03_windowing_tsfresh.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "79001523",
   "metadata": {},
   "source": [
    "# Tsfresh (Time Series Feature Extractor)\n",
    "\n",
    "The idea of this notebook is providing a quick way to obtain strong baselines with a library to automatically extract time-series features"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6a962903",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-28T13:55:57.437692Z",
     "start_time": "2022-08-28T13:55:57.433675Z"
    }
   },
   "source": [
    "\n",
    "From time-series data like:\n",
    "<img src=\"images/introduction_ts_exa.png\" width=\"400\" height=\"200\">\n",
    "\n",
    "Extracts features like:\n",
    "<img src=\"images/introduction_ts_exa_features.png\" width=\"400\" height=\"200\">\n",
    "\n",
    "- Relativelly simple to use\n",
    "- Extract +700 features with few lines of code (statistical, trends, wavelets, fourier transformations...)\n",
    "- Feature prunning (if needed) with Benjamini-Yekutieli procedure."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "91f60579",
   "metadata": {},
   "source": [
    "#### Important but not covered here\n",
    "Domain specific features, like HRV:\n",
    " - PyHRV: https://pyhrv.readthedocs.io/en/latest/\n",
    " - HRV-Analysis https://aura-healthcare.github.io/hrvanalysis/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "e877c35b",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-09-04T13:12:38.656746Z",
     "start_time": "2022-09-04T13:12:38.652634Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/joao/miniforge3/envs/tutorial-tsfresh/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:7: 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 (to_datetime, Int64Index, DatetimeIndex, Period,\n",
      "/Users/joao/miniforge3/envs/tutorial-tsfresh/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:7: FutureWarning: pandas.Float64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "  from pandas import (to_datetime, Int64Index, DatetimeIndex, Period,\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from glob import glob\n",
    "import tsfresh\n",
    "import os\n",
    "from tsfresh.feature_extraction import extract_features\n",
    "from tsfresh.feature_extraction import MinimalFCParameters, ComprehensiveFCParameters, EfficientFCParameters\n",
    "from tsfresh.utilities.dataframe_functions import make_forecasting_frame, roll_time_series\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "eebe04c0",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-09-04T13:12:42.259769Z",
     "start_time": "2022-09-04T13:12:40.923965Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original Dataframe has 102859 rows\n"
     ]
    }
   ],
   "source": [
    "df = pd.read_csv(\"./datasets/tutorial_sleep_training_data.csv.gz\")\n",
    "# Removing Nan values from HR\n",
    "df = df.dropna()\n",
    "\n",
    "print(\"Original Dataframe has %d rows\" % df.shape[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "34da2493",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-09-04T09:20:09.314528Z",
     "start_time": "2022-09-04T09:20:09.272267Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\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>time</th>\n",
       "      <th>act</th>\n",
       "      <th>sleep_phase</th>\n",
       "      <th>hr</th>\n",
       "      <th>pid</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <td>16</td>\n",
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       "      <td>22.0</td>\n",
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       "      <td>61.0</td>\n",
       "      <td>16</td>\n",
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       "      <td>16.0</td>\n",
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       "      <td>81.0</td>\n",
       "      <td>16</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1458 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         time   act  sleep_phase    hr  pid\n",
       "38399      29   1.0          0.0  66.0    1\n",
       "38429      59   0.0          0.0  67.0    1\n",
       "38459      89   0.0          1.0  66.0    1\n",
       "38489     119   1.0          1.0  67.0    1\n",
       "38519     149   1.0          1.0  64.0    1\n",
       "...       ...   ...          ...   ...  ...\n",
       "499958  15869   4.0          1.0  95.0   16\n",
       "499988  15899  22.0          0.0  61.0   16\n",
       "500018  15929  15.0          0.0  73.0   16\n",
       "500048  15959  16.0          1.0  81.0   16\n",
       "500078  15989   0.0          1.0  68.0   16\n",
       "\n",
       "[1458 rows x 5 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_small = df[df[\"pid\"].isin([1, 16])].dropna()\n",
    "df_small"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "1655efb8",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-09-04T09:20:12.178289Z",
     "start_time": "2022-09-04T09:20:10.894544Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Feature Extraction: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:09<00:00,  4.99s/it]\n",
      "/Users/joao/miniforge3/envs/tutorial-tsfresh/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:7: 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 (to_datetime, Int64Index, DatetimeIndex, Period,\n",
      "/Users/joao/miniforge3/envs/tutorial-tsfresh/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:7: FutureWarning: pandas.Float64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "  from pandas import (to_datetime, Int64Index, DatetimeIndex, Period,\n",
      "/Users/joao/miniforge3/envs/tutorial-tsfresh/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:7: 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 (to_datetime, Int64Index, DatetimeIndex, Period,\n",
      "/Users/joao/miniforge3/envs/tutorial-tsfresh/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:7: FutureWarning: pandas.Float64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "  from pandas import (to_datetime, Int64Index, DatetimeIndex, Period,\n",
      "/Users/joao/miniforge3/envs/tutorial-tsfresh/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:7: 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 (to_datetime, Int64Index, DatetimeIndex, Period,\n",
      "/Users/joao/miniforge3/envs/tutorial-tsfresh/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:7: FutureWarning: pandas.Float64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "  from pandas import (to_datetime, Int64Index, DatetimeIndex, Period,\n",
      "/Users/joao/miniforge3/envs/tutorial-tsfresh/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:7: 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 (to_datetime, Int64Index, DatetimeIndex, Period,\n",
      "/Users/joao/miniforge3/envs/tutorial-tsfresh/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:7: FutureWarning: pandas.Float64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "  from pandas import (to_datetime, Int64Index, DatetimeIndex, Period,\n",
      "/Users/joao/miniforge3/envs/tutorial-tsfresh/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:7: 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 (to_datetime, Int64Index, DatetimeIndex, Period,\n",
      "/Users/joao/miniforge3/envs/tutorial-tsfresh/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:7: FutureWarning: pandas.Float64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "  from pandas import (to_datetime, Int64Index, DatetimeIndex, Period,\n"
     ]
    }
   ],
   "source": [
    "df_extracted_features = tsfresh.extract_features(df_small[[\"time\", \"pid\", \"act\"]],\n",
    "                                                 column_id=\"pid\", \n",
    "                                                 column_sort=\"time\", show_warnings=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "dd2835da",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-09-04T09:20:13.511950Z",
     "start_time": "2022-09-04T09:20:13.479060Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>act__variance_larger_than_standard_deviation</th>\n",
       "      <th>act__has_duplicate_max</th>\n",
       "      <th>act__has_duplicate_min</th>\n",
       "      <th>act__has_duplicate</th>\n",
       "      <th>act__sum_values</th>\n",
       "      <th>act__abs_energy</th>\n",
       "      <th>act__mean_abs_change</th>\n",
       "      <th>act__mean_change</th>\n",
       "      <th>act__mean_second_derivative_central</th>\n",
       "      <th>act__median</th>\n",
       "      <th>...</th>\n",
       "      <th>act__fourier_entropy__bins_2</th>\n",
       "      <th>act__fourier_entropy__bins_3</th>\n",
       "      <th>act__fourier_entropy__bins_5</th>\n",
       "      <th>act__fourier_entropy__bins_10</th>\n",
       "      <th>act__fourier_entropy__bins_100</th>\n",
       "      <th>act__permutation_entropy__dimension_3__tau_1</th>\n",
       "      <th>act__permutation_entropy__dimension_4__tau_1</th>\n",
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       "      <th>act__permutation_entropy__dimension_6__tau_1</th>\n",
       "      <th>act__permutation_entropy__dimension_7__tau_1</th>\n",
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       "  </thead>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4008.0</td>\n",
       "      <td>707198.0</td>\n",
       "      <td>6.735931</td>\n",
       "      <td>0.155844</td>\n",
       "      <td>0.055255</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.417974</td>\n",
       "      <td>0.779345</td>\n",
       "      <td>1.249572</td>\n",
       "      <td>1.922716</td>\n",
       "      <td>3.854080</td>\n",
       "      <td>1.351787</td>\n",
       "      <td>2.226125</td>\n",
       "      <td>3.107142</td>\n",
       "      <td>3.938264</td>\n",
       "      <td>4.690248</td>\n",
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       "    <tr>\n",
       "      <th>16</th>\n",
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       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4629.0</td>\n",
       "      <td>500191.0</td>\n",
       "      <td>12.530075</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.015066</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.326783</td>\n",
       "      <td>0.617950</td>\n",
       "      <td>1.123223</td>\n",
       "      <td>1.747968</td>\n",
       "      <td>3.723715</td>\n",
       "      <td>1.571942</td>\n",
       "      <td>2.676353</td>\n",
       "      <td>3.842873</td>\n",
       "      <td>4.790484</td>\n",
       "      <td>5.456311</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 779 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    act__variance_larger_than_standard_deviation  act__has_duplicate_max  \\\n",
       "1                                            1.0                     0.0   \n",
       "16                                           1.0                     0.0   \n",
       "\n",
       "    act__has_duplicate_min  act__has_duplicate  act__sum_values  \\\n",
       "1                      1.0                 1.0           4008.0   \n",
       "16                     1.0                 1.0           4629.0   \n",
       "\n",
       "    act__abs_energy  act__mean_abs_change  act__mean_change  \\\n",
       "1          707198.0              6.735931          0.155844   \n",
       "16         500191.0             12.530075          0.000000   \n",
       "\n",
       "    act__mean_second_derivative_central  act__median  ...  \\\n",
       "1                              0.055255          0.0  ...   \n",
       "16                            -0.015066          1.0  ...   \n",
       "\n",
       "    act__fourier_entropy__bins_2  act__fourier_entropy__bins_3  \\\n",
       "1                       0.417974                      0.779345   \n",
       "16                      0.326783                      0.617950   \n",
       "\n",
       "    act__fourier_entropy__bins_5  act__fourier_entropy__bins_10  \\\n",
       "1                       1.249572                       1.922716   \n",
       "16                      1.123223                       1.747968   \n",
       "\n",
       "    act__fourier_entropy__bins_100  \\\n",
       "1                         3.854080   \n",
       "16                        3.723715   \n",
       "\n",
       "    act__permutation_entropy__dimension_3__tau_1  \\\n",
       "1                                       1.351787   \n",
       "16                                      1.571942   \n",
       "\n",
       "    act__permutation_entropy__dimension_4__tau_1  \\\n",
       "1                                       2.226125   \n",
       "16                                      2.676353   \n",
       "\n",
       "    act__permutation_entropy__dimension_5__tau_1  \\\n",
       "1                                       3.107142   \n",
       "16                                      3.842873   \n",
       "\n",
       "    act__permutation_entropy__dimension_6__tau_1  \\\n",
       "1                                       3.938264   \n",
       "16                                      4.790484   \n",
       "\n",
       "    act__permutation_entropy__dimension_7__tau_1  \n",
       "1                                       4.690248  \n",
       "16                                      5.456311  \n",
       "\n",
       "[2 rows x 779 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_extracted_features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "fd6db718",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['act__variance_larger_than_standard_deviation',\n",
       "       'act__has_duplicate_max', 'act__has_duplicate_min',\n",
       "       'act__has_duplicate', 'act__sum_values', 'act__abs_energy',\n",
       "       'act__mean_abs_change', 'act__mean_change',\n",
       "       'act__mean_second_derivative_central', 'act__median',\n",
       "       ...\n",
       "       'act__fourier_entropy__bins_2', 'act__fourier_entropy__bins_3',\n",
       "       'act__fourier_entropy__bins_5', 'act__fourier_entropy__bins_10',\n",
       "       'act__fourier_entropy__bins_100',\n",
       "       'act__permutation_entropy__dimension_3__tau_1',\n",
       "       'act__permutation_entropy__dimension_4__tau_1',\n",
       "       'act__permutation_entropy__dimension_5__tau_1',\n",
       "       'act__permutation_entropy__dimension_6__tau_1',\n",
       "       'act__permutation_entropy__dimension_7__tau_1'],\n",
       "      dtype='object', length=779)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_extracted_features.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "7849cf2a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-09-04T09:20:15.632256Z",
     "start_time": "2022-09-04T09:20:14.411619Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/joao/miniforge3/envs/tutorial-tsfresh/lib/python3.8/site-packages/tsfresh/utilities/dataframe_functions.py:467: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df[\"sort\"] = range(df.shape[0])\n",
      "Rolling: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 15/15 [00:01<00:00, 13.80it/s]\n",
      "/Users/joao/miniforge3/envs/tutorial-tsfresh/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:7: 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 (to_datetime, Int64Index, DatetimeIndex, Period,\n",
      "/Users/joao/miniforge3/envs/tutorial-tsfresh/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:7: FutureWarning: pandas.Float64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "  from pandas import (to_datetime, Int64Index, DatetimeIndex, Period,\n",
      "/Users/joao/miniforge3/envs/tutorial-tsfresh/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:7: 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 (to_datetime, Int64Index, DatetimeIndex, Period,\n",
      "/Users/joao/miniforge3/envs/tutorial-tsfresh/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:7: FutureWarning: pandas.Float64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "  from pandas import (to_datetime, Int64Index, DatetimeIndex, Period,\n",
      "/Users/joao/miniforge3/envs/tutorial-tsfresh/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:7: 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 (to_datetime, Int64Index, DatetimeIndex, Period,\n",
      "/Users/joao/miniforge3/envs/tutorial-tsfresh/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:7: FutureWarning: pandas.Float64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "  from pandas import (to_datetime, Int64Index, DatetimeIndex, Period,\n"
     ]
    }
   ],
   "source": [
    "df_rolled = roll_time_series(df_small[[\"time\", \"hr\", \"act\", \"pid\", \"sleep_phase\"]], \n",
    "                             column_id=\"pid\", \n",
    "                             min_timeshift=0,  # it actually uses this number +1\n",
    "                             max_timeshift=2,  # it also uses this number +1\n",
    "                             n_jobs=3, show_warnings=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "4f582311",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-09-04T09:20:20.226449Z",
     "start_time": "2022-09-04T09:20:20.209862Z"
    }
   },
   "outputs": [
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       "</div>"
      ],
      "text/plain": [
       "     time    hr  act  pid  sleep_phase  sort      id\n",
       "372    29  66.0  1.0    1          0.0     0  (1, 0)\n",
       "374    29  66.0  1.0    1          0.0     0  (1, 1)\n",
       "375    59  67.0  0.0    1          0.0     1  (1, 1)\n",
       "378    29  66.0  1.0    1          0.0     0  (1, 2)\n",
       "379    59  67.0  0.0    1          0.0     1  (1, 2)\n",
       "380    89  66.0  0.0    1          1.0     2  (1, 2)\n",
       "384    59  67.0  0.0    1          0.0     1  (1, 3)\n",
       "385    89  66.0  0.0    1          1.0     2  (1, 3)\n",
       "386   119  67.0  1.0    1          1.0     3  (1, 3)\n",
       "390    89  66.0  0.0    1          1.0     2  (1, 4)\n",
       "391   119  67.0  1.0    1          1.0     3  (1, 4)\n",
       "392   149  64.0  1.0    1          1.0     4  (1, 4)\n",
       "396   119  67.0  1.0    1          1.0     3  (1, 5)\n",
       "397   149  64.0  1.0    1          1.0     4  (1, 5)\n",
       "398   179  66.0  0.0    1          2.0     5  (1, 5)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_rolled.head(15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "961decee",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-09-04T09:20:30.501201Z",
     "start_time": "2022-09-04T09:20:29.322319Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/joao/miniforge3/envs/tutorial-tsfresh/lib/python3.8/site-packages/tsfresh/utilities/dataframe_functions.py:467: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df[\"sort\"] = range(df.shape[0])\n",
      "Rolling: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 15/15 [00:01<00:00, 13.78it/s]\n",
      "/Users/joao/miniforge3/envs/tutorial-tsfresh/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:7: 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 (to_datetime, Int64Index, DatetimeIndex, Period,\n",
      "/Users/joao/miniforge3/envs/tutorial-tsfresh/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:7: FutureWarning: pandas.Float64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "  from pandas import (to_datetime, Int64Index, DatetimeIndex, Period,\n",
      "/Users/joao/miniforge3/envs/tutorial-tsfresh/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:7: 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 (to_datetime, Int64Index, DatetimeIndex, Period,\n",
      "/Users/joao/miniforge3/envs/tutorial-tsfresh/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:7: FutureWarning: pandas.Float64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "  from pandas import (to_datetime, Int64Index, DatetimeIndex, Period,\n",
      "/Users/joao/miniforge3/envs/tutorial-tsfresh/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:7: 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 (to_datetime, Int64Index, DatetimeIndex, Period,\n",
      "/Users/joao/miniforge3/envs/tutorial-tsfresh/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:7: FutureWarning: pandas.Float64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "  from pandas import (to_datetime, Int64Index, DatetimeIndex, Period,\n"
     ]
    }
   ],
   "source": [
    "# What win size should we use? 11 = 5 minutes and 30 seconds\n",
    "df_rolled = roll_time_series(df_small[[\"time\", \"hr\", \"act\", \"pid\", \"sleep_phase\"]], \n",
    "                             column_id=\"pid\", \n",
    "                             min_timeshift=0, \n",
    "                             max_timeshift=10,\n",
    "                             n_jobs=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "2b75a862",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-09-04T09:20:33.837587Z",
     "start_time": "2022-09-04T09:20:33.801372Z"
    }
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       "    <tr>\n",
       "      <th>10251</th>\n",
       "      <td>15989</td>\n",
       "      <td>68.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1457</td>\n",
       "      <td>(16, 532)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        time    hr   act  pid  sleep_phase  sort         id\n",
       "10226  15869  95.0   4.0   16          1.0  1453  (16, 531)\n",
       "10227  15899  61.0  22.0   16          0.0  1454  (16, 531)\n",
       "10228  15929  73.0  15.0   16          0.0  1455  (16, 531)\n",
       "10229  15959  81.0  16.0   16          1.0  1456  (16, 531)\n",
       "10241  15689  50.0  18.0   16          2.0  1447  (16, 532)\n",
       "10242  15719  76.0   0.0   16          1.0  1448  (16, 532)\n",
       "10243  15749  80.0   6.0   16          1.0  1449  (16, 532)\n",
       "10244  15779  55.0  10.0   16          1.0  1450  (16, 532)\n",
       "10245  15809  54.0   8.0   16          0.0  1451  (16, 532)\n",
       "10246  15839  74.0   0.0   16          1.0  1452  (16, 532)\n",
       "10247  15869  95.0   4.0   16          1.0  1453  (16, 532)\n",
       "10248  15899  61.0  22.0   16          0.0  1454  (16, 532)\n",
       "10249  15929  73.0  15.0   16          0.0  1455  (16, 532)\n",
       "10250  15959  81.0  16.0   16          1.0  1456  (16, 532)\n",
       "10251  15989  68.0   0.0   16          1.0  1457  (16, 532)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_rolled.tail(15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "4796c9af",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-09-04T09:20:42.822337Z",
     "start_time": "2022-09-04T09:20:41.982685Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Feature Extraction: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 15/15 [00:00<00:00, 15.87it/s]\n",
      "/Users/joao/miniforge3/envs/tutorial-tsfresh/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:7: 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 (to_datetime, Int64Index, DatetimeIndex, Period,\n",
      "/Users/joao/miniforge3/envs/tutorial-tsfresh/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:7: FutureWarning: pandas.Float64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "  from pandas import (to_datetime, Int64Index, DatetimeIndex, Period,\n",
      "/Users/joao/miniforge3/envs/tutorial-tsfresh/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:7: 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 (to_datetime, Int64Index, DatetimeIndex, Period,\n",
      "/Users/joao/miniforge3/envs/tutorial-tsfresh/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:7: FutureWarning: pandas.Float64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "  from pandas import (to_datetime, Int64Index, DatetimeIndex, Period,\n",
      "/Users/joao/miniforge3/envs/tutorial-tsfresh/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:7: 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 (to_datetime, Int64Index, DatetimeIndex, Period,\n",
      "/Users/joao/miniforge3/envs/tutorial-tsfresh/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:7: FutureWarning: pandas.Float64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "  from pandas import (to_datetime, Int64Index, DatetimeIndex, Period,\n"
     ]
    }
   ],
   "source": [
    "df_extracted_features = tsfresh.extract_features(df_rolled[[\"id\", \"time\", \"act\", \"hr\"]], \n",
    "                                                 column_id=\"id\",\n",
    "                                                 column_sort=\"time\",\n",
    "                                                 #\n",
    "                                                 # In order to be quick, we are just extracting the Minimal \n",
    "                                                 # Parameters (Min, max, mean, std...)\n",
    "                                                 default_fc_parameters=MinimalFCParameters(),\n",
    "                                                 n_jobs=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "c3914fab",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>act__sum_values</th>\n",
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       "      <td>11.0</td>\n",
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      "text/plain": [
       "        act__sum_values  act__median  act__mean  act__length  \\\n",
       "16 528            131.0          8.0  11.909091         11.0   \n",
       "   529            144.0          8.0  13.090909         11.0   \n",
       "   530            159.0          8.0  14.454545         11.0   \n",
       "   531            107.0          8.0   9.727273         11.0   \n",
       "   532             99.0          8.0   9.000000         11.0   \n",
       "\n",
       "        act__standard_deviation  act__variance  act__maximum  act__minimum  \\\n",
       "16 528                18.461038     340.809917          68.0           0.0   \n",
       "   529                18.652100     347.900826          68.0           0.0   \n",
       "   530                18.187726     330.793388          68.0           0.0   \n",
       "   531                 6.929396      48.016529          22.0           0.0   \n",
       "   532                 7.471157      55.818182          22.0           0.0   \n",
       "\n",
       "        hr__sum_values  hr__median   hr__mean  hr__length  \\\n",
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       "   532           767.0        73.0  69.727273        11.0   \n",
       "\n",
       "        hr__standard_deviation  hr__variance  hr__maximum  hr__minimum  \n",
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       "   532               13.046009    170.198347         95.0         50.0  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_extracted_features.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "012946af",
   "metadata": {},
   "source": [
    "Note that the index used in df_extracted_features are the \"pid\" and \"time\".\n",
    "\n",
    "We can get this index back to our dataframe with `df.reset_index()`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "6f338fbd",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-09-04T09:20:43.645166Z",
     "start_time": "2022-09-04T09:20:43.623619Z"
    }
   },
   "outputs": [
    {
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       "      <th>1455</th>\n",
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       "      <td>530</td>\n",
       "      <td>159.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>14.454545</td>\n",
       "      <td>11.0</td>\n",
       "      <td>18.187726</td>\n",
       "      <td>330.793388</td>\n",
       "      <td>68.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>755.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>68.636364</td>\n",
       "      <td>11.0</td>\n",
       "      <td>13.988779</td>\n",
       "      <td>195.685950</td>\n",
       "      <td>95.0</td>\n",
       "      <td>50.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1456</th>\n",
       "      <td>16</td>\n",
       "      <td>531</td>\n",
       "      <td>107.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>9.727273</td>\n",
       "      <td>11.0</td>\n",
       "      <td>6.929396</td>\n",
       "      <td>48.016529</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>753.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>68.454545</td>\n",
       "      <td>11.0</td>\n",
       "      <td>13.812798</td>\n",
       "      <td>190.793388</td>\n",
       "      <td>95.0</td>\n",
       "      <td>50.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1457</th>\n",
       "      <td>16</td>\n",
       "      <td>532</td>\n",
       "      <td>99.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>11.0</td>\n",
       "      <td>7.471157</td>\n",
       "      <td>55.818182</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>767.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>69.727273</td>\n",
       "      <td>11.0</td>\n",
       "      <td>13.046009</td>\n",
       "      <td>170.198347</td>\n",
       "      <td>95.0</td>\n",
       "      <td>50.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1458 rows × 18 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      pid  time  act__sum_values  act__median  act__mean  act__length  \\\n",
       "0       1     0              1.0          1.0   1.000000          1.0   \n",
       "1       1     1              1.0          0.5   0.500000          2.0   \n",
       "2       1     2              1.0          0.0   0.333333          3.0   \n",
       "3       1     3              2.0          0.5   0.500000          4.0   \n",
       "4       1     4              3.0          1.0   0.600000          5.0   \n",
       "...   ...   ...              ...          ...        ...          ...   \n",
       "1453   16   528            131.0          8.0  11.909091         11.0   \n",
       "1454   16   529            144.0          8.0  13.090909         11.0   \n",
       "1455   16   530            159.0          8.0  14.454545         11.0   \n",
       "1456   16   531            107.0          8.0   9.727273         11.0   \n",
       "1457   16   532             99.0          8.0   9.000000         11.0   \n",
       "\n",
       "      act__standard_deviation  act__variance  act__maximum  act__minimum  \\\n",
       "0                    0.000000       0.000000           1.0           1.0   \n",
       "1                    0.500000       0.250000           1.0           0.0   \n",
       "2                    0.471405       0.222222           1.0           0.0   \n",
       "3                    0.500000       0.250000           1.0           0.0   \n",
       "4                    0.489898       0.240000           1.0           0.0   \n",
       "...                       ...            ...           ...           ...   \n",
       "1453                18.461038     340.809917          68.0           0.0   \n",
       "1454                18.652100     347.900826          68.0           0.0   \n",
       "1455                18.187726     330.793388          68.0           0.0   \n",
       "1456                 6.929396      48.016529          22.0           0.0   \n",
       "1457                 7.471157      55.818182          22.0           0.0   \n",
       "\n",
       "      hr__sum_values  hr__median   hr__mean  hr__length  \\\n",
       "0               66.0        66.0  66.000000         1.0   \n",
       "1              133.0        66.5  66.500000         2.0   \n",
       "2              199.0        66.0  66.333333         3.0   \n",
       "3              266.0        66.5  66.500000         4.0   \n",
       "4              330.0        66.0  66.000000         5.0   \n",
       "...              ...         ...        ...         ...   \n",
       "1453           754.0        74.0  68.545455        11.0   \n",
       "1454           764.0        74.0  69.454545        11.0   \n",
       "1455           755.0        73.0  68.636364        11.0   \n",
       "1456           753.0        73.0  68.454545        11.0   \n",
       "1457           767.0        73.0  69.727273        11.0   \n",
       "\n",
       "      hr__standard_deviation  hr__variance  hr__maximum  hr__minimum  \n",
       "0                   0.000000      0.000000         66.0         66.0  \n",
       "1                   0.500000      0.250000         67.0         66.0  \n",
       "2                   0.471405      0.222222         67.0         66.0  \n",
       "3                   0.500000      0.250000         67.0         66.0  \n",
       "4                   1.095445      1.200000         67.0         64.0  \n",
       "...                      ...           ...          ...          ...  \n",
       "1453               15.269480    233.157025         95.0         50.0  \n",
       "1454               14.474829    209.520661         95.0         50.0  \n",
       "1455               13.988779    195.685950         95.0         50.0  \n",
       "1456               13.812798    190.793388         95.0         50.0  \n",
       "1457               13.046009    170.198347         95.0         50.0  \n",
       "\n",
       "[1458 rows x 18 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_features = df_extracted_features.reset_index().rename(columns={\"level_0\":\"pid\", \"level_1\": \"time\"})\n",
    "df_features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "ec13880e",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-09-04T09:20:44.554372Z",
     "start_time": "2022-09-04T09:20:44.507646Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pid</th>\n",
       "      <th>time</th>\n",
       "      <th>act__sum_values</th>\n",
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       "      <th>hr__minimum</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <th>928</th>\n",
       "      <td>16</td>\n",
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       "      <td>754.0</td>\n",
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       "      <td>11.0</td>\n",
       "      <td>15.269480</td>\n",
       "      <td>233.157025</td>\n",
       "      <td>95.0</td>\n",
       "      <td>50.0</td>\n",
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       "    <tr>\n",
       "      <th>1454</th>\n",
       "      <td>16</td>\n",
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       "      <td>18.652100</td>\n",
       "      <td>347.900826</td>\n",
       "      <td>68.0</td>\n",
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       "      <td>14.474829</td>\n",
       "      <td>209.520661</td>\n",
       "      <td>95.0</td>\n",
       "      <td>50.0</td>\n",
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       "    <tr>\n",
       "      <th>1455</th>\n",
       "      <td>16</td>\n",
       "      <td>530</td>\n",
       "      <td>159.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>14.454545</td>\n",
       "      <td>11.0</td>\n",
       "      <td>18.187726</td>\n",
       "      <td>330.793388</td>\n",
       "      <td>68.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>73.0</td>\n",
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       "      <td>95.0</td>\n",
       "      <td>50.0</td>\n",
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       "    <tr>\n",
       "      <th>1456</th>\n",
       "      <td>16</td>\n",
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       "      <td>190.793388</td>\n",
       "      <td>95.0</td>\n",
       "      <td>50.0</td>\n",
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       "    <tr>\n",
       "      <th>1457</th>\n",
       "      <td>16</td>\n",
       "      <td>532</td>\n",
       "      <td>99.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>11.0</td>\n",
       "      <td>7.471157</td>\n",
       "      <td>55.818182</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>767.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>69.727273</td>\n",
       "      <td>11.0</td>\n",
       "      <td>13.046009</td>\n",
       "      <td>170.198347</td>\n",
       "      <td>95.0</td>\n",
       "      <td>50.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>533 rows × 18 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      pid  time  act__sum_values  act__median  act__mean  act__length  \\\n",
       "925    16     0              0.0          0.0   0.000000          1.0   \n",
       "926    16     1              0.0          0.0   0.000000          2.0   \n",
       "927    16     2              0.0          0.0   0.000000          3.0   \n",
       "928    16     3              1.0          0.0   0.250000          4.0   \n",
       "929    16     4              1.0          0.0   0.200000          5.0   \n",
       "...   ...   ...              ...          ...        ...          ...   \n",
       "1453   16   528            131.0          8.0  11.909091         11.0   \n",
       "1454   16   529            144.0          8.0  13.090909         11.0   \n",
       "1455   16   530            159.0          8.0  14.454545         11.0   \n",
       "1456   16   531            107.0          8.0   9.727273         11.0   \n",
       "1457   16   532             99.0          8.0   9.000000         11.0   \n",
       "\n",
       "      act__standard_deviation  act__variance  act__maximum  act__minimum  \\\n",
       "925                  0.000000       0.000000           0.0           0.0   \n",
       "926                  0.000000       0.000000           0.0           0.0   \n",
       "927                  0.000000       0.000000           0.0           0.0   \n",
       "928                  0.433013       0.187500           1.0           0.0   \n",
       "929                  0.400000       0.160000           1.0           0.0   \n",
       "...                       ...            ...           ...           ...   \n",
       "1453                18.461038     340.809917          68.0           0.0   \n",
       "1454                18.652100     347.900826          68.0           0.0   \n",
       "1455                18.187726     330.793388          68.0           0.0   \n",
       "1456                 6.929396      48.016529          22.0           0.0   \n",
       "1457                 7.471157      55.818182          22.0           0.0   \n",
       "\n",
       "      hr__sum_values  hr__median   hr__mean  hr__length  \\\n",
       "925             48.0        48.0  48.000000         1.0   \n",
       "926            100.0        50.0  50.000000         2.0   \n",
       "927            156.0        52.0  52.000000         3.0   \n",
       "928            206.0        51.0  51.500000         4.0   \n",
       "929            255.0        50.0  51.000000         5.0   \n",
       "...              ...         ...        ...         ...   \n",
       "1453           754.0        74.0  68.545455        11.0   \n",
       "1454           764.0        74.0  69.454545        11.0   \n",
       "1455           755.0        73.0  68.636364        11.0   \n",
       "1456           753.0        73.0  68.454545        11.0   \n",
       "1457           767.0        73.0  69.727273        11.0   \n",
       "\n",
       "      hr__standard_deviation  hr__variance  hr__maximum  hr__minimum  \n",
       "925                 0.000000      0.000000         48.0         48.0  \n",
       "926                 2.000000      4.000000         52.0         48.0  \n",
       "927                 3.265986     10.666667         56.0         48.0  \n",
       "928                 2.958040      8.750000         56.0         48.0  \n",
       "929                 2.828427      8.000000         56.0         48.0  \n",
       "...                      ...           ...          ...          ...  \n",
       "1453               15.269480    233.157025         95.0         50.0  \n",
       "1454               14.474829    209.520661         95.0         50.0  \n",
       "1455               13.988779    195.685950         95.0         50.0  \n",
       "1456               13.812798    190.793388         95.0         50.0  \n",
       "1457               13.046009    170.198347         95.0         50.0  \n",
       "\n",
       "[533 rows x 18 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_features[df_features[\"pid\"] == 16]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "0b93bab5",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-09-04T09:20:47.948783Z",
     "start_time": "2022-09-04T09:20:47.924464Z"
    }
   },
   "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>sleep_phase</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>484118</th>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>484148</th>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>484178</th>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>484208</th>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>484238</th>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>499958</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>499988</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>500018</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>500048</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>500078</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>533 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        sleep_phase\n",
       "484118          2.0\n",
       "484148          2.0\n",
       "484178          2.0\n",
       "484208          2.0\n",
       "484238          2.0\n",
       "...             ...\n",
       "499958          1.0\n",
       "499988          0.0\n",
       "500018          0.0\n",
       "500048          1.0\n",
       "500078          1.0\n",
       "\n",
       "[533 rows x 1 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Do we have the same number of rows?\n",
    "df_small[df_small[\"pid\"] == 16][[\"sleep_phase\"]]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bdf3d3f2",
   "metadata": {},
   "source": [
    "# Let's do it for the whole dataset\n",
    "\n",
    "Here I am dropping NAs and simplifying the use of HR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "072989ee",
   "metadata": {},
   "outputs": [],
   "source": [
    "WINSIZE = 11\n",
    "df = df.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "d68da717",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-09-04T13:16:52.871441Z",
     "start_time": "2022-09-04T13:16:52.841602Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Before, our dataframe had 102859. Now the new dataframe has 1125949 rows.\n"
     ]
    },
    {
     "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>time</th>\n",
       "      <th>hr</th>\n",
       "      <th>act</th>\n",
       "      <th>pid</th>\n",
       "      <th>sleep_phase</th>\n",
       "      <th>sort</th>\n",
       "      <th>id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>29</td>\n",
       "      <td>71.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>(0, 0)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>29</td>\n",
       "      <td>71.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>(0, 1)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>59</td>\n",
       "      <td>76.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>(0, 1)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>300</th>\n",
       "      <td>29</td>\n",
       "      <td>71.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>(0, 2)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>301</th>\n",
       "      <td>59</td>\n",
       "      <td>76.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>(0, 2)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     time    hr  act  pid  sleep_phase  sort      id\n",
       "0      29  71.0  2.0    0          0.0     0  (0, 0)\n",
       "100    29  71.0  2.0    0          0.0     0  (0, 1)\n",
       "101    59  76.0  0.0    0          0.0     1  (0, 1)\n",
       "300    29  71.0  2.0    0          0.0     0  (0, 2)\n",
       "301    59  76.0  0.0    0          0.0     1  (0, 2)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(f\"Before, our dataframe had {df.shape[0]}. Now the new dataframe has {df_rolled.shape[0]} rows.\")\n",
    "df_rolled.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3e07cb42",
   "metadata": {},
   "source": [
    "<hr>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "34a15323",
   "metadata": {},
   "source": [
    "# The first feature set - TSFresh Features \n",
    "\n",
    "Now lets get more advanced features using TSFresh"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "ec78939c",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-28T18:47:37.720283Z",
     "start_time": "2022-08-28T18:47:33.229983Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Rolling: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 25/25 [00:01<00:00, 21.55it/s]\n",
      "/Users/joao/miniforge3/envs/tutorial-tsfresh/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:7: 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 (to_datetime, Int64Index, DatetimeIndex, Period,\n",
      "/Users/joao/miniforge3/envs/tutorial-tsfresh/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:7: FutureWarning: pandas.Float64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "  from pandas import (to_datetime, Int64Index, DatetimeIndex, Period,\n",
      "/Users/joao/miniforge3/envs/tutorial-tsfresh/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:7: 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 (to_datetime, Int64Index, DatetimeIndex, Period,\n",
      "/Users/joao/miniforge3/envs/tutorial-tsfresh/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:7: FutureWarning: pandas.Float64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "  from pandas import (to_datetime, Int64Index, DatetimeIndex, Period,\n",
      "/Users/joao/miniforge3/envs/tutorial-tsfresh/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:7: 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 (to_datetime, Int64Index, DatetimeIndex, Period,\n",
      "/Users/joao/miniforge3/envs/tutorial-tsfresh/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:7: FutureWarning: pandas.Float64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "  from pandas import (to_datetime, Int64Index, DatetimeIndex, Period,\n",
      "/Users/joao/miniforge3/envs/tutorial-tsfresh/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:7: 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 (to_datetime, Int64Index, DatetimeIndex, Period,\n",
      "/Users/joao/miniforge3/envs/tutorial-tsfresh/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:7: FutureWarning: pandas.Float64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "  from pandas import (to_datetime, Int64Index, DatetimeIndex, Period,\n",
      "/Users/joao/miniforge3/envs/tutorial-tsfresh/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:7: 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 (to_datetime, Int64Index, DatetimeIndex, Period,\n",
      "/Users/joao/miniforge3/envs/tutorial-tsfresh/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:7: FutureWarning: pandas.Float64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "  from pandas import (to_datetime, Int64Index, DatetimeIndex, Period,\n",
      "Feature Extraction: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 25/25 [00:16<00:00,  1.53it/s]\n",
      "/Users/joao/miniforge3/envs/tutorial-tsfresh/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:7: 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 (to_datetime, Int64Index, DatetimeIndex, Period,\n",
      "/Users/joao/miniforge3/envs/tutorial-tsfresh/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:7: FutureWarning: pandas.Float64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "  from pandas import (to_datetime, Int64Index, DatetimeIndex, Period,\n",
      "/Users/joao/miniforge3/envs/tutorial-tsfresh/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:7: 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 (to_datetime, Int64Index, DatetimeIndex, Period,\n",
      "/Users/joao/miniforge3/envs/tutorial-tsfresh/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:7: FutureWarning: pandas.Float64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "  from pandas import (to_datetime, Int64Index, DatetimeIndex, Period,\n",
      "/Users/joao/miniforge3/envs/tutorial-tsfresh/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:7: 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 (to_datetime, Int64Index, DatetimeIndex, Period,\n",
      "/Users/joao/miniforge3/envs/tutorial-tsfresh/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:7: FutureWarning: pandas.Float64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "  from pandas import (to_datetime, Int64Index, DatetimeIndex, Period,\n",
      "/Users/joao/miniforge3/envs/tutorial-tsfresh/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:7: 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 (to_datetime, Int64Index, DatetimeIndex, Period,\n",
      "/Users/joao/miniforge3/envs/tutorial-tsfresh/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:7: FutureWarning: pandas.Float64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "  from pandas import (to_datetime, Int64Index, DatetimeIndex, Period,\n",
      "/Users/joao/miniforge3/envs/tutorial-tsfresh/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:7: 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 (to_datetime, Int64Index, DatetimeIndex, Period,\n",
      "/Users/joao/miniforge3/envs/tutorial-tsfresh/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:7: FutureWarning: pandas.Float64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "  from pandas import (to_datetime, Int64Index, DatetimeIndex, Period,\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 4.3 s, sys: 962 ms, total: 5.26 s\n",
      "Wall time: 20.3 s\n"
     ]
    }
   ],
   "source": [
    "%%time \n",
    "# Let's start experimenting with a few subjects, before moving to the whole dataset:\n",
    "\n",
    "# df_small has only 3 subjects\n",
    "df_small = df[df[\"pid\"].isin(df[\"pid\"].unique()[:3])].dropna()\n",
    "\n",
    "\n",
    "\n",
    "# Roll the dataset\n",
    "df_rolled = roll_time_series(df_small[[\"time\", \"hr\", \"act\", \"pid\", \"sleep_phase\"]].copy(), \n",
    "                             column_id=\"pid\", \n",
    "                             min_timeshift=0,\n",
    "                             max_timeshift=WINSIZE-1,\n",
    "                             n_jobs=5)\n",
    "\n",
    "\n",
    "# Get the \"subset\" of features that are \"efficient\" to calculate\n",
    "settings = EfficientFCParameters()\n",
    "del settings[\"friedrich_coefficients\"]\n",
    "del settings[\"max_langevin_fixed_point\"]\n",
    "\n",
    "df_extracted_features = tsfresh.extract_features(df_rolled[[\"id\", \"sort\", \"act\", \"hr\"]], \n",
    "                                                 column_id=\"id\",\n",
    "                                                 column_sort=\"sort\",\n",
    "                                                 default_fc_parameters=settings,\n",
    "                                                 n_jobs=5)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "9aeac8a0",
   "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>act__variance_larger_than_standard_deviation</th>\n",
       "      <th>act__has_duplicate_max</th>\n",
       "      <th>act__has_duplicate_min</th>\n",
       "      <th>act__has_duplicate</th>\n",
       "      <th>act__sum_values</th>\n",
       "      <th>act__abs_energy</th>\n",
       "      <th>act__mean_abs_change</th>\n",
       "      <th>act__mean_change</th>\n",
       "      <th>act__mean_second_derivative_central</th>\n",
       "      <th>act__median</th>\n",
       "      <th>...</th>\n",
       "      <th>hr__fourier_entropy__bins_2</th>\n",
       "      <th>hr__fourier_entropy__bins_3</th>\n",
       "      <th>hr__fourier_entropy__bins_5</th>\n",
       "      <th>hr__fourier_entropy__bins_10</th>\n",
       "      <th>hr__fourier_entropy__bins_100</th>\n",
       "      <th>hr__permutation_entropy__dimension_3__tau_1</th>\n",
       "      <th>hr__permutation_entropy__dimension_4__tau_1</th>\n",
       "      <th>hr__permutation_entropy__dimension_5__tau_1</th>\n",
       "      <th>hr__permutation_entropy__dimension_6__tau_1</th>\n",
       "      <th>hr__permutation_entropy__dimension_7__tau_1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">2</th>\n",
       "      <th>1070</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>255.0</td>\n",
       "      <td>60567.0</td>\n",
       "      <td>49.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.450561</td>\n",
       "      <td>0.867563</td>\n",
       "      <td>0.867563</td>\n",
       "      <td>1.242453</td>\n",
       "      <td>1.791759</td>\n",
       "      <td>1.676988</td>\n",
       "      <td>1.906155</td>\n",
       "      <td>1.94591</td>\n",
       "      <td>1.791759</td>\n",
       "      <td>1.609438</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1071</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>255.0</td>\n",
       "      <td>60567.0</td>\n",
       "      <td>49.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.636514</td>\n",
       "      <td>0.867563</td>\n",
       "      <td>0.867563</td>\n",
       "      <td>0.867563</td>\n",
       "      <td>1.791759</td>\n",
       "      <td>1.676988</td>\n",
       "      <td>1.906155</td>\n",
       "      <td>1.94591</td>\n",
       "      <td>1.791759</td>\n",
       "      <td>1.609438</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1072</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>255.0</td>\n",
       "      <td>60567.0</td>\n",
       "      <td>49.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.388889</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.636514</td>\n",
       "      <td>0.636514</td>\n",
       "      <td>0.636514</td>\n",
       "      <td>0.636514</td>\n",
       "      <td>1.329661</td>\n",
       "      <td>1.464816</td>\n",
       "      <td>1.906155</td>\n",
       "      <td>1.94591</td>\n",
       "      <td>1.791759</td>\n",
       "      <td>1.609438</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1073</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>256.0</td>\n",
       "      <td>60568.0</td>\n",
       "      <td>48.8</td>\n",
       "      <td>-0.6</td>\n",
       "      <td>-13.222222</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.450561</td>\n",
       "      <td>0.867563</td>\n",
       "      <td>0.867563</td>\n",
       "      <td>1.242453</td>\n",
       "      <td>1.791759</td>\n",
       "      <td>1.676988</td>\n",
       "      <td>2.079442</td>\n",
       "      <td>1.94591</td>\n",
       "      <td>1.791759</td>\n",
       "      <td>1.609438</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1074</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>251.0</td>\n",
       "      <td>60523.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>-24.4</td>\n",
       "      <td>13.722222</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.450561</td>\n",
       "      <td>0.867563</td>\n",
       "      <td>1.011404</td>\n",
       "      <td>1.560710</td>\n",
       "      <td>1.791759</td>\n",
       "      <td>1.735126</td>\n",
       "      <td>2.079442</td>\n",
       "      <td>1.94591</td>\n",
       "      <td>1.791759</td>\n",
       "      <td>1.609438</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 1536 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        act__variance_larger_than_standard_deviation  act__has_duplicate_max  \\\n",
       "2 1070                                           1.0                     0.0   \n",
       "  1071                                           1.0                     0.0   \n",
       "  1072                                           1.0                     0.0   \n",
       "  1073                                           1.0                     0.0   \n",
       "  1074                                           1.0                     0.0   \n",
       "\n",
       "        act__has_duplicate_min  act__has_duplicate  act__sum_values  \\\n",
       "2 1070                     1.0                 1.0            255.0   \n",
       "  1071                     1.0                 1.0            255.0   \n",
       "  1072                     1.0                 1.0            255.0   \n",
       "  1073                     1.0                 1.0            256.0   \n",
       "  1074                     1.0                 1.0            251.0   \n",
       "\n",
       "        act__abs_energy  act__mean_abs_change  act__mean_change  \\\n",
       "2 1070          60567.0                  49.4               0.0   \n",
       "  1071          60567.0                  49.4               0.0   \n",
       "  1072          60567.0                  49.4               0.0   \n",
       "  1073          60568.0                  48.8              -0.6   \n",
       "  1074          60523.0                  25.0             -24.4   \n",
       "\n",
       "        act__mean_second_derivative_central  act__median  ...  \\\n",
       "2 1070                             0.000000          0.0  ...   \n",
       "  1071                             0.000000          0.0  ...   \n",
       "  1072                            -0.388889          0.0  ...   \n",
       "  1073                           -13.222222          0.0  ...   \n",
       "  1074                            13.722222          0.0  ...   \n",
       "\n",
       "        hr__fourier_entropy__bins_2  hr__fourier_entropy__bins_3  \\\n",
       "2 1070                     0.450561                     0.867563   \n",
       "  1071                     0.636514                     0.867563   \n",
       "  1072                     0.636514                     0.636514   \n",
       "  1073                     0.450561                     0.867563   \n",
       "  1074                     0.450561                     0.867563   \n",
       "\n",
       "        hr__fourier_entropy__bins_5  hr__fourier_entropy__bins_10  \\\n",
       "2 1070                     0.867563                      1.242453   \n",
       "  1071                     0.867563                      0.867563   \n",
       "  1072                     0.636514                      0.636514   \n",
       "  1073                     0.867563                      1.242453   \n",
       "  1074                     1.011404                      1.560710   \n",
       "\n",
       "        hr__fourier_entropy__bins_100  \\\n",
       "2 1070                       1.791759   \n",
       "  1071                       1.791759   \n",
       "  1072                       1.329661   \n",
       "  1073                       1.791759   \n",
       "  1074                       1.791759   \n",
       "\n",
       "        hr__permutation_entropy__dimension_3__tau_1  \\\n",
       "2 1070                                     1.676988   \n",
       "  1071                                     1.676988   \n",
       "  1072                                     1.464816   \n",
       "  1073                                     1.676988   \n",
       "  1074                                     1.735126   \n",
       "\n",
       "        hr__permutation_entropy__dimension_4__tau_1  \\\n",
       "2 1070                                     1.906155   \n",
       "  1071                                     1.906155   \n",
       "  1072                                     1.906155   \n",
       "  1073                                     2.079442   \n",
       "  1074                                     2.079442   \n",
       "\n",
       "        hr__permutation_entropy__dimension_5__tau_1  \\\n",
       "2 1070                                      1.94591   \n",
       "  1071                                      1.94591   \n",
       "  1072                                      1.94591   \n",
       "  1073                                      1.94591   \n",
       "  1074                                      1.94591   \n",
       "\n",
       "        hr__permutation_entropy__dimension_6__tau_1  \\\n",
       "2 1070                                     1.791759   \n",
       "  1071                                     1.791759   \n",
       "  1072                                     1.791759   \n",
       "  1073                                     1.791759   \n",
       "  1074                                     1.791759   \n",
       "\n",
       "        hr__permutation_entropy__dimension_7__tau_1  \n",
       "2 1070                                     1.609438  \n",
       "  1071                                     1.609438  \n",
       "  1072                                     1.609438  \n",
       "  1073                                     1.609438  \n",
       "  1074                                     1.609438  \n",
       "\n",
       "[5 rows x 1536 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_extracted_features.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "2ccb218e",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-29T07:35:08.628511Z",
     "start_time": "2022-08-29T05:50:19.978516Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/joao/miniforge3/envs/tutorial-tsfresh/lib/python3.8/site-packages/tsfresh/utilities/dataframe_functions.py:467: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df[\"sort\"] = range(df.shape[0])\n",
      "Rolling: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 35/35 [00:06<00:00,  5.43it/s]\n",
      "/Users/joao/miniforge3/envs/tutorial-tsfresh/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:7: 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 (to_datetime, Int64Index, DatetimeIndex, Period,\n",
      "/Users/joao/miniforge3/envs/tutorial-tsfresh/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:7: FutureWarning: pandas.Float64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "  from pandas import (to_datetime, Int64Index, DatetimeIndex, Period,\n",
      "/Users/joao/miniforge3/envs/tutorial-tsfresh/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:7: 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 (to_datetime, Int64Index, DatetimeIndex, Period,\n",
      "/Users/joao/miniforge3/envs/tutorial-tsfresh/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:7: FutureWarning: pandas.Float64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "  from pandas import (to_datetime, Int64Index, DatetimeIndex, Period,\n",
      "/Users/joao/miniforge3/envs/tutorial-tsfresh/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:7: 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 (to_datetime, Int64Index, DatetimeIndex, Period,\n",
      "/Users/joao/miniforge3/envs/tutorial-tsfresh/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:7: FutureWarning: pandas.Float64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "  from pandas import (to_datetime, Int64Index, DatetimeIndex, Period,\n",
      "/Users/joao/miniforge3/envs/tutorial-tsfresh/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:7: 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 (to_datetime, Int64Index, DatetimeIndex, Period,\n",
      "/Users/joao/miniforge3/envs/tutorial-tsfresh/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:7: FutureWarning: pandas.Float64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "  from pandas import (to_datetime, Int64Index, DatetimeIndex, Period,\n",
      "/Users/joao/miniforge3/envs/tutorial-tsfresh/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:7: 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 (to_datetime, Int64Index, DatetimeIndex, Period,\n",
      "/Users/joao/miniforge3/envs/tutorial-tsfresh/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:7: FutureWarning: pandas.Float64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "  from pandas import (to_datetime, Int64Index, DatetimeIndex, Period,\n",
      "/Users/joao/miniforge3/envs/tutorial-tsfresh/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:7: 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 (to_datetime, Int64Index, DatetimeIndex, Period,\n",
      "/Users/joao/miniforge3/envs/tutorial-tsfresh/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:7: FutureWarning: pandas.Float64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "  from pandas import (to_datetime, Int64Index, DatetimeIndex, Period,\n",
      "/Users/joao/miniforge3/envs/tutorial-tsfresh/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:7: 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 (to_datetime, Int64Index, DatetimeIndex, Period,\n",
      "/Users/joao/miniforge3/envs/tutorial-tsfresh/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:7: FutureWarning: pandas.Float64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "  from pandas import (to_datetime, Int64Index, DatetimeIndex, Period,\n",
      "Feature Extraction: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 35/35 [00:34<00:00,  1.00it/s]\n",
      "/Users/joao/miniforge3/envs/tutorial-tsfresh/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:7: 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 (to_datetime, Int64Index, DatetimeIndex, Period,\n",
      "/Users/joao/miniforge3/envs/tutorial-tsfresh/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:7: FutureWarning: pandas.Float64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "  from pandas import (to_datetime, Int64Index, DatetimeIndex, Period,\n",
      "/Users/joao/miniforge3/envs/tutorial-tsfresh/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:7: 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 (to_datetime, Int64Index, DatetimeIndex, Period,\n",
      "/Users/joao/miniforge3/envs/tutorial-tsfresh/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:7: FutureWarning: pandas.Float64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "  from pandas import (to_datetime, Int64Index, DatetimeIndex, Period,\n",
      "/Users/joao/miniforge3/envs/tutorial-tsfresh/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:7: 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 (to_datetime, Int64Index, DatetimeIndex, Period,\n",
      "/Users/joao/miniforge3/envs/tutorial-tsfresh/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:7: FutureWarning: pandas.Float64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "  from pandas import (to_datetime, Int64Index, DatetimeIndex, Period,\n",
      "/Users/joao/miniforge3/envs/tutorial-tsfresh/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:7: 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 (to_datetime, Int64Index, DatetimeIndex, Period,\n",
      "/Users/joao/miniforge3/envs/tutorial-tsfresh/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:7: FutureWarning: pandas.Float64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "  from pandas import (to_datetime, Int64Index, DatetimeIndex, Period,\n",
      "/Users/joao/miniforge3/envs/tutorial-tsfresh/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:7: 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 (to_datetime, Int64Index, DatetimeIndex, Period,\n",
      "/Users/joao/miniforge3/envs/tutorial-tsfresh/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:7: FutureWarning: pandas.Float64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "  from pandas import (to_datetime, Int64Index, DatetimeIndex, Period,\n",
      "/Users/joao/miniforge3/envs/tutorial-tsfresh/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:7: 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 (to_datetime, Int64Index, DatetimeIndex, Period,\n",
      "/Users/joao/miniforge3/envs/tutorial-tsfresh/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:7: FutureWarning: pandas.Float64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "  from pandas import (to_datetime, Int64Index, DatetimeIndex, Period,\n",
      "/Users/joao/miniforge3/envs/tutorial-tsfresh/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:7: 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 (to_datetime, Int64Index, DatetimeIndex, Period,\n",
      "/Users/joao/miniforge3/envs/tutorial-tsfresh/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:7: FutureWarning: pandas.Float64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "  from pandas import (to_datetime, Int64Index, DatetimeIndex, Period,\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Feature Extraction: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 35/35 [00:38<00:00,  1.10s/it]\n",
      "/Users/joao/miniforge3/envs/tutorial-tsfresh/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:7: 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 (to_datetime, Int64Index, DatetimeIndex, Period,\n",
      "/Users/joao/miniforge3/envs/tutorial-tsfresh/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:7: FutureWarning: pandas.Float64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "  from pandas import (to_datetime, Int64Index, DatetimeIndex, Period,\n",
      "/Users/joao/miniforge3/envs/tutorial-tsfresh/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:7: 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 (to_datetime, Int64Index, DatetimeIndex, Period,\n",
      "/Users/joao/miniforge3/envs/tutorial-tsfresh/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:7: FutureWarning: pandas.Float64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "  from pandas import (to_datetime, Int64Index, DatetimeIndex, Period,\n",
      "/Users/joao/miniforge3/envs/tutorial-tsfresh/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:7: 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 (to_datetime, Int64Index, DatetimeIndex, Period,\n",
      "/Users/joao/miniforge3/envs/tutorial-tsfresh/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:7: FutureWarning: pandas.Float64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "  from pandas import (to_datetime, Int64Index, DatetimeIndex, Period,\n",
      "/Users/joao/miniforge3/envs/tutorial-tsfresh/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:7: 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 (to_datetime, Int64Index, DatetimeIndex, Period,\n",
      "/Users/joao/miniforge3/envs/tutorial-tsfresh/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:7: FutureWarning: pandas.Float64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "  from pandas import (to_datetime, Int64Index, DatetimeIndex, Period,\n",
      "/Users/joao/miniforge3/envs/tutorial-tsfresh/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:7: 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 (to_datetime, Int64Index, DatetimeIndex, Period,\n",
      "/Users/joao/miniforge3/envs/tutorial-tsfresh/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:7: FutureWarning: pandas.Float64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "  from pandas import (to_datetime, Int64Index, DatetimeIndex, Period,\n",
      "/Users/joao/miniforge3/envs/tutorial-tsfresh/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:7: 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 (to_datetime, Int64Index, DatetimeIndex, Period,\n",
      "/Users/joao/miniforge3/envs/tutorial-tsfresh/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:7: FutureWarning: pandas.Float64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "  from pandas import (to_datetime, Int64Index, DatetimeIndex, Period,\n",
      "/Users/joao/miniforge3/envs/tutorial-tsfresh/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:7: 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 (to_datetime, Int64Index, DatetimeIndex, Period,\n",
      "/Users/joao/miniforge3/envs/tutorial-tsfresh/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:7: FutureWarning: pandas.Float64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "  from pandas import (to_datetime, Int64Index, DatetimeIndex, Period,\n",
      "Feature Extraction: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 35/35 [00:38<00:00,  1.11s/it]\n",
      "/Users/joao/miniforge3/envs/tutorial-tsfresh/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:7: 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 (to_datetime, Int64Index, DatetimeIndex, Period,\n",
      "/Users/joao/miniforge3/envs/tutorial-tsfresh/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:7: FutureWarning: pandas.Float64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "  from pandas import (to_datetime, Int64Index, DatetimeIndex, Period,\n",
      "/Users/joao/miniforge3/envs/tutorial-tsfresh/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:7: 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 (to_datetime, Int64Index, DatetimeIndex, Period,\n",
      "/Users/joao/miniforge3/envs/tutorial-tsfresh/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:7: FutureWarning: pandas.Float64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "  from pandas import (to_datetime, Int64Index, DatetimeIndex, Period,\n",
      "/Users/joao/miniforge3/envs/tutorial-tsfresh/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:7: 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 (to_datetime, Int64Index, DatetimeIndex, Period,\n",
      "/Users/joao/miniforge3/envs/tutorial-tsfresh/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:7: FutureWarning: pandas.Float64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "  from pandas import (to_datetime, Int64Index, DatetimeIndex, Period,\n",
      "/Users/joao/miniforge3/envs/tutorial-tsfresh/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:7: 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 (to_datetime, Int64Index, DatetimeIndex, Period,\n",
      "/Users/joao/miniforge3/envs/tutorial-tsfresh/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:7: FutureWarning: pandas.Float64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "  from pandas import (to_datetime, Int64Index, DatetimeIndex, Period,\n",
      "/Users/joao/miniforge3/envs/tutorial-tsfresh/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:7: 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 (to_datetime, Int64Index, DatetimeIndex, Period,\n",
      "/Users/joao/miniforge3/envs/tutorial-tsfresh/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:7: FutureWarning: pandas.Float64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "  from pandas import (to_datetime, Int64Index, DatetimeIndex, Period,\n",
      "/Users/joao/miniforge3/envs/tutorial-tsfresh/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:7: 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 (to_datetime, Int64Index, DatetimeIndex, Period,\n",
      "/Users/joao/miniforge3/envs/tutorial-tsfresh/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:7: FutureWarning: pandas.Float64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "  from pandas import (to_datetime, Int64Index, DatetimeIndex, Period,\n",
      "/Users/joao/miniforge3/envs/tutorial-tsfresh/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:7: 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 (to_datetime, Int64Index, DatetimeIndex, Period,\n",
      "/Users/joao/miniforge3/envs/tutorial-tsfresh/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:7: FutureWarning: pandas.Float64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "  from pandas import (to_datetime, Int64Index, DatetimeIndex, Period,\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Feature Extraction: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 35/35 [00:42<00:00,  1.20s/it]\n",
      "/Users/joao/miniforge3/envs/tutorial-tsfresh/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:7: 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 (to_datetime, Int64Index, DatetimeIndex, Period,\n",
      "/Users/joao/miniforge3/envs/tutorial-tsfresh/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:7: FutureWarning: pandas.Float64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "  from pandas import (to_datetime, Int64Index, DatetimeIndex, Period,\n",
      "/Users/joao/miniforge3/envs/tutorial-tsfresh/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:7: 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 (to_datetime, Int64Index, DatetimeIndex, Period,\n",
      "/Users/joao/miniforge3/envs/tutorial-tsfresh/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:7: FutureWarning: pandas.Float64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "  from pandas import (to_datetime, Int64Index, DatetimeIndex, Period,\n",
      "/Users/joao/miniforge3/envs/tutorial-tsfresh/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:7: 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 (to_datetime, Int64Index, DatetimeIndex, Period,\n",
      "/Users/joao/miniforge3/envs/tutorial-tsfresh/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:7: FutureWarning: pandas.Float64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "  from pandas import (to_datetime, Int64Index, DatetimeIndex, Period,\n",
      "/Users/joao/miniforge3/envs/tutorial-tsfresh/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:7: 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 (to_datetime, Int64Index, DatetimeIndex, Period,\n",
      "/Users/joao/miniforge3/envs/tutorial-tsfresh/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:7: FutureWarning: pandas.Float64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "  from pandas import (to_datetime, Int64Index, DatetimeIndex, Period,\n",
      "/Users/joao/miniforge3/envs/tutorial-tsfresh/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:7: 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 (to_datetime, Int64Index, DatetimeIndex, Period,\n",
      "/Users/joao/miniforge3/envs/tutorial-tsfresh/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:7: FutureWarning: pandas.Float64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "  from pandas import (to_datetime, Int64Index, DatetimeIndex, Period,\n",
      "/Users/joao/miniforge3/envs/tutorial-tsfresh/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:7: 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 (to_datetime, Int64Index, DatetimeIndex, Period,\n",
      "/Users/joao/miniforge3/envs/tutorial-tsfresh/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:7: FutureWarning: pandas.Float64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "  from pandas import (to_datetime, Int64Index, DatetimeIndex, Period,\n",
      "/Users/joao/miniforge3/envs/tutorial-tsfresh/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:7: 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 (to_datetime, Int64Index, DatetimeIndex, Period,\n",
      "/Users/joao/miniforge3/envs/tutorial-tsfresh/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:7: FutureWarning: pandas.Float64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "  from pandas import (to_datetime, Int64Index, DatetimeIndex, Period,\n",
      "Feature Extraction: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 35/35 [00:40<00:00,  1.17s/it]\n",
      "/Users/joao/miniforge3/envs/tutorial-tsfresh/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:7: 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 (to_datetime, Int64Index, DatetimeIndex, Period,\n",
      "/Users/joao/miniforge3/envs/tutorial-tsfresh/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:7: FutureWarning: pandas.Float64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "  from pandas import (to_datetime, Int64Index, DatetimeIndex, Period,\n",
      "/Users/joao/miniforge3/envs/tutorial-tsfresh/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:7: 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 (to_datetime, Int64Index, DatetimeIndex, Period,\n",
      "/Users/joao/miniforge3/envs/tutorial-tsfresh/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:7: FutureWarning: pandas.Float64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "  from pandas import (to_datetime, Int64Index, DatetimeIndex, Period,\n",
      "/Users/joao/miniforge3/envs/tutorial-tsfresh/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:7: 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 (to_datetime, Int64Index, DatetimeIndex, Period,\n",
      "/Users/joao/miniforge3/envs/tutorial-tsfresh/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:7: FutureWarning: pandas.Float64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "  from pandas import (to_datetime, Int64Index, DatetimeIndex, Period,\n",
      "/Users/joao/miniforge3/envs/tutorial-tsfresh/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:7: 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 (to_datetime, Int64Index, DatetimeIndex, Period,\n",
      "/Users/joao/miniforge3/envs/tutorial-tsfresh/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:7: FutureWarning: pandas.Float64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "  from pandas import (to_datetime, Int64Index, DatetimeIndex, Period,\n",
      "/Users/joao/miniforge3/envs/tutorial-tsfresh/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:7: 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 (to_datetime, Int64Index, DatetimeIndex, Period,\n",
      "/Users/joao/miniforge3/envs/tutorial-tsfresh/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:7: FutureWarning: pandas.Float64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "  from pandas import (to_datetime, Int64Index, DatetimeIndex, Period,\n",
      "/Users/joao/miniforge3/envs/tutorial-tsfresh/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:7: 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 (to_datetime, Int64Index, DatetimeIndex, Period,\n",
      "/Users/joao/miniforge3/envs/tutorial-tsfresh/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:7: FutureWarning: pandas.Float64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "  from pandas import (to_datetime, Int64Index, DatetimeIndex, Period,\n",
      "/Users/joao/miniforge3/envs/tutorial-tsfresh/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:7: 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 (to_datetime, Int64Index, DatetimeIndex, Period,\n",
      "/Users/joao/miniforge3/envs/tutorial-tsfresh/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:7: FutureWarning: pandas.Float64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "  from pandas import (to_datetime, Int64Index, DatetimeIndex, Period,\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Feature Extraction: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 35/35 [00:37<00:00,  1.07s/it]\n",
      "/Users/joao/miniforge3/envs/tutorial-tsfresh/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:7: 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 (to_datetime, Int64Index, DatetimeIndex, Period,\n",
      "/Users/joao/miniforge3/envs/tutorial-tsfresh/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:7: FutureWarning: pandas.Float64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "  from pandas import (to_datetime, Int64Index, DatetimeIndex, Period,\n",
      "/Users/joao/miniforge3/envs/tutorial-tsfresh/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:7: 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 (to_datetime, Int64Index, DatetimeIndex, Period,\n",
      "/Users/joao/miniforge3/envs/tutorial-tsfresh/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:7: FutureWarning: pandas.Float64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "  from pandas import (to_datetime, Int64Index, DatetimeIndex, Period,\n",
      "/Users/joao/miniforge3/envs/tutorial-tsfresh/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:7: 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 (to_datetime, Int64Index, DatetimeIndex, Period,\n",
      "/Users/joao/miniforge3/envs/tutorial-tsfresh/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:7: FutureWarning: pandas.Float64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "  from pandas import (to_datetime, Int64Index, DatetimeIndex, Period,\n",
      "/Users/joao/miniforge3/envs/tutorial-tsfresh/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:7: 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 (to_datetime, Int64Index, DatetimeIndex, Period,\n",
      "/Users/joao/miniforge3/envs/tutorial-tsfresh/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:7: FutureWarning: pandas.Float64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "  from pandas import (to_datetime, Int64Index, DatetimeIndex, Period,\n",
      "/Users/joao/miniforge3/envs/tutorial-tsfresh/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:7: 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 (to_datetime, Int64Index, DatetimeIndex, Period,\n",
      "/Users/joao/miniforge3/envs/tutorial-tsfresh/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:7: FutureWarning: pandas.Float64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "  from pandas import (to_datetime, Int64Index, DatetimeIndex, Period,\n",
      "/Users/joao/miniforge3/envs/tutorial-tsfresh/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:7: 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 (to_datetime, Int64Index, DatetimeIndex, Period,\n",
      "/Users/joao/miniforge3/envs/tutorial-tsfresh/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:7: FutureWarning: pandas.Float64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "  from pandas import (to_datetime, Int64Index, DatetimeIndex, Period,\n",
      "/Users/joao/miniforge3/envs/tutorial-tsfresh/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:7: 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 (to_datetime, Int64Index, DatetimeIndex, Period,\n",
      "/Users/joao/miniforge3/envs/tutorial-tsfresh/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:7: FutureWarning: pandas.Float64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "  from pandas import (to_datetime, Int64Index, DatetimeIndex, Period,\n",
      "Feature Extraction: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 35/35 [00:38<00:00,  1.11s/it]\n",
      "/Users/joao/miniforge3/envs/tutorial-tsfresh/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:7: 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 (to_datetime, Int64Index, DatetimeIndex, Period,\n",
      "/Users/joao/miniforge3/envs/tutorial-tsfresh/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:7: FutureWarning: pandas.Float64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "  from pandas import (to_datetime, Int64Index, DatetimeIndex, Period,\n",
      "/Users/joao/miniforge3/envs/tutorial-tsfresh/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:7: 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 (to_datetime, Int64Index, DatetimeIndex, Period,\n",
      "/Users/joao/miniforge3/envs/tutorial-tsfresh/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:7: FutureWarning: pandas.Float64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "  from pandas import (to_datetime, Int64Index, DatetimeIndex, Period,\n",
      "/Users/joao/miniforge3/envs/tutorial-tsfresh/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:7: 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 (to_datetime, Int64Index, DatetimeIndex, Period,\n",
      "/Users/joao/miniforge3/envs/tutorial-tsfresh/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:7: FutureWarning: pandas.Float64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "  from pandas import (to_datetime, Int64Index, DatetimeIndex, Period,\n",
      "/Users/joao/miniforge3/envs/tutorial-tsfresh/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:7: 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 (to_datetime, Int64Index, DatetimeIndex, Period,\n",
      "/Users/joao/miniforge3/envs/tutorial-tsfresh/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:7: FutureWarning: pandas.Float64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "  from pandas import (to_datetime, Int64Index, DatetimeIndex, Period,\n",
      "/Users/joao/miniforge3/envs/tutorial-tsfresh/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:7: 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 (to_datetime, Int64Index, DatetimeIndex, Period,\n",
      "/Users/joao/miniforge3/envs/tutorial-tsfresh/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:7: FutureWarning: pandas.Float64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "  from pandas import (to_datetime, Int64Index, DatetimeIndex, Period,\n",
      "/Users/joao/miniforge3/envs/tutorial-tsfresh/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:7: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  from pandas import (to_datetime, Int64Index, DatetimeIndex, Period,\n",
      "/Users/joao/miniforge3/envs/tutorial-tsfresh/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:7: FutureWarning: pandas.Float64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "  from pandas import (to_datetime, Int64Index, DatetimeIndex, Period,\n",
      "/Users/joao/miniforge3/envs/tutorial-tsfresh/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:7: 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 (to_datetime, Int64Index, DatetimeIndex, Period,\n",
      "/Users/joao/miniforge3/envs/tutorial-tsfresh/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:7: FutureWarning: pandas.Float64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "  from pandas import (to_datetime, Int64Index, DatetimeIndex, Period,\n",
      "Feature Extraction: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 35/35 [00:38<00:00,  1.10s/it]\n",
      "/Users/joao/miniforge3/envs/tutorial-tsfresh/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:7: 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 (to_datetime, Int64Index, DatetimeIndex, Period,\n",
      "/Users/joao/miniforge3/envs/tutorial-tsfresh/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:7: FutureWarning: pandas.Float64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "  from pandas import (to_datetime, Int64Index, DatetimeIndex, Period,\n",
      "/Users/joao/miniforge3/envs/tutorial-tsfresh/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:7: 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 (to_datetime, Int64Index, DatetimeIndex, Period,\n",
      "/Users/joao/miniforge3/envs/tutorial-tsfresh/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:7: FutureWarning: pandas.Float64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "  from pandas import (to_datetime, Int64Index, DatetimeIndex, Period,\n",
      "/Users/joao/miniforge3/envs/tutorial-tsfresh/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:7: 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 (to_datetime, Int64Index, DatetimeIndex, Period,\n",
      "/Users/joao/miniforge3/envs/tutorial-tsfresh/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:7: FutureWarning: pandas.Float64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "  from pandas import (to_datetime, Int64Index, DatetimeIndex, Period,\n",
      "/Users/joao/miniforge3/envs/tutorial-tsfresh/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:7: 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 (to_datetime, Int64Index, DatetimeIndex, Period,\n",
      "/Users/joao/miniforge3/envs/tutorial-tsfresh/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:7: FutureWarning: pandas.Float64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "  from pandas import (to_datetime, Int64Index, DatetimeIndex, Period,\n",
      "/Users/joao/miniforge3/envs/tutorial-tsfresh/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:7: 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 (to_datetime, Int64Index, DatetimeIndex, Period,\n",
      "/Users/joao/miniforge3/envs/tutorial-tsfresh/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:7: FutureWarning: pandas.Float64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "  from pandas import (to_datetime, Int64Index, DatetimeIndex, Period,\n",
      "/Users/joao/miniforge3/envs/tutorial-tsfresh/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:7: 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 (to_datetime, Int64Index, DatetimeIndex, Period,\n",
      "/Users/joao/miniforge3/envs/tutorial-tsfresh/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:7: FutureWarning: pandas.Float64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "  from pandas import (to_datetime, Int64Index, DatetimeIndex, Period,\n",
      "/Users/joao/miniforge3/envs/tutorial-tsfresh/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:7: 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 (to_datetime, Int64Index, DatetimeIndex, Period,\n",
      "/Users/joao/miniforge3/envs/tutorial-tsfresh/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:7: FutureWarning: pandas.Float64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "  from pandas import (to_datetime, Int64Index, DatetimeIndex, Period,\n",
      "Feature Extraction: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 35/35 [00:37<00:00,  1.06s/it]\n",
      "/Users/joao/miniforge3/envs/tutorial-tsfresh/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:7: 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 (to_datetime, Int64Index, DatetimeIndex, Period,\n",
      "/Users/joao/miniforge3/envs/tutorial-tsfresh/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:7: FutureWarning: pandas.Float64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "  from pandas import (to_datetime, Int64Index, DatetimeIndex, Period,\n",
      "/Users/joao/miniforge3/envs/tutorial-tsfresh/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:7: 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 (to_datetime, Int64Index, DatetimeIndex, Period,\n",
      "/Users/joao/miniforge3/envs/tutorial-tsfresh/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:7: FutureWarning: pandas.Float64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "  from pandas import (to_datetime, Int64Index, DatetimeIndex, Period,\n",
      "/Users/joao/miniforge3/envs/tutorial-tsfresh/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:7: 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 (to_datetime, Int64Index, DatetimeIndex, Period,\n",
      "/Users/joao/miniforge3/envs/tutorial-tsfresh/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:7: FutureWarning: pandas.Float64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "  from pandas import (to_datetime, Int64Index, DatetimeIndex, Period,\n",
      "/Users/joao/miniforge3/envs/tutorial-tsfresh/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:7: 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 (to_datetime, Int64Index, DatetimeIndex, Period,\n",
      "/Users/joao/miniforge3/envs/tutorial-tsfresh/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:7: FutureWarning: pandas.Float64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "  from pandas import (to_datetime, Int64Index, DatetimeIndex, Period,\n",
      "/Users/joao/miniforge3/envs/tutorial-tsfresh/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:7: 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 (to_datetime, Int64Index, DatetimeIndex, Period,\n",
      "/Users/joao/miniforge3/envs/tutorial-tsfresh/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:7: FutureWarning: pandas.Float64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "  from pandas import (to_datetime, Int64Index, DatetimeIndex, Period,\n",
      "/Users/joao/miniforge3/envs/tutorial-tsfresh/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:7: 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 (to_datetime, Int64Index, DatetimeIndex, Period,\n",
      "/Users/joao/miniforge3/envs/tutorial-tsfresh/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:7: FutureWarning: pandas.Float64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "  from pandas import (to_datetime, Int64Index, DatetimeIndex, Period,\n",
      "/Users/joao/miniforge3/envs/tutorial-tsfresh/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:7: 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 (to_datetime, Int64Index, DatetimeIndex, Period,\n",
      "/Users/joao/miniforge3/envs/tutorial-tsfresh/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:7: FutureWarning: pandas.Float64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "  from pandas import (to_datetime, Int64Index, DatetimeIndex, Period,\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Feature Extraction: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 35/35 [00:38<00:00,  1.10s/it]\n",
      "/Users/joao/miniforge3/envs/tutorial-tsfresh/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:7: 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 (to_datetime, Int64Index, DatetimeIndex, Period,\n",
      "/Users/joao/miniforge3/envs/tutorial-tsfresh/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:7: FutureWarning: pandas.Float64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "  from pandas import (to_datetime, Int64Index, DatetimeIndex, Period,\n",
      "/Users/joao/miniforge3/envs/tutorial-tsfresh/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:7: 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 (to_datetime, Int64Index, DatetimeIndex, Period,\n",
      "/Users/joao/miniforge3/envs/tutorial-tsfresh/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:7: FutureWarning: pandas.Float64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "  from pandas import (to_datetime, Int64Index, DatetimeIndex, Period,\n",
      "/Users/joao/miniforge3/envs/tutorial-tsfresh/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:7: 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 (to_datetime, Int64Index, DatetimeIndex, Period,\n",
      "/Users/joao/miniforge3/envs/tutorial-tsfresh/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:7: FutureWarning: pandas.Float64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "  from pandas import (to_datetime, Int64Index, DatetimeIndex, Period,\n",
      "/Users/joao/miniforge3/envs/tutorial-tsfresh/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:7: 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 (to_datetime, Int64Index, DatetimeIndex, Period,\n",
      "/Users/joao/miniforge3/envs/tutorial-tsfresh/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:7: FutureWarning: pandas.Float64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "  from pandas import (to_datetime, Int64Index, DatetimeIndex, Period,\n",
      "/Users/joao/miniforge3/envs/tutorial-tsfresh/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:7: 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 (to_datetime, Int64Index, DatetimeIndex, Period,\n",
      "/Users/joao/miniforge3/envs/tutorial-tsfresh/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:7: FutureWarning: pandas.Float64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "  from pandas import (to_datetime, Int64Index, DatetimeIndex, Period,\n",
      "/Users/joao/miniforge3/envs/tutorial-tsfresh/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:7: 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 (to_datetime, Int64Index, DatetimeIndex, Period,\n",
      "/Users/joao/miniforge3/envs/tutorial-tsfresh/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:7: FutureWarning: pandas.Float64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "  from pandas import (to_datetime, Int64Index, DatetimeIndex, Period,\n",
      "/Users/joao/miniforge3/envs/tutorial-tsfresh/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:7: 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 (to_datetime, Int64Index, DatetimeIndex, Period,\n",
      "/Users/joao/miniforge3/envs/tutorial-tsfresh/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:7: FutureWarning: pandas.Float64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "  from pandas import (to_datetime, Int64Index, DatetimeIndex, Period,\n"
     ]
    },
    {
     "ename": "ZeroDivisionError",
     "evalue": "division by zero",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mZeroDivisionError\u001b[0m                         Traceback (most recent call last)",
      "File \u001b[0;32m<timed exec>:25\u001b[0m\n",
      "File \u001b[0;32m~/miniforge3/envs/tutorial-tsfresh/lib/python3.8/site-packages/tsfresh/feature_extraction/extraction.py:151\u001b[0m, in \u001b[0;36mextract_features\u001b[0;34m(timeseries_container, default_fc_parameters, kind_to_fc_parameters, column_id, column_sort, column_kind, column_value, chunksize, n_jobs, show_warnings, disable_progressbar, impute_function, profile, profiling_filename, profiling_sorting, distributor, pivot)\u001b[0m\n\u001b[1;32m    148\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    149\u001b[0m     warnings\u001b[38;5;241m.\u001b[39msimplefilter(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdefault\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m--> 151\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[43m_do_extraction\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdf\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtimeseries_container\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    152\u001b[0m \u001b[43m                        \u001b[49m\u001b[43mcolumn_id\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcolumn_id\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcolumn_value\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcolumn_value\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    153\u001b[0m \u001b[43m                        \u001b[49m\u001b[43mcolumn_kind\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcolumn_kind\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    154\u001b[0m \u001b[43m                        \u001b[49m\u001b[43mcolumn_sort\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcolumn_sort\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    155\u001b[0m \u001b[43m                        \u001b[49m\u001b[43mn_jobs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mn_jobs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mchunk_size\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mchunksize\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    156\u001b[0m \u001b[43m                        \u001b[49m\u001b[43mdisable_progressbar\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdisable_progressbar\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    157\u001b[0m \u001b[43m                        \u001b[49m\u001b[43mshow_warnings\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mshow_warnings\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    158\u001b[0m \u001b[43m                        \u001b[49m\u001b[43mdefault_fc_parameters\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdefault_fc_parameters\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    159\u001b[0m \u001b[43m                        \u001b[49m\u001b[43mkind_to_fc_parameters\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mkind_to_fc_parameters\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    160\u001b[0m \u001b[43m                        \u001b[49m\u001b[43mdistributor\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdistributor\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    161\u001b[0m \u001b[43m                        \u001b[49m\u001b[43mpivot\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpivot\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    163\u001b[0m \u001b[38;5;66;03m# Impute the result if requested\u001b[39;00m\n\u001b[1;32m    164\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m impute_function \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
      "File \u001b[0;32m~/miniforge3/envs/tutorial-tsfresh/lib/python3.8/site-packages/tsfresh/feature_extraction/extraction.py:253\u001b[0m, in \u001b[0;36m_do_extraction\u001b[0;34m(df, column_id, column_value, column_kind, column_sort, default_fc_parameters, kind_to_fc_parameters, n_jobs, chunk_size, disable_progressbar, show_warnings, distributor, pivot)\u001b[0m\n\u001b[1;32m    248\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mthe passed distributor is not an DistributorBaseClass object\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m    250\u001b[0m kwargs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mdict\u001b[39m(default_fc_parameters\u001b[38;5;241m=\u001b[39mdefault_fc_parameters,\n\u001b[1;32m    251\u001b[0m               kind_to_fc_parameters\u001b[38;5;241m=\u001b[39mkind_to_fc_parameters)\n\u001b[0;32m--> 253\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[43mdistributor\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmap_reduce\u001b[49m\u001b[43m(\u001b[49m\u001b[43m_do_extraction_on_chunk\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    254\u001b[0m \u001b[43m                                \u001b[49m\u001b[43mchunk_size\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mchunk_size\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    255\u001b[0m \u001b[43m                                \u001b[49m\u001b[43mfunction_kwargs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    257\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m pivot:\n\u001b[1;32m    258\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m result\n",
      "File \u001b[0;32m~/miniforge3/envs/tutorial-tsfresh/lib/python3.8/site-packages/tsfresh/utilities/distribution.py:206\u001b[0m, in \u001b[0;36mIterableDistributorBaseClass.map_reduce\u001b[0;34m(self, map_function, data, function_kwargs, chunk_size, data_length)\u001b[0m\n\u001b[1;32m    203\u001b[0m map_kwargs \u001b[38;5;241m=\u001b[39m {\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmap_function\u001b[39m\u001b[38;5;124m\"\u001b[39m: map_function, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mkwargs\u001b[39m\u001b[38;5;124m\"\u001b[39m: function_kwargs}\n\u001b[1;32m    205\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mprogressbar_title\u001b[39m\u001b[38;5;124m\"\u001b[39m):\n\u001b[0;32m--> 206\u001b[0m     total_number_of_expected_results \u001b[38;5;241m=\u001b[39m math\u001b[38;5;241m.\u001b[39mceil(\u001b[43mdata_length\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m/\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mchunk_size\u001b[49m)\n\u001b[1;32m    207\u001b[0m     result \u001b[38;5;241m=\u001b[39m tqdm(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdistribute(_function_with_partly_reduce, chunk_generator, map_kwargs),\n\u001b[1;32m    208\u001b[0m                   total\u001b[38;5;241m=\u001b[39mtotal_number_of_expected_results,\n\u001b[1;32m    209\u001b[0m                   desc\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mprogressbar_title, disable\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdisable_progressbar)\n\u001b[1;32m    210\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n",
      "\u001b[0;31mZeroDivisionError\u001b[0m: division by zero"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "# WARNING: This cell might take very long to run. \n",
    "# Last time, it took me 20 minutes (but it can take much longer if you dont break the df into pieces)\n",
    "\n",
    "# If you dony want to run this cell, you can get the generated dataset from my dropbox:\n",
    "# https://www.dropbox.com/scl/fo/5llpuwwtcuo22p9jnfxuo/h?dl=0&rlkey=nm0kqrfbk3z9s8qns8hjh4437\n",
    "\n",
    "\n",
    "# Roll the dataset\n",
    "df_rolled = roll_time_series(df[[\"time\", \"hr\", \"act\", \"pid\", \"sleep_phase\"]], \n",
    "                             column_id=\"pid\", \n",
    "                             min_timeshift=0,\n",
    "                             max_timeshift=WINSIZE-1,\n",
    "                             n_jobs=7)\n",
    "\n",
    "\n",
    "settings = EfficientFCParameters()\n",
    "del settings[\"friedrich_coefficients\"]\n",
    "del settings[\"max_langevin_fixed_point\"]\n",
    "\n",
    "start = 0\n",
    "steps = 10\n",
    "\n",
    "for start in range(0, df[\"pid\"].unique().shape[0] + 1, steps):\n",
    "    idxs = range(start, start+steps)\n",
    "    \n",
    "    \n",
    "    df_rolled_small = df_rolled[df_rolled[\"pid\"].isin(idxs)]\n",
    "    \n",
    "    df_extracted_features = tsfresh.extract_features(df_rolled_small[[\"id\", \"sort\", \"act\", \"hr\"]], \n",
    "                                                     column_id=\"id\",\n",
    "                                                     column_sort=\"sort\",\n",
    "                                                     default_fc_parameters=settings,\n",
    "                                                     n_jobs=7)\n",
    "    \n",
    "    df_features = df_extracted_features.reset_index().rename(columns={\"level_0\":\"pid\", \"level_1\": \"time\"})\n",
    "    df_features.to_csv(f\"datasets/df_tsfresh_features_{start}.tar.gz\", index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "e5d8542d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Merge all data\n",
    "dfs = []\n",
    "for file in glob(\"datasets/df_tsfresh_features_*.tar.gz\"):\n",
    "    dfs.append(pd.read_csv(file))\n",
    "\n",
    "df_features = pd.concat(dfs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "3e209fbf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pid</th>\n",
       "      <th>time</th>\n",
       "      <th>act__variance_larger_than_standard_deviation</th>\n",
       "      <th>act__has_duplicate_max</th>\n",
       "      <th>act__has_duplicate_min</th>\n",
       "      <th>act__has_duplicate</th>\n",
       "      <th>act__sum_values</th>\n",
       "      <th>act__abs_energy</th>\n",
       "      <th>act__mean_abs_change</th>\n",
       "      <th>act__mean_change</th>\n",
       "      <th>...</th>\n",
       "      <th>hr__fourier_entropy__bins_2</th>\n",
       "      <th>hr__fourier_entropy__bins_3</th>\n",
       "      <th>hr__fourier_entropy__bins_5</th>\n",
       "      <th>hr__fourier_entropy__bins_10</th>\n",
       "      <th>hr__fourier_entropy__bins_100</th>\n",
       "      <th>hr__permutation_entropy__dimension_3__tau_1</th>\n",
       "      <th>hr__permutation_entropy__dimension_4__tau_1</th>\n",
       "      <th>hr__permutation_entropy__dimension_5__tau_1</th>\n",
       "      <th>hr__permutation_entropy__dimension_6__tau_1</th>\n",
       "      <th>hr__permutation_entropy__dimension_7__tau_1</th>\n",
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       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>1</th>\n",
       "      <td>0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>-2.00</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.000000</td>\n",
       "      <td>-0.000000</td>\n",
       "      <td>-0.000000</td>\n",
       "      <td>-0.000000</td>\n",
       "      <td>-0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>-0.50</td>\n",
       "      <td>...</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>-0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1.333333</td>\n",
       "      <td>0.00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.636514</td>\n",
       "      <td>0.636514</td>\n",
       "      <td>0.636514</td>\n",
       "      <td>0.636514</td>\n",
       "      <td>0.636514</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>-0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>92.0</td>\n",
       "      <td>7578.0</td>\n",
       "      <td>22.250000</td>\n",
       "      <td>21.25</td>\n",
       "      <td>...</td>\n",
       "      <td>0.636514</td>\n",
       "      <td>0.636514</td>\n",
       "      <td>0.636514</td>\n",
       "      <td>0.636514</td>\n",
       "      <td>1.098612</td>\n",
       "      <td>1.098612</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 1538 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   pid  time  act__variance_larger_than_standard_deviation  \\\n",
       "0    0     0                                           0.0   \n",
       "1    0     1                                           0.0   \n",
       "2    0     2                                           0.0   \n",
       "3    0     3                                           0.0   \n",
       "4    0     4                                           1.0   \n",
       "\n",
       "   act__has_duplicate_max  act__has_duplicate_min  act__has_duplicate  \\\n",
       "0                     0.0                     0.0                 0.0   \n",
       "1                     0.0                     0.0                 0.0   \n",
       "2                     0.0                     0.0                 0.0   \n",
       "3                     1.0                     0.0                 1.0   \n",
       "4                     0.0                     0.0                 1.0   \n",
       "\n",
       "   act__sum_values  act__abs_energy  act__mean_abs_change  act__mean_change  \\\n",
       "0              2.0              4.0                   NaN               NaN   \n",
       "1              2.0              4.0              2.000000             -2.00   \n",
       "2              3.0              5.0              1.500000             -0.50   \n",
       "3              5.0              9.0              1.333333              0.00   \n",
       "4             92.0           7578.0             22.250000             21.25   \n",
       "\n",
       "   ...  hr__fourier_entropy__bins_2  hr__fourier_entropy__bins_3  \\\n",
       "0  ...                          NaN                          NaN   \n",
       "1  ...                    -0.000000                    -0.000000   \n",
       "2  ...                     0.693147                     0.693147   \n",
       "3  ...                     0.636514                     0.636514   \n",
       "4  ...                     0.636514                     0.636514   \n",
       "\n",
       "   hr__fourier_entropy__bins_5  hr__fourier_entropy__bins_10  \\\n",
       "0                          NaN                           NaN   \n",
       "1                    -0.000000                     -0.000000   \n",
       "2                     0.693147                      0.693147   \n",
       "3                     0.636514                      0.636514   \n",
       "4                     0.636514                      0.636514   \n",
       "\n",
       "   hr__fourier_entropy__bins_100  hr__permutation_entropy__dimension_3__tau_1  \\\n",
       "0                            NaN                                          NaN   \n",
       "1                      -0.000000                                          NaN   \n",
       "2                       0.693147                                    -0.000000   \n",
       "3                       0.636514                                     0.693147   \n",
       "4                       1.098612                                     1.098612   \n",
       "\n",
       "   hr__permutation_entropy__dimension_4__tau_1  \\\n",
       "0                                          NaN   \n",
       "1                                          NaN   \n",
       "2                                          NaN   \n",
       "3                                    -0.000000   \n",
       "4                                     0.693147   \n",
       "\n",
       "   hr__permutation_entropy__dimension_5__tau_1  \\\n",
       "0                                          NaN   \n",
       "1                                          NaN   \n",
       "2                                          NaN   \n",
       "3                                          NaN   \n",
       "4                                         -0.0   \n",
       "\n",
       "   hr__permutation_entropy__dimension_6__tau_1  \\\n",
       "0                                          NaN   \n",
       "1                                          NaN   \n",
       "2                                          NaN   \n",
       "3                                          NaN   \n",
       "4                                          NaN   \n",
       "\n",
       "   hr__permutation_entropy__dimension_7__tau_1  \n",
       "0                                          NaN  \n",
       "1                                          NaN  \n",
       "2                                          NaN  \n",
       "3                                          NaN  \n",
       "4                                          NaN  \n",
       "\n",
       "[5 rows x 1538 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_features = df_features.sort_values(by=[\"pid\", \"time\"]).reset_index(drop=True)\n",
    "df_features.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "dda795f6",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-29T11:07:23.896884Z",
     "start_time": "2022-08-29T11:03:53.423477Z"
    }
   },
   "outputs": [],
   "source": [
    "# Reset time col\n",
    "df_nonan = df.dropna().copy()\n",
    "df_nonan[\"time\"] = 1\n",
    "df_nonan[\"time\"] = df_nonan.groupby(\"pid\")[\"time\"].cumsum()\n",
    "\n",
    "\n",
    "df_merged = pd.merge(df_nonan, df_features)\n",
    "df_merged.to_csv(\"datasets/df_tsfresh_features.tar.gz\", index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "251993da",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Removing... datasets/df_tsfresh_features_60.tar.gz\n",
      "Removing... datasets/df_tsfresh_features_70.tar.gz\n",
      "Removing... datasets/df_tsfresh_features_50.tar.gz\n",
      "Removing... datasets/df_tsfresh_features_40.tar.gz\n",
      "Removing... datasets/df_tsfresh_features_10.tar.gz\n",
      "Removing... datasets/df_tsfresh_features_80.tar.gz\n",
      "Removing... datasets/df_tsfresh_features_90.tar.gz\n",
      "Removing... datasets/df_tsfresh_features_0.tar.gz\n",
      "Removing... datasets/df_tsfresh_features_20.tar.gz\n",
      "Removing... datasets/df_tsfresh_features_30.tar.gz\n"
     ]
    }
   ],
   "source": [
    "# Clean dataset folder by removing temporary files\n",
    "for file in glob(\"datasets/df_tsfresh_features_*.tar.gz\"):\n",
    "    if os.path.isfile(file):\n",
    "        print(\"Removing...\", file)\n",
    "        os.remove(file)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "19db1289",
   "metadata": {},
   "source": [
    "## The second feature set from rolling windows: raw signals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "8ef7ca62",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-09-04T13:13:43.383013Z",
     "start_time": "2022-09-04T13:12:51.660514Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/joao/miniforge3/envs/tutorial-tsfresh/lib/python3.8/site-packages/tsfresh/utilities/dataframe_functions.py:467: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df[\"sort\"] = range(df.shape[0])\n",
      "Rolling: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 25/25 [00:08<00:00,  2.88it/s]\n",
      "/Users/joao/miniforge3/envs/tutorial-tsfresh/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:7: 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 (to_datetime, Int64Index, DatetimeIndex, Period,\n",
      "/Users/joao/miniforge3/envs/tutorial-tsfresh/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:7: FutureWarning: pandas.Float64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "  from pandas import (to_datetime, Int64Index, DatetimeIndex, Period,\n",
      "/Users/joao/miniforge3/envs/tutorial-tsfresh/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:7: 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 (to_datetime, Int64Index, DatetimeIndex, Period,\n",
      "/Users/joao/miniforge3/envs/tutorial-tsfresh/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:7: FutureWarning: pandas.Float64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "  from pandas import (to_datetime, Int64Index, DatetimeIndex, Period,\n",
      "/Users/joao/miniforge3/envs/tutorial-tsfresh/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:7: 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 (to_datetime, Int64Index, DatetimeIndex, Period,\n",
      "/Users/joao/miniforge3/envs/tutorial-tsfresh/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:7: FutureWarning: pandas.Float64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "  from pandas import (to_datetime, Int64Index, DatetimeIndex, Period,\n",
      "/Users/joao/miniforge3/envs/tutorial-tsfresh/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:7: 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 (to_datetime, Int64Index, DatetimeIndex, Period,\n",
      "/Users/joao/miniforge3/envs/tutorial-tsfresh/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:7: FutureWarning: pandas.Float64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "  from pandas import (to_datetime, Int64Index, DatetimeIndex, Period,\n",
      "/Users/joao/miniforge3/envs/tutorial-tsfresh/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:7: 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 (to_datetime, Int64Index, DatetimeIndex, Period,\n",
      "/Users/joao/miniforge3/envs/tutorial-tsfresh/lib/python3.8/site-packages/statsmodels/tsa/base/tsa_model.py:7: FutureWarning: pandas.Float64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "  from pandas import (to_datetime, Int64Index, DatetimeIndex, Period,\n"
     ]
    }
   ],
   "source": [
    "df_rolled = roll_time_series(df[[\"time\", \"hr\", \"act\", \"pid\", \"sleep_phase\"]], \n",
    "                             column_id=\"pid\", \n",
    "                             min_timeshift=0, \n",
    "                             max_timeshift=WINSIZE-1,\n",
    "                             n_jobs=5)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "9e59b930",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-09-04T13:34:12.679815Z",
     "start_time": "2022-09-04T13:34:12.653144Z"
    }
   },
   "outputs": [],
   "source": [
    "def make_cols(l, size):\n",
    "    \n",
    "    l = np.array(l)\n",
    "    # print(len(l))\n",
    "    \n",
    "    # This will pad L with 0s at the end\n",
    "    # l = np.pad(l, (0, size-len(l)),  \"constant\")\n",
    "    \n",
    "    # this will pad with 0s at the begining\n",
    "    l = np.pad(l, (size-len(l), 0),  \"constant\")\n",
    "    return l\n",
    "\n",
    "def get_raw_win_features(df, winsize=WINSIZE):\n",
    "    \n",
    "    df_acts = df[[\"pid\", \"id\", \"act\"]].groupby('id')[\"act\"].apply(lambda x: make_cols(x, size=winsize))\n",
    "    df_hrs = df[[\"pid\", \"id\", \"hr\"]].groupby('id')[\"hr\"].apply(lambda x: make_cols(x, size=winsize))\n",
    "    \n",
    "    df_acts = df_acts.apply(pd.Series).rename(columns=dict([(i, \"act_%d\" % i) for i in range(winsize)]))\n",
    "    df_hrs = df_hrs.apply(pd.Series).rename(columns=dict([(i, \"hr_%d\" % i) for i in range(winsize)]))\n",
    "    \n",
    "    df_new = pd.concat((df_acts, df_hrs), axis=1).reset_index()\n",
    "    \n",
    "    # Get the sorted unique ids for this dataframe\n",
    "    df_uniqueids_sorted = df[[\"pid\", \"id\", \"time\", \"sort\", \"sleep_phase\"]].groupby([\"pid\", \"time\"]).first().reset_index()\n",
    "    \n",
    "    return pd.merge(df_uniqueids_sorted, df_new, on=\"id\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "e618cce4",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-09-04T13:35:00.703691Z",
     "start_time": "2022-09-04T13:34:15.263258Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of rows in the df now (102859) and in the original df (102859) should be the same!\n",
      "CPU times: user 11.1 s, sys: 172 ms, total: 11.3 s\n",
      "Wall time: 11.2 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "df_features_raw_win = get_raw_win_features(df_rolled)\n",
    "\n",
    "print(f\"The number of rows in the df now ({df_features_raw_win.shape[0]}) and in the original df ({df.shape[0]}) \"\n",
    "      \"should be the same!\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "4f6f08f4",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pid</th>\n",
       "      <th>time</th>\n",
       "      <th>id</th>\n",
       "      <th>sort</th>\n",
       "      <th>sleep_phase</th>\n",
       "      <th>act_0</th>\n",
       "      <th>act_1</th>\n",
       "      <th>act_2</th>\n",
       "      <th>act_3</th>\n",
       "      <th>act_4</th>\n",
       "      <th>...</th>\n",
       "      <th>hr_1</th>\n",
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       "      <th>hr_3</th>\n",
       "      <th>hr_4</th>\n",
       "      <th>hr_5</th>\n",
       "      <th>hr_6</th>\n",
       "      <th>hr_7</th>\n",
       "      <th>hr_8</th>\n",
       "      <th>hr_9</th>\n",
       "      <th>hr_10</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>2</th>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>78.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>119</td>\n",
       "      <td>(0, 3)</td>\n",
       "      <td>3</td>\n",
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       "      <td>78.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>80.0</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   pid  time      id  sort  sleep_phase  act_0  act_1  act_2  act_3  act_4  \\\n",
       "0    0    29  (0, 0)     0          0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "1    0    59  (0, 1)     1          0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "2    0    89  (0, 2)     2          0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "3    0   119  (0, 3)     3          0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "4    0   149  (0, 4)     4          0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "\n",
       "   ...  hr_1  hr_2  hr_3  hr_4  hr_5  hr_6  hr_7  hr_8  hr_9  hr_10  \n",
       "0  ...   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   71.0  \n",
       "1  ...   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0  71.0   76.0  \n",
       "2  ...   0.0   0.0   0.0   0.0   0.0   0.0   0.0  71.0  76.0   78.0  \n",
       "3  ...   0.0   0.0   0.0   0.0   0.0   0.0  71.0  76.0  78.0   73.0  \n",
       "4  ...   0.0   0.0   0.0   0.0   0.0  71.0  76.0  78.0  73.0   80.0  \n",
       "\n",
       "[5 rows x 27 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Result:\n",
    "df_features_raw_win.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "d98d0ea5",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-09-04T15:18:51.291096Z",
     "start_time": "2022-09-04T15:18:51.277590Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      2.0\n",
       "1      0.0\n",
       "2      1.0\n",
       "3      2.0\n",
       "4     87.0\n",
       "5      0.0\n",
       "6      1.0\n",
       "7      0.0\n",
       "8      0.0\n",
       "9      0.0\n",
       "10     0.0\n",
       "11     1.0\n",
       "12     1.0\n",
       "13    66.0\n",
       "14     0.0\n",
       "15     0.0\n",
       "16     3.0\n",
       "17     0.0\n",
       "18     5.0\n",
       "19    34.0\n",
       "Name: act_10, dtype: float64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The most recent signal is at the last index (i.e., act_10, hr_10)\n",
    "df_features_raw_win[f\"act_{WINSIZE-1}\"].head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "0c9a5b0c",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-09-04T15:18:54.478033Z",
     "start_time": "2022-09-04T15:18:54.463696Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>time</th>\n",
       "      <th>act</th>\n",
       "      <th>sleep_phase</th>\n",
       "      <th>hr</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>29</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>59</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>89</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119</th>\n",
       "      <td>119</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>149</td>\n",
       "      <td>87.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179</th>\n",
       "      <td>179</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>209</th>\n",
       "      <td>209</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>239</th>\n",
       "      <td>239</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>269</th>\n",
       "      <td>269</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>299</th>\n",
       "      <td>299</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>329</th>\n",
       "      <td>329</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>359</th>\n",
       "      <td>359</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>389</th>\n",
       "      <td>389</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>419</th>\n",
       "      <td>419</td>\n",
       "      <td>66.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>449</th>\n",
       "      <td>449</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>479</th>\n",
       "      <td>479</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>509</th>\n",
       "      <td>509</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>539</th>\n",
       "      <td>539</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>569</th>\n",
       "      <td>569</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>599</th>\n",
       "      <td>599</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     time   act  sleep_phase    hr  pid\n",
       "29     29   2.0          0.0  71.0    0\n",
       "59     59   0.0          0.0  76.0    0\n",
       "89     89   1.0          0.0  78.0    0\n",
       "119   119   2.0          0.0  73.0    0\n",
       "149   149  87.0          0.0  80.0    0\n",
       "179   179   0.0          0.0  75.0    0\n",
       "209   209   1.0          0.0  77.0    0\n",
       "239   239   0.0          0.0  78.0    0\n",
       "269   269   0.0          0.0  78.0    0\n",
       "299   299   0.0          0.0  78.0    0\n",
       "329   329   0.0          0.0  74.0    0\n",
       "359   359   1.0          0.0  75.0    0\n",
       "389   389   1.0          0.0  74.0    0\n",
       "419   419  66.0          0.0  71.0    0\n",
       "449   449   0.0          0.0  73.0    0\n",
       "479   479   0.0          0.0  60.0    0\n",
       "509   509   3.0          0.0  73.0    0\n",
       "539   539   0.0          0.0  76.0    0\n",
       "569   569   5.0          0.0  73.0    0\n",
       "599   599  34.0          0.0  74.0    0"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "a033a2d6",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-09-04T13:36:32.943763Z",
     "start_time": "2022-09-04T13:36:27.433005Z"
    }
   },
   "outputs": [],
   "source": [
    "df_features_raw_win.to_csv(\"./datasets/df_raw_features.tar.gz\", index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2951e0be",
   "metadata": {},
   "source": [
    "<b> Done with feature sets creation. Now we have two sets ready to be used by ML models </b>"
   ]
  }
 ],
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