Experiment_TD.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "colab_type": "code",
    "id": "b-3bwn2hNwag",
    "outputId": "8cd00148-d87f-4961-8005-61471c447568"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting zeugma\n",
      "  Downloading https://files.pythonhosted.org/packages/37/06/987f0c591e4f46fc31446d541d17d63981394f099c455955680dfe7bd980/zeugma-0.46.tar.gz\n",
      "Requirement already satisfied: numpy>=1.13.3 in /usr/local/lib/python3.6/dist-packages (from zeugma) (1.17.3)\n",
      "Requirement already satisfied: Cython>=0.27.3 in /usr/local/lib/python3.6/dist-packages (from zeugma) (0.29.13)\n",
      "Requirement already satisfied: pandas>=0.20.3 in /usr/local/lib/python3.6/dist-packages (from zeugma) (0.25.3)\n",
      "Requirement already satisfied: gensim>=3.5.0 in /usr/local/lib/python3.6/dist-packages (from zeugma) (3.6.0)\n",
      "Requirement already satisfied: scikit_learn>=0.19.1 in /usr/local/lib/python3.6/dist-packages (from zeugma) (0.21.3)\n",
      "Requirement already satisfied: tensorflow>=1.5.0 in /usr/local/lib/python3.6/dist-packages (from zeugma) (1.15.0)\n",
      "Requirement already satisfied: keras>=2.1.3 in /usr/local/lib/python3.6/dist-packages (from zeugma) (2.2.5)\n",
      "Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.6/dist-packages (from pandas>=0.20.3->zeugma) (2018.9)\n",
      "Requirement already satisfied: python-dateutil>=2.6.1 in /usr/local/lib/python3.6/dist-packages (from pandas>=0.20.3->zeugma) (2.6.1)\n",
      "Requirement already satisfied: six>=1.5.0 in /usr/local/lib/python3.6/dist-packages (from gensim>=3.5.0->zeugma) (1.12.0)\n",
      "Requirement already satisfied: scipy>=0.18.1 in /usr/local/lib/python3.6/dist-packages (from gensim>=3.5.0->zeugma) (1.3.1)\n",
      "Requirement already satisfied: smart-open>=1.2.1 in /usr/local/lib/python3.6/dist-packages (from gensim>=3.5.0->zeugma) (1.8.4)\n",
      "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.6/dist-packages (from scikit_learn>=0.19.1->zeugma) (0.14.0)\n",
      "Requirement already satisfied: tensorflow-estimator==1.15.1 in /usr/local/lib/python3.6/dist-packages (from tensorflow>=1.5.0->zeugma) (1.15.1)\n",
      "Requirement already satisfied: gast==0.2.2 in /usr/local/lib/python3.6/dist-packages (from tensorflow>=1.5.0->zeugma) (0.2.2)\n",
      "Requirement already satisfied: termcolor>=1.1.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow>=1.5.0->zeugma) (1.1.0)\n",
      "Requirement already satisfied: wrapt>=1.11.1 in /usr/local/lib/python3.6/dist-packages (from tensorflow>=1.5.0->zeugma) (1.11.2)\n",
      "Requirement already satisfied: grpcio>=1.8.6 in /usr/local/lib/python3.6/dist-packages (from tensorflow>=1.5.0->zeugma) (1.15.0)\n",
      "Requirement already satisfied: opt-einsum>=2.3.2 in /usr/local/lib/python3.6/dist-packages (from tensorflow>=1.5.0->zeugma) (3.1.0)\n",
      "Requirement already satisfied: keras-applications>=1.0.8 in /usr/local/lib/python3.6/dist-packages (from tensorflow>=1.5.0->zeugma) (1.0.8)\n",
      "Requirement already satisfied: protobuf>=3.6.1 in /usr/local/lib/python3.6/dist-packages (from tensorflow>=1.5.0->zeugma) (3.10.0)\n",
      "Requirement already satisfied: tensorboard<1.16.0,>=1.15.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow>=1.5.0->zeugma) (1.15.0)\n",
      "Requirement already satisfied: absl-py>=0.7.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow>=1.5.0->zeugma) (0.8.1)\n",
      "Requirement already satisfied: google-pasta>=0.1.6 in /usr/local/lib/python3.6/dist-packages (from tensorflow>=1.5.0->zeugma) (0.1.7)\n",
      "Requirement already satisfied: astor>=0.6.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow>=1.5.0->zeugma) (0.8.0)\n",
      "Requirement already satisfied: wheel>=0.26 in /usr/local/lib/python3.6/dist-packages (from tensorflow>=1.5.0->zeugma) (0.33.6)\n",
      "Requirement already satisfied: keras-preprocessing>=1.0.5 in /usr/local/lib/python3.6/dist-packages (from tensorflow>=1.5.0->zeugma) (1.1.0)\n",
      "Requirement already satisfied: pyyaml in /usr/local/lib/python3.6/dist-packages (from keras>=2.1.3->zeugma) (3.13)\n",
      "Requirement already satisfied: h5py in /usr/local/lib/python3.6/dist-packages (from keras>=2.1.3->zeugma) (2.8.0)\n",
      "Requirement already satisfied: boto>=2.32 in /usr/local/lib/python3.6/dist-packages (from smart-open>=1.2.1->gensim>=3.5.0->zeugma) (2.49.0)\n",
      "Requirement already satisfied: requests in /usr/local/lib/python3.6/dist-packages (from smart-open>=1.2.1->gensim>=3.5.0->zeugma) (2.21.0)\n",
      "Requirement already satisfied: boto3 in /usr/local/lib/python3.6/dist-packages (from smart-open>=1.2.1->gensim>=3.5.0->zeugma) (1.10.7)\n",
      "Requirement already satisfied: setuptools in /usr/local/lib/python3.6/dist-packages (from protobuf>=3.6.1->tensorflow>=1.5.0->zeugma) (41.4.0)\n",
      "Requirement already satisfied: markdown>=2.6.8 in /usr/local/lib/python3.6/dist-packages (from tensorboard<1.16.0,>=1.15.0->tensorflow>=1.5.0->zeugma) (3.1.1)\n",
      "Requirement already satisfied: werkzeug>=0.11.15 in /usr/local/lib/python3.6/dist-packages (from tensorboard<1.16.0,>=1.15.0->tensorflow>=1.5.0->zeugma) (0.16.0)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.6/dist-packages (from requests->smart-open>=1.2.1->gensim>=3.5.0->zeugma) (2019.9.11)\n",
      "Requirement already satisfied: chardet<3.1.0,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from requests->smart-open>=1.2.1->gensim>=3.5.0->zeugma) (3.0.4)\n",
      "Requirement already satisfied: idna<2.9,>=2.5 in /usr/local/lib/python3.6/dist-packages (from requests->smart-open>=1.2.1->gensim>=3.5.0->zeugma) (2.8)\n",
      "Requirement already satisfied: urllib3<1.25,>=1.21.1 in /usr/local/lib/python3.6/dist-packages (from requests->smart-open>=1.2.1->gensim>=3.5.0->zeugma) (1.24.3)\n",
      "Requirement already satisfied: s3transfer<0.3.0,>=0.2.0 in /usr/local/lib/python3.6/dist-packages (from boto3->smart-open>=1.2.1->gensim>=3.5.0->zeugma) (0.2.1)\n",
      "Requirement already satisfied: jmespath<1.0.0,>=0.7.1 in /usr/local/lib/python3.6/dist-packages (from boto3->smart-open>=1.2.1->gensim>=3.5.0->zeugma) (0.9.4)\n",
      "Requirement already satisfied: botocore<1.14.0,>=1.13.7 in /usr/local/lib/python3.6/dist-packages (from boto3->smart-open>=1.2.1->gensim>=3.5.0->zeugma) (1.13.7)\n",
      "Requirement already satisfied: docutils<0.16,>=0.10 in /usr/local/lib/python3.6/dist-packages (from botocore<1.14.0,>=1.13.7->boto3->smart-open>=1.2.1->gensim>=3.5.0->zeugma) (0.15.2)\n",
      "Building wheels for collected packages: zeugma\n",
      "  Building wheel for zeugma (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
      "  Created wheel for zeugma: filename=zeugma-0.46-cp36-none-any.whl size=8611 sha256=2f10ecd92acfe6e51e1adab8d33f7f8d0e3e88e38404a75be612b121d1354cb0\n",
      "  Stored in directory: /root/.cache/pip/wheels/49/ce/d3/22bc15de9112558b220d9dba3bfcd7d9ad0d8cc4d44d3e7813\n",
      "Successfully built zeugma\n",
      "Installing collected packages: zeugma\n",
      "Successfully installed zeugma-0.46\n",
      "Collecting deslib\n",
      "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/4b/36/89c48ec8fbb01c41d9262453141c9aabdc243acc896e46070fedc973a08a/DESlib-0.3-py3-none-any.whl (152kB)\n",
      "\u001b[K     |████████████████████████████████| 153kB 2.8MB/s \n",
      "\u001b[?25hRequirement already satisfied: scikit-learn>=0.19.0 in /usr/local/lib/python3.6/dist-packages (from deslib) (0.21.3)\n",
      "Requirement already satisfied: numpy>=1.10.4 in /usr/local/lib/python3.6/dist-packages (from deslib) (1.17.3)\n",
      "Requirement already satisfied: scipy>=1.2.0 in /usr/local/lib/python3.6/dist-packages (from deslib) (1.3.1)\n",
      "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.6/dist-packages (from scikit-learn>=0.19.0->deslib) (0.14.0)\n",
      "Installing collected packages: deslib\n",
      "Successfully installed deslib-0.3\n"
     ]
    }
   ],
   "source": [
    "!pip install zeugma\n",
    "!pip install deslib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 97
    },
    "colab_type": "code",
    "id": "N7b-yuJONlIA",
    "outputId": "0400b028-dc63-44fa-c0cc-4ce15dc65f1d"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<p style=\"color: red;\">\n",
       "The default version of TensorFlow in Colab will soon switch to TensorFlow 2.x.<br>\n",
       "We recommend you <a href=\"https://www.tensorflow.org/guide/migrate\" target=\"_blank\">upgrade</a> now \n",
       "or ensure your notebook will continue to use TensorFlow 1.x via the <code>%tensorflow_version 1.x</code> magic:\n",
       "<a href=\"https://colab.research.google.com/notebooks/tensorflow_version.ipynb\" target=\"_blank\">more info</a>.</p>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.metrics import accuracy_score, f1_score\n",
    "from sklearn.model_selection import ParameterGrid\n",
    "from sklearn.pipeline import Pipeline\n",
    "from zeugma.embeddings import EmbeddingTransformer\n",
    "from deslib.util.diversity import double_fault\n",
    "from sklearn.manifold import TSNE\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "cKSFfy25MDTB"
   },
   "outputs": [],
   "source": [
    "from keras.models import Sequential\n",
    "from keras.layers import *\n",
    "from keras.layers.embeddings import Embedding\n",
    "from keras.preprocessing.text import Tokenizer\n",
    "from keras.preprocessing.sequence import pad_sequences\n",
    "from keras.utils import to_categorical"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 88
    },
    "colab_type": "code",
    "id": "yiaZR6Ws9ftQ",
    "outputId": "ac43f782-9cbf-4889-9db6-cc66f3a9f9bc"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow_core/python/compat/v2_compat.py:68: disable_resource_variables (from tensorflow.python.ops.variable_scope) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "non-resource variables are not supported in the long term\n"
     ]
    }
   ],
   "source": [
    "import tensorflow.compat.v1 as tf\n",
    "tf.disable_v2_behavior()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "0FTJfofRN_OG"
   },
   "source": [
    "# Extractors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "pd3GfZlBOA4B"
   },
   "outputs": [],
   "source": [
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from preprocessing import *\n",
    "from gensim.models.word2vec import Word2Vec\n",
    "import matplotlib.lines as mlines\n",
    "from matplotlib_venn import venn3_unweighted, venn3, venn3_circles, venn2, venn2_unweighted, venn2_circles\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "BvellogVODEe"
   },
   "source": [
    "# Classifiers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "dATufhhOOEFZ"
   },
   "outputs": [],
   "source": [
    "from sklearn.svm import SVC\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.naive_bayes import MultinomialNB, BernoulliNB\n",
    "from sklearn.ensemble import RandomForestClassifier, ExtraTreesClassifier\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from deslib.util.aggregation import *"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "L010307gOO0t"
   },
   "source": [
    "# Embedding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 122
    },
    "colab_type": "code",
    "id": "3AFZlSL1OPx4",
    "outputId": "914ea17b-8a07-4611-9f59-63e8e2dd5a17"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[==================================================] 100.0% 1662.8/1662.8MB downloaded\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.6/dist-packages/smart_open/smart_open_lib.py:398: UserWarning: This function is deprecated, use smart_open.open instead. See the migration notes for details: https://github.com/RaRe-Technologies/smart_open/blob/master/README.rst#migrating-to-the-new-open-function\n",
      "  'See the migration notes for details: %s' % _MIGRATION_NOTES_URL\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[==================================================] 100.0% 104.8/104.8MB downloaded\n",
      "[==================================================] 100.0% 958.5/958.4MB downloaded\n"
     ]
    }
   ],
   "source": [
    "w2v = EmbeddingTransformer('word2vec')\n",
    "glove = EmbeddingTransformer('glove')\n",
    "fasttext = EmbeddingTransformer('fasttext')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "zKHIlsLgOc9V"
   },
   "outputs": [],
   "source": [
    "uri_train = 'https://raw.githubusercontent.com/wvs2/data-hate/master/tdavidson/train.csv'\n",
    "uri_val   = 'https://raw.githubusercontent.com/wvs2/data-hate/master/tdavidson/val.csv'\n",
    "uri_teste = 'https://raw.githubusercontent.com/wvs2/data-hate/master/tdavidson/test.csv'\n",
    "\n",
    "df_train = pd.read_table(uri_train, sep=',')\n",
    "df_val   = pd.read_table(uri_val, sep=',')\n",
    "df_test = pd.read_table(uri_teste, sep=',')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "4NB0PUyNd-XP"
   },
   "outputs": [],
   "source": [
    "train = df_train['text'].fillna(' ').apply(pre_processing)\n",
    "val = df_val['text'].fillna(' ').apply(pre_processing)\n",
    "test, class_test = df_test['text'].fillna(' ').apply(pre_processing), df_test['class']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 51
    },
    "colab_type": "code",
    "id": "HzPj6FyvBqFq",
    "outputId": "2d39dbd3-6463-4a32-9b47-6b00d9b6a696"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<16604x15777 sparse matrix of type '<class 'numpy.float64'>'\n",
       "\twith 111010 stored elements in Compressed Sparse Row format>"
      ]
     },
     "execution_count": 10,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cv = CountVectorizer(analyzer='word', lowercase=True, stop_words='english')\n",
    "cv.fit_transform(train.values.astype('U'))\n",
    "     \n",
    "tfidf =  TfidfVectorizer(analyzer='word', lowercase=True, use_idf=True, stop_words='english')\n",
    "tfidf.fit_transform(train.values.astype('U'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "AyJLkXuHeDm2"
   },
   "source": [
    "# Funções"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "EIIXIL-ceEk4"
   },
   "outputs": [],
   "source": [
    "def calc_delta(labels = {}, pred = {}, clfs=[]):\n",
    "    size = len(clfs)\n",
    "    delta = np.zeros(shape=(size, size))\n",
    "    for i, clf1 in enumerate(clfs):\n",
    "        for k, clf2 in enumerate(clfs):\n",
    "            delta[i][k] = double_fault(labels, pred[clf1], pred[clf2])\n",
    "    return delta, clfs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "mZel_M2HeHV5"
   },
   "outputs": [],
   "source": [
    "def gera_tsne(delta=[], title='', classifiers=[], size=5, escala=0, x_ini=0, y_ini=0,x_fim=0,y_fim=0, legend=False):\n",
    "    simbolos = {}\n",
    "    simbolos[0] = 'X'\n",
    "    simbolos[1] = 'd'\n",
    "    simbolos[2] = '*'\n",
    "    simbolos[3] = \"^\"\n",
    "    simbolos[4] = 'o'\n",
    "    \n",
    "    tsne_model = TSNE(perplexity=50, init='pca', n_iter=2500, random_state=23)\n",
    "    new_values = tsne_model.fit_transform(delta)\n",
    "    \n",
    "    x = []\n",
    "    y = []\n",
    "    for value in new_values:\n",
    "        x.append(value[0])\n",
    "        y.append(value[1])\n",
    "\n",
    "    plt.figure(figsize=(size, size))\n",
    "    plt.title(title)\n",
    "    if escala > 0:\n",
    "        plt.ylim(escala * -1, escala)\n",
    "        plt.xlim(escala * -1, escala)\n",
    "    else:\n",
    "        plt.ylim(y_ini, y_fim)\n",
    "        plt.xlim(x_ini, x_fim)\n",
    "    \n",
    "    dot = 0\n",
    "    for i in range(len(x)):\n",
    "        plt.scatter(x[i], y[i], marker=simbolos[dot], label=classifiers[i], s=100)        \n",
    "        dot = dot+1\n",
    "    if legend:\n",
    "        plt.legend()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "ao4G4qqXSxdx"
   },
   "outputs": [],
   "source": [
    "def get_classifier(clf, statement, label, ext):\n",
    "    pipe_clf = Pipeline([\n",
    "        ('extractor', ext),\n",
    "        ('clf', clf)\n",
    "    ])\n",
    "    pipe_clf.fit(statement, label)\n",
    "    return pipe_clf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "6oI9hmsaGYyA"
   },
   "outputs": [],
   "source": [
    "def get_CNN(ext, tokenizer, MAX_NB_WORDS, EMBEDDING_DIM=300, MAX_SEQUENCE_LENGTH=300, activation='sigmoid', word_embedding=False):\n",
    "    if word_embedding==False:\n",
    "        X_ext = ext.get_feature_names()\n",
    "        model = Word2Vec([X_ext], min_count=1, workers=1, size=300)\n",
    "    else:\n",
    "        model = ext.model\n",
    "\n",
    "    word_index = tokenizer.word_index\n",
    "    \n",
    "    nb_words = min(MAX_NB_WORDS, len(word_index))+1\n",
    "\n",
    "    embedding_matrix = np.zeros((nb_words, EMBEDDING_DIM))\n",
    "    for word, i in word_index.items():\n",
    "        if word in model.wv.vocab:\n",
    "            embedding_matrix[i] = model.wv.word_vec(word)\n",
    "\n",
    "    embedding_layer = Embedding(nb_words,\n",
    "        EMBEDDING_DIM,\n",
    "        weights=[embedding_matrix],\n",
    "        input_length=MAX_SEQUENCE_LENGTH,\n",
    "        trainable=False\n",
    "    )\n",
    "    cnn = Sequential()\n",
    "    cnn.add(embedding_layer)\n",
    "    cnn.add(Dropout(0.2))\n",
    "    cnn.add(Conv1D(64, 3, padding='valid', activation='relu', strides=1))\n",
    "    cnn.add(GlobalMaxPooling1D())\n",
    "    cnn.add(Dense(256))\n",
    "    cnn.add(Dropout(0.2))\n",
    "    cnn.add(Activation('relu'))\n",
    "    cnn.add(Dense(3))\n",
    "    cnn.add(Activation(activation))\n",
    "    cnn.summary()\n",
    "    cnn.compile(loss='categorical_crossentropy', optimizer='rmsprop', metrics=['accuracy'])\n",
    "\n",
    "    return cnn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Dy6vRjn0qWp4"
   },
   "outputs": [],
   "source": [
    "def get_oracle(labels, pred={}, clfs=[]):\n",
    "    cont = 0;\n",
    "    list_yes = np.zeros((len(labels)))\n",
    "    for i, k in enumerate(labels):\n",
    "        acertou = False\n",
    "        for clf in clfs:\n",
    "            if pred[clf][i] == k:\n",
    "                acertou = True\n",
    "                break\n",
    "        if acertou:\n",
    "            cont += 1\n",
    "            list_yes[i] = k\n",
    "        else:\n",
    "            if k == 0:\n",
    "                list_yes[i] = 1\n",
    "            else:\n",
    "                list_yes[i] = 0\n",
    "    return cont, list_yes\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "9NSBiaYxt7YV"
   },
   "outputs": [],
   "source": [
    "def tsne_full(labels,  delta , title='' , escala=0, size=5, x_ini=0, x_fim=0, y_ini=0, y_fim=0, espacamento=50, per=50 , lern=200.0, iterations=2500, ang=0.5, size_dot=10, cores={}, simbolos={}):\n",
    "    tsne_model = TSNE(init='pca', early_exaggeration=espacamento, perplexity=per, learning_rate=lern, random_state=42, n_iter=iterations, angle=ang)\n",
    "    new_values = tsne_model.fit_transform(delta)\n",
    "\n",
    "    x = []\n",
    "    y = []\n",
    "    for value in new_values:\n",
    "        x.append(value[0])\n",
    "        y.append(value[1])\n",
    "\n",
    "    plt.figure(figsize=(size, size))\n",
    "    plt.title(title)\n",
    "\n",
    "    if escala > 0:\n",
    "        plt.ylim(escala * -1, escala)\n",
    "        plt.xlim(escala * -1, escala)\n",
    "    else:\n",
    "        plt.ylim(y_ini, y_fim)\n",
    "        plt.xlim(x_ini, x_fim)\n",
    "    \n",
    "    dot = 0\n",
    "    for i in range(len(x)):\n",
    "        plt.scatter(x[i], y[i], s=size_dot,  marker=simbolos[dot], c=cores[dot])        \n",
    "        \n",
    "        if labels[i] == 'SVM-CV' and i==40:\n",
    "            posicao=(5, -20)\n",
    "        elif labels[i] == 'MNB-GL':\n",
    "            posicao=(5, 25)\n",
    "        elif i == 56:\n",
    "            posicao=(5, 25)\n",
    "        else:  \n",
    "            posicao=(5, 8)\n",
    "\n",
    "\n",
    "        plt.annotate(labels[i], xy=(x[i], y[i]), xytext=posicao, textcoords='offset points', ha='center', va='bottom')  \n",
    "        dot = dot+1\n",
    "        if dot == 5:\n",
    "            dot = 0\n",
    "\n",
    "    \n",
    "\n",
    "    m1 = mlines.Line2D([], [], color=cores[0], marker=simbolos[0], linestyle='None', markersize=10, label='CV')\n",
    "    m2 = mlines.Line2D([], [], color=cores[1], marker=simbolos[1], linestyle='None', markersize=10, label='TFIDF')\n",
    "    m3 = mlines.Line2D([], [], color=cores[2], marker=simbolos[2], linestyle='None', markersize=10, label='Glove')\n",
    "    m4 = mlines.Line2D([], [], color=cores[3], marker=simbolos[3], linestyle='None', markersize=10, label='Word2Vec')\n",
    "    m5 = mlines.Line2D([], [], color=cores[4], marker=simbolos[4], linestyle='None', markersize=10, label='FastText')\n",
    "    \n",
    "\n",
    "    plt.legend(handles=[m1, m2, m3, m4, m5])\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "5xtxw46fxnwz"
   },
   "outputs": [],
   "source": [
    "def diagrama_venn(p_a, p_b, p_c, label_a, label_b, label_c, txt):\n",
    "  acertos_a = 0 \n",
    "  acertos_a_b = 0\n",
    "  acertos_a_c = 0\n",
    "  acertos_b = 0\n",
    "  acertos_b_c = 0\n",
    "  acertos_c = 0\n",
    "  acertos_juncao = 0\n",
    "  \n",
    "\n",
    "  for index, row in df_test.iterrows():\n",
    "    #   print(p_a[index], row['norm'])\n",
    "      pred_a = p_a[index]\n",
    "      pred_b = p_b[index]\n",
    "      pred_c = p_c[index]\n",
    "\n",
    "      label = row['class']\n",
    "      if pred_a == label and pred_b == label and pred_c == label :\n",
    "        acertos_juncao += 1\n",
    "      if pred_a == label and pred_b != label and pred_c != label :\n",
    "        acertos_a += 1 \n",
    "      if pred_a != label and pred_b == label and pred_c != label :\n",
    "        acertos_b += 1\n",
    "      if pred_a != label and pred_b != label and pred_c == label :\n",
    "        acertos_c += 1\n",
    "      if pred_a == label and pred_b == label and pred_c != label :\n",
    "        acertos_a_b += 1  \n",
    "      if pred_a != label and pred_b == label and pred_c == label :\n",
    "        acertos_b_c += 1   \n",
    "      if pred_a == label and pred_b != label and pred_c == label :\n",
    "        acertos_a_c += 1 \n",
    "\n",
    "\n",
    "  plt.figure(figsize=(4,4))\n",
    "  v = venn3_unweighted(subsets=(acertos_a, acertos_b, acertos_a_b ,acertos_c, acertos_a_c, acertos_b_c , acertos_juncao), set_labels = (label_a,  label_b,  label_c))\n",
    "  v.get_patch_by_id('001').set_color('white')\n",
    "  v.get_patch_by_id('100').set_color('white')\n",
    "  v.get_patch_by_id('010').set_color('white')\n",
    "  v.get_patch_by_id('111').set_color('white')\n",
    "  v.get_patch_by_id('011').set_color('white')\n",
    "  v.get_patch_by_id('110').set_color('white')\n",
    "  v.get_patch_by_id('101').set_color('white')\n",
    "\n",
    "  c = venn3_circles(subsets=(1, 1, 1, 1, 1, 1, 1), linestyle='solid')\n",
    "  plt.title(txt)\n",
    "  plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "y_Uw2tyI44Zx"
   },
   "outputs": [],
   "source": [
    "def diagrama_venn2(label, pred_a, pred_b, label_a, label_b, title):\n",
    "    acertos_a = 0\n",
    "    acertos_b = 0\n",
    "    acertos_juncao = 0\n",
    "\n",
    "    for i, l in enumerate(label):\n",
    "        if pred_a[i] == l and pred_b[i] != l:\n",
    "            acertos_a += 1\n",
    "        if pred_a[i] != l and pred_b[i] == l:\n",
    "            acertos_b += 1\n",
    "        if pred_a[i] == l and pred_b[i] == l:\n",
    "            acertos_juncao += 1\n",
    "\n",
    "    plt.figure(figsize=(4,4))\n",
    "    v = venn2_unweighted(subsets=(acertos_a, acertos_b, acertos_juncao), set_labels = (label_a,  label_b))\n",
    "    v.get_patch_by_id('01').set_color('white')\n",
    "    v.get_patch_by_id('10').set_color('white')\n",
    "    v.get_patch_by_id('11').set_color('white')\n",
    "\n",
    "    c = venn2_circles(subsets=(1, 1, 1), linestyle='solid')\n",
    "    plt.title(title)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "yOLW8HgL-ggK"
   },
   "outputs": [],
   "source": [
    "def autolabel(rects, ax):\n",
    "  for rect in rects:\n",
    "    height = rect.get_height()\n",
    "    ax.annotate('{}'.format(height),\n",
    "    xy=(\n",
    "      rect.get_x() + rect.get_width() / 2, height),\n",
    "      xytext=(0, 3),  # 3 points vertical offset\n",
    "      textcoords=\"offset points\",\n",
    "      ha='center', va='bottom'\n",
    "    )\n",
    "\n",
    "def render_bar(list_certos, title='', size_x=0, size_y=0):\n",
    "    \n",
    "    labels = [l for l in range(len(list_certos)-1, -1,-1)]\n",
    "    \n",
    "    # labels = [5, 4, 3, 2, 1, 0]\n",
    "    x = np.arange(len(labels))\n",
    "    width = 0.35 \n",
    "\n",
    "    fig, ax = plt.subplots()\n",
    "    rects1 = ax.bar(x - width/2, list_certos.values(), width)\n",
    "\n",
    "    ax.set_ylabel('Instâncias')\n",
    "    ax.set_xlabel('Classificadores')\n",
    "    ax.set_title('Classificadores x Instâncias: '+title)\n",
    "    ax.set_xticks(x)\n",
    "    ax.set_xticklabels(labels)\n",
    "    ax.legend()\n",
    "    autolabel(rects1, ax)\n",
    "    fig.tight_layout()\n",
    "    if size_x != 0:        \n",
    "        plt.rcParams[\"figure.figsize\"] = (size_y, size_x)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "tu8QgCFuDg_N"
   },
   "outputs": [],
   "source": [
    "def get_combine(data_test, clfs={}, cnn=False):\n",
    "    list_proba = []\n",
    "\n",
    "    for ext, clf in clfs.items():\n",
    "        list_proba.append(clf.predict_proba(data_test))\n",
    "    \n",
    "    ensemble = np.array(list_proba).transpose((1, 0, 2))\n",
    "    \n",
    "    votes = np.zeros((test.shape[0], len(clfs)))\n",
    "    k = 0\n",
    "    for clf_index, clf in clfs.items():    \n",
    "        if cnn == False:\n",
    "            votes[:, k] = clf.predict(data_test).reshape(test.shape[0])\n",
    "        else:\n",
    "            votes[:, k] = np.argmax(clf.predict(data_test),axis=1).reshape(test.shape[0])\n",
    "        k += 1\n",
    "    \n",
    "    return majority_voting_rule(votes), average_rule(ensemble), maximum_rule(ensemble), minimum_rule(ensemble), median_rule(ensemble), product_rule(ensemble)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "F6aFRH3LewdT"
   },
   "outputs": [],
   "source": [
    "def get_combine_all(d_test, d_test_cnn, clfs={}):\n",
    "    list_proba = []\n",
    "\n",
    "    for key, clf in clfs.items():\n",
    "        if key[:3]=='CNN':\n",
    "            list_proba.append(clf.predict_proba(d_test_cnn))\n",
    "        else:\n",
    "            list_proba.append(clf.predict_proba(d_test))\n",
    "    \n",
    "    ensemble = np.array(list_proba).transpose((1, 0, 2))\n",
    "    \n",
    "    votes = np.zeros((d_test.shape[0], len(clfs)))\n",
    "    k = 0\n",
    "    for key, clf in clfs.items():    \n",
    "        if key[:3]=='CNN':\n",
    "            votes[:, k] = np.argmax(clf.predict(d_test_cnn), axis=1).reshape(d_test.shape[0])            \n",
    "        else:\n",
    "            votes[:, k] = clf.predict(d_test).reshape(d_test.shape[0])            \n",
    "        k += 1\n",
    "    \n",
    "    return majority_voting_rule(votes), average_rule(ensemble), maximum_rule(ensemble), minimum_rule(ensemble), median_rule(ensemble), product_rule(ensemble)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "0lJqzqv1eAGu"
   },
   "source": [
    "# SVM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "osZyNtRvUvB_"
   },
   "outputs": [],
   "source": [
    "svm = {\n",
    "    'CV': {\n",
    "        'CLF': SVC(random_state=42, verbose=100, kernel='linear', gamma=0.1, probability=True),\n",
    "        'EXT': cv,\n",
    "    },\n",
    "    'TFIDF': {\n",
    "        'CLF': SVC(random_state=42, verbose=100, kernel='linear', gamma=0.1, probability=True),\n",
    "        'EXT': tfidf,\n",
    "    },\n",
    "    'W2V': {\n",
    "        'CLF': SVC(random_state=42, verbose=100, kernel='rbf', gamma=1, probability=True),\n",
    "        'EXT': w2v,\n",
    "    },\n",
    "    'GLOVE': {\n",
    "        'CLF': SVC(random_state=42, verbose=100, kernel='rbf', gamma=0.5, probability=True),\n",
    "        'EXT':  glove,\n",
    "    },\n",
    "    'FAST': {\n",
    "        'CLF': SVC(random_state=42, verbose=100, kernel='rbf', gamma=1, probability=True),\n",
    "        'EXT': fasttext,\n",
    "    }\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "5kttfGP7Wq4C",
    "outputId": "46bfdc20-c1a9-401f-bea8-23ad959ce6ec"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[LibSVM][LibSVM][LibSVM][LibSVM][LibSVM]"
     ]
    }
   ],
   "source": [
    "svm_clfs = {\n",
    "\n",
    "}\n",
    "for ext, clf in svm.items():\n",
    "    svm_clfs[ext] = get_classifier(clf['CLF'], train, df_train['class'], clf['EXT'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "ve3DdJBwXrM4"
   },
   "outputs": [],
   "source": [
    "pre = {\n",
    "\n",
    "}\n",
    "for ext, clf in svm_clfs.items():\n",
    "    pre['SVM-'+ext] = clf.predict(val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "GPSYIaW1eXxP"
   },
   "outputs": [],
   "source": [
    "delta, labels = calc_delta(df_val['class'], pre, pre.keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 336
    },
    "colab_type": "code",
    "id": "ebagSe-cexAz",
    "outputId": "af8df5d8-1a61-42cf-f080-d16708d8945b"
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAVEAAAE/CAYAAAD/iPBoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nZfj/HzrIvy/6q40AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "gera_tsne(delta, 'SVM', list(labels), escala=400)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "-8M-bIeFgAGA"
   },
   "source": [
    "# LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "B-uvzYWAgBSg"
   },
   "outputs": [],
   "source": [
    "lr = {\n",
    "    'CV': {\n",
    "        'CLF': LogisticRegression(random_state=42, verbose=100, multi_class='auto', solver='liblinear', penalty='l1'),\n",
    "        'EXT': cv,\n",
    "    },\n",
    "    'TFIDF': {\n",
    "        'CLF': LogisticRegression(random_state=42, verbose=100, multi_class='auto', solver='liblinear', penalty='l1'),\n",
    "        'EXT': tfidf,\n",
    "    },\n",
    "    'W2V': {\n",
    "        'CLF': LogisticRegression(random_state=42, verbose=100, multi_class='auto', solver='liblinear', penalty='l1'),\n",
    "        'EXT': w2v,\n",
    "    },\n",
    "    'GLOVE': {\n",
    "        'CLF': LogisticRegression(random_state=42, verbose=100, multi_class='auto', solver='liblinear', penalty='l1'),\n",
    "        'EXT':  glove,\n",
    "    },\n",
    "    'FAST': {\n",
    "        'CLF': LogisticRegression(random_state=42, verbose=100, multi_class='auto', solver='liblinear', penalty='l1'),\n",
    "        'EXT': fasttext,\n",
    "    }\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "638ZQmbrgBvQ",
    "outputId": "1fe66b85-3f42-4922-e8b7-ac2141f66b96"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[LibLinear][LibLinear][LibLinear][LibLinear][LibLinear]"
     ]
    }
   ],
   "source": [
    "lr_clfs = {\n",
    "\n",
    "}\n",
    "for ext, clf in lr.items():\n",
    "    lr_clfs[ext] = get_classifier(clf['CLF'], train, df_train['class'], clf['EXT'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "oInqnv4LgFt8"
   },
   "outputs": [],
   "source": [
    "pre_lr = {\n",
    "\n",
    "}\n",
    "for ext, clf in lr_clfs.items():\n",
    "    pre_lr['LR-'+ext] = clf.predict(val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "vE2Avd0dgNgd"
   },
   "outputs": [],
   "source": [
    "delta, labels = calc_delta(df_val['class'], pre_lr, pre_lr.keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 336
    },
    "colab_type": "code",
    "id": "wL5xsliqgjAC",
    "outputId": "848c2b21-09c2-41cc-eec2-0a030e81fb71"
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAVEAAAE/CAYAAAD/iPBoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nmpllcIiamWVwiJqZZfj/gj/nec4X5xwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "gera_tsne(delta, 'LogisticRegression', list(labels), escala=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "60kEPa9EhUlm"
   },
   "outputs": [],
   "source": [
    "pre.update(pre_lr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "-sDXVT4ehmi8"
   },
   "source": [
    "# RandomForest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Kv1PjOulhhwC"
   },
   "outputs": [],
   "source": [
    "rf = {\n",
    "    'CV': {\n",
    "        'CLF': RandomForestClassifier(random_state=42, verbose=100, n_estimators=20),\n",
    "        'EXT': cv,\n",
    "    },\n",
    "    'TFIDF': {\n",
    "        'CLF': RandomForestClassifier(random_state=42, verbose=100, n_estimators=50),\n",
    "        'EXT': tfidf,\n",
    "    },\n",
    "    'W2V': {\n",
    "        'CLF': RandomForestClassifier(random_state=42, verbose=100, n_estimators=50),\n",
    "        'EXT': w2v,\n",
    "    },\n",
    "    'GLOVE': {\n",
    "        'CLF': RandomForestClassifier(random_state=42, verbose=100, n_estimators=50),\n",
    "        'EXT':  glove,\n",
    "    },\n",
    "    'FAST': {\n",
    "        'CLF': RandomForestClassifier(random_state=42, verbose=100, n_estimators=50),\n",
    "        'EXT': fasttext,\n",
    "    }\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "colab_type": "code",
    "id": "LvYypgBgh239",
    "outputId": "a1826a2c-ed4e-4de8-bf02-ea3ffed4a128"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "building tree 1 of 20\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.2s remaining:    0.0s\n",
      "building tree 2 of 20\n",
      "[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:    0.3s remaining:    0.0s\n",
      "building tree 3 of 20\n",
      "[Parallel(n_jobs=1)]: Done   3 out of   3 | elapsed:    0.5s remaining:    0.0s\n",
      "building tree 4 of 20\n",
      "[Parallel(n_jobs=1)]: Done   4 out of   4 | elapsed:    0.6s remaining:    0.0s\n",
      "building tree 5 of 20\n",
      "[Parallel(n_jobs=1)]: Done   5 out of   5 | elapsed:    0.8s remaining:    0.0s\n",
      "building tree 6 of 20\n",
      "[Parallel(n_jobs=1)]: Done   6 out of   6 | elapsed:    1.0s remaining:    0.0s\n",
      "building tree 7 of 20\n",
      "[Parallel(n_jobs=1)]: Done   7 out of   7 | elapsed:    1.1s remaining:    0.0s\n",
      "building tree 8 of 20\n",
      "[Parallel(n_jobs=1)]: Done   8 out of   8 | elapsed:    1.3s remaining:    0.0s\n",
      "building tree 9 of 20\n",
      "[Parallel(n_jobs=1)]: Done   9 out of   9 | elapsed:    1.5s remaining:    0.0s\n",
      "building tree 10 of 20\n",
      "[Parallel(n_jobs=1)]: Done  10 out of  10 | elapsed:    1.6s remaining:    0.0s\n",
      "building tree 11 of 20\n",
      "[Parallel(n_jobs=1)]: Done  11 out of  11 | elapsed:    1.8s remaining:    0.0s\n",
      "building tree 12 of 20\n",
      "[Parallel(n_jobs=1)]: Done  12 out of  12 | elapsed:    1.9s remaining:    0.0s\n",
      "building tree 13 of 20\n",
      "[Parallel(n_jobs=1)]: Done  13 out of  13 | elapsed:    2.1s remaining:    0.0s\n",
      "building tree 14 of 20\n",
      "[Parallel(n_jobs=1)]: Done  14 out of  14 | elapsed:    2.3s remaining:    0.0s\n",
      "building tree 15 of 20\n",
      "[Parallel(n_jobs=1)]: Done  15 out of  15 | elapsed:    2.4s remaining:    0.0s\n",
      "building tree 16 of 20\n",
      "[Parallel(n_jobs=1)]: Done  16 out of  16 | elapsed:    2.6s remaining:    0.0s\n",
      "building tree 17 of 20\n",
      "[Parallel(n_jobs=1)]: Done  17 out of  17 | elapsed:    2.7s remaining:    0.0s\n",
      "building tree 18 of 20\n",
      "[Parallel(n_jobs=1)]: Done  18 out of  18 | elapsed:    2.9s remaining:    0.0s\n",
      "building tree 19 of 20\n",
      "[Parallel(n_jobs=1)]: Done  19 out of  19 | elapsed:    3.1s remaining:    0.0s\n",
      "building tree 20 of 20\n",
      "[Parallel(n_jobs=1)]: Done  20 out of  20 | elapsed:    3.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  20 out of  20 | elapsed:    3.2s finished\n",
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "building tree 1 of 50\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.1s remaining:    0.0s\n",
      "building tree 2 of 50\n",
      "[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:    0.3s remaining:    0.0s\n",
      "building tree 3 of 50\n",
      "[Parallel(n_jobs=1)]: Done   3 out of   3 | elapsed:    0.4s remaining:    0.0s\n",
      "building tree 4 of 50\n",
      "[Parallel(n_jobs=1)]: Done   4 out of   4 | elapsed:    0.6s remaining:    0.0s\n",
      "building tree 5 of 50\n",
      "[Parallel(n_jobs=1)]: Done   5 out of   5 | elapsed:    0.7s remaining:    0.0s\n",
      "building tree 6 of 50\n",
      "[Parallel(n_jobs=1)]: Done   6 out of   6 | elapsed:    0.8s remaining:    0.0s\n",
      "building tree 7 of 50\n",
      "[Parallel(n_jobs=1)]: Done   7 out of   7 | elapsed:    1.0s remaining:    0.0s\n",
      "building tree 8 of 50\n",
      "[Parallel(n_jobs=1)]: Done   8 out of   8 | elapsed:    1.1s remaining:    0.0s\n",
      "building tree 9 of 50\n",
      "[Parallel(n_jobs=1)]: Done   9 out of   9 | elapsed:    1.2s remaining:    0.0s\n",
      "building tree 10 of 50\n",
      "[Parallel(n_jobs=1)]: Done  10 out of  10 | elapsed:    1.4s remaining:    0.0s\n",
      "building tree 11 of 50\n",
      "[Parallel(n_jobs=1)]: Done  11 out of  11 | elapsed:    1.5s remaining:    0.0s\n",
      "building tree 12 of 50\n",
      "[Parallel(n_jobs=1)]: Done  12 out of  12 | elapsed:    1.6s remaining:    0.0s\n",
      "building tree 13 of 50\n",
      "[Parallel(n_jobs=1)]: Done  13 out of  13 | elapsed:    1.8s remaining:    0.0s\n",
      "building tree 14 of 50\n",
      "[Parallel(n_jobs=1)]: Done  14 out of  14 | elapsed:    1.9s remaining:    0.0s\n",
      "building tree 15 of 50\n",
      "[Parallel(n_jobs=1)]: Done  15 out of  15 | elapsed:    2.0s remaining:    0.0s\n",
      "building tree 16 of 50\n",
      "[Parallel(n_jobs=1)]: Done  16 out of  16 | elapsed:    2.2s remaining:    0.0s\n",
      "building tree 17 of 50\n",
      "[Parallel(n_jobs=1)]: Done  17 out of  17 | elapsed:    2.3s remaining:    0.0s\n",
      "building tree 18 of 50\n",
      "[Parallel(n_jobs=1)]: Done  18 out of  18 | elapsed:    2.4s remaining:    0.0s\n",
      "building tree 19 of 50\n",
      "[Parallel(n_jobs=1)]: Done  19 out of  19 | elapsed:    2.6s remaining:    0.0s\n",
      "building tree 20 of 50\n",
      "[Parallel(n_jobs=1)]: Done  20 out of  20 | elapsed:    2.7s remaining:    0.0s\n",
      "building tree 21 of 50\n",
      "[Parallel(n_jobs=1)]: Done  21 out of  21 | elapsed:    2.8s remaining:    0.0s\n",
      "building tree 22 of 50\n",
      "[Parallel(n_jobs=1)]: Done  22 out of  22 | elapsed:    3.0s remaining:    0.0s\n",
      "building tree 23 of 50\n",
      "[Parallel(n_jobs=1)]: Done  23 out of  23 | elapsed:    3.1s remaining:    0.0s\n",
      "building tree 24 of 50\n",
      "[Parallel(n_jobs=1)]: Done  24 out of  24 | elapsed:    3.2s remaining:    0.0s\n",
      "building tree 25 of 50\n",
      "[Parallel(n_jobs=1)]: Done  25 out of  25 | elapsed:    3.4s remaining:    0.0s\n",
      "building tree 26 of 50\n",
      "[Parallel(n_jobs=1)]: Done  26 out of  26 | elapsed:    3.5s remaining:    0.0s\n",
      "building tree 27 of 50\n",
      "[Parallel(n_jobs=1)]: Done  27 out of  27 | elapsed:    3.7s remaining:    0.0s\n",
      "building tree 28 of 50\n",
      "[Parallel(n_jobs=1)]: Done  28 out of  28 | elapsed:    3.8s remaining:    0.0s\n",
      "building tree 29 of 50\n",
      "[Parallel(n_jobs=1)]: Done  29 out of  29 | elapsed:    3.9s remaining:    0.0s\n",
      "building tree 30 of 50\n",
      "[Parallel(n_jobs=1)]: Done  30 out of  30 | elapsed:    4.1s remaining:    0.0s\n",
      "building tree 31 of 50\n",
      "[Parallel(n_jobs=1)]: Done  31 out of  31 | elapsed:    4.2s remaining:    0.0s\n",
      "building tree 32 of 50\n",
      "[Parallel(n_jobs=1)]: Done  32 out of  32 | elapsed:    4.3s remaining:    0.0s\n",
      "building tree 33 of 50\n",
      "[Parallel(n_jobs=1)]: Done  33 out of  33 | elapsed:    4.5s remaining:    0.0s\n",
      "building tree 34 of 50\n",
      "[Parallel(n_jobs=1)]: Done  34 out of  34 | elapsed:    4.6s remaining:    0.0s\n",
      "building tree 35 of 50\n",
      "[Parallel(n_jobs=1)]: Done  35 out of  35 | elapsed:    4.8s remaining:    0.0s\n",
      "building tree 36 of 50\n",
      "[Parallel(n_jobs=1)]: Done  36 out of  36 | elapsed:    4.9s remaining:    0.0s\n",
      "building tree 37 of 50\n",
      "[Parallel(n_jobs=1)]: Done  37 out of  37 | elapsed:    5.0s remaining:    0.0s\n",
      "building tree 38 of 50\n",
      "[Parallel(n_jobs=1)]: Done  38 out of  38 | elapsed:    5.2s remaining:    0.0s\n",
      "building tree 39 of 50\n",
      "[Parallel(n_jobs=1)]: Done  39 out of  39 | elapsed:    5.3s remaining:    0.0s\n",
      "building tree 40 of 50\n",
      "[Parallel(n_jobs=1)]: Done  40 out of  40 | elapsed:    5.4s remaining:    0.0s\n",
      "building tree 41 of 50\n",
      "[Parallel(n_jobs=1)]: Done  41 out of  41 | elapsed:    5.6s remaining:    0.0s\n",
      "building tree 42 of 50\n",
      "[Parallel(n_jobs=1)]: Done  42 out of  42 | elapsed:    5.7s remaining:    0.0s\n",
      "building tree 43 of 50\n",
      "[Parallel(n_jobs=1)]: Done  43 out of  43 | elapsed:    5.8s remaining:    0.0s\n",
      "building tree 44 of 50\n",
      "[Parallel(n_jobs=1)]: Done  44 out of  44 | elapsed:    6.0s remaining:    0.0s\n",
      "building tree 45 of 50\n",
      "[Parallel(n_jobs=1)]: Done  45 out of  45 | elapsed:    6.1s remaining:    0.0s\n",
      "building tree 46 of 50\n",
      "[Parallel(n_jobs=1)]: Done  46 out of  46 | elapsed:    6.2s remaining:    0.0s\n",
      "building tree 47 of 50\n",
      "[Parallel(n_jobs=1)]: Done  47 out of  47 | elapsed:    6.4s remaining:    0.0s\n",
      "building tree 48 of 50\n",
      "[Parallel(n_jobs=1)]: Done  48 out of  48 | elapsed:    6.5s remaining:    0.0s\n",
      "building tree 49 of 50\n",
      "[Parallel(n_jobs=1)]: Done  49 out of  49 | elapsed:    6.7s remaining:    0.0s\n",
      "building tree 50 of 50\n",
      "[Parallel(n_jobs=1)]: Done  50 out of  50 | elapsed:    6.8s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  50 out of  50 | elapsed:    6.8s finished\n",
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "building tree 1 of 50\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.3s remaining:    0.0s\n",
      "building tree 2 of 50\n",
      "[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:    0.7s remaining:    0.0s\n",
      "building tree 3 of 50\n",
      "[Parallel(n_jobs=1)]: Done   3 out of   3 | elapsed:    1.0s remaining:    0.0s\n",
      "building tree 4 of 50\n",
      "[Parallel(n_jobs=1)]: Done   4 out of   4 | elapsed:    1.3s remaining:    0.0s\n",
      "building tree 5 of 50\n",
      "[Parallel(n_jobs=1)]: Done   5 out of   5 | elapsed:    1.7s remaining:    0.0s\n",
      "building tree 6 of 50\n",
      "[Parallel(n_jobs=1)]: Done   6 out of   6 | elapsed:    2.0s remaining:    0.0s\n",
      "building tree 7 of 50\n",
      "[Parallel(n_jobs=1)]: Done   7 out of   7 | elapsed:    2.3s remaining:    0.0s\n",
      "building tree 8 of 50\n",
      "[Parallel(n_jobs=1)]: Done   8 out of   8 | elapsed:    2.7s remaining:    0.0s\n",
      "building tree 9 of 50\n",
      "[Parallel(n_jobs=1)]: Done   9 out of   9 | elapsed:    3.1s remaining:    0.0s\n",
      "building tree 10 of 50\n",
      "[Parallel(n_jobs=1)]: Done  10 out of  10 | elapsed:    3.4s remaining:    0.0s\n",
      "building tree 11 of 50\n",
      "[Parallel(n_jobs=1)]: Done  11 out of  11 | elapsed:    3.7s remaining:    0.0s\n",
      "building tree 12 of 50\n",
      "[Parallel(n_jobs=1)]: Done  12 out of  12 | elapsed:    4.0s remaining:    0.0s\n",
      "building tree 13 of 50\n",
      "[Parallel(n_jobs=1)]: Done  13 out of  13 | elapsed:    4.3s remaining:    0.0s\n",
      "building tree 14 of 50\n",
      "[Parallel(n_jobs=1)]: Done  14 out of  14 | elapsed:    4.7s remaining:    0.0s\n",
      "building tree 15 of 50\n",
      "[Parallel(n_jobs=1)]: Done  15 out of  15 | elapsed:    5.0s remaining:    0.0s\n",
      "building tree 16 of 50\n",
      "[Parallel(n_jobs=1)]: Done  16 out of  16 | elapsed:    5.4s remaining:    0.0s\n",
      "building tree 17 of 50\n",
      "[Parallel(n_jobs=1)]: Done  17 out of  17 | elapsed:    5.7s remaining:    0.0s\n",
      "building tree 18 of 50\n",
      "[Parallel(n_jobs=1)]: Done  18 out of  18 | elapsed:    6.0s remaining:    0.0s\n",
      "building tree 19 of 50\n",
      "[Parallel(n_jobs=1)]: Done  19 out of  19 | elapsed:    6.3s remaining:    0.0s\n",
      "building tree 20 of 50\n",
      "[Parallel(n_jobs=1)]: Done  20 out of  20 | elapsed:    6.6s remaining:    0.0s\n",
      "building tree 21 of 50\n",
      "[Parallel(n_jobs=1)]: Done  21 out of  21 | elapsed:    7.0s remaining:    0.0s\n",
      "building tree 22 of 50\n",
      "[Parallel(n_jobs=1)]: Done  22 out of  22 | elapsed:    7.3s remaining:    0.0s\n",
      "building tree 23 of 50\n",
      "[Parallel(n_jobs=1)]: Done  23 out of  23 | elapsed:    7.6s remaining:    0.0s\n",
      "building tree 24 of 50\n",
      "[Parallel(n_jobs=1)]: Done  24 out of  24 | elapsed:    8.0s remaining:    0.0s\n",
      "building tree 25 of 50\n",
      "[Parallel(n_jobs=1)]: Done  25 out of  25 | elapsed:    8.3s remaining:    0.0s\n",
      "building tree 26 of 50\n",
      "[Parallel(n_jobs=1)]: Done  26 out of  26 | elapsed:    8.6s remaining:    0.0s\n",
      "building tree 27 of 50\n",
      "[Parallel(n_jobs=1)]: Done  27 out of  27 | elapsed:    9.0s remaining:    0.0s\n",
      "building tree 28 of 50\n",
      "[Parallel(n_jobs=1)]: Done  28 out of  28 | elapsed:    9.3s remaining:    0.0s\n",
      "building tree 29 of 50\n",
      "[Parallel(n_jobs=1)]: Done  29 out of  29 | elapsed:    9.7s remaining:    0.0s\n",
      "building tree 30 of 50\n",
      "[Parallel(n_jobs=1)]: Done  30 out of  30 | elapsed:   10.0s remaining:    0.0s\n",
      "building tree 31 of 50\n",
      "[Parallel(n_jobs=1)]: Done  31 out of  31 | elapsed:   10.3s remaining:    0.0s\n",
      "building tree 32 of 50\n",
      "[Parallel(n_jobs=1)]: Done  32 out of  32 | elapsed:   10.7s remaining:    0.0s\n",
      "building tree 33 of 50\n",
      "[Parallel(n_jobs=1)]: Done  33 out of  33 | elapsed:   11.0s remaining:    0.0s\n",
      "building tree 34 of 50\n",
      "[Parallel(n_jobs=1)]: Done  34 out of  34 | elapsed:   11.3s remaining:    0.0s\n",
      "building tree 35 of 50\n",
      "[Parallel(n_jobs=1)]: Done  35 out of  35 | elapsed:   11.7s remaining:    0.0s\n",
      "building tree 36 of 50\n",
      "[Parallel(n_jobs=1)]: Done  36 out of  36 | elapsed:   12.0s remaining:    0.0s\n",
      "building tree 37 of 50\n",
      "[Parallel(n_jobs=1)]: Done  37 out of  37 | elapsed:   12.3s remaining:    0.0s\n",
      "building tree 38 of 50\n",
      "[Parallel(n_jobs=1)]: Done  38 out of  38 | elapsed:   12.7s remaining:    0.0s\n",
      "building tree 39 of 50\n",
      "[Parallel(n_jobs=1)]: Done  39 out of  39 | elapsed:   13.0s remaining:    0.0s\n",
      "building tree 40 of 50\n",
      "[Parallel(n_jobs=1)]: Done  40 out of  40 | elapsed:   13.3s remaining:    0.0s\n",
      "building tree 41 of 50\n",
      "[Parallel(n_jobs=1)]: Done  41 out of  41 | elapsed:   13.6s remaining:    0.0s\n",
      "building tree 42 of 50\n",
      "[Parallel(n_jobs=1)]: Done  42 out of  42 | elapsed:   14.0s remaining:    0.0s\n",
      "building tree 43 of 50\n",
      "[Parallel(n_jobs=1)]: Done  43 out of  43 | elapsed:   14.3s remaining:    0.0s\n",
      "building tree 44 of 50\n",
      "[Parallel(n_jobs=1)]: Done  44 out of  44 | elapsed:   14.7s remaining:    0.0s\n",
      "building tree 45 of 50\n",
      "[Parallel(n_jobs=1)]: Done  45 out of  45 | elapsed:   15.0s remaining:    0.0s\n",
      "building tree 46 of 50\n",
      "[Parallel(n_jobs=1)]: Done  46 out of  46 | elapsed:   15.4s remaining:    0.0s\n",
      "building tree 47 of 50\n",
      "[Parallel(n_jobs=1)]: Done  47 out of  47 | elapsed:   15.7s remaining:    0.0s\n",
      "building tree 48 of 50\n",
      "[Parallel(n_jobs=1)]: Done  48 out of  48 | elapsed:   16.0s remaining:    0.0s\n",
      "building tree 49 of 50\n",
      "[Parallel(n_jobs=1)]: Done  49 out of  49 | elapsed:   16.4s remaining:    0.0s\n",
      "building tree 50 of 50\n",
      "[Parallel(n_jobs=1)]: Done  50 out of  50 | elapsed:   16.7s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  50 out of  50 | elapsed:   16.7s finished\n",
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "building tree 1 of 50\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.1s remaining:    0.0s\n",
      "building tree 2 of 50\n",
      "[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:    0.2s remaining:    0.0s\n",
      "building tree 3 of 50\n",
      "[Parallel(n_jobs=1)]: Done   3 out of   3 | elapsed:    0.3s remaining:    0.0s\n",
      "building tree 4 of 50\n",
      "[Parallel(n_jobs=1)]: Done   4 out of   4 | elapsed:    0.4s remaining:    0.0s\n",
      "building tree 5 of 50\n",
      "[Parallel(n_jobs=1)]: Done   5 out of   5 | elapsed:    0.5s remaining:    0.0s\n",
      "building tree 6 of 50\n",
      "[Parallel(n_jobs=1)]: Done   6 out of   6 | elapsed:    0.6s remaining:    0.0s\n",
      "building tree 7 of 50\n",
      "[Parallel(n_jobs=1)]: Done   7 out of   7 | elapsed:    0.7s remaining:    0.0s\n",
      "building tree 8 of 50\n",
      "[Parallel(n_jobs=1)]: Done   8 out of   8 | elapsed:    0.8s remaining:    0.0s\n",
      "building tree 9 of 50\n",
      "[Parallel(n_jobs=1)]: Done   9 out of   9 | elapsed:    0.9s remaining:    0.0s\n",
      "building tree 10 of 50\n",
      "[Parallel(n_jobs=1)]: Done  10 out of  10 | elapsed:    1.0s remaining:    0.0s\n",
      "building tree 11 of 50\n",
      "[Parallel(n_jobs=1)]: Done  11 out of  11 | elapsed:    1.1s remaining:    0.0s\n",
      "building tree 12 of 50\n",
      "[Parallel(n_jobs=1)]: Done  12 out of  12 | elapsed:    1.1s remaining:    0.0s\n",
      "building tree 13 of 50\n",
      "[Parallel(n_jobs=1)]: Done  13 out of  13 | elapsed:    1.2s remaining:    0.0s\n",
      "building tree 14 of 50\n",
      "[Parallel(n_jobs=1)]: Done  14 out of  14 | elapsed:    1.3s remaining:    0.0s\n",
      "building tree 15 of 50\n",
      "[Parallel(n_jobs=1)]: Done  15 out of  15 | elapsed:    1.4s remaining:    0.0s\n",
      "building tree 16 of 50\n",
      "[Parallel(n_jobs=1)]: Done  16 out of  16 | elapsed:    1.5s remaining:    0.0s\n",
      "building tree 17 of 50\n",
      "[Parallel(n_jobs=1)]: Done  17 out of  17 | elapsed:    1.6s remaining:    0.0s\n",
      "building tree 18 of 50\n",
      "[Parallel(n_jobs=1)]: Done  18 out of  18 | elapsed:    1.7s remaining:    0.0s\n",
      "building tree 19 of 50\n",
      "[Parallel(n_jobs=1)]: Done  19 out of  19 | elapsed:    1.8s remaining:    0.0s\n",
      "building tree 20 of 50\n",
      "[Parallel(n_jobs=1)]: Done  20 out of  20 | elapsed:    1.9s remaining:    0.0s\n",
      "building tree 21 of 50\n",
      "[Parallel(n_jobs=1)]: Done  21 out of  21 | elapsed:    2.0s remaining:    0.0s\n",
      "building tree 22 of 50\n",
      "[Parallel(n_jobs=1)]: Done  22 out of  22 | elapsed:    2.1s remaining:    0.0s\n",
      "building tree 23 of 50\n",
      "[Parallel(n_jobs=1)]: Done  23 out of  23 | elapsed:    2.2s remaining:    0.0s\n",
      "building tree 24 of 50\n",
      "[Parallel(n_jobs=1)]: Done  24 out of  24 | elapsed:    2.3s remaining:    0.0s\n",
      "building tree 25 of 50\n",
      "[Parallel(n_jobs=1)]: Done  25 out of  25 | elapsed:    2.4s remaining:    0.0s\n",
      "building tree 26 of 50\n",
      "[Parallel(n_jobs=1)]: Done  26 out of  26 | elapsed:    2.4s remaining:    0.0s\n",
      "building tree 27 of 50\n",
      "[Parallel(n_jobs=1)]: Done  27 out of  27 | elapsed:    2.5s remaining:    0.0s\n",
      "building tree 28 of 50\n",
      "[Parallel(n_jobs=1)]: Done  28 out of  28 | elapsed:    2.7s remaining:    0.0s\n",
      "building tree 29 of 50\n",
      "[Parallel(n_jobs=1)]: Done  29 out of  29 | elapsed:    2.8s remaining:    0.0s\n",
      "building tree 30 of 50\n",
      "[Parallel(n_jobs=1)]: Done  30 out of  30 | elapsed:    2.8s remaining:    0.0s\n",
      "building tree 31 of 50\n",
      "[Parallel(n_jobs=1)]: Done  31 out of  31 | elapsed:    2.9s remaining:    0.0s\n",
      "building tree 32 of 50\n",
      "[Parallel(n_jobs=1)]: Done  32 out of  32 | elapsed:    3.0s remaining:    0.0s\n",
      "building tree 33 of 50\n",
      "[Parallel(n_jobs=1)]: Done  33 out of  33 | elapsed:    3.1s remaining:    0.0s\n",
      "building tree 34 of 50\n",
      "[Parallel(n_jobs=1)]: Done  34 out of  34 | elapsed:    3.2s remaining:    0.0s\n",
      "building tree 35 of 50\n",
      "[Parallel(n_jobs=1)]: Done  35 out of  35 | elapsed:    3.3s remaining:    0.0s\n",
      "building tree 36 of 50\n",
      "[Parallel(n_jobs=1)]: Done  36 out of  36 | elapsed:    3.4s remaining:    0.0s\n",
      "building tree 37 of 50\n",
      "[Parallel(n_jobs=1)]: Done  37 out of  37 | elapsed:    3.5s remaining:    0.0s\n",
      "building tree 38 of 50\n",
      "[Parallel(n_jobs=1)]: Done  38 out of  38 | elapsed:    3.6s remaining:    0.0s\n",
      "building tree 39 of 50\n",
      "[Parallel(n_jobs=1)]: Done  39 out of  39 | elapsed:    3.7s remaining:    0.0s\n",
      "building tree 40 of 50\n",
      "[Parallel(n_jobs=1)]: Done  40 out of  40 | elapsed:    3.8s remaining:    0.0s\n",
      "building tree 41 of 50\n",
      "[Parallel(n_jobs=1)]: Done  41 out of  41 | elapsed:    3.8s remaining:    0.0s\n",
      "building tree 42 of 50\n",
      "[Parallel(n_jobs=1)]: Done  42 out of  42 | elapsed:    3.9s remaining:    0.0s\n",
      "building tree 43 of 50\n",
      "[Parallel(n_jobs=1)]: Done  43 out of  43 | elapsed:    4.0s remaining:    0.0s\n",
      "building tree 44 of 50\n",
      "[Parallel(n_jobs=1)]: Done  44 out of  44 | elapsed:    4.1s remaining:    0.0s\n",
      "building tree 45 of 50\n",
      "[Parallel(n_jobs=1)]: Done  45 out of  45 | elapsed:    4.2s remaining:    0.0s\n",
      "building tree 46 of 50\n",
      "[Parallel(n_jobs=1)]: Done  46 out of  46 | elapsed:    4.3s remaining:    0.0s\n",
      "building tree 47 of 50\n",
      "[Parallel(n_jobs=1)]: Done  47 out of  47 | elapsed:    4.4s remaining:    0.0s\n",
      "building tree 48 of 50\n",
      "[Parallel(n_jobs=1)]: Done  48 out of  48 | elapsed:    4.5s remaining:    0.0s\n",
      "building tree 49 of 50\n",
      "[Parallel(n_jobs=1)]: Done  49 out of  49 | elapsed:    4.6s remaining:    0.0s\n",
      "building tree 50 of 50\n",
      "[Parallel(n_jobs=1)]: Done  50 out of  50 | elapsed:    4.7s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  50 out of  50 | elapsed:    4.7s finished\n",
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "building tree 1 of 50\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.3s remaining:    0.0s\n",
      "building tree 2 of 50\n",
      "[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:    0.7s remaining:    0.0s\n",
      "building tree 3 of 50\n",
      "[Parallel(n_jobs=1)]: Done   3 out of   3 | elapsed:    1.0s remaining:    0.0s\n",
      "building tree 4 of 50\n",
      "[Parallel(n_jobs=1)]: Done   4 out of   4 | elapsed:    1.4s remaining:    0.0s\n",
      "building tree 5 of 50\n",
      "[Parallel(n_jobs=1)]: Done   5 out of   5 | elapsed:    1.7s remaining:    0.0s\n",
      "building tree 6 of 50\n",
      "[Parallel(n_jobs=1)]: Done   6 out of   6 | elapsed:    2.0s remaining:    0.0s\n",
      "building tree 7 of 50\n",
      "[Parallel(n_jobs=1)]: Done   7 out of   7 | elapsed:    2.3s remaining:    0.0s\n",
      "building tree 8 of 50\n",
      "[Parallel(n_jobs=1)]: Done   8 out of   8 | elapsed:    2.6s remaining:    0.0s\n",
      "building tree 9 of 50\n",
      "[Parallel(n_jobs=1)]: Done   9 out of   9 | elapsed:    2.9s remaining:    0.0s\n",
      "building tree 10 of 50\n",
      "[Parallel(n_jobs=1)]: Done  10 out of  10 | elapsed:    3.2s remaining:    0.0s\n",
      "building tree 11 of 50\n",
      "[Parallel(n_jobs=1)]: Done  11 out of  11 | elapsed:    3.6s remaining:    0.0s\n",
      "building tree 12 of 50\n",
      "[Parallel(n_jobs=1)]: Done  12 out of  12 | elapsed:    3.9s remaining:    0.0s\n",
      "building tree 13 of 50\n",
      "[Parallel(n_jobs=1)]: Done  13 out of  13 | elapsed:    4.2s remaining:    0.0s\n",
      "building tree 14 of 50\n",
      "[Parallel(n_jobs=1)]: Done  14 out of  14 | elapsed:    4.6s remaining:    0.0s\n",
      "building tree 15 of 50\n",
      "[Parallel(n_jobs=1)]: Done  15 out of  15 | elapsed:    4.9s remaining:    0.0s\n",
      "building tree 16 of 50\n",
      "[Parallel(n_jobs=1)]: Done  16 out of  16 | elapsed:    5.2s remaining:    0.0s\n",
      "building tree 17 of 50\n",
      "[Parallel(n_jobs=1)]: Done  17 out of  17 | elapsed:    5.5s remaining:    0.0s\n",
      "building tree 18 of 50\n",
      "[Parallel(n_jobs=1)]: Done  18 out of  18 | elapsed:    5.9s remaining:    0.0s\n",
      "building tree 19 of 50\n",
      "[Parallel(n_jobs=1)]: Done  19 out of  19 | elapsed:    6.2s remaining:    0.0s\n",
      "building tree 20 of 50\n",
      "[Parallel(n_jobs=1)]: Done  20 out of  20 | elapsed:    6.5s remaining:    0.0s\n",
      "building tree 21 of 50\n",
      "[Parallel(n_jobs=1)]: Done  21 out of  21 | elapsed:    6.9s remaining:    0.0s\n",
      "building tree 22 of 50\n",
      "[Parallel(n_jobs=1)]: Done  22 out of  22 | elapsed:    7.2s remaining:    0.0s\n",
      "building tree 23 of 50\n",
      "[Parallel(n_jobs=1)]: Done  23 out of  23 | elapsed:    7.5s remaining:    0.0s\n",
      "building tree 24 of 50\n",
      "[Parallel(n_jobs=1)]: Done  24 out of  24 | elapsed:    7.8s remaining:    0.0s\n",
      "building tree 25 of 50\n",
      "[Parallel(n_jobs=1)]: Done  25 out of  25 | elapsed:    8.2s remaining:    0.0s\n",
      "building tree 26 of 50\n",
      "[Parallel(n_jobs=1)]: Done  26 out of  26 | elapsed:    8.5s remaining:    0.0s\n",
      "building tree 27 of 50\n",
      "[Parallel(n_jobs=1)]: Done  27 out of  27 | elapsed:    8.9s remaining:    0.0s\n",
      "building tree 28 of 50\n",
      "[Parallel(n_jobs=1)]: Done  28 out of  28 | elapsed:    9.2s remaining:    0.0s\n",
      "building tree 29 of 50\n",
      "[Parallel(n_jobs=1)]: Done  29 out of  29 | elapsed:    9.5s remaining:    0.0s\n",
      "building tree 30 of 50\n",
      "[Parallel(n_jobs=1)]: Done  30 out of  30 | elapsed:    9.8s remaining:    0.0s\n",
      "building tree 31 of 50\n",
      "[Parallel(n_jobs=1)]: Done  31 out of  31 | elapsed:   10.2s remaining:    0.0s\n",
      "building tree 32 of 50\n",
      "[Parallel(n_jobs=1)]: Done  32 out of  32 | elapsed:   10.5s remaining:    0.0s\n",
      "building tree 33 of 50\n",
      "[Parallel(n_jobs=1)]: Done  33 out of  33 | elapsed:   10.8s remaining:    0.0s\n",
      "building tree 34 of 50\n",
      "[Parallel(n_jobs=1)]: Done  34 out of  34 | elapsed:   11.1s remaining:    0.0s\n",
      "building tree 35 of 50\n",
      "[Parallel(n_jobs=1)]: Done  35 out of  35 | elapsed:   11.5s remaining:    0.0s\n",
      "building tree 36 of 50\n",
      "[Parallel(n_jobs=1)]: Done  36 out of  36 | elapsed:   11.8s remaining:    0.0s\n",
      "building tree 37 of 50\n",
      "[Parallel(n_jobs=1)]: Done  37 out of  37 | elapsed:   12.1s remaining:    0.0s\n",
      "building tree 38 of 50\n",
      "[Parallel(n_jobs=1)]: Done  38 out of  38 | elapsed:   12.5s remaining:    0.0s\n",
      "building tree 39 of 50\n",
      "[Parallel(n_jobs=1)]: Done  39 out of  39 | elapsed:   12.8s remaining:    0.0s\n",
      "building tree 40 of 50\n",
      "[Parallel(n_jobs=1)]: Done  40 out of  40 | elapsed:   13.1s remaining:    0.0s\n",
      "building tree 41 of 50\n",
      "[Parallel(n_jobs=1)]: Done  41 out of  41 | elapsed:   13.5s remaining:    0.0s\n",
      "building tree 42 of 50\n",
      "[Parallel(n_jobs=1)]: Done  42 out of  42 | elapsed:   13.8s remaining:    0.0s\n",
      "building tree 43 of 50\n",
      "[Parallel(n_jobs=1)]: Done  43 out of  43 | elapsed:   14.1s remaining:    0.0s\n",
      "building tree 44 of 50\n",
      "[Parallel(n_jobs=1)]: Done  44 out of  44 | elapsed:   14.5s remaining:    0.0s\n",
      "building tree 45 of 50\n",
      "[Parallel(n_jobs=1)]: Done  45 out of  45 | elapsed:   14.8s remaining:    0.0s\n",
      "building tree 46 of 50\n",
      "[Parallel(n_jobs=1)]: Done  46 out of  46 | elapsed:   15.1s remaining:    0.0s\n",
      "building tree 47 of 50\n",
      "[Parallel(n_jobs=1)]: Done  47 out of  47 | elapsed:   15.5s remaining:    0.0s\n",
      "building tree 48 of 50\n",
      "[Parallel(n_jobs=1)]: Done  48 out of  48 | elapsed:   15.8s remaining:    0.0s\n",
      "building tree 49 of 50\n",
      "[Parallel(n_jobs=1)]: Done  49 out of  49 | elapsed:   16.1s remaining:    0.0s\n",
      "building tree 50 of 50\n",
      "[Parallel(n_jobs=1)]: Done  50 out of  50 | elapsed:   16.5s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  50 out of  50 | elapsed:   16.5s finished\n"
     ]
    }
   ],
   "source": [
    "rf_clfs = {\n",
    "\n",
    "}\n",
    "for ext, clf in rf.items():\n",
    "    rf_clfs[ext] = get_classifier(clf['CLF'], train, df_train['class'], clf['EXT'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "colab_type": "code",
    "id": "FPeIOoZJh7Fd",
    "outputId": "b1d75186-ec49-455e-8739-0e6035879e36"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   3 out of   3 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   4 out of   4 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   5 out of   5 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   6 out of   6 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   7 out of   7 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   8 out of   8 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   9 out of   9 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  10 out of  10 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  11 out of  11 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  12 out of  12 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  13 out of  13 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  14 out of  14 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  15 out of  15 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  16 out of  16 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  17 out of  17 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  18 out of  18 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  19 out of  19 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  20 out of  20 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  20 out of  20 | elapsed:    0.1s finished\n",
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   3 out of   3 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   4 out of   4 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   5 out of   5 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   6 out of   6 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   7 out of   7 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   8 out of   8 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   9 out of   9 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  10 out of  10 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  11 out of  11 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  12 out of  12 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  13 out of  13 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  14 out of  14 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  15 out of  15 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  16 out of  16 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  17 out of  17 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  18 out of  18 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  19 out of  19 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  20 out of  20 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  21 out of  21 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  22 out of  22 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  23 out of  23 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  24 out of  24 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  25 out of  25 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  26 out of  26 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  27 out of  27 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  28 out of  28 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  29 out of  29 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  30 out of  30 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  31 out of  31 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  32 out of  32 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  33 out of  33 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  34 out of  34 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  35 out of  35 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  36 out of  36 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  37 out of  37 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  38 out of  38 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  39 out of  39 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  40 out of  40 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  41 out of  41 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  42 out of  42 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  43 out of  43 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  44 out of  44 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  45 out of  45 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  46 out of  46 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  47 out of  47 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  48 out of  48 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  49 out of  49 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  50 out of  50 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  50 out of  50 | elapsed:    0.2s finished\n",
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   3 out of   3 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   4 out of   4 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   5 out of   5 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   6 out of   6 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   7 out of   7 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   8 out of   8 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   9 out of   9 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  10 out of  10 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  11 out of  11 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  12 out of  12 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  13 out of  13 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  14 out of  14 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  15 out of  15 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  16 out of  16 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  17 out of  17 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  18 out of  18 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  19 out of  19 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  20 out of  20 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  21 out of  21 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  22 out of  22 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  23 out of  23 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  24 out of  24 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  25 out of  25 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  26 out of  26 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  27 out of  27 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  28 out of  28 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  29 out of  29 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  30 out of  30 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  31 out of  31 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  32 out of  32 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  33 out of  33 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  34 out of  34 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  35 out of  35 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  36 out of  36 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  37 out of  37 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  38 out of  38 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  39 out of  39 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  40 out of  40 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  41 out of  41 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  42 out of  42 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  43 out of  43 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  44 out of  44 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  45 out of  45 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  46 out of  46 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  47 out of  47 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  48 out of  48 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  49 out of  49 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  50 out of  50 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  50 out of  50 | elapsed:    0.1s finished\n",
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   3 out of   3 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   4 out of   4 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   5 out of   5 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   6 out of   6 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   7 out of   7 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   8 out of   8 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   9 out of   9 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  10 out of  10 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  11 out of  11 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  12 out of  12 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  13 out of  13 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  14 out of  14 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  15 out of  15 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  16 out of  16 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  17 out of  17 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  18 out of  18 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  19 out of  19 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  20 out of  20 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  21 out of  21 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  22 out of  22 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  23 out of  23 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  24 out of  24 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  25 out of  25 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  26 out of  26 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  27 out of  27 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  28 out of  28 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  29 out of  29 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  30 out of  30 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  31 out of  31 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  32 out of  32 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  33 out of  33 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  34 out of  34 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  35 out of  35 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  36 out of  36 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  37 out of  37 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  38 out of  38 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  39 out of  39 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  40 out of  40 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  41 out of  41 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  42 out of  42 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  43 out of  43 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  44 out of  44 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  45 out of  45 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  46 out of  46 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  47 out of  47 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  48 out of  48 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  49 out of  49 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  50 out of  50 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  50 out of  50 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   3 out of   3 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   4 out of   4 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   5 out of   5 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   6 out of   6 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   7 out of   7 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   8 out of   8 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   9 out of   9 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  10 out of  10 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  11 out of  11 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  12 out of  12 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  13 out of  13 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  14 out of  14 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  15 out of  15 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  16 out of  16 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  17 out of  17 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  18 out of  18 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  19 out of  19 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  20 out of  20 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  21 out of  21 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  22 out of  22 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  23 out of  23 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  24 out of  24 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  25 out of  25 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  26 out of  26 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  27 out of  27 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  28 out of  28 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  29 out of  29 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  30 out of  30 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  31 out of  31 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  32 out of  32 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  33 out of  33 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  34 out of  34 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  35 out of  35 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  36 out of  36 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  37 out of  37 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  38 out of  38 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  39 out of  39 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  40 out of  40 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  41 out of  41 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  42 out of  42 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  43 out of  43 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  44 out of  44 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  45 out of  45 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  46 out of  46 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  47 out of  47 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  48 out of  48 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  49 out of  49 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  50 out of  50 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  50 out of  50 | elapsed:    0.1s finished\n"
     ]
    }
   ],
   "source": [
    "pre_rf = {\n",
    "\n",
    "}\n",
    "for ext, clf in rf_clfs.items():\n",
    "    pre_rf['RF-'+ext] = clf.predict(val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "msmq4AM9iAoK"
   },
   "outputs": [],
   "source": [
    "delta, labels = calc_delta(df_val['class'], pre_rf, pre_rf.keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 336
    },
    "colab_type": "code",
    "id": "oYEC6bpAiEug",
    "outputId": "3a9513f8-be78-48d6-b7f2-6a1d9bad8b6c"
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAVEAAAE/CAYAAAD/iPBoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "gera_tsne(delta, 'RandomForest', list(labels), escala=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "D7p6Lo2diH0o"
   },
   "outputs": [],
   "source": [
    "pre.update(pre_rf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "rqjoNKjNiPMF"
   },
   "source": [
    "# NaiveBayes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Py7MPTQziQkk"
   },
   "outputs": [],
   "source": [
    "nb = {\n",
    "    'CV': {\n",
    "        'CLF': MultinomialNB(alpha=1, fit_prior=False),\n",
    "        'EXT': cv,\n",
    "    },\n",
    "    'TFIDF': {\n",
    "        'CLF': MultinomialNB(alpha=0.5, fit_prior=False),\n",
    "        'EXT': tfidf,\n",
    "    },\n",
    "    'W2V': {\n",
    "        'CLF': BernoulliNB(alpha=0.5, fit_prior=True),\n",
    "        'EXT': w2v,\n",
    "    },\n",
    "    'GLOVE': {\n",
    "        'CLF': BernoulliNB(alpha=0.1, fit_prior=True),\n",
    "        'EXT':  glove,\n",
    "    },\n",
    "    'FAST': {\n",
    "        'CLF': BernoulliNB(alpha=1, fit_prior=True),\n",
    "        'EXT': fasttext,\n",
    "    }\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "EFM6vWcXinx1"
   },
   "outputs": [],
   "source": [
    "nb_clfs = {\n",
    "\n",
    "}\n",
    "for ext, clf in nb.items():\n",
    "    nb_clfs[ext] = get_classifier(clf['CLF'], train, df_train['class'], clf['EXT'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "d6Egk7_5iseR"
   },
   "outputs": [],
   "source": [
    "pre_nb = {\n",
    "\n",
    "}\n",
    "for ext, clf in nb_clfs.items():\n",
    "    pre_nb['NB-'+ext] = clf.predict(val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "me3Q71VSi1sx"
   },
   "outputs": [],
   "source": [
    "delta, labels = calc_delta(df_val['class'], pre_nb, pre_nb.keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 336
    },
    "colab_type": "code",
    "id": "X1ewhlhdi_ER",
    "outputId": "1d8a54ec-80a2-4b08-c743-5d0167397f38"
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAUcAAAE/CAYAAADVOAHHAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nh6OZWcb/B+ye8dt4I1LgAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "gera_tsne(delta, 'NaiveBayes', list(labels), escala=350)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "jvXLLTDijCOy"
   },
   "outputs": [],
   "source": [
    "pre.update(pre_nb)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "bADgN3QvjQcU"
   },
   "source": [
    "# MLP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "uYtF4P3pjFmP"
   },
   "outputs": [],
   "source": [
    "mlp = {\n",
    "    'CV': {\n",
    "        'CLF': MLPClassifier(random_state=42, batch_size=20, max_iter=20, verbose=100, activation='relu', solver='lbfgs'),\n",
    "        'EXT': cv,\n",
    "    },\n",
    "    'TFIDF': {\n",
    "        'CLF': MLPClassifier(random_state=42, batch_size=20, max_iter=20, verbose=100, activation='logistic', solver='adam'),\n",
    "        'EXT': tfidf,\n",
    "    },\n",
    "    'W2V': {\n",
    "        'CLF': MLPClassifier(random_state=42, batch_size=20, max_iter=20, verbose=100, activation='relu', solver='adam'),\n",
    "        'EXT': w2v,\n",
    "    },\n",
    "    'GLOVE': {\n",
    "        'CLF': MLPClassifier(random_state=42, batch_size=20, max_iter=20, verbose=100, activation='relu', solver='adam'),\n",
    "        'EXT':  glove,\n",
    "    },\n",
    "    'FAST': {\n",
    "        'CLF': MLPClassifier(random_state=42, batch_size=20, max_iter=20, verbose=100, activation='relu', solver='adam'),\n",
    "        'EXT': fasttext,\n",
    "    }\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "colab_type": "code",
    "id": "mXWz0oPhjlfh",
    "outputId": "f239ccd8-d977-44e3-dee0-570a3f3e25b0"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 1, loss = 0.60941986\n",
      "Iteration 2, loss = 0.41521054\n",
      "Iteration 3, loss = 0.30750962\n",
      "Iteration 4, loss = 0.25374964\n",
      "Iteration 5, loss = 0.21959876\n",
      "Iteration 6, loss = 0.19513646\n",
      "Iteration 7, loss = 0.17652194\n",
      "Iteration 8, loss = 0.16174140\n",
      "Iteration 9, loss = 0.14984757\n",
      "Iteration 10, loss = 0.13980109\n",
      "Iteration 11, loss = 0.13132903\n",
      "Iteration 12, loss = 0.12438988\n",
      "Iteration 13, loss = 0.11802265\n",
      "Iteration 14, loss = 0.11269725\n",
      "Iteration 15, loss = 0.10788111\n",
      "Iteration 16, loss = 0.10334254\n",
      "Iteration 17, loss = 0.10010296\n",
      "Iteration 18, loss = 0.09632147\n",
      "Iteration 19, loss = 0.09383296\n",
      "Iteration 20, loss = 0.09094837\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.6/dist-packages/sklearn/neural_network/multilayer_perceptron.py:566: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (20) reached and the optimization hasn't converged yet.\n",
      "  % self.max_iter, ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 1, loss = 0.47096438\n",
      "Iteration 2, loss = 0.40325746\n",
      "Iteration 3, loss = 0.38423555\n",
      "Iteration 4, loss = 0.37239274\n",
      "Iteration 5, loss = 0.36178605\n",
      "Iteration 6, loss = 0.35188223\n",
      "Iteration 7, loss = 0.34173046\n",
      "Iteration 8, loss = 0.33450757\n",
      "Iteration 9, loss = 0.32556258\n",
      "Iteration 10, loss = 0.31724949\n",
      "Iteration 11, loss = 0.31054098\n",
      "Iteration 12, loss = 0.30246049\n",
      "Iteration 13, loss = 0.29497105\n",
      "Iteration 14, loss = 0.28648034\n",
      "Iteration 15, loss = 0.27953092\n",
      "Iteration 16, loss = 0.27336929\n",
      "Iteration 17, loss = 0.26443955\n",
      "Iteration 18, loss = 0.25756238\n",
      "Iteration 19, loss = 0.25052649\n",
      "Iteration 20, loss = 0.24392953\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.6/dist-packages/sklearn/neural_network/multilayer_perceptron.py:566: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (20) reached and the optimization hasn't converged yet.\n",
      "  % self.max_iter, ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 1, loss = 0.51324616\n",
      "Iteration 2, loss = 0.48328811\n",
      "Iteration 3, loss = 0.47602715\n",
      "Iteration 4, loss = 0.46867845\n",
      "Iteration 5, loss = 0.46579464\n",
      "Iteration 6, loss = 0.46111646\n",
      "Iteration 7, loss = 0.45723375\n",
      "Iteration 8, loss = 0.45548453\n",
      "Iteration 9, loss = 0.45215457\n",
      "Iteration 10, loss = 0.44906359\n",
      "Iteration 11, loss = 0.44807205\n",
      "Iteration 12, loss = 0.44462039\n",
      "Iteration 13, loss = 0.44277098\n",
      "Iteration 14, loss = 0.44035827\n",
      "Iteration 15, loss = 0.43859533\n",
      "Iteration 16, loss = 0.43706094\n",
      "Iteration 17, loss = 0.43554432\n",
      "Iteration 18, loss = 0.43325094\n",
      "Iteration 19, loss = 0.43281282\n",
      "Iteration 20, loss = 0.43097964\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.6/dist-packages/sklearn/neural_network/multilayer_perceptron.py:566: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (20) reached and the optimization hasn't converged yet.\n",
      "  % self.max_iter, ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 1, loss = 0.50055697\n",
      "Iteration 2, loss = 0.40483245\n",
      "Iteration 3, loss = 0.38585786\n",
      "Iteration 4, loss = 0.37354835\n",
      "Iteration 5, loss = 0.36551086\n",
      "Iteration 6, loss = 0.35913265\n",
      "Iteration 7, loss = 0.35193565\n",
      "Iteration 8, loss = 0.34805301\n",
      "Iteration 9, loss = 0.34321217\n",
      "Iteration 10, loss = 0.33780297\n",
      "Iteration 11, loss = 0.33535158\n",
      "Iteration 12, loss = 0.33160899\n",
      "Iteration 13, loss = 0.32785932\n",
      "Iteration 14, loss = 0.32211126\n",
      "Iteration 15, loss = 0.31972493\n",
      "Iteration 16, loss = 0.31649134\n",
      "Iteration 17, loss = 0.31222652\n",
      "Iteration 18, loss = 0.30881306\n",
      "Iteration 19, loss = 0.30596117\n",
      "Iteration 20, loss = 0.30269560\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.6/dist-packages/sklearn/neural_network/multilayer_perceptron.py:566: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (20) reached and the optimization hasn't converged yet.\n",
      "  % self.max_iter, ConvergenceWarning)\n"
     ]
    }
   ],
   "source": [
    "mlp_clfs = {\n",
    "\n",
    "}\n",
    "for ext, clf in mlp.items():\n",
    "    mlp_clfs[ext] = get_classifier(clf['CLF'], train, df_train['class'], clf['EXT'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "1esFI2Tdjp2v"
   },
   "outputs": [],
   "source": [
    "pre_mlp = {\n",
    "\n",
    "}\n",
    "for ext, clf in mlp_clfs.items():\n",
    "    pre_mlp['MLP-'+ext] = clf.predict(val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "t30ark_1jvE8"
   },
   "outputs": [],
   "source": [
    "delta, labels = calc_delta(df_val['class'], pre_mlp, pre_mlp.keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 336
    },
    "colab_type": "code",
    "id": "87MJpG_Tjyb1",
    "outputId": "3c0f802d-573c-4f6d-eb51-d782e33af514"
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAVEAAAE/CAYAAAD/iPBoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "gera_tsne(delta, 'MLP', list(labels), escala=400)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Jth3maC5j1ZF"
   },
   "outputs": [],
   "source": [
    "pre.update(pre_mlp)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "r7qV51yUB8fc"
   },
   "source": [
    "# ExtraTrees"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "uePbrkFJB-tm"
   },
   "outputs": [],
   "source": [
    "extra = {\n",
    "    'CV': {\n",
    "        'CLF': ExtraTreesClassifier(random_state=42, verbose=100, n_estimators=50),\n",
    "        'EXT': cv,\n",
    "    },\n",
    "    'TFIDF': {\n",
    "        'CLF': ExtraTreesClassifier(random_state=42, verbose=100, n_estimators=50),\n",
    "        'EXT': tfidf,\n",
    "    },\n",
    "    'W2V': {\n",
    "        'CLF': ExtraTreesClassifier(random_state=42, verbose=100, n_estimators=50),\n",
    "        'EXT': w2v,\n",
    "    },\n",
    "    'GLOVE': {\n",
    "        'CLF': ExtraTreesClassifier(random_state=42, verbose=100, n_estimators=50),\n",
    "        'EXT':  glove,\n",
    "    },\n",
    "    'FAST': {\n",
    "        'CLF': ExtraTreesClassifier(random_state=42, verbose=100, n_estimators=50),\n",
    "        'EXT': fasttext,\n",
    "    }\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "colab_type": "code",
    "id": "hVnG6LtKkBGO",
    "outputId": "c3fd721f-05ac-47f7-86d6-15f0a529c54a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "building tree 1 of 50\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.3s remaining:    0.0s\n",
      "building tree 2 of 50\n",
      "[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:    0.5s remaining:    0.0s\n",
      "building tree 3 of 50\n",
      "[Parallel(n_jobs=1)]: Done   3 out of   3 | elapsed:    0.8s remaining:    0.0s\n",
      "building tree 4 of 50\n",
      "[Parallel(n_jobs=1)]: Done   4 out of   4 | elapsed:    1.1s remaining:    0.0s\n",
      "building tree 5 of 50\n",
      "[Parallel(n_jobs=1)]: Done   5 out of   5 | elapsed:    1.4s remaining:    0.0s\n",
      "building tree 6 of 50\n",
      "[Parallel(n_jobs=1)]: Done   6 out of   6 | elapsed:    1.6s remaining:    0.0s\n",
      "building tree 7 of 50\n",
      "[Parallel(n_jobs=1)]: Done   7 out of   7 | elapsed:    1.9s remaining:    0.0s\n",
      "building tree 8 of 50\n",
      "[Parallel(n_jobs=1)]: Done   8 out of   8 | elapsed:    2.2s remaining:    0.0s\n",
      "building tree 9 of 50\n",
      "[Parallel(n_jobs=1)]: Done   9 out of   9 | elapsed:    2.4s remaining:    0.0s\n",
      "building tree 10 of 50\n",
      "[Parallel(n_jobs=1)]: Done  10 out of  10 | elapsed:    2.7s remaining:    0.0s\n",
      "building tree 11 of 50\n",
      "[Parallel(n_jobs=1)]: Done  11 out of  11 | elapsed:    2.9s remaining:    0.0s\n",
      "building tree 12 of 50\n",
      "[Parallel(n_jobs=1)]: Done  12 out of  12 | elapsed:    3.2s remaining:    0.0s\n",
      "building tree 13 of 50\n",
      "[Parallel(n_jobs=1)]: Done  13 out of  13 | elapsed:    3.5s remaining:    0.0s\n",
      "building tree 14 of 50\n",
      "[Parallel(n_jobs=1)]: Done  14 out of  14 | elapsed:    3.8s remaining:    0.0s\n",
      "building tree 15 of 50\n",
      "[Parallel(n_jobs=1)]: Done  15 out of  15 | elapsed:    4.0s remaining:    0.0s\n",
      "building tree 16 of 50\n",
      "[Parallel(n_jobs=1)]: Done  16 out of  16 | elapsed:    4.2s remaining:    0.0s\n",
      "building tree 17 of 50\n",
      "[Parallel(n_jobs=1)]: Done  17 out of  17 | elapsed:    4.5s remaining:    0.0s\n",
      "building tree 18 of 50\n",
      "[Parallel(n_jobs=1)]: Done  18 out of  18 | elapsed:    4.7s remaining:    0.0s\n",
      "building tree 19 of 50\n",
      "[Parallel(n_jobs=1)]: Done  19 out of  19 | elapsed:    5.0s remaining:    0.0s\n",
      "building tree 20 of 50\n",
      "[Parallel(n_jobs=1)]: Done  20 out of  20 | elapsed:    5.3s remaining:    0.0s\n",
      "building tree 21 of 50\n",
      "[Parallel(n_jobs=1)]: Done  21 out of  21 | elapsed:    5.6s remaining:    0.0s\n",
      "building tree 22 of 50\n",
      "[Parallel(n_jobs=1)]: Done  22 out of  22 | elapsed:    5.8s remaining:    0.0s\n",
      "building tree 23 of 50\n",
      "[Parallel(n_jobs=1)]: Done  23 out of  23 | elapsed:    6.1s remaining:    0.0s\n",
      "building tree 24 of 50\n",
      "[Parallel(n_jobs=1)]: Done  24 out of  24 | elapsed:    6.4s remaining:    0.0s\n",
      "building tree 25 of 50\n",
      "[Parallel(n_jobs=1)]: Done  25 out of  25 | elapsed:    6.6s remaining:    0.0s\n",
      "building tree 26 of 50\n",
      "[Parallel(n_jobs=1)]: Done  26 out of  26 | elapsed:    6.9s remaining:    0.0s\n",
      "building tree 27 of 50\n",
      "[Parallel(n_jobs=1)]: Done  27 out of  27 | elapsed:    7.1s remaining:    0.0s\n",
      "building tree 28 of 50\n",
      "[Parallel(n_jobs=1)]: Done  28 out of  28 | elapsed:    7.4s remaining:    0.0s\n",
      "building tree 29 of 50\n",
      "[Parallel(n_jobs=1)]: Done  29 out of  29 | elapsed:    7.6s remaining:    0.0s\n",
      "building tree 30 of 50\n",
      "[Parallel(n_jobs=1)]: Done  30 out of  30 | elapsed:    7.9s remaining:    0.0s\n",
      "building tree 31 of 50\n",
      "[Parallel(n_jobs=1)]: Done  31 out of  31 | elapsed:    8.2s remaining:    0.0s\n",
      "building tree 32 of 50\n",
      "[Parallel(n_jobs=1)]: Done  32 out of  32 | elapsed:    8.4s remaining:    0.0s\n",
      "building tree 33 of 50\n",
      "[Parallel(n_jobs=1)]: Done  33 out of  33 | elapsed:    8.7s remaining:    0.0s\n",
      "building tree 34 of 50\n",
      "[Parallel(n_jobs=1)]: Done  34 out of  34 | elapsed:    9.0s remaining:    0.0s\n",
      "building tree 35 of 50\n",
      "[Parallel(n_jobs=1)]: Done  35 out of  35 | elapsed:    9.3s remaining:    0.0s\n",
      "building tree 36 of 50\n",
      "[Parallel(n_jobs=1)]: Done  36 out of  36 | elapsed:    9.5s remaining:    0.0s\n",
      "building tree 37 of 50\n",
      "[Parallel(n_jobs=1)]: Done  37 out of  37 | elapsed:    9.8s remaining:    0.0s\n",
      "building tree 38 of 50\n",
      "[Parallel(n_jobs=1)]: Done  38 out of  38 | elapsed:   10.1s remaining:    0.0s\n",
      "building tree 39 of 50\n",
      "[Parallel(n_jobs=1)]: Done  39 out of  39 | elapsed:   10.3s remaining:    0.0s\n",
      "building tree 40 of 50\n",
      "[Parallel(n_jobs=1)]: Done  40 out of  40 | elapsed:   10.6s remaining:    0.0s\n",
      "building tree 41 of 50\n",
      "[Parallel(n_jobs=1)]: Done  41 out of  41 | elapsed:   10.8s remaining:    0.0s\n",
      "building tree 42 of 50\n",
      "[Parallel(n_jobs=1)]: Done  42 out of  42 | elapsed:   11.1s remaining:    0.0s\n",
      "building tree 43 of 50\n",
      "[Parallel(n_jobs=1)]: Done  43 out of  43 | elapsed:   11.4s remaining:    0.0s\n",
      "building tree 44 of 50\n",
      "[Parallel(n_jobs=1)]: Done  44 out of  44 | elapsed:   11.6s remaining:    0.0s\n",
      "building tree 45 of 50\n",
      "[Parallel(n_jobs=1)]: Done  45 out of  45 | elapsed:   11.9s remaining:    0.0s\n",
      "building tree 46 of 50\n",
      "[Parallel(n_jobs=1)]: Done  46 out of  46 | elapsed:   12.1s remaining:    0.0s\n",
      "building tree 47 of 50\n",
      "[Parallel(n_jobs=1)]: Done  47 out of  47 | elapsed:   12.4s remaining:    0.0s\n",
      "building tree 48 of 50\n",
      "[Parallel(n_jobs=1)]: Done  48 out of  48 | elapsed:   12.7s remaining:    0.0s\n",
      "building tree 49 of 50\n",
      "[Parallel(n_jobs=1)]: Done  49 out of  49 | elapsed:   12.9s remaining:    0.0s\n",
      "building tree 50 of 50\n",
      "[Parallel(n_jobs=1)]: Done  50 out of  50 | elapsed:   13.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  50 out of  50 | elapsed:   13.2s finished\n",
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "building tree 1 of 50\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.2s remaining:    0.0s\n",
      "building tree 2 of 50\n",
      "[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:    0.5s remaining:    0.0s\n",
      "building tree 3 of 50\n",
      "[Parallel(n_jobs=1)]: Done   3 out of   3 | elapsed:    0.7s remaining:    0.0s\n",
      "building tree 4 of 50\n",
      "[Parallel(n_jobs=1)]: Done   4 out of   4 | elapsed:    1.0s remaining:    0.0s\n",
      "building tree 5 of 50\n",
      "[Parallel(n_jobs=1)]: Done   5 out of   5 | elapsed:    1.2s remaining:    0.0s\n",
      "building tree 6 of 50\n",
      "[Parallel(n_jobs=1)]: Done   6 out of   6 | elapsed:    1.5s remaining:    0.0s\n",
      "building tree 7 of 50\n",
      "[Parallel(n_jobs=1)]: Done   7 out of   7 | elapsed:    1.8s remaining:    0.0s\n",
      "building tree 8 of 50\n",
      "[Parallel(n_jobs=1)]: Done   8 out of   8 | elapsed:    2.0s remaining:    0.0s\n",
      "building tree 9 of 50\n",
      "[Parallel(n_jobs=1)]: Done   9 out of   9 | elapsed:    2.3s remaining:    0.0s\n",
      "building tree 10 of 50\n",
      "[Parallel(n_jobs=1)]: Done  10 out of  10 | elapsed:    2.5s remaining:    0.0s\n",
      "building tree 11 of 50\n",
      "[Parallel(n_jobs=1)]: Done  11 out of  11 | elapsed:    2.7s remaining:    0.0s\n",
      "building tree 12 of 50\n",
      "[Parallel(n_jobs=1)]: Done  12 out of  12 | elapsed:    3.0s remaining:    0.0s\n",
      "building tree 13 of 50\n",
      "[Parallel(n_jobs=1)]: Done  13 out of  13 | elapsed:    3.2s remaining:    0.0s\n",
      "building tree 14 of 50\n",
      "[Parallel(n_jobs=1)]: Done  14 out of  14 | elapsed:    3.5s remaining:    0.0s\n",
      "building tree 15 of 50\n",
      "[Parallel(n_jobs=1)]: Done  15 out of  15 | elapsed:    3.7s remaining:    0.0s\n",
      "building tree 16 of 50\n",
      "[Parallel(n_jobs=1)]: Done  16 out of  16 | elapsed:    4.0s remaining:    0.0s\n",
      "building tree 17 of 50\n",
      "[Parallel(n_jobs=1)]: Done  17 out of  17 | elapsed:    4.2s remaining:    0.0s\n",
      "building tree 18 of 50\n",
      "[Parallel(n_jobs=1)]: Done  18 out of  18 | elapsed:    4.5s remaining:    0.0s\n",
      "building tree 19 of 50\n",
      "[Parallel(n_jobs=1)]: Done  19 out of  19 | elapsed:    4.7s remaining:    0.0s\n",
      "building tree 20 of 50\n",
      "[Parallel(n_jobs=1)]: Done  20 out of  20 | elapsed:    5.0s remaining:    0.0s\n",
      "building tree 21 of 50\n",
      "[Parallel(n_jobs=1)]: Done  21 out of  21 | elapsed:    5.2s remaining:    0.0s\n",
      "building tree 22 of 50\n",
      "[Parallel(n_jobs=1)]: Done  22 out of  22 | elapsed:    5.5s remaining:    0.0s\n",
      "building tree 23 of 50\n",
      "[Parallel(n_jobs=1)]: Done  23 out of  23 | elapsed:    5.7s remaining:    0.0s\n",
      "building tree 24 of 50\n",
      "[Parallel(n_jobs=1)]: Done  24 out of  24 | elapsed:    6.0s remaining:    0.0s\n",
      "building tree 25 of 50\n",
      "[Parallel(n_jobs=1)]: Done  25 out of  25 | elapsed:    6.2s remaining:    0.0s\n",
      "building tree 26 of 50\n",
      "[Parallel(n_jobs=1)]: Done  26 out of  26 | elapsed:    6.5s remaining:    0.0s\n",
      "building tree 27 of 50\n",
      "[Parallel(n_jobs=1)]: Done  27 out of  27 | elapsed:    6.7s remaining:    0.0s\n",
      "building tree 28 of 50\n",
      "[Parallel(n_jobs=1)]: Done  28 out of  28 | elapsed:    6.9s remaining:    0.0s\n",
      "building tree 29 of 50\n",
      "[Parallel(n_jobs=1)]: Done  29 out of  29 | elapsed:    7.2s remaining:    0.0s\n",
      "building tree 30 of 50\n",
      "[Parallel(n_jobs=1)]: Done  30 out of  30 | elapsed:    7.4s remaining:    0.0s\n",
      "building tree 31 of 50\n",
      "[Parallel(n_jobs=1)]: Done  31 out of  31 | elapsed:    7.6s remaining:    0.0s\n",
      "building tree 32 of 50\n",
      "[Parallel(n_jobs=1)]: Done  32 out of  32 | elapsed:    7.9s remaining:    0.0s\n",
      "building tree 33 of 50\n",
      "[Parallel(n_jobs=1)]: Done  33 out of  33 | elapsed:    8.2s remaining:    0.0s\n",
      "building tree 34 of 50\n",
      "[Parallel(n_jobs=1)]: Done  34 out of  34 | elapsed:    8.4s remaining:    0.0s\n",
      "building tree 35 of 50\n",
      "[Parallel(n_jobs=1)]: Done  35 out of  35 | elapsed:    8.7s remaining:    0.0s\n",
      "building tree 36 of 50\n",
      "[Parallel(n_jobs=1)]: Done  36 out of  36 | elapsed:    8.9s remaining:    0.0s\n",
      "building tree 37 of 50\n",
      "[Parallel(n_jobs=1)]: Done  37 out of  37 | elapsed:    9.2s remaining:    0.0s\n",
      "building tree 38 of 50\n",
      "[Parallel(n_jobs=1)]: Done  38 out of  38 | elapsed:    9.4s remaining:    0.0s\n",
      "building tree 39 of 50\n",
      "[Parallel(n_jobs=1)]: Done  39 out of  39 | elapsed:    9.7s remaining:    0.0s\n",
      "building tree 40 of 50\n",
      "[Parallel(n_jobs=1)]: Done  40 out of  40 | elapsed:    9.9s remaining:    0.0s\n",
      "building tree 41 of 50\n",
      "[Parallel(n_jobs=1)]: Done  41 out of  41 | elapsed:   10.2s remaining:    0.0s\n",
      "building tree 42 of 50\n",
      "[Parallel(n_jobs=1)]: Done  42 out of  42 | elapsed:   10.4s remaining:    0.0s\n",
      "building tree 43 of 50\n",
      "[Parallel(n_jobs=1)]: Done  43 out of  43 | elapsed:   10.7s remaining:    0.0s\n",
      "building tree 44 of 50\n",
      "[Parallel(n_jobs=1)]: Done  44 out of  44 | elapsed:   10.9s remaining:    0.0s\n",
      "building tree 45 of 50\n",
      "[Parallel(n_jobs=1)]: Done  45 out of  45 | elapsed:   11.1s remaining:    0.0s\n",
      "building tree 46 of 50\n",
      "[Parallel(n_jobs=1)]: Done  46 out of  46 | elapsed:   11.4s remaining:    0.0s\n",
      "building tree 47 of 50\n",
      "[Parallel(n_jobs=1)]: Done  47 out of  47 | elapsed:   11.6s remaining:    0.0s\n",
      "building tree 48 of 50\n",
      "[Parallel(n_jobs=1)]: Done  48 out of  48 | elapsed:   11.9s remaining:    0.0s\n",
      "building tree 49 of 50\n",
      "[Parallel(n_jobs=1)]: Done  49 out of  49 | elapsed:   12.1s remaining:    0.0s\n",
      "building tree 50 of 50\n",
      "[Parallel(n_jobs=1)]: Done  50 out of  50 | elapsed:   12.4s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  50 out of  50 | elapsed:   12.4s finished\n",
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "building tree 1 of 50\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.1s remaining:    0.0s\n",
      "building tree 2 of 50\n",
      "[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:    0.1s remaining:    0.0s\n",
      "building tree 3 of 50\n",
      "[Parallel(n_jobs=1)]: Done   3 out of   3 | elapsed:    0.1s remaining:    0.0s\n",
      "building tree 4 of 50\n",
      "[Parallel(n_jobs=1)]: Done   4 out of   4 | elapsed:    0.2s remaining:    0.0s\n",
      "building tree 5 of 50\n",
      "[Parallel(n_jobs=1)]: Done   5 out of   5 | elapsed:    0.2s remaining:    0.0s\n",
      "building tree 6 of 50\n",
      "[Parallel(n_jobs=1)]: Done   6 out of   6 | elapsed:    0.3s remaining:    0.0s\n",
      "building tree 7 of 50\n",
      "[Parallel(n_jobs=1)]: Done   7 out of   7 | elapsed:    0.3s remaining:    0.0s\n",
      "building tree 8 of 50\n",
      "[Parallel(n_jobs=1)]: Done   8 out of   8 | elapsed:    0.4s remaining:    0.0s\n",
      "building tree 9 of 50\n",
      "[Parallel(n_jobs=1)]: Done   9 out of   9 | elapsed:    0.4s remaining:    0.0s\n",
      "building tree 10 of 50\n",
      "[Parallel(n_jobs=1)]: Done  10 out of  10 | elapsed:    0.5s remaining:    0.0s\n",
      "building tree 11 of 50\n",
      "[Parallel(n_jobs=1)]: Done  11 out of  11 | elapsed:    0.5s remaining:    0.0s\n",
      "building tree 12 of 50\n",
      "[Parallel(n_jobs=1)]: Done  12 out of  12 | elapsed:    0.6s remaining:    0.0s\n",
      "building tree 13 of 50\n",
      "[Parallel(n_jobs=1)]: Done  13 out of  13 | elapsed:    0.6s remaining:    0.0s\n",
      "building tree 14 of 50\n",
      "[Parallel(n_jobs=1)]: Done  14 out of  14 | elapsed:    0.7s remaining:    0.0s\n",
      "building tree 15 of 50\n",
      "[Parallel(n_jobs=1)]: Done  15 out of  15 | elapsed:    0.7s remaining:    0.0s\n",
      "building tree 16 of 50\n",
      "[Parallel(n_jobs=1)]: Done  16 out of  16 | elapsed:    0.8s remaining:    0.0s\n",
      "building tree 17 of 50\n",
      "[Parallel(n_jobs=1)]: Done  17 out of  17 | elapsed:    0.8s remaining:    0.0s\n",
      "building tree 18 of 50\n",
      "[Parallel(n_jobs=1)]: Done  18 out of  18 | elapsed:    0.8s remaining:    0.0s\n",
      "building tree 19 of 50\n",
      "[Parallel(n_jobs=1)]: Done  19 out of  19 | elapsed:    0.9s remaining:    0.0s\n",
      "building tree 20 of 50\n",
      "[Parallel(n_jobs=1)]: Done  20 out of  20 | elapsed:    0.9s remaining:    0.0s\n",
      "building tree 21 of 50\n",
      "[Parallel(n_jobs=1)]: Done  21 out of  21 | elapsed:    1.0s remaining:    0.0s\n",
      "building tree 22 of 50\n",
      "[Parallel(n_jobs=1)]: Done  22 out of  22 | elapsed:    1.0s remaining:    0.0s\n",
      "building tree 23 of 50\n",
      "[Parallel(n_jobs=1)]: Done  23 out of  23 | elapsed:    1.1s remaining:    0.0s\n",
      "building tree 24 of 50\n",
      "[Parallel(n_jobs=1)]: Done  24 out of  24 | elapsed:    1.1s remaining:    0.0s\n",
      "building tree 25 of 50\n",
      "[Parallel(n_jobs=1)]: Done  25 out of  25 | elapsed:    1.1s remaining:    0.0s\n",
      "building tree 26 of 50\n",
      "[Parallel(n_jobs=1)]: Done  26 out of  26 | elapsed:    1.2s remaining:    0.0s\n",
      "building tree 27 of 50\n",
      "[Parallel(n_jobs=1)]: Done  27 out of  27 | elapsed:    1.2s remaining:    0.0s\n",
      "building tree 28 of 50\n",
      "[Parallel(n_jobs=1)]: Done  28 out of  28 | elapsed:    1.3s remaining:    0.0s\n",
      "building tree 29 of 50\n",
      "[Parallel(n_jobs=1)]: Done  29 out of  29 | elapsed:    1.3s remaining:    0.0s\n",
      "building tree 30 of 50\n",
      "[Parallel(n_jobs=1)]: Done  30 out of  30 | elapsed:    1.4s remaining:    0.0s\n",
      "building tree 31 of 50\n",
      "[Parallel(n_jobs=1)]: Done  31 out of  31 | elapsed:    1.4s remaining:    0.0s\n",
      "building tree 32 of 50\n",
      "[Parallel(n_jobs=1)]: Done  32 out of  32 | elapsed:    1.5s remaining:    0.0s\n",
      "building tree 33 of 50\n",
      "[Parallel(n_jobs=1)]: Done  33 out of  33 | elapsed:    1.5s remaining:    0.0s\n",
      "building tree 34 of 50\n",
      "[Parallel(n_jobs=1)]: Done  34 out of  34 | elapsed:    1.5s remaining:    0.0s\n",
      "building tree 35 of 50\n",
      "[Parallel(n_jobs=1)]: Done  35 out of  35 | elapsed:    1.6s remaining:    0.0s\n",
      "building tree 36 of 50\n",
      "[Parallel(n_jobs=1)]: Done  36 out of  36 | elapsed:    1.6s remaining:    0.0s\n",
      "building tree 37 of 50\n",
      "[Parallel(n_jobs=1)]: Done  37 out of  37 | elapsed:    1.7s remaining:    0.0s\n",
      "building tree 38 of 50\n",
      "[Parallel(n_jobs=1)]: Done  38 out of  38 | elapsed:    1.7s remaining:    0.0s\n",
      "building tree 39 of 50\n",
      "[Parallel(n_jobs=1)]: Done  39 out of  39 | elapsed:    1.8s remaining:    0.0s\n",
      "building tree 40 of 50\n",
      "[Parallel(n_jobs=1)]: Done  40 out of  40 | elapsed:    1.8s remaining:    0.0s\n",
      "building tree 41 of 50\n",
      "[Parallel(n_jobs=1)]: Done  41 out of  41 | elapsed:    1.9s remaining:    0.0s\n",
      "building tree 42 of 50\n",
      "[Parallel(n_jobs=1)]: Done  42 out of  42 | elapsed:    1.9s remaining:    0.0s\n",
      "building tree 43 of 50\n",
      "[Parallel(n_jobs=1)]: Done  43 out of  43 | elapsed:    2.0s remaining:    0.0s\n",
      "building tree 44 of 50\n",
      "[Parallel(n_jobs=1)]: Done  44 out of  44 | elapsed:    2.0s remaining:    0.0s\n",
      "building tree 45 of 50\n",
      "[Parallel(n_jobs=1)]: Done  45 out of  45 | elapsed:    2.0s remaining:    0.0s\n",
      "building tree 46 of 50\n",
      "[Parallel(n_jobs=1)]: Done  46 out of  46 | elapsed:    2.1s remaining:    0.0s\n",
      "building tree 47 of 50\n",
      "[Parallel(n_jobs=1)]: Done  47 out of  47 | elapsed:    2.1s remaining:    0.0s\n",
      "building tree 48 of 50\n",
      "[Parallel(n_jobs=1)]: Done  48 out of  48 | elapsed:    2.2s remaining:    0.0s\n",
      "building tree 49 of 50\n",
      "[Parallel(n_jobs=1)]: Done  49 out of  49 | elapsed:    2.2s remaining:    0.0s\n",
      "building tree 50 of 50\n",
      "[Parallel(n_jobs=1)]: Done  50 out of  50 | elapsed:    2.3s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  50 out of  50 | elapsed:    2.3s finished\n",
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "building tree 1 of 50\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
      "building tree 2 of 50\n",
      "[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:    0.0s remaining:    0.0s\n",
      "building tree 3 of 50\n",
      "[Parallel(n_jobs=1)]: Done   3 out of   3 | elapsed:    0.1s remaining:    0.0s\n",
      "building tree 4 of 50\n",
      "[Parallel(n_jobs=1)]: Done   4 out of   4 | elapsed:    0.1s remaining:    0.0s\n",
      "building tree 5 of 50\n",
      "[Parallel(n_jobs=1)]: Done   5 out of   5 | elapsed:    0.1s remaining:    0.0s\n",
      "building tree 6 of 50\n",
      "[Parallel(n_jobs=1)]: Done   6 out of   6 | elapsed:    0.1s remaining:    0.0s\n",
      "building tree 7 of 50\n",
      "[Parallel(n_jobs=1)]: Done   7 out of   7 | elapsed:    0.1s remaining:    0.0s\n",
      "building tree 8 of 50\n",
      "[Parallel(n_jobs=1)]: Done   8 out of   8 | elapsed:    0.1s remaining:    0.0s\n",
      "building tree 9 of 50\n",
      "[Parallel(n_jobs=1)]: Done   9 out of   9 | elapsed:    0.2s remaining:    0.0s\n",
      "building tree 10 of 50\n",
      "[Parallel(n_jobs=1)]: Done  10 out of  10 | elapsed:    0.2s remaining:    0.0s\n",
      "building tree 11 of 50\n",
      "[Parallel(n_jobs=1)]: Done  11 out of  11 | elapsed:    0.2s remaining:    0.0s\n",
      "building tree 12 of 50\n",
      "[Parallel(n_jobs=1)]: Done  12 out of  12 | elapsed:    0.2s remaining:    0.0s\n",
      "building tree 13 of 50\n",
      "[Parallel(n_jobs=1)]: Done  13 out of  13 | elapsed:    0.2s remaining:    0.0s\n",
      "building tree 14 of 50\n",
      "[Parallel(n_jobs=1)]: Done  14 out of  14 | elapsed:    0.3s remaining:    0.0s\n",
      "building tree 15 of 50\n",
      "[Parallel(n_jobs=1)]: Done  15 out of  15 | elapsed:    0.3s remaining:    0.0s\n",
      "building tree 16 of 50\n",
      "[Parallel(n_jobs=1)]: Done  16 out of  16 | elapsed:    0.3s remaining:    0.0s\n",
      "building tree 17 of 50\n",
      "[Parallel(n_jobs=1)]: Done  17 out of  17 | elapsed:    0.3s remaining:    0.0s\n",
      "building tree 18 of 50\n",
      "[Parallel(n_jobs=1)]: Done  18 out of  18 | elapsed:    0.3s remaining:    0.0s\n",
      "building tree 19 of 50\n",
      "[Parallel(n_jobs=1)]: Done  19 out of  19 | elapsed:    0.3s remaining:    0.0s\n",
      "building tree 20 of 50\n",
      "[Parallel(n_jobs=1)]: Done  20 out of  20 | elapsed:    0.4s remaining:    0.0s\n",
      "building tree 21 of 50\n",
      "[Parallel(n_jobs=1)]: Done  21 out of  21 | elapsed:    0.4s remaining:    0.0s\n",
      "building tree 22 of 50\n",
      "[Parallel(n_jobs=1)]: Done  22 out of  22 | elapsed:    0.4s remaining:    0.0s\n",
      "building tree 23 of 50\n",
      "[Parallel(n_jobs=1)]: Done  23 out of  23 | elapsed:    0.4s remaining:    0.0s\n",
      "building tree 24 of 50\n",
      "[Parallel(n_jobs=1)]: Done  24 out of  24 | elapsed:    0.4s remaining:    0.0s\n",
      "building tree 25 of 50\n",
      "[Parallel(n_jobs=1)]: Done  25 out of  25 | elapsed:    0.5s remaining:    0.0s\n",
      "building tree 26 of 50\n",
      "[Parallel(n_jobs=1)]: Done  26 out of  26 | elapsed:    0.5s remaining:    0.0s\n",
      "building tree 27 of 50\n",
      "[Parallel(n_jobs=1)]: Done  27 out of  27 | elapsed:    0.5s remaining:    0.0s\n",
      "building tree 28 of 50\n",
      "[Parallel(n_jobs=1)]: Done  28 out of  28 | elapsed:    0.5s remaining:    0.0s\n",
      "building tree 29 of 50\n",
      "[Parallel(n_jobs=1)]: Done  29 out of  29 | elapsed:    0.5s remaining:    0.0s\n",
      "building tree 30 of 50\n",
      "[Parallel(n_jobs=1)]: Done  30 out of  30 | elapsed:    0.6s remaining:    0.0s\n",
      "building tree 31 of 50\n",
      "[Parallel(n_jobs=1)]: Done  31 out of  31 | elapsed:    0.6s remaining:    0.0s\n",
      "building tree 32 of 50\n",
      "[Parallel(n_jobs=1)]: Done  32 out of  32 | elapsed:    0.6s remaining:    0.0s\n",
      "building tree 33 of 50\n",
      "[Parallel(n_jobs=1)]: Done  33 out of  33 | elapsed:    0.6s remaining:    0.0s\n",
      "building tree 34 of 50\n",
      "[Parallel(n_jobs=1)]: Done  34 out of  34 | elapsed:    0.6s remaining:    0.0s\n",
      "building tree 35 of 50\n",
      "[Parallel(n_jobs=1)]: Done  35 out of  35 | elapsed:    0.6s remaining:    0.0s\n",
      "building tree 36 of 50\n",
      "[Parallel(n_jobs=1)]: Done  36 out of  36 | elapsed:    0.7s remaining:    0.0s\n",
      "building tree 37 of 50\n",
      "[Parallel(n_jobs=1)]: Done  37 out of  37 | elapsed:    0.7s remaining:    0.0s\n",
      "building tree 38 of 50\n",
      "[Parallel(n_jobs=1)]: Done  38 out of  38 | elapsed:    0.7s remaining:    0.0s\n",
      "building tree 39 of 50\n",
      "[Parallel(n_jobs=1)]: Done  39 out of  39 | elapsed:    0.7s remaining:    0.0s\n",
      "building tree 40 of 50\n",
      "[Parallel(n_jobs=1)]: Done  40 out of  40 | elapsed:    0.7s remaining:    0.0s\n",
      "building tree 41 of 50\n",
      "[Parallel(n_jobs=1)]: Done  41 out of  41 | elapsed:    0.8s remaining:    0.0s\n",
      "building tree 42 of 50\n",
      "[Parallel(n_jobs=1)]: Done  42 out of  42 | elapsed:    0.8s remaining:    0.0s\n",
      "building tree 43 of 50\n",
      "[Parallel(n_jobs=1)]: Done  43 out of  43 | elapsed:    0.8s remaining:    0.0s\n",
      "building tree 44 of 50\n",
      "[Parallel(n_jobs=1)]: Done  44 out of  44 | elapsed:    0.8s remaining:    0.0s\n",
      "building tree 45 of 50\n",
      "[Parallel(n_jobs=1)]: Done  45 out of  45 | elapsed:    0.8s remaining:    0.0s\n",
      "building tree 46 of 50\n",
      "[Parallel(n_jobs=1)]: Done  46 out of  46 | elapsed:    0.8s remaining:    0.0s\n",
      "building tree 47 of 50\n",
      "[Parallel(n_jobs=1)]: Done  47 out of  47 | elapsed:    0.9s remaining:    0.0s\n",
      "building tree 48 of 50\n",
      "[Parallel(n_jobs=1)]: Done  48 out of  48 | elapsed:    0.9s remaining:    0.0s\n",
      "building tree 49 of 50\n",
      "[Parallel(n_jobs=1)]: Done  49 out of  49 | elapsed:    0.9s remaining:    0.0s\n",
      "building tree 50 of 50\n",
      "[Parallel(n_jobs=1)]: Done  50 out of  50 | elapsed:    0.9s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  50 out of  50 | elapsed:    0.9s finished\n",
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "building tree 1 of 50\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.1s remaining:    0.0s\n",
      "building tree 2 of 50\n",
      "[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:    0.1s remaining:    0.0s\n",
      "building tree 3 of 50\n",
      "[Parallel(n_jobs=1)]: Done   3 out of   3 | elapsed:    0.2s remaining:    0.0s\n",
      "building tree 4 of 50\n",
      "[Parallel(n_jobs=1)]: Done   4 out of   4 | elapsed:    0.2s remaining:    0.0s\n",
      "building tree 5 of 50\n",
      "[Parallel(n_jobs=1)]: Done   5 out of   5 | elapsed:    0.3s remaining:    0.0s\n",
      "building tree 6 of 50\n",
      "[Parallel(n_jobs=1)]: Done   6 out of   6 | elapsed:    0.3s remaining:    0.0s\n",
      "building tree 7 of 50\n",
      "[Parallel(n_jobs=1)]: Done   7 out of   7 | elapsed:    0.4s remaining:    0.0s\n",
      "building tree 8 of 50\n",
      "[Parallel(n_jobs=1)]: Done   8 out of   8 | elapsed:    0.4s remaining:    0.0s\n",
      "building tree 9 of 50\n",
      "[Parallel(n_jobs=1)]: Done   9 out of   9 | elapsed:    0.5s remaining:    0.0s\n",
      "building tree 10 of 50\n",
      "[Parallel(n_jobs=1)]: Done  10 out of  10 | elapsed:    0.5s remaining:    0.0s\n",
      "building tree 11 of 50\n",
      "[Parallel(n_jobs=1)]: Done  11 out of  11 | elapsed:    0.6s remaining:    0.0s\n",
      "building tree 12 of 50\n",
      "[Parallel(n_jobs=1)]: Done  12 out of  12 | elapsed:    0.6s remaining:    0.0s\n",
      "building tree 13 of 50\n",
      "[Parallel(n_jobs=1)]: Done  13 out of  13 | elapsed:    0.7s remaining:    0.0s\n",
      "building tree 14 of 50\n",
      "[Parallel(n_jobs=1)]: Done  14 out of  14 | elapsed:    0.7s remaining:    0.0s\n",
      "building tree 15 of 50\n",
      "[Parallel(n_jobs=1)]: Done  15 out of  15 | elapsed:    0.8s remaining:    0.0s\n",
      "building tree 16 of 50\n",
      "[Parallel(n_jobs=1)]: Done  16 out of  16 | elapsed:    0.8s remaining:    0.0s\n",
      "building tree 17 of 50\n",
      "[Parallel(n_jobs=1)]: Done  17 out of  17 | elapsed:    0.9s remaining:    0.0s\n",
      "building tree 18 of 50\n",
      "[Parallel(n_jobs=1)]: Done  18 out of  18 | elapsed:    0.9s remaining:    0.0s\n",
      "building tree 19 of 50\n",
      "[Parallel(n_jobs=1)]: Done  19 out of  19 | elapsed:    1.0s remaining:    0.0s\n",
      "building tree 20 of 50\n",
      "[Parallel(n_jobs=1)]: Done  20 out of  20 | elapsed:    1.0s remaining:    0.0s\n",
      "building tree 21 of 50\n",
      "[Parallel(n_jobs=1)]: Done  21 out of  21 | elapsed:    1.1s remaining:    0.0s\n",
      "building tree 22 of 50\n",
      "[Parallel(n_jobs=1)]: Done  22 out of  22 | elapsed:    1.2s remaining:    0.0s\n",
      "building tree 23 of 50\n",
      "[Parallel(n_jobs=1)]: Done  23 out of  23 | elapsed:    1.2s remaining:    0.0s\n",
      "building tree 24 of 50\n",
      "[Parallel(n_jobs=1)]: Done  24 out of  24 | elapsed:    1.3s remaining:    0.0s\n",
      "building tree 25 of 50\n",
      "[Parallel(n_jobs=1)]: Done  25 out of  25 | elapsed:    1.3s remaining:    0.0s\n",
      "building tree 26 of 50\n",
      "[Parallel(n_jobs=1)]: Done  26 out of  26 | elapsed:    1.4s remaining:    0.0s\n",
      "building tree 27 of 50\n",
      "[Parallel(n_jobs=1)]: Done  27 out of  27 | elapsed:    1.4s remaining:    0.0s\n",
      "building tree 28 of 50\n",
      "[Parallel(n_jobs=1)]: Done  28 out of  28 | elapsed:    1.5s remaining:    0.0s\n",
      "building tree 29 of 50\n",
      "[Parallel(n_jobs=1)]: Done  29 out of  29 | elapsed:    1.5s remaining:    0.0s\n",
      "building tree 30 of 50\n",
      "[Parallel(n_jobs=1)]: Done  30 out of  30 | elapsed:    1.6s remaining:    0.0s\n",
      "building tree 31 of 50\n",
      "[Parallel(n_jobs=1)]: Done  31 out of  31 | elapsed:    1.6s remaining:    0.0s\n",
      "building tree 32 of 50\n",
      "[Parallel(n_jobs=1)]: Done  32 out of  32 | elapsed:    1.7s remaining:    0.0s\n",
      "building tree 33 of 50\n",
      "[Parallel(n_jobs=1)]: Done  33 out of  33 | elapsed:    1.7s remaining:    0.0s\n",
      "building tree 34 of 50\n",
      "[Parallel(n_jobs=1)]: Done  34 out of  34 | elapsed:    1.8s remaining:    0.0s\n",
      "building tree 35 of 50\n",
      "[Parallel(n_jobs=1)]: Done  35 out of  35 | elapsed:    1.8s remaining:    0.0s\n",
      "building tree 36 of 50\n",
      "[Parallel(n_jobs=1)]: Done  36 out of  36 | elapsed:    1.9s remaining:    0.0s\n",
      "building tree 37 of 50\n",
      "[Parallel(n_jobs=1)]: Done  37 out of  37 | elapsed:    1.9s remaining:    0.0s\n",
      "building tree 38 of 50\n",
      "[Parallel(n_jobs=1)]: Done  38 out of  38 | elapsed:    2.0s remaining:    0.0s\n",
      "building tree 39 of 50\n",
      "[Parallel(n_jobs=1)]: Done  39 out of  39 | elapsed:    2.0s remaining:    0.0s\n",
      "building tree 40 of 50\n",
      "[Parallel(n_jobs=1)]: Done  40 out of  40 | elapsed:    2.1s remaining:    0.0s\n",
      "building tree 41 of 50\n",
      "[Parallel(n_jobs=1)]: Done  41 out of  41 | elapsed:    2.1s remaining:    0.0s\n",
      "building tree 42 of 50\n",
      "[Parallel(n_jobs=1)]: Done  42 out of  42 | elapsed:    2.2s remaining:    0.0s\n",
      "building tree 43 of 50\n",
      "[Parallel(n_jobs=1)]: Done  43 out of  43 | elapsed:    2.2s remaining:    0.0s\n",
      "building tree 44 of 50\n",
      "[Parallel(n_jobs=1)]: Done  44 out of  44 | elapsed:    2.3s remaining:    0.0s\n",
      "building tree 45 of 50\n",
      "[Parallel(n_jobs=1)]: Done  45 out of  45 | elapsed:    2.3s remaining:    0.0s\n",
      "building tree 46 of 50\n",
      "[Parallel(n_jobs=1)]: Done  46 out of  46 | elapsed:    2.4s remaining:    0.0s\n",
      "building tree 47 of 50\n",
      "[Parallel(n_jobs=1)]: Done  47 out of  47 | elapsed:    2.4s remaining:    0.0s\n",
      "building tree 48 of 50\n",
      "[Parallel(n_jobs=1)]: Done  48 out of  48 | elapsed:    2.5s remaining:    0.0s\n",
      "building tree 49 of 50\n",
      "[Parallel(n_jobs=1)]: Done  49 out of  49 | elapsed:    2.5s remaining:    0.0s\n",
      "building tree 50 of 50\n",
      "[Parallel(n_jobs=1)]: Done  50 out of  50 | elapsed:    2.6s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  50 out of  50 | elapsed:    2.6s finished\n"
     ]
    }
   ],
   "source": [
    "extra_clfs = {\n",
    "\n",
    "}\n",
    "for ext, clf in extra.items():\n",
    "    extra_clfs[ext] = get_classifier(clf['CLF'], train, df_train['class'], clf['EXT'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "colab_type": "code",
    "id": "qTA_Ao8lCzxP",
    "outputId": "bda75200-c222-4b0f-bf6f-5277767c772c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   3 out of   3 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   4 out of   4 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   5 out of   5 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   6 out of   6 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   7 out of   7 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   8 out of   8 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   9 out of   9 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  10 out of  10 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  11 out of  11 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  12 out of  12 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  13 out of  13 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  14 out of  14 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  15 out of  15 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  16 out of  16 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  17 out of  17 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  18 out of  18 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  19 out of  19 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  20 out of  20 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  21 out of  21 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  22 out of  22 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  23 out of  23 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  24 out of  24 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  25 out of  25 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  26 out of  26 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  27 out of  27 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  28 out of  28 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  29 out of  29 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  30 out of  30 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  31 out of  31 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  32 out of  32 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  33 out of  33 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  34 out of  34 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  35 out of  35 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  36 out of  36 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  37 out of  37 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  38 out of  38 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  39 out of  39 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  40 out of  40 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  41 out of  41 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  42 out of  42 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  43 out of  43 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  44 out of  44 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  45 out of  45 | elapsed:    0.3s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  46 out of  46 | elapsed:    0.3s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  47 out of  47 | elapsed:    0.3s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  48 out of  48 | elapsed:    0.3s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  49 out of  49 | elapsed:    0.3s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  50 out of  50 | elapsed:    0.3s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  50 out of  50 | elapsed:    0.3s finished\n",
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   3 out of   3 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   4 out of   4 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   5 out of   5 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   6 out of   6 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   7 out of   7 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   8 out of   8 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   9 out of   9 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  10 out of  10 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  11 out of  11 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  12 out of  12 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  13 out of  13 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  14 out of  14 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  15 out of  15 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  16 out of  16 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  17 out of  17 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  18 out of  18 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  19 out of  19 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  20 out of  20 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  21 out of  21 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  22 out of  22 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  23 out of  23 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  24 out of  24 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  25 out of  25 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  26 out of  26 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  27 out of  27 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  28 out of  28 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  29 out of  29 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  30 out of  30 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  31 out of  31 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  32 out of  32 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  33 out of  33 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  34 out of  34 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  35 out of  35 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  36 out of  36 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  37 out of  37 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  38 out of  38 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  39 out of  39 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  40 out of  40 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  41 out of  41 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  42 out of  42 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  43 out of  43 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  44 out of  44 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  45 out of  45 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  46 out of  46 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  47 out of  47 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  48 out of  48 | elapsed:    0.3s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  49 out of  49 | elapsed:    0.3s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  50 out of  50 | elapsed:    0.3s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  50 out of  50 | elapsed:    0.3s finished\n",
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   3 out of   3 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   4 out of   4 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   5 out of   5 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   6 out of   6 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   7 out of   7 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   8 out of   8 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   9 out of   9 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  10 out of  10 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  11 out of  11 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  12 out of  12 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  13 out of  13 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  14 out of  14 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  15 out of  15 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  16 out of  16 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  17 out of  17 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  18 out of  18 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  19 out of  19 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  20 out of  20 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  21 out of  21 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  22 out of  22 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  23 out of  23 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  24 out of  24 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  25 out of  25 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  26 out of  26 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  27 out of  27 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  28 out of  28 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  29 out of  29 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  30 out of  30 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  31 out of  31 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  32 out of  32 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  33 out of  33 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  34 out of  34 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  35 out of  35 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  36 out of  36 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  37 out of  37 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  38 out of  38 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  39 out of  39 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  40 out of  40 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  41 out of  41 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  42 out of  42 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  43 out of  43 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  44 out of  44 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  45 out of  45 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  46 out of  46 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  47 out of  47 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  48 out of  48 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  49 out of  49 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  50 out of  50 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  50 out of  50 | elapsed:    0.1s finished\n",
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   3 out of   3 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   4 out of   4 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   5 out of   5 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   6 out of   6 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   7 out of   7 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   8 out of   8 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   9 out of   9 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  10 out of  10 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  11 out of  11 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  12 out of  12 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  13 out of  13 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  14 out of  14 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  15 out of  15 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  16 out of  16 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  17 out of  17 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  18 out of  18 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  19 out of  19 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  20 out of  20 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  21 out of  21 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  22 out of  22 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  23 out of  23 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  24 out of  24 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  25 out of  25 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  26 out of  26 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  27 out of  27 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  28 out of  28 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  29 out of  29 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  30 out of  30 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  31 out of  31 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  32 out of  32 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  33 out of  33 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  34 out of  34 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  35 out of  35 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  36 out of  36 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  37 out of  37 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  38 out of  38 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  39 out of  39 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  40 out of  40 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  41 out of  41 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  42 out of  42 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  43 out of  43 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  44 out of  44 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  45 out of  45 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  46 out of  46 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  47 out of  47 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  48 out of  48 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  49 out of  49 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  50 out of  50 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  50 out of  50 | elapsed:    0.1s finished\n",
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   3 out of   3 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   4 out of   4 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   5 out of   5 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   6 out of   6 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   7 out of   7 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   8 out of   8 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   9 out of   9 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  10 out of  10 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  11 out of  11 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  12 out of  12 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  13 out of  13 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  14 out of  14 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  15 out of  15 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  16 out of  16 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  17 out of  17 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  18 out of  18 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  19 out of  19 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  20 out of  20 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  21 out of  21 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  22 out of  22 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  23 out of  23 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  24 out of  24 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  25 out of  25 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  26 out of  26 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  27 out of  27 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  28 out of  28 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  29 out of  29 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  30 out of  30 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  31 out of  31 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  32 out of  32 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  33 out of  33 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  34 out of  34 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  35 out of  35 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  36 out of  36 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  37 out of  37 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  38 out of  38 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  39 out of  39 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  40 out of  40 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  41 out of  41 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  42 out of  42 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  43 out of  43 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  44 out of  44 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  45 out of  45 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  46 out of  46 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  47 out of  47 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  48 out of  48 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  49 out of  49 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  50 out of  50 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  50 out of  50 | elapsed:    0.1s finished\n"
     ]
    }
   ],
   "source": [
    "pre_extra = {\n",
    "\n",
    "}\n",
    "for ext, clf in extra_clfs.items():\n",
    "    pre_extra['EXTRA-'+ext] = clf.predict(val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "OEf0UtSDCwIh"
   },
   "outputs": [],
   "source": [
    "delta, labels = calc_delta(df_val['class'], pre_extra, pre_extra.keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 336
    },
    "colab_type": "code",
    "id": "_BIkfHuuDBys",
    "outputId": "d9eabb88-aab7-4791-8790-44b09782ddaa"
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAVEAAAE/CAYAAAD/iPBoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nQmCC\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "gera_tsne(delta, 'EXTRATREES', list(labels), escala=400)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "hvvb-kgZDEtf"
   },
   "outputs": [],
   "source": [
    "pre.update(pre_extra)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "rcX82GBrE1XA"
   },
   "source": [
    "# KNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Dzqc-SqYE2ae"
   },
   "outputs": [],
   "source": [
    "knn = {\n",
    "    'CV': {\n",
    "        'CLF': KNeighborsClassifier(n_neighbors=3, algorithm='auto'),\n",
    "        'EXT': cv,\n",
    "    },\n",
    "    'TFIDF': {\n",
    "        'CLF': KNeighborsClassifier(n_neighbors=5, algorithm='auto'),\n",
    "        'EXT': tfidf,\n",
    "    },\n",
    "    'W2V': {\n",
    "        'CLF': KNeighborsClassifier(n_neighbors=5, algorithm='auto'),\n",
    "        'EXT': w2v,\n",
    "    },\n",
    "    'GLOVE': {\n",
    "        'CLF': KNeighborsClassifier(n_neighbors=5, algorithm='ball_tree'),\n",
    "        'EXT':  glove,\n",
    "    },\n",
    "    'FAST': {\n",
    "        'CLF': KNeighborsClassifier(n_neighbors=5, algorithm='auto'),\n",
    "        'EXT': fasttext,\n",
    "    }\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "XkFX34wYFYSq"
   },
   "outputs": [],
   "source": [
    "knn_clfs = { }\n",
    "for ext, clf in knn.items():\n",
    "    knn_clfs[ext] = get_classifier(clf['CLF'], train, df_train['class'], clf['EXT'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "tSTzqVOMFd2i"
   },
   "outputs": [],
   "source": [
    "pre_knn = { }\n",
    "for ext, clf in knn_clfs.items():\n",
    "    pre_knn['KNN-'+ext] = clf.predict(val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "R7QJK01-Fl-M"
   },
   "outputs": [],
   "source": [
    "delta, labels = calc_delta(df_val['class'], pre_knn, pre_knn.keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 336
    },
    "colab_type": "code",
    "id": "twWncWeEFr6K",
    "outputId": "7dcc9351-435e-4cd3-8fb6-71678d12ae58"
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAVEAAAE/CAYAAAD/iPBoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nChiiklTAEJWkAv8PauCxO/ebJTQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "gera_tsne(delta, 'KNN', list(labels), escala=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Yv6kEPI6q-mf"
   },
   "outputs": [],
   "source": [
    "pre.update(pre_knn)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "BZah9uMIGTEF"
   },
   "source": [
    "# CNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "1YsOFwIPLXSB"
   },
   "outputs": [],
   "source": [
    "MAX_NB_WORDS = 20000\n",
    "MAX_SEQUENCE_LENGTH=300\n",
    "\n",
    "y_train = to_categorical(df_train['class'])\n",
    "y_val = to_categorical(df_val['class'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "mYtIRDDAOy0K"
   },
   "outputs": [],
   "source": [
    "tokenizer = Tokenizer(num_words=MAX_NB_WORDS)\n",
    "tokenizer.fit_on_texts(train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Sc2mZae_OSyx"
   },
   "outputs": [],
   "source": [
    "seq_train = tokenizer.texts_to_sequences(train)\n",
    "seq_val = tokenizer.texts_to_sequences(val)\n",
    "seq_test = tokenizer.texts_to_sequences(test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "AgNORNP1OUw_"
   },
   "outputs": [],
   "source": [
    "data_train = pad_sequences(seq_train, maxlen=MAX_SEQUENCE_LENGTH)\n",
    "data_val = pad_sequences(seq_val, maxlen=MAX_SEQUENCE_LENGTH)\n",
    "data_test = pad_sequences(seq_test, maxlen=MAX_SEQUENCE_LENGTH)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 938
    },
    "colab_type": "code",
    "id": "RHDmm1rRNPb-",
    "outputId": "dffed554-aa5b-44d3-f66f-26135d2bf04e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:66: The name tf.get_default_graph is deprecated. Please use tf.compat.v1.get_default_graph instead.\n",
      "\n",
      "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:541: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.\n",
      "\n",
      "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:4432: The name tf.random_uniform is deprecated. Please use tf.random.uniform instead.\n",
      "\n",
      "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:190: The name tf.get_default_session is deprecated. Please use tf.compat.v1.get_default_session instead.\n",
      "\n",
      "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:197: The name tf.ConfigProto is deprecated. Please use tf.compat.v1.ConfigProto instead.\n",
      "\n",
      "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:203: The name tf.Session is deprecated. Please use tf.compat.v1.Session instead.\n",
      "\n",
      "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:207: The name tf.global_variables is deprecated. Please use tf.compat.v1.global_variables instead.\n",
      "\n",
      "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:216: The name tf.is_variable_initialized is deprecated. Please use tf.compat.v1.is_variable_initialized instead.\n",
      "\n",
      "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:223: The name tf.variables_initializer is deprecated. Please use tf.compat.v1.variables_initializer instead.\n",
      "\n",
      "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:148: The name tf.placeholder_with_default is deprecated. Please use tf.compat.v1.placeholder_with_default instead.\n",
      "\n",
      "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:3733: calling dropout (from tensorflow.python.ops.nn_ops) with keep_prob is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please use `rate` instead of `keep_prob`. Rate should be set to `rate = 1 - keep_prob`.\n",
      "Model: \"sequential_1\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "embedding_1 (Embedding)      (None, 300, 300)          4950600   \n",
      "_________________________________________________________________\n",
      "dropout_1 (Dropout)          (None, 300, 300)          0         \n",
      "_________________________________________________________________\n",
      "conv1d_1 (Conv1D)            (None, 298, 64)           57664     \n",
      "_________________________________________________________________\n",
      "global_max_pooling1d_1 (Glob (None, 64)                0         \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 256)               16640     \n",
      "_________________________________________________________________\n",
      "dropout_2 (Dropout)          (None, 256)               0         \n",
      "_________________________________________________________________\n",
      "activation_1 (Activation)    (None, 256)               0         \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 3)                 771       \n",
      "_________________________________________________________________\n",
      "activation_2 (Activation)    (None, 3)                 0         \n",
      "=================================================================\n",
      "Total params: 5,025,675\n",
      "Trainable params: 75,075\n",
      "Non-trainable params: 4,950,600\n",
      "_________________________________________________________________\n",
      "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/optimizers.py:793: The name tf.train.Optimizer is deprecated. Please use tf.compat.v1.train.Optimizer instead.\n",
      "\n",
      "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:3576: The name tf.log is deprecated. Please use tf.math.log instead.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "cnn_cv = get_CNN(cv, tokenizer, MAX_NB_WORDS, EMBEDDING_DIM=300, activation='sigmoid')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 870
    },
    "colab_type": "code",
    "id": "sUdMQOOiOFBZ",
    "outputId": "5088ef09-adf6-48e2-df17-3e988504ae2d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow_core/python/ops/math_grad.py:1424: where (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use tf.where in 2.0, which has the same broadcast rule as np.where\n",
      "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:1033: The name tf.assign_add is deprecated. Please use tf.compat.v1.assign_add instead.\n",
      "\n",
      "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:1020: The name tf.assign is deprecated. Please use tf.compat.v1.assign instead.\n",
      "\n",
      "Train on 16604 samples, validate on 4090 samples\n",
      "Epoch 1/20\n",
      "16604/16604 [==============================] - 6s 370us/step - loss: 0.5554 - acc: 0.7777 - val_loss: 0.4231 - val_acc: 0.8161\n",
      "Epoch 2/20\n",
      "16604/16604 [==============================] - 3s 166us/step - loss: 0.4211 - acc: 0.8197 - val_loss: 0.3945 - val_acc: 0.8401\n",
      "Epoch 3/20\n",
      "16604/16604 [==============================] - 3s 166us/step - loss: 0.3969 - acc: 0.8441 - val_loss: 0.3976 - val_acc: 0.8403\n",
      "Epoch 4/20\n",
      "16604/16604 [==============================] - 3s 166us/step - loss: 0.3700 - acc: 0.8686 - val_loss: 0.3490 - val_acc: 0.8719\n",
      "Epoch 5/20\n",
      "16604/16604 [==============================] - 3s 165us/step - loss: 0.3486 - acc: 0.8801 - val_loss: 0.3357 - val_acc: 0.8797\n",
      "Epoch 6/20\n",
      "16604/16604 [==============================] - 3s 167us/step - loss: 0.3299 - acc: 0.8888 - val_loss: 0.3124 - val_acc: 0.8917\n",
      "Epoch 7/20\n",
      "16604/16604 [==============================] - 3s 166us/step - loss: 0.3127 - acc: 0.8926 - val_loss: 0.3119 - val_acc: 0.8856\n",
      "Epoch 8/20\n",
      "16604/16604 [==============================] - 3s 166us/step - loss: 0.2953 - acc: 0.8986 - val_loss: 0.3041 - val_acc: 0.8949\n",
      "Epoch 9/20\n",
      "16604/16604 [==============================] - 3s 170us/step - loss: 0.2859 - acc: 0.9001 - val_loss: 0.3040 - val_acc: 0.8885\n",
      "Epoch 10/20\n",
      "16604/16604 [==============================] - 3s 170us/step - loss: 0.2720 - acc: 0.9044 - val_loss: 0.2796 - val_acc: 0.8990\n",
      "Epoch 11/20\n",
      "16604/16604 [==============================] - 3s 167us/step - loss: 0.2655 - acc: 0.9057 - val_loss: 0.3149 - val_acc: 0.8748\n",
      "Epoch 12/20\n",
      "16604/16604 [==============================] - 3s 168us/step - loss: 0.2596 - acc: 0.9078 - val_loss: 0.2850 - val_acc: 0.8990\n",
      "Epoch 13/20\n",
      "16604/16604 [==============================] - 3s 167us/step - loss: 0.2496 - acc: 0.9116 - val_loss: 0.3023 - val_acc: 0.8995\n",
      "Epoch 14/20\n",
      "16604/16604 [==============================] - 3s 170us/step - loss: 0.2462 - acc: 0.9124 - val_loss: 0.2751 - val_acc: 0.9010\n",
      "Epoch 15/20\n",
      "16604/16604 [==============================] - 3s 169us/step - loss: 0.2407 - acc: 0.9142 - val_loss: 0.3409 - val_acc: 0.8826\n",
      "Epoch 16/20\n",
      "16604/16604 [==============================] - 3s 169us/step - loss: 0.2332 - acc: 0.9160 - val_loss: 0.2875 - val_acc: 0.9002\n",
      "Epoch 17/20\n",
      "16604/16604 [==============================] - 3s 167us/step - loss: 0.2283 - acc: 0.9174 - val_loss: 0.2966 - val_acc: 0.9000\n",
      "Epoch 18/20\n",
      "16604/16604 [==============================] - 3s 165us/step - loss: 0.2230 - acc: 0.9200 - val_loss: 0.2884 - val_acc: 0.9039\n",
      "Epoch 19/20\n",
      "16604/16604 [==============================] - 3s 165us/step - loss: 0.2176 - acc: 0.9221 - val_loss: 0.2935 - val_acc: 0.8993\n",
      "Epoch 20/20\n",
      "16604/16604 [==============================] - 3s 167us/step - loss: 0.2155 - acc: 0.9242 - val_loss: 0.2891 - val_acc: 0.9020\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7f56107a7dd8>"
      ]
     },
     "execution_count": 68,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cnn_cv.fit(data_train, y_train, validation_data=(data_val, y_val), epochs=20, batch_size=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 459
    },
    "colab_type": "code",
    "id": "UblW-NhUPKw8",
    "outputId": "ea8fac5b-9454-4daf-eb90-baae2dc6d9e3"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_2\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "embedding_2 (Embedding)      (None, 300, 300)          4950600   \n",
      "_________________________________________________________________\n",
      "dropout_3 (Dropout)          (None, 300, 300)          0         \n",
      "_________________________________________________________________\n",
      "conv1d_2 (Conv1D)            (None, 298, 64)           57664     \n",
      "_________________________________________________________________\n",
      "global_max_pooling1d_2 (Glob (None, 64)                0         \n",
      "_________________________________________________________________\n",
      "dense_3 (Dense)              (None, 256)               16640     \n",
      "_________________________________________________________________\n",
      "dropout_4 (Dropout)          (None, 256)               0         \n",
      "_________________________________________________________________\n",
      "activation_3 (Activation)    (None, 256)               0         \n",
      "_________________________________________________________________\n",
      "dense_4 (Dense)              (None, 3)                 771       \n",
      "_________________________________________________________________\n",
      "activation_4 (Activation)    (None, 3)                 0         \n",
      "=================================================================\n",
      "Total params: 5,025,675\n",
      "Trainable params: 75,075\n",
      "Non-trainable params: 4,950,600\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "cnn_tfidf = get_CNN(tfidf, tokenizer, MAX_NB_WORDS, EMBEDDING_DIM=300, activation='softmax')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 751
    },
    "colab_type": "code",
    "id": "8az0g6xFP8j2",
    "outputId": "55d1243d-fad3-48dc-c0ba-3c8c29f799cb"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 16604 samples, validate on 4090 samples\n",
      "Epoch 1/20\n",
      "16604/16604 [==============================] - 1s 65us/step - loss: 0.6839 - acc: 0.7690 - val_loss: 0.6070 - val_acc: 0.7773\n",
      "Epoch 2/20\n",
      "16604/16604 [==============================] - 1s 48us/step - loss: 0.5367 - acc: 0.7742 - val_loss: 0.4693 - val_acc: 0.7773\n",
      "Epoch 3/20\n",
      "16604/16604 [==============================] - 1s 48us/step - loss: 0.4524 - acc: 0.7993 - val_loss: 0.4481 - val_acc: 0.8088\n",
      "Epoch 4/20\n",
      "16604/16604 [==============================] - 1s 48us/step - loss: 0.4231 - acc: 0.8216 - val_loss: 0.4150 - val_acc: 0.8134\n",
      "Epoch 5/20\n",
      "16604/16604 [==============================] - 1s 48us/step - loss: 0.4078 - acc: 0.8337 - val_loss: 0.3995 - val_acc: 0.8274\n",
      "Epoch 6/20\n",
      "16604/16604 [==============================] - 1s 48us/step - loss: 0.3950 - acc: 0.8456 - val_loss: 0.4173 - val_acc: 0.8306\n",
      "Epoch 7/20\n",
      "16604/16604 [==============================] - 1s 48us/step - loss: 0.3829 - acc: 0.8571 - val_loss: 0.3679 - val_acc: 0.8601\n",
      "Epoch 8/20\n",
      "16604/16604 [==============================] - 1s 48us/step - loss: 0.3694 - acc: 0.8680 - val_loss: 0.3530 - val_acc: 0.8756\n",
      "Epoch 9/20\n",
      "16604/16604 [==============================] - 1s 48us/step - loss: 0.3573 - acc: 0.8769 - val_loss: 0.5108 - val_acc: 0.8210\n",
      "Epoch 10/20\n",
      "16604/16604 [==============================] - 1s 48us/step - loss: 0.3504 - acc: 0.8801 - val_loss: 0.3651 - val_acc: 0.8650\n",
      "Epoch 11/20\n",
      "16604/16604 [==============================] - 1s 48us/step - loss: 0.3407 - acc: 0.8854 - val_loss: 0.3501 - val_acc: 0.8641\n",
      "Epoch 12/20\n",
      "16604/16604 [==============================] - 1s 48us/step - loss: 0.3367 - acc: 0.8865 - val_loss: 0.4021 - val_acc: 0.8570\n",
      "Epoch 13/20\n",
      "16604/16604 [==============================] - 1s 48us/step - loss: 0.3308 - acc: 0.8890 - val_loss: 0.4212 - val_acc: 0.8237\n",
      "Epoch 14/20\n",
      "16604/16604 [==============================] - 1s 48us/step - loss: 0.3253 - acc: 0.8912 - val_loss: 0.3279 - val_acc: 0.8858\n",
      "Epoch 15/20\n",
      "16604/16604 [==============================] - 1s 48us/step - loss: 0.3201 - acc: 0.8923 - val_loss: 0.3420 - val_acc: 0.8687\n",
      "Epoch 16/20\n",
      "16604/16604 [==============================] - 1s 48us/step - loss: 0.3138 - acc: 0.8959 - val_loss: 0.3733 - val_acc: 0.8672\n",
      "Epoch 17/20\n",
      "16604/16604 [==============================] - 1s 48us/step - loss: 0.3076 - acc: 0.8974 - val_loss: 0.3813 - val_acc: 0.8667\n",
      "Epoch 18/20\n",
      "16604/16604 [==============================] - 1s 48us/step - loss: 0.3010 - acc: 0.9009 - val_loss: 0.3661 - val_acc: 0.8702\n",
      "Epoch 19/20\n",
      "16604/16604 [==============================] - 1s 48us/step - loss: 0.2981 - acc: 0.8996 - val_loss: 0.3772 - val_acc: 0.8680\n",
      "Epoch 20/20\n",
      "16604/16604 [==============================] - 1s 48us/step - loss: 0.2916 - acc: 0.9017 - val_loss: 0.3061 - val_acc: 0.8890\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7f56107ccd68>"
      ]
     },
     "execution_count": 70,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cnn_tfidf.fit(data_train, y_train, validation_data=(data_val, y_val), epochs=20, batch_size=200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 547
    },
    "colab_type": "code",
    "id": "cfIHzRg3QB5-",
    "outputId": "c85ebed1-656a-4ca2-922e-53f235665ce4"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:14: DeprecationWarning: Call to deprecated `wv` (Attribute will be removed in 4.0.0, use self instead).\n",
      "  \n",
      "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:15: DeprecationWarning: Call to deprecated `wv` (Attribute will be removed in 4.0.0, use self instead).\n",
      "  from ipykernel import kernelapp as app\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_3\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "embedding_3 (Embedding)      (None, 300, 300)          4950600   \n",
      "_________________________________________________________________\n",
      "dropout_5 (Dropout)          (None, 300, 300)          0         \n",
      "_________________________________________________________________\n",
      "conv1d_3 (Conv1D)            (None, 298, 64)           57664     \n",
      "_________________________________________________________________\n",
      "global_max_pooling1d_3 (Glob (None, 64)                0         \n",
      "_________________________________________________________________\n",
      "dense_5 (Dense)              (None, 256)               16640     \n",
      "_________________________________________________________________\n",
      "dropout_6 (Dropout)          (None, 256)               0         \n",
      "_________________________________________________________________\n",
      "activation_5 (Activation)    (None, 256)               0         \n",
      "_________________________________________________________________\n",
      "dense_6 (Dense)              (None, 3)                 771       \n",
      "_________________________________________________________________\n",
      "activation_6 (Activation)    (None, 3)                 0         \n",
      "=================================================================\n",
      "Total params: 5,025,675\n",
      "Trainable params: 75,075\n",
      "Non-trainable params: 4,950,600\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "cnn_w2v = get_CNN(w2v, tokenizer, MAX_NB_WORDS, EMBEDDING_DIM=300, activation='sigmoid', word_embedding=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 751
    },
    "colab_type": "code",
    "id": "FH4PoJ7dQdZ7",
    "outputId": "c8a8b72e-4db0-4acd-bbe4-e0d7d898c0b8"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 16604 samples, validate on 4090 samples\n",
      "Epoch 1/20\n",
      "16604/16604 [==============================] - 1s 65us/step - loss: 0.4348 - acc: 0.8416 - val_loss: 0.3186 - val_acc: 0.8826\n",
      "Epoch 2/20\n",
      "16604/16604 [==============================] - 1s 48us/step - loss: 0.3146 - acc: 0.8869 - val_loss: 0.3264 - val_acc: 0.8738\n",
      "Epoch 3/20\n",
      "16604/16604 [==============================] - 1s 48us/step - loss: 0.2867 - acc: 0.8965 - val_loss: 0.3110 - val_acc: 0.8812\n",
      "Epoch 4/20\n",
      "16604/16604 [==============================] - 1s 48us/step - loss: 0.2627 - acc: 0.9058 - val_loss: 0.2699 - val_acc: 0.9000\n",
      "Epoch 5/20\n",
      "16604/16604 [==============================] - 1s 48us/step - loss: 0.2485 - acc: 0.9115 - val_loss: 0.2764 - val_acc: 0.8968\n",
      "Epoch 6/20\n",
      "16604/16604 [==============================] - 1s 48us/step - loss: 0.2328 - acc: 0.9164 - val_loss: 0.2743 - val_acc: 0.8995\n",
      "Epoch 7/20\n",
      "16604/16604 [==============================] - 1s 48us/step - loss: 0.2154 - acc: 0.9233 - val_loss: 0.3860 - val_acc: 0.8675\n",
      "Epoch 8/20\n",
      "16604/16604 [==============================] - 1s 48us/step - loss: 0.2048 - acc: 0.9271 - val_loss: 0.3286 - val_acc: 0.8785\n",
      "Epoch 9/20\n",
      "16604/16604 [==============================] - 1s 48us/step - loss: 0.1904 - acc: 0.9324 - val_loss: 0.2730 - val_acc: 0.9022\n",
      "Epoch 10/20\n",
      "16604/16604 [==============================] - 1s 48us/step - loss: 0.1797 - acc: 0.9336 - val_loss: 0.3230 - val_acc: 0.8875\n",
      "Epoch 11/20\n",
      "16604/16604 [==============================] - 1s 48us/step - loss: 0.1652 - acc: 0.9398 - val_loss: 0.2932 - val_acc: 0.8966\n",
      "Epoch 12/20\n",
      "16604/16604 [==============================] - 1s 48us/step - loss: 0.1646 - acc: 0.9395 - val_loss: 0.2953 - val_acc: 0.8910\n",
      "Epoch 13/20\n",
      "16604/16604 [==============================] - 1s 48us/step - loss: 0.1506 - acc: 0.9450 - val_loss: 0.2952 - val_acc: 0.8946\n",
      "Epoch 14/20\n",
      "16604/16604 [==============================] - 1s 49us/step - loss: 0.1369 - acc: 0.9520 - val_loss: 0.3506 - val_acc: 0.9029\n",
      "Epoch 15/20\n",
      "16604/16604 [==============================] - 1s 48us/step - loss: 0.1369 - acc: 0.9503 - val_loss: 0.3240 - val_acc: 0.8995\n",
      "Epoch 16/20\n",
      "16604/16604 [==============================] - 1s 48us/step - loss: 0.1266 - acc: 0.9536 - val_loss: 0.3326 - val_acc: 0.8946\n",
      "Epoch 17/20\n",
      "16604/16604 [==============================] - 1s 48us/step - loss: 0.1218 - acc: 0.9567 - val_loss: 0.5194 - val_acc: 0.8330\n",
      "Epoch 18/20\n",
      "16604/16604 [==============================] - 1s 48us/step - loss: 0.1173 - acc: 0.9566 - val_loss: 0.4097 - val_acc: 0.9000\n",
      "Epoch 19/20\n",
      "16604/16604 [==============================] - 1s 48us/step - loss: 0.1135 - acc: 0.9589 - val_loss: 0.3784 - val_acc: 0.8993\n",
      "Epoch 20/20\n",
      "16604/16604 [==============================] - 1s 48us/step - loss: 0.1115 - acc: 0.9594 - val_loss: 0.5439 - val_acc: 0.7897\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7f56c71076a0>"
      ]
     },
     "execution_count": 72,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cnn_w2v.fit(data_train, y_train, validation_data=(data_val, y_val), epochs=20, batch_size=200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 547
    },
    "colab_type": "code",
    "id": "26ZolVDnRP_m",
    "outputId": "110ca6cc-b7ec-4387-a269-b22bbc7c1ebb"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:14: DeprecationWarning: Call to deprecated `wv` (Attribute will be removed in 4.0.0, use self instead).\n",
      "  \n",
      "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:15: DeprecationWarning: Call to deprecated `wv` (Attribute will be removed in 4.0.0, use self instead).\n",
      "  from ipykernel import kernelapp as app\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_4\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "embedding_4 (Embedding)      (None, 300, 25)           412550    \n",
      "_________________________________________________________________\n",
      "dropout_7 (Dropout)          (None, 300, 25)           0         \n",
      "_________________________________________________________________\n",
      "conv1d_4 (Conv1D)            (None, 298, 64)           4864      \n",
      "_________________________________________________________________\n",
      "global_max_pooling1d_4 (Glob (None, 64)                0         \n",
      "_________________________________________________________________\n",
      "dense_7 (Dense)              (None, 256)               16640     \n",
      "_________________________________________________________________\n",
      "dropout_8 (Dropout)          (None, 256)               0         \n",
      "_________________________________________________________________\n",
      "activation_7 (Activation)    (None, 256)               0         \n",
      "_________________________________________________________________\n",
      "dense_8 (Dense)              (None, 3)                 771       \n",
      "_________________________________________________________________\n",
      "activation_8 (Activation)    (None, 3)                 0         \n",
      "=================================================================\n",
      "Total params: 434,825\n",
      "Trainable params: 22,275\n",
      "Non-trainable params: 412,550\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "cnn_glove = get_CNN(glove, tokenizer, MAX_NB_WORDS, EMBEDDING_DIM=25, activation='sigmoid', word_embedding=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 751
    },
    "colab_type": "code",
    "id": "YGDEprwaRXQl",
    "outputId": "21759d2b-6f4d-4016-822e-11410c452610"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 16604 samples, validate on 4090 samples\n",
      "Epoch 1/20\n",
      "16604/16604 [==============================] - 1s 43us/step - loss: 0.5391 - acc: 0.8040 - val_loss: 0.5148 - val_acc: 0.8286\n",
      "Epoch 2/20\n",
      "16604/16604 [==============================] - 0s 21us/step - loss: 0.4298 - acc: 0.8536 - val_loss: 0.4304 - val_acc: 0.8677\n",
      "Epoch 3/20\n",
      "16604/16604 [==============================] - 0s 21us/step - loss: 0.3970 - acc: 0.8656 - val_loss: 0.4935 - val_acc: 0.8298\n",
      "Epoch 4/20\n",
      "16604/16604 [==============================] - 0s 20us/step - loss: 0.3788 - acc: 0.8735 - val_loss: 0.4865 - val_acc: 0.8704\n",
      "Epoch 5/20\n",
      "16604/16604 [==============================] - 0s 20us/step - loss: 0.3662 - acc: 0.8772 - val_loss: 0.3538 - val_acc: 0.8861\n",
      "Epoch 6/20\n",
      "16604/16604 [==============================] - 0s 21us/step - loss: 0.3579 - acc: 0.8787 - val_loss: 0.3502 - val_acc: 0.8667\n",
      "Epoch 7/20\n",
      "16604/16604 [==============================] - 0s 20us/step - loss: 0.3496 - acc: 0.8794 - val_loss: 0.3204 - val_acc: 0.8897\n",
      "Epoch 8/20\n",
      "16604/16604 [==============================] - 0s 20us/step - loss: 0.3447 - acc: 0.8808 - val_loss: 0.3919 - val_acc: 0.8866\n",
      "Epoch 9/20\n",
      "16604/16604 [==============================] - 0s 21us/step - loss: 0.3389 - acc: 0.8821 - val_loss: 0.3491 - val_acc: 0.8812\n",
      "Epoch 10/20\n",
      "16604/16604 [==============================] - 0s 20us/step - loss: 0.3394 - acc: 0.8827 - val_loss: 0.3243 - val_acc: 0.8856\n",
      "Epoch 11/20\n",
      "16604/16604 [==============================] - 0s 20us/step - loss: 0.3291 - acc: 0.8844 - val_loss: 0.3355 - val_acc: 0.8831\n",
      "Epoch 12/20\n",
      "16604/16604 [==============================] - 0s 20us/step - loss: 0.3312 - acc: 0.8853 - val_loss: 0.3087 - val_acc: 0.8875\n",
      "Epoch 13/20\n",
      "16604/16604 [==============================] - 0s 20us/step - loss: 0.3216 - acc: 0.8870 - val_loss: 0.3007 - val_acc: 0.8873\n",
      "Epoch 14/20\n",
      "16604/16604 [==============================] - 0s 21us/step - loss: 0.3246 - acc: 0.8875 - val_loss: 0.3178 - val_acc: 0.8819\n",
      "Epoch 15/20\n",
      "16604/16604 [==============================] - 0s 21us/step - loss: 0.3225 - acc: 0.8871 - val_loss: 0.3984 - val_acc: 0.8587\n",
      "Epoch 16/20\n",
      "16604/16604 [==============================] - 0s 21us/step - loss: 0.3195 - acc: 0.8880 - val_loss: 0.3086 - val_acc: 0.8875\n",
      "Epoch 17/20\n",
      "16604/16604 [==============================] - 0s 21us/step - loss: 0.3166 - acc: 0.8864 - val_loss: 0.3368 - val_acc: 0.8689\n",
      "Epoch 18/20\n",
      "16604/16604 [==============================] - 0s 20us/step - loss: 0.3174 - acc: 0.8869 - val_loss: 0.3106 - val_acc: 0.8917\n",
      "Epoch 19/20\n",
      "16604/16604 [==============================] - 0s 20us/step - loss: 0.3144 - acc: 0.8881 - val_loss: 0.2939 - val_acc: 0.8890\n",
      "Epoch 20/20\n",
      "16604/16604 [==============================] - 0s 20us/step - loss: 0.3140 - acc: 0.8877 - val_loss: 0.3018 - val_acc: 0.8910\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7f56c6f62dd8>"
      ]
     },
     "execution_count": 74,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cnn_glove.fit(data_train, y_train, validation_data=(data_val, y_val), epochs=20, batch_size=200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 547
    },
    "colab_type": "code",
    "id": "9CWqFsbpRbka",
    "outputId": "b9eeed3d-9c2e-4eda-c966-f7a492dc1b28"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:14: DeprecationWarning: Call to deprecated `wv` (Attribute will be removed in 4.0.0, use self instead).\n",
      "  \n",
      "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:15: DeprecationWarning: Call to deprecated `wv` (Attribute will be removed in 4.0.0, use self instead).\n",
      "  from ipykernel import kernelapp as app\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_5\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "embedding_5 (Embedding)      (None, 300, 300)          4950600   \n",
      "_________________________________________________________________\n",
      "dropout_9 (Dropout)          (None, 300, 300)          0         \n",
      "_________________________________________________________________\n",
      "conv1d_5 (Conv1D)            (None, 298, 64)           57664     \n",
      "_________________________________________________________________\n",
      "global_max_pooling1d_5 (Glob (None, 64)                0         \n",
      "_________________________________________________________________\n",
      "dense_9 (Dense)              (None, 256)               16640     \n",
      "_________________________________________________________________\n",
      "dropout_10 (Dropout)         (None, 256)               0         \n",
      "_________________________________________________________________\n",
      "activation_9 (Activation)    (None, 256)               0         \n",
      "_________________________________________________________________\n",
      "dense_10 (Dense)             (None, 3)                 771       \n",
      "_________________________________________________________________\n",
      "activation_10 (Activation)   (None, 3)                 0         \n",
      "=================================================================\n",
      "Total params: 5,025,675\n",
      "Trainable params: 75,075\n",
      "Non-trainable params: 4,950,600\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "cnn_fast = get_CNN(fasttext, tokenizer, MAX_NB_WORDS, EMBEDDING_DIM=300, activation='sigmoid', word_embedding=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 751
    },
    "colab_type": "code",
    "id": "mBWlvlH5RoKd",
    "outputId": "49217a1f-3018-4d04-e6d4-50f3e34039ba"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 16604 samples, validate on 4090 samples\n",
      "Epoch 1/20\n",
      "16604/16604 [==============================] - 1s 70us/step - loss: 0.4848 - acc: 0.8206 - val_loss: 0.5288 - val_acc: 0.8315\n",
      "Epoch 2/20\n",
      "16604/16604 [==============================] - 1s 49us/step - loss: 0.3255 - acc: 0.8860 - val_loss: 0.3540 - val_acc: 0.8636\n",
      "Epoch 3/20\n",
      "16604/16604 [==============================] - 1s 48us/step - loss: 0.2953 - acc: 0.8928 - val_loss: 0.3058 - val_acc: 0.8829\n",
      "Epoch 4/20\n",
      "16604/16604 [==============================] - 1s 48us/step - loss: 0.2727 - acc: 0.9005 - val_loss: 0.3240 - val_acc: 0.8927\n",
      "Epoch 5/20\n",
      "16604/16604 [==============================] - 1s 48us/step - loss: 0.2591 - acc: 0.9071 - val_loss: 0.3383 - val_acc: 0.8753\n",
      "Epoch 6/20\n",
      "16604/16604 [==============================] - 1s 48us/step - loss: 0.2496 - acc: 0.9097 - val_loss: 0.2761 - val_acc: 0.9002\n",
      "Epoch 7/20\n",
      "16604/16604 [==============================] - 1s 48us/step - loss: 0.2372 - acc: 0.9148 - val_loss: 0.3291 - val_acc: 0.8851\n",
      "Epoch 8/20\n",
      "16604/16604 [==============================] - 1s 49us/step - loss: 0.2284 - acc: 0.9184 - val_loss: 0.3160 - val_acc: 0.8785\n",
      "Epoch 9/20\n",
      "16604/16604 [==============================] - 1s 48us/step - loss: 0.2176 - acc: 0.9228 - val_loss: 0.3405 - val_acc: 0.8822\n",
      "Epoch 10/20\n",
      "16604/16604 [==============================] - 1s 48us/step - loss: 0.2089 - acc: 0.9236 - val_loss: 0.3070 - val_acc: 0.8858\n",
      "Epoch 11/20\n",
      "16604/16604 [==============================] - 1s 48us/step - loss: 0.1991 - acc: 0.9278 - val_loss: 0.3211 - val_acc: 0.8831\n",
      "Epoch 12/20\n",
      "16604/16604 [==============================] - 1s 48us/step - loss: 0.1931 - acc: 0.9326 - val_loss: 0.2705 - val_acc: 0.8995\n",
      "Epoch 13/20\n",
      "16604/16604 [==============================] - 1s 48us/step - loss: 0.1865 - acc: 0.9317 - val_loss: 0.4066 - val_acc: 0.8611\n",
      "Epoch 14/20\n",
      "16604/16604 [==============================] - 1s 48us/step - loss: 0.1782 - acc: 0.9358 - val_loss: 0.3374 - val_acc: 0.8866\n",
      "Epoch 15/20\n",
      "16604/16604 [==============================] - 1s 48us/step - loss: 0.1689 - acc: 0.9394 - val_loss: 0.2791 - val_acc: 0.8980\n",
      "Epoch 16/20\n",
      "16604/16604 [==============================] - 1s 48us/step - loss: 0.1649 - acc: 0.9398 - val_loss: 0.2998 - val_acc: 0.9022\n",
      "Epoch 17/20\n",
      "16604/16604 [==============================] - 1s 48us/step - loss: 0.1568 - acc: 0.9421 - val_loss: 0.3123 - val_acc: 0.8831\n",
      "Epoch 18/20\n",
      "16604/16604 [==============================] - 1s 48us/step - loss: 0.1538 - acc: 0.9421 - val_loss: 0.5334 - val_acc: 0.8452\n",
      "Epoch 19/20\n",
      "16604/16604 [==============================] - 1s 48us/step - loss: 0.1406 - acc: 0.9488 - val_loss: 0.3035 - val_acc: 0.9002\n",
      "Epoch 20/20\n",
      "16604/16604 [==============================] - 1s 48us/step - loss: 0.1416 - acc: 0.9478 - val_loss: 0.3382 - val_acc: 0.8990\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7f56c6e99eb8>"
      ]
     },
     "execution_count": 76,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cnn_fast.fit(data_train, y_train, validation_data=(data_val, y_val), epochs=20, batch_size=200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "M-gnBDLyRrSE"
   },
   "outputs": [],
   "source": [
    "pre_cnn = {\n",
    "    'CNN-CV': np.argmax(cnn_cv.predict(data_val), axis=1),\n",
    "    'CNN-TFIDF': np.argmax(cnn_tfidf.predict(data_val), axis=1),\n",
    "    'CNN-W2V': np.argmax(cnn_w2v.predict(data_val), axis=1),\n",
    "    'CNN-GLOVE': np.argmax(cnn_glove.predict(data_val), axis=1),\n",
    "    'CNN-FAST': np.argmax(cnn_fast.predict(data_val), axis=1),\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "DlBlGac5R6n9"
   },
   "outputs": [],
   "source": [
    "delta, labels = calc_delta(df_val['class'], pre_cnn, pre_cnn.keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 336
    },
    "colab_type": "code",
    "id": "wlDBeP3pSWuP",
    "outputId": "d6f0d790-d672-436a-f7c6-9f084e6272c2"
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAVEAAAE/CAYAAAD/iPBoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "gera_tsne(delta, 'CNN', list(labels), escala=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "sWz1wHJkSeuG"
   },
   "outputs": [],
   "source": [
    "pre.update(pre_cnn)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "3thDFXCGo1AE"
   },
   "source": [
    "# PREDICT TEST"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "MN75qpvwo2i-"
   },
   "outputs": [],
   "source": [
    "pre_test = {}\n",
    "\n",
    "for ext, clf in svm_clfs.items():\n",
    "    pre_test['SVM-'+ext] = clf.predict(test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "WoSYVCqSpj0m"
   },
   "outputs": [],
   "source": [
    "for ext, clf in lr_clfs.items():\n",
    "    pre_test['LR-'+ext] = clf.predict(test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "colab_type": "code",
    "id": "oyShoq_vprPV",
    "outputId": "ce3afc5e-5d4c-4239-fa0c-38e59e9dca07"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   3 out of   3 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   4 out of   4 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   5 out of   5 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   6 out of   6 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   7 out of   7 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   8 out of   8 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   9 out of   9 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  10 out of  10 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  11 out of  11 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  12 out of  12 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  13 out of  13 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  14 out of  14 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  15 out of  15 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  16 out of  16 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  17 out of  17 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  18 out of  18 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  19 out of  19 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  20 out of  20 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  20 out of  20 | elapsed:    0.1s finished\n",
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   3 out of   3 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   4 out of   4 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   5 out of   5 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   6 out of   6 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   7 out of   7 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   8 out of   8 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   9 out of   9 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  10 out of  10 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  11 out of  11 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  12 out of  12 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  13 out of  13 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  14 out of  14 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  15 out of  15 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  16 out of  16 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  17 out of  17 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  18 out of  18 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  19 out of  19 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  20 out of  20 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  21 out of  21 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  22 out of  22 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  23 out of  23 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  24 out of  24 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  25 out of  25 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  26 out of  26 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  27 out of  27 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  28 out of  28 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  29 out of  29 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  30 out of  30 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  31 out of  31 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  32 out of  32 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  33 out of  33 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  34 out of  34 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  35 out of  35 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  36 out of  36 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  37 out of  37 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  38 out of  38 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  39 out of  39 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  40 out of  40 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  41 out of  41 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  42 out of  42 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  43 out of  43 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  44 out of  44 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  45 out of  45 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  46 out of  46 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  47 out of  47 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  48 out of  48 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  49 out of  49 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  50 out of  50 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  50 out of  50 | elapsed:    0.1s finished\n",
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   3 out of   3 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   4 out of   4 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   5 out of   5 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   6 out of   6 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   7 out of   7 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   8 out of   8 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   9 out of   9 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  10 out of  10 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  11 out of  11 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  12 out of  12 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  13 out of  13 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  14 out of  14 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  15 out of  15 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  16 out of  16 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  17 out of  17 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  18 out of  18 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  19 out of  19 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  20 out of  20 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  21 out of  21 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  22 out of  22 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  23 out of  23 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  24 out of  24 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  25 out of  25 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  26 out of  26 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  27 out of  27 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  28 out of  28 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  29 out of  29 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  30 out of  30 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  31 out of  31 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  32 out of  32 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  33 out of  33 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  34 out of  34 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  35 out of  35 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  36 out of  36 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  37 out of  37 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  38 out of  38 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  39 out of  39 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  40 out of  40 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  41 out of  41 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  42 out of  42 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  43 out of  43 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  44 out of  44 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  45 out of  45 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  46 out of  46 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  47 out of  47 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  48 out of  48 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  49 out of  49 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  50 out of  50 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  50 out of  50 | elapsed:    0.1s finished\n",
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   3 out of   3 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   4 out of   4 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   5 out of   5 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   6 out of   6 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   7 out of   7 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   8 out of   8 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   9 out of   9 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  10 out of  10 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  11 out of  11 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  12 out of  12 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  13 out of  13 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  14 out of  14 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  15 out of  15 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  16 out of  16 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  17 out of  17 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  18 out of  18 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  19 out of  19 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  20 out of  20 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  21 out of  21 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  22 out of  22 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  23 out of  23 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  24 out of  24 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  25 out of  25 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  26 out of  26 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  27 out of  27 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  28 out of  28 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  29 out of  29 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  30 out of  30 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  31 out of  31 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  32 out of  32 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  33 out of  33 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  34 out of  34 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  35 out of  35 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  36 out of  36 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  37 out of  37 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  38 out of  38 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  39 out of  39 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  40 out of  40 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  41 out of  41 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  42 out of  42 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  43 out of  43 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  44 out of  44 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  45 out of  45 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  46 out of  46 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  47 out of  47 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  48 out of  48 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  49 out of  49 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  50 out of  50 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  50 out of  50 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   3 out of   3 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   4 out of   4 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   5 out of   5 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   6 out of   6 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   7 out of   7 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   8 out of   8 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   9 out of   9 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  10 out of  10 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  11 out of  11 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  12 out of  12 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  13 out of  13 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  14 out of  14 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  15 out of  15 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  16 out of  16 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  17 out of  17 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  18 out of  18 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  19 out of  19 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  20 out of  20 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  21 out of  21 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  22 out of  22 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  23 out of  23 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  24 out of  24 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  25 out of  25 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  26 out of  26 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  27 out of  27 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  28 out of  28 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  29 out of  29 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  30 out of  30 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  31 out of  31 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  32 out of  32 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  33 out of  33 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  34 out of  34 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  35 out of  35 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  36 out of  36 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  37 out of  37 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  38 out of  38 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  39 out of  39 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  40 out of  40 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  41 out of  41 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  42 out of  42 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  43 out of  43 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  44 out of  44 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  45 out of  45 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  46 out of  46 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  47 out of  47 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  48 out of  48 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  49 out of  49 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  50 out of  50 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  50 out of  50 | elapsed:    0.0s finished\n"
     ]
    }
   ],
   "source": [
    "for ext, clf in rf_clfs.items():\n",
    "    pre_test['RF-'+ext] = clf.predict(test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "QJXk393yptuW"
   },
   "outputs": [],
   "source": [
    "for ext, clf in nb_clfs.items():\n",
    "    pre_test['NB-'+ext] = clf.predict(test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "0m4AawQrpyeV"
   },
   "outputs": [],
   "source": [
    "for ext, clf in mlp_clfs.items():\n",
    "    pre_test['MLP-'+ext] = clf.predict(test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "colab_type": "code",
    "id": "J8xIyAyXp1Rq",
    "outputId": "5634be83-099e-49e3-e338-ef3e5ecd03be"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   3 out of   3 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   4 out of   4 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   5 out of   5 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   6 out of   6 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   7 out of   7 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   8 out of   8 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   9 out of   9 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  10 out of  10 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  11 out of  11 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  12 out of  12 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  13 out of  13 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  14 out of  14 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  15 out of  15 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  16 out of  16 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  17 out of  17 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  18 out of  18 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  19 out of  19 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  20 out of  20 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  21 out of  21 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  22 out of  22 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  23 out of  23 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  24 out of  24 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  25 out of  25 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  26 out of  26 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  27 out of  27 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  28 out of  28 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  29 out of  29 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  30 out of  30 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  31 out of  31 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  32 out of  32 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  33 out of  33 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  34 out of  34 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  35 out of  35 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  36 out of  36 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  37 out of  37 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  38 out of  38 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  39 out of  39 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  40 out of  40 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  41 out of  41 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  42 out of  42 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  43 out of  43 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  44 out of  44 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  45 out of  45 | elapsed:    0.3s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  46 out of  46 | elapsed:    0.3s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  47 out of  47 | elapsed:    0.3s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  48 out of  48 | elapsed:    0.3s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  49 out of  49 | elapsed:    0.3s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  50 out of  50 | elapsed:    0.3s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  50 out of  50 | elapsed:    0.3s finished\n",
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   3 out of   3 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   4 out of   4 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   5 out of   5 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   6 out of   6 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   7 out of   7 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   8 out of   8 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   9 out of   9 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  10 out of  10 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  11 out of  11 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  12 out of  12 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  13 out of  13 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  14 out of  14 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  15 out of  15 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  16 out of  16 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  17 out of  17 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  18 out of  18 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  19 out of  19 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  20 out of  20 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  21 out of  21 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  22 out of  22 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  23 out of  23 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  24 out of  24 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  25 out of  25 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  26 out of  26 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  27 out of  27 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  28 out of  28 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  29 out of  29 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  30 out of  30 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  31 out of  31 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  32 out of  32 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  33 out of  33 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  34 out of  34 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  35 out of  35 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  36 out of  36 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  37 out of  37 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  38 out of  38 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  39 out of  39 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  40 out of  40 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  41 out of  41 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  42 out of  42 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  43 out of  43 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  44 out of  44 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  45 out of  45 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  46 out of  46 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  47 out of  47 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  48 out of  48 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  49 out of  49 | elapsed:    0.3s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  50 out of  50 | elapsed:    0.3s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  50 out of  50 | elapsed:    0.3s finished\n",
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   3 out of   3 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   4 out of   4 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   5 out of   5 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   6 out of   6 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   7 out of   7 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   8 out of   8 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   9 out of   9 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  10 out of  10 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  11 out of  11 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  12 out of  12 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  13 out of  13 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  14 out of  14 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  15 out of  15 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  16 out of  16 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  17 out of  17 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  18 out of  18 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  19 out of  19 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  20 out of  20 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  21 out of  21 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  22 out of  22 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  23 out of  23 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  24 out of  24 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  25 out of  25 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  26 out of  26 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  27 out of  27 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  28 out of  28 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  29 out of  29 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  30 out of  30 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  31 out of  31 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  32 out of  32 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  33 out of  33 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  34 out of  34 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  35 out of  35 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  36 out of  36 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  37 out of  37 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  38 out of  38 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  39 out of  39 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  40 out of  40 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  41 out of  41 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  42 out of  42 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  43 out of  43 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  44 out of  44 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  45 out of  45 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  46 out of  46 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  47 out of  47 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  48 out of  48 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  49 out of  49 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  50 out of  50 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  50 out of  50 | elapsed:    0.1s finished\n",
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   3 out of   3 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   4 out of   4 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   5 out of   5 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   6 out of   6 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   7 out of   7 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   8 out of   8 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   9 out of   9 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  10 out of  10 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  11 out of  11 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  12 out of  12 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  13 out of  13 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  14 out of  14 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  15 out of  15 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  16 out of  16 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  17 out of  17 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  18 out of  18 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  19 out of  19 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  20 out of  20 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  21 out of  21 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  22 out of  22 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  23 out of  23 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  24 out of  24 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  25 out of  25 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  26 out of  26 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  27 out of  27 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  28 out of  28 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  29 out of  29 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  30 out of  30 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  31 out of  31 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  32 out of  32 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  33 out of  33 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  34 out of  34 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  35 out of  35 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  36 out of  36 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  37 out of  37 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  38 out of  38 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  39 out of  39 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  40 out of  40 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  41 out of  41 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  42 out of  42 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  43 out of  43 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  44 out of  44 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  45 out of  45 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  46 out of  46 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  47 out of  47 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  48 out of  48 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  49 out of  49 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  50 out of  50 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  50 out of  50 | elapsed:    0.1s finished\n",
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   3 out of   3 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   4 out of   4 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   5 out of   5 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   6 out of   6 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   7 out of   7 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   8 out of   8 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   9 out of   9 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  10 out of  10 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  11 out of  11 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  12 out of  12 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  13 out of  13 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  14 out of  14 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  15 out of  15 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  16 out of  16 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  17 out of  17 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  18 out of  18 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  19 out of  19 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  20 out of  20 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  21 out of  21 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  22 out of  22 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  23 out of  23 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  24 out of  24 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  25 out of  25 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  26 out of  26 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  27 out of  27 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  28 out of  28 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  29 out of  29 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  30 out of  30 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  31 out of  31 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  32 out of  32 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  33 out of  33 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  34 out of  34 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  35 out of  35 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  36 out of  36 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  37 out of  37 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  38 out of  38 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  39 out of  39 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  40 out of  40 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  41 out of  41 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  42 out of  42 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  43 out of  43 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  44 out of  44 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  45 out of  45 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  46 out of  46 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  47 out of  47 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  48 out of  48 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  49 out of  49 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  50 out of  50 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  50 out of  50 | elapsed:    0.1s finished\n"
     ]
    }
   ],
   "source": [
    "for ext, clf in extra_clfs.items():\n",
    "    pre_test['EXTRA-'+ext] = clf.predict(test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "m_tl1wh0p3Uy"
   },
   "outputs": [],
   "source": [
    "for ext, clf in knn_clfs.items():\n",
    "    pre_test['KNN-'+ext] = clf.predict(test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "y7lZKQXvp5eU"
   },
   "outputs": [],
   "source": [
    "pre_test['CNN-CV'] = np.argmax(cnn_cv.predict(data_test), axis=1)\n",
    "pre_test['CNN-TFIDF'] = np.argmax(cnn_tfidf.predict(data_test), axis=1)\n",
    "pre_test['CNN-W2V'] = np.argmax(cnn_w2v.predict(data_test), axis=1)\n",
    "pre_test['CNN-GLOVE'] = np.argmax(cnn_glove.predict(data_test), axis=1)\n",
    "pre_test['CNN-FAST'] = np.argmax(cnn_fast.predict(data_test), axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "colab_type": "code",
    "id": "euKKlCZXB2xw",
    "outputId": "7690b5ba-14e8-401f-8169-9328c08482a1"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SVM-CV\n",
      "0.6996949300977103\n",
      "0.8975299584250428\n",
      "SVM-TFIDF\n",
      "0.683903106810294\n",
      "0.9060895084372708\n",
      "SVM-W2V\n",
      "0.5403381320039579\n",
      "0.8473954512105649\n",
      "SVM-GLOVE\n",
      "0.5072339434574643\n",
      "0.8290535583272194\n",
      "SVM-FAST\n",
      "0.5372873051525895\n",
      "0.85155294693079\n",
      "\n",
      "LR-CV\n",
      "0.7061946317764202\n",
      "0.9068231841526045\n",
      "LR-TFIDF\n",
      "0.6798583410033484\n",
      "0.9026656884323796\n",
      "LR-W2V\n",
      "0.5397536876895024\n",
      "0.8378576669112252\n",
      "LR-GLOVE\n",
      "0.48428563655007156\n",
      "0.8217168011738811\n",
      "LR-FAST\n",
      "0.5481235089092872\n",
      "0.8456835412081194\n",
      "\n",
      "RF-CV\n",
      "0.6996712135588782\n",
      "0.8938615798483737\n",
      "RF-TFIDF\n",
      "0.6718619085460568\n",
      "0.9002201027146002\n",
      "RF-W2V\n",
      "0.4836414163407839\n",
      "0.8202494497432135\n",
      "RF-GLOVE\n",
      "0.5005444316590708\n",
      "0.8219613597456591\n",
      "RF-FAST\n",
      "0.49473525749826747\n",
      "0.8268525311812179\n",
      "\n",
      "NB-CV\n",
      "0.678153415932599\n",
      "0.880410858400587\n",
      "NB-TFIDF\n",
      "0.6431472612527828\n",
      "0.8542430912203473\n",
      "NB-W2V\n",
      "0.5304511068909745\n",
      "0.7493274639276106\n",
      "NB-GLOVE\n",
      "0.48194756102954345\n",
      "0.7994619711420885\n",
      "NB-FAST\n",
      "0.5734328322927237\n",
      "0.7723159696747371\n",
      "\n",
      "MLP-CV\n",
      "0.6926028578467603\n",
      "0.8953289312790413\n",
      "MLP-TFIDF\n",
      "0.6876818000461355\n",
      "0.8862802641232576\n",
      "MLP-W2V\n",
      "0.5650170902845967\n",
      "0.8410369283443385\n",
      "MLP-GLOVE\n",
      "0.5308938900805913\n",
      "0.826363414037662\n",
      "MLP-FAST\n",
      "0.586442657004018\n",
      "0.8549767669356811\n",
      "\n",
      "EXTRA-CV\n",
      "0.6872668423012103\n",
      "0.8818782098312545\n",
      "EXTRA-TFIDF\n",
      "0.6616989790476028\n",
      "0.8916605527023722\n",
      "EXTRA-W2V\n",
      "0.4637544697534071\n",
      "0.8170701883101003\n",
      "EXTRA-GLOVE\n",
      "0.4935620882973814\n",
      "0.8222059183174371\n",
      "EXTRA-FAST\n",
      "0.4503475578453726\n",
      "0.8116898997309856\n",
      "\n",
      "KNN-CV\n",
      "0.6188510227598402\n",
      "0.8290535583272194\n",
      "KNN-TFIDF\n",
      "0.5426204934077559\n",
      "0.7945707997065297\n",
      "KNN-W2V\n",
      "0.5065238233767334\n",
      "0.822450476889215\n",
      "KNN-GLOVE\n",
      "0.4933713450104677\n",
      "0.8014184397163121\n",
      "KNN-FAST\n",
      "0.4779889979138013\n",
      "0.8065541697236488\n",
      "\n",
      "CNN-CV\n",
      "0.7014580834629718\n",
      "0.896551724137931\n",
      "CNN-TFIDF\n",
      "0.5816789541599594\n",
      "0.8889704084128148\n",
      "CNN-W2V\n",
      "0.6628573105635795\n",
      "0.7857666911225238\n",
      "CNN-GLOVE\n",
      "0.599602778252181\n",
      "0.8941061384201516\n",
      "CNN-FAST\n",
      "0.7035341029724355\n",
      "0.8958180484225972\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.6/dist-packages/sklearn/metrics/classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n"
     ]
    }
   ],
   "source": [
    "k = 0\n",
    "for clf, pre_clf in pre_test.items():\n",
    "    print(clf)\n",
    "    print(f1_score(class_test, pre_clf, average='macro'))\n",
    "    print(accuracy_score(class_test, pre_clf))\n",
    "    k += 1\n",
    "    if k % 5 == 0:\n",
    "        print(\"\")\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Az-Rw-clqPtU"
   },
   "source": [
    "# ORACLE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 68
    },
    "colab_type": "code",
    "id": "MT3R88LxqRJ3",
    "outputId": "826468d0-ae34-4e18-966c-01a351370b90"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3882 de 4089\n",
      "Acc:  0.9493763756419662\n",
      "F1:  0.7814545785826867\n"
     ]
    }
   ],
   "source": [
    "clfs = ['SVM-CV', 'SVM-TFIDF', 'SVM-W2V', 'SVM-GLOVE', 'SVM-FAST']\n",
    "\n",
    "oracle, df_yes = get_oracle(df_test['class'], pre_test, clfs)\n",
    "\n",
    "print(\"{} de {}\".format(oracle, df_test.shape[0]))\n",
    "print('Acc: ', accuracy_score(df_test['class'], df_yes))\n",
    "print('F1: ', f1_score(df_test['class'], df_yes, average='macro'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 68
    },
    "colab_type": "code",
    "id": "oLkabHCZqMyJ",
    "outputId": "57637c88-70b6-4aac-c758-fd729870283c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3882 de 4089\n",
      "Acc:  0.9493763756419662\n",
      "F1:  0.7769870171923238\n"
     ]
    }
   ],
   "source": [
    "clfs = ['LR-CV', 'LR-TFIDF', 'LR-W2V', 'LR-GLOVE', 'LR-FAST']\n",
    "\n",
    "oracle, df_yes = get_oracle(df_test['class'], pre_test, clfs)\n",
    "\n",
    "print(\"{} de {}\".format(oracle, df_test.shape[0]))\n",
    "print('Acc: ', accuracy_score(df_test['class'], df_yes))\n",
    "print('F1: ', f1_score(df_test['class'], df_yes, average='macro'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 68
    },
    "colab_type": "code",
    "id": "P1UHO-kQsIqf",
    "outputId": "2687470a-c782-4a50-e947-f718152ba0da"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3858 de 4089\n",
      "Acc:  0.9435069699192957\n",
      "F1:  0.7596445883390107\n"
     ]
    }
   ],
   "source": [
    "clfs = ['RF-CV', 'RF-TFIDF', 'RF-W2V', 'RF-GLOVE', 'RF-FAST']\n",
    "\n",
    "oracle, df_yes = get_oracle(df_test['class'], pre_test, clfs)\n",
    "\n",
    "print(\"{} de {}\".format(oracle, df_test.shape[0]))\n",
    "print('Acc: ', accuracy_score(df_test['class'], df_yes))\n",
    "print('F1: ', f1_score(df_test['class'], df_yes, average='macro'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 68
    },
    "colab_type": "code",
    "id": "_clGW9dis4wk",
    "outputId": "289d429c-2c3d-450a-d397-4d18baaca86f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3871 de 4089\n",
      "Acc:  0.9466862313524089\n",
      "F1:  0.8102401792105702\n"
     ]
    }
   ],
   "source": [
    "clfs = ['NB-CV', 'NB-TFIDF', 'NB-W2V', 'NB-GLOVE', 'NB-FAST']\n",
    "\n",
    "oracle, df_yes = get_oracle(df_test['class'], pre_test, clfs)\n",
    "\n",
    "print(\"{} de {}\".format(oracle, df_test.shape[0]))\n",
    "print('Acc: ', accuracy_score(df_test['class'], df_yes))\n",
    "print('F1: ', f1_score(df_test['class'], df_yes, average='macro'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 68
    },
    "colab_type": "code",
    "id": "eeTjVRDxs8ll",
    "outputId": "85047b5d-5aee-4948-9274-1f673a8cce65"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3907 de 4089\n",
      "Acc:  0.9554903399364147\n",
      "F1:  0.8176106620615481\n"
     ]
    }
   ],
   "source": [
    "clfs = ['MLP-CV', 'MLP-TFIDF', 'MLP-W2V', 'MLP-GLOVE', 'MLP-FAST']\n",
    "\n",
    "oracle, df_yes = get_oracle(df_test['class'], pre_test, clfs)\n",
    "\n",
    "print(\"{} de {}\".format(oracle, df_test.shape[0]))\n",
    "print('Acc: ', accuracy_score(df_test['class'], df_yes))\n",
    "print('F1: ', f1_score(df_test['class'], df_yes, average='macro'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 68
    },
    "colab_type": "code",
    "id": "RpWfFTUMtAJ4",
    "outputId": "414e0c0c-88c0-4ea1-ef96-42bb24b6757e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3845 de 4089\n",
      "Acc:  0.9403277084861824\n",
      "F1:  0.7515660761734061\n"
     ]
    }
   ],
   "source": [
    "clfs = ['EXTRA-CV', 'EXTRA-TFIDF', 'EXTRA-W2V', 'EXTRA-GLOVE', 'EXTRA-FAST']\n",
    "\n",
    "oracle, df_yes = get_oracle(df_test['class'], pre_test, clfs)\n",
    "\n",
    "print(\"{} de {}\".format(oracle, df_test.shape[0]))\n",
    "print('Acc: ', accuracy_score(df_test['class'], df_yes))\n",
    "print('F1: ', f1_score(df_test['class'], df_yes, average='macro'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 68
    },
    "colab_type": "code",
    "id": "TyjokiKutFGq",
    "outputId": "7188db2f-2b3f-4b56-fa54-56b4965e95ec"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3834 de 4089\n",
      "Acc:  0.9376375641966251\n",
      "F1:  0.7724467484817703\n"
     ]
    }
   ],
   "source": [
    "clfs = ['KNN-CV', 'KNN-TFIDF', 'KNN-W2V', 'KNN-GLOVE', 'KNN-FAST']\n",
    "\n",
    "oracle, df_yes = get_oracle(df_test['class'], pre_test, clfs)\n",
    "\n",
    "print(\"{} de {}\".format(oracle, df_test.shape[0]))\n",
    "print('Acc: ', accuracy_score(df_test['class'], df_yes))\n",
    "print('F1: ', f1_score(df_test['class'], df_yes, average='macro'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 68
    },
    "colab_type": "code",
    "id": "nS2agY4qtI6p",
    "outputId": "fc2f41f2-c1fd-4e5c-cbf8-37fe93ac1bce"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3976 de 4089\n",
      "Acc:  0.9723648813890927\n",
      "F1:  0.9081084960997048\n"
     ]
    }
   ],
   "source": [
    "clfs = ['CNN-CV', 'CNN-TFIDF', 'CNN-W2V', 'CNN-GLOVE', 'CNN-FAST']\n",
    "\n",
    "oracle, df_yes = get_oracle(df_test['class'], pre_test, clfs)\n",
    "\n",
    "print(\"{} de {}\".format(oracle, df_test.shape[0]))\n",
    "print('Acc: ', accuracy_score(df_test['class'], df_yes))\n",
    "print('F1: ', f1_score(df_test['class'], df_yes, average='macro'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "TBmJnHDltRLU"
   },
   "source": [
    "# TSNE-FULL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "dXSbDtpZtNdO"
   },
   "outputs": [],
   "source": [
    "delta, cols = calc_delta(df_val['class'], pre, pre.keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 899
    },
    "colab_type": "code",
    "id": "q1oaGeYhueD9",
    "outputId": "1df3515e-4707-4276-bb79-0eb6dffe1608"
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA38AAANeCAYAAACiR7ccAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cores = {}\n",
    "cores[0] = '#DAA520'\n",
    "cores[1] = '#FF0000'\n",
    "cores[2] = '#0000FF'\n",
    "cores[3] = '#228B22'\n",
    "cores[4] = '#000000'\n",
    "\n",
    "simbolos = {}\n",
    "simbolos[0] = 'X'\n",
    "simbolos[1] = 'd'\n",
    "simbolos[2] = '*'\n",
    "simbolos[3] = \"^\"\n",
    "simbolos[4] = 'o'\n",
    "\n",
    "tsne_full(list(cols), delta, 'Todos', cores=cores, simbolos=simbolos, escala=200, per=25, size=15, size_dot=70)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "FH2cEv8PwlU8"
   },
   "source": [
    "# Oracle Combinados"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 68
    },
    "colab_type": "code",
    "id": "nRdyG7SWLyIO",
    "outputId": "b90b5655-8c0c-41c3-a059-df7d3b08ba28"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4043 de 4089\n",
      "Acc:  0.9887503056982148\n",
      "F1:  0.9604786119459551\n"
     ]
    }
   ],
   "source": [
    "# TODOS\n",
    "clfs = list(pre.keys())\n",
    "\n",
    "oracle, df_yes = get_oracle(df_test['class'], pre_test, clfs)\n",
    "\n",
    "print(\"{} de {}\".format(oracle, df_test.shape[0]))\n",
    "print('Acc: ', accuracy_score(df_test['class'], df_yes))\n",
    "print('F1: ', f1_score(df_test['class'], df_yes, average='macro'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 68
    },
    "colab_type": "code",
    "id": "DefuGr4Dwm3m",
    "outputId": "cedf4f2d-acc7-44d9-ddb5-1fb99ec73c6c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3931 de 4089\n",
      "Acc:  0.9613597456590853\n",
      "F1:  0.8364097882833813\n"
     ]
    }
   ],
   "source": [
    "# EXTERNOS\n",
    "clfs = ['CNN-FAST', 'RF-CV', 'KNN-TFIDF', 'LR-W2V', 'SVM-GLOVE']\n",
    "\n",
    "oracle, df_yes = get_oracle(df_test['class'], pre_test, clfs)\n",
    "\n",
    "print(\"{} de {}\".format(oracle, df_test.shape[0]))\n",
    "print('Acc: ', accuracy_score(df_test['class'], df_yes))\n",
    "print('F1: ', f1_score(df_test['class'], df_yes, average='macro'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 68
    },
    "colab_type": "code",
    "id": "9ImDessUw3YD",
    "outputId": "219e6e5b-de3c-43be-f1a2-145d8731e9eb"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3894 de 4089\n",
      "Acc:  0.9523110785033015\n",
      "F1:  0.8277069366247168\n"
     ]
    }
   ],
   "source": [
    "clfs = ['CNN-FAST', 'SVM-CV', 'LR-TFIDF', 'LR-CV', 'NB-CV']\n",
    "\n",
    "oracle, df_yes = get_oracle(df_test['class'], pre_test, clfs)\n",
    "\n",
    "print(\"{} de {}\".format(oracle, df_test.shape[0]))\n",
    "print('Acc: ', accuracy_score(df_test['class'], df_yes))\n",
    "print('F1: ', f1_score(df_test['class'], df_yes, average='macro'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "cbcEiqVexr33"
   },
   "source": [
    "# VENN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 267
    },
    "colab_type": "code",
    "id": "9NDrEAToxsw8",
    "outputId": "9b2cce9e-a83f-44c9-ccd5-0fd81c1ea400"
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAO4AAAD6CAYAAACmqXuvAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nZCUQW1HsogAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# clfs = ['CNN-CV', 'NB-FAST', 'MLP-FAST', 'KNN-FAST', 'NB-GLOVE']\n",
    "diagrama_venn(pre_test['CNN-CV'], pre_test['NB-FAST'], pre_test['MLP-FAST'], 'CNN-CV', 'NB-FAST', 'MLP-FAST', 'Conjunto 1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 267
    },
    "colab_type": "code",
    "id": "glnRdDtRz9ej",
    "outputId": "88186a0b-7c99-461f-abc0-adcfed9222a4"
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAPAAAAD6CAYAAACfzsr0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# clfs = ['CNN-CV', 'LR-TFIDF', 'SVM-CV', 'RF-CV', 'MLP-TFIDF']\n",
    "diagrama_venn(pre_test['CNN-CV'], pre_test['LR-TFIDF'], pre_test['SVM-CV'], 'CNN-CV', 'LR-TFIDF', 'SVM-CV', 'Conjunto 2')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "7Ksstwwr8waY"
   },
   "source": [
    "# BAR\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "BCeVM4sw8-BG"
   },
   "outputs": [],
   "source": [
    "list_certos = {\n",
    "    5: 0, 4: 0, 3: 0, 2:0, 1:0, 0:0\n",
    "}\n",
    "EXTS = ['CV','TFIDF', 'W2V','GLOVE', 'FAST']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 314
    },
    "colab_type": "code",
    "id": "q9PfYSWA9Uhx",
    "outputId": "baffe89b-7d83-4563-d3fc-5c9a32b9bf70"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No handles with labels found to put in legend.\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAagAAAEYCAYAAAAJeGK1AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nz0qZaGjmAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "svm_certos = list_certos.copy()\n",
    "for i, l in enumerate(df_test['class']):\n",
    "    k = 0\n",
    "    for ext in EXTS:\n",
    "        if pre_test['SVM-'+ext][i]==l:\n",
    "            k += 1\n",
    "    svm_certos[k] = svm_certos[k]+1\n",
    "\n",
    "render_bar(svm_certos, title='SVM')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 314
    },
    "colab_type": "code",
    "id": "Z2_Z5DGT9_sH",
    "outputId": "d0e1c65e-2cad-4ea7-d982-36eb24fff3c8"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No handles with labels found to put in legend.\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAagAAAEYCAYAAAAJeGK1AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "lr_certos = list_certos.copy()\n",
    "for i, l in enumerate(df_test['class']):\n",
    "    k = 0\n",
    "    for ext in EXTS:\n",
    "        if pre_test['LR-'+ext][i]==l:\n",
    "            k += 1\n",
    "    lr_certos[k] = lr_certos[k]+1\n",
    "\n",
    "render_bar(lr_certos, title='LogisticRegression')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 314
    },
    "colab_type": "code",
    "id": "jY1Qws_P9WbD",
    "outputId": "40bdd046-6129-4861-d6aa-0fdc5cda6ff4"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No handles with labels found to put in legend.\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAagAAAEYCAYAAAAJeGK1AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nZpZLTlBmZpZLTlBmZpZLTlBmZpZL/x8NgAaghpn/NQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "rf_certos = list_certos.copy()\n",
    "for i, l in enumerate(df_test['class']):\n",
    "    k = 0\n",
    "    for ext in EXTS:\n",
    "        if pre_test['RF-'+ext][i]==l:\n",
    "            k += 1\n",
    "    rf_certos[k] = rf_certos[k]+1\n",
    "\n",
    "render_bar(rf_certos, title='RandomForest')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 314
    },
    "colab_type": "code",
    "id": "pggmZleZ_4vP",
    "outputId": "853e5d73-151f-4495-976a-1aa425e37a1f"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No handles with labels found to put in legend.\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAagAAAEYCAYAAAAJeGK1AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nXHKCMjOzXHKCMjOzXPr/bGpzAqeM5hwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "nb_certos = list_certos.copy()\n",
    "for i, l in enumerate(df_test['class']):\n",
    "    k = 0\n",
    "    for ext in EXTS:\n",
    "        if pre_test['NB-'+ext][i]==l:\n",
    "            k += 1\n",
    "    nb_certos[k] = nb_certos[k]+1\n",
    "\n",
    "render_bar(nb_certos, title='NaiveBayes')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 314
    },
    "colab_type": "code",
    "id": "LNOV-YK-AA6X",
    "outputId": "6f792653-0c1c-4b6f-feaf-cae5f23e1824"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No handles with labels found to put in legend.\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAagAAAEYCAYAAAAJeGK1AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nzHLp/wMUAUbWwp+U/QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "mlp_certos = list_certos.copy()\n",
    "for i, l in enumerate(df_test['class']):\n",
    "    k = 0\n",
    "    for ext in EXTS:\n",
    "        if pre_test['MLP-'+ext][i]==l:\n",
    "            k += 1\n",
    "    mlp_certos[k] = mlp_certos[k]+1\n",
    "\n",
    "render_bar(mlp_certos, title='MLP')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 314
    },
    "colab_type": "code",
    "id": "TWD0UWrJAIY0",
    "outputId": "d11d5eee-27da-4b67-c458-f73e4fc0c2ca"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No handles with labels found to put in legend.\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAagAAAEYCAYAAAAJeGK1AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "extra_certos = list_certos.copy()\n",
    "for i, l in enumerate(df_test['class']):\n",
    "    k = 0\n",
    "    for ext in EXTS:\n",
    "        if pre_test['EXTRA-'+ext][i]==l:\n",
    "            k += 1\n",
    "    extra_certos[k] = extra_certos[k]+1\n",
    "\n",
    "render_bar(extra_certos, title='ExtraTrees')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 314
    },
    "colab_type": "code",
    "id": "60uO4nmpAXAT",
    "outputId": "57a558ba-29db-4678-bd79-62ca1b3894b8"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No handles with labels found to put in legend.\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAagAAAEYCAYAAAAJeGK1AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nCcrMzHLJCcrMzHLp/wMyz8Ya/kEscQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "knn_certos = list_certos.copy()\n",
    "for i, l in enumerate(df_test['class']):\n",
    "    k = 0\n",
    "    for ext in EXTS:\n",
    "        if pre_test['KNN-'+ext][i]==l:\n",
    "            k += 1\n",
    "    knn_certos[k] = knn_certos[k]+1\n",
    "\n",
    "render_bar(knn_certos, title='KNN')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 314
    },
    "colab_type": "code",
    "id": "jHLPbvPKAkrP",
    "outputId": "93448dd5-9bb5-4de9-b1ab-158ce1a8efc5"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No handles with labels found to put in legend.\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAagAAAEYCAYAAAAJeGK1AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cnn_certos = list_certos.copy()\n",
    "for i, l in enumerate(df_test['class']):\n",
    "    k = 0\n",
    "    for ext in EXTS:\n",
    "        if pre_test['CNN-'+ext][i]==l:\n",
    "            k += 1\n",
    "    cnn_certos[k] = cnn_certos[k]+1\n",
    "\n",
    "render_bar(cnn_certos, title='CNN')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 314
    },
    "colab_type": "code",
    "id": "sMiYx9FnA1vy",
    "outputId": "9faf2407-9b55-4bd7-8100-a090fa104b42"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No handles with labels found to put in legend.\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAagAAAEYCAYAAAAJeGK1AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nUGZmlktOUGZmlktOUGZmlkv/H1LFsUhSULClAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "clfs_combinados = ['CNN-FAST', 'RF-CV', 'KNN-TFIDF', 'LR-W2V', 'SVM-GLOVE']\n",
    "clfs_combinados_certos = list_certos.copy()\n",
    "for i, l in enumerate(df_test['class']):\n",
    "    k = 0\n",
    "    for clf in clfs_combinados:\n",
    "        if pre_test[clf][i]==l:\n",
    "            k += 1\n",
    "    clfs_combinados_certos[k] = clfs_combinados_certos[k]+1\n",
    "\n",
    "render_bar(clfs_combinados_certos, title='Combinados (B)', size_x=4, size_y=6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 386
    },
    "colab_type": "code",
    "id": "lUs2NRL_ezjz",
    "outputId": "f4b05169-b846-4360-bc26-68b65eaa24c5"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No handles with labels found to put in legend.\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAsgAAAFgCAYAAACmDI9oAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nIAiCIAiCoMD/AYb1iXs57lhcAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "clfs_todos = list(pre.keys())\n",
    "clfs_todos_certos = {k:0 for k in range(len(clfs_todos), -1, -1)}\n",
    "# print(clfs_todos_certos)\n",
    "for i, l in enumerate(df_test['class']):\n",
    "    k = 0\n",
    "    for clf in clfs_todos:\n",
    "        if pre_test[clf][i]==l:\n",
    "            k += 1\n",
    "    clfs_todos_certos[k] = clfs_todos_certos[k]+1\n",
    "render_bar(clfs_todos_certos, title='Todos', size_x=5, size_y=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "k3389HWmBWHS"
   },
   "source": [
    "# COMBINE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "cZLAHI8GTyYS"
   },
   "outputs": [],
   "source": [
    "y_pred_voting, y_pred_average, y_pred_maximun, y_pred_minimun, y_pred_median, y_pred_product = get_combine(test, svm_clfs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 340
    },
    "colab_type": "code",
    "id": "1vsQqPpwhPFw",
    "outputId": "7d9216b6-9599-45f4-c94a-d23fe497e5d9"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SVM\n",
      "Voting\n",
      "0.6056341907550814\n",
      "0.880410858400587\n",
      "Average\n",
      "0.6130555154027507\n",
      "0.892638786989484\n",
      "Maximun\n",
      "0.6426386645074987\n",
      "0.8945952555637076\n",
      "Minimun\n",
      "0.6310058755587278\n",
      "0.8938615798483737\n",
      "Median\n",
      "0.5902673759788791\n",
      "0.879921741257031\n",
      "Product\n",
      "0.6177547535303702\n",
      "0.8945952555637076\n"
     ]
    }
   ],
   "source": [
    "print(\"SVM\")\n",
    "print(\"Voting\")\n",
    "print(f1_score(class_test, y_pred_voting, average='macro'))\n",
    "print(accuracy_score(class_test, y_pred_voting))\n",
    "\n",
    "print(\"Average\")\n",
    "print(f1_score(class_test, y_pred_average, average='macro'))\n",
    "print(accuracy_score(class_test, y_pred_average))\n",
    "\n",
    "print(\"Maximun\")\n",
    "print(f1_score(class_test, y_pred_maximun, average='macro'))\n",
    "print(accuracy_score(class_test, y_pred_maximun))\n",
    "\n",
    "print(\"Minimun\")\n",
    "print(f1_score(class_test, y_pred_minimun, average='macro'))\n",
    "print(accuracy_score(class_test, y_pred_minimun))\n",
    "\n",
    "print(\"Median\")\n",
    "print(f1_score(class_test, y_pred_median, average='macro'))\n",
    "print(accuracy_score(class_test, y_pred_median))\n",
    "\n",
    "print(\"Product\")\n",
    "print(f1_score(class_test, y_pred_product, average='macro'))\n",
    "print(accuracy_score(class_test, y_pred_product))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "PAbKv8WyCVHi"
   },
   "outputs": [],
   "source": [
    "y_pred_voting, y_pred_average, y_pred_maximun, y_pred_minimun, y_pred_median, y_pred_product = get_combine(test, lr_clfs)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 340
    },
    "colab_type": "code",
    "id": "IrRxaur_hT6n",
    "outputId": "b7f72d64-d2d6-46b5-8d5d-319f61e91e37"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic Regression\n",
      "Voting\n",
      "0.5967779460372734\n",
      "0.8755196869650281\n",
      "Average\n",
      "0.6011302481723882\n",
      "0.8875030569821473\n",
      "Maximun\n",
      "0.6027191784176119\n",
      "0.8831010026901442\n",
      "Minimun\n",
      "0.5915829542773894\n",
      "0.8818782098312545\n",
      "Median\n",
      "0.5916513721925662\n",
      "0.8755196869650281\n",
      "Product\n",
      "0.5962378583037995\n",
      "0.8855465884079237\n"
     ]
    }
   ],
   "source": [
    "print(\"Logistic Regression\")\n",
    "\n",
    "print(\"Voting\")\n",
    "print(f1_score(class_test, y_pred_voting, average='macro'))\n",
    "print(accuracy_score(class_test, y_pred_voting))\n",
    "\n",
    "print(\"Average\")\n",
    "print(f1_score(class_test, y_pred_average, average='macro'))\n",
    "print(accuracy_score(class_test, y_pred_average))\n",
    "\n",
    "print(\"Maximun\")\n",
    "print(f1_score(class_test, y_pred_maximun, average='macro'))\n",
    "print(accuracy_score(class_test, y_pred_maximun))\n",
    "\n",
    "print(\"Minimun\")\n",
    "print(f1_score(class_test, y_pred_minimun, average='macro'))\n",
    "print(accuracy_score(class_test, y_pred_minimun))\n",
    "\n",
    "print(\"Median\")\n",
    "print(f1_score(class_test, y_pred_median, average='macro'))\n",
    "print(accuracy_score(class_test, y_pred_median))\n",
    "\n",
    "print(\"Product\")\n",
    "print(f1_score(class_test, y_pred_product, average='macro'))\n",
    "print(accuracy_score(class_test, y_pred_product))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "colab_type": "code",
    "id": "p52CSCD5UEo4",
    "outputId": "70e9a04b-c187-4ee2-b026-d5f5236d93fc"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   3 out of   3 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   4 out of   4 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   5 out of   5 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   6 out of   6 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   7 out of   7 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   8 out of   8 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   9 out of   9 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  10 out of  10 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  11 out of  11 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  12 out of  12 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  13 out of  13 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  14 out of  14 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  15 out of  15 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  16 out of  16 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  17 out of  17 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  18 out of  18 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  19 out of  19 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  20 out of  20 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  20 out of  20 | elapsed:    0.1s finished\n",
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   3 out of   3 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   4 out of   4 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   5 out of   5 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   6 out of   6 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   7 out of   7 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   8 out of   8 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   9 out of   9 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  10 out of  10 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  11 out of  11 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  12 out of  12 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  13 out of  13 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  14 out of  14 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  15 out of  15 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  16 out of  16 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  17 out of  17 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  18 out of  18 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  19 out of  19 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  20 out of  20 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  21 out of  21 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  22 out of  22 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  23 out of  23 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  24 out of  24 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  25 out of  25 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  26 out of  26 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  27 out of  27 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  28 out of  28 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  29 out of  29 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  30 out of  30 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  31 out of  31 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  32 out of  32 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  33 out of  33 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  34 out of  34 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  35 out of  35 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  36 out of  36 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  37 out of  37 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  38 out of  38 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  39 out of  39 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  40 out of  40 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  41 out of  41 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  42 out of  42 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  43 out of  43 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  44 out of  44 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  45 out of  45 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  46 out of  46 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  47 out of  47 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  48 out of  48 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  49 out of  49 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  50 out of  50 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  50 out of  50 | elapsed:    0.1s finished\n",
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   3 out of   3 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   4 out of   4 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   5 out of   5 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   6 out of   6 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   7 out of   7 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   8 out of   8 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   9 out of   9 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  10 out of  10 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  11 out of  11 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  12 out of  12 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  13 out of  13 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  14 out of  14 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  15 out of  15 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  16 out of  16 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  17 out of  17 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  18 out of  18 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  19 out of  19 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  20 out of  20 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  21 out of  21 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  22 out of  22 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  23 out of  23 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  24 out of  24 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  25 out of  25 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  26 out of  26 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  27 out of  27 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  28 out of  28 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  29 out of  29 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  30 out of  30 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  31 out of  31 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  32 out of  32 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  33 out of  33 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  34 out of  34 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  35 out of  35 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  36 out of  36 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  37 out of  37 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  38 out of  38 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  39 out of  39 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  40 out of  40 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  41 out of  41 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  42 out of  42 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  43 out of  43 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  44 out of  44 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  45 out of  45 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  46 out of  46 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  47 out of  47 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  48 out of  48 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  49 out of  49 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  50 out of  50 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  50 out of  50 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   3 out of   3 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   4 out of   4 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   5 out of   5 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   6 out of   6 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   7 out of   7 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   8 out of   8 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   9 out of   9 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  10 out of  10 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  11 out of  11 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  12 out of  12 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  13 out of  13 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  14 out of  14 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  15 out of  15 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  16 out of  16 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  17 out of  17 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  18 out of  18 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  19 out of  19 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  20 out of  20 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  21 out of  21 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  22 out of  22 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  23 out of  23 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  24 out of  24 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  25 out of  25 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  26 out of  26 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  27 out of  27 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  28 out of  28 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  29 out of  29 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  30 out of  30 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  31 out of  31 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  32 out of  32 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  33 out of  33 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  34 out of  34 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  35 out of  35 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  36 out of  36 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  37 out of  37 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  38 out of  38 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  39 out of  39 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  40 out of  40 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  41 out of  41 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  42 out of  42 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  43 out of  43 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  44 out of  44 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  45 out of  45 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  46 out of  46 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  47 out of  47 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  48 out of  48 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  49 out of  49 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  50 out of  50 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  50 out of  50 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   3 out of   3 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   4 out of   4 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   5 out of   5 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   6 out of   6 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   7 out of   7 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   8 out of   8 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   9 out of   9 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  10 out of  10 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  11 out of  11 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  12 out of  12 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  13 out of  13 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  14 out of  14 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  15 out of  15 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  16 out of  16 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  17 out of  17 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  18 out of  18 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  19 out of  19 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  20 out of  20 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  21 out of  21 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  22 out of  22 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  23 out of  23 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  24 out of  24 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  25 out of  25 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  26 out of  26 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  27 out of  27 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  28 out of  28 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  29 out of  29 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  30 out of  30 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  31 out of  31 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  32 out of  32 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  33 out of  33 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  34 out of  34 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  35 out of  35 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  36 out of  36 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  37 out of  37 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  38 out of  38 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  39 out of  39 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  40 out of  40 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  41 out of  41 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  42 out of  42 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  43 out of  43 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  44 out of  44 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  45 out of  45 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  46 out of  46 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  47 out of  47 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  48 out of  48 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  49 out of  49 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  50 out of  50 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  50 out of  50 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   3 out of   3 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   4 out of   4 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   5 out of   5 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   6 out of   6 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   7 out of   7 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   8 out of   8 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   9 out of   9 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  10 out of  10 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  11 out of  11 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  12 out of  12 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  13 out of  13 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  14 out of  14 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  15 out of  15 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  16 out of  16 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  17 out of  17 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  18 out of  18 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  19 out of  19 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  20 out of  20 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  20 out of  20 | elapsed:    0.1s finished\n",
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   3 out of   3 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   4 out of   4 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   5 out of   5 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   6 out of   6 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   7 out of   7 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   8 out of   8 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   9 out of   9 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  10 out of  10 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  11 out of  11 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  12 out of  12 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  13 out of  13 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  14 out of  14 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  15 out of  15 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  16 out of  16 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  17 out of  17 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  18 out of  18 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  19 out of  19 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  20 out of  20 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  21 out of  21 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  22 out of  22 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  23 out of  23 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  24 out of  24 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  25 out of  25 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  26 out of  26 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  27 out of  27 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  28 out of  28 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  29 out of  29 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  30 out of  30 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  31 out of  31 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  32 out of  32 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  33 out of  33 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  34 out of  34 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  35 out of  35 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  36 out of  36 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  37 out of  37 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  38 out of  38 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  39 out of  39 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  40 out of  40 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  41 out of  41 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  42 out of  42 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  43 out of  43 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  44 out of  44 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  45 out of  45 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  46 out of  46 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  47 out of  47 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  48 out of  48 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  49 out of  49 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  50 out of  50 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  50 out of  50 | elapsed:    0.1s finished\n",
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   3 out of   3 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   4 out of   4 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   5 out of   5 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   6 out of   6 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   7 out of   7 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   8 out of   8 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   9 out of   9 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  10 out of  10 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  11 out of  11 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  12 out of  12 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  13 out of  13 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  14 out of  14 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  15 out of  15 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  16 out of  16 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  17 out of  17 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  18 out of  18 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  19 out of  19 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  20 out of  20 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  21 out of  21 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  22 out of  22 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  23 out of  23 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  24 out of  24 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  25 out of  25 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  26 out of  26 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  27 out of  27 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  28 out of  28 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  29 out of  29 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  30 out of  30 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  31 out of  31 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  32 out of  32 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  33 out of  33 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  34 out of  34 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  35 out of  35 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  36 out of  36 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  37 out of  37 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  38 out of  38 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  39 out of  39 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  40 out of  40 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  41 out of  41 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  42 out of  42 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  43 out of  43 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  44 out of  44 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  45 out of  45 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  46 out of  46 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  47 out of  47 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  48 out of  48 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  49 out of  49 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  50 out of  50 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  50 out of  50 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   3 out of   3 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   4 out of   4 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   5 out of   5 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   6 out of   6 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   7 out of   7 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   8 out of   8 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   9 out of   9 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  10 out of  10 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  11 out of  11 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  12 out of  12 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  13 out of  13 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  14 out of  14 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  15 out of  15 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  16 out of  16 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  17 out of  17 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  18 out of  18 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  19 out of  19 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  20 out of  20 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  21 out of  21 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  22 out of  22 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  23 out of  23 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  24 out of  24 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  25 out of  25 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  26 out of  26 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  27 out of  27 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  28 out of  28 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  29 out of  29 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  30 out of  30 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  31 out of  31 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  32 out of  32 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  33 out of  33 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  34 out of  34 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  35 out of  35 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  36 out of  36 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  37 out of  37 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  38 out of  38 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  39 out of  39 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  40 out of  40 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  41 out of  41 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  42 out of  42 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  43 out of  43 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  44 out of  44 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  45 out of  45 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  46 out of  46 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  47 out of  47 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  48 out of  48 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  49 out of  49 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  50 out of  50 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  50 out of  50 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   3 out of   3 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   4 out of   4 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   5 out of   5 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   6 out of   6 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   7 out of   7 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   8 out of   8 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   9 out of   9 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  10 out of  10 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  11 out of  11 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  12 out of  12 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  13 out of  13 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  14 out of  14 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  15 out of  15 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  16 out of  16 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  17 out of  17 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  18 out of  18 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  19 out of  19 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  20 out of  20 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  21 out of  21 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  22 out of  22 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  23 out of  23 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  24 out of  24 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  25 out of  25 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  26 out of  26 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  27 out of  27 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  28 out of  28 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  29 out of  29 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  30 out of  30 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  31 out of  31 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  32 out of  32 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  33 out of  33 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  34 out of  34 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  35 out of  35 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  36 out of  36 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  37 out of  37 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  38 out of  38 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  39 out of  39 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  40 out of  40 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  41 out of  41 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  42 out of  42 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  43 out of  43 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  44 out of  44 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  45 out of  45 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  46 out of  46 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  47 out of  47 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  48 out of  48 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  49 out of  49 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  50 out of  50 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  50 out of  50 | elapsed:    0.1s finished\n"
     ]
    }
   ],
   "source": [
    "y_pred_voting, y_pred_average, y_pred_maximun, y_pred_minimun, y_pred_median, y_pred_product = get_combine(test, rf_clfs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 340
    },
    "colab_type": "code",
    "id": "yDk5jCZyhZoa",
    "outputId": "20ad7089-65ad-440f-b001-a3bcef5e9aab"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RandomForest\n",
      "Voting\n",
      "0.5800281913201396\n",
      "0.8640254340914649\n",
      "Average\n",
      "0.5952990902528279\n",
      "0.8813890926876987\n",
      "Maximun\n",
      "0.6307301318547309\n",
      "0.8872584984103693\n",
      "Minimun\n",
      "0.5949109831931229\n",
      "0.8816336512594767\n",
      "Median\n",
      "0.5595610749955092\n",
      "0.8628026412325752\n",
      "Product\n",
      "0.5983126755471758\n",
      "0.8821227684030325\n"
     ]
    }
   ],
   "source": [
    "print(\"RandomForest\")\n",
    "\n",
    "print(\"Voting\")\n",
    "print(f1_score(class_test, y_pred_voting, average='macro'))\n",
    "print(accuracy_score(class_test, y_pred_voting))\n",
    "\n",
    "print(\"Average\")\n",
    "print(f1_score(class_test, y_pred_average, average='macro'))\n",
    "print(accuracy_score(class_test, y_pred_average))\n",
    "\n",
    "print(\"Maximun\")\n",
    "print(f1_score(class_test, y_pred_maximun, average='macro'))\n",
    "print(accuracy_score(class_test, y_pred_maximun))\n",
    "\n",
    "print(\"Minimun\")\n",
    "print(f1_score(class_test, y_pred_minimun, average='macro'))\n",
    "print(accuracy_score(class_test, y_pred_minimun))\n",
    "\n",
    "print(\"Median\")\n",
    "print(f1_score(class_test, y_pred_median, average='macro'))\n",
    "print(accuracy_score(class_test, y_pred_median))\n",
    "\n",
    "print(\"Product\")\n",
    "print(f1_score(class_test, y_pred_product, average='macro'))\n",
    "print(accuracy_score(class_test, y_pred_product))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "MGu1XrzQUGvV"
   },
   "outputs": [],
   "source": [
    "y_pred_voting, y_pred_average, y_pred_maximun, y_pred_minimun, y_pred_median, y_pred_product = get_combine(test, nb_clfs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 340
    },
    "colab_type": "code",
    "id": "PusC5H3Bhe1k",
    "outputId": "cf753a6a-8065-4adc-f6e7-8815af82b6a4"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Naive Bayes\n",
      "Voting\n",
      "0.6138298361261093\n",
      "0.8498410369283443\n",
      "Average\n",
      "0.5987293430498138\n",
      "0.8410369283443385\n",
      "Maximun\n",
      "0.5819314503546419\n",
      "0.8050868182929811\n",
      "Minimun\n",
      "0.5833034561392721\n",
      "0.8031303497187576\n",
      "Median\n",
      "0.6043424367712436\n",
      "0.8508192712154561\n",
      "Product\n",
      "0.6039507713785883\n",
      "0.8290535583272194\n"
     ]
    }
   ],
   "source": [
    "print(\"Naive Bayes\")\n",
    "\n",
    "print(\"Voting\")\n",
    "print(f1_score(class_test, y_pred_voting, average='macro'))\n",
    "print(accuracy_score(class_test, y_pred_voting))\n",
    "\n",
    "print(\"Average\")\n",
    "print(f1_score(class_test, y_pred_average, average='macro'))\n",
    "print(accuracy_score(class_test, y_pred_average))\n",
    "\n",
    "print(\"Maximun\")\n",
    "print(f1_score(class_test, y_pred_maximun, average='macro'))\n",
    "print(accuracy_score(class_test, y_pred_maximun))\n",
    "\n",
    "print(\"Minimun\")\n",
    "print(f1_score(class_test, y_pred_minimun, average='macro'))\n",
    "print(accuracy_score(class_test, y_pred_minimun))\n",
    "\n",
    "print(\"Median\")\n",
    "print(f1_score(class_test, y_pred_median, average='macro'))\n",
    "print(accuracy_score(class_test, y_pred_median))\n",
    "\n",
    "print(\"Product\")\n",
    "print(f1_score(class_test, y_pred_product, average='macro'))\n",
    "print(accuracy_score(class_test, y_pred_product))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "s6W5gvofUIPz"
   },
   "outputs": [],
   "source": [
    "y_pred_voting, y_pred_average, y_pred_maximun, y_pred_minimun, y_pred_median, y_pred_product = get_combine(test, mlp_clfs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 340
    },
    "colab_type": "code",
    "id": "J_fDKfW4hifb",
    "outputId": "2fad2dc8-b6e0-4f70-d0d7-0891360024dd"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MLP\n",
      "Voting\n",
      "0.6181147289555564\n",
      "0.8823673269748105\n",
      "Average\n",
      "0.6268473647012168\n",
      "0.8909268769870384\n",
      "Maximun\n",
      "0.6634907137042804\n",
      "0.892394228417706\n",
      "Minimun\n",
      "0.6390735294695034\n",
      "0.8901932012717045\n",
      "Median\n",
      "0.612929957801457\n",
      "0.8823673269748105\n",
      "Product\n",
      "0.6341281643768025\n",
      "0.8933724627048178\n"
     ]
    }
   ],
   "source": [
    "print(\"MLP\")\n",
    "\n",
    "print(\"Voting\")\n",
    "print(f1_score(class_test, y_pred_voting, average='macro'))\n",
    "print(accuracy_score(class_test, y_pred_voting))\n",
    "\n",
    "print(\"Average\")\n",
    "print(f1_score(class_test, y_pred_average, average='macro'))\n",
    "print(accuracy_score(class_test, y_pred_average))\n",
    "\n",
    "print(\"Maximun\")\n",
    "print(f1_score(class_test, y_pred_maximun, average='macro'))\n",
    "print(accuracy_score(class_test, y_pred_maximun))\n",
    "\n",
    "print(\"Minimun\")\n",
    "print(f1_score(class_test, y_pred_minimun, average='macro'))\n",
    "print(accuracy_score(class_test, y_pred_minimun))\n",
    "\n",
    "print(\"Median\")\n",
    "print(f1_score(class_test, y_pred_median, average='macro'))\n",
    "print(accuracy_score(class_test, y_pred_median))\n",
    "\n",
    "print(\"Product\")\n",
    "print(f1_score(class_test, y_pred_product, average='macro'))\n",
    "print(accuracy_score(class_test, y_pred_product))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "colab_type": "code",
    "id": "vVye-vYNUKXp",
    "outputId": "ca08e047-509f-4aa6-bc52-f142fb85ff23"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   3 out of   3 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   4 out of   4 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   5 out of   5 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   6 out of   6 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   7 out of   7 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   8 out of   8 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   9 out of   9 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  10 out of  10 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  11 out of  11 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  12 out of  12 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  13 out of  13 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  14 out of  14 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  15 out of  15 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  16 out of  16 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  17 out of  17 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  18 out of  18 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  19 out of  19 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  20 out of  20 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  21 out of  21 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  22 out of  22 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  23 out of  23 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  24 out of  24 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  25 out of  25 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  26 out of  26 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  27 out of  27 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  28 out of  28 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  29 out of  29 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  30 out of  30 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  31 out of  31 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  32 out of  32 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  33 out of  33 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  34 out of  34 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  35 out of  35 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  36 out of  36 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  37 out of  37 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  38 out of  38 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  39 out of  39 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  40 out of  40 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  41 out of  41 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  42 out of  42 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  43 out of  43 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  44 out of  44 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  45 out of  45 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  46 out of  46 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  47 out of  47 | elapsed:    0.3s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  48 out of  48 | elapsed:    0.3s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  49 out of  49 | elapsed:    0.3s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  50 out of  50 | elapsed:    0.3s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  50 out of  50 | elapsed:    0.3s finished\n",
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   3 out of   3 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   4 out of   4 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   5 out of   5 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   6 out of   6 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   7 out of   7 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   8 out of   8 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   9 out of   9 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  10 out of  10 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  11 out of  11 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  12 out of  12 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  13 out of  13 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  14 out of  14 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  15 out of  15 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  16 out of  16 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  17 out of  17 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  18 out of  18 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  19 out of  19 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  20 out of  20 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  21 out of  21 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  22 out of  22 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  23 out of  23 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  24 out of  24 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  25 out of  25 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  26 out of  26 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  27 out of  27 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  28 out of  28 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  29 out of  29 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  30 out of  30 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  31 out of  31 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  32 out of  32 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  33 out of  33 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  34 out of  34 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  35 out of  35 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  36 out of  36 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  37 out of  37 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  38 out of  38 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  39 out of  39 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  40 out of  40 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  41 out of  41 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  42 out of  42 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  43 out of  43 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  44 out of  44 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  45 out of  45 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  46 out of  46 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  47 out of  47 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  48 out of  48 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  49 out of  49 | elapsed:    0.3s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  50 out of  50 | elapsed:    0.3s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  50 out of  50 | elapsed:    0.3s finished\n",
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   3 out of   3 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   4 out of   4 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   5 out of   5 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   6 out of   6 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   7 out of   7 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   8 out of   8 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   9 out of   9 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  10 out of  10 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  11 out of  11 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  12 out of  12 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  13 out of  13 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  14 out of  14 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  15 out of  15 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  16 out of  16 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  17 out of  17 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  18 out of  18 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  19 out of  19 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  20 out of  20 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  21 out of  21 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  22 out of  22 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  23 out of  23 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  24 out of  24 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  25 out of  25 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  26 out of  26 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  27 out of  27 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  28 out of  28 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  29 out of  29 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  30 out of  30 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  31 out of  31 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  32 out of  32 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  33 out of  33 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  34 out of  34 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  35 out of  35 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  36 out of  36 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  37 out of  37 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  38 out of  38 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  39 out of  39 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  40 out of  40 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  41 out of  41 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  42 out of  42 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  43 out of  43 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  44 out of  44 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  45 out of  45 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  46 out of  46 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  47 out of  47 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  48 out of  48 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  49 out of  49 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  50 out of  50 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  50 out of  50 | elapsed:    0.1s finished\n",
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   3 out of   3 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   4 out of   4 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   5 out of   5 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   6 out of   6 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   7 out of   7 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   8 out of   8 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   9 out of   9 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  10 out of  10 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  11 out of  11 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  12 out of  12 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  13 out of  13 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  14 out of  14 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  15 out of  15 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  16 out of  16 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  17 out of  17 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  18 out of  18 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  19 out of  19 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  20 out of  20 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  21 out of  21 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  22 out of  22 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  23 out of  23 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  24 out of  24 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  25 out of  25 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  26 out of  26 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  27 out of  27 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  28 out of  28 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  29 out of  29 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  30 out of  30 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  31 out of  31 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  32 out of  32 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  33 out of  33 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  34 out of  34 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  35 out of  35 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  36 out of  36 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  37 out of  37 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  38 out of  38 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  39 out of  39 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  40 out of  40 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  41 out of  41 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  42 out of  42 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  43 out of  43 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  44 out of  44 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  45 out of  45 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  46 out of  46 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  47 out of  47 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  48 out of  48 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  49 out of  49 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  50 out of  50 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  50 out of  50 | elapsed:    0.1s finished\n",
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   3 out of   3 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   4 out of   4 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   5 out of   5 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   6 out of   6 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   7 out of   7 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   8 out of   8 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   9 out of   9 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  10 out of  10 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  11 out of  11 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  12 out of  12 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  13 out of  13 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  14 out of  14 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  15 out of  15 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  16 out of  16 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  17 out of  17 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  18 out of  18 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  19 out of  19 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  20 out of  20 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  21 out of  21 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  22 out of  22 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  23 out of  23 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  24 out of  24 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  25 out of  25 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  26 out of  26 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  27 out of  27 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  28 out of  28 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  29 out of  29 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  30 out of  30 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  31 out of  31 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  32 out of  32 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  33 out of  33 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  34 out of  34 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  35 out of  35 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  36 out of  36 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  37 out of  37 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  38 out of  38 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  39 out of  39 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  40 out of  40 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  41 out of  41 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  42 out of  42 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  43 out of  43 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  44 out of  44 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  45 out of  45 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  46 out of  46 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  47 out of  47 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  48 out of  48 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  49 out of  49 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  50 out of  50 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  50 out of  50 | elapsed:    0.1s finished\n",
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   3 out of   3 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   4 out of   4 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   5 out of   5 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   6 out of   6 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   7 out of   7 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   8 out of   8 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   9 out of   9 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  10 out of  10 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  11 out of  11 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  12 out of  12 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  13 out of  13 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  14 out of  14 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  15 out of  15 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  16 out of  16 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  17 out of  17 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  18 out of  18 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  19 out of  19 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  20 out of  20 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  21 out of  21 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  22 out of  22 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  23 out of  23 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  24 out of  24 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  25 out of  25 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  26 out of  26 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  27 out of  27 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  28 out of  28 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  29 out of  29 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  30 out of  30 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  31 out of  31 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  32 out of  32 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  33 out of  33 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  34 out of  34 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  35 out of  35 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  36 out of  36 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  37 out of  37 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  38 out of  38 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  39 out of  39 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  40 out of  40 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  41 out of  41 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  42 out of  42 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  43 out of  43 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  44 out of  44 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  45 out of  45 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  46 out of  46 | elapsed:    0.3s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  47 out of  47 | elapsed:    0.3s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  48 out of  48 | elapsed:    0.3s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  49 out of  49 | elapsed:    0.3s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  50 out of  50 | elapsed:    0.3s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  50 out of  50 | elapsed:    0.3s finished\n",
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   3 out of   3 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   4 out of   4 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   5 out of   5 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   6 out of   6 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   7 out of   7 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   8 out of   8 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   9 out of   9 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  10 out of  10 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  11 out of  11 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  12 out of  12 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  13 out of  13 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  14 out of  14 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  15 out of  15 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  16 out of  16 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  17 out of  17 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  18 out of  18 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  19 out of  19 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  20 out of  20 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  21 out of  21 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  22 out of  22 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  23 out of  23 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  24 out of  24 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  25 out of  25 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  26 out of  26 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  27 out of  27 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  28 out of  28 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  29 out of  29 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  30 out of  30 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  31 out of  31 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  32 out of  32 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  33 out of  33 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  34 out of  34 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  35 out of  35 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  36 out of  36 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  37 out of  37 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  38 out of  38 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  39 out of  39 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  40 out of  40 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  41 out of  41 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  42 out of  42 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  43 out of  43 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  44 out of  44 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  45 out of  45 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  46 out of  46 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  47 out of  47 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  48 out of  48 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  49 out of  49 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  50 out of  50 | elapsed:    0.3s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  50 out of  50 | elapsed:    0.3s finished\n",
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   3 out of   3 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   4 out of   4 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   5 out of   5 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   6 out of   6 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   7 out of   7 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   8 out of   8 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   9 out of   9 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  10 out of  10 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  11 out of  11 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  12 out of  12 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  13 out of  13 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  14 out of  14 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  15 out of  15 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  16 out of  16 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  17 out of  17 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  18 out of  18 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  19 out of  19 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  20 out of  20 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  21 out of  21 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  22 out of  22 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  23 out of  23 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  24 out of  24 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  25 out of  25 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  26 out of  26 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  27 out of  27 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  28 out of  28 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  29 out of  29 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  30 out of  30 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  31 out of  31 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  32 out of  32 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  33 out of  33 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  34 out of  34 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  35 out of  35 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  36 out of  36 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  37 out of  37 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  38 out of  38 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  39 out of  39 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  40 out of  40 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  41 out of  41 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  42 out of  42 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  43 out of  43 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  44 out of  44 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  45 out of  45 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  46 out of  46 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  47 out of  47 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  48 out of  48 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  49 out of  49 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  50 out of  50 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  50 out of  50 | elapsed:    0.1s finished\n",
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   3 out of   3 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   4 out of   4 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   5 out of   5 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   6 out of   6 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   7 out of   7 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   8 out of   8 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   9 out of   9 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  10 out of  10 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  11 out of  11 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  12 out of  12 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  13 out of  13 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  14 out of  14 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  15 out of  15 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  16 out of  16 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  17 out of  17 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  18 out of  18 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  19 out of  19 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  20 out of  20 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  21 out of  21 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  22 out of  22 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  23 out of  23 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  24 out of  24 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  25 out of  25 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  26 out of  26 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  27 out of  27 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  28 out of  28 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  29 out of  29 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  30 out of  30 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  31 out of  31 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  32 out of  32 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  33 out of  33 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  34 out of  34 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  35 out of  35 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  36 out of  36 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  37 out of  37 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  38 out of  38 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  39 out of  39 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  40 out of  40 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  41 out of  41 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  42 out of  42 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  43 out of  43 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  44 out of  44 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  45 out of  45 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  46 out of  46 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  47 out of  47 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  48 out of  48 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  49 out of  49 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  50 out of  50 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  50 out of  50 | elapsed:    0.1s finished\n",
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   3 out of   3 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   4 out of   4 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   5 out of   5 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   6 out of   6 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   7 out of   7 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   8 out of   8 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   9 out of   9 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  10 out of  10 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  11 out of  11 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  12 out of  12 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  13 out of  13 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  14 out of  14 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  15 out of  15 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  16 out of  16 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  17 out of  17 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  18 out of  18 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  19 out of  19 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  20 out of  20 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  21 out of  21 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  22 out of  22 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  23 out of  23 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  24 out of  24 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  25 out of  25 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  26 out of  26 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  27 out of  27 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  28 out of  28 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  29 out of  29 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  30 out of  30 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  31 out of  31 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  32 out of  32 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  33 out of  33 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  34 out of  34 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  35 out of  35 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  36 out of  36 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  37 out of  37 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  38 out of  38 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  39 out of  39 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  40 out of  40 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  41 out of  41 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  42 out of  42 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  43 out of  43 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  44 out of  44 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  45 out of  45 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  46 out of  46 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  47 out of  47 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  48 out of  48 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  49 out of  49 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  50 out of  50 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  50 out of  50 | elapsed:    0.1s finished\n"
     ]
    }
   ],
   "source": [
    "y_pred_voting, y_pred_average, y_pred_maximun, y_pred_minimun, y_pred_median, y_pred_product = get_combine(test, extra_clfs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 340
    },
    "colab_type": "code",
    "id": "08NKMhxthlZn",
    "outputId": "dc31016f-91f1-4ca2-ea5d-262b9aaa7b51"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ExtraTrees\n",
      "Voting\n",
      "0.5442460871257405\n",
      "0.8503301540719003\n",
      "Average\n",
      "0.5832438827539957\n",
      "0.8764979212521399\n",
      "Maximun\n",
      "0.6268006044547468\n",
      "0.8816336512594767\n",
      "Minimun\n",
      "0.6087747581301431\n",
      "0.8806554169723649\n",
      "Median\n",
      "0.5318878584752618\n",
      "0.8500855955001223\n",
      "Product\n",
      "0.5966209766819871\n",
      "0.8772315969674737\n"
     ]
    }
   ],
   "source": [
    "print(\"ExtraTrees\")\n",
    "\n",
    "print(\"Voting\")\n",
    "print(f1_score(class_test, y_pred_voting, average='macro'))\n",
    "print(accuracy_score(class_test, y_pred_voting))\n",
    "\n",
    "print(\"Average\")\n",
    "print(f1_score(class_test, y_pred_average, average='macro'))\n",
    "print(accuracy_score(class_test, y_pred_average))\n",
    "\n",
    "print(\"Maximun\")\n",
    "print(f1_score(class_test, y_pred_maximun, average='macro'))\n",
    "print(accuracy_score(class_test, y_pred_maximun))\n",
    "\n",
    "print(\"Minimun\")\n",
    "print(f1_score(class_test, y_pred_minimun, average='macro'))\n",
    "print(accuracy_score(class_test, y_pred_minimun))\n",
    "\n",
    "print(\"Median\")\n",
    "print(f1_score(class_test, y_pred_median, average='macro'))\n",
    "print(accuracy_score(class_test, y_pred_median))\n",
    "\n",
    "print(\"Product\")\n",
    "print(f1_score(class_test, y_pred_product, average='macro'))\n",
    "print(accuracy_score(class_test, y_pred_product))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "dKCfnBDPDRLL"
   },
   "outputs": [],
   "source": [
    "y_pred_voting, y_pred_average, y_pred_maximun, y_pred_minimun, y_pred_median, y_pred_product = get_combine(test, knn_clfs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 340
    },
    "colab_type": "code",
    "id": "KyWbyl7Ehotz",
    "outputId": "78f80905-4fae-4dd0-cd34-948aac4a94cc"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KNN\n",
      "Voting\n",
      "0.5311373317284772\n",
      "0.8332110540474443\n",
      "Average\n",
      "0.517065605795631\n",
      "0.8366348740523355\n",
      "Maximun\n",
      "0.5248397823309824\n",
      "0.8310100269014429\n",
      "Minimun\n",
      "0.5080163592497143\n",
      "0.8195157740278797\n",
      "Median\n",
      "0.5115390873677789\n",
      "0.8327219369038885\n",
      "Product\n",
      "0.5149415273488113\n",
      "0.8253851797505503\n"
     ]
    }
   ],
   "source": [
    "print(\"KNN\")\n",
    "\n",
    "print(\"Voting\")\n",
    "print(f1_score(class_test, y_pred_voting, average='macro'))\n",
    "print(accuracy_score(class_test, y_pred_voting))\n",
    "\n",
    "print(\"Average\")\n",
    "print(f1_score(class_test, y_pred_average, average='macro'))\n",
    "print(accuracy_score(class_test, y_pred_average))\n",
    "\n",
    "print(\"Maximun\")\n",
    "print(f1_score(class_test, y_pred_maximun, average='macro'))\n",
    "print(accuracy_score(class_test, y_pred_maximun))\n",
    "\n",
    "print(\"Minimun\")\n",
    "print(f1_score(class_test, y_pred_minimun, average='macro'))\n",
    "print(accuracy_score(class_test, y_pred_minimun))\n",
    "\n",
    "print(\"Median\")\n",
    "print(f1_score(class_test, y_pred_median, average='macro'))\n",
    "print(accuracy_score(class_test, y_pred_median))\n",
    "\n",
    "print(\"Product\")\n",
    "print(f1_score(class_test, y_pred_product, average='macro'))\n",
    "print(accuracy_score(class_test, y_pred_product))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "11MWVssOVMGg"
   },
   "outputs": [],
   "source": [
    "cnn_clfs = {\n",
    "    'CV':cnn_cv, 'TFIDF':cnn_tfidf, 'W2V': cnn_w2v, 'GLOVE': cnn_glove, 'FAST': cnn_fast\n",
    "}\n",
    "y_pred_voting, y_pred_average, y_pred_maximun, y_pred_minimun, y_pred_median, y_pred_product = get_combine(data_test, cnn_clfs, cnn=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 340
    },
    "colab_type": "code",
    "id": "SG8JyrsfhrSr",
    "outputId": "4b9ddc7c-503a-48ab-817e-da157da6c6a1"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CNN\n",
      "Voting\n",
      "0.7006418160397528\n",
      "0.9060895084372708\n",
      "Average\n",
      "0.6867772951608643\n",
      "0.9095133284421619\n",
      "Maximun\n",
      "0.7045838525593021\n",
      "0.9065786255808266\n",
      "Minimun\n",
      "0.7161602905856782\n",
      "0.9029102470041575\n",
      "Median\n",
      "0.6535126809316004\n",
      "0.9075568598679383\n",
      "Product\n",
      "0.7196055645762912\n",
      "0.9107361213010516\n"
     ]
    }
   ],
   "source": [
    "print(\"CNN\")\n",
    "\n",
    "print(\"Voting\")\n",
    "print(f1_score(class_test, y_pred_voting, average='macro'))\n",
    "print(accuracy_score(class_test, y_pred_voting))\n",
    "\n",
    "print(\"Average\")\n",
    "print(f1_score(class_test, y_pred_average, average='macro'))\n",
    "print(accuracy_score(class_test, y_pred_average))\n",
    "\n",
    "print(\"Maximun\")\n",
    "print(f1_score(class_test, y_pred_maximun, average='macro'))\n",
    "print(accuracy_score(class_test, y_pred_maximun))\n",
    "\n",
    "print(\"Minimun\")\n",
    "print(f1_score(class_test, y_pred_minimun, average='macro'))\n",
    "print(accuracy_score(class_test, y_pred_minimun))\n",
    "\n",
    "print(\"Median\")\n",
    "print(f1_score(class_test, y_pred_median, average='macro'))\n",
    "print(accuracy_score(class_test, y_pred_median))\n",
    "\n",
    "print(\"Product\")\n",
    "print(f1_score(class_test, y_pred_product, average='macro'))\n",
    "print(accuracy_score(class_test, y_pred_product))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "NVkL7SOtVOwO"
   },
   "outputs": [],
   "source": [
    "full_clfs = {}\n",
    "for ext, clf in svm_clfs.items():\n",
    "    full_clfs['SVM-'+ext] = clf\n",
    "\n",
    "for ext, clf in lr_clfs.items():\n",
    "    full_clfs['LR-'+ext] = clf\n",
    "\n",
    "for ext, clf in rf_clfs.items():\n",
    "    full_clfs['RF-'+ext] = clf\n",
    "\n",
    "for ext, clf in nb_clfs.items():\n",
    "    full_clfs['NB-'+ext] = clf\n",
    "\n",
    "for ext, clf in mlp_clfs.items():\n",
    "    full_clfs['MLP-'+ext] = clf\n",
    "\n",
    "for ext, clf in extra_clfs.items():\n",
    "    full_clfs['EXTRA-'+ext] = clf\n",
    "\n",
    "for ext, clf in knn_clfs.items():\n",
    "    full_clfs['KNN-'+ext] = clf\n",
    "\n",
    "for ext, clf in cnn_clfs.items():\n",
    "    full_clfs['CNN-'+ext] = clf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "colab_type": "code",
    "id": "4C_-hLsfWSD0",
    "outputId": "4028eb02-f350-4874-c694-08db6b37e0c1"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   3 out of   3 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   4 out of   4 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   5 out of   5 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   6 out of   6 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   7 out of   7 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   8 out of   8 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   9 out of   9 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  10 out of  10 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  11 out of  11 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  12 out of  12 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  13 out of  13 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  14 out of  14 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  15 out of  15 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  16 out of  16 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  17 out of  17 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  18 out of  18 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  19 out of  19 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  20 out of  20 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  20 out of  20 | elapsed:    0.1s finished\n",
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   3 out of   3 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   4 out of   4 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   5 out of   5 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   6 out of   6 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   7 out of   7 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   8 out of   8 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   9 out of   9 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  10 out of  10 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  11 out of  11 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  12 out of  12 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  13 out of  13 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  14 out of  14 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  15 out of  15 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  16 out of  16 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  17 out of  17 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  18 out of  18 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  19 out of  19 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  20 out of  20 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  21 out of  21 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  22 out of  22 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  23 out of  23 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  24 out of  24 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  25 out of  25 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  26 out of  26 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  27 out of  27 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  28 out of  28 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  29 out of  29 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  30 out of  30 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  31 out of  31 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  32 out of  32 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  33 out of  33 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  34 out of  34 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  35 out of  35 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  36 out of  36 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  37 out of  37 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  38 out of  38 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  39 out of  39 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  40 out of  40 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  41 out of  41 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  42 out of  42 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  43 out of  43 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  44 out of  44 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  45 out of  45 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  46 out of  46 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  47 out of  47 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  48 out of  48 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  49 out of  49 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  50 out of  50 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  50 out of  50 | elapsed:    0.1s finished\n",
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   3 out of   3 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   4 out of   4 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   5 out of   5 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   6 out of   6 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   7 out of   7 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   8 out of   8 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   9 out of   9 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  10 out of  10 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  11 out of  11 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  12 out of  12 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  13 out of  13 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  14 out of  14 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  15 out of  15 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  16 out of  16 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  17 out of  17 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  18 out of  18 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  19 out of  19 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  20 out of  20 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  21 out of  21 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  22 out of  22 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  23 out of  23 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  24 out of  24 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  25 out of  25 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  26 out of  26 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  27 out of  27 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  28 out of  28 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  29 out of  29 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  30 out of  30 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  31 out of  31 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  32 out of  32 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  33 out of  33 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  34 out of  34 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  35 out of  35 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  36 out of  36 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  37 out of  37 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  38 out of  38 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  39 out of  39 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  40 out of  40 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  41 out of  41 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  42 out of  42 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  43 out of  43 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  44 out of  44 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  45 out of  45 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  46 out of  46 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  47 out of  47 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  48 out of  48 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  49 out of  49 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  50 out of  50 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  50 out of  50 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   3 out of   3 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   4 out of   4 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   5 out of   5 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   6 out of   6 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   7 out of   7 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   8 out of   8 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   9 out of   9 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  10 out of  10 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  11 out of  11 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  12 out of  12 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  13 out of  13 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  14 out of  14 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  15 out of  15 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  16 out of  16 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  17 out of  17 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  18 out of  18 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  19 out of  19 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  20 out of  20 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  21 out of  21 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  22 out of  22 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  23 out of  23 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  24 out of  24 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  25 out of  25 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  26 out of  26 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  27 out of  27 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  28 out of  28 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  29 out of  29 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  30 out of  30 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  31 out of  31 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  32 out of  32 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  33 out of  33 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  34 out of  34 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  35 out of  35 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  36 out of  36 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  37 out of  37 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  38 out of  38 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  39 out of  39 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  40 out of  40 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  41 out of  41 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  42 out of  42 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  43 out of  43 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  44 out of  44 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  45 out of  45 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  46 out of  46 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  47 out of  47 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  48 out of  48 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  49 out of  49 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  50 out of  50 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  50 out of  50 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   3 out of   3 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   4 out of   4 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   5 out of   5 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   6 out of   6 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   7 out of   7 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   8 out of   8 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   9 out of   9 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  10 out of  10 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  11 out of  11 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  12 out of  12 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  13 out of  13 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  14 out of  14 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  15 out of  15 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  16 out of  16 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  17 out of  17 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  18 out of  18 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  19 out of  19 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  20 out of  20 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  21 out of  21 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  22 out of  22 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  23 out of  23 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  24 out of  24 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  25 out of  25 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  26 out of  26 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  27 out of  27 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  28 out of  28 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  29 out of  29 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  30 out of  30 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  31 out of  31 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  32 out of  32 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  33 out of  33 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  34 out of  34 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  35 out of  35 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  36 out of  36 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  37 out of  37 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  38 out of  38 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  39 out of  39 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  40 out of  40 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  41 out of  41 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  42 out of  42 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  43 out of  43 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  44 out of  44 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  45 out of  45 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  46 out of  46 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  47 out of  47 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  48 out of  48 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  49 out of  49 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  50 out of  50 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  50 out of  50 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   3 out of   3 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   4 out of   4 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   5 out of   5 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   6 out of   6 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   7 out of   7 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   8 out of   8 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   9 out of   9 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  10 out of  10 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  11 out of  11 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  12 out of  12 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  13 out of  13 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  14 out of  14 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  15 out of  15 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  16 out of  16 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  17 out of  17 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  18 out of  18 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  19 out of  19 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  20 out of  20 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  21 out of  21 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  22 out of  22 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  23 out of  23 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  24 out of  24 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  25 out of  25 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  26 out of  26 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  27 out of  27 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  28 out of  28 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  29 out of  29 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  30 out of  30 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  31 out of  31 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  32 out of  32 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  33 out of  33 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  34 out of  34 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  35 out of  35 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  36 out of  36 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  37 out of  37 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  38 out of  38 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  39 out of  39 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  40 out of  40 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  41 out of  41 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  42 out of  42 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  43 out of  43 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  44 out of  44 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  45 out of  45 | elapsed:    0.3s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  46 out of  46 | elapsed:    0.3s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  47 out of  47 | elapsed:    0.3s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  48 out of  48 | elapsed:    0.3s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  49 out of  49 | elapsed:    0.3s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  50 out of  50 | elapsed:    0.3s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  50 out of  50 | elapsed:    0.3s finished\n",
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   3 out of   3 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   4 out of   4 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   5 out of   5 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   6 out of   6 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   7 out of   7 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   8 out of   8 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   9 out of   9 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  10 out of  10 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  11 out of  11 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  12 out of  12 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  13 out of  13 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  14 out of  14 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  15 out of  15 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  16 out of  16 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  17 out of  17 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  18 out of  18 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  19 out of  19 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  20 out of  20 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  21 out of  21 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  22 out of  22 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  23 out of  23 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  24 out of  24 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  25 out of  25 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  26 out of  26 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  27 out of  27 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  28 out of  28 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  29 out of  29 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  30 out of  30 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  31 out of  31 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  32 out of  32 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  33 out of  33 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  34 out of  34 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  35 out of  35 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  36 out of  36 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  37 out of  37 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  38 out of  38 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  39 out of  39 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  40 out of  40 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  41 out of  41 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  42 out of  42 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  43 out of  43 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  44 out of  44 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  45 out of  45 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  46 out of  46 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  47 out of  47 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  48 out of  48 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  49 out of  49 | elapsed:    0.3s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  50 out of  50 | elapsed:    0.3s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  50 out of  50 | elapsed:    0.3s finished\n",
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   3 out of   3 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   4 out of   4 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   5 out of   5 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   6 out of   6 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   7 out of   7 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   8 out of   8 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   9 out of   9 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  10 out of  10 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  11 out of  11 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  12 out of  12 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  13 out of  13 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  14 out of  14 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  15 out of  15 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  16 out of  16 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  17 out of  17 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  18 out of  18 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  19 out of  19 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  20 out of  20 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  21 out of  21 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  22 out of  22 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  23 out of  23 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  24 out of  24 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  25 out of  25 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  26 out of  26 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  27 out of  27 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  28 out of  28 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  29 out of  29 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  30 out of  30 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  31 out of  31 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  32 out of  32 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  33 out of  33 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  34 out of  34 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  35 out of  35 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  36 out of  36 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  37 out of  37 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  38 out of  38 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  39 out of  39 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  40 out of  40 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  41 out of  41 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  42 out of  42 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  43 out of  43 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  44 out of  44 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  45 out of  45 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  46 out of  46 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  47 out of  47 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  48 out of  48 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  49 out of  49 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  50 out of  50 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  50 out of  50 | elapsed:    0.1s finished\n",
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   3 out of   3 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   4 out of   4 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   5 out of   5 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   6 out of   6 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   7 out of   7 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   8 out of   8 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   9 out of   9 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  10 out of  10 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  11 out of  11 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  12 out of  12 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  13 out of  13 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  14 out of  14 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  15 out of  15 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  16 out of  16 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  17 out of  17 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  18 out of  18 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  19 out of  19 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  20 out of  20 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  21 out of  21 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  22 out of  22 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  23 out of  23 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  24 out of  24 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  25 out of  25 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  26 out of  26 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  27 out of  27 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  28 out of  28 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  29 out of  29 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  30 out of  30 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  31 out of  31 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  32 out of  32 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  33 out of  33 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  34 out of  34 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  35 out of  35 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  36 out of  36 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  37 out of  37 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  38 out of  38 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  39 out of  39 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  40 out of  40 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  41 out of  41 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  42 out of  42 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  43 out of  43 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  44 out of  44 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  45 out of  45 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  46 out of  46 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  47 out of  47 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  48 out of  48 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  49 out of  49 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  50 out of  50 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  50 out of  50 | elapsed:    0.1s finished\n",
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   3 out of   3 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   4 out of   4 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   5 out of   5 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   6 out of   6 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   7 out of   7 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   8 out of   8 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   9 out of   9 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  10 out of  10 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  11 out of  11 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  12 out of  12 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  13 out of  13 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  14 out of  14 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  15 out of  15 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  16 out of  16 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  17 out of  17 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  18 out of  18 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  19 out of  19 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  20 out of  20 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  21 out of  21 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  22 out of  22 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  23 out of  23 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  24 out of  24 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  25 out of  25 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  26 out of  26 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  27 out of  27 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  28 out of  28 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  29 out of  29 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  30 out of  30 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  31 out of  31 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  32 out of  32 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  33 out of  33 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  34 out of  34 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  35 out of  35 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  36 out of  36 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  37 out of  37 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  38 out of  38 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  39 out of  39 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  40 out of  40 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  41 out of  41 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  42 out of  42 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  43 out of  43 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  44 out of  44 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  45 out of  45 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  46 out of  46 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  47 out of  47 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  48 out of  48 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  49 out of  49 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  50 out of  50 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  50 out of  50 | elapsed:    0.1s finished\n",
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   3 out of   3 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   4 out of   4 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   5 out of   5 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   6 out of   6 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   7 out of   7 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   8 out of   8 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   9 out of   9 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  10 out of  10 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  11 out of  11 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  12 out of  12 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  13 out of  13 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  14 out of  14 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  15 out of  15 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  16 out of  16 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  17 out of  17 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  18 out of  18 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  19 out of  19 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  20 out of  20 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  20 out of  20 | elapsed:    0.1s finished\n",
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   3 out of   3 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   4 out of   4 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   5 out of   5 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   6 out of   6 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   7 out of   7 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   8 out of   8 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   9 out of   9 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  10 out of  10 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  11 out of  11 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  12 out of  12 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  13 out of  13 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  14 out of  14 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  15 out of  15 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  16 out of  16 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  17 out of  17 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  18 out of  18 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  19 out of  19 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  20 out of  20 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  21 out of  21 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  22 out of  22 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  23 out of  23 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  24 out of  24 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  25 out of  25 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  26 out of  26 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  27 out of  27 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  28 out of  28 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  29 out of  29 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  30 out of  30 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  31 out of  31 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  32 out of  32 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  33 out of  33 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  34 out of  34 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  35 out of  35 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  36 out of  36 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  37 out of  37 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  38 out of  38 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  39 out of  39 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  40 out of  40 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  41 out of  41 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  42 out of  42 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  43 out of  43 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  44 out of  44 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  45 out of  45 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  46 out of  46 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  47 out of  47 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  48 out of  48 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  49 out of  49 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  50 out of  50 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  50 out of  50 | elapsed:    0.1s finished\n",
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   3 out of   3 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   4 out of   4 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   5 out of   5 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   6 out of   6 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   7 out of   7 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   8 out of   8 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   9 out of   9 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  10 out of  10 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  11 out of  11 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  12 out of  12 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  13 out of  13 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  14 out of  14 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  15 out of  15 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  16 out of  16 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  17 out of  17 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  18 out of  18 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  19 out of  19 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  20 out of  20 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  21 out of  21 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  22 out of  22 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  23 out of  23 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  24 out of  24 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  25 out of  25 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  26 out of  26 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  27 out of  27 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  28 out of  28 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  29 out of  29 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  30 out of  30 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  31 out of  31 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  32 out of  32 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  33 out of  33 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  34 out of  34 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  35 out of  35 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  36 out of  36 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  37 out of  37 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  38 out of  38 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  39 out of  39 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  40 out of  40 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  41 out of  41 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  42 out of  42 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  43 out of  43 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  44 out of  44 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  45 out of  45 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  46 out of  46 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  47 out of  47 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  48 out of  48 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  49 out of  49 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  50 out of  50 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  50 out of  50 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   3 out of   3 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   4 out of   4 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   5 out of   5 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   6 out of   6 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   7 out of   7 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   8 out of   8 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   9 out of   9 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  10 out of  10 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  11 out of  11 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  12 out of  12 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  13 out of  13 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  14 out of  14 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  15 out of  15 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  16 out of  16 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  17 out of  17 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  18 out of  18 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  19 out of  19 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  20 out of  20 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  21 out of  21 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  22 out of  22 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  23 out of  23 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  24 out of  24 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  25 out of  25 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  26 out of  26 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  27 out of  27 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  28 out of  28 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  29 out of  29 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  30 out of  30 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  31 out of  31 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  32 out of  32 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  33 out of  33 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  34 out of  34 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  35 out of  35 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  36 out of  36 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  37 out of  37 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  38 out of  38 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  39 out of  39 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  40 out of  40 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  41 out of  41 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  42 out of  42 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  43 out of  43 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  44 out of  44 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  45 out of  45 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  46 out of  46 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  47 out of  47 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  48 out of  48 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  49 out of  49 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  50 out of  50 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  50 out of  50 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   3 out of   3 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   4 out of   4 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   5 out of   5 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   6 out of   6 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   7 out of   7 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   8 out of   8 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   9 out of   9 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  10 out of  10 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  11 out of  11 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  12 out of  12 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  13 out of  13 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  14 out of  14 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  15 out of  15 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  16 out of  16 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  17 out of  17 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  18 out of  18 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  19 out of  19 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  20 out of  20 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  21 out of  21 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  22 out of  22 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  23 out of  23 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  24 out of  24 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  25 out of  25 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  26 out of  26 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  27 out of  27 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  28 out of  28 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  29 out of  29 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  30 out of  30 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  31 out of  31 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  32 out of  32 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  33 out of  33 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  34 out of  34 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  35 out of  35 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  36 out of  36 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  37 out of  37 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  38 out of  38 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  39 out of  39 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  40 out of  40 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  41 out of  41 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  42 out of  42 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  43 out of  43 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  44 out of  44 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  45 out of  45 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  46 out of  46 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  47 out of  47 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  48 out of  48 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  49 out of  49 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  50 out of  50 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  50 out of  50 | elapsed:    0.0s finished\n",
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   3 out of   3 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   4 out of   4 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   5 out of   5 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   6 out of   6 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   7 out of   7 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   8 out of   8 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   9 out of   9 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  10 out of  10 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  11 out of  11 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  12 out of  12 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  13 out of  13 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  14 out of  14 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  15 out of  15 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  16 out of  16 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  17 out of  17 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  18 out of  18 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  19 out of  19 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  20 out of  20 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  21 out of  21 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  22 out of  22 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  23 out of  23 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  24 out of  24 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  25 out of  25 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  26 out of  26 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  27 out of  27 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  28 out of  28 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  29 out of  29 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  30 out of  30 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  31 out of  31 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  32 out of  32 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  33 out of  33 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  34 out of  34 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  35 out of  35 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  36 out of  36 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  37 out of  37 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  38 out of  38 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  39 out of  39 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  40 out of  40 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  41 out of  41 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  42 out of  42 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  43 out of  43 | elapsed:    0.3s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  44 out of  44 | elapsed:    0.3s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  45 out of  45 | elapsed:    0.3s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  46 out of  46 | elapsed:    0.3s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  47 out of  47 | elapsed:    0.3s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  48 out of  48 | elapsed:    0.3s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  49 out of  49 | elapsed:    0.3s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  50 out of  50 | elapsed:    0.3s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  50 out of  50 | elapsed:    0.3s finished\n",
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   3 out of   3 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   4 out of   4 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   5 out of   5 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   6 out of   6 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   7 out of   7 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   8 out of   8 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   9 out of   9 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  10 out of  10 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  11 out of  11 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  12 out of  12 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  13 out of  13 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  14 out of  14 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  15 out of  15 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  16 out of  16 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  17 out of  17 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  18 out of  18 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  19 out of  19 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  20 out of  20 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  21 out of  21 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  22 out of  22 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  23 out of  23 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  24 out of  24 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  25 out of  25 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  26 out of  26 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  27 out of  27 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  28 out of  28 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  29 out of  29 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  30 out of  30 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  31 out of  31 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  32 out of  32 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  33 out of  33 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  34 out of  34 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  35 out of  35 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  36 out of  36 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  37 out of  37 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  38 out of  38 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  39 out of  39 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  40 out of  40 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  41 out of  41 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  42 out of  42 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  43 out of  43 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  44 out of  44 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  45 out of  45 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  46 out of  46 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  47 out of  47 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  48 out of  48 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  49 out of  49 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  50 out of  50 | elapsed:    0.3s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  50 out of  50 | elapsed:    0.3s finished\n",
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   3 out of   3 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   4 out of   4 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   5 out of   5 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   6 out of   6 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   7 out of   7 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   8 out of   8 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   9 out of   9 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  10 out of  10 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  11 out of  11 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  12 out of  12 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  13 out of  13 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  14 out of  14 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  15 out of  15 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  16 out of  16 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  17 out of  17 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  18 out of  18 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  19 out of  19 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  20 out of  20 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  21 out of  21 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  22 out of  22 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  23 out of  23 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  24 out of  24 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  25 out of  25 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  26 out of  26 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  27 out of  27 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  28 out of  28 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  29 out of  29 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  30 out of  30 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  31 out of  31 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  32 out of  32 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  33 out of  33 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  34 out of  34 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  35 out of  35 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  36 out of  36 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  37 out of  37 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  38 out of  38 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  39 out of  39 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  40 out of  40 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  41 out of  41 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  42 out of  42 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  43 out of  43 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  44 out of  44 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  45 out of  45 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  46 out of  46 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  47 out of  47 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  48 out of  48 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  49 out of  49 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  50 out of  50 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  50 out of  50 | elapsed:    0.1s finished\n",
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   3 out of   3 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   4 out of   4 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   5 out of   5 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   6 out of   6 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   7 out of   7 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   8 out of   8 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   9 out of   9 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  10 out of  10 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  11 out of  11 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  12 out of  12 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  13 out of  13 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  14 out of  14 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  15 out of  15 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  16 out of  16 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  17 out of  17 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  18 out of  18 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  19 out of  19 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  20 out of  20 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  21 out of  21 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  22 out of  22 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  23 out of  23 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  24 out of  24 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  25 out of  25 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  26 out of  26 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  27 out of  27 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  28 out of  28 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  29 out of  29 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  30 out of  30 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  31 out of  31 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  32 out of  32 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  33 out of  33 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  34 out of  34 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  35 out of  35 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  36 out of  36 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  37 out of  37 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  38 out of  38 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  39 out of  39 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  40 out of  40 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  41 out of  41 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  42 out of  42 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  43 out of  43 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  44 out of  44 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  45 out of  45 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  46 out of  46 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  47 out of  47 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  48 out of  48 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  49 out of  49 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  50 out of  50 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  50 out of  50 | elapsed:    0.1s finished\n",
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   3 out of   3 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   4 out of   4 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   5 out of   5 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   6 out of   6 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   7 out of   7 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   8 out of   8 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   9 out of   9 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  10 out of  10 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  11 out of  11 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  12 out of  12 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  13 out of  13 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  14 out of  14 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  15 out of  15 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  16 out of  16 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  17 out of  17 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  18 out of  18 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  19 out of  19 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  20 out of  20 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  21 out of  21 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  22 out of  22 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  23 out of  23 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  24 out of  24 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  25 out of  25 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  26 out of  26 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  27 out of  27 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  28 out of  28 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  29 out of  29 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  30 out of  30 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  31 out of  31 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  32 out of  32 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  33 out of  33 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  34 out of  34 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  35 out of  35 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  36 out of  36 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  37 out of  37 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  38 out of  38 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  39 out of  39 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  40 out of  40 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  41 out of  41 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  42 out of  42 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  43 out of  43 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  44 out of  44 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  45 out of  45 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  46 out of  46 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  47 out of  47 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  48 out of  48 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  49 out of  49 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  50 out of  50 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  50 out of  50 | elapsed:    0.1s finished\n"
     ]
    }
   ],
   "source": [
    "y_pred_voting, y_pred_average, y_pred_maximun, y_pred_minimun, y_pred_median, y_pred_product = get_combine_all(test, data_test, clfs=full_clfs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 340
    },
    "colab_type": "code",
    "id": "G7FFXNDchuq1",
    "outputId": "91ec68ab-5470-40a8-d980-d75a0c4a006b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Completo\n",
      "Voting\n",
      "0.6074008353548263\n",
      "0.8875030569821473\n",
      "Average\n",
      "0.618645019426193\n",
      "0.892394228417706\n",
      "Maximun\n",
      "0.6004478265464812\n",
      "0.8549767669356811\n",
      "Minimun\n",
      "0.5677162608479321\n",
      "0.8412814869161164\n",
      "Median\n",
      "0.6039050656412567\n",
      "0.8872584984103693\n",
      "Product\n",
      "0.5670424614258592\n",
      "0.8417706040596723\n"
     ]
    }
   ],
   "source": [
    "print(\"Completo\")\n",
    "\n",
    "print(\"Voting\")\n",
    "print(f1_score(class_test, y_pred_voting, average='macro'))\n",
    "print(accuracy_score(class_test, y_pred_voting))\n",
    "\n",
    "print(\"Average\")\n",
    "print(f1_score(class_test, y_pred_average, average='macro'))\n",
    "print(accuracy_score(class_test, y_pred_average))\n",
    "\n",
    "print(\"Maximun\")\n",
    "print(f1_score(class_test, y_pred_maximun, average='macro'))\n",
    "print(accuracy_score(class_test, y_pred_maximun))\n",
    "\n",
    "print(\"Minimun\")\n",
    "print(f1_score(class_test, y_pred_minimun, average='macro'))\n",
    "print(accuracy_score(class_test, y_pred_minimun))\n",
    "\n",
    "print(\"Median\")\n",
    "print(f1_score(class_test, y_pred_median, average='macro'))\n",
    "print(accuracy_score(class_test, y_pred_median))\n",
    "\n",
    "print(\"Product\")\n",
    "print(f1_score(class_test, y_pred_product, average='macro'))\n",
    "print(accuracy_score(class_test, y_pred_product))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 765
    },
    "colab_type": "code",
    "id": "_vfkrbEYb9_W",
    "outputId": "b2e38f3d-866d-4328-d262-e90137067c22"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   3 out of   3 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   4 out of   4 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   5 out of   5 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   6 out of   6 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   7 out of   7 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   8 out of   8 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   9 out of   9 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  10 out of  10 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  11 out of  11 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  12 out of  12 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  13 out of  13 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  14 out of  14 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  15 out of  15 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  16 out of  16 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  17 out of  17 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  18 out of  18 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  19 out of  19 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  20 out of  20 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  20 out of  20 | elapsed:    0.1s finished\n",
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   3 out of   3 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   4 out of   4 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   5 out of   5 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   6 out of   6 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   7 out of   7 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   8 out of   8 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   9 out of   9 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  10 out of  10 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  11 out of  11 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  12 out of  12 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  13 out of  13 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  14 out of  14 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  15 out of  15 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  16 out of  16 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  17 out of  17 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  18 out of  18 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  19 out of  19 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  20 out of  20 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done  20 out of  20 | elapsed:    0.1s finished\n"
     ]
    }
   ],
   "source": [
    "clfs_combine = {\n",
    "    'CNN-FAST': full_clfs['CNN-FAST'], 'RF-CV': full_clfs['RF-CV'], 'KNN-TFIDF': full_clfs['KNN-TFIDF'], \n",
    "    'LR-W2V': full_clfs['LR-W2V'], 'SVM-GLOVE': full_clfs['SVM-GLOVE']\n",
    "}\n",
    "y_pred_voting, y_pred_average, y_pred_maximun, y_pred_minimun, y_pred_median, y_pred_product = get_combine_all(test, data_test, clfs=clfs_combine)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 340
    },
    "colab_type": "code",
    "id": "RTa28hC7hyCB",
    "outputId": "2c17d583-5fd2-4b8b-bc9a-f670015ab0f8"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Combinado dissimilar\n",
      "Voting\n",
      "0.6431351599649967\n",
      "0.8835901198337002\n",
      "Average\n",
      "0.6070005587797063\n",
      "0.8877476155539251\n",
      "Maximun\n",
      "0.60393532041009\n",
      "0.8789435069699193\n",
      "Minimun\n",
      "0.6693218112095\n",
      "0.9033993641477134\n",
      "Median\n",
      "0.5988115437568801\n",
      "0.8752751283932502\n",
      "Product\n",
      "0.6333614324984921\n",
      "0.9036439227194913\n"
     ]
    }
   ],
   "source": [
    "print(\"Combinado dissimilar\")\n",
    "\n",
    "print(\"Voting\")\n",
    "print(f1_score(class_test, y_pred_voting, average='macro'))\n",
    "print(accuracy_score(class_test, y_pred_voting))\n",
    "\n",
    "print(\"Average\")\n",
    "print(f1_score(class_test, y_pred_average, average='macro'))\n",
    "print(accuracy_score(class_test, y_pred_average))\n",
    "\n",
    "print(\"Maximun\")\n",
    "print(f1_score(class_test, y_pred_maximun, average='macro'))\n",
    "print(accuracy_score(class_test, y_pred_maximun))\n",
    "\n",
    "print(\"Minimun\")\n",
    "print(f1_score(class_test, y_pred_minimun, average='macro'))\n",
    "print(accuracy_score(class_test, y_pred_minimun))\n",
    "\n",
    "print(\"Median\")\n",
    "print(f1_score(class_test, y_pred_median, average='macro'))\n",
    "print(accuracy_score(class_test, y_pred_median))\n",
    "\n",
    "print(\"Product\")\n",
    "print(f1_score(class_test, y_pred_product, average='macro'))\n",
    "print(accuracy_score(class_test, y_pred_product))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "swDxkMfYgYJP"
   },
   "outputs": [],
   "source": [
    "clfs_combine = {\n",
    "    'CNN-FAST': full_clfs['CNN-FAST'], 'SVM-CV': full_clfs['SVM-CV'], 'LR-TFIDF': full_clfs['LR-TFIDF'], \n",
    "    'LR-CV': full_clfs['LR-CV'], 'NB-CV': full_clfs['NB-CV']\n",
    "}\n",
    "y_pred_voting, y_pred_average, y_pred_maximun, y_pred_minimun, y_pred_median, y_pred_product = get_combine_all(test, data_test, clfs=clfs_combine)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 340
    },
    "colab_type": "code",
    "id": "TKUEOLSjhJOx",
    "outputId": "9d339d98-5d82-4ab1-e019-b71d2beedfe4"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Combinado Similar\n",
      "Voting\n",
      "0.7104829815904035\n",
      "0.9095133284421619\n",
      "Average\n",
      "0.6973736715472453\n",
      "0.9073123012961605\n",
      "Maximun\n",
      "0.6888265016875851\n",
      "0.9016874541452677\n",
      "Minimun\n",
      "0.7045730348397367\n",
      "0.9060895084372708\n",
      "Median\n",
      "0.7065177705247808\n",
      "0.9097578870139399\n",
      "Product\n",
      "0.6987636679235072\n",
      "0.9107361213010516\n"
     ]
    }
   ],
   "source": [
    "print(\"Combinado Similar\")\n",
    "\n",
    "print(\"Voting\")\n",
    "print(f1_score(class_test, y_pred_voting, average='macro'))\n",
    "print(accuracy_score(class_test, y_pred_voting))\n",
    "\n",
    "print(\"Average\")\n",
    "print(f1_score(class_test, y_pred_average, average='macro'))\n",
    "print(accuracy_score(class_test, y_pred_average))\n",
    "\n",
    "print(\"Maximun\")\n",
    "print(f1_score(class_test, y_pred_maximun, average='macro'))\n",
    "print(accuracy_score(class_test, y_pred_maximun))\n",
    "\n",
    "print(\"Minimun\")\n",
    "print(f1_score(class_test, y_pred_minimun, average='macro'))\n",
    "print(accuracy_score(class_test, y_pred_minimun))\n",
    "\n",
    "print(\"Median\")\n",
    "print(f1_score(class_test, y_pred_median, average='macro'))\n",
    "print(accuracy_score(class_test, y_pred_median))\n",
    "\n",
    "print(\"Product\")\n",
    "print(f1_score(class_test, y_pred_product, average='macro'))\n",
    "print(accuracy_score(class_test, y_pred_product))"
   ]
  }
 ],
 "metadata": {
  "accelerator": "GPU",
  "colab": {
   "collapsed_sections": [],
   "machine_shape": "hm",
   "name": "Central_TD.ipynb",
   "provenance": [],
   "toc_visible": true
  },
  "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": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}