example.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "9d41e6a3",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-05-30 20:55:36.690897: I tensorflow/tsl/cuda/cudart_stub.cc:28] Could not find cuda drivers on your machine, GPU will not be used.\n",
      "2023-05-30 20:55:36.742078: I tensorflow/tsl/cuda/cudart_stub.cc:28] Could not find cuda drivers on your machine, GPU will not be used.\n",
      "2023-05-30 20:55:36.743211: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
      "To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
      "2023-05-30 20:55:37.670198: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n",
      "/venv/lib/python3.10/site-packages/xgboost/compat.py:31: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "  from pandas import MultiIndex, Int64Index\n",
      "WARNING:root:[Memory(location=/tmp/time_series_models_cache/joblib)]: Flushing completely the cache\n"
     ]
    }
   ],
   "source": [
    "import logging\n",
    "import time\n",
    "\n",
    "import sklearn\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from joblib import parallel_backend, Memory\n",
    "\n",
    "from time_series_models.estimators import XgbRegressor, IdentityRegressor\n",
    "from time_series_models.filters import RowFilter, InterpolateLinear\n",
    "from time_series_models.processes import AmiHourlyForecast, PVForecast\n",
    "from time_series_models.time_series_models import RegularTimeSeriesModel\n",
    "from time_series_models.time_series_models import make_domain\n",
    "from time_series_models.data_fetchers.ami_fetcher import AmiFetcher\n",
    "from time_series_models.data_fetchers.hrrr_fetcher import HrrrFetcher\n",
    "\n",
    "Memory(\"/tmp/time_series_models_cache\").clear()\n",
    "\n",
    "logger = logging.getLogger()\n",
    "logging.Formatter.converter = time.gmtime\n",
    "logging.basicConfig(\n",
    "    format=\"%(asctime)s.%(msecs)03dZ %(processName)s %(threadName)s %(levelname)s:%(name)s:%(message)s\",\n",
    "    datefmt=\"%Y-%m-%dT%H:%M:%S\",\n",
    "    level=logging.INFO,\n",
    ")\n",
    "logger.info(\"configured logging\")\n",
    "\n",
    "pd.set_option('display.max_columns', None)\n",
    "sklearn.set_config(display=\"diagram\")\n",
    "\n",
    "AmiFetcher.GCS_BUCKET = \"seto2243-forecasting\"\n",
    "# HrrrFetcher.HRRR_GCS_BUCKET = \"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "566120e3",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-503af349-37af-487c-9641-356e58d2d23c {color: black;background-color: white;}#sk-503af349-37af-487c-9641-356e58d2d23c pre{padding: 0;}#sk-503af349-37af-487c-9641-356e58d2d23c div.sk-toggleable {background-color: white;}#sk-503af349-37af-487c-9641-356e58d2d23c label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-503af349-37af-487c-9641-356e58d2d23c label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-503af349-37af-487c-9641-356e58d2d23c label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-503af349-37af-487c-9641-356e58d2d23c div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-503af349-37af-487c-9641-356e58d2d23c div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-503af349-37af-487c-9641-356e58d2d23c div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-503af349-37af-487c-9641-356e58d2d23c input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-503af349-37af-487c-9641-356e58d2d23c input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-503af349-37af-487c-9641-356e58d2d23c div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-503af349-37af-487c-9641-356e58d2d23c div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-503af349-37af-487c-9641-356e58d2d23c input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-503af349-37af-487c-9641-356e58d2d23c div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-503af349-37af-487c-9641-356e58d2d23c div.sk-estimator:hover {background-color: #d4ebff;}#sk-503af349-37af-487c-9641-356e58d2d23c div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-503af349-37af-487c-9641-356e58d2d23c div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-503af349-37af-487c-9641-356e58d2d23c div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 2em;bottom: 0;left: 50%;}#sk-503af349-37af-487c-9641-356e58d2d23c div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;}#sk-503af349-37af-487c-9641-356e58d2d23c div.sk-item {z-index: 1;}#sk-503af349-37af-487c-9641-356e58d2d23c div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;}#sk-503af349-37af-487c-9641-356e58d2d23c div.sk-parallel::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 2em;bottom: 0;left: 50%;}#sk-503af349-37af-487c-9641-356e58d2d23c div.sk-parallel-item {display: flex;flex-direction: column;position: relative;background-color: white;}#sk-503af349-37af-487c-9641-356e58d2d23c div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-503af349-37af-487c-9641-356e58d2d23c div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-503af349-37af-487c-9641-356e58d2d23c div.sk-parallel-item:only-child::after {width: 0;}#sk-503af349-37af-487c-9641-356e58d2d23c div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;position: relative;}#sk-503af349-37af-487c-9641-356e58d2d23c div.sk-label label {font-family: monospace;font-weight: bold;background-color: white;display: inline-block;line-height: 1.2em;}#sk-503af349-37af-487c-9641-356e58d2d23c div.sk-label-container {position: relative;z-index: 2;text-align: center;}#sk-503af349-37af-487c-9641-356e58d2d23c div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-503af349-37af-487c-9641-356e58d2d23c div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-503af349-37af-487c-9641-356e58d2d23c\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;feature_builder&#x27;,\n",
       "                 ColumnTransformer(transformers=[(&#x27;count_locs&#x27;,\n",
       "                                                  FunctionTransformer(func=&lt;function count_domain_locs_into_column at 0x7fe0c7afbbe0&gt;),\n",
       "                                                  [0]),\n",
       "                                                 (&#x27;correlated_load&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;extend&#x27;,\n",
       "                                                                   FunctionTransformer(func=&lt;function shift_time at 0x7fe0c7afb7f0&gt;,\n",
       "                                                                                       kw_args={&#x27;extend&#x27;: True,\n",
       "                                                                                                &#x27;time_delta&#x27;: numpy.timedelta64(168,&#x27;h&#x27;),\n",
       "                                                                                                &#x27;tstep&#x27;: nump...\n",
       "                              importance_type=&#x27;gain&#x27;,\n",
       "                              interaction_constraints=None, learning_rate=None,\n",
       "                              max_delta_step=None, max_depth=None,\n",
       "                              min_child_weight=None, missing=nan,\n",
       "                              monotone_constraints=None, n_estimators=100,\n",
       "                              n_jobs=None, num_parallel_tree=None,\n",
       "                              random_state=None, reg_alpha=None,\n",
       "                              reg_lambda=None, scale_pos_weight=None,\n",
       "                              subsample=None, tree_method=None,\n",
       "                              validate_parameters=None, verbosity=None))])</pre><b>Please rerun this cell to show the HTML repr or trust the notebook.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"bf30144b-0315-4e46-a04d-89b468ac95bc\" type=\"checkbox\" ><label for=\"bf30144b-0315-4e46-a04d-89b468ac95bc\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;feature_builder&#x27;,\n",
       "                 ColumnTransformer(transformers=[(&#x27;count_locs&#x27;,\n",
       "                                                  FunctionTransformer(func=&lt;function count_domain_locs_into_column at 0x7fe0c7afbbe0&gt;),\n",
       "                                                  [0]),\n",
       "                                                 (&#x27;correlated_load&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;extend&#x27;,\n",
       "                                                                   FunctionTransformer(func=&lt;function shift_time at 0x7fe0c7afb7f0&gt;,\n",
       "                                                                                       kw_args={&#x27;extend&#x27;: True,\n",
       "                                                                                                &#x27;time_delta&#x27;: numpy.timedelta64(168,&#x27;h&#x27;),\n",
       "                                                                                                &#x27;tstep&#x27;: nump...\n",
       "                              importance_type=&#x27;gain&#x27;,\n",
       "                              interaction_constraints=None, learning_rate=None,\n",
       "                              max_delta_step=None, max_depth=None,\n",
       "                              min_child_weight=None, missing=nan,\n",
       "                              monotone_constraints=None, n_estimators=100,\n",
       "                              n_jobs=None, num_parallel_tree=None,\n",
       "                              random_state=None, reg_alpha=None,\n",
       "                              reg_lambda=None, scale_pos_weight=None,\n",
       "                              subsample=None, tree_method=None,\n",
       "                              validate_parameters=None, verbosity=None))])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"ac267e6c-1b2e-4313-bb8e-2815abae817b\" type=\"checkbox\" ><label for=\"ac267e6c-1b2e-4313-bb8e-2815abae817b\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">feature_builder: ColumnTransformer</label><div class=\"sk-toggleable__content\"><pre>ColumnTransformer(transformers=[(&#x27;count_locs&#x27;,\n",
       "                                 FunctionTransformer(func=&lt;function count_domain_locs_into_column at 0x7fe0c7afbbe0&gt;),\n",
       "                                 [0]),\n",
       "                                (&#x27;correlated_load&#x27;,\n",
       "                                 Pipeline(steps=[(&#x27;extend&#x27;,\n",
       "                                                  FunctionTransformer(func=&lt;function shift_time at 0x7fe0c7afb7f0&gt;,\n",
       "                                                                      kw_args={&#x27;extend&#x27;: True,\n",
       "                                                                               &#x27;time_delta&#x27;: numpy.timedelta64(168,&#x27;h&#x27;),\n",
       "                                                                               &#x27;tstep&#x27;: numpy.timedelta64(1,&#x27;h&#x27;)})),\n",
       "                                                 (&#x27;count_and...\n",
       "                                                  FunctionTransformer(func=&lt;function split_domain at 0x7fe0c7b08040&gt;,\n",
       "                                                                      kw_args={&#x27;key&#x27;: &#x27;date_time&#x27;})),\n",
       "                                                 (&#x27;delta_value&#x27;,\n",
       "                                                  FunctionTransformer(func=&lt;function delta_value at 0x7fe0c60e7640&gt;,\n",
       "                                                                      kw_args={&#x27;zero_value&#x27;: numpy.datetime64(&#x27;1970-01-01&#x27;)})),\n",
       "                                                 (&#x27;harmonic&#x27;,\n",
       "                                                  FunctionTransformer(func=&lt;function harmonic at 0x7fe0c5f14f70&gt;,\n",
       "                                                                      kw_args={&#x27;period&#x27;: numpy.timedelta64(8760,&#x27;h&#x27;)}))]),\n",
       "                                 [0])])</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"41f94c35-cc5d-40f3-a1c7-a7415b08e08d\" type=\"checkbox\" ><label for=\"41f94c35-cc5d-40f3-a1c7-a7415b08e08d\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">count_locs</label><div class=\"sk-toggleable__content\"><pre>[0]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"b42ad8f7-bc3c-4fe0-8e2a-432f5eaab8ad\" type=\"checkbox\" ><label for=\"b42ad8f7-bc3c-4fe0-8e2a-432f5eaab8ad\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">FunctionTransformer</label><div class=\"sk-toggleable__content\"><pre>FunctionTransformer(func=&lt;function count_domain_locs_into_column at 0x7fe0c7afbbe0&gt;)</pre></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"b0d94abd-00fb-47bc-87b4-1fd5236d0c4c\" type=\"checkbox\" ><label for=\"b0d94abd-00fb-47bc-87b4-1fd5236d0c4c\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">correlated_load</label><div class=\"sk-toggleable__content\"><pre>[0]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"7f9048ed-2740-45ba-8a9b-678c6ce3ace6\" type=\"checkbox\" ><label for=\"7f9048ed-2740-45ba-8a9b-678c6ce3ace6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">FunctionTransformer</label><div class=\"sk-toggleable__content\"><pre>FunctionTransformer(func=&lt;function shift_time at 0x7fe0c7afb7f0&gt;,\n",
       "                    kw_args={&#x27;extend&#x27;: True,\n",
       "                             &#x27;time_delta&#x27;: numpy.timedelta64(168,&#x27;h&#x27;),\n",
       "                             &#x27;tstep&#x27;: numpy.timedelta64(1,&#x27;h&#x27;)})</pre></div></div></div><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"bb37019f-0979-4717-8845-16d7777e22f9\" type=\"checkbox\" ><label for=\"bb37019f-0979-4717-8845-16d7777e22f9\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">count_and_fetch: ColumnTransformer</label><div class=\"sk-toggleable__content\"><pre>ColumnTransformer(transformers=[(&#x27;count_domain_locs&#x27;,\n",
       "                                 FunctionTransformer(func=&lt;function count_domain_locs_into_column at 0x7fe0c7afbbe0&gt;),\n",
       "                                 [0]),\n",
       "                                (&#x27;fetch&#x27;,\n",
       "                                 Pipeline(steps=[(&#x27;fetch&#x27;,\n",
       "                                                  AmiFetcher(location_mapper=&lt;function AmiHourlyForecast.__init__.&lt;locals&gt;.&lt;lambda&gt; at 0x7fe0c2b115a0&gt;,\n",
       "                                                             uri_formatter=&#x27;Arroyo_Hondo_AMI_data/{units:s}/{meter:s}_{units:s}.csv.gz&#x27;)),\n",
       "                                                 (&#x27;combine&#x27;,\n",
       "                                                  FunctionTransformer(func=&lt;function subtract_arrays at 0x7fe0c7b085e0&gt;))]),\n",
       "                                 [0])])</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"016bac66-7873-440c-9d77-56a1bb00fcaf\" type=\"checkbox\" ><label for=\"016bac66-7873-440c-9d77-56a1bb00fcaf\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">count_domain_locs</label><div class=\"sk-toggleable__content\"><pre>[0]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"3051f3fd-639a-4f8f-b1e4-50d3a81ba683\" type=\"checkbox\" ><label for=\"3051f3fd-639a-4f8f-b1e4-50d3a81ba683\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">FunctionTransformer</label><div class=\"sk-toggleable__content\"><pre>FunctionTransformer(func=&lt;function count_domain_locs_into_column at 0x7fe0c7afbbe0&gt;)</pre></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"bf03b2db-af67-49e1-b18f-36fcaf1b9c0b\" type=\"checkbox\" ><label for=\"bf03b2db-af67-49e1-b18f-36fcaf1b9c0b\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">fetch</label><div class=\"sk-toggleable__content\"><pre>[0]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"1bea4da4-5387-463b-b848-2e63e219f40e\" type=\"checkbox\" ><label for=\"1bea4da4-5387-463b-b848-2e63e219f40e\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">AmiFetcher</label><div class=\"sk-toggleable__content\"><pre>AmiFetcher(location_mapper=&lt;function AmiHourlyForecast.__init__.&lt;locals&gt;.&lt;lambda&gt; at 0x7fe0c2b115a0&gt;,\n",
       "           uri_formatter=&#x27;Arroyo_Hondo_AMI_data/{units:s}/{meter:s}_{units:s}.csv.gz&#x27;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"2b255dbb-c1ef-4ffa-a443-455d35316060\" type=\"checkbox\" ><label for=\"2b255dbb-c1ef-4ffa-a443-455d35316060\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">FunctionTransformer</label><div class=\"sk-toggleable__content\"><pre>FunctionTransformer(func=&lt;function subtract_arrays at 0x7fe0c7b085e0&gt;)</pre></div></div></div></div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"5af9494e-195b-4e1d-92f7-dfbe5187a3b0\" type=\"checkbox\" ><label for=\"5af9494e-195b-4e1d-92f7-dfbe5187a3b0\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">FunctionTransformer</label><div class=\"sk-toggleable__content\"><pre>FunctionTransformer(func=&lt;function slice_and_hstack.&lt;locals&gt;.slice_and_stack at 0x7fe0c2b117e0&gt;)</pre></div></div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"37786e10-6bbf-47ab-94da-c55e6ab4ec0d\" type=\"checkbox\" ><label for=\"37786e10-6bbf-47ab-94da-c55e6ab4ec0d\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">day_of_week</label><div class=\"sk-toggleable__content\"><pre>[0]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"a4d57ac6-3a6a-42bf-8c37-812115ad9bf2\" type=\"checkbox\" ><label for=\"a4d57ac6-3a6a-42bf-8c37-812115ad9bf2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">FunctionTransformer</label><div class=\"sk-toggleable__content\"><pre>FunctionTransformer(func=&lt;function split_domain at 0x7fe0c7b08040&gt;,\n",
       "                    kw_args={&#x27;key&#x27;: &#x27;date_time&#x27;})</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"b84f27e4-d58f-4249-8fed-aedf3631a073\" type=\"checkbox\" ><label for=\"b84f27e4-d58f-4249-8fed-aedf3631a073\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">FunctionTransformer</label><div class=\"sk-toggleable__content\"><pre>FunctionTransformer(func=&lt;bound method TimeUnitEnum.as_unit of &lt;TimeUnitEnum.DAY_OF_WEEK: &#x27;W&#x27;&gt;&gt;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"245a51d2-cffd-439c-9df5-2314b7ef5411\" type=\"checkbox\" ><label for=\"245a51d2-cffd-439c-9df5-2314b7ef5411\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">OneHotEncoder</label><div class=\"sk-toggleable__content\"><pre>OneHotEncoder()</pre></div></div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"0e5ab559-379a-4bdb-95f4-fd3bae3e7339\" type=\"checkbox\" ><label for=\"0e5ab559-379a-4bdb-95f4-fd3bae3e7339\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">harmonic_24_hr</label><div class=\"sk-toggleable__content\"><pre>[0]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"e4198efd-fe16-4e68-b971-a268c3848e26\" type=\"checkbox\" ><label for=\"e4198efd-fe16-4e68-b971-a268c3848e26\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">FunctionTransformer</label><div class=\"sk-toggleable__content\"><pre>FunctionTransformer(func=&lt;function split_domain at 0x7fe0c7b08040&gt;,\n",
       "                    kw_args={&#x27;key&#x27;: &#x27;date_time&#x27;})</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"e3c46551-b891-4891-9497-1fb0850d79f5\" type=\"checkbox\" ><label for=\"e3c46551-b891-4891-9497-1fb0850d79f5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">FunctionTransformer</label><div class=\"sk-toggleable__content\"><pre>FunctionTransformer(func=&lt;function delta_value at 0x7fe0c60e7640&gt;,\n",
       "                    kw_args={&#x27;zero_value&#x27;: numpy.datetime64(&#x27;1970-01-01&#x27;)})</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"036d7653-7e14-42f8-8960-fea512e3a0d5\" type=\"checkbox\" ><label for=\"036d7653-7e14-42f8-8960-fea512e3a0d5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">FunctionTransformer</label><div class=\"sk-toggleable__content\"><pre>FunctionTransformer(func=&lt;function harmonic at 0x7fe0c5f14f70&gt;,\n",
       "                    kw_args={&#x27;period&#x27;: numpy.timedelta64(24,&#x27;h&#x27;)})</pre></div></div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"1f47aaab-7472-4d78-8fe2-d19b4cc3fbfa\" type=\"checkbox\" ><label for=\"1f47aaab-7472-4d78-8fe2-d19b4cc3fbfa\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">harmonic_168_hr</label><div class=\"sk-toggleable__content\"><pre>[0]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"0c8443a3-8f94-4d91-85cb-707b9de18ea3\" type=\"checkbox\" ><label for=\"0c8443a3-8f94-4d91-85cb-707b9de18ea3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">FunctionTransformer</label><div class=\"sk-toggleable__content\"><pre>FunctionTransformer(func=&lt;function split_domain at 0x7fe0c7b08040&gt;,\n",
       "                    kw_args={&#x27;key&#x27;: &#x27;date_time&#x27;})</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"09aa37ef-eaa6-49d5-a1a8-08b7758c413a\" type=\"checkbox\" ><label for=\"09aa37ef-eaa6-49d5-a1a8-08b7758c413a\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">FunctionTransformer</label><div class=\"sk-toggleable__content\"><pre>FunctionTransformer(func=&lt;function delta_value at 0x7fe0c60e7640&gt;,\n",
       "                    kw_args={&#x27;zero_value&#x27;: numpy.datetime64(&#x27;1970-01-01&#x27;)})</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"cbc286f9-f1a7-47a0-9835-374230ed6dfe\" type=\"checkbox\" ><label for=\"cbc286f9-f1a7-47a0-9835-374230ed6dfe\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">FunctionTransformer</label><div class=\"sk-toggleable__content\"><pre>FunctionTransformer(func=&lt;function harmonic at 0x7fe0c5f14f70&gt;,\n",
       "                    kw_args={&#x27;period&#x27;: numpy.timedelta64(168,&#x27;h&#x27;)})</pre></div></div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"bca4c2ad-cf98-4781-a432-52662950f8b9\" type=\"checkbox\" ><label for=\"bca4c2ad-cf98-4781-a432-52662950f8b9\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">harmonic_8760_hr</label><div class=\"sk-toggleable__content\"><pre>[0]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"35340bc8-29a5-4ff9-be1e-1ec02111cca4\" type=\"checkbox\" ><label for=\"35340bc8-29a5-4ff9-be1e-1ec02111cca4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">FunctionTransformer</label><div class=\"sk-toggleable__content\"><pre>FunctionTransformer(func=&lt;function split_domain at 0x7fe0c7b08040&gt;,\n",
       "                    kw_args={&#x27;key&#x27;: &#x27;date_time&#x27;})</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"a79b71ef-e24a-4d7f-b0af-98d4e28fc7f3\" type=\"checkbox\" ><label for=\"a79b71ef-e24a-4d7f-b0af-98d4e28fc7f3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">FunctionTransformer</label><div class=\"sk-toggleable__content\"><pre>FunctionTransformer(func=&lt;function delta_value at 0x7fe0c60e7640&gt;,\n",
       "                    kw_args={&#x27;zero_value&#x27;: numpy.datetime64(&#x27;1970-01-01&#x27;)})</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"47cbc57c-871b-4a4b-948d-e0b6e84e6f91\" type=\"checkbox\" ><label for=\"47cbc57c-871b-4a4b-948d-e0b6e84e6f91\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">FunctionTransformer</label><div class=\"sk-toggleable__content\"><pre>FunctionTransformer(func=&lt;function harmonic at 0x7fe0c5f14f70&gt;,\n",
       "                    kw_args={&#x27;period&#x27;: numpy.timedelta64(8760,&#x27;h&#x27;)})</pre></div></div></div></div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"9946a238-7512-491d-aca2-fdb743df4939\" type=\"checkbox\" ><label for=\"9946a238-7512-491d-aca2-fdb743df4939\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">ForecastDataMonitor</label><div class=\"sk-toggleable__content\"><pre>ForecastDataMonitor(use_locs=True)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"aae3538c-a8c5-48c0-b39c-dd05b57ab2c2\" type=\"checkbox\" ><label for=\"aae3538c-a8c5-48c0-b39c-dd05b57ab2c2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">feature_filter: Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;linear_interpolation&#x27;,\n",
       "                 FunctionTransformer(func=&lt;function interpolate_array_by_group at 0x7fe0c7afbb50&gt;,\n",
       "                                     kw_args={&#x27;interp_func&#x27;: &lt;function interpolate_array_interior at 0x7fe0c7afb250&gt;,\n",
       "                                              &#x27;n_col&#x27;: -1})),\n",
       "                (&#x27;row_filter&#x27;, RowFilteringFunctionTransformer())])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"70695a1d-79c7-4f01-a3b5-3c40627af267\" type=\"checkbox\" ><label for=\"70695a1d-79c7-4f01-a3b5-3c40627af267\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">FunctionTransformer</label><div class=\"sk-toggleable__content\"><pre>FunctionTransformer(func=&lt;function interpolate_array_by_group at 0x7fe0c7afbb50&gt;,\n",
       "                    kw_args={&#x27;interp_func&#x27;: &lt;function interpolate_array_interior at 0x7fe0c7afb250&gt;,\n",
       "                             &#x27;n_col&#x27;: -1})</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"9f07575f-f637-434f-9206-4c19d1ba3cea\" type=\"checkbox\" ><label for=\"9f07575f-f637-434f-9206-4c19d1ba3cea\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RowFilteringFunctionTransformer</label><div class=\"sk-toggleable__content\"><pre>RowFilteringFunctionTransformer()</pre></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"f19c8d8a-cc8c-4fb7-9635-fd7369c7b782\" type=\"checkbox\" ><label for=\"f19c8d8a-cc8c-4fb7-9635-fd7369c7b782\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">ForecastDataMonitor</label><div class=\"sk-toggleable__content\"><pre>ForecastDataMonitor()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"13dd845a-c7fd-4941-9e32-f04d5e675ca3\" type=\"checkbox\" ><label for=\"13dd845a-c7fd-4941-9e32-f04d5e675ca3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">FunctionTransformer</label><div class=\"sk-toggleable__content\"><pre>FunctionTransformer(func=&lt;function drop_first_column at 0x7fe0c7b08550&gt;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"f144594f-40d5-428d-beb2-c8b68a7174b6\" type=\"checkbox\" ><label for=\"f144594f-40d5-428d-beb2-c8b68a7174b6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">XGBRegressor</label><div class=\"sk-toggleable__content\"><pre>XGBRegressor(base_score=None, booster=None, colsample_bylevel=None,\n",
       "             colsample_bynode=None, colsample_bytree=None, gamma=None,\n",
       "             gpu_id=None, importance_type=&#x27;gain&#x27;, interaction_constraints=None,\n",
       "             learning_rate=None, max_delta_step=None, max_depth=None,\n",
       "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "             n_estimators=100, n_jobs=None, num_parallel_tree=None,\n",
       "             random_state=None, reg_alpha=None, reg_lambda=None,\n",
       "             scale_pos_weight=None, subsample=None, tree_method=None,\n",
       "             validate_parameters=None, verbosity=None)</pre></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "Pipeline(steps=[('feature_builder',\n",
       "                 ColumnTransformer(transformers=[('count_locs',\n",
       "                                                  FunctionTransformer(func=<function count_domain_locs_into_column at 0x7fe0c7afbbe0>),\n",
       "                                                  [0]),\n",
       "                                                 ('correlated_load',\n",
       "                                                  Pipeline(steps=[('extend',\n",
       "                                                                   FunctionTransformer(func=<function shift_time at 0x7fe0c7afb7f0>,\n",
       "                                                                                       kw_args={'extend': True,\n",
       "                                                                                                'time_delta': numpy.timedelta64(168,'h'),\n",
       "                                                                                                'tstep': nump...\n",
       "                              importance_type='gain',\n",
       "                              interaction_constraints=None, learning_rate=None,\n",
       "                              max_delta_step=None, max_depth=None,\n",
       "                              min_child_weight=None, missing=nan,\n",
       "                              monotone_constraints=None, n_estimators=100,\n",
       "                              n_jobs=None, num_parallel_tree=None,\n",
       "                              random_state=None, reg_alpha=None,\n",
       "                              reg_lambda=None, scale_pos_weight=None,\n",
       "                              subsample=None, tree_method=None,\n",
       "                              validate_parameters=None, verbosity=None))])"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "class Model(\n",
    "    AmiHourlyForecast,\n",
    "    RowFilter,\n",
    "    InterpolateLinear,\n",
    "    XgbRegressor,\n",
    "    RegularTimeSeriesModel,\n",
    "):\n",
    "    pass\n",
    "\n",
    "config = dict(\n",
    "    lags = np.array([24, 48, 168], dtype=\"timedelta64[h]\"),\n",
    "    day_of_week=True,\n",
    "    harmonics = np.array([24, 168, 365*24], dtype=\"timedelta64[h]\")\n",
    ")\n",
    "\n",
    "instance = Model(**config)\n",
    "instance.model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "1160a1e0",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:time_series_models.filters:RowFilter removed 891 missings from fit_range of length 3289, leaving 2398 observations in fit_range prior to sklearn pipeline fit\n"
     ]
    }
   ],
   "source": [
    "instance.fit(\"2021-01-15\", \"2021-06-01\", \"55797646\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "73b4e97f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>predicted</th>\n",
       "      <th>true</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>location</th>\n",
       "      <th>date_time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">55797646</th>\n",
       "      <th>2021-03-15 00:00:00</th>\n",
       "      <td>1.906638</td>\n",
       "      <td>0.640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-03-15 01:00:00</th>\n",
       "      <td>1.630097</td>\n",
       "      <td>0.472</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-03-15 02:00:00</th>\n",
       "      <td>1.227366</td>\n",
       "      <td>0.640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-03-15 03:00:00</th>\n",
       "      <td>1.051105</td>\n",
       "      <td>0.872</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-03-15 04:00:00</th>\n",
       "      <td>1.478648</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              predicted   true\n",
       "location date_time                            \n",
       "55797646 2021-03-15 00:00:00   1.906638  0.640\n",
       "         2021-03-15 01:00:00   1.630097  0.472\n",
       "         2021-03-15 02:00:00   1.227366  0.640\n",
       "         2021-03-15 03:00:00   1.051105  0.872\n",
       "         2021-03-15 04:00:00   1.478648    NaN"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "instance.predict_dataframe(\n",
    "    \"2021-03-15\",\n",
    "    \"2021-03-15T04\",\n",
    "    \"55797646\",\n",
    "    range=True,\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "f5bc0568",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-c06fd688-af90-4cab-847e-8c16cdc3c6c2 {color: black;background-color: white;}#sk-c06fd688-af90-4cab-847e-8c16cdc3c6c2 pre{padding: 0;}#sk-c06fd688-af90-4cab-847e-8c16cdc3c6c2 div.sk-toggleable {background-color: white;}#sk-c06fd688-af90-4cab-847e-8c16cdc3c6c2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-c06fd688-af90-4cab-847e-8c16cdc3c6c2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-c06fd688-af90-4cab-847e-8c16cdc3c6c2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-c06fd688-af90-4cab-847e-8c16cdc3c6c2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-c06fd688-af90-4cab-847e-8c16cdc3c6c2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-c06fd688-af90-4cab-847e-8c16cdc3c6c2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-c06fd688-af90-4cab-847e-8c16cdc3c6c2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-c06fd688-af90-4cab-847e-8c16cdc3c6c2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-c06fd688-af90-4cab-847e-8c16cdc3c6c2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-c06fd688-af90-4cab-847e-8c16cdc3c6c2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-c06fd688-af90-4cab-847e-8c16cdc3c6c2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-c06fd688-af90-4cab-847e-8c16cdc3c6c2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-c06fd688-af90-4cab-847e-8c16cdc3c6c2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-c06fd688-af90-4cab-847e-8c16cdc3c6c2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-c06fd688-af90-4cab-847e-8c16cdc3c6c2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-c06fd688-af90-4cab-847e-8c16cdc3c6c2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 2em;bottom: 0;left: 50%;}#sk-c06fd688-af90-4cab-847e-8c16cdc3c6c2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;}#sk-c06fd688-af90-4cab-847e-8c16cdc3c6c2 div.sk-item {z-index: 1;}#sk-c06fd688-af90-4cab-847e-8c16cdc3c6c2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;}#sk-c06fd688-af90-4cab-847e-8c16cdc3c6c2 div.sk-parallel::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 2em;bottom: 0;left: 50%;}#sk-c06fd688-af90-4cab-847e-8c16cdc3c6c2 div.sk-parallel-item {display: flex;flex-direction: column;position: relative;background-color: white;}#sk-c06fd688-af90-4cab-847e-8c16cdc3c6c2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-c06fd688-af90-4cab-847e-8c16cdc3c6c2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-c06fd688-af90-4cab-847e-8c16cdc3c6c2 div.sk-parallel-item:only-child::after {width: 0;}#sk-c06fd688-af90-4cab-847e-8c16cdc3c6c2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;position: relative;}#sk-c06fd688-af90-4cab-847e-8c16cdc3c6c2 div.sk-label label {font-family: monospace;font-weight: bold;background-color: white;display: inline-block;line-height: 1.2em;}#sk-c06fd688-af90-4cab-847e-8c16cdc3c6c2 div.sk-label-container {position: relative;z-index: 2;text-align: center;}#sk-c06fd688-af90-4cab-847e-8c16cdc3c6c2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-c06fd688-af90-4cab-847e-8c16cdc3c6c2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-c06fd688-af90-4cab-847e-8c16cdc3c6c2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;feature_builder&#x27;,\n",
       "                 ColumnTransformer(transformers=[(&#x27;count_locs&#x27;,\n",
       "                                                  FunctionTransformer(func=&lt;function count_domain_locs_into_column at 0x7fe0c7afbbe0&gt;),\n",
       "                                                  [0]),\n",
       "                                                 (&#x27;pv_physical&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;hrrr_domain&#x27;,\n",
       "                                                                   ColumnTypeTransformer(transformers=[(&#x27;domain&#x27;,\n",
       "                                                                                                        &#x27;passthrough&#x27;,\n",
       "                                                                                                        [0]),\n",
       "                                                                                                       (&#x27;hrrr_data&#x27;,\n",
       "                                                                                                        HrrrFetcher(location_mapper=&lt;function PVForecast.__init__.&lt;...\n",
       "                                                                   FunctionTransformer(func=&lt;function nan_like at 0x7fe0c7b08940&gt;))]),\n",
       "                                                  [0])])),\n",
       "                (&#x27;data_monitor_unfiltered&#x27;, ForecastDataMonitor(use_locs=True)),\n",
       "                (&#x27;feature_filter&#x27;,\n",
       "                 Pipeline(steps=[(&#x27;passthrough&#x27;, &#x27;passthrough&#x27;)])),\n",
       "                (&#x27;data_monitor_filtered&#x27;, ForecastDataMonitor()),\n",
       "                (&#x27;drop_location_count&#x27;,\n",
       "                 FunctionTransformer(func=&lt;function drop_first_column at 0x7fe0c7b08550&gt;)),\n",
       "                (&#x27;estimator&#x27;, IdentityEstimator())])</pre><b>Please rerun this cell to show the HTML repr or trust the notebook.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"d0dfc6d3-bbad-4cf4-be3a-2bce0d0abed5\" type=\"checkbox\" ><label for=\"d0dfc6d3-bbad-4cf4-be3a-2bce0d0abed5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;feature_builder&#x27;,\n",
       "                 ColumnTransformer(transformers=[(&#x27;count_locs&#x27;,\n",
       "                                                  FunctionTransformer(func=&lt;function count_domain_locs_into_column at 0x7fe0c7afbbe0&gt;),\n",
       "                                                  [0]),\n",
       "                                                 (&#x27;pv_physical&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;hrrr_domain&#x27;,\n",
       "                                                                   ColumnTypeTransformer(transformers=[(&#x27;domain&#x27;,\n",
       "                                                                                                        &#x27;passthrough&#x27;,\n",
       "                                                                                                        [0]),\n",
       "                                                                                                       (&#x27;hrrr_data&#x27;,\n",
       "                                                                                                        HrrrFetcher(location_mapper=&lt;function PVForecast.__init__.&lt;...\n",
       "                                                                   FunctionTransformer(func=&lt;function nan_like at 0x7fe0c7b08940&gt;))]),\n",
       "                                                  [0])])),\n",
       "                (&#x27;data_monitor_unfiltered&#x27;, ForecastDataMonitor(use_locs=True)),\n",
       "                (&#x27;feature_filter&#x27;,\n",
       "                 Pipeline(steps=[(&#x27;passthrough&#x27;, &#x27;passthrough&#x27;)])),\n",
       "                (&#x27;data_monitor_filtered&#x27;, ForecastDataMonitor()),\n",
       "                (&#x27;drop_location_count&#x27;,\n",
       "                 FunctionTransformer(func=&lt;function drop_first_column at 0x7fe0c7b08550&gt;)),\n",
       "                (&#x27;estimator&#x27;, IdentityEstimator())])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"a395b4f0-40a8-4de5-b2c4-1fdcd34db521\" type=\"checkbox\" ><label for=\"a395b4f0-40a8-4de5-b2c4-1fdcd34db521\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">feature_builder: ColumnTransformer</label><div class=\"sk-toggleable__content\"><pre>ColumnTransformer(transformers=[(&#x27;count_locs&#x27;,\n",
       "                                 FunctionTransformer(func=&lt;function count_domain_locs_into_column at 0x7fe0c7afbbe0&gt;),\n",
       "                                 [0]),\n",
       "                                (&#x27;pv_physical&#x27;,\n",
       "                                 Pipeline(steps=[(&#x27;hrrr_domain&#x27;,\n",
       "                                                  ColumnTypeTransformer(transformers=[(&#x27;domain&#x27;,\n",
       "                                                                                       &#x27;passthrough&#x27;,\n",
       "                                                                                       [0]),\n",
       "                                                                                      (&#x27;hrrr_data&#x27;,\n",
       "                                                                                       HrrrFetcher(location_mapper=&lt;function PVForecast.__init__.&lt;locals&gt;.&lt;lambda&gt; at 0x7fe0c2bd44c0&gt;,\n",
       "                                                                                                   s...\n",
       "                                                                                                   source_mode=&#x27;12_hour_horizon&#x27;),\n",
       "                                                                                       [0])])),\n",
       "                                                 (&#x27;pv_forecast&#x27;,\n",
       "                                                  FunctionTransformer(func=&lt;function pv_function at 0x7fe0c5f153f0&gt;,\n",
       "                                                                      kw_args={&#x27;pv_model_builder&#x27;: &lt;time_series_models.transformers_pv.ConfigBuilder object at 0x7fe0c2b4f730&gt;}))]),\n",
       "                                 [0]),\n",
       "                                (&#x27;correlated_generation&#x27;,\n",
       "                                 Pipeline(steps=[(&#x27;no_ar_features&#x27;,\n",
       "                                                  FunctionTransformer(func=&lt;function nan_like at 0x7fe0c7b08940&gt;))]),\n",
       "                                 [0])])</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"290064ec-5b77-4275-a415-975964540ec3\" type=\"checkbox\" ><label for=\"290064ec-5b77-4275-a415-975964540ec3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">count_locs</label><div class=\"sk-toggleable__content\"><pre>[0]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"05c5365f-7512-48f6-af7f-8d802be72592\" type=\"checkbox\" ><label for=\"05c5365f-7512-48f6-af7f-8d802be72592\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">FunctionTransformer</label><div class=\"sk-toggleable__content\"><pre>FunctionTransformer(func=&lt;function count_domain_locs_into_column at 0x7fe0c7afbbe0&gt;)</pre></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"adf19a95-9ed7-46ca-8e40-7ca3a4ab2d81\" type=\"checkbox\" ><label for=\"adf19a95-9ed7-46ca-8e40-7ca3a4ab2d81\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">pv_physical</label><div class=\"sk-toggleable__content\"><pre>[0]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"addf92b7-f910-4b43-b46d-8314837e8263\" type=\"checkbox\" ><label for=\"addf92b7-f910-4b43-b46d-8314837e8263\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">hrrr_domain: ColumnTypeTransformer</label><div class=\"sk-toggleable__content\"><pre>ColumnTypeTransformer(transformers=[(&#x27;domain&#x27;, &#x27;passthrough&#x27;, [0]),\n",
       "                                    (&#x27;hrrr_data&#x27;,\n",
       "                                     HrrrFetcher(location_mapper=&lt;function PVForecast.__init__.&lt;locals&gt;.&lt;lambda&gt; at 0x7fe0c2bd44c0&gt;,\n",
       "                                                 selector=&#x27;select&#x27;,\n",
       "                                                 selector_args={&#x27;variables&#x27;: [&#x27;valid_time&#x27;,\n",
       "                                                                              &#x27;dswrf&#x27;,\n",
       "                                                                              &#x27;vbdsf&#x27;,\n",
       "                                                                              &#x27;vddsf&#x27;,\n",
       "                                                                              &#x27;t&#x27;,\n",
       "                                                                              &#x27;gust&#x27;]},\n",
       "                                                 source_mode=&#x27;12_hour_horizon&#x27;),\n",
       "                                     [0])])</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"aa8eed15-63a4-46d4-9c65-f01ff316f1e5\" type=\"checkbox\" ><label for=\"aa8eed15-63a4-46d4-9c65-f01ff316f1e5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">domain</label><div class=\"sk-toggleable__content\"><pre>[0]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"a8fd850a-9655-4568-87e1-3e4bb73796bf\" type=\"checkbox\" ><label for=\"a8fd850a-9655-4568-87e1-3e4bb73796bf\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">passthrough</label><div class=\"sk-toggleable__content\"><pre>passthrough</pre></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"a1adbd6e-0566-4942-b653-edb2c9b83ee6\" type=\"checkbox\" ><label for=\"a1adbd6e-0566-4942-b653-edb2c9b83ee6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">hrrr_data</label><div class=\"sk-toggleable__content\"><pre>[0]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"59051c4a-1dd3-4c1e-94b6-24848765cdb5\" type=\"checkbox\" ><label for=\"59051c4a-1dd3-4c1e-94b6-24848765cdb5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">HrrrFetcher</label><div class=\"sk-toggleable__content\"><pre>HrrrFetcher(location_mapper=&lt;function PVForecast.__init__.&lt;locals&gt;.&lt;lambda&gt; at 0x7fe0c2bd44c0&gt;,\n",
       "            selector=&#x27;select&#x27;,\n",
       "            selector_args={&#x27;variables&#x27;: [&#x27;valid_time&#x27;, &#x27;dswrf&#x27;, &#x27;vbdsf&#x27;,\n",
       "                                         &#x27;vddsf&#x27;, &#x27;t&#x27;, &#x27;gust&#x27;]},\n",
       "            source_mode=&#x27;12_hour_horizon&#x27;)</pre></div></div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"71b10936-b59e-4639-9e5c-a3cd1c9a7753\" type=\"checkbox\" ><label for=\"71b10936-b59e-4639-9e5c-a3cd1c9a7753\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">FunctionTransformer</label><div class=\"sk-toggleable__content\"><pre>FunctionTransformer(func=&lt;function pv_function at 0x7fe0c5f153f0&gt;,\n",
       "                    kw_args={&#x27;pv_model_builder&#x27;: &lt;time_series_models.transformers_pv.ConfigBuilder object at 0x7fe0c2b4f730&gt;})</pre></div></div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"3b96dc61-3f18-4a21-a12e-6e493b004601\" type=\"checkbox\" ><label for=\"3b96dc61-3f18-4a21-a12e-6e493b004601\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">correlated_generation</label><div class=\"sk-toggleable__content\"><pre>[0]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"a756de27-b223-466f-bd05-b1873c9fc6ae\" type=\"checkbox\" ><label for=\"a756de27-b223-466f-bd05-b1873c9fc6ae\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">FunctionTransformer</label><div class=\"sk-toggleable__content\"><pre>FunctionTransformer(func=&lt;function nan_like at 0x7fe0c7b08940&gt;)</pre></div></div></div></div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"1527c3ac-12b0-4200-8edd-4d5fdd94ef9d\" type=\"checkbox\" ><label for=\"1527c3ac-12b0-4200-8edd-4d5fdd94ef9d\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">ForecastDataMonitor</label><div class=\"sk-toggleable__content\"><pre>ForecastDataMonitor(use_locs=True)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"e267050b-83b5-42f1-b180-e6c49f0ea8b4\" type=\"checkbox\" ><label for=\"e267050b-83b5-42f1-b180-e6c49f0ea8b4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">feature_filter: Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;passthrough&#x27;, &#x27;passthrough&#x27;)])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"3dc9588d-c074-4295-b65c-57f6f0edcb27\" type=\"checkbox\" ><label for=\"3dc9588d-c074-4295-b65c-57f6f0edcb27\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">passthrough</label><div class=\"sk-toggleable__content\"><pre>passthrough</pre></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"58de4059-1e14-4819-b538-b5b0f68e30d9\" type=\"checkbox\" ><label for=\"58de4059-1e14-4819-b538-b5b0f68e30d9\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">ForecastDataMonitor</label><div class=\"sk-toggleable__content\"><pre>ForecastDataMonitor()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"031221c9-fe51-441f-89b8-7281cedf7d4d\" type=\"checkbox\" ><label for=\"031221c9-fe51-441f-89b8-7281cedf7d4d\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">FunctionTransformer</label><div class=\"sk-toggleable__content\"><pre>FunctionTransformer(func=&lt;function drop_first_column at 0x7fe0c7b08550&gt;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"8345d1eb-81ed-47ff-8aff-99b89ea92cc8\" type=\"checkbox\" ><label for=\"8345d1eb-81ed-47ff-8aff-99b89ea92cc8\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">IdentityEstimator</label><div class=\"sk-toggleable__content\"><pre>IdentityEstimator()</pre></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "Pipeline(steps=[('feature_builder',\n",
       "                 ColumnTransformer(transformers=[('count_locs',\n",
       "                                                  FunctionTransformer(func=<function count_domain_locs_into_column at 0x7fe0c7afbbe0>),\n",
       "                                                  [0]),\n",
       "                                                 ('pv_physical',\n",
       "                                                  Pipeline(steps=[('hrrr_domain',\n",
       "                                                                   ColumnTypeTransformer(transformers=[('domain',\n",
       "                                                                                                        'passthrough',\n",
       "                                                                                                        [0]),\n",
       "                                                                                                       ('hrrr_data',\n",
       "                                                                                                        HrrrFetcher(location_mapper=<function PVForecast.__init__.<...\n",
       "                                                                   FunctionTransformer(func=<function nan_like at 0x7fe0c7b08940>))]),\n",
       "                                                  [0])])),\n",
       "                ('data_monitor_unfiltered', ForecastDataMonitor(use_locs=True)),\n",
       "                ('feature_filter',\n",
       "                 Pipeline(steps=[('passthrough', 'passthrough')])),\n",
       "                ('data_monitor_filtered', ForecastDataMonitor()),\n",
       "                ('drop_location_count',\n",
       "                 FunctionTransformer(func=<function drop_first_column at 0x7fe0c7b08550>)),\n",
       "                ('estimator', IdentityEstimator())])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pv_config = dict(\n",
    "    site_config_mapping=\"RESOURCE_SELF\",\n",
    "    site_meter_mapping=None,\n",
    "    site_latlong_mapping=\"RESOURCE_SELF\",\n",
    "    source_mode=\"12_hour_horizon\",\n",
    "    lags=None,\n",
    ")\n",
    "\n",
    "\n",
    "\n",
    "class PVForecastModel(\n",
    "    PVForecast,\n",
    "    IdentityRegressor,\n",
    "    RegularTimeSeriesModel,\n",
    "):\n",
    "    pass\n",
    "\n",
    "pv_instance = PVForecastModel(**pv_config)\n",
    "\n",
    "\n",
    "pv_instance.model\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ab8df7c6",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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