8zT1QuDSzLw/Mx8E/g3405YzTSgzfwDctd7Y9+rcAJcAC4Ye7HFMlLv2eeDDQCdvON5A7k6bJHcC684+PYMO/vucJPf2wA/q1yWw/1BDNbCB35P7AWfWu50JvKmdhDPPgtYxEbEQ2B24tN0kjSXwvYi4vF7VYTZYBNwJnB4RP46IUyLiqW2HejyZeRvwGaozIauBezLze+2mauwa4A8j4tkRsRnwemCbljNN1TuAf2k7RBMRsR9wW2Ze1XaWKXhvfUn5tFl02er9wAkRcSvVv9UunmmdyLU8ss71W+j4v831fk9umZmr6023A1u2FGvGWdA6JCKeBpwHvD8z7207T0OvyMyXAK+jOuX8yrYDNTAPeAnwpczcHfhPZsFp8fqX1H5UBfP3gKdGxJ+1m6qZzLwe+DTwPeC7wJXAb1sNNQUR8RGqSy1fbTvL46mL8LFUl9pmmy8B2wK7Uf3HyGfbjdPYXwAfyMxtgA9Qn6WfBd4BHB4RlwNPByZceqgLNvR7MqtpKTp5pngqLGgdERFPpPpL99XM/EbbeZqqz+qQmWuo7l3o3E2xE1gFrMrMdWcpz6UqbF3XA27OzDsz8zfAN4D/1nKmxjLz1Mx8aWa+Erib6l6uWSMiDgH2Bd6Ws2N+om2pyvxVEbGC6rLsFRHxu62maiAz78jM32bmQ8DJzI6fKwAHU/27BPhHZknuzLwhM1+TmS+lWs/6/7WdaSKT/J68IyK2qrdvBaxpK99Ms6B1QEQE1X9pXZ+Zn2s7T1MR8dSIePq611Q3Ij/mCbKuyczbgVsjYod6aG/guhYjNbUS2CsiNqv/zuzNLHi4YZ2IeE7953Op7j/7WruJmouIfaju43pjZt7fdp4mMvPqzHxOZi7MzIVU/2Hykvrvf6et+4Vb+xNmwc+V2s+AP6pfvxr4aYtZGhv3b/MJwHHAl9tN9Fgb+D15AVUxpv7z/GFnGxQnqu2AiHgF8EPgauChevjYjj659LCIeD7VWTOoLht+LTM/2WKkxiJiN6onlzYFbgIOzcy72031+CLiE8BiqstsPwbemZm/bjdVMxHxQ+DZwG+AD2bmhS1HmlBEnA28CtgCuAP4GNW9RE8CflHvdklm/nkrAScxUe7MPHXc9hVUTwD/vJWAk5jk/+9XUV3eTGAF8O5x9xl1wiS5l1E9IT4P+BVweGZe3lbGiUyS+2nAe+pdvgEc07WzxJP9nqS6D+0c4LnALcABmTnrHjqZiAVNkiSpY7zEKUmS1DEWNEmSpI6xoEmSJHWMBU2SJKljLGiSJEkdM6/tAJI0U4qitxC4GdiuLPvLZ+D7/TXV8j13lWX/eettOwOYV5b9CVdzKIreKuC4suyfMd0ckkaPBU2SJlAUvWdSTdr5bmCiOQmPGG4iSaPEgiZJE9u8/vOisuyvWn9jWfbvGXIeSSPEgiZpziqK3jOAk4A3Af9FtSzMkWXZv6/evi/w34GdgF9TLeT+Lqq1WS+qv82NRdH7RFn2P77e9z6DcZc4i6L3bqozbpsDx6+37y7AF4GXAvdRLbZ+VFn2H5zZ/8WS5gofEpA0l51GtaTNHwJvAHYAzgAoit4iqoW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z66B1HJuAj/Tl/JIkSUPFegu0LGuc3upJ8rz56YGJI0mSpBdrQTugxXP0eeSlJEmS1m+9BVqeN/9mzW1Z1og8b6by+ZZ53ny8neEkSZK6UasrCWwLzAP+lz/de/bLLGvcDEzN8+bv2pRPkiSp67Q6ivNb5eOcXtsOAIYBXxnQRJIkSV2upRY04G+AN+R58/7VG/K8eU+WNT4GXN+WZNIgt3zpMpZd2FN1jD5bueL/qo4gSV2v1QJtBTAO+NUa20cDKwc0kTRE7Lrj9kyePL7qGH12zjlXVx1BkrpeqwXaecCcLGt8imItTiiWezoNmNuGXJIkSV2r1QJtJhAUM/2vXpPzEeBLwFkDH0uSJKl7tbqSwCrgU8CnsqwxEliZ580n25pMkiSpS7XagvY8p9SQJElqr1an2ZAkSVKHWKBJkiTVTEsFWpY19mp3EEmSJBVavQft+ixrPABcBFyU58172hdJkiSpu7VaoL0KeAdwBHBqljXuoCjWLs7z5pJ2hZMkSepGrU6zsYJisfR5WdbYEng78HfAzCxr3ApcSNGy5tQbkiRJ/bQhgwR2Bv4CGA8kYDFFy9qDWdY4cgCzSZIkdaWWWtCyrLEPRRH2LmB74AfAZ4Cr8rz5dHnMdOBrwMXtiSpJktQdWr0H7Ubgv4Ezge/kefOJdRxzA3DZQAWTJEnqVq0WaDvkeXPpix2Q583/pijiJElD3PKly1h2YU/VMfps2Ko+L6AjVaLVQQJLs6xxIPAhivvPVgJ3AZ/P8+bP25hPklRDu+64PZMnj686Rp/Nm7ew6ghSS1qdqPYE4PvA74FvAeeXu25wYIAkSdLAarWt9yTg2Dxvzuu9McsaPwX+FQcGSJIkDZhWp9nYDPjfdWz/H2DkwMWRJElSqwXaV4Czsqyx1eoNWdYYAcyk6PLsl4j4p4hYFBELI+KiiBgeEeMi4qaIuDciFkTEJv29jiRJ0mCw3i7OLGssppiIFiCA0cBvsqxxP7AKGAe8DOjXIIGIGA0cD+yaUvpDRFwCHAkcCnwxpXRxRHwTOBr4Rn+uJUmSNBi82D1oMzqWosixaUQ8A4wAlgIHAu8t959P0VpngSZJkoa89RZoed48f337BlJKaUlEnAs8BPwB+BFwC/B4SunZ8rAeihY8SZKkIW9D1uIcUBGxFXAYRZfp9sDLgYP78P5jI+Jm4LIFCxa0J6QkSVIHVV6gAQ3g/pTSIymlZ4DLgf2BLSNidQvfGGDJut6cUpqVUtoHeOekSZM6EliSJKmd6lCgPQTsGxEjIiKAg4A7gR9TLM4OMAW4sqJ8kiRJHdWnRcmyrPFned5clWWN7YADgNvyvHlPfwKklG6KiO8AtwLPUowKnQV8D7g4Is4ot83pz3UEs+fN4bGn1rXOfb1tNWILjpl8dNUxJEnqmJYKtCxr7AtcCrwvyxp3ATcDmwMvy7LGkXne/M/+hEgpfQb4zBqb7wPe2J/z6oUee+oJtjtol6pj9Nmya+6uOoIkSR3VahfnFyi6GG+mmI/sWWAb4MPAZ9sTTZIkqTu1WqDtBZyT583fA28Drsjz5h+BJvCadoWTJEnqRq0WaMuBP8+yxljgDRT3hwHsQzGprCRJkgZIq4MEzgOuAFYC9wDXZFnjw8A5wKltyiZJktSVWmpBy/Pmp4EPAmcBb8rz5irgAWBSnjf/rX3xJEmSuk/L86CVIzUvBXbNssamwC153vxu25JJkiR1qVan2dgM+HfgncAq4LXAF7OssQ3w9jxvPty+iJIkSd2l1Ra0zwMjKdbL/EO57eNAAr7chlySJEldq9UC7W3AiXnefHD1hjxv3ksxD9rftiOYJElSt2q1QNuUYgTnml4GxMDFkSRJUqsF2pXAv2ZZY8vydcqyxs7AVwAHCkiSJA2gVgu044BnKCasfTlwG/DL8vUJ7YkmSZLUnVoaxZnnzSeBd2ZZY0fgL8r33Z3nzV+2M5wkSVI3Wm+BVhZj63LXmsfkefO+Ac4lSZLUtV6sBe1eimk0VltzMEAqtyVgowHOJUmS1LVerEAb17EUkiRJet56C7Tec56tlmWNPSjuQVsF/CLPm79qYzZJkqSu1OpST6OAy4GJwKMUXZqbZ1njGuCIPG8+3r6IkiRJ3aXVaTbmUCzxtGOeN0fmeXMrYBeKiWq/3q5wkiRJ3ajVAu2vgY/mefOB1RvKpZ6OA97ahlySJEldq9UC7R5gz3VsHws8MFBhJEmS1OI9aMD5wNeyrLEPcAPwLEXBdhwwN8saH1h9YJ43zxvwlJIkSV2k1QLtBOBx4B3l12pPrLEtARZokiRJ/dDqUk/OiSZJktQhrbagkWWN3YDXUYzc7C3lefOiAU0lSZLUxVqdB+1M4GTgSYrpNnpLQL8KtIjYEvg2ML483weAu4EF/GkgwhEppcf6cx1JkqTBoNUWtGnAtDxvfqtNOb4EXJ1SeldEbAKMAE4BrkkpfS4ipgPTgU+26fqSJEm10eo0GyuA/2lHgIjYgmKetTkAKaWVKaXHgcMoRo9SPr69HdeXJEmqm1Zb0P6ZYpqNTwMPUazF+bw8bz7UjwzjgEeAf4+ICcAtFKNGt00pLS2PWQZs249rSJIkDRqttqBtDOwN/Bj4NXB/+fVA+dgfGwOvB76RUtoL+D+K7sznpZQSxb1pa4mIYyPiZuCyBQsW9DOKJElS9Vot0D5PMb/ZBOC1vb52Lh/7owfoSSndVL7+DkXB9tuIGAVQPj68rjenlGallPYB3jlp0qR+RpEkSapeq12cw4Ev5XnzvoEOkFJaFhGLI2KXlNLdwEHAneXXFOBz5eOVA31tSZKkOmq1Be0cYEaWNUa0KcdxwAURcTvFElL/QlGYZRFxD9AoX0uSJA15rbagHQq8AfiHLGv8jmItzuflefPP+xMipXQbsM86dh3Un/NKkiQNRq0WaN8uvyRJktRmra7Fef769mVZY82lnyRJktQPrS71NAo4FdgN2KjcHBTrcr4O2Lwt6SRJkrpQq4MEzqO4Uf8GYF/gZ8BSiukwTmlPNEmSpO7U6j1oBwBZnjdvyLJGBnw3z5s/y7LGJ4G3Al9tW0INmOVLl7Hswp6qY/TZsFWt/phKkjQ0tPrJF8CS8vmdFC1nPwMuAT7Rhlxqg1133J7Jk8dXHaPP5s1bWHUESZI6qtUuzluAyeXz24C3lM9fM+CJJEmSulyrLWifBL6bZY2ngPOBk7KscRcwGpjfrnCSJEndqKUWtDxv3gC8Grggz5vLKSaV/SpwLMUqAJIkSRogrXZxrj72ifL5K4ERwMN53lw14KkkSZK6WEsFWpY1/g74DfBXWdYYB/wU+CDwX1nW+Mc25pMkSeo6rbagnUmxgPk1wNHAMooJao8CTmpPNEmSpO7UaoG2CzA/z5sJeBtwRfn858D27QonSZLUjVot0H4DTMiyxh7AeOC75fa3AA+0IZckSVLXanWajc8DlwGrgBvLVQRmAJ8GprQrnCRJUjdqdZqNrwMTgfcAB5Wbm8Ab8rx5UZuySZIkdaWWFznM8+ZtFKsIrH59Y1sSSZIkdbm+zIMmSZKkDrBAkyRJqhkLNEmSpJqxQJMkSaoZCzRJkqSaaXkUpyRJg93Cex/k7G9+oeoYG2SrEVtwzOSjq46hDrFAkyR1jec2DrY7aJeqY2yQZdfcXXUEdZBdnJIkSTVTmwItIjaKiJ9HxHfL1+Mi4qaIuDciFkTEJlVnlCRJ6oTaFGjACcBdvV6fBXwxpbQT8Bhgx7skSeoKtSjQImIM8HfAt8vXARwIfKc85Hzg7dWkkyRJ6qxaFGjAvwGfAFaVr7cGHk8pPVu+7gFGVxFMkiSp0yov0CLircDDKaVbNvD9x0bEzcBlCxYsGNhwkiRJFajDNBv7A2+LiEOB4cDmwJeALSNi47IVbQywZF1vTinNAmZFxGsnTZrkGGRJkjToVd6CllI6OaU0JqU0FjgSuDaldBTwY+Bd5WFTgCsriihJktRRlRdoL+KTwIkRcS/FPWlzKs4jSZLUEXXo4nxeSuknwE/K5/cBb6wyjyRJUhXq3IImSZLUlSzQJEmSasYCTZIkqWYs0CRJkmrGAk2SJKlmLNAkSZJqplbTbEiSpHV76MGHOPubX6g6Rp9tNWILjpl8dNUxBh0LNElDwsJ7H/TDS0NaDPsztjtol6pj9Nmya1yFcUNYoEkaEp7bOPzwkjRkeA+aJElSzVigSZIk1YwFmiRJUs1YoEmSJNWMBZokSVLNOIpTkiS1jfO3bRgLNEmS1DbO37Zh7OKUJEmqGQs0SZKkmrFAkyRJqhnvQdsAs+fN4bGnnqg6Rp89smQxML7qGJIk6SVYoG2Ax556YnDe8HhhT9URJElSC+zilCRJqhkLNEmSpJqxi1OSKjRYJ/H0nlapvSzQJKlCg3YST+9pldrKLk5JkqSaqbwFLSJ2AOYB2wIJmJVS+lJEvBJYAIwFHgCOSCk9VlVOVWfhvQ/aBSRJ6iqVF2jAs8DHU0q3RsQrgFsiIgemAteklD4XEdOB6cAnK8ypijy3cdgFJEnqKpV3caaUlqaUbi2frwDuAkYDhwHnl4edD7y9moSSJEmdVXmB1ltEjAX2Am4Ctk0pLS13LaPoApUkSRryalOgRcRmwGXAx1JKT/bel1JKFPenret9x0bEzcBlCxYsaH9QSZKkNqvDPWhExDCK4uyClNLl5ebfRsSolNLSiBgFPLyu96aUZgGzIuK1kyZNurtDkSVpQDy14vfcduE1Vcfos5Ur/q/qCNKQVnmBFhEBzAHuSin1Hqp3FTAF+Fz5eGUF8SSprbbYcjM+/+lDq47RZ+ecc3XVEaQhrfICDdgfeB9wR0TcVm47haIwuyQijgYeBI6oKJ8kSVJHVV6gpZT+B4j17D6ok1kkSZLqoDaDBCRJklSwQJMkSaoZCzRJkqSasUCTJEmqGQs0SZKkmql8FKckSZ3yh8eeHJQTAwM8s3Jl1RHUQRZokqSuMW77LTnppIOrjrFBPn7696uOoA6yi1OSJKl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PclassSex_labelEmbarked_labelAgeCabin_labelFare
count78.078.00000078.00000078.00000078.00000078.000000
mean3.00.6666670.51282128.0136160.8461548.075326
std0.00.4744560.5030717.88029516.3047891.975028
min3.00.0000000.00000012.000000-1.0000004.012500
25%3.00.0000000.00000025.250000-1.0000007.229200
50%3.01.0000001.00000029.699118-1.0000007.750000
75%3.01.0000001.00000029.699118-1.0000007.750000
max3.01.0000001.00000070.500000143.00000014.458300
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" + ], + "text/plain": [ + " Pclass Sex_label Embarked_label Age Cabin_label Fare\n", + "count 78.0 78.000000 78.000000 78.000000 78.000000 78.000000\n", + "mean 3.0 0.666667 0.512821 28.013616 0.846154 8.075326\n", + "std 0.0 0.474456 0.503071 7.880295 16.304789 1.975028\n", + "min 3.0 0.000000 0.000000 12.000000 -1.000000 4.012500\n", + "25% 3.0 0.000000 0.000000 25.250000 -1.000000 7.229200\n", + "50% 3.0 1.000000 1.000000 29.699118 -1.000000 7.750000\n", + "75% 3.0 1.000000 1.000000 29.699118 -1.000000 7.750000\n", + "max 3.0 1.000000 1.000000 70.500000 143.000000 14.458300" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "trees.describe_node_sample(light_dtree, node_id=5)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Pclass 2.0\n", + "Sex_label 0.0\n", + "Embarked_label 2.0\n", + "Age 42.0\n", + "Cabin_label -1.0\n", + "Fare 13.0\n", + "Name: 865, dtype: float64\n" + ] + } + ], + "source": [ + "x = X_train[features].iloc[10]\n", "print(x)\n" ] }, @@ -1329,7 +4641,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 41, "metadata": {}, "outputs": [], "source": [ @@ -1364,14 +4676,14 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 42, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000667 seconds.\n", + "[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000979 seconds.\n", "You can set `force_row_wise=true` to remove the overhead.\n", "And if memory is not enough, you can set `force_col_wise=true`.\n", "[LightGBM] [Info] Total Bins 65\n", @@ -1430,16 +4742,16 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 43, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 22, + "execution_count": 43, "metadata": {}, "output_type": "execute_result" }, @@ -1462,7 +4774,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 44, "metadata": { "scrolled": false }, @@ -1813,10 +5125,10 @@ "\n" ], "text/plain": [ - "" + "" ] }, - "execution_count": 23, + "execution_count": 44, "metadata": {}, "output_type": "execute_result" } @@ -1834,7 +5146,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 45, "metadata": {}, "outputs": [], "source": [ @@ -1843,7 +5155,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 46, "metadata": {}, "outputs": [ { @@ -1873,96 +5185,96 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -1972,10 +5284,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1993,7 +5305,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2040,10 +5352,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2061,7 +5373,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2078,7 +5390,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2094,13 +5406,13 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2111,7 +5423,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2137,40 +5449,40 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -2178,10 +5490,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2199,7 +5511,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2216,7 +5528,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2232,7 +5544,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2275,7 +5587,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2307,65 +5619,65 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -2373,10 +5685,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2394,7 +5706,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2411,7 +5723,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2427,7 +5739,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2472,7 +5784,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2504,31 +5816,31 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -2536,10 +5848,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2557,7 +5869,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2574,7 +5886,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2590,7 +5902,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2631,7 +5943,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2657,118 +5969,118 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -2778,10 +6090,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2799,7 +6111,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2818,7 +6130,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2863,10 +6175,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2884,7 +6196,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2898,7 +6210,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2914,13 +6226,13 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2931,7 +6243,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2962,63 +6274,63 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -3028,10 +6340,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3049,7 +6361,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3068,7 +6380,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3114,10 +6426,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3134,7 +6446,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3151,7 +6463,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3167,13 +6479,13 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3184,7 +6496,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3217,29 +6529,29 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -3247,10 +6559,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3268,7 +6580,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3285,7 +6597,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3301,7 +6613,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3345,7 +6657,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3377,43 +6689,43 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -3421,10 +6733,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3442,7 +6754,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3459,7 +6771,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3475,7 +6787,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3518,7 +6830,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3549,172 +6861,172 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -3724,10 +7036,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3749,7 +7061,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3770,7 +7082,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3818,10 +7130,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3838,7 +7150,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3852,7 +7164,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3865,13 +7177,13 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3882,7 +7194,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3919,558 +7231,558 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " 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\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -4480,10 +7792,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4501,7 +7813,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4517,7 +7829,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4566,10 +7878,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4586,7 +7898,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4600,7 +7912,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4616,13 +7928,13 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4633,7 +7945,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4659,94 +7971,94 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " 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" \n", @@ -5386,10 +8698,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5411,7 +8723,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5429,7 +8741,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5480,10 +8792,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5497,7 +8809,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5511,7 +8823,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5527,13 +8839,13 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5544,7 +8856,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5571,53 +8883,53 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -5625,10 +8937,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5646,7 +8958,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5663,7 +8975,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5679,7 +8991,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5723,7 +9035,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5755,33 +9067,33 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -5789,10 +9101,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5810,7 +9122,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5827,7 +9139,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5843,7 +9155,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5887,7 +9199,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5918,84 +9230,84 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6011,7 +9323,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6052,10 +9364,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6073,7 +9385,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6087,7 +9399,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6103,10 +9415,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6117,7 +9429,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " 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\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6600,7 +9912,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6616,7 +9928,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6652,10 +9964,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6672,7 +9984,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6689,7 +10001,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6705,10 +10017,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6719,7 +10031,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6746,43 +10058,43 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -6790,10 +10102,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6811,7 +10123,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6828,7 +10140,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6844,7 +10156,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6887,7 +10199,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6919,33 +10231,33 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -6953,10 +10265,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6974,7 +10286,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6991,7 +10303,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7007,7 +10319,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7051,7 +10363,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7083,300 +10395,300 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " 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\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -7384,10 +10696,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7405,7 +10717,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7422,7 +10734,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7438,7 +10750,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7484,7 +10796,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7516,141 +10828,141 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - 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\n", " \n", " \n", @@ -7658,10 +10970,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7679,7 +10991,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7696,7 +11008,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7712,7 +11024,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7758,7 +11070,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7813,738 +11125,738 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", 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\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8560,7 +11872,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8576,7 +11888,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8612,10 +11924,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8632,7 +11944,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8649,7 +11961,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8678,10 +11990,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8692,7 +12004,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8702,14 +12014,14 @@ "node0->node1\n", "\n", "\n", - "<\n", + "≤\n", "\n", "\n", "\n", "node0->node10\n", "\n", "\n", - "≥\n", + ">\n", "\n", "\n", "\n", @@ -8720,10 +12032,10 @@ "" ], "text/plain": [ - "" + "" ] }, - "execution_count": 26, + "execution_count": 46, "metadata": {}, "output_type": "execute_result" } diff --git a/notebooks/dtreeviz_sklearn_visualisations.ipynb b/notebooks/dtreeviz_sklearn_visualisations.ipynb index 0f2053fa..d896a405 100644 --- a/notebooks/dtreeviz_sklearn_visualisations.ipynb +++ b/notebooks/dtreeviz_sklearn_visualisations.ipynb @@ -219,7 +219,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 10, "metadata": {}, "outputs": [ { @@ -250,118 +250,118 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -370,10 +370,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -389,7 +389,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -425,10 +425,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -444,7 +444,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -479,7 +479,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -502,118 +502,118 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -622,10 +622,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -644,7 +644,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -684,10 +684,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -700,7 +700,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -741,7 +741,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -959,118 +959,118 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1079,10 +1079,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1104,7 +1104,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1139,10 +1139,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1155,7 +1155,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1194,7 +1194,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1229,118 +1229,118 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1349,10 +1349,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1371,7 +1371,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1411,10 +1411,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1427,7 +1427,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1468,7 +1468,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1492,118 +1492,118 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1612,10 +1612,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1637,7 +1637,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1672,10 +1672,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1688,7 +1688,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1729,7 +1729,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1752,118 +1752,118 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1872,10 +1872,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1893,7 +1893,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1942,10 +1942,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1961,7 +1961,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2000,7 +2000,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2238,118 +2238,118 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2358,10 +2358,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2377,7 +2377,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2413,10 +2413,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2432,7 +2432,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2472,7 +2472,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2507,118 +2507,118 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2627,10 +2627,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2648,7 +2648,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2695,10 +2695,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2714,7 +2714,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2754,7 +2754,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2778,118 +2778,118 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2898,10 +2898,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2920,7 +2920,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2960,10 +2960,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2976,7 +2976,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3015,7 +3015,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3038,118 +3038,118 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3158,10 +3158,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3183,7 +3183,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3218,10 +3218,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3234,7 +3234,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3276,7 +3276,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3500,118 +3500,118 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3620,10 +3620,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3645,7 +3645,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3680,10 +3680,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3696,7 +3696,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3737,7 +3737,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3772,118 +3772,118 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3892,10 +3892,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3917,7 +3917,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3952,10 +3952,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3968,7 +3968,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4009,7 +4009,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4033,118 +4033,118 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4153,10 +4153,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4175,7 +4175,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4215,10 +4215,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4231,7 +4231,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4270,7 +4270,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4456,118 +4456,118 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4576,10 +4576,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4595,7 +4595,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4636,10 +4636,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4652,7 +4652,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4692,7 +4692,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4702,14 +4702,14 @@ "node0->node1\n", "\n", "\n", - "<\n", + "≤\n", "\n", "\n", "\n", "node0->node16\n", "\n", "\n", - "≥\n", + ">\n", "\n", "\n", "\n", @@ -4792,10 +4792,10 @@ "" ], "text/plain": [ - "" + "" ] }, - "execution_count": 12, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } @@ -7606,7 +7606,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -7620,7 +7620,7 @@ " random_state=1234, splitter='best')" ] }, - "execution_count": 15, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } @@ -7634,7 +7634,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ @@ -7657,274 +7657,7 @@ }, { "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "data": { - "image/svg+xml": [ - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "Tree\n", - "\n", - "\n", - "\n", - "0\n", - "\n", - "node #0\n", - "Pclass <= 1.5\n", - "mae = 9.074\n", - "samples = 891\n", - "value = 29.699\n", - "\n", - "\n", - "\n", - "1\n", - "\n", - "node #1\n", - "Cabin_label <= 79.5\n", - "mae = 11.234\n", - "samples = 216\n", - "value = 35.0\n", - "\n", - "\n", - "\n", - "0->1\n", - "\n", - "\n", - "True\n", - "\n", - "\n", - "\n", - "8\n", - "\n", - "node #8\n", - "Fare <= 27.825\n", - "mae = 8.214\n", - "samples = 675\n", - "value = 29.699\n", - "\n", - "\n", - "\n", - "0->8\n", - "\n", - "\n", - "False\n", - "\n", - "\n", - "\n", - "2\n", - "\n", - "node #2\n", - "Fare <= 64.29\n", - "mae = 11.217\n", - "samples = 148\n", - "value = 31.0\n", - "\n", - "\n", - "\n", - "1->2\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "5\n", - "\n", - "node #5\n", - "Survived <= 0.5\n", - "mae = 10.36\n", - "samples = 68\n", - "value = 39.0\n", - "\n", - "\n", - "\n", - "1->5\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "3\n", - "\n", - "node #3\n", - "mae = 10.99\n", - "samples = 76\n", - "value = 35.0\n", - "\n", - "\n", - "\n", - "2->3\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "4\n", - "\n", - "node #4\n", - "mae = 10.912\n", - "samples = 72\n", - "value = 29.699\n", - "\n", - "\n", - "\n", - "2->4\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "6\n", - "\n", - "node #6\n", - "mae = 9.315\n", - "samples = 20\n", - "value = 47.0\n", - "\n", - "\n", - "\n", - "5->6\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "7\n", - "\n", - "node #7\n", - "mae = 9.046\n", - "samples = 48\n", - "value = 36.0\n", - "\n", - "\n", - "\n", - "5->7\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "9\n", - "\n", - "node #9\n", - "Cabin_label <= 139.5\n", - "mae = 7.472\n", - "samples = 596\n", - "value = 29.699\n", - "\n", - "\n", - "\n", - "8->9\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "12\n", - "\n", - "node #12\n", - "Fare <= 51.698\n", - "mae = 12.384\n", - "samples = 79\n", - "value = 21.0\n", - "\n", - "\n", - "\n", - "8->12\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "10\n", - "\n", - "node #10\n", - "mae = 7.386\n", - "samples = 583\n", - "value = 29.699\n", - "\n", - "\n", - "\n", - "9->10\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "11\n", - "\n", - "node #11\n", - "mae = 9.938\n", - "samples = 13\n", - "value = 24.0\n", - "\n", - "\n", - "\n", - "9->11\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "13\n", - "\n", - "node #13\n", - "mae = 12.317\n", - "samples = 58\n", - "value = 11.0\n", - "\n", - "\n", - "\n", - "12->13\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "14\n", - "\n", - "node #14\n", - "mae = 3.386\n", - "samples = 21\n", - "value = 29.699\n", - "\n", - "\n", - "\n", - "12->14\n", - "\n", - "\n", - "\n", - "\n", - "\n" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from sklearn import tree as sklearn_tree\n", - "\n", - "dot_data = sklearn_tree.export_graphviz(tree_regressor, out_file=None, feature_names=features_reg,\n", - " filled=True, node_ids=True)\n", - "graphviz.Source(dot_data)" - ] - }, - { - "cell_type": "code", - "execution_count": 21, + "execution_count": 14, "metadata": {}, "outputs": [ { @@ -7953,157 +7686,157 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -8113,10 +7846,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8135,7 +7868,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8158,7 +7891,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8195,10 +7928,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8212,7 +7945,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8226,7 +7959,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8242,13 +7975,13 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8259,7 +7992,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8283,77 +8016,77 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -8363,10 +8096,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8385,7 +8118,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8403,7 +8136,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8445,10 +8178,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8465,7 +8198,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8479,7 +8212,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8495,13 +8228,13 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8512,7 +8245,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8538,85 +8271,85 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -8624,10 +8357,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8645,7 +8378,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8662,7 +8395,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8678,7 +8411,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8722,7 +8455,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8753,81 +8486,81 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -8835,10 +8568,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8856,7 +8589,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8873,7 +8606,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8889,7 +8622,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8932,7 +8665,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8963,29 +8696,29 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -8993,10 +8726,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9014,7 +8747,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9031,7 +8764,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9047,7 +8780,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9092,7 +8825,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9123,57 +8856,57 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -9181,10 +8914,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9202,7 +8935,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9219,7 +8952,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9235,7 +8968,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9280,7 +9013,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9310,225 +9043,225 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " 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\n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -11200,7 +10933,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -11247,10 +10980,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -11268,7 +11001,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -11285,7 +11018,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -11301,13 +11034,13 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -11318,7 +11051,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -11342,88 +11075,88 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -11433,10 +11166,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -11455,7 +11188,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -11478,7 +11211,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -11514,10 +11247,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -11531,7 +11264,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -11545,7 +11278,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -11558,13 +11291,13 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -11575,7 +11308,7 @@ " \n", " \n", " \n", - " 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\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -12194,10 +11927,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -12215,7 +11948,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -12232,7 +11965,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -12248,7 +11981,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -12293,7 +12026,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -12324,22 +12057,22 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -12347,10 +12080,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -12368,7 +12101,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -12385,7 +12118,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -12401,7 +12134,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -12445,7 +12178,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -12476,67 +12209,67 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", 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" \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -12544,10 +12277,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -12565,7 +12298,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -12582,7 +12315,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -12598,7 +12331,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -12642,7 +12375,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -12673,30 +12406,30 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ 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\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -13746,10 +13479,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -13769,7 +13502,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -13787,7 +13520,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -13825,10 +13558,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -13845,7 +13578,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -13859,7 +13592,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -13888,13 +13621,13 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -13905,7 +13638,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -13915,14 +13648,14 @@ "node0->node1\n", "\n", "\n", - "<\n", + "≤\n", "\n", "\n", "\n", "node0->node8\n", "\n", "\n", - "≥\n", + ">\n", "\n", "\n", "\n", @@ -13932,10 +13665,10 @@ "" ], "text/plain": [ - "" + "" ] }, - "execution_count": 21, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" } @@ -13946,7 +13679,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 17, "metadata": {}, "outputs": [ { @@ -13975,157 +13708,157 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -14135,10 +13868,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -14157,7 +13890,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -14180,7 +13913,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -14217,10 +13950,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -14234,7 +13967,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -14248,7 +13981,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -14264,13 +13997,13 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -14281,7 +14014,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -14305,77 +14038,77 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -14385,10 +14118,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -14407,7 +14140,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -14425,7 +14158,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -14467,10 +14200,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -14487,7 +14220,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -14501,7 +14234,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -14517,13 +14250,13 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -14534,7 +14267,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -14560,85 +14293,85 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -14646,10 +14379,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -14667,7 +14400,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -14684,7 +14417,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -14700,7 +14433,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -14744,7 +14477,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -14775,81 +14508,81 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -14857,10 +14590,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -14878,7 +14611,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -14895,7 +14628,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -14911,7 +14644,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -14954,7 +14687,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -14985,29 +14718,29 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -15015,10 +14748,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -15036,7 +14769,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -15053,7 +14786,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -15069,7 +14802,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -15114,7 +14847,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -15145,57 +14878,57 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -15203,10 +14936,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -15224,7 +14957,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -15241,7 +14974,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -15257,7 +14990,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -15302,7 +15035,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -15332,225 +15065,225 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " 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\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -15560,10 +15293,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -15585,7 +15318,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -15606,7 +15339,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -15655,10 +15388,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -15675,7 +15408,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -15689,7 +15422,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -15702,13 +15435,13 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -15719,7 +15452,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -15755,684 +15488,684 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " 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\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -17201,10 +16934,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -17222,7 +16955,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -17269,10 +17002,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -17290,7 +17023,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -17307,7 +17040,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -17323,13 +17056,13 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -17340,7 +17073,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -17364,88 +17097,88 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -17455,10 +17188,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -17477,7 +17210,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -17500,7 +17233,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -17536,10 +17269,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -17553,7 +17286,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -17567,7 +17300,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -17580,13 +17313,13 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -17597,7 +17330,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -17623,592 +17356,592 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " 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\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -18216,10 +17949,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -18237,7 +17970,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -18254,7 +17987,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -18270,7 +18003,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -18315,7 +18048,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -18346,22 +18079,22 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -18369,10 +18102,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -18390,7 +18123,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -18407,7 +18140,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -18423,7 +18156,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -18467,7 +18200,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -18498,67 +18231,67 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -18566,10 +18299,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -18587,7 +18320,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -18604,7 +18337,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -18620,7 +18353,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -18664,7 +18397,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -18695,30 +18428,30 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -18726,10 +18459,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -18747,7 +18480,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -18764,7 +18497,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -18780,7 +18513,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -18823,7 +18556,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -18865,900 +18598,900 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " 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\n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -19768,10 +19501,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -19791,7 +19524,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -19809,7 +19542,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -19847,10 +19580,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -19867,7 +19600,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -19881,7 +19614,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -19910,13 +19643,13 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -19927,7 +19660,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -19937,14 +19670,14 @@ "node0->node1\n", "\n", "\n", - "<\n", + "≤\n", "\n", "\n", "\n", "node0->node8\n", "\n", "\n", - "≥\n", + ">\n", "\n", "\n", "\n", @@ -19954,16 +19687,16 @@ "" ], "text/plain": [ - "" + "" ] }, - "execution_count": 22, + "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "trees.dtreeviz(tree_regressor, dataset[features_reg], dataset[target_reg], features_reg, target_reg)" + "trees.dtreeviz(sk_dtree_reg)" ] }, { diff --git a/notebooks/dtreeviz_spark_visualisations.ipynb b/notebooks/dtreeviz_spark_visualisations.ipynb index 430851c9..4241fc4e 100644 --- a/notebooks/dtreeviz_spark_visualisations.ipynb +++ b/notebooks/dtreeviz_spark_visualisations.ipynb @@ -18,7 +18,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -30,7 +30,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -65,7 +65,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -74,7 +74,7 @@ "3" ] }, - "execution_count": 4, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } @@ -86,7 +86,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -98,7 +98,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -107,7 +107,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 6, "metadata": { "scrolled": true }, @@ -165,7 +165,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -192,7 +192,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -201,14 +201,14 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "DecisionTreeClassificationModel: uid=DecisionTreeClassifier_b9c1e8ba2f3b, depth=4, numNodes=17, numClasses=2, numFeatures=7\n", + "DecisionTreeClassificationModel: uid=DecisionTreeClassifier_d26859ba0ec0, depth=4, numNodes=17, numClasses=2, numFeatures=7\n", " If (feature 1 in {0.0})\n", " If (feature 3 <= 3.5)\n", " If (feature 4 <= 2.5)\n", @@ -244,7 +244,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ @@ -254,7 +254,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -263,7 +263,7 @@ "'InternalNode(prediction = 0.0, impurity = 0.4730129578614428, split = org.apache.spark.ml.tree.CategoricalSplit@ca78837e)'" ] }, - "execution_count": 12, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } @@ -274,16 +274,16 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ - "df = spark.read.parquet(\"../../dtreeviz/testing/dtreeviz/models/fixtures/spark_3_0_decision_tree_classifier.model/data\")" + "df = spark.read.parquet(\"../../dtreeviz/testing/testlib/models/fixtures/spark_3_0_decision_tree_classifier.model/data\")" ] }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 13, "metadata": {}, "outputs": [ { @@ -315,7 +315,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 14, "metadata": {}, "outputs": [ { @@ -393,2896 +393,210 @@ " 1.0\n", " 0.000000\n", " [0.0, 12.0]\n", - " 12\n", - " -1.000000\n", - " -1\n", - " -1\n", - " (-1, [], -1)\n", - " \n", - " \n", - " 4\n", - " 4\n", - " 0.0\n", - " 0.277778\n", - " [5.0, 1.0]\n", - " 6\n", - " -1.000000\n", - " -1\n", - " -1\n", - " (-1, [], -1)\n", - " \n", - " \n", - " 5\n", - " 5\n", - " 0.0\n", - " 0.284484\n", - " [463.0, 96.0]\n", - " 559\n", - " -1.000000\n", - " -1\n", - " -1\n", - " (-1, [], -1)\n", - " \n", - " \n", - " 6\n", - " 6\n", - " 1.0\n", - " 0.382835\n", - " [81.0, 233.0]\n", - " 314\n", - " 0.099246\n", - " 7\n", - " 12\n", - " (0, [2.5], -1)\n", - " \n", - " \n", - " 7\n", - " 7\n", - " 1.0\n", - " 0.100277\n", - " [9.0, 161.0]\n", - " 170\n", - " 0.002825\n", - " 8\n", - " 11\n", - " (3, [3.5], -1)\n", - " \n", - " \n", - " 8\n", - " 8\n", - " 1.0\n", - " 0.444444\n", - " [1.0, 2.0]\n", - " 3\n", - " 0.444444\n", - " 9\n", - " 10\n", - " (0, [1.5], -1)\n", - " \n", - " \n", - " 9\n", - " 9\n", - " 0.0\n", - " 0.000000\n", - " [1.0, 0.0]\n", - " 1\n", - " -1.000000\n", - " -1\n", - " -1\n", - " (-1, [], -1)\n", - " \n", - " \n", - " 10\n", - " 10\n", - " 1.0\n", - " 0.000000\n", - " [0.0, 2.0]\n", - " 2\n", - " -1.000000\n", - " -1\n", - " -1\n", - " (-1, [], -1)\n", - " \n", - " \n", - " 11\n", - " 11\n", - " 1.0\n", - " 0.091219\n", - " [8.0, 159.0]\n", - " 167\n", - " -1.000000\n", - " -1\n", - " -1\n", - " (-1, [], -1)\n", - " \n", - " \n", - " 12\n", - " 12\n", - " 0.0\n", - " 0.500000\n", - " [72.0, 72.0]\n", - " 144\n", - " 0.064858\n", - " 13\n", - " 16\n", - " (6, [24.808349999999997], -1)\n", - " \n", - " \n", - " 13\n", - " 13\n", - " 1.0\n", - " 0.487672\n", - " [51.0, 70.0]\n", - " 121\n", - " 0.028418\n", - " 14\n", - " 15\n", - " (2, [1.0, 2.0], 4)\n", - " \n", - " \n", - " 14\n", - " 14\n", - " 1.0\n", - " 0.412562\n", - " [16.0, 39.0]\n", - " 55\n", - " -1.000000\n", - " -1\n", - " -1\n", - " (-1, [], -1)\n", - " \n", - " \n", - " 15\n", - " 15\n", - " 0.0\n", - " 0.498163\n", - " [35.0, 31.0]\n", - " 66\n", - " -1.000000\n", - " -1\n", - " -1\n", - " (-1, [], -1)\n", - " \n", - " \n", - " 16\n", - " 16\n", - " 0.0\n", - " 0.158790\n", - " [21.0, 2.0]\n", - " 23\n", - " -1.000000\n", - " -1\n", - " -1\n", - " (-1, [], -1)\n", - " \n", - " \n", - "\n", - "" - ], - "text/plain": [ - " id prediction impurity impurityStats rawCount gain leftChild \\\n", - "0 0 0.0 0.473013 [549.0, 342.0] 891 0.139648 1 \n", - "1 1 0.0 0.306444 [468.0, 109.0] 577 0.018317 2 \n", - "2 2 1.0 0.401235 [5.0, 13.0] 18 0.308642 3 \n", - "3 3 1.0 0.000000 [0.0, 12.0] 12 -1.000000 -1 \n", - "4 4 0.0 0.277778 [5.0, 1.0] 6 -1.000000 -1 \n", - "5 5 0.0 0.284484 [463.0, 96.0] 559 -1.000000 -1 \n", - "6 6 1.0 0.382835 [81.0, 233.0] 314 0.099246 7 \n", - "7 7 1.0 0.100277 [9.0, 161.0] 170 0.002825 8 \n", - "8 8 1.0 0.444444 [1.0, 2.0] 3 0.444444 9 \n", - "9 9 0.0 0.000000 [1.0, 0.0] 1 -1.000000 -1 \n", - "10 10 1.0 0.000000 [0.0, 2.0] 2 -1.000000 -1 \n", - "11 11 1.0 0.091219 [8.0, 159.0] 167 -1.000000 -1 \n", - "12 12 0.0 0.500000 [72.0, 72.0] 144 0.064858 13 \n", - "13 13 1.0 0.487672 [51.0, 70.0] 121 0.028418 14 \n", - "14 14 1.0 0.412562 [16.0, 39.0] 55 -1.000000 -1 \n", - "15 15 0.0 0.498163 [35.0, 31.0] 66 -1.000000 -1 \n", - "16 16 0.0 0.158790 [21.0, 2.0] 23 -1.000000 -1 \n", - "\n", - " rightChild split \n", - "0 6 (1, [0.0], 3) \n", - "1 5 (3, [3.5], -1) \n", - "2 4 (4, [2.5], -1) \n", - "3 -1 (-1, [], -1) \n", - "4 -1 (-1, [], -1) \n", - "5 -1 (-1, [], -1) \n", - "6 12 (0, [2.5], -1) \n", - "7 11 (3, [3.5], -1) \n", - "8 10 (0, [1.5], -1) \n", - "9 -1 (-1, [], -1) \n", - "10 -1 (-1, [], -1) \n", - "11 -1 (-1, [], -1) \n", - "12 16 (6, [24.808349999999997], -1) \n", - "13 15 (2, [1.0, 2.0], 4) \n", - "14 -1 (-1, [], -1) \n", - "15 -1 (-1, [], -1) \n", - "16 -1 (-1, [], -1) " - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df.toPandas()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## dtreeviz visualisations" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "tree_model_classifier = model.stages[4]\n", - "\n", - "dataset = Pipeline(stages=[sex_label_indexer, embarked_label_indexer, age_imputer]) \\\n", - " .fit(data) \\\n", - " .transform(data) \\\n", - " .toPandas()[features + [target]]\n", - "\n", - "spark_dtree = ShadowSparkTree(tree_model_classifier, dataset[features], dataset[target], feature_names=features, target_name=target, class_names=[0, 1])\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### dtreeviz" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "data": { - "image/svg+xml": [ - "\n", - "\n", - "G\n", - "\n", - "\n", - "cluster_legend\n", - "\n", - "\n", - "\n", - "node2\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", 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" id prediction impurity impurityStats rawCount gain leftChild \\\n", + "0 0 0.0 0.473013 [549.0, 342.0] 891 0.139648 1 \n", + "1 1 0.0 0.306444 [468.0, 109.0] 577 0.018317 2 \n", + "2 2 1.0 0.401235 [5.0, 13.0] 18 0.308642 3 \n", + "3 3 1.0 0.000000 [0.0, 12.0] 12 -1.000000 -1 \n", + "4 4 0.0 0.277778 [5.0, 1.0] 6 -1.000000 -1 \n", + "5 5 0.0 0.284484 [463.0, 96.0] 559 -1.000000 -1 \n", + "6 6 1.0 0.382835 [81.0, 233.0] 314 0.099246 7 \n", + "7 7 1.0 0.100277 [9.0, 161.0] 170 0.002825 8 \n", + "8 8 1.0 0.444444 [1.0, 2.0] 3 0.444444 9 \n", + "9 9 0.0 0.000000 [1.0, 0.0] 1 -1.000000 -1 \n", + "10 10 1.0 0.000000 [0.0, 2.0] 2 -1.000000 -1 \n", + "11 11 1.0 0.091219 [8.0, 159.0] 167 -1.000000 -1 \n", + "12 12 0.0 0.500000 [72.0, 72.0] 144 0.064858 13 \n", + "13 13 1.0 0.487672 [51.0, 70.0] 121 0.028418 14 \n", + "14 14 1.0 0.412562 [16.0, 39.0] 55 -1.000000 -1 \n", + "15 15 0.0 0.498163 [35.0, 31.0] 66 -1.000000 -1 \n", + "16 16 0.0 0.158790 [21.0, 2.0] 23 -1.000000 -1 \n", + "\n", + " rightChild split \n", + "0 6 (1, [0.0], 3) \n", + "1 5 (3, [3.5], -1) \n", + "2 4 (4, [2.5], -1) \n", + "3 -1 (-1, [], -1) \n", + "4 -1 (-1, [], -1) \n", + "5 -1 (-1, [], -1) \n", + "6 12 (0, [2.5], -1) \n", + "7 11 (3, [3.5], -1) \n", + "8 10 (0, [1.5], -1) \n", + "9 -1 (-1, [], -1) \n", + "10 -1 (-1, [], -1) \n", + "11 -1 (-1, [], -1) \n", + "12 16 (6, [24.808349999999997], -1) \n", + "13 15 (2, [1.0, 2.0], 4) \n", + "14 -1 (-1, [], -1) \n", + "15 -1 (-1, [], -1) \n", + "16 -1 (-1, [], -1) " ] }, "execution_count": 14, @@ -3291,12 +605,42 @@ } ], "source": [ - "trees.dtreeviz(spark_dtree, fancy=True)" + "df.toPandas()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## dtreeviz visualisations" ] }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "tree_model_classifier = model.stages[4]\n", + "\n", + "dataset = Pipeline(stages=[sex_label_indexer, embarked_label_indexer, age_imputer]) \\\n", + " .fit(data) \\\n", + " .transform(data) \\\n", + " .toPandas()[features + [target]]\n", + "\n", + "spark_dtree = ShadowSparkTree(tree_model_classifier, dataset[features], dataset[target], feature_names=features, target_name=target, class_names=[0, 1])\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### dtreeviz" + ] + }, + { + "cell_type": "code", + "execution_count": 16, "metadata": {}, "outputs": [ { @@ -3327,118 +671,118 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3447,10 +791,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3466,7 +810,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3500,10 +844,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3516,7 +860,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3551,7 +895,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3718,118 +1062,118 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3838,10 +1182,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3863,7 +1207,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3907,10 +1251,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3923,7 +1267,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3963,7 +1307,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3992,118 +1336,118 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4112,10 +1456,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4131,7 +1475,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4167,10 +1511,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4186,7 +1530,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4226,7 +1570,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4256,118 +1600,118 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4376,10 +1720,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4395,7 +1739,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4431,10 +1775,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4450,7 +1794,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4488,7 +1832,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4651,118 +1995,118 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4771,10 +2115,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4796,7 +2140,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4840,10 +2184,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4856,7 +2200,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4895,7 +2239,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4924,118 +2268,118 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5044,10 +2388,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5066,7 +2410,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5106,10 +2450,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5122,7 +2466,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5164,7 +2508,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5194,118 +2538,118 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5317,10 +2661,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5336,7 +2680,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5385,10 +2729,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5401,7 +2745,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5425,7 +2769,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5621,118 +2965,118 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5741,10 +3085,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5760,7 +3104,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5801,10 +3145,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5817,7 +3161,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5843,7 +3187,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5853,14 +3197,14 @@ "node0->node1\n", "\n", "\n", - "<\n", + "≤\n", "\n", "\n", "\n", "node0->node6\n", "\n", "\n", - "≥\n", + ">\n", "\n", "\n", "\n", @@ -5938,10 +3282,10 @@ "" ], "text/plain": [ - "" + "" ] }, - "execution_count": 18, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" } @@ -9102,7 +6446,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 17, "metadata": {}, "outputs": [], "source": [ @@ -9111,7 +6455,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 18, "metadata": {}, "outputs": [], "source": [ @@ -9121,7 +6465,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 19, "metadata": {}, "outputs": [], "source": [ @@ -9133,7 +6477,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 20, "metadata": {}, "outputs": [], "source": [ @@ -9145,7 +6489,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 21, "metadata": {}, "outputs": [ { @@ -9336,7 +6680,7 @@ "[891 rows x 8 columns]" ] }, - "execution_count": 19, + "execution_count": 21, "metadata": {}, "output_type": "execute_result" } @@ -9354,7 +6698,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 22, "metadata": {}, "outputs": [], "source": [ @@ -9371,7 +6715,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 23, "metadata": {}, "outputs": [ { @@ -9387,7 +6731,7 @@ "Name: 10, dtype: float64" ] }, - "execution_count": 21, + "execution_count": 23, "metadata": {}, "output_type": "execute_result" } @@ -9399,7 +6743,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 24, "metadata": {}, "outputs": [ { @@ -9428,189 +6772,189 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9626,7 +6970,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9667,10 +7011,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9683,7 +7027,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9701,7 +7045,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9718,10 +7062,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9732,7 +7076,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9756,62 +7100,62 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -9821,10 +7165,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9843,7 +7187,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9861,7 +7205,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9898,10 +7242,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9914,7 +7258,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9932,7 +7276,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9949,13 +7293,13 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9966,7 +7310,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9992,108 +7336,108 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -10101,10 +7445,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -10120,7 +7464,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -10137,7 +7481,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -10183,7 +7527,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -10214,73 +7558,73 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -10288,10 +7632,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -10307,7 +7651,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -10324,7 +7668,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -10371,7 +7715,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -10402,57 +7746,57 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -10460,10 +7804,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -10479,7 +7823,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -10496,7 +7840,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -10542,7 +7886,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -10573,14 +7917,14 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -10588,10 +7932,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -10607,7 +7951,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -10624,7 +7968,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -10669,7 +8013,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -10699,225 +8043,225 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -10927,10 +8271,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -10946,7 +8290,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -10981,10 +8325,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -10997,7 +8341,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -11015,7 +8359,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -11032,13 +8376,13 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -11049,7 +8393,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -11085,684 +8429,684 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " 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\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -12401,10 +9745,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -12420,7 +9764,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -12455,10 +9799,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -12471,7 +9815,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -12489,7 +9833,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -12506,13 +9850,13 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -12523,7 +9867,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -12547,213 +9891,213 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", 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\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -12763,10 +10107,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -12785,7 +10129,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -12803,7 +10147,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -12840,10 +10184,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -12856,7 +10200,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -12874,7 +10218,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -12888,13 +10232,13 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -12905,7 +10249,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -12931,437 +10275,437 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " 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" \n", @@ -13369,10 +10713,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -13388,7 +10732,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -13405,7 +10749,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -13452,7 +10796,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -13483,52 +10827,52 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -13536,10 +10880,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -13555,7 +10899,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -13572,7 +10916,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -13619,7 +10963,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -13650,201 +10994,201 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " 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\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -13852,10 +11196,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -13871,7 +11215,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -13888,7 +11232,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -13934,7 +11278,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -13965,21 +11309,21 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -13987,10 +11331,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", 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\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -15013,10 +12357,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -15036,7 +12380,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -15054,7 +12398,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -15092,10 +12436,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -15108,7 +12452,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -15126,7 +12470,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -15157,13 +12501,13 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -15174,7 +12518,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -15184,14 +12528,14 @@ "node0->node1\n", "\n", "\n", - "<\n", + "≤\n", "\n", "\n", "\n", "node0->node8\n", "\n", "\n", - "≥\n", + ">\n", "\n", "\n", "\n", @@ -15201,10 +12545,10 @@ "" ], "text/plain": [ - "" + "" ] }, - "execution_count": 22, + "execution_count": 24, "metadata": {}, "output_type": "execute_result" } diff --git a/notebooks/dtreeviz_xgboost_visualisations.ipynb b/notebooks/dtreeviz_xgboost_visualisations.ipynb index 9649b94c..9532ade3 100644 --- a/notebooks/dtreeviz_xgboost_visualisations.ipynb +++ b/notebooks/dtreeviz_xgboost_visualisations.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 39, + "execution_count": 18, "metadata": {}, "outputs": [ { @@ -30,7 +30,7 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 19, "metadata": {}, "outputs": [ { @@ -39,7 +39,7 @@ "'/Users/tudorl/Documents/workspace/personal/dtreeviz/venv/bin/python'" ] }, - "execution_count": 40, + "execution_count": 19, "metadata": {}, "output_type": "execute_result" } @@ -51,7 +51,7 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 20, "metadata": {}, "outputs": [], "source": [ @@ -79,7 +79,7 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 21, "metadata": {}, "outputs": [], "source": [ @@ -95,7 +95,7 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": 22, "metadata": {}, "outputs": [], "source": [ @@ -113,7 +113,7 @@ }, { "cell_type": "code", - "execution_count": 44, + "execution_count": 23, "metadata": {}, "outputs": [], "source": [ @@ -122,7 +122,7 @@ }, { "cell_type": "code", - "execution_count": 45, + "execution_count": 24, "metadata": {}, "outputs": [], "source": [ @@ -132,7 +132,7 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 25, "metadata": { "scrolled": true }, @@ -376,7 +376,7 @@ "12 24.00 " ] }, - "execution_count": 46, + "execution_count": 25, "metadata": {}, "output_type": "execute_result" } @@ -387,16 +387,16 @@ }, { "cell_type": "code", - "execution_count": 47, + "execution_count": 26, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 47, + "execution_count": 26, "metadata": {}, "output_type": "execute_result" }, @@ -427,7 +427,7 @@ }, { "cell_type": "code", - "execution_count": 48, + "execution_count": 27, "metadata": {}, "outputs": [], "source": [ @@ -444,7 +444,7 @@ }, { "cell_type": "code", - "execution_count": 49, + "execution_count": 28, "metadata": {}, "outputs": [], "source": [ @@ -454,7 +454,7 @@ }, { "cell_type": "code", - "execution_count": 50, + "execution_count": 29, "metadata": {}, "outputs": [], "source": [ @@ -477,7 +477,7 @@ }, { "cell_type": "code", - "execution_count": 51, + "execution_count": 30, "metadata": {}, "outputs": [ { @@ -499,7 +499,7 @@ }, { "cell_type": "code", - "execution_count": 52, + "execution_count": 31, "metadata": {}, "outputs": [ { @@ -528,7 +528,7 @@ }, { "cell_type": "code", - "execution_count": 53, + "execution_count": 32, "metadata": {}, "outputs": [ { @@ -550,7 +550,7 @@ }, { "cell_type": "code", - "execution_count": 54, + "execution_count": 33, "metadata": { "scrolled": true }, @@ -581,7 +581,7 @@ }, { "cell_type": "code", - "execution_count": 55, + "execution_count": 35, "metadata": {}, "outputs": [ { @@ -612,118 +612,118 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -732,10 +732,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -754,7 +754,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -794,10 +794,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -810,7 +810,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -851,7 +851,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -976,118 +976,118 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1096,10 +1096,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1115,7 +1115,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1151,10 +1151,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1170,7 +1170,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1209,7 +1209,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1238,118 +1238,118 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1358,10 +1358,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1379,7 +1379,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1426,10 +1426,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1445,7 +1445,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1484,7 +1484,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1559,118 +1559,118 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1679,10 +1679,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1704,7 +1704,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1739,10 +1739,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1755,7 +1755,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1796,7 +1796,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1819,118 +1819,118 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1939,10 +1939,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1964,7 +1964,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1999,10 +1999,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2015,7 +2015,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2056,7 +2056,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2296,118 +2296,118 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2416,10 +2416,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2435,7 +2435,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2476,10 +2476,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2492,7 +2492,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2532,7 +2532,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2628,10 +2628,10 @@ "" ], "text/plain": [ - "" + "" ] }, - "execution_count": 55, + "execution_count": 35, "metadata": {}, "output_type": "execute_result" } @@ -2640,6 +2640,13 @@ "trees.dtreeviz(xgb_model, d[features], d[target], features, target, class_names=[0, 1], tree_index=1)" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, { "cell_type": "code", "execution_count": 56, @@ -5092,7 +5099,7 @@ }, { "cell_type": "code", - "execution_count": 63, + "execution_count": 36, "metadata": {}, "outputs": [], "source": [ @@ -5102,7 +5109,7 @@ }, { "cell_type": "code", - "execution_count": 64, + "execution_count": 37, "metadata": {}, "outputs": [], "source": [ @@ -5113,7 +5120,7 @@ }, { "cell_type": "code", - "execution_count": 65, + "execution_count": 38, "metadata": {}, "outputs": [], "source": [ @@ -5129,7 +5136,7 @@ }, { "cell_type": "code", - "execution_count": 66, + "execution_count": 39, "metadata": {}, "outputs": [ { @@ -5158,83 +5165,83 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -5244,10 +5251,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5266,7 +5273,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5289,7 +5296,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5325,10 +5332,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5342,7 +5349,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5356,7 +5363,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5372,13 +5379,13 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5389,7 +5396,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5414,10 +5421,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -5425,10 +5432,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5446,7 +5453,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5463,7 +5470,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5479,7 +5486,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5519,7 +5526,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5550,82 +5557,82 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -5633,10 +5640,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5654,7 +5661,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5671,7 +5678,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5687,7 +5694,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5731,7 +5738,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5761,225 +5768,225 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", 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" \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -5989,10 +5996,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6011,7 +6018,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6034,7 +6041,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", 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@@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6931,7 +6938,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6945,7 +6952,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6961,13 +6968,13 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6978,7 +6985,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7004,151 +7011,151 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " 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\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -7156,10 +7163,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7177,7 +7184,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7194,7 +7201,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7210,7 +7217,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7256,7 +7263,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7286,605 +7293,605 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", 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\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -7894,10 +7901,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7913,7 +7920,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7953,10 +7960,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7974,7 +7981,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7988,7 +7995,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8004,13 +8011,13 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8021,7 +8028,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8045,88 +8052,88 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -8136,10 +8143,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8158,7 +8165,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8181,7 +8188,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8217,10 +8224,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8234,7 +8241,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8248,7 +8255,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8261,13 +8268,13 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8278,7 +8285,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8304,423 +8311,423 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " 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\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -8728,10 +8735,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8749,7 +8756,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8766,7 +8773,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8782,7 +8789,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8827,7 +8834,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8858,191 +8865,191 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -9050,10 +9057,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9071,7 +9078,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9088,7 +9095,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9104,7 +9111,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9150,7 +9157,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9181,67 +9188,67 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -9249,10 +9256,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9270,7 +9277,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9287,7 +9294,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9303,7 +9310,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9347,7 +9354,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9378,30 +9385,30 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -9409,10 +9416,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9430,7 +9437,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9447,7 +9454,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9463,7 +9470,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9506,7 +9513,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9548,900 +9555,900 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " 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\n", " \n", " \n", @@ -10451,10 +10458,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -10470,7 +10477,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -10486,7 +10493,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -10522,10 +10529,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -10542,7 +10549,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -10559,7 +10566,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -10588,13 +10595,13 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -10605,7 +10612,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -10632,10 +10639,10 @@ "" ], "text/plain": [ - "" + "" ] }, - "execution_count": 66, + "execution_count": 39, "metadata": {}, "output_type": "execute_result" } From b4384ba3d7e66db0c75f86bec269ef64b1cd233f Mon Sep 17 00:00:00 2001 From: tlapusan Date: Sat, 6 Feb 2021 18:53:13 +0200 Subject: [PATCH 15/16] Changes based on PR suggestions --- README.md | 7 +- dtreeviz/models/lightgbm_decision_tree.py | 4 +- dtreeviz/models/shadow_decision_tree.py | 2 +- dtreeviz/models/sklearn_decision_trees.py | 26 +- dtreeviz/models/spark_decision_tree.py | 19 +- dtreeviz/models/xgb_decision_tree.py | 2 +- dtreeviz/trees.py | 4 +- .../dtreeviz_lightgbm_visualisations.ipynb | 14525 ++++++++-------- 8 files changed, 7702 insertions(+), 6887 deletions(-) diff --git a/README.md b/README.md index b95a4dda..823b2869 100644 --- a/README.md +++ b/README.md @@ -43,9 +43,10 @@ conda uninstall graphviz To install (Python >=3.6 only), do this (from Anaconda Prompt on Windows!): ```bash -pip install dtreeviz # Install dtreeviz for sklearn -pip install dtreeviz[boost] # install XGBoost related dependency -pip install dtreeviz[pyspark] # install spark related dependency +pip install dtreeviz # install dtreeviz for sklearn +pip install dtreeviz[xgboost] # install XGBoost related dependency +pip install dtreeviz[pyspark] # install pyspark related dependency +pip install dtreeviz[lightgbm] # install LightGBM related dependency ``` This should also pull in the `graphviz` Python library (>=0.9), which we are using for platform specific stuff. diff --git a/dtreeviz/models/lightgbm_decision_tree.py b/dtreeviz/models/lightgbm_decision_tree.py index aeabf14f..f0423336 100644 --- a/dtreeviz/models/lightgbm_decision_tree.py +++ b/dtreeviz/models/lightgbm_decision_tree.py @@ -103,7 +103,7 @@ def get_thresholds(self) -> np.ndarray: else: node_thresholds[i] = round(self.tree_nodes[i]["threshold"], 2) - self.thresholds = np.array(node_thresholds) + self.thresholds = np.array(node_thresholds, dtype="object") return self.thresholds def get_features(self) -> np.ndarray: @@ -160,7 +160,7 @@ def get_split_samples(self, id): right = np.nonzero(node_X_data > split)[0] return left, right - def get_root_node_labels(self): + def get_root_edge_labels(self): return ["≤", ">"] def get_node_nsamples(self, id): diff --git a/dtreeviz/models/shadow_decision_tree.py b/dtreeviz/models/shadow_decision_tree.py index cb79672b..c7522c94 100644 --- a/dtreeviz/models/shadow_decision_tree.py +++ b/dtreeviz/models/shadow_decision_tree.py @@ -253,7 +253,7 @@ def shouldGoLeftAtSplit(self, id, x): """Return true if it should go to the left node child based on node split criterion and x value""" pass - def get_root_node_labels(self): + def get_root_edge_labels(self): pass def is_categorical_split(self, id) -> bool: diff --git a/dtreeviz/models/sklearn_decision_trees.py b/dtreeviz/models/sklearn_decision_trees.py index e9a69788..55ba5104 100644 --- a/dtreeviz/models/sklearn_decision_trees.py +++ b/dtreeviz/models/sklearn_decision_trees.py @@ -15,7 +15,6 @@ def __init__(self, tree_model, target_name: str = None, class_names: (List[str], Mapping[int, str]) = None): - self.tree_model = tree_model self.node_to_samples = None super().__init__(tree_model, x_data, y_data, feature_names, target_name, class_names) @@ -75,7 +74,7 @@ def get_split_samples(self, id): return left, right - def get_root_node_labels(self): + def get_root_edge_labels(self): return ["≤", ">"] def get_node_nsamples(self, id): @@ -111,26 +110,20 @@ def get_node_criterion(self, id): return self.tree_model.tree_.impurity[id] def get_feature_path_importance(self, node_list): - gini_importance = np.zeros(self.tree_model.tree_.n_features) + gini = np.zeros(self.tree_model.tree_.n_features) + tree_ = self.tree_model.tree_ for node in node_list: if self.tree_model.tree_.children_left[node] != -1: node_left = self.tree_model.tree_.children_left[node] node_right = self.tree_model.tree_.children_right[node] - - gini_importance[self.tree_model.tree_.feature[node]] += self.tree_model.tree_.weighted_n_node_samples[ - node] * \ - self.tree_model.tree_.impurity[node] \ - - self.tree_model.tree_.weighted_n_node_samples[ - node_left] * \ - self.tree_model.tree_.impurity[node_left] \ - - self.tree_model.tree_.weighted_n_node_samples[ - node_right] * \ - self.tree_model.tree_.impurity[node_right] - normalizer = np.sum(gini_importance) + gini[tree_.feature[node]] += tree_.weighted_n_node_samples[node] * tree_.impurity[node] \ + - tree_.weighted_n_node_samples[node_left] * tree_.impurity[node_left] \ + - tree_.weighted_n_node_samples[node_right] * tree_.impurity[node_right] + normalizer = np.sum(gini) if normalizer > 0.0: - gini_importance /= normalizer + gini /= normalizer - return gini_importance + return gini def get_max_depth(self): return self.tree_model.max_depth @@ -143,4 +136,3 @@ def get_min_samples_leaf(self): def shouldGoLeftAtSplit(self, id, x): return x < self.get_node_split(id) - diff --git a/dtreeviz/models/spark_decision_tree.py b/dtreeviz/models/spark_decision_tree.py index 884ac6d5..ee294f03 100644 --- a/dtreeviz/models/spark_decision_tree.py +++ b/dtreeviz/models/spark_decision_tree.py @@ -20,17 +20,16 @@ def __init__(self, tree_model: (DecisionTreeClassificationModel, DecisionTreeReg target_name: str = None, class_names: (List[str], Mapping[int, str]) = None): - self.tree_model = tree_model - self.tree_nodes, self.children_left, self.children_right = self._get_nodes_info() + self.tree_nodes, self.children_left, self.children_right = self._get_nodes_info(tree_model) self.features = None # lazy initialization self.thresholds = None # lazy initialization self.node_to_samples = None # lazy initialization super().__init__(tree_model, x_data, y_data, feature_names, target_name, class_names) - def _get_nodes_info(self): - tree_nodes = [None] * self.tree_model.numNodes - children_left = [-1] * self.tree_model.numNodes - children_right = [-1] * self.tree_model.numNodes + def _get_nodes_info(self, tree_model): + tree_nodes = [None] * tree_model.numNodes + children_left = [-1] * tree_model.numNodes + children_right = [-1] * tree_model.numNodes node_index = 0 def recur(node, node_id): @@ -47,7 +46,7 @@ def recur(node, node_id): children_right[node_id] = node_index recur(node.rightChild(), node_index) - recur(self.tree_model._call_java('rootNode'), 0) + recur(tree_model._call_java('rootNode'), 0) return tree_nodes, children_left, children_right def is_fit(self) -> bool: @@ -84,7 +83,7 @@ def get_thresholds(self) -> np.ndarray: elif "ContinuousSplit" in node.split().toString(): node_thresholds[i] = node.split().threshold() - self.thresholds = np.array(node_thresholds) + self.thresholds = np.array(node_thresholds, dtype="object") return self.thresholds def get_features(self) -> np.ndarray: @@ -123,7 +122,7 @@ def get_node_samples(self): node_to_samples = defaultdict(list) for i in range(self.x_data.shape[0]): - prediction, path = self.predict(self.x_data[i]) + path = self.predict(self.x_data[i], path_only=True) for node in path: node_to_samples[node.id].append(i) @@ -144,7 +143,7 @@ def get_split_samples(self, id): right = np.nonzero(node_X_data > split)[0] return left, right - def get_root_node_labels(self): + def get_root_edge_labels(self): return ["≤", ">"] def get_node_nsamples(self, id): diff --git a/dtreeviz/models/xgb_decision_tree.py b/dtreeviz/models/xgb_decision_tree.py index 69db2bd7..b5506a95 100644 --- a/dtreeviz/models/xgb_decision_tree.py +++ b/dtreeviz/models/xgb_decision_tree.py @@ -118,7 +118,7 @@ def get_split_samples(self, id): return left, right - def get_root_node_labels(self): + def get_root_edge_labels(self): return ["<", "≥"] def get_node_nsamples(self, id): diff --git a/dtreeviz/trees.py b/dtreeviz/trees.py index eb789f6f..2ebccfc4 100644 --- a/dtreeviz/trees.py +++ b/dtreeviz/trees.py @@ -848,8 +848,8 @@ def get_leaves(): show_edge_labels = False all_llabel = '<' if show_edge_labels else '' all_rlabel = '≥' if show_edge_labels else '' - root_llabel = shadow_tree.get_root_node_labels()[0] if show_root_edge_labels else '' - root_rlabel = shadow_tree.get_root_node_labels()[1] if show_root_edge_labels else '' + root_llabel = shadow_tree.get_root_edge_labels()[0] if show_root_edge_labels else '' + root_rlabel = shadow_tree.get_root_edge_labels()[1] if show_root_edge_labels else '' edges = [] # non leaf edges with > and <= diff --git a/notebooks/dtreeviz_lightgbm_visualisations.ipynb b/notebooks/dtreeviz_lightgbm_visualisations.ipynb index b2395a5a..f4fc5cab 100644 --- a/notebooks/dtreeviz_lightgbm_visualisations.ipynb +++ b/notebooks/dtreeviz_lightgbm_visualisations.ipynb @@ -9,18 +9,9 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 1, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The autoreload extension is already loaded. To reload it, use:\n", - " %reload_ext autoreload\n" - ] - } - ], + "outputs": [], "source": [ "%load_ext autoreload\n", "%autoreload 2\n", @@ -30,7 +21,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -60,7 +51,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -69,7 +60,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -321,7 +312,7 @@ "[891 rows x 12 columns]" ] }, - "execution_count": 32, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -332,7 +323,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -359,7 +350,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 39, "metadata": {}, "outputs": [], "source": [ @@ -381,15 +372,17 @@ }, { "cell_type": "code", - "execution_count": 35, - "metadata": {}, + "execution_count": 40, + "metadata": { + "scrolled": true + }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[LightGBM] [Info] Number of positive: 268, number of negative: 444\n", - "[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000974 seconds.\n", + "[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000717 seconds.\n", "You can set `force_row_wise=true` to remove the overhead.\n", "And if memory is not enough, you can set `force_col_wise=true`.\n", "[LightGBM] [Info] Total Bins 234\n", @@ -397,205 +390,62 @@ "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.376404 -> initscore=-0.504838\n", "[LightGBM] [Info] Start training from score -0.504838\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", - "[1]\ttraining's binary_logloss: 0.65258\tvalid_1's binary_logloss: 0.669903\n", - 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"[74]\ttraining's binary_logloss: 0.445934\tvalid_1's binary_logloss: 0.473522\n", - "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", - "[75]\ttraining's binary_logloss: 0.44778\tvalid_1's binary_logloss: 0.475082\n", - "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", - "[76]\ttraining's binary_logloss: 0.448649\tvalid_1's binary_logloss: 0.476007\n", - "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", - "[77]\ttraining's binary_logloss: 0.44374\tvalid_1's binary_logloss: 0.47244\n", - "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", - "[78]\ttraining's binary_logloss: 0.441121\tvalid_1's binary_logloss: 0.470451\n", - "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", - "[79]\ttraining's binary_logloss: 0.4361\tvalid_1's binary_logloss: 0.466223\n", - "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", - "[80]\ttraining's binary_logloss: 0.431753\tvalid_1's binary_logloss: 0.463265\n", - "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", - "[81]\ttraining's binary_logloss: 0.427485\tvalid_1's binary_logloss: 0.45983\n", - "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", - "[82]\ttraining's binary_logloss: 0.425612\tvalid_1's binary_logloss: 0.458425\n", - "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", - "[83]\ttraining's binary_logloss: 0.427913\tvalid_1's binary_logloss: 0.460055\n", - "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", - "[84]\ttraining's binary_logloss: 0.42986\tvalid_1's binary_logloss: 0.461585\n", - "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", - "[85]\ttraining's binary_logloss: 0.431663\tvalid_1's binary_logloss: 0.463038\n", - "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", - "[86]\ttraining's binary_logloss: 0.427474\tvalid_1's binary_logloss: 0.459983\n", - "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", - "[87]\ttraining's binary_logloss: 0.428849\tvalid_1's binary_logloss: 0.460959\n", - "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", - "[88]\ttraining's binary_logloss: 0.426592\tvalid_1's binary_logloss: 0.458425\n", - "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", - "[89]\ttraining's binary_logloss: 0.428585\tvalid_1's binary_logloss: 0.459928\n", - "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", - "[90]\ttraining's binary_logloss: 0.424486\tvalid_1's binary_logloss: 0.457091\n", - "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", - "[91]\ttraining's binary_logloss: 0.426458\tvalid_1's binary_logloss: 0.458469\n", - "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", - "[92]\ttraining's binary_logloss: 0.424398\tvalid_1's binary_logloss: 0.456956\n", - "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", - "[93]\ttraining's binary_logloss: 0.426646\tvalid_1's binary_logloss: 0.458664\n", - "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", - "[94]\ttraining's binary_logloss: 0.428622\tvalid_1's binary_logloss: 0.460124\n", - "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", - "[95]\ttraining's binary_logloss: 0.430696\tvalid_1's binary_logloss: 0.461634\n", - "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", - "[96]\ttraining's binary_logloss: 0.432746\tvalid_1's binary_logloss: 0.463187\n", - "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", - "[97]\ttraining's binary_logloss: 0.429157\tvalid_1's binary_logloss: 0.460482\n", - "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", - "[98]\ttraining's binary_logloss: 0.430924\tvalid_1's binary_logloss: 0.461824\n", - "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", - "[99]\ttraining's binary_logloss: 0.432548\tvalid_1's binary_logloss: 0.463037\n", - "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", - "[100]\ttraining's binary_logloss: 0.428043\tvalid_1's binary_logloss: 0.459459\n" + "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n" ] }, { @@ -616,14 +466,15 @@ " 'boosting': 'dart', # dart (drop out trees) often performs better\n", " 'application': 'binary', # Binary classification\n", " 'learning_rate': 0.05, # Learning rate, controls size of a gradient descent step\n", - " 'min_data_in_leaf': 22, # Data set is quite small so reduce this a bit\n", + " 'min_data_in_leaf': 2, # Data set is quite small so reduce this a bit\n", + " 'feature_pre_filter': False,\n", " 'feature_fraction': 0.7, # Proportion of features in each boost, controls overfitting\n", " 'num_leaves': 41, # Controls size of tree since LGBM uses leaf wise splits\n", " 'drop_rate': 0.15,\n", " 'max_depth':4,\n", " \"seed\":1212}\n", "\n", - "lgbm_model = lgb.train(lgbm_params, train_data, valid_sets=[train_data, valid_data])" + "lgbm_model = lgb.train(lgbm_params, train_data, valid_sets=[train_data, valid_data], verbose_eval=False)\n" ] }, { @@ -641,7 +492,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 36, @@ -650,7 +501,7 @@ }, { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] @@ -667,27 +518,7 @@ }, { "cell_type": "code", - "execution_count": 37, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "lightgbm.basic.Booster" - ] - }, - "execution_count": 37, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "type(lgbm_model)" - ] - }, - { - "cell_type": "code", - "execution_count": 38, + "execution_count": 9, "metadata": {}, "outputs": [ { @@ -699,347 +530,443 @@ "\n", "\n", - "\n", - "\n", + "\n", + "\n", "%3\n", - "\n", + "\n", "\n", "\n", "split0\n", - "\n", - "Pclass\n", - " = \n", - "3\n", - "0.000 value\n", - "count: 712\n", + "\n", + "Pclass\n", + " ≤ \n", + "2.500\n", + "0.000 value\n", + "count: 712\n", "\n", - "\n", + "\n", "\n", + "split1\n", + "\n", + "Fare\n", + " ≤ \n", + "52.277\n", + "0.007 value\n", + "count: 314\n", + "\n", + "\n", + "\n", + "split0->split1\n", + "\n", + "\n", + "yes\n", + "\n", + "\n", + "\n", "split3\n", - "\n", - "Age\n", - " ≤ \n", - "6.500\n", - "-0.005 value\n", - "count: 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+ "-0.009\n", + "count: 151\n", + "\n", + "\n", + "\n", + "split11->leaf12\n", + "\n", + "\n", + "no\n", "\n", "\n", "\n" ], "text/plain": [ - "" + "" ] }, - "execution_count": 38, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -1057,7 +984,7 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ @@ -1066,23 +993,23 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "image/svg+xml": [ - "\n", - "\n", + "\n", + "\n", "G\n", - "\n", - "\n", + "\n", + "\n", "cluster_legend\n", "\n", "\n", "\n", "node4\n", - "\n", + "\n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1229,7 +1156,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1239,7 +1166,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1250,7 +1177,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1267,7 +1194,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1279,15 +1206,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1295,57 +1222,156 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", " \n", - " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " 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"\n", + "\n", + "\n", + "\n", "\n", - "node7\n", - "\n", + "node2\n", + "\n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1494,7 +1520,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1505,7 +1531,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1513,7 +1539,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1529,7 +1555,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1540,15 +1566,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1556,36 +1582,35 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -1594,17 +1619,21 @@ " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", "\n", "\n", + "\n", + "node2->node4\n", + "\n", + "\n", + "\n", "\n", - "\n", - "leaf5\n", - "\n", - "\n", + "\n", + "node7\n", + "\n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", - " \n", - "\n", - "\n", - "\n", - "node4->leaf5\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "leaf6\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", - " \n", - "\n", - "\n", - "\n", - "node4->leaf6\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "leaf8\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", - " \n", - "\n", - "\n", - "\n", - "node7->leaf8\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "leaf9\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", "\n", "\n", - "\n", - "node7->leaf9\n", - "\n", - "\n", + "\n", + "\n", + "leaf3\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n", + "\n", + "\n", + "node2->leaf3\n", + "\n", + "\n", "\n", "\n", "\n", - "node3\n", - "\n", + "node8\n", + "\n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -2017,10 +2130,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2028,7 +2141,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2036,38 +2149,724 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n", + "\n", + "\n", + "leaf9\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n", + "\n", + "\n", + "node8->leaf9\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "leaf10\n", + "\n", + "\n", + " \n", + " 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\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n", + "\n", + "\n", + "node1->node2\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "node1->node7\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "node12\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -2076,27 +2875,16 @@ " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", "\n", "\n", - "\n", - "node3->node4\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "node3->node7\n", - "\n", - "\n", - "\n", "\n", - "\n", - "node1\n", - "\n", + "\n", + "node14\n", + "\n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2242,48 +3030,50 @@ " \n", " \n", " \n", - " \n", - " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -2291,15 +3081,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2307,62 +3097,52 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", - " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", + " \n", " \n", " \n", - " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", "\n", "\n", - "\n", - "node1->node3\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "node10\n", - "\n", + "\n", + "node17\n", + "\n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2508,50 +3288,48 @@ " \n", " \n", " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -2559,15 +3337,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2575,59 +3353,199 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", " \n", " \n", - " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n", + "\n", + "\n", + "\n", + "leaf15\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n", + "\n", + "\n", + "node14->leaf15\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "leaf16\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n", + "\n", + "\n", + "node14->leaf16\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "leaf18\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", "\n", "\n", + "\n", + "node17->leaf18\n", + "\n", + "\n", + "\n", "\n", - "\n", - "leaf2\n", - "\n", - "\n", + "\n", + "leaf19\n", + "\n", + "\n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", "\n", "\n", - "\n", - "node1->leaf2\n", - "\n", - "\n", + "\n", + "node17->leaf19\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "node13\n", - "\n", + "\n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2825,7 +3741,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2835,7 +3751,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2846,7 +3762,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2863,7 +3779,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2875,15 +3791,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -2891,159 +3807,65 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", " \n", - " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", " \n", + " \n", " \n", - " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", "\n", "\n", - "\n", - "leaf14\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "\n", - "\n", - "\n", - "node13->leaf14\n", - "\n", - "\n", + "\n", + "node13->node14\n", + "\n", + "\n", "\n", "\n", - "\n", - "leaf15\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "\n", - "\n", - "\n", - "node13->leaf15\n", - "\n", - "\n", + "\n", + "node13->node17\n", + "\n", + "\n", "\n", "\n", - "\n", - "node11\n", - "\n", + "\n", + "node20\n", + "\n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -3238,15 +4070,18 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -3254,58 +4089,56 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", "\n", "\n", - "\n", - "node11->node13\n", - "\n", - "\n", - "\n", "\n", - "\n", - "node16\n", - "\n", + "\n", + "node21\n", + "\n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3454,7 +4287,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3465,7 +4298,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3473,7 +4306,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3489,7 +4322,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3500,15 +4333,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3516,108 +4349,57 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "\n", - "\n", - "\n", - "\n", - "leaf12\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", "\n", "\n", - "\n", - "node11->leaf12\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "node17\n", - "\n", + "\n", + "node24\n", + "\n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3766,7 +4548,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3777,7 +4559,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3785,7 +4567,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3801,7 +4583,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3812,15 +4594,15 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -3828,57 +4610,55 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", " \n", + " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", "\n", "\n", - "\n", - "leaf18\n", - "\n", - "\n", + "\n", + "\n", + "leaf22\n", + "\n", + "\n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", + " \n", + " \n", + " \n", + "\n", + "\n", + "\n", + "node21->leaf22\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "leaf23\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", + " \n", + " \n", " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", "\n", "\n", - "\n", - "node17->leaf18\n", - "\n", - "\n", + "\n", + "node21->leaf23\n", + "\n", + "\n", "\n", "\n", - "\n", - "leaf19\n", - "\n", - "\n", + "\n", + "leaf25\n", + "\n", + "\n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", "\n", "\n", - "\n", - "node17->leaf19\n", - "\n", - "\n", + "\n", + "node24->leaf25\n", + "\n", + "\n", "\n", "\n", - "\n", - "leaf20\n", - "\n", - "\n", + "\n", + "leaf26\n", + "\n", + "\n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", "\n", "\n", + "\n", + "node24->leaf26\n", + "\n", + "\n", + "\n", "\n", - "\n", - "node16->node17\n", - "\n", - "\n", + "\n", + "node20->node21\n", + "\n", + "\n", "\n", "\n", - "\n", - "node16->leaf20\n", - "\n", - "\n", + "\n", + "node20->node24\n", + "\n", + "\n", "\n", "\n", - "\n", - "node10->node11\n", - "\n", - "\n", + "\n", + "node12->node13\n", + "\n", + "\n", "\n", "\n", - "\n", - "node10->node16\n", - "\n", - "\n", + "\n", + "node12->node20\n", + "\n", + "\n", "\n", "\n", - "\n", + "\n", "node0\n", - "\n", + "\n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4205,7 +5041,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4241,10 +5077,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4260,7 +5096,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -4283,27 +5119,42 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", "\n", "\n", - "\n", + "\n", "node0->node1\n", - "\n", - "\n", - "≤\n", + "\n", + "\n", + "≤\n", "\n", "\n", - "\n", - "node0->node10\n", - "\n", - "\n", - ">\n", + "\n", + "node0->node12\n", + "\n", + "\n", + ">\n", "\n", "\n", "\n", @@ -4312,10 +5163,11 @@ "\n", "\n", "\n", - "\n", + "\n", + "\n", "legend\n", - "\n", - "\n", + "\n", + "\n", " \n", " " ], "text/plain": [ - "" + "" ] }, - "execution_count": 40, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } @@ -4398,19 +5250,12 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 15, + "execution_count": 18, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -4427,14 +5272,14 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 19, "metadata": { "scrolled": true }, "outputs": [ { "data": { - "image/png": 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\n", 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2LbfnURRvkiRJ6iOtXuL8NjA1yxofBW4FrsuyxkyKCWYfalc4SZKkgajVHrTPAv8KbJPnzSYwmWIOs7cDn2hTNkmSpAGp1XnQXqHHSM08b14AXNCuUJIkSQPZKgu0LGtMbPUged78XN/EkSRJ0up60A5u8RipL4JIkiSpsMoCLc+bf7lyW5Y1Is+bqdzeMs+bz7UznCRJ0kDU6koC21FMSvsr/nTv2QNZ1rgbGJ/nzd+3KZ8kSdKA0+oozm+Wn3subn4wMBj4Sp8mkiRJGuBaLdD+Ejgjz5uPLW/I8+ZDFCsJHNGOYJIkSQNVqwXaYmBEL+3DgKV9F0eSJEmtriRwNTAlyxoXUqzFCcVyTxcB17QhlyRJ0oDVaoE2AQjgC/xpTc5ngH8GLuv7WJIkSQNXqysJLAMuBC7MssYQYGmeN19oazJJkqQBqtUetG5OqSFJktRerQ4SkCRJUodYoEmSJNVMSwValjX2bXcQSZIkFVq9B+3OLGs8DtwA3FBOUitJkqQ2aLVAexPwfuA44Pwsa9xHUax9O8+b89oVTpIkaSBqdZqNxRSLpU/NssaWwPuAvwYmZFnj18D1FD1rTr0hSZK0jtZmkMAuwO7AKCABT1L0rD2RZY3j+zCbJEnSgNRSD1qWNfanKML+FtgB+A/g88Ated58qdznXOBrwLfbE1Xri4XzF7Dg+q6qY3QbvGyNp/uTJKlSrf7mugv4GXAp8G953ny+l31+AcxY3UEi4mrgPcDTKaVRZdvWwHRgJ+Bx4LiU0qKICIqlpI4ClgDjU0q/7u24qpc9Ru7AuHGjqo7RberUWVVHkCRpjbRaoO2Y5835q9shz5s/oyjiVuca4KsU97Mtdy5wW0rpCxFxbvn4H4AjKS6n7gKMAb5RfpYkSerXWh0kMD/LGocCp1Hcf7YUmANcmefN37R6spTSHRGx00rNRwPvLrevBX5KUaAdDUxNKSXgrojYMiKGppRWWyhKkiSt71qdqPYs4IfAH4BvUhRSAL/og4EB2/UouhYA25XbwygGICzXVbZJkiT1a61e4jwHODXPmz0vTZJljZ8D/0QfDQxIKaWISGvymog4FTgV2Gj69OlceOGFfRFF/cish5/gi1d9qeoYK9hq0y04ZdzJVceQJNVUqwXaZsCvemn/T2DIOmZ4avmly4gYCjxdts8Dduyx3/CybQUppUnApIjYdezYsXPXMYv6oVc3CLY/bLeqY6xgwW3+qEqSVq3VedC+AlyWZY2tljdkWWNTYALFJc91cQvw4XL7w8D3erSPi8IBwPPefyZJkgaCVfagZVnjSYqJaAGC4v6v32VZ4zFgGTAC2AhoeZBARNxAMSBgSER0Ucyl9gXgxog4GXiCYr41KO55Owp4mGKajZNa/qokSZLWY6u7xHlBX58spXTCKp46rJd9E/DJvs4gSZJUd6ss0PK8ee2qnpMkSVL7rM1anJIkSWojCzRJkqSacRVpSZIGmIXzF7Dg+q6qY6xg6eL/rTpCraxRgZZljT/L8+ayLGtsDxwMzMzz5kPtiSZJktphj5E7MG7cqKpjrODyy2+tOkKttLrU0wHltBvvyrLGdsDdwBRgVpY13t/OgJIkSQNNq/egfYliAtm7gZOBV4BtgU8AF7cnmiRJ0sDUaoG2L3B5njf/ALwXuDnPm38EmsBb2hVOkiRpIGq1QFsIvDnLGjsBbwd+ULbvD7j8kiRJUh9qdZDA1cDNwFLgIeC2LGt8ArgcOL9N2SRJkgaklnrQ8rz5OeCjwGXAIXneXAY8DozN8+b/bV88SZKkgafliWrzvPld4CZgjyxrbALck+fN77ctmSRJ0gDV0iXOLGtsBvw/4APAMmBX4MtZ1tgWeF+eN59uX0RJktbM5KlTWLTk+apjdNtq0y04ZdzJVcfQeqTVe9CuBIYAI4BZZdungWuBfwGO7/tokiStnUVLnmf7w3arOka3BbfNrTqC1jOtXuJ8L3B2njefWN6Q582HKeZB+6t2BJMkSRqoWi3QNqEYwbmyjYDouziSJElqtUD7HvBPWdbYsnycsqyxC/AVwIECkiRJfajVAu0M4GWKCWvfAMwEHigfn9WeaJIkSQNTS4ME8rz5AvCBLGuMBHYvXzc3z5sPtDOcJEnSQLTKAq0sxnozZ+V98rz5aB/nkiRJGrBW14P2MJB6PF55MEAq2xIwqI9zSZIkDVirK9BGdCyFJEmSuq2yQOs559lyWdbYm+IetGXAb/O8+WAbs0mSJA1IrS71NBT4DjAGeJbikubmWda4DTguz5vPtS+iJEnSwNLqNBtTgBeBkXneHJLnza2A3Sgmqv16u8JJkiQNRK0WaO8CTs/z5uPLG8qlns4A3tOGXJIkSQNWqwXaQ8DoXtp3Ah7vqzCSJElq8R404Frga1nW2B/4BfAKRcF2BnBNljU+snzHPG9evSYBImI3YHqPppHA54AtgVOAZ8r281JKP1yTY0uSJK2PWi3QzgKeA95ffiz3/EptCVijAi2lNJeydy4iBgHzgO8CJwFfTildsSbHkyRJWt+1utRTp+ZEOwx4JKX0RMTK8+JKkiQNDK32oJFljT2Bt1KM3Owp5Xnzhj7KczzQ81inR8Q44G7g0ymlRX10HkmSpNpqaZBAljUuBe6jmG7jyl4+1llEbAi8F7ipbPoG8BaKy5/zV3WeiDg1Iu4GZkyfPr23XSRJktYrrfagfRz4eJ43v9nGLEcCv04pPQWw/DNAREwGvt/bi1JKk4BJEbHr2LFj57YxnyRJUke0Os3GYuA/2xkEOIEelzcjYmiP594PzGrz+SVJkmqh1R60v6eYZuNzwP9QrMXZLc+b/7MuISLiDUAGfKxH8xcjYjTFyNDHV3pOkiSp32q1QNsA2A/4yUrtQVFADVqXECml/wW2WantQ+tyTEmSpPVVqwXalRTzm02mWJNTkiRJbdJqgbYx8M953ny0nWEkSZLU+iCBy4ELsqyxaTvDSJIkqfUetKOAtwP/J8sav6dYi7Nbnjff3NfBJEmSBqpWC7RvlR+SJElqs1bX4rx2Vc9lWWPlpZ8kSZK0Dloq0LKsMRQ4H9iTP02pERTrcr4V2Lwt6SRJkgagVgcJXA00gF8ABwD/RbE+5tuA89oTTZIkaWBqtUA7GDgpz5vnAb8Fvp/nzeMoetXe065wkiRJA1GrBVoA88rt+yl6zgBupBjdKUmSpD7SaoF2DzCu3J4JHF5uv6XPE0mSJA1wrU6z8Q/A97OssQS4FjgnyxpzgGHAtHaFkyRJGoha6kHL8+YvgD8Hrsvz5kJgf+CrwKnAGe2LJ0mSNPC0eolz+b7Pl9tbA5sCT+d5c1mfp5IkSRrAWirQsqzx18DvgHdmWWME8HPgo8C/Z1njY23MJ0mSNOC02oN2KfCPwG3AycACiglqTwTOaU80SZKkganVAm03YFqeNxPwXuDmcvs3wA7tCidJkjQQtVqg/Q7YJ8saewOjgO+X7YcDj7chlyRJ0oDV6jQbVwIzgGXAXXne/K8sa1wAfA74cLvCSZIkDUStTrPxdWAMcAJwWNncBN6e580b2pRNkiRpQGq1B408b86kWEVg+eO72pJIkiRpgFuTedAkSZLUARZokiRJNWOBJkmSVDMWaJIkSTXT8iABSZLWFwvnL2DB9V1Vx+g2eJm/brVmavMTExGPA4uBV4FXUkr7R8TWwHRgJ4oJcY9LKS2qKqMkaf2wx8gdGDduVNUxuk2dOqvqCFrP1O0S51+mlEanlPYvH58L3JZS2oViHdBzq4smSZLUGXUr0FZ2NHBtuX0t8L4Ks0iSJHVEnQq0BPw4Iu6JiFPLtu1SSvPL7QXAdtVEkyRJ6pw6FWjvTCm9DTgS+GREvKvnkymlRFHErSAiTo2Iu4EZ06dP70xSSZKkNqpNgZZSmld+fhr4LvAO4KmIGApQfn66l9dNKu9Z+8DYsWM7mFiSJKk9alGgRcQbIuKNy7eBvwJmAbcAHy53+zDwvWoSSpIkdU5dptnYDvhuRECR6fqU0q0R8Svgxog4GXgCOK7CjJIkSR1RiwItpfQosE8v7QuBwzqfSJIkqTq1uMQpSZKkP7FAkyRJqhkLNEmSpJqxQJMkSaoZCzRJkqSasUCTJEmqGQs0SZKkmrFAkyRJqhkLNEmSpJqxQJMkSaoZCzRJkqSasUCTJEmqGQs0SZKkmrFAkyRJqhkLNEmSpJqxQJMkSaoZCzRJkqSasUCTJEmqGQs0SZKkmrFAkyRJqhkLNEmSpJqxQJMkSaoZCzRJkqSasUCTJEmqGQs0SZKkmrFAkyRJqhkLNEmSpJqpvECLiB0j4icRcX9EzI6Is8r2CRExLyJmlh9HVZ1VkiSpEzaoOgDwCvDplNKvI+KNwD0RkZfPfTmldEWF2SRJkjqu8gItpTQfmF9uL46IOcCwalNJkiRVp/JLnD1FxE7AvsAvy6bTI+LeiLg6IraqLJgkSVIH1aZAi4jNgBnAp1JKLwDfAN4CjKboYbtyFa87NSLuBmZMnz69U3ElSZLapvJLnAARMZiiOLsupfQdgJTSUz2enwx8v7fXppQmAZMiYtexY8fO7URerV9eXPQCM6+/reoYKxi8rBZvPUlSTVX+WyIiApgCzEkpfalH+9Dy/jSA9wOzqsin9d+IHbbknHOOqDrGCqZO9cdZkrRqlRdowEHAh4D7ImJm2XYecEJEjAYS8DjwsWriSZIkdVblBVpK6T+B6OWpH3Y6iyRJUh3UZpCAJEmSChZokiRJNWOBJkmSVDMWaJIkSTVjgSZJklQzFmiSJEk1Y4EmSZJUMxZokiRJNVP5RLXro8lTp7BoyfNVx+i21aZbcMq4k6uOIUmS+ogF2lpYtOR5tj9st6pjdFtwm2vES5LUn3iJU5IkqWYs0CRJkmrGAk2SJKlmLNAkSZJqxkECkiS12cyZ9/Pb33686hjdNtoogFFVx9BqWKBJktRmQ4duzjnnHFF1jG6XX35r1RH0OizQ+oFHH3yIs8/2LzOpXeo29yE4/6HU31mg9QObbrEJV37uqKpjdPMvM/U3dZv7EJz/UOrvHCQgSZJUMxZokiRJNWOBJkmSVDPegyZJ66G6DQ56ZdAGDH/LiKpjdHtm3pM4WEnrMws0SVoP1W1w0Kcn/rBWAykWXN9VdQRpnXiJU5IkqWbsQZMqMOvhJ/jiVV+qOkY359SSpHqxQJMq8OoGUa/LQc6ppXX04qIXmHn9bVXH6LZ08f9WHUFaJxZokqR1NmKHLV3KSOpDtS/QIuII4J+BQcC3UkpfqDiStM7q1tsweFnt/yuQpAGl1v8rR8Qg4GtABnQBv4qIW1JK91ebTFo3dettOPvsG52yYTXmP7WA7anPJWlJ/V+tCzTgHcDDKaVHASLi28DRgAWa1IeGDt28VgXjJ/7u2yx4qj7TJLz44tKqI0gaYOpeoA0DnuzxuAsYs4p9Bz/22GPtTwQ889Qz8NhmHTlXKxb9fhEPPlifX2bPPPOseV5H3TLVLc9mg5fx0eP3rjpGty989WcseGxe1TFW4Pt+9cyzenXLA/XLtGDB0zz44INtPcduu+22YUqp178AI6XU1pOvi4j4W+CIlNJHy8cfAsaklE7vsc+pwKkUc7p9D7ihiqxr6TjgxqpD9GCe11e3TOZZPfO8vrplMs/q1S0P1C9T3fKszuPra4F2IDAhpXR4+fizACmlf6o0WB+JiLtTSvtXnWM587y+umUyz+qZ5/XVLZN5Vq9ueaB+meqWZ23VfSWBXwG7RMSIiNgQOB64peJMkiRJbVXre9BSSq9ExOnAjyim2bg6pTS74liSJEltVesCDSCl9EPgh1XnaJNJVQdYiXleX90ymWf1zPP66pbJPKtXtzxQv0x1y7NWan0PmiRJ0kBU93vQJEmSBhwLtA6LiI0j4r8j4rcRMTsiLqo6E0BEPB4R90XEzIi4uwZ5toyIf4uIByJiTjmit8o8Z0XErPLf7FMVZbg6Ip6OiFk92raOiDwiHio/b1VxnmPL79GyiOjoKKpV5JkQEfPKn+uZEXFUlXnK9jPKn+vZEfHFKvNExOiIuGv5+z4i3tGpPKvJtE9E/KL8/+jfI2LzDubZMSJ+EhH3l/8+Z5Xtl5f/ZvdGxHcjYsuK81xcZpkZEXmrgx4AAAgOSURBVD+OiB0qzlPJ+2xVecrnKnmf9amUkh8d/AAC2KzcHgz8EjigBrkeB4ZUnaNHnmuBj5bbGwJbVphlFDAL2JTivs0msHMFOd4FvA2Y1aPti8C55fa5wGUV59kd2A34KbB/Db4/E4C/r+jnprc8f1n+/GxUPn5TxXl+DBxZbh8F/LQG36NfAYeU2x8BLu5gnqHA28rtNwIPAnsAfwVsULZf1qn32WrybN5jnzOBqyrOU8n7bDV5Knuf9eWHPWgdlgp/KB8OLj+8EbCHiNiC4j/uKQAppaUppecqjLQ78MuU0pKU0ivAz4BjOh0ipXQH8OxKzUdTFLOUn99XZZ6U0pyU0txOZXi9PFVaRZ6PA19IKf2x3OfpivMkYHkP1RbA7zqVZzWZdgXuKLdz4AMdzDM/pfTrcnsxMAcYllL6cfneB7gLGF5xnhd67PYGOvQ7ZFV5OnHuNcxT2fusL1mgVSAiBkXETOBpIE8p/bLqTBRv8B9HxD3l6gxVGgE8A/y/iPhNRHwrIt5QYZ5ZwMERsU1EbErR07BjhXl62i6lNL/cXgBsV2WYmjq9vBx0dScvAa/CrhQ/S7+MiJ9FxNsrzvMp4PKIeBK4AvhsxXkAZlP84QFwLBW91yJiJ2BfiqscPX0E+I+q80TEpeW/24nA56rOQ8Xvs5Xy1O19tlYs0CqQUno1pTSa4q+wd0TEqKozAe9MKb0NOBL4ZES8q8IsG1Bc9vhGSmlf4H8pLt9VIqU0h+Kyxo+BW4GZwKtV5VmVVPTl2xu7om8AbwFGA/OBK6uNwwbA1sABwDnAjRERFeb5OPB3KaUdgb+j7LWu2EeAT0TEPRSXrTq+Un1EbAbMAD7Vs7cqIs4HXgGuqzpPSun88t/tOuD01b2+A3kqfZ/1kqdu77O1YoFWofKy3U+AI2qQZV75+Wngu0BHbxZeSRfQ1aNn8d8oCrbKpJSmpJT2Sym9C1hEca9DHTwVEUMBys/rZVd+u6SUnir/IFoGTKban2sofra/U97q8N/AMmBIhXk+DHyn3L6J6r8/pJQeSCn9VUppP4q1lR/p5PkjYjDFL/vrUkrf6dE+HngPcGL5x1CleXq4jg5eBu4tT5Xvs1V8f+r2PlsrFmgdFhHbLh8BFBGbABnwQMWZ3hARb1y+TXFD7KzVv6p9UkoLgCcjYrey6TDg/qryAETEm8rPb6a4/+z6KvP0cAvFL1nKz9+rMEvtLC9eS++nwp/r0s0UNzATEbtSDID5fYV5fgccUm4fCjxUYRZghffanwEXAFd18NxB0Ys4J6X0pR7tRwCfAd6bUlpSgzy79NjtaDr0O2Q1eSp5n60qD/V7n60VJ6rtsIjYm+Jm7kEUBfKNKaWJFWcaSdFrBkXX8PUppUsrjEREjAa+RfHGehQ4KaW0qMI8Pwe2AV4Gzk4p3VZBhhuAd1P8JfgU8HmK/4huBN4MPAEcl1LqyI3yq8jzLPAVYFvgOWBmSunwCvO8m+KyS6IYqfyxHvfsVZFnGnB1mWkpxci32yvMMxf4Z4r3/UvAJ1JK93Qiz2oybQZ8stzlO8BnO9VjFRHvBH4O3EfR6wJwHvAvwEbAwrLtrpTSaRXmOZlitPQyivf9acuvglSU5wQqeJ+tJk+Tit5nfckCTZIkqWa8xClJklQzFmiSJEk1Y4EmSZJUMxZokiRJNWOBJkmSVDMbVB1AkvpKljV2Ah4Ddsnz5sN9cLyLKZZDejbPm3++0nPXABvkefP/rOK1XcAFed68Zl1zSBp4LNAkqRdZ1tiKYqLUjwE/7GWXszqbSNJAYoEmSb3bvPz8kzxvdq38ZJ43n+9wHkkDiAWapH4ryxpbUMwC/z7gRYqlsT6d583F5fPvASYCewB/BG4FTqFY+/Un5WEezLLGRXnenLDSsa+hxyXOLGt8jKLHbXPgCyvtuxfwNWA/YDHF+on/kOfNV/r2K5bUXzhIQFJ/djXFMkIHA39NsTzONQBZ1hhBscjyVcBbgWMp1qM8DbiTPy34fCBwxepOkmWNwymWTDoP+AvgAGBYj13+lWK9xL2A44APUSzXI0m9sgdNUr+UZY23UCzcPCTPm8+WbeOAx7OssSPF/39n5XlzUvmSx7Os0QT2zPPm0ixrPFO2/z7Pm394ndN9FPh2njenlec5Geh5WXQn4AfAE3nefDTLGkfyp3UdJek1LNAk9Ve7AwH8T5Y1Vn5u1zxv3pZljT9mWeN8YBSwZ/lxw1qcaw/gW8sf5Hnz91nWeLzH85dQXPY8Ncsa/0FRzHVsUXJJ6x8vcUrqrzYA/gCMXuljF+CuLGvsA9xPUZz9nOKS47fX4Xyx0uOXl2/kefNyYARwEbAt8L0sa0xYh3NJ6ufsQZPUX80FNgMG5XlzLkCWNXYGvkQxdcaHgP/K8+YJy1+QZY1dgIfW4lyzgLf3OM7mwMhye2PgMuCKPG9+BfhKljUuAE4EJqzFuSQNABZokvqlPG/OybLGrcC0LGucAbwEfIOiYJufZY2FwKgsa4wBnqUYHPB24H/W4nRfA5rlSM6fUfSUbVzmeCnLGu8E/jzLGp+l+H/3SMBLnJJWyUuckvqzD1H0iP2YonCaBxxdPvcvwH8BOcWozZ0oCqt91/Qked68AxgP/ANwd3me+3rsMpaiYLsL+E+K1Q7OWNPzSBo4IqVUdQZJkiT1YA+aJElSzVigSZIk1YwFmiRJUs1YoEmSJNWMBZokSVLNWKBJkiTVjAWaJElSzVigSZIk1YwFmiRJUs38fy9DGz95kbDSAAAAAElFTkSuQmCC\n", "text/plain": [ "
" ] @@ -4451,7 +5296,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 20, "metadata": {}, "outputs": [ { @@ -4486,93 +5331,93 @@ " \n", "
\n", " count\n", - " 78.0\n", - " 78.000000\n", - " 78.000000\n", - " 78.000000\n", - " 78.000000\n", - " 78.000000\n", + " 10.000000\n", + " 10.0\n", + " 10.0\n", + " 10.000000\n", + " 10.000000\n", + " 10.000000\n", "
\n", "
\n", " mean\n", - " 3.0\n", - " 0.666667\n", - " 0.512821\n", - " 28.013616\n", - " 0.846154\n", - " 8.075326\n", + " 1.500000\n", + " 1.0\n", + " 2.0\n", + " 31.989382\n", + " 10.100000\n", + " 0.500000\n", "
\n", "
\n", " std\n", + " 0.527046\n", " 0.0\n", - " 0.474456\n", - " 0.503071\n", - " 7.880295\n", - " 16.304789\n", - " 1.975028\n", + " 0.0\n", + " 4.096846\n", + " 17.110426\n", + " 1.581139\n", "
\n", "
\n", " min\n", - " 3.0\n", - " 0.000000\n", - " 0.000000\n", - " 12.000000\n", + " 1.000000\n", + " 1.0\n", + " 2.0\n", + " 29.699118\n", " -1.000000\n", - " 4.012500\n", + " 0.000000\n", "
\n", "
\n", " 25%\n", - " 3.0\n", - " 0.000000\n", - " 0.000000\n", - " 25.250000\n", + " 1.000000\n", + " 1.0\n", + " 2.0\n", + " 29.699118\n", " -1.000000\n", - " 7.229200\n", + " 0.000000\n", "
\n", "
\n", " 50%\n", - " 3.0\n", - " 1.000000\n", - " 1.000000\n", + " 1.500000\n", + " 1.0\n", + " 2.0\n", " 29.699118\n", " -1.000000\n", - " 7.750000\n", + " 0.000000\n", "
\n", "
\n", " 75%\n", - " 3.0\n", - " 1.000000\n", - " 1.000000\n", - " 29.699118\n", - " -1.000000\n", - " 7.750000\n", + " 2.000000\n", + " 1.0\n", + " 2.0\n", + " 32.174779\n", + " 14.750000\n", + " 0.000000\n", "
\n", "
\n", " max\n", - " 3.0\n", - " 1.000000\n", - " 1.000000\n", - " 70.500000\n", - " 143.000000\n", - " 14.458300\n", + " 2.000000\n", + " 1.0\n", + " 2.0\n", + " 40.000000\n", + " 46.000000\n", + " 5.000000\n", "
\n", " \n", "\n", "" ], "text/plain": [ - " Pclass Sex_label Embarked_label Age Cabin_label Fare\n", - "count 78.0 78.000000 78.000000 78.000000 78.000000 78.000000\n", - "mean 3.0 0.666667 0.512821 28.013616 0.846154 8.075326\n", - "std 0.0 0.474456 0.503071 7.880295 16.304789 1.975028\n", - "min 3.0 0.000000 0.000000 12.000000 -1.000000 4.012500\n", - "25% 3.0 0.000000 0.000000 25.250000 -1.000000 7.229200\n", - "50% 3.0 1.000000 1.000000 29.699118 -1.000000 7.750000\n", - "75% 3.0 1.000000 1.000000 29.699118 -1.000000 7.750000\n", - "max 3.0 1.000000 1.000000 70.500000 143.000000 14.458300" + " Pclass Sex_label Embarked_label Age Cabin_label Fare\n", + "count 10.000000 10.0 10.0 10.000000 10.000000 10.000000\n", + "mean 1.500000 1.0 2.0 31.989382 10.100000 0.500000\n", + "std 0.527046 0.0 0.0 4.096846 17.110426 1.581139\n", + "min 1.000000 1.0 2.0 29.699118 -1.000000 0.000000\n", + "25% 1.000000 1.0 2.0 29.699118 -1.000000 0.000000\n", + "50% 1.500000 1.0 2.0 29.699118 -1.000000 0.000000\n", + "75% 2.000000 1.0 2.0 32.174779 14.750000 0.000000\n", + "max 2.000000 1.0 2.0 40.000000 46.000000 5.000000" ] }, - "execution_count": 17, + "execution_count": 20, "metadata": {}, "output_type": "execute_result" } @@ -4583,8 +5428,10 @@ }, { "cell_type": "code", - "execution_count": 18, - "metadata": {}, + "execution_count": 27, + "metadata": { + "scrolled": true + }, "outputs": [ { "name": "stdout", @@ -4602,21 +5449,21 @@ ], "source": [ "x = X_train[features].iloc[10]\n", - "print(x)\n" + "print(x)" ] }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 28, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "22.5 <= Age \n", - "12.32 <= Fare < 52.28\n", - "Pclass in [] \n", + "Pclass < 2.5\n", + "15.5 <= Age \n", + "10.32 <= Fare < 52.28\n", "\n" ] } @@ -4650,9 +5497,9 @@ "\n", "X_train_reg, X_test_reg, y_train_reg, y_test_reg = train_test_split(dataset[features_reg], dataset[target_reg], test_size=0.2, random_state=42)\n", "\n", - "# categorical_feature=[\"Pclass\", \"Survived\", \"Sex_label\", \"Embarked_label\", \"Cabin_label\"]\n", - "train_data_reg = lgb.Dataset(data=X_train_reg, label=y_train_reg, categorical_feature=[\"Pclass\", \"Survived\", \"Sex_label\"])\n", - "valid_data_reg = lgb.Dataset(data=X_test_reg, label=y_test_reg, categorical_feature=[\"Pclass\", \"Survived\", \"Sex_label\"])\n", + "categorical_feature=[\"Pclass\", \"Survived\", \"Sex_label\"]\n", + "train_data_reg = lgb.Dataset(data=X_train_reg, label=y_train_reg)\n", + "valid_data_reg = lgb.Dataset(data=X_test_reg, label=y_test_reg)\n", "\n", "lgbm_params = {\n", " 'num_tree':10,\n", @@ -4676,61 +5523,37 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 43, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000979 seconds.\n", + "[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000825 seconds.\n", "You can set `force_row_wise=true` to remove the overhead.\n", "And if memory is not enough, you can set `force_col_wise=true`.\n", "[LightGBM] [Info] Total Bins 65\n", "[LightGBM] [Info] Number of data points in the train set: 712, number of used features: 5\n", "[LightGBM] [Info] Start training from score 29.699118\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", - "[1]\ttraining's l1: 9.04215\tvalid_1's l1: 9.06466\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", - "[2]\ttraining's l1: 9.009\tvalid_1's l1: 9.03808\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", - "[3]\ttraining's l1: 8.97758\tvalid_1's l1: 9.02326\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", - "[4]\ttraining's l1: 8.97783\tvalid_1's l1: 9.02642\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", - "[5]\ttraining's l1: 8.95896\tvalid_1's l1: 9.01841\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", - "[6]\ttraining's l1: 8.93382\tvalid_1's l1: 9.00164\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", - "[7]\ttraining's l1: 8.90782\tvalid_1's l1: 8.98947\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n", - "[8]\ttraining's l1: 29.438\tvalid_1's l1: 8.98947\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n", - "[9]\ttraining's l1: 29.4549\tvalid_1's l1: 8.98947\n", "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n", - "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n", - "[10]\ttraining's l1: 29.4847\tvalid_1's l1: 8.98947\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/tudorl/Documents/workspace/personal/dtreeviz/venv/lib/python3.7/site-packages/lightgbm/engine.py:151: UserWarning: Found `num_tree` in params. Will use it instead of argument\n", - " warnings.warn(\"Found `{}` in params. Will use it instead of argument\".format(alias))\n", - "/Users/tudorl/Documents/workspace/personal/dtreeviz/venv/lib/python3.7/site-packages/lightgbm/basic.py:1551: UserWarning: Using categorical_feature in Dataset.\n", - " warnings.warn('Using categorical_feature in Dataset.')\n", - "/Users/tudorl/Documents/workspace/personal/dtreeviz/venv/lib/python3.7/site-packages/lightgbm/basic.py:1286: UserWarning: Overriding the parameters from Reference Dataset.\n", - " warnings.warn('Overriding the parameters from Reference Dataset.')\n", - "/Users/tudorl/Documents/workspace/personal/dtreeviz/venv/lib/python3.7/site-packages/lightgbm/basic.py:1098: UserWarning: categorical_column in param dict is overridden.\n", - " warnings.warn('{} in param dict is overridden.'.format(cat_alias))\n" + "[LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements\n" ] } ], "source": [ - "lgbm_reg_model = lgb.train(lgbm_params, train_data_reg, valid_sets=[train_data_reg, valid_data_reg])" + "lgbm_reg_model = lgb.train(lgbm_params, train_data_reg, valid_sets=[train_data_reg, valid_data_reg], categorical_feature=categorical_feature, verbose_eval=False)" ] }, { @@ -4742,16 +5565,16 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 43, + "execution_count": 19, "metadata": {}, "output_type": "execute_result" }, @@ -4774,7 +5597,7 @@ }, { "cell_type": "code", - "execution_count": 44, + "execution_count": 20, "metadata": { "scrolled": false }, @@ -5125,10 +5948,10 @@ "
\n" ], "text/plain": [ - "" + "" ] }, - "execution_count": 44, + "execution_count": 20, "metadata": {}, "output_type": "execute_result" } @@ -5146,7 +5969,7 @@ }, { "cell_type": "code", - "execution_count": 45, + "execution_count": 21, "metadata": {}, "outputs": [], "source": [ @@ -5155,7 +5978,7 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 22, "metadata": {}, "outputs": [ { @@ -5185,96 +6008,96 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -5284,10 +6107,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5305,7 +6128,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5352,10 +6175,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5373,7 +6196,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5390,7 +6213,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5406,13 +6229,13 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5423,7 +6246,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5449,40 +6272,40 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -5490,10 +6313,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5511,7 +6334,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5528,7 +6351,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5544,7 +6367,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5587,7 +6410,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5619,65 +6442,65 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -5685,10 +6508,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5706,7 +6529,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5723,7 +6546,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5739,7 +6562,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5784,7 +6607,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5816,31 +6639,31 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -5848,10 +6671,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5869,7 +6692,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5886,7 +6709,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5902,7 +6725,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5943,7 +6766,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -5969,118 +6792,118 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -6090,10 +6913,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6111,7 +6934,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6130,7 +6953,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6175,10 +6998,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6196,7 +7019,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6210,7 +7033,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6226,13 +7049,13 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6243,7 +7066,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6274,63 +7097,63 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -6340,10 +7163,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6361,7 +7184,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6380,7 +7203,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6426,10 +7249,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6446,7 +7269,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6463,7 +7286,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6479,13 +7302,13 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6496,7 +7319,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6529,29 +7352,29 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -6559,10 +7382,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6580,7 +7403,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6597,7 +7420,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6613,7 +7436,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6657,7 +7480,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6689,43 +7512,43 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -6733,10 +7556,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6754,7 +7577,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6771,7 +7594,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6787,7 +7610,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6830,7 +7653,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -6861,172 +7684,172 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -7036,10 +7859,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7061,7 +7884,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7082,7 +7905,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7130,10 +7953,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7150,7 +7973,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7164,7 +7987,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7177,13 +8000,13 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7194,7 +8017,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7231,558 +8054,558 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " 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\n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -7792,10 +8615,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7813,7 +8636,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7829,7 +8652,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7878,10 +8701,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7898,7 +8721,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7912,7 +8735,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7928,13 +8751,13 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7945,7 +8768,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -7971,94 +8794,94 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " 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\n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8074,7 +8897,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8115,10 +8938,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8136,7 +8959,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8150,7 +8973,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8166,10 +8989,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8180,7 +9003,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8205,490 +9028,490 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " 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\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -8698,10 +9521,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8723,7 +9546,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8741,7 +9564,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8792,10 +9615,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8809,7 +9632,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8823,7 +9646,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8839,13 +9662,13 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8856,7 +9679,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8883,53 +9706,53 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -8937,10 +9760,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8958,7 +9781,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8975,7 +9798,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -8991,7 +9814,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9035,7 +9858,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9067,33 +9890,33 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -9101,10 +9924,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9122,7 +9945,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9139,7 +9962,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9155,7 +9978,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9199,7 +10022,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9230,84 +10053,84 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9323,7 +10146,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9364,10 +10187,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9385,7 +10208,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9399,7 +10222,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9415,10 +10238,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9429,7 +10252,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9454,449 +10277,449 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " 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\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9912,7 +10735,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9928,7 +10751,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9964,10 +10787,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -9984,7 +10807,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -10001,7 +10824,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -10017,10 +10840,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -10031,7 +10854,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -10058,43 +10881,43 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -10102,10 +10925,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -10123,7 +10946,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -10140,7 +10963,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -10156,7 +10979,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -10199,7 +11022,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -10231,33 +11054,33 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -10265,10 +11088,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -10286,7 +11109,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -10303,7 +11126,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -10319,7 +11142,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -10363,7 +11186,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -10395,300 +11218,300 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " 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\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -10696,10 +11519,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -10717,7 +11540,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -10734,7 +11557,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -10750,7 +11573,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -10796,7 +11619,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -10828,141 +11651,141 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - 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\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -10970,10 +11793,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -10991,7 +11814,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -11008,7 +11831,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", 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\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -11872,7 +12695,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -11888,7 +12711,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -11924,10 +12747,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -11944,7 +12767,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -11961,7 +12784,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -11990,10 +12813,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -12004,7 +12827,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -12032,10 +12855,10 @@ "" ], "text/plain": [ - "" + "" ] }, - "execution_count": 46, + "execution_count": 22, "metadata": {}, "output_type": "execute_result" } @@ -12046,7 +12869,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 23, "metadata": {}, "outputs": [ { @@ -12068,7 +12891,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 24, "metadata": {}, "outputs": [ { @@ -12180,7 +13003,7 @@ "max 3.000000 1.000000 1.000000 145.000000 2.000000" ] }, - "execution_count": 28, + "execution_count": 24, "metadata": {}, "output_type": "execute_result" } @@ -12191,7 +13014,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 25, "metadata": {}, "outputs": [ { @@ -12213,7 +13036,7 @@ }, { "cell_type": "code", - "execution_count": 45, + "execution_count": 26, "metadata": {}, "outputs": [ { @@ -12227,7 +13050,7 @@ "Name: 762, dtype: int64" ] }, - "execution_count": 45, + "execution_count": 26, "metadata": {}, "output_type": "execute_result" } @@ -12239,7 +13062,7 @@ }, { "cell_type": "code", - "execution_count": 51, + "execution_count": 27, "metadata": {}, "outputs": [ { @@ -12272,84 +13095,84 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -12359,10 +13182,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -12378,7 +13201,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -12419,10 +13242,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -12440,7 +13263,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -12454,7 +13277,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ 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+ " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -12563,10 +13386,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -12584,7 +13407,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -12601,7 +13424,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -12617,7 +13440,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -12661,7 +13484,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -12692,558 +13515,558 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - 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\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -13256,10 +14079,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -13277,7 +14100,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -13293,7 +14116,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -13342,10 +14165,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -13362,7 +14185,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -13376,7 +14199,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -13392,13 +14215,13 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -13409,7 +14232,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -13440,728 +14263,728 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " 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\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -14171,10 +14994,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -14190,7 +15013,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -14206,7 +15029,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -14242,10 +15065,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -14262,7 +15085,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -14279,7 +15102,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -14308,10 +15131,10 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -14322,7 +15145,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -14370,10 +15193,10 @@ "" ], "text/plain": [ - "" + "" ] }, - "execution_count": 51, + "execution_count": 27, "metadata": {}, "output_type": "execute_result" } @@ -14384,7 +15207,7 @@ }, { "cell_type": "code", - "execution_count": 52, + "execution_count": 28, "metadata": {}, "outputs": [ { From 78d89d9c72f7f5c1b80d3280a9399213b7bd380e Mon Sep 17 00:00:00 2001 From: tlapusan Date: Sun, 7 Feb 2021 17:29:57 +0200 Subject: [PATCH 16/16] Remove tree_index from spark shadow tree initialization --- dtreeviz/models/shadow_decision_tree.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/dtreeviz/models/shadow_decision_tree.py b/dtreeviz/models/shadow_decision_tree.py index c7522c94..c5761f12 100644 --- a/dtreeviz/models/shadow_decision_tree.py +++ b/dtreeviz/models/shadow_decision_tree.py @@ -464,7 +464,7 @@ def get_shadow_tree(tree_model, x_data, y_data, feature_names, target_name, clas elif (str(type(tree_model)).endswith("pyspark.ml.classification.DecisionTreeClassificationModel'>") or str(type(tree_model)).endswith("pyspark.ml.classification.DecisionTreeClassificationModel'>")): from dtreeviz.models import spark_decision_tree - return spark_decision_tree.ShadowSparkTree(tree_model, tree_index, x_data, y_data, + return spark_decision_tree.ShadowSparkTree(tree_model, x_data, y_data, feature_names, target_name, class_names) elif "lightgbm.basic.Booster" in str(type(tree_model)): from dtreeviz.models import lightgbm_decision_tree