preprocess.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from tsfresh.utilities.dataframe_functions import roll_time_series, impute\n",
    "from tsfresh.feature_selection.relevance import calculate_relevance_table\n",
    "\n",
    "from tsfresh import extract_features, select_features\n",
    "from third_party.feature_selector import FeatureSelector"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "window_size = 60\n",
    "n_features = 106\n",
    "filename = 'data/trace_201708/server_usage.csv'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(filename, header=None)\n",
    "df = df[[0, 1, 2]]\n",
    "df.rename(columns={\n",
    "    0: 'time',\n",
    "    1: 'machine_id',\n",
    "    2: 'cpu'\n",
    "}, inplace=True)\n",
    "\n",
    "df['cpu'] = df['cpu'].fillna(0)\n",
    "df.sort_values(['machine_id', 'time'], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>cpu</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>machine_id</th>\n",
       "      <th>time</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">1</th>\n",
       "      <th>39600</th>\n",
       "      <td>44.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39900</th>\n",
       "      <td>35.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40200</th>\n",
       "      <td>30.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40500</th>\n",
       "      <td>29.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40800</th>\n",
       "      <td>30.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">1313</th>\n",
       "      <th>81300</th>\n",
       "      <td>13.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81600</th>\n",
       "      <td>14.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81900</th>\n",
       "      <td>13.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82200</th>\n",
       "      <td>13.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82500</th>\n",
       "      <td>14.32</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>187963 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    cpu\n",
       "machine_id time        \n",
       "1          39600  44.62\n",
       "           39900  35.60\n",
       "           40200  30.98\n",
       "           40500  29.52\n",
       "           40800  30.18\n",
       "...                 ...\n",
       "1313       81300  13.76\n",
       "           81600  14.72\n",
       "           81900  13.42\n",
       "           82200  13.56\n",
       "           82500  14.32\n",
       "\n",
       "[187963 rows x 1 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_grp = df.copy().set_index(['machine_id', 'time'], drop=True)\n",
    "df_grp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/envs/py39/lib/python3.9/site-packages/tsfresh/utilities/dataframe_functions.py:520: UserWarning: Your time stamps are not uniformly sampled, which makes rolling nonsensical in some domains.\n",
      "  warnings.warn(\n",
      "Rolling: 100%|██████████| 72/72 [00:09<00:00,  7.26it/s]\n"
     ]
    }
   ],
   "source": [
    "df_rolled = roll_time_series(\n",
    "    df,\n",
    "    column_id='machine_id',\n",
    "    column_sort='time',\n",
    "    max_timeshift=window_size-1,\n",
    "    min_timeshift=window_size-1,\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>time</th>\n",
       "      <th>machine_id</th>\n",
       "      <th>cpu</th>\n",
       "      <th>id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>39600</td>\n",
       "      <td>1</td>\n",
       "      <td>44.62</td>\n",
       "      <td>(1, 57300)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>39900</td>\n",
       "      <td>1</td>\n",
       "      <td>35.60</td>\n",
       "      <td>(1, 57300)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>40200</td>\n",
       "      <td>1</td>\n",
       "      <td>30.98</td>\n",
       "      <td>(1, 57300)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>40500</td>\n",
       "      <td>1</td>\n",
       "      <td>29.52</td>\n",
       "      <td>(1, 57300)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>40800</td>\n",
       "      <td>1</td>\n",
       "      <td>30.18</td>\n",
       "      <td>(1, 57300)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5068795</th>\n",
       "      <td>81300</td>\n",
       "      <td>1313</td>\n",
       "      <td>13.76</td>\n",
       "      <td>(1313, 82500)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5068796</th>\n",
       "      <td>81600</td>\n",
       "      <td>1313</td>\n",
       "      <td>14.72</td>\n",
       "      <td>(1313, 82500)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5068797</th>\n",
       "      <td>81900</td>\n",
       "      <td>1313</td>\n",
       "      <td>13.42</td>\n",
       "      <td>(1313, 82500)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5068798</th>\n",
       "      <td>82200</td>\n",
       "      <td>1313</td>\n",
       "      <td>13.56</td>\n",
       "      <td>(1313, 82500)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5068799</th>\n",
       "      <td>82500</td>\n",
       "      <td>1313</td>\n",
       "      <td>14.32</td>\n",
       "      <td>(1313, 82500)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6640380 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          time  machine_id    cpu             id\n",
       "0        39600           1  44.62     (1, 57300)\n",
       "1        39900           1  35.60     (1, 57300)\n",
       "2        40200           1  30.98     (1, 57300)\n",
       "3        40500           1  29.52     (1, 57300)\n",
       "4        40800           1  30.18     (1, 57300)\n",
       "...        ...         ...    ...            ...\n",
       "5068795  81300        1313  13.76  (1313, 82500)\n",
       "5068796  81600        1313  14.72  (1313, 82500)\n",
       "5068797  81900        1313  13.42  (1313, 82500)\n",
       "5068798  82200        1313  13.56  (1313, 82500)\n",
       "5068799  82500        1313  14.32  (1313, 82500)\n",
       "\n",
       "[6640380 rows x 4 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_rolled"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Feature Extraction: 100%|██████████| 120/120 [07:26<00:00,  3.72s/it]\n"
     ]
    }
   ],
   "source": [
    "from tsfresh.feature_extraction import EfficientFCParameters, MinimalFCParameters\n",
    "\n",
    "extracted_features = extract_features(\n",
    "    df_rolled.drop('machine_id', axis=1),\n",
    "    column_id='id',\n",
    "    column_sort='time',\n",
    "    column_value='cpu',\n",
    "    impute_function=impute,\n",
    "    default_fc_parameters=EfficientFCParameters(),\n",
    ") "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>cpu__variance_larger_than_standard_deviation</th>\n",
       "      <th>cpu__has_duplicate_max</th>\n",
       "      <th>cpu__has_duplicate_min</th>\n",
       "      <th>cpu__has_duplicate</th>\n",
       "      <th>cpu__sum_values</th>\n",
       "      <th>cpu__abs_energy</th>\n",
       "      <th>cpu__mean_abs_change</th>\n",
       "      <th>cpu__mean_change</th>\n",
       "      <th>cpu__mean_second_derivative_central</th>\n",
       "      <th>cpu__median</th>\n",
       "      <th>...</th>\n",
       "      <th>cpu__fourier_entropy__bins_5</th>\n",
       "      <th>cpu__fourier_entropy__bins_10</th>\n",
       "      <th>cpu__fourier_entropy__bins_100</th>\n",
       "      <th>cpu__permutation_entropy__dimension_3__tau_1</th>\n",
       "      <th>cpu__permutation_entropy__dimension_4__tau_1</th>\n",
       "      <th>cpu__permutation_entropy__dimension_5__tau_1</th>\n",
       "      <th>cpu__permutation_entropy__dimension_6__tau_1</th>\n",
       "      <th>cpu__permutation_entropy__dimension_7__tau_1</th>\n",
       "      <th>cpu__query_similarity_count__query_None__threshold_0.0</th>\n",
       "      <th>cpu__mean_n_absolute_max__number_of_maxima_7</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">1</th>\n",
       "      <th>57300</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2075.200004</td>\n",
       "      <td>73049.099452</td>\n",
       "      <td>4.749830</td>\n",
       "      <td>-0.223051</td>\n",
       "      <td>0.162414</td>\n",
       "      <td>34.38</td>\n",
       "      <td>...</td>\n",
       "      <td>0.999869</td>\n",
       "      <td>1.631021</td>\n",
       "      <td>2.846720</td>\n",
       "      <td>1.777805</td>\n",
       "      <td>2.985166</td>\n",
       "      <td>3.768455</td>\n",
       "      <td>3.982128</td>\n",
       "      <td>3.988984</td>\n",
       "      <td>NaN</td>\n",
       "      <td>42.731429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57600</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2061.060004</td>\n",
       "      <td>71987.185429</td>\n",
       "      <td>4.613559</td>\n",
       "      <td>-0.086780</td>\n",
       "      <td>0.031379</td>\n",
       "      <td>34.20</td>\n",
       "      <td>...</td>\n",
       "      <td>1.131502</td>\n",
       "      <td>1.678262</td>\n",
       "      <td>2.827608</td>\n",
       "      <td>1.771253</td>\n",
       "      <td>2.975986</td>\n",
       "      <td>3.768455</td>\n",
       "      <td>3.982128</td>\n",
       "      <td>3.988984</td>\n",
       "      <td>NaN</td>\n",
       "      <td>42.142857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57900</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2055.940004</td>\n",
       "      <td>71648.855848</td>\n",
       "      <td>4.535254</td>\n",
       "      <td>-0.008475</td>\n",
       "      <td>0.012586</td>\n",
       "      <td>33.92</td>\n",
       "      <td>...</td>\n",
       "      <td>1.165271</td>\n",
       "      <td>1.643434</td>\n",
       "      <td>2.969917</td>\n",
       "      <td>1.757079</td>\n",
       "      <td>2.928559</td>\n",
       "      <td>3.768455</td>\n",
       "      <td>3.982128</td>\n",
       "      <td>3.988984</td>\n",
       "      <td>NaN</td>\n",
       "      <td>42.142857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58200</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2053.760004</td>\n",
       "      <td>71518.535453</td>\n",
       "      <td>4.538983</td>\n",
       "      <td>-0.012203</td>\n",
       "      <td>-0.020172</td>\n",
       "      <td>33.92</td>\n",
       "      <td>...</td>\n",
       "      <td>1.165271</td>\n",
       "      <td>1.689065</td>\n",
       "      <td>3.165672</td>\n",
       "      <td>1.758518</td>\n",
       "      <td>2.920594</td>\n",
       "      <td>3.734356</td>\n",
       "      <td>3.982128</td>\n",
       "      <td>3.988984</td>\n",
       "      <td>NaN</td>\n",
       "      <td>42.142857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58500</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2049.680004</td>\n",
       "      <td>71294.298656</td>\n",
       "      <td>4.584746</td>\n",
       "      <td>-0.080339</td>\n",
       "      <td>-0.032586</td>\n",
       "      <td>33.92</td>\n",
       "      <td>...</td>\n",
       "      <td>1.165271</td>\n",
       "      <td>1.707492</td>\n",
       "      <td>3.076234</td>\n",
       "      <td>1.767957</td>\n",
       "      <td>2.887093</td>\n",
       "      <td>3.700257</td>\n",
       "      <td>3.982128</td>\n",
       "      <td>3.988984</td>\n",
       "      <td>NaN</td>\n",
       "      <td>42.142857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">1313</th>\n",
       "      <th>81300</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1638.439997</td>\n",
       "      <td>47027.796582</td>\n",
       "      <td>3.217627</td>\n",
       "      <td>-0.240000</td>\n",
       "      <td>-0.026207</td>\n",
       "      <td>27.18</td>\n",
       "      <td>...</td>\n",
       "      <td>0.731802</td>\n",
       "      <td>1.018214</td>\n",
       "      <td>2.558358</td>\n",
       "      <td>1.700411</td>\n",
       "      <td>2.820881</td>\n",
       "      <td>3.485568</td>\n",
       "      <td>3.796177</td>\n",
       "      <td>3.937640</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.605714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81600</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1625.239998</td>\n",
       "      <td>46464.948601</td>\n",
       "      <td>3.210508</td>\n",
       "      <td>-0.247119</td>\n",
       "      <td>0.026724</td>\n",
       "      <td>27.14</td>\n",
       "      <td>...</td>\n",
       "      <td>0.687083</td>\n",
       "      <td>0.928839</td>\n",
       "      <td>2.463937</td>\n",
       "      <td>1.700411</td>\n",
       "      <td>2.801307</td>\n",
       "      <td>3.485568</td>\n",
       "      <td>3.796177</td>\n",
       "      <td>3.937640</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.605714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81900</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1609.359998</td>\n",
       "      <td>45786.554998</td>\n",
       "      <td>3.196271</td>\n",
       "      <td>-0.232881</td>\n",
       "      <td>-0.016897</td>\n",
       "      <td>26.72</td>\n",
       "      <td>...</td>\n",
       "      <td>0.687083</td>\n",
       "      <td>0.928839</td>\n",
       "      <td>2.483049</td>\n",
       "      <td>1.700411</td>\n",
       "      <td>2.807268</td>\n",
       "      <td>3.476224</td>\n",
       "      <td>3.821382</td>\n",
       "      <td>3.937640</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.605714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82200</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1595.759998</td>\n",
       "      <td>45232.763007</td>\n",
       "      <td>3.187458</td>\n",
       "      <td>-0.241695</td>\n",
       "      <td>-0.031897</td>\n",
       "      <td>26.29</td>\n",
       "      <td>...</td>\n",
       "      <td>0.611952</td>\n",
       "      <td>0.928839</td>\n",
       "      <td>2.405088</td>\n",
       "      <td>1.702331</td>\n",
       "      <td>2.816448</td>\n",
       "      <td>3.500979</td>\n",
       "      <td>3.821382</td>\n",
       "      <td>3.937640</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.605714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82500</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1582.259998</td>\n",
       "      <td>44663.873000</td>\n",
       "      <td>3.135254</td>\n",
       "      <td>-0.293898</td>\n",
       "      <td>-0.021724</td>\n",
       "      <td>26.15</td>\n",
       "      <td>...</td>\n",
       "      <td>0.595073</td>\n",
       "      <td>0.822522</td>\n",
       "      <td>2.137709</td>\n",
       "      <td>1.702331</td>\n",
       "      <td>2.816448</td>\n",
       "      <td>3.476224</td>\n",
       "      <td>3.821382</td>\n",
       "      <td>3.937640</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.605714</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>110673 rows × 777 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            cpu__variance_larger_than_standard_deviation  \\\n",
       "1    57300                                           1.0   \n",
       "     57600                                           1.0   \n",
       "     57900                                           1.0   \n",
       "     58200                                           1.0   \n",
       "     58500                                           1.0   \n",
       "...                                                  ...   \n",
       "1313 81300                                           1.0   \n",
       "     81600                                           1.0   \n",
       "     81900                                           1.0   \n",
       "     82200                                           1.0   \n",
       "     82500                                           1.0   \n",
       "\n",
       "            cpu__has_duplicate_max  cpu__has_duplicate_min  \\\n",
       "1    57300                     0.0                     0.0   \n",
       "     57600                     0.0                     0.0   \n",
       "     57900                     0.0                     0.0   \n",
       "     58200                     0.0                     0.0   \n",
       "     58500                     0.0                     0.0   \n",
       "...                            ...                     ...   \n",
       "1313 81300                     0.0                     0.0   \n",
       "     81600                     0.0                     0.0   \n",
       "     81900                     0.0                     0.0   \n",
       "     82200                     0.0                     0.0   \n",
       "     82500                     0.0                     0.0   \n",
       "\n",
       "            cpu__has_duplicate  cpu__sum_values  cpu__abs_energy  \\\n",
       "1    57300                 1.0      2075.200004     73049.099452   \n",
       "     57600                 1.0      2061.060004     71987.185429   \n",
       "     57900                 0.0      2055.940004     71648.855848   \n",
       "     58200                 0.0      2053.760004     71518.535453   \n",
       "     58500                 0.0      2049.680004     71294.298656   \n",
       "...                        ...              ...              ...   \n",
       "1313 81300                 0.0      1638.439997     47027.796582   \n",
       "     81600                 0.0      1625.239998     46464.948601   \n",
       "     81900                 0.0      1609.359998     45786.554998   \n",
       "     82200                 0.0      1595.759998     45232.763007   \n",
       "     82500                 0.0      1582.259998     44663.873000   \n",
       "\n",
       "            cpu__mean_abs_change  cpu__mean_change  \\\n",
       "1    57300              4.749830         -0.223051   \n",
       "     57600              4.613559         -0.086780   \n",
       "     57900              4.535254         -0.008475   \n",
       "     58200              4.538983         -0.012203   \n",
       "     58500              4.584746         -0.080339   \n",
       "...                          ...               ...   \n",
       "1313 81300              3.217627         -0.240000   \n",
       "     81600              3.210508         -0.247119   \n",
       "     81900              3.196271         -0.232881   \n",
       "     82200              3.187458         -0.241695   \n",
       "     82500              3.135254         -0.293898   \n",
       "\n",
       "            cpu__mean_second_derivative_central  cpu__median  ...  \\\n",
       "1    57300                             0.162414        34.38  ...   \n",
       "     57600                             0.031379        34.20  ...   \n",
       "     57900                             0.012586        33.92  ...   \n",
       "     58200                            -0.020172        33.92  ...   \n",
       "     58500                            -0.032586        33.92  ...   \n",
       "...                                         ...          ...  ...   \n",
       "1313 81300                            -0.026207        27.18  ...   \n",
       "     81600                             0.026724        27.14  ...   \n",
       "     81900                            -0.016897        26.72  ...   \n",
       "     82200                            -0.031897        26.29  ...   \n",
       "     82500                            -0.021724        26.15  ...   \n",
       "\n",
       "            cpu__fourier_entropy__bins_5  cpu__fourier_entropy__bins_10  \\\n",
       "1    57300                      0.999869                       1.631021   \n",
       "     57600                      1.131502                       1.678262   \n",
       "     57900                      1.165271                       1.643434   \n",
       "     58200                      1.165271                       1.689065   \n",
       "     58500                      1.165271                       1.707492   \n",
       "...                                  ...                            ...   \n",
       "1313 81300                      0.731802                       1.018214   \n",
       "     81600                      0.687083                       0.928839   \n",
       "     81900                      0.687083                       0.928839   \n",
       "     82200                      0.611952                       0.928839   \n",
       "     82500                      0.595073                       0.822522   \n",
       "\n",
       "            cpu__fourier_entropy__bins_100  \\\n",
       "1    57300                        2.846720   \n",
       "     57600                        2.827608   \n",
       "     57900                        2.969917   \n",
       "     58200                        3.165672   \n",
       "     58500                        3.076234   \n",
       "...                                    ...   \n",
       "1313 81300                        2.558358   \n",
       "     81600                        2.463937   \n",
       "     81900                        2.483049   \n",
       "     82200                        2.405088   \n",
       "     82500                        2.137709   \n",
       "\n",
       "            cpu__permutation_entropy__dimension_3__tau_1  \\\n",
       "1    57300                                      1.777805   \n",
       "     57600                                      1.771253   \n",
       "     57900                                      1.757079   \n",
       "     58200                                      1.758518   \n",
       "     58500                                      1.767957   \n",
       "...                                                  ...   \n",
       "1313 81300                                      1.700411   \n",
       "     81600                                      1.700411   \n",
       "     81900                                      1.700411   \n",
       "     82200                                      1.702331   \n",
       "     82500                                      1.702331   \n",
       "\n",
       "            cpu__permutation_entropy__dimension_4__tau_1  \\\n",
       "1    57300                                      2.985166   \n",
       "     57600                                      2.975986   \n",
       "     57900                                      2.928559   \n",
       "     58200                                      2.920594   \n",
       "     58500                                      2.887093   \n",
       "...                                                  ...   \n",
       "1313 81300                                      2.820881   \n",
       "     81600                                      2.801307   \n",
       "     81900                                      2.807268   \n",
       "     82200                                      2.816448   \n",
       "     82500                                      2.816448   \n",
       "\n",
       "            cpu__permutation_entropy__dimension_5__tau_1  \\\n",
       "1    57300                                      3.768455   \n",
       "     57600                                      3.768455   \n",
       "     57900                                      3.768455   \n",
       "     58200                                      3.734356   \n",
       "     58500                                      3.700257   \n",
       "...                                                  ...   \n",
       "1313 81300                                      3.485568   \n",
       "     81600                                      3.485568   \n",
       "     81900                                      3.476224   \n",
       "     82200                                      3.500979   \n",
       "     82500                                      3.476224   \n",
       "\n",
       "            cpu__permutation_entropy__dimension_6__tau_1  \\\n",
       "1    57300                                      3.982128   \n",
       "     57600                                      3.982128   \n",
       "     57900                                      3.982128   \n",
       "     58200                                      3.982128   \n",
       "     58500                                      3.982128   \n",
       "...                                                  ...   \n",
       "1313 81300                                      3.796177   \n",
       "     81600                                      3.796177   \n",
       "     81900                                      3.821382   \n",
       "     82200                                      3.821382   \n",
       "     82500                                      3.821382   \n",
       "\n",
       "            cpu__permutation_entropy__dimension_7__tau_1  \\\n",
       "1    57300                                      3.988984   \n",
       "     57600                                      3.988984   \n",
       "     57900                                      3.988984   \n",
       "     58200                                      3.988984   \n",
       "     58500                                      3.988984   \n",
       "...                                                  ...   \n",
       "1313 81300                                      3.937640   \n",
       "     81600                                      3.937640   \n",
       "     81900                                      3.937640   \n",
       "     82200                                      3.937640   \n",
       "     82500                                      3.937640   \n",
       "\n",
       "            cpu__query_similarity_count__query_None__threshold_0.0  \\\n",
       "1    57300                                                NaN        \n",
       "     57600                                                NaN        \n",
       "     57900                                                NaN        \n",
       "     58200                                                NaN        \n",
       "     58500                                                NaN        \n",
       "...                                                       ...        \n",
       "1313 81300                                                NaN        \n",
       "     81600                                                NaN        \n",
       "     81900                                                NaN        \n",
       "     82200                                                NaN        \n",
       "     82500                                                NaN        \n",
       "\n",
       "            cpu__mean_n_absolute_max__number_of_maxima_7  \n",
       "1    57300                                     42.731429  \n",
       "     57600                                     42.142857  \n",
       "     57900                                     42.142857  \n",
       "     58200                                     42.142857  \n",
       "     58500                                     42.142857  \n",
       "...                                                  ...  \n",
       "1313 81300                                     37.605714  \n",
       "     81600                                     37.605714  \n",
       "     81900                                     37.605714  \n",
       "     82200                                     37.605714  \n",
       "     82500                                     37.605714  \n",
       "\n",
       "[110673 rows x 777 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "extracted_features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = df.groupby('machine_id').apply(lambda x: x.set_index('time')['cpu'].shift(-1)[:-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = y[y.index.isin(extracted_features.index)]\n",
    "extracted_features = extracted_features[extracted_features.index.isin(y.index)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((109363,), (109363, 777))"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.shape, extracted_features.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature Selector"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "fs = FeatureSelector(data=extracted_features, labels=y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "42 features with a single unique value.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "fs.identify_single_unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "70 features with a correlation magnitude greater than 0.98.\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Projects/adaptive_prediction_models/third_party/feature_selector.py:223: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  record_collinear = record_collinear.append(temp_df, ignore_index = True)\n",
      "/Projects/adaptive_prediction_models/third_party/feature_selector.py:223: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  record_collinear = record_collinear.append(temp_df, ignore_index = True)\n",
      "/Projects/adaptive_prediction_models/third_party/feature_selector.py:223: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  record_collinear = record_collinear.append(temp_df, ignore_index = True)\n",
      "/Projects/adaptive_prediction_models/third_party/feature_selector.py:223: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  record_collinear = record_collinear.append(temp_df, ignore_index = True)\n",
      "/Projects/adaptive_prediction_models/third_party/feature_selector.py:223: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  record_collinear = record_collinear.append(temp_df, ignore_index = True)\n",
      "/Projects/adaptive_prediction_models/third_party/feature_selector.py:223: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  record_collinear = record_collinear.append(temp_df, ignore_index = True)\n",
      "/Projects/adaptive_prediction_models/third_party/feature_selector.py:223: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  record_collinear = record_collinear.append(temp_df, ignore_index = True)\n",
      "/Projects/adaptive_prediction_models/third_party/feature_selector.py:223: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  record_collinear = record_collinear.append(temp_df, ignore_index = True)\n",
      "/Projects/adaptive_prediction_models/third_party/feature_selector.py:223: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  record_collinear = record_collinear.append(temp_df, ignore_index = True)\n",
      "/Projects/adaptive_prediction_models/third_party/feature_selector.py:223: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  record_collinear = record_collinear.append(temp_df, ignore_index = True)\n",
      "/Projects/adaptive_prediction_models/third_party/feature_selector.py:223: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  record_collinear = record_collinear.append(temp_df, ignore_index = True)\n",
      "/Projects/adaptive_prediction_models/third_party/feature_selector.py:223: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  record_collinear = record_collinear.append(temp_df, ignore_index = True)\n",
      "/Projects/adaptive_prediction_models/third_party/feature_selector.py:223: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  record_collinear = record_collinear.append(temp_df, ignore_index = True)\n",
      "/Projects/adaptive_prediction_models/third_party/feature_selector.py:223: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  record_collinear = record_collinear.append(temp_df, ignore_index = True)\n",
      "/Projects/adaptive_prediction_models/third_party/feature_selector.py:223: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  record_collinear = record_collinear.append(temp_df, ignore_index = True)\n",
      "/Projects/adaptive_prediction_models/third_party/feature_selector.py:223: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  record_collinear = record_collinear.append(temp_df, ignore_index = True)\n",
      "/Projects/adaptive_prediction_models/third_party/feature_selector.py:223: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  record_collinear = record_collinear.append(temp_df, ignore_index = True)\n",
      "/Projects/adaptive_prediction_models/third_party/feature_selector.py:223: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  record_collinear = record_collinear.append(temp_df, ignore_index = True)\n",
      "/Projects/adaptive_prediction_models/third_party/feature_selector.py:223: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  record_collinear = record_collinear.append(temp_df, ignore_index = True)\n",
      "/Projects/adaptive_prediction_models/third_party/feature_selector.py:223: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  record_collinear = record_collinear.append(temp_df, ignore_index = True)\n",
      "/Projects/adaptive_prediction_models/third_party/feature_selector.py:223: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  record_collinear = record_collinear.append(temp_df, ignore_index = True)\n",
      "/Projects/adaptive_prediction_models/third_party/feature_selector.py:223: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  record_collinear = record_collinear.append(temp_df, ignore_index = True)\n",
      "/Projects/adaptive_prediction_models/third_party/feature_selector.py:223: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  record_collinear = record_collinear.append(temp_df, ignore_index = True)\n",
      "/Projects/adaptive_prediction_models/third_party/feature_selector.py:223: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  record_collinear = record_collinear.append(temp_df, ignore_index = True)\n",
      "/Projects/adaptive_prediction_models/third_party/feature_selector.py:223: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  record_collinear = record_collinear.append(temp_df, ignore_index = True)\n",
      "/Projects/adaptive_prediction_models/third_party/feature_selector.py:223: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  record_collinear = record_collinear.append(temp_df, ignore_index = True)\n",
      "/Projects/adaptive_prediction_models/third_party/feature_selector.py:223: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  record_collinear = record_collinear.append(temp_df, ignore_index = True)\n",
      "/Projects/adaptive_prediction_models/third_party/feature_selector.py:223: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  record_collinear = record_collinear.append(temp_df, ignore_index = True)\n",
      "/Projects/adaptive_prediction_models/third_party/feature_selector.py:223: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  record_collinear = record_collinear.append(temp_df, ignore_index = True)\n",
      "/Projects/adaptive_prediction_models/third_party/feature_selector.py:223: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  record_collinear = record_collinear.append(temp_df, ignore_index = True)\n",
      "/Projects/adaptive_prediction_models/third_party/feature_selector.py:223: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  record_collinear = record_collinear.append(temp_df, ignore_index = True)\n",
      "/Projects/adaptive_prediction_models/third_party/feature_selector.py:223: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  record_collinear = record_collinear.append(temp_df, ignore_index = True)\n",
      "/Projects/adaptive_prediction_models/third_party/feature_selector.py:223: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  record_collinear = record_collinear.append(temp_df, ignore_index = True)\n",
      "/Projects/adaptive_prediction_models/third_party/feature_selector.py:223: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  record_collinear = record_collinear.append(temp_df, ignore_index = True)\n",
      "/Projects/adaptive_prediction_models/third_party/feature_selector.py:223: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  record_collinear = record_collinear.append(temp_df, ignore_index = True)\n",
      "/Projects/adaptive_prediction_models/third_party/feature_selector.py:223: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  record_collinear = record_collinear.append(temp_df, ignore_index = True)\n",
      "/Projects/adaptive_prediction_models/third_party/feature_selector.py:223: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  record_collinear = record_collinear.append(temp_df, ignore_index = True)\n",
      "/Projects/adaptive_prediction_models/third_party/feature_selector.py:223: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  record_collinear = record_collinear.append(temp_df, ignore_index = True)\n",
      "/Projects/adaptive_prediction_models/third_party/feature_selector.py:223: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  record_collinear = record_collinear.append(temp_df, ignore_index = True)\n",
      "/Projects/adaptive_prediction_models/third_party/feature_selector.py:223: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  record_collinear = record_collinear.append(temp_df, ignore_index = True)\n",
      "/Projects/adaptive_prediction_models/third_party/feature_selector.py:223: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  record_collinear = record_collinear.append(temp_df, ignore_index = True)\n",
      "/Projects/adaptive_prediction_models/third_party/feature_selector.py:223: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  record_collinear = record_collinear.append(temp_df, ignore_index = True)\n",
      "/Projects/adaptive_prediction_models/third_party/feature_selector.py:223: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  record_collinear = record_collinear.append(temp_df, ignore_index = True)\n",
      "/Projects/adaptive_prediction_models/third_party/feature_selector.py:223: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  record_collinear = record_collinear.append(temp_df, ignore_index = True)\n",
      "/Projects/adaptive_prediction_models/third_party/feature_selector.py:223: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  record_collinear = record_collinear.append(temp_df, ignore_index = True)\n",
      "/Projects/adaptive_prediction_models/third_party/feature_selector.py:223: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  record_collinear = record_collinear.append(temp_df, ignore_index = True)\n",
      "/Projects/adaptive_prediction_models/third_party/feature_selector.py:223: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  record_collinear = record_collinear.append(temp_df, ignore_index = True)\n",
      "/Projects/adaptive_prediction_models/third_party/feature_selector.py:223: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  record_collinear = record_collinear.append(temp_df, ignore_index = True)\n",
      "/Projects/adaptive_prediction_models/third_party/feature_selector.py:223: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  record_collinear = record_collinear.append(temp_df, ignore_index = True)\n",
      "/Projects/adaptive_prediction_models/third_party/feature_selector.py:223: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  record_collinear = record_collinear.append(temp_df, ignore_index = True)\n",
      "/Projects/adaptive_prediction_models/third_party/feature_selector.py:223: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  record_collinear = record_collinear.append(temp_df, ignore_index = True)\n",
      "/Projects/adaptive_prediction_models/third_party/feature_selector.py:223: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  record_collinear = record_collinear.append(temp_df, ignore_index = True)\n",
      "/Projects/adaptive_prediction_models/third_party/feature_selector.py:223: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  record_collinear = record_collinear.append(temp_df, ignore_index = True)\n",
      "/Projects/adaptive_prediction_models/third_party/feature_selector.py:223: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  record_collinear = record_collinear.append(temp_df, ignore_index = True)\n",
      "/Projects/adaptive_prediction_models/third_party/feature_selector.py:223: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  record_collinear = record_collinear.append(temp_df, ignore_index = True)\n",
      "/Projects/adaptive_prediction_models/third_party/feature_selector.py:223: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  record_collinear = record_collinear.append(temp_df, ignore_index = True)\n",
      "/Projects/adaptive_prediction_models/third_party/feature_selector.py:223: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  record_collinear = record_collinear.append(temp_df, ignore_index = True)\n",
      "/Projects/adaptive_prediction_models/third_party/feature_selector.py:223: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  record_collinear = record_collinear.append(temp_df, ignore_index = True)\n",
      "/Projects/adaptive_prediction_models/third_party/feature_selector.py:223: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  record_collinear = record_collinear.append(temp_df, ignore_index = True)\n",
      "/Projects/adaptive_prediction_models/third_party/feature_selector.py:223: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  record_collinear = record_collinear.append(temp_df, ignore_index = True)\n",
      "/Projects/adaptive_prediction_models/third_party/feature_selector.py:223: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  record_collinear = record_collinear.append(temp_df, ignore_index = True)\n",
      "/Projects/adaptive_prediction_models/third_party/feature_selector.py:223: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  record_collinear = record_collinear.append(temp_df, ignore_index = True)\n",
      "/Projects/adaptive_prediction_models/third_party/feature_selector.py:223: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  record_collinear = record_collinear.append(temp_df, ignore_index = True)\n",
      "/Projects/adaptive_prediction_models/third_party/feature_selector.py:223: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  record_collinear = record_collinear.append(temp_df, ignore_index = True)\n",
      "/Projects/adaptive_prediction_models/third_party/feature_selector.py:223: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  record_collinear = record_collinear.append(temp_df, ignore_index = True)\n",
      "/Projects/adaptive_prediction_models/third_party/feature_selector.py:223: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  record_collinear = record_collinear.append(temp_df, ignore_index = True)\n",
      "/Projects/adaptive_prediction_models/third_party/feature_selector.py:223: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  record_collinear = record_collinear.append(temp_df, ignore_index = True)\n",
      "/Projects/adaptive_prediction_models/third_party/feature_selector.py:223: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  record_collinear = record_collinear.append(temp_df, ignore_index = True)\n",
      "/Projects/adaptive_prediction_models/third_party/feature_selector.py:223: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  record_collinear = record_collinear.append(temp_df, ignore_index = True)\n",
      "/Projects/adaptive_prediction_models/third_party/feature_selector.py:223: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  record_collinear = record_collinear.append(temp_df, ignore_index = True)\n"
     ]
    }
   ],
   "source": [
    "fs.identify_collinear(correlation_threshold=0.98)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Gradient Boosting Model\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/envs/py39/lib/python3.9/site-packages/lightgbm/sklearn.py:726: UserWarning: 'early_stopping_rounds' argument is deprecated and will be removed in a future release of LightGBM. Pass 'early_stopping()' callback via 'callbacks' argument instead.\n",
      "  _log_warning(\"'early_stopping_rounds' argument is deprecated and will be removed in a future release of LightGBM. \"\n",
      "/opt/conda/envs/py39/lib/python3.9/site-packages/lightgbm/sklearn.py:736: UserWarning: 'verbose' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.\n",
      "  _log_warning(\"'verbose' argument is deprecated and will be removed in a future release of LightGBM. \"\n",
      "/opt/conda/envs/py39/lib/python3.9/site-packages/lightgbm/sklearn.py:726: UserWarning: 'early_stopping_rounds' argument is deprecated and will be removed in a future release of LightGBM. Pass 'early_stopping()' callback via 'callbacks' argument instead.\n",
      "  _log_warning(\"'early_stopping_rounds' argument is deprecated and will be removed in a future release of LightGBM. \"\n",
      "/opt/conda/envs/py39/lib/python3.9/site-packages/lightgbm/sklearn.py:736: UserWarning: 'verbose' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.\n",
      "  _log_warning(\"'verbose' argument is deprecated and will be removed in a future release of LightGBM. \"\n",
      "/opt/conda/envs/py39/lib/python3.9/site-packages/lightgbm/sklearn.py:726: UserWarning: 'early_stopping_rounds' argument is deprecated and will be removed in a future release of LightGBM. Pass 'early_stopping()' callback via 'callbacks' argument instead.\n",
      "  _log_warning(\"'early_stopping_rounds' argument is deprecated and will be removed in a future release of LightGBM. \"\n",
      "/opt/conda/envs/py39/lib/python3.9/site-packages/lightgbm/sklearn.py:736: UserWarning: 'verbose' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.\n",
      "  _log_warning(\"'verbose' argument is deprecated and will be removed in a future release of LightGBM. \"\n",
      "/opt/conda/envs/py39/lib/python3.9/site-packages/lightgbm/sklearn.py:726: UserWarning: 'early_stopping_rounds' argument is deprecated and will be removed in a future release of LightGBM. Pass 'early_stopping()' callback via 'callbacks' argument instead.\n",
      "  _log_warning(\"'early_stopping_rounds' argument is deprecated and will be removed in a future release of LightGBM. \"\n",
      "/opt/conda/envs/py39/lib/python3.9/site-packages/lightgbm/sklearn.py:736: UserWarning: 'verbose' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.\n",
      "  _log_warning(\"'verbose' argument is deprecated and will be removed in a future release of LightGBM. \"\n",
      "/opt/conda/envs/py39/lib/python3.9/site-packages/lightgbm/sklearn.py:726: UserWarning: 'early_stopping_rounds' argument is deprecated and will be removed in a future release of LightGBM. Pass 'early_stopping()' callback via 'callbacks' argument instead.\n",
      "  _log_warning(\"'early_stopping_rounds' argument is deprecated and will be removed in a future release of LightGBM. \"\n",
      "/opt/conda/envs/py39/lib/python3.9/site-packages/lightgbm/sklearn.py:736: UserWarning: 'verbose' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.\n",
      "  _log_warning(\"'verbose' argument is deprecated and will be removed in a future release of LightGBM. \"\n",
      "/opt/conda/envs/py39/lib/python3.9/site-packages/lightgbm/sklearn.py:726: UserWarning: 'early_stopping_rounds' argument is deprecated and will be removed in a future release of LightGBM. Pass 'early_stopping()' callback via 'callbacks' argument instead.\n",
      "  _log_warning(\"'early_stopping_rounds' argument is deprecated and will be removed in a future release of LightGBM. \"\n",
      "/opt/conda/envs/py39/lib/python3.9/site-packages/lightgbm/sklearn.py:736: UserWarning: 'verbose' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.\n",
      "  _log_warning(\"'verbose' argument is deprecated and will be removed in a future release of LightGBM. \"\n",
      "/opt/conda/envs/py39/lib/python3.9/site-packages/lightgbm/sklearn.py:726: UserWarning: 'early_stopping_rounds' argument is deprecated and will be removed in a future release of LightGBM. Pass 'early_stopping()' callback via 'callbacks' argument instead.\n",
      "  _log_warning(\"'early_stopping_rounds' argument is deprecated and will be removed in a future release of LightGBM. \"\n",
      "/opt/conda/envs/py39/lib/python3.9/site-packages/lightgbm/sklearn.py:736: UserWarning: 'verbose' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.\n",
      "  _log_warning(\"'verbose' argument is deprecated and will be removed in a future release of LightGBM. \"\n",
      "/opt/conda/envs/py39/lib/python3.9/site-packages/lightgbm/sklearn.py:726: UserWarning: 'early_stopping_rounds' argument is deprecated and will be removed in a future release of LightGBM. Pass 'early_stopping()' callback via 'callbacks' argument instead.\n",
      "  _log_warning(\"'early_stopping_rounds' argument is deprecated and will be removed in a future release of LightGBM. \"\n",
      "/opt/conda/envs/py39/lib/python3.9/site-packages/lightgbm/sklearn.py:736: UserWarning: 'verbose' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.\n",
      "  _log_warning(\"'verbose' argument is deprecated and will be removed in a future release of LightGBM. \"\n",
      "/opt/conda/envs/py39/lib/python3.9/site-packages/lightgbm/sklearn.py:726: UserWarning: 'early_stopping_rounds' argument is deprecated and will be removed in a future release of LightGBM. Pass 'early_stopping()' callback via 'callbacks' argument instead.\n",
      "  _log_warning(\"'early_stopping_rounds' argument is deprecated and will be removed in a future release of LightGBM. \"\n",
      "/opt/conda/envs/py39/lib/python3.9/site-packages/lightgbm/sklearn.py:736: UserWarning: 'verbose' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.\n",
      "  _log_warning(\"'verbose' argument is deprecated and will be removed in a future release of LightGBM. \"\n",
      "/opt/conda/envs/py39/lib/python3.9/site-packages/lightgbm/sklearn.py:726: UserWarning: 'early_stopping_rounds' argument is deprecated and will be removed in a future release of LightGBM. Pass 'early_stopping()' callback via 'callbacks' argument instead.\n",
      "  _log_warning(\"'early_stopping_rounds' argument is deprecated and will be removed in a future release of LightGBM. \"\n",
      "/opt/conda/envs/py39/lib/python3.9/site-packages/lightgbm/sklearn.py:736: UserWarning: 'verbose' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.\n",
      "  _log_warning(\"'verbose' argument is deprecated and will be removed in a future release of LightGBM. \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "347 features with zero or negative importance after one-hot encoding.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "fs.identify_zero_importance(task='regression', eval_metric='l2', n_iterations=10, early_stopping=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Removed 405 features.\n"
     ]
    }
   ],
   "source": [
    "extracted_features = fs.remove(methods=['single_unique', 'collinear', 'zero_importance'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>cpu__has_duplicate</th>\n",
       "      <th>cpu__sum_values</th>\n",
       "      <th>cpu__abs_energy</th>\n",
       "      <th>cpu__mean_abs_change</th>\n",
       "      <th>cpu__mean_change</th>\n",
       "      <th>cpu__mean_second_derivative_central</th>\n",
       "      <th>cpu__standard_deviation</th>\n",
       "      <th>cpu__variation_coefficient</th>\n",
       "      <th>cpu__variance</th>\n",
       "      <th>cpu__skewness</th>\n",
       "      <th>...</th>\n",
       "      <th>cpu__fourier_entropy__bins_2</th>\n",
       "      <th>cpu__fourier_entropy__bins_3</th>\n",
       "      <th>cpu__fourier_entropy__bins_5</th>\n",
       "      <th>cpu__fourier_entropy__bins_10</th>\n",
       "      <th>cpu__fourier_entropy__bins_100</th>\n",
       "      <th>cpu__permutation_entropy__dimension_3__tau_1</th>\n",
       "      <th>cpu__permutation_entropy__dimension_4__tau_1</th>\n",
       "      <th>cpu__permutation_entropy__dimension_5__tau_1</th>\n",
       "      <th>cpu__permutation_entropy__dimension_6__tau_1</th>\n",
       "      <th>cpu__permutation_entropy__dimension_7__tau_1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">1</th>\n",
       "      <th>57300</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2075.200004</td>\n",
       "      <td>73049.099452</td>\n",
       "      <td>4.749830</td>\n",
       "      <td>-0.223051</td>\n",
       "      <td>0.162414</td>\n",
       "      <td>4.609498</td>\n",
       "      <td>0.133274</td>\n",
       "      <td>21.247475</td>\n",
       "      <td>-0.009936</td>\n",
       "      <td>...</td>\n",
       "      <td>0.384543</td>\n",
       "      <td>0.688364</td>\n",
       "      <td>0.999869</td>\n",
       "      <td>1.631021</td>\n",
       "      <td>2.846720</td>\n",
       "      <td>1.777805</td>\n",
       "      <td>2.985166</td>\n",
       "      <td>3.768455</td>\n",
       "      <td>3.982128</td>\n",
       "      <td>3.988984</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57600</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2061.060004</td>\n",
       "      <td>71987.185429</td>\n",
       "      <td>4.613559</td>\n",
       "      <td>-0.086780</td>\n",
       "      <td>0.031379</td>\n",
       "      <td>4.449182</td>\n",
       "      <td>0.129521</td>\n",
       "      <td>19.795219</td>\n",
       "      <td>-0.057404</td>\n",
       "      <td>...</td>\n",
       "      <td>0.384543</td>\n",
       "      <td>0.688364</td>\n",
       "      <td>1.131502</td>\n",
       "      <td>1.678262</td>\n",
       "      <td>2.827608</td>\n",
       "      <td>1.771253</td>\n",
       "      <td>2.975986</td>\n",
       "      <td>3.768455</td>\n",
       "      <td>3.982128</td>\n",
       "      <td>3.988984</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57900</th>\n",
       "      <td>0.0</td>\n",
       "      <td>2055.940004</td>\n",
       "      <td>71648.855848</td>\n",
       "      <td>4.535254</td>\n",
       "      <td>-0.008475</td>\n",
       "      <td>0.012586</td>\n",
       "      <td>4.473442</td>\n",
       "      <td>0.130552</td>\n",
       "      <td>20.011681</td>\n",
       "      <td>-0.009214</td>\n",
       "      <td>...</td>\n",
       "      <td>0.317937</td>\n",
       "      <td>0.688364</td>\n",
       "      <td>1.165271</td>\n",
       "      <td>1.643434</td>\n",
       "      <td>2.969917</td>\n",
       "      <td>1.757079</td>\n",
       "      <td>2.928559</td>\n",
       "      <td>3.768455</td>\n",
       "      <td>3.982128</td>\n",
       "      <td>3.988984</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58200</th>\n",
       "      <td>0.0</td>\n",
       "      <td>2053.760004</td>\n",
       "      <td>71518.535453</td>\n",
       "      <td>4.538983</td>\n",
       "      <td>-0.012203</td>\n",
       "      <td>-0.020172</td>\n",
       "      <td>4.508694</td>\n",
       "      <td>0.131720</td>\n",
       "      <td>20.328326</td>\n",
       "      <td>-0.008040</td>\n",
       "      <td>...</td>\n",
       "      <td>0.317937</td>\n",
       "      <td>0.741749</td>\n",
       "      <td>1.165271</td>\n",
       "      <td>1.689065</td>\n",
       "      <td>3.165672</td>\n",
       "      <td>1.758518</td>\n",
       "      <td>2.920594</td>\n",
       "      <td>3.734356</td>\n",
       "      <td>3.982128</td>\n",
       "      <td>3.988984</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58500</th>\n",
       "      <td>0.0</td>\n",
       "      <td>2049.680004</td>\n",
       "      <td>71294.298656</td>\n",
       "      <td>4.584746</td>\n",
       "      <td>-0.080339</td>\n",
       "      <td>-0.032586</td>\n",
       "      <td>4.608862</td>\n",
       "      <td>0.134915</td>\n",
       "      <td>21.241611</td>\n",
       "      <td>-0.062458</td>\n",
       "      <td>...</td>\n",
       "      <td>0.317937</td>\n",
       "      <td>0.787235</td>\n",
       "      <td>1.165271</td>\n",
       "      <td>1.707492</td>\n",
       "      <td>3.076234</td>\n",
       "      <td>1.767957</td>\n",
       "      <td>2.887093</td>\n",
       "      <td>3.700257</td>\n",
       "      <td>3.982128</td>\n",
       "      <td>3.988984</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">1313</th>\n",
       "      <th>81000</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1655.259997</td>\n",
       "      <td>47773.595376</td>\n",
       "      <td>3.234576</td>\n",
       "      <td>-0.256949</td>\n",
       "      <td>-0.009828</td>\n",
       "      <td>5.928511</td>\n",
       "      <td>0.214897</td>\n",
       "      <td>35.147240</td>\n",
       "      <td>-0.027028</td>\n",
       "      <td>...</td>\n",
       "      <td>0.239217</td>\n",
       "      <td>0.379535</td>\n",
       "      <td>0.731802</td>\n",
       "      <td>0.956616</td>\n",
       "      <td>2.547396</td>\n",
       "      <td>1.713828</td>\n",
       "      <td>2.834808</td>\n",
       "      <td>3.510323</td>\n",
       "      <td>3.796177</td>\n",
       "      <td>3.937640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81300</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1638.439997</td>\n",
       "      <td>47027.796582</td>\n",
       "      <td>3.217627</td>\n",
       "      <td>-0.240000</td>\n",
       "      <td>-0.026207</td>\n",
       "      <td>6.173019</td>\n",
       "      <td>0.226057</td>\n",
       "      <td>38.106158</td>\n",
       "      <td>-0.078203</td>\n",
       "      <td>...</td>\n",
       "      <td>0.239217</td>\n",
       "      <td>0.379535</td>\n",
       "      <td>0.731802</td>\n",
       "      <td>1.018214</td>\n",
       "      <td>2.558358</td>\n",
       "      <td>1.700411</td>\n",
       "      <td>2.820881</td>\n",
       "      <td>3.485568</td>\n",
       "      <td>3.796177</td>\n",
       "      <td>3.937640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81600</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1625.239998</td>\n",
       "      <td>46464.948601</td>\n",
       "      <td>3.210508</td>\n",
       "      <td>-0.247119</td>\n",
       "      <td>0.026724</td>\n",
       "      <td>6.379043</td>\n",
       "      <td>0.235499</td>\n",
       "      <td>40.692185</td>\n",
       "      <td>-0.096063</td>\n",
       "      <td>...</td>\n",
       "      <td>0.239217</td>\n",
       "      <td>0.379535</td>\n",
       "      <td>0.687083</td>\n",
       "      <td>0.928839</td>\n",
       "      <td>2.463937</td>\n",
       "      <td>1.700411</td>\n",
       "      <td>2.801307</td>\n",
       "      <td>3.485568</td>\n",
       "      <td>3.796177</td>\n",
       "      <td>3.937640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81900</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1609.359998</td>\n",
       "      <td>45786.554998</td>\n",
       "      <td>3.196271</td>\n",
       "      <td>-0.232881</td>\n",
       "      <td>-0.016897</td>\n",
       "      <td>6.607103</td>\n",
       "      <td>0.246325</td>\n",
       "      <td>43.653805</td>\n",
       "      <td>-0.115089</td>\n",
       "      <td>...</td>\n",
       "      <td>0.239217</td>\n",
       "      <td>0.379535</td>\n",
       "      <td>0.687083</td>\n",
       "      <td>0.928839</td>\n",
       "      <td>2.483049</td>\n",
       "      <td>1.700411</td>\n",
       "      <td>2.807268</td>\n",
       "      <td>3.476224</td>\n",
       "      <td>3.821382</td>\n",
       "      <td>3.937640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82200</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1595.759998</td>\n",
       "      <td>45232.763007</td>\n",
       "      <td>3.187458</td>\n",
       "      <td>-0.241695</td>\n",
       "      <td>-0.031897</td>\n",
       "      <td>6.821449</td>\n",
       "      <td>0.256484</td>\n",
       "      <td>46.532169</td>\n",
       "      <td>-0.127938</td>\n",
       "      <td>...</td>\n",
       "      <td>0.239217</td>\n",
       "      <td>0.379535</td>\n",
       "      <td>0.611952</td>\n",
       "      <td>0.928839</td>\n",
       "      <td>2.405088</td>\n",
       "      <td>1.702331</td>\n",
       "      <td>2.816448</td>\n",
       "      <td>3.500979</td>\n",
       "      <td>3.821382</td>\n",
       "      <td>3.937640</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>109363 rows × 372 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            cpu__has_duplicate  cpu__sum_values  cpu__abs_energy  \\\n",
       "1    57300                 1.0      2075.200004     73049.099452   \n",
       "     57600                 1.0      2061.060004     71987.185429   \n",
       "     57900                 0.0      2055.940004     71648.855848   \n",
       "     58200                 0.0      2053.760004     71518.535453   \n",
       "     58500                 0.0      2049.680004     71294.298656   \n",
       "...                        ...              ...              ...   \n",
       "1313 81000                 0.0      1655.259997     47773.595376   \n",
       "     81300                 0.0      1638.439997     47027.796582   \n",
       "     81600                 0.0      1625.239998     46464.948601   \n",
       "     81900                 0.0      1609.359998     45786.554998   \n",
       "     82200                 0.0      1595.759998     45232.763007   \n",
       "\n",
       "            cpu__mean_abs_change  cpu__mean_change  \\\n",
       "1    57300              4.749830         -0.223051   \n",
       "     57600              4.613559         -0.086780   \n",
       "     57900              4.535254         -0.008475   \n",
       "     58200              4.538983         -0.012203   \n",
       "     58500              4.584746         -0.080339   \n",
       "...                          ...               ...   \n",
       "1313 81000              3.234576         -0.256949   \n",
       "     81300              3.217627         -0.240000   \n",
       "     81600              3.210508         -0.247119   \n",
       "     81900              3.196271         -0.232881   \n",
       "     82200              3.187458         -0.241695   \n",
       "\n",
       "            cpu__mean_second_derivative_central  cpu__standard_deviation  \\\n",
       "1    57300                             0.162414                 4.609498   \n",
       "     57600                             0.031379                 4.449182   \n",
       "     57900                             0.012586                 4.473442   \n",
       "     58200                            -0.020172                 4.508694   \n",
       "     58500                            -0.032586                 4.608862   \n",
       "...                                         ...                      ...   \n",
       "1313 81000                            -0.009828                 5.928511   \n",
       "     81300                            -0.026207                 6.173019   \n",
       "     81600                             0.026724                 6.379043   \n",
       "     81900                            -0.016897                 6.607103   \n",
       "     82200                            -0.031897                 6.821449   \n",
       "\n",
       "            cpu__variation_coefficient  cpu__variance  cpu__skewness  ...  \\\n",
       "1    57300                    0.133274      21.247475      -0.009936  ...   \n",
       "     57600                    0.129521      19.795219      -0.057404  ...   \n",
       "     57900                    0.130552      20.011681      -0.009214  ...   \n",
       "     58200                    0.131720      20.328326      -0.008040  ...   \n",
       "     58500                    0.134915      21.241611      -0.062458  ...   \n",
       "...                                ...            ...            ...  ...   \n",
       "1313 81000                    0.214897      35.147240      -0.027028  ...   \n",
       "     81300                    0.226057      38.106158      -0.078203  ...   \n",
       "     81600                    0.235499      40.692185      -0.096063  ...   \n",
       "     81900                    0.246325      43.653805      -0.115089  ...   \n",
       "     82200                    0.256484      46.532169      -0.127938  ...   \n",
       "\n",
       "            cpu__fourier_entropy__bins_2  cpu__fourier_entropy__bins_3  \\\n",
       "1    57300                      0.384543                      0.688364   \n",
       "     57600                      0.384543                      0.688364   \n",
       "     57900                      0.317937                      0.688364   \n",
       "     58200                      0.317937                      0.741749   \n",
       "     58500                      0.317937                      0.787235   \n",
       "...                                  ...                           ...   \n",
       "1313 81000                      0.239217                      0.379535   \n",
       "     81300                      0.239217                      0.379535   \n",
       "     81600                      0.239217                      0.379535   \n",
       "     81900                      0.239217                      0.379535   \n",
       "     82200                      0.239217                      0.379535   \n",
       "\n",
       "            cpu__fourier_entropy__bins_5  cpu__fourier_entropy__bins_10  \\\n",
       "1    57300                      0.999869                       1.631021   \n",
       "     57600                      1.131502                       1.678262   \n",
       "     57900                      1.165271                       1.643434   \n",
       "     58200                      1.165271                       1.689065   \n",
       "     58500                      1.165271                       1.707492   \n",
       "...                                  ...                            ...   \n",
       "1313 81000                      0.731802                       0.956616   \n",
       "     81300                      0.731802                       1.018214   \n",
       "     81600                      0.687083                       0.928839   \n",
       "     81900                      0.687083                       0.928839   \n",
       "     82200                      0.611952                       0.928839   \n",
       "\n",
       "            cpu__fourier_entropy__bins_100  \\\n",
       "1    57300                        2.846720   \n",
       "     57600                        2.827608   \n",
       "     57900                        2.969917   \n",
       "     58200                        3.165672   \n",
       "     58500                        3.076234   \n",
       "...                                    ...   \n",
       "1313 81000                        2.547396   \n",
       "     81300                        2.558358   \n",
       "     81600                        2.463937   \n",
       "     81900                        2.483049   \n",
       "     82200                        2.405088   \n",
       "\n",
       "            cpu__permutation_entropy__dimension_3__tau_1  \\\n",
       "1    57300                                      1.777805   \n",
       "     57600                                      1.771253   \n",
       "     57900                                      1.757079   \n",
       "     58200                                      1.758518   \n",
       "     58500                                      1.767957   \n",
       "...                                                  ...   \n",
       "1313 81000                                      1.713828   \n",
       "     81300                                      1.700411   \n",
       "     81600                                      1.700411   \n",
       "     81900                                      1.700411   \n",
       "     82200                                      1.702331   \n",
       "\n",
       "            cpu__permutation_entropy__dimension_4__tau_1  \\\n",
       "1    57300                                      2.985166   \n",
       "     57600                                      2.975986   \n",
       "     57900                                      2.928559   \n",
       "     58200                                      2.920594   \n",
       "     58500                                      2.887093   \n",
       "...                                                  ...   \n",
       "1313 81000                                      2.834808   \n",
       "     81300                                      2.820881   \n",
       "     81600                                      2.801307   \n",
       "     81900                                      2.807268   \n",
       "     82200                                      2.816448   \n",
       "\n",
       "            cpu__permutation_entropy__dimension_5__tau_1  \\\n",
       "1    57300                                      3.768455   \n",
       "     57600                                      3.768455   \n",
       "     57900                                      3.768455   \n",
       "     58200                                      3.734356   \n",
       "     58500                                      3.700257   \n",
       "...                                                  ...   \n",
       "1313 81000                                      3.510323   \n",
       "     81300                                      3.485568   \n",
       "     81600                                      3.485568   \n",
       "     81900                                      3.476224   \n",
       "     82200                                      3.500979   \n",
       "\n",
       "            cpu__permutation_entropy__dimension_6__tau_1  \\\n",
       "1    57300                                      3.982128   \n",
       "     57600                                      3.982128   \n",
       "     57900                                      3.982128   \n",
       "     58200                                      3.982128   \n",
       "     58500                                      3.982128   \n",
       "...                                                  ...   \n",
       "1313 81000                                      3.796177   \n",
       "     81300                                      3.796177   \n",
       "     81600                                      3.796177   \n",
       "     81900                                      3.821382   \n",
       "     82200                                      3.821382   \n",
       "\n",
       "            cpu__permutation_entropy__dimension_7__tau_1  \n",
       "1    57300                                      3.988984  \n",
       "     57600                                      3.988984  \n",
       "     57900                                      3.988984  \n",
       "     58200                                      3.988984  \n",
       "     58500                                      3.988984  \n",
       "...                                                  ...  \n",
       "1313 81000                                      3.937640  \n",
       "     81300                                      3.937640  \n",
       "     81600                                      3.937640  \n",
       "     81900                                      3.937640  \n",
       "     82200                                      3.937640  \n",
       "\n",
       "[109363 rows x 372 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "extracted_features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "extracted_features.isnull().values.any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "extracted_features = impute(extracted_features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(109363, 372)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "extracted_features.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "extracted_features = select_features(extracted_features, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(109363, 354)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "extracted_features.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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>cpu__sum_values</th>\n",
       "      <th>cpu__change_quantiles__f_agg_\"mean\"__isabs_True__qh_1.0__ql_0.8</th>\n",
       "      <th>cpu__fft_coefficient__attr_\"real\"__coeff_3</th>\n",
       "      <th>cpu__fft_coefficient__attr_\"real\"__coeff_15</th>\n",
       "      <th>cpu__fft_coefficient__attr_\"real\"__coeff_18</th>\n",
       "      <th>cpu__large_standard_deviation__r_0.25</th>\n",
       "      <th>cpu__fft_coefficient__attr_\"real\"__coeff_29</th>\n",
       "      <th>cpu__fft_coefficient__attr_\"imag\"__coeff_2</th>\n",
       "      <th>cpu__fft_coefficient__attr_\"imag\"__coeff_3</th>\n",
       "      <th>cpu__fft_coefficient__attr_\"imag\"__coeff_4</th>\n",
       "      <th>...</th>\n",
       "      <th>cpu__energy_ratio_by_chunks__num_segments_10__segment_focus_1</th>\n",
       "      <th>cpu__fft_coefficient__attr_\"real\"__coeff_7</th>\n",
       "      <th>cpu__fft_coefficient__attr_\"real\"__coeff_16</th>\n",
       "      <th>cpu__number_cwt_peaks__n_1</th>\n",
       "      <th>cpu__fft_coefficient__attr_\"real\"__coeff_6</th>\n",
       "      <th>cpu__cwt_coefficients__coeff_7__w_2__widths_(2, 5, 10, 20)</th>\n",
       "      <th>cpu__cwt_coefficients__coeff_0__w_2__widths_(2, 5, 10, 20)</th>\n",
       "      <th>cpu__fft_coefficient__attr_\"imag\"__coeff_25</th>\n",
       "      <th>cpu__agg_linear_trend__attr_\"slope\"__chunk_len_5__f_agg_\"max\"</th>\n",
       "      <th>cpu__change_quantiles__f_agg_\"mean\"__isabs_False__qh_0.8__ql_0.6</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">1</th>\n",
       "      <th>57300</th>\n",
       "      <td>2075.200004</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-50.406326</td>\n",
       "      <td>-20.279996</td>\n",
       "      <td>27.047164</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-15.658072</td>\n",
       "      <td>-63.727188</td>\n",
       "      <td>20.935206</td>\n",
       "      <td>18.730944</td>\n",
       "      <td>...</td>\n",
       "      <td>0.115371</td>\n",
       "      <td>6.397208</td>\n",
       "      <td>20.931446</td>\n",
       "      <td>13.0</td>\n",
       "      <td>-7.567163</td>\n",
       "      <td>-0.341500</td>\n",
       "      <td>8.955725</td>\n",
       "      <td>-3.491165</td>\n",
       "      <td>-0.266294</td>\n",
       "      <td>0.780001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57600</th>\n",
       "      <td>2061.060004</td>\n",
       "      <td>5.340001</td>\n",
       "      <td>-67.856539</td>\n",
       "      <td>-2.599996</td>\n",
       "      <td>-17.479656</td>\n",
       "      <td>0.0</td>\n",
       "      <td>28.266603</td>\n",
       "      <td>-70.726773</td>\n",
       "      <td>-0.035348</td>\n",
       "      <td>19.646036</td>\n",
       "      <td>...</td>\n",
       "      <td>0.122298</td>\n",
       "      <td>13.956393</td>\n",
       "      <td>30.671857</td>\n",
       "      <td>12.0</td>\n",
       "      <td>-17.770342</td>\n",
       "      <td>2.281701</td>\n",
       "      <td>2.503732</td>\n",
       "      <td>-2.978702</td>\n",
       "      <td>-0.109021</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57900</th>\n",
       "      <td>2055.940004</td>\n",
       "      <td>5.340001</td>\n",
       "      <td>-69.393889</td>\n",
       "      <td>34.419996</td>\n",
       "      <td>-0.521976</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-21.333487</td>\n",
       "      <td>-75.699765</td>\n",
       "      <td>-22.584608</td>\n",
       "      <td>15.081658</td>\n",
       "      <td>...</td>\n",
       "      <td>0.125453</td>\n",
       "      <td>24.681134</td>\n",
       "      <td>-12.668424</td>\n",
       "      <td>12.0</td>\n",
       "      <td>-11.188020</td>\n",
       "      <td>3.533864</td>\n",
       "      <td>-1.593988</td>\n",
       "      <td>6.090428</td>\n",
       "      <td>-0.315804</td>\n",
       "      <td>-2.660001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58200</th>\n",
       "      <td>2053.760004</td>\n",
       "      <td>5.340001</td>\n",
       "      <td>-61.091786</td>\n",
       "      <td>7.719996</td>\n",
       "      <td>23.595911</td>\n",
       "      <td>0.0</td>\n",
       "      <td>21.454694</td>\n",
       "      <td>-77.817562</td>\n",
       "      <td>-43.596787</td>\n",
       "      <td>7.022839</td>\n",
       "      <td>...</td>\n",
       "      <td>0.133684</td>\n",
       "      <td>26.226865</td>\n",
       "      <td>-22.675564</td>\n",
       "      <td>12.0</td>\n",
       "      <td>3.024087</td>\n",
       "      <td>4.165031</td>\n",
       "      <td>-3.830059</td>\n",
       "      <td>-8.660229</td>\n",
       "      <td>-0.319930</td>\n",
       "      <td>-2.660001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58500</th>\n",
       "      <td>2049.680004</td>\n",
       "      <td>5.340001</td>\n",
       "      <td>-48.509904</td>\n",
       "      <td>-32.239996</td>\n",
       "      <td>-10.620310</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-15.103190</td>\n",
       "      <td>-77.382638</td>\n",
       "      <td>-61.602198</td>\n",
       "      <td>-3.909779</td>\n",
       "      <td>...</td>\n",
       "      <td>0.130947</td>\n",
       "      <td>13.447552</td>\n",
       "      <td>20.015384</td>\n",
       "      <td>12.0</td>\n",
       "      <td>14.960307</td>\n",
       "      <td>4.562926</td>\n",
       "      <td>-4.573604</td>\n",
       "      <td>6.869529</td>\n",
       "      <td>-0.426224</td>\n",
       "      <td>-2.660001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">1313</th>\n",
       "      <th>81000</th>\n",
       "      <td>1655.259997</td>\n",
       "      <td>1.523333</td>\n",
       "      <td>-78.930134</td>\n",
       "      <td>18.359999</td>\n",
       "      <td>16.350396</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15.033972</td>\n",
       "      <td>26.117726</td>\n",
       "      <td>-142.406780</td>\n",
       "      <td>-48.378235</td>\n",
       "      <td>...</td>\n",
       "      <td>0.089302</td>\n",
       "      <td>26.393936</td>\n",
       "      <td>-26.105569</td>\n",
       "      <td>11.0</td>\n",
       "      <td>-21.640398</td>\n",
       "      <td>7.354004</td>\n",
       "      <td>0.948451</td>\n",
       "      <td>13.555468</td>\n",
       "      <td>-0.384196</td>\n",
       "      <td>0.533333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81300</th>\n",
       "      <td>1638.439997</td>\n",
       "      <td>1.523333</td>\n",
       "      <td>-47.057674</td>\n",
       "      <td>8.780002</td>\n",
       "      <td>-7.175498</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.120160</td>\n",
       "      <td>25.913401</td>\n",
       "      <td>-165.025315</td>\n",
       "      <td>-32.745803</td>\n",
       "      <td>...</td>\n",
       "      <td>0.078716</td>\n",
       "      <td>-21.796226</td>\n",
       "      <td>17.219849</td>\n",
       "      <td>11.0</td>\n",
       "      <td>5.905498</td>\n",
       "      <td>1.739043</td>\n",
       "      <td>-0.916139</td>\n",
       "      <td>-21.558924</td>\n",
       "      <td>-0.565524</td>\n",
       "      <td>0.533333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81600</th>\n",
       "      <td>1625.239998</td>\n",
       "      <td>1.523333</td>\n",
       "      <td>-6.312826</td>\n",
       "      <td>-1.539999</td>\n",
       "      <td>8.983330</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.696624</td>\n",
       "      <td>21.832101</td>\n",
       "      <td>-175.569046</td>\n",
       "      <td>-16.820247</td>\n",
       "      <td>...</td>\n",
       "      <td>0.080308</td>\n",
       "      <td>-51.778952</td>\n",
       "      <td>40.705415</td>\n",
       "      <td>12.0</td>\n",
       "      <td>37.336671</td>\n",
       "      <td>-4.542828</td>\n",
       "      <td>-1.622003</td>\n",
       "      <td>17.185684</td>\n",
       "      <td>-0.504895</td>\n",
       "      <td>0.533333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81900</th>\n",
       "      <td>1609.359998</td>\n",
       "      <td>1.523333</td>\n",
       "      <td>33.147187</td>\n",
       "      <td>4.419998</td>\n",
       "      <td>19.730684</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.596793</td>\n",
       "      <td>13.494996</td>\n",
       "      <td>-173.834046</td>\n",
       "      <td>-4.445296</td>\n",
       "      <td>...</td>\n",
       "      <td>0.079157</td>\n",
       "      <td>-53.763435</td>\n",
       "      <td>-10.869687</td>\n",
       "      <td>12.0</td>\n",
       "      <td>54.859314</td>\n",
       "      <td>-7.780561</td>\n",
       "      <td>-2.470852</td>\n",
       "      <td>-16.147553</td>\n",
       "      <td>-0.534825</td>\n",
       "      <td>1.360000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82200</th>\n",
       "      <td>1595.759998</td>\n",
       "      <td>1.523333</td>\n",
       "      <td>72.308154</td>\n",
       "      <td>17.419999</td>\n",
       "      <td>-1.094932</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7.576607</td>\n",
       "      <td>1.740496</td>\n",
       "      <td>-159.285589</td>\n",
       "      <td>3.166669</td>\n",
       "      <td>...</td>\n",
       "      <td>0.084137</td>\n",
       "      <td>-22.355855</td>\n",
       "      <td>-21.131445</td>\n",
       "      <td>11.0</td>\n",
       "      <td>56.304933</td>\n",
       "      <td>-6.779307</td>\n",
       "      <td>0.724182</td>\n",
       "      <td>3.982699</td>\n",
       "      <td>-1.029720</td>\n",
       "      <td>1.360000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>109363 rows × 354 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            cpu__sum_values  \\\n",
       "1    57300      2075.200004   \n",
       "     57600      2061.060004   \n",
       "     57900      2055.940004   \n",
       "     58200      2053.760004   \n",
       "     58500      2049.680004   \n",
       "...                     ...   \n",
       "1313 81000      1655.259997   \n",
       "     81300      1638.439997   \n",
       "     81600      1625.239998   \n",
       "     81900      1609.359998   \n",
       "     82200      1595.759998   \n",
       "\n",
       "            cpu__change_quantiles__f_agg_\"mean\"__isabs_True__qh_1.0__ql_0.8  \\\n",
       "1    57300                                           0.000000                 \n",
       "     57600                                           5.340001                 \n",
       "     57900                                           5.340001                 \n",
       "     58200                                           5.340001                 \n",
       "     58500                                           5.340001                 \n",
       "...                                                       ...                 \n",
       "1313 81000                                           1.523333                 \n",
       "     81300                                           1.523333                 \n",
       "     81600                                           1.523333                 \n",
       "     81900                                           1.523333                 \n",
       "     82200                                           1.523333                 \n",
       "\n",
       "            cpu__fft_coefficient__attr_\"real\"__coeff_3  \\\n",
       "1    57300                                  -50.406326   \n",
       "     57600                                  -67.856539   \n",
       "     57900                                  -69.393889   \n",
       "     58200                                  -61.091786   \n",
       "     58500                                  -48.509904   \n",
       "...                                                ...   \n",
       "1313 81000                                  -78.930134   \n",
       "     81300                                  -47.057674   \n",
       "     81600                                   -6.312826   \n",
       "     81900                                   33.147187   \n",
       "     82200                                   72.308154   \n",
       "\n",
       "            cpu__fft_coefficient__attr_\"real\"__coeff_15  \\\n",
       "1    57300                                   -20.279996   \n",
       "     57600                                    -2.599996   \n",
       "     57900                                    34.419996   \n",
       "     58200                                     7.719996   \n",
       "     58500                                   -32.239996   \n",
       "...                                                 ...   \n",
       "1313 81000                                    18.359999   \n",
       "     81300                                     8.780002   \n",
       "     81600                                    -1.539999   \n",
       "     81900                                     4.419998   \n",
       "     82200                                    17.419999   \n",
       "\n",
       "            cpu__fft_coefficient__attr_\"real\"__coeff_18  \\\n",
       "1    57300                                    27.047164   \n",
       "     57600                                   -17.479656   \n",
       "     57900                                    -0.521976   \n",
       "     58200                                    23.595911   \n",
       "     58500                                   -10.620310   \n",
       "...                                                 ...   \n",
       "1313 81000                                    16.350396   \n",
       "     81300                                    -7.175498   \n",
       "     81600                                     8.983330   \n",
       "     81900                                    19.730684   \n",
       "     82200                                    -1.094932   \n",
       "\n",
       "            cpu__large_standard_deviation__r_0.25  \\\n",
       "1    57300                                    0.0   \n",
       "     57600                                    0.0   \n",
       "     57900                                    0.0   \n",
       "     58200                                    0.0   \n",
       "     58500                                    0.0   \n",
       "...                                           ...   \n",
       "1313 81000                                    0.0   \n",
       "     81300                                    0.0   \n",
       "     81600                                    0.0   \n",
       "     81900                                    0.0   \n",
       "     82200                                    1.0   \n",
       "\n",
       "            cpu__fft_coefficient__attr_\"real\"__coeff_29  \\\n",
       "1    57300                                   -15.658072   \n",
       "     57600                                    28.266603   \n",
       "     57900                                   -21.333487   \n",
       "     58200                                    21.454694   \n",
       "     58500                                   -15.103190   \n",
       "...                                                 ...   \n",
       "1313 81000                                    15.033972   \n",
       "     81300                                     2.120160   \n",
       "     81600                                    10.696624   \n",
       "     81900                                     5.596793   \n",
       "     82200                                     7.576607   \n",
       "\n",
       "            cpu__fft_coefficient__attr_\"imag\"__coeff_2  \\\n",
       "1    57300                                  -63.727188   \n",
       "     57600                                  -70.726773   \n",
       "     57900                                  -75.699765   \n",
       "     58200                                  -77.817562   \n",
       "     58500                                  -77.382638   \n",
       "...                                                ...   \n",
       "1313 81000                                   26.117726   \n",
       "     81300                                   25.913401   \n",
       "     81600                                   21.832101   \n",
       "     81900                                   13.494996   \n",
       "     82200                                    1.740496   \n",
       "\n",
       "            cpu__fft_coefficient__attr_\"imag\"__coeff_3  \\\n",
       "1    57300                                   20.935206   \n",
       "     57600                                   -0.035348   \n",
       "     57900                                  -22.584608   \n",
       "     58200                                  -43.596787   \n",
       "     58500                                  -61.602198   \n",
       "...                                                ...   \n",
       "1313 81000                                 -142.406780   \n",
       "     81300                                 -165.025315   \n",
       "     81600                                 -175.569046   \n",
       "     81900                                 -173.834046   \n",
       "     82200                                 -159.285589   \n",
       "\n",
       "            cpu__fft_coefficient__attr_\"imag\"__coeff_4  ...  \\\n",
       "1    57300                                   18.730944  ...   \n",
       "     57600                                   19.646036  ...   \n",
       "     57900                                   15.081658  ...   \n",
       "     58200                                    7.022839  ...   \n",
       "     58500                                   -3.909779  ...   \n",
       "...                                                ...  ...   \n",
       "1313 81000                                  -48.378235  ...   \n",
       "     81300                                  -32.745803  ...   \n",
       "     81600                                  -16.820247  ...   \n",
       "     81900                                   -4.445296  ...   \n",
       "     82200                                    3.166669  ...   \n",
       "\n",
       "            cpu__energy_ratio_by_chunks__num_segments_10__segment_focus_1  \\\n",
       "1    57300                                           0.115371               \n",
       "     57600                                           0.122298               \n",
       "     57900                                           0.125453               \n",
       "     58200                                           0.133684               \n",
       "     58500                                           0.130947               \n",
       "...                                                       ...               \n",
       "1313 81000                                           0.089302               \n",
       "     81300                                           0.078716               \n",
       "     81600                                           0.080308               \n",
       "     81900                                           0.079157               \n",
       "     82200                                           0.084137               \n",
       "\n",
       "            cpu__fft_coefficient__attr_\"real\"__coeff_7  \\\n",
       "1    57300                                    6.397208   \n",
       "     57600                                   13.956393   \n",
       "     57900                                   24.681134   \n",
       "     58200                                   26.226865   \n",
       "     58500                                   13.447552   \n",
       "...                                                ...   \n",
       "1313 81000                                   26.393936   \n",
       "     81300                                  -21.796226   \n",
       "     81600                                  -51.778952   \n",
       "     81900                                  -53.763435   \n",
       "     82200                                  -22.355855   \n",
       "\n",
       "            cpu__fft_coefficient__attr_\"real\"__coeff_16  \\\n",
       "1    57300                                    20.931446   \n",
       "     57600                                    30.671857   \n",
       "     57900                                   -12.668424   \n",
       "     58200                                   -22.675564   \n",
       "     58500                                    20.015384   \n",
       "...                                                 ...   \n",
       "1313 81000                                   -26.105569   \n",
       "     81300                                    17.219849   \n",
       "     81600                                    40.705415   \n",
       "     81900                                   -10.869687   \n",
       "     82200                                   -21.131445   \n",
       "\n",
       "            cpu__number_cwt_peaks__n_1  \\\n",
       "1    57300                        13.0   \n",
       "     57600                        12.0   \n",
       "     57900                        12.0   \n",
       "     58200                        12.0   \n",
       "     58500                        12.0   \n",
       "...                                ...   \n",
       "1313 81000                        11.0   \n",
       "     81300                        11.0   \n",
       "     81600                        12.0   \n",
       "     81900                        12.0   \n",
       "     82200                        11.0   \n",
       "\n",
       "            cpu__fft_coefficient__attr_\"real\"__coeff_6  \\\n",
       "1    57300                                   -7.567163   \n",
       "     57600                                  -17.770342   \n",
       "     57900                                  -11.188020   \n",
       "     58200                                    3.024087   \n",
       "     58500                                   14.960307   \n",
       "...                                                ...   \n",
       "1313 81000                                  -21.640398   \n",
       "     81300                                    5.905498   \n",
       "     81600                                   37.336671   \n",
       "     81900                                   54.859314   \n",
       "     82200                                   56.304933   \n",
       "\n",
       "            cpu__cwt_coefficients__coeff_7__w_2__widths_(2, 5, 10, 20)  \\\n",
       "1    57300                                          -0.341500            \n",
       "     57600                                           2.281701            \n",
       "     57900                                           3.533864            \n",
       "     58200                                           4.165031            \n",
       "     58500                                           4.562926            \n",
       "...                                                       ...            \n",
       "1313 81000                                           7.354004            \n",
       "     81300                                           1.739043            \n",
       "     81600                                          -4.542828            \n",
       "     81900                                          -7.780561            \n",
       "     82200                                          -6.779307            \n",
       "\n",
       "            cpu__cwt_coefficients__coeff_0__w_2__widths_(2, 5, 10, 20)  \\\n",
       "1    57300                                           8.955725            \n",
       "     57600                                           2.503732            \n",
       "     57900                                          -1.593988            \n",
       "     58200                                          -3.830059            \n",
       "     58500                                          -4.573604            \n",
       "...                                                       ...            \n",
       "1313 81000                                           0.948451            \n",
       "     81300                                          -0.916139            \n",
       "     81600                                          -1.622003            \n",
       "     81900                                          -2.470852            \n",
       "     82200                                           0.724182            \n",
       "\n",
       "            cpu__fft_coefficient__attr_\"imag\"__coeff_25  \\\n",
       "1    57300                                    -3.491165   \n",
       "     57600                                    -2.978702   \n",
       "     57900                                     6.090428   \n",
       "     58200                                    -8.660229   \n",
       "     58500                                     6.869529   \n",
       "...                                                 ...   \n",
       "1313 81000                                    13.555468   \n",
       "     81300                                   -21.558924   \n",
       "     81600                                    17.185684   \n",
       "     81900                                   -16.147553   \n",
       "     82200                                     3.982699   \n",
       "\n",
       "            cpu__agg_linear_trend__attr_\"slope\"__chunk_len_5__f_agg_\"max\"  \\\n",
       "1    57300                                          -0.266294               \n",
       "     57600                                          -0.109021               \n",
       "     57900                                          -0.315804               \n",
       "     58200                                          -0.319930               \n",
       "     58500                                          -0.426224               \n",
       "...                                                       ...               \n",
       "1313 81000                                          -0.384196               \n",
       "     81300                                          -0.565524               \n",
       "     81600                                          -0.504895               \n",
       "     81900                                          -0.534825               \n",
       "     82200                                          -1.029720               \n",
       "\n",
       "            cpu__change_quantiles__f_agg_\"mean\"__isabs_False__qh_0.8__ql_0.6  \n",
       "1    57300                                           0.780001                 \n",
       "     57600                                           0.000000                 \n",
       "     57900                                          -2.660001                 \n",
       "     58200                                          -2.660001                 \n",
       "     58500                                          -2.660001                 \n",
       "...                                                       ...                 \n",
       "1313 81000                                           0.533333                 \n",
       "     81300                                           0.533333                 \n",
       "     81600                                           0.533333                 \n",
       "     81900                                           1.360000                 \n",
       "     82200                                           1.360000                 \n",
       "\n",
       "[109363 rows x 354 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "extracted_features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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>feature</th>\n",
       "      <th>type</th>\n",
       "      <th>p_value</th>\n",
       "      <th>relevant</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>feature</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>cpu__sum_values</th>\n",
       "      <td>cpu__sum_values</td>\n",
       "      <td>real</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cpu__fft_coefficient__attr_\"real\"__coeff_15</th>\n",
       "      <td>cpu__fft_coefficient__attr_\"real\"__coeff_15</td>\n",
       "      <td>real</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cpu__fft_coefficient__attr_\"real\"__coeff_3</th>\n",
       "      <td>cpu__fft_coefficient__attr_\"real\"__coeff_3</td>\n",
       "      <td>real</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cpu__fft_aggregated__aggtype_\"centroid\"</th>\n",
       "      <td>cpu__fft_aggregated__aggtype_\"centroid\"</td>\n",
       "      <td>real</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cpu__fft_coefficient__attr_\"imag\"__coeff_3</th>\n",
       "      <td>cpu__fft_coefficient__attr_\"imag\"__coeff_3</td>\n",
       "      <td>real</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cpu__cwt_coefficients__coeff_7__w_2__widths_(2, 5, 10, 20)</th>\n",
       "      <td>cpu__cwt_coefficients__coeff_7__w_2__widths_(2...</td>\n",
       "      <td>real</td>\n",
       "      <td>0.001300</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cpu__cwt_coefficients__coeff_0__w_2__widths_(2, 5, 10, 20)</th>\n",
       "      <td>cpu__cwt_coefficients__coeff_0__w_2__widths_(2...</td>\n",
       "      <td>real</td>\n",
       "      <td>0.002629</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cpu__fft_coefficient__attr_\"imag\"__coeff_25</th>\n",
       "      <td>cpu__fft_coefficient__attr_\"imag\"__coeff_25</td>\n",
       "      <td>real</td>\n",
       "      <td>0.004480</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cpu__agg_linear_trend__attr_\"slope\"__chunk_len_5__f_agg_\"max\"</th>\n",
       "      <td>cpu__agg_linear_trend__attr_\"slope\"__chunk_len...</td>\n",
       "      <td>real</td>\n",
       "      <td>0.005114</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cpu__change_quantiles__f_agg_\"mean\"__isabs_False__qh_0.8__ql_0.6</th>\n",
       "      <td>cpu__change_quantiles__f_agg_\"mean\"__isabs_Fal...</td>\n",
       "      <td>real</td>\n",
       "      <td>0.007065</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>354 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                                              feature  \\\n",
       "feature                                                                                                 \n",
       "cpu__sum_values                                                                       cpu__sum_values   \n",
       "cpu__fft_coefficient__attr_\"real\"__coeff_15               cpu__fft_coefficient__attr_\"real\"__coeff_15   \n",
       "cpu__fft_coefficient__attr_\"real\"__coeff_3                 cpu__fft_coefficient__attr_\"real\"__coeff_3   \n",
       "cpu__fft_aggregated__aggtype_\"centroid\"                       cpu__fft_aggregated__aggtype_\"centroid\"   \n",
       "cpu__fft_coefficient__attr_\"imag\"__coeff_3                 cpu__fft_coefficient__attr_\"imag\"__coeff_3   \n",
       "...                                                                                               ...   \n",
       "cpu__cwt_coefficients__coeff_7__w_2__widths_(2,...  cpu__cwt_coefficients__coeff_7__w_2__widths_(2...   \n",
       "cpu__cwt_coefficients__coeff_0__w_2__widths_(2,...  cpu__cwt_coefficients__coeff_0__w_2__widths_(2...   \n",
       "cpu__fft_coefficient__attr_\"imag\"__coeff_25               cpu__fft_coefficient__attr_\"imag\"__coeff_25   \n",
       "cpu__agg_linear_trend__attr_\"slope\"__chunk_len_...  cpu__agg_linear_trend__attr_\"slope\"__chunk_len...   \n",
       "cpu__change_quantiles__f_agg_\"mean\"__isabs_Fals...  cpu__change_quantiles__f_agg_\"mean\"__isabs_Fal...   \n",
       "\n",
       "                                                    type   p_value  relevant  \n",
       "feature                                                                       \n",
       "cpu__sum_values                                     real  0.000000      True  \n",
       "cpu__fft_coefficient__attr_\"real\"__coeff_15         real  0.000000      True  \n",
       "cpu__fft_coefficient__attr_\"real\"__coeff_3          real  0.000000      True  \n",
       "cpu__fft_aggregated__aggtype_\"centroid\"             real  0.000000      True  \n",
       "cpu__fft_coefficient__attr_\"imag\"__coeff_3          real  0.000000      True  \n",
       "...                                                  ...       ...       ...  \n",
       "cpu__cwt_coefficients__coeff_7__w_2__widths_(2,...  real  0.001300      True  \n",
       "cpu__cwt_coefficients__coeff_0__w_2__widths_(2,...  real  0.002629      True  \n",
       "cpu__fft_coefficient__attr_\"imag\"__coeff_25         real  0.004480      True  \n",
       "cpu__agg_linear_trend__attr_\"slope\"__chunk_len_...  real  0.005114      True  \n",
       "cpu__change_quantiles__f_agg_\"mean\"__isabs_Fals...  real  0.007065      True  \n",
       "\n",
       "[354 rows x 4 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "relevance_table = calculate_relevance_table(extracted_features, y)\n",
    "relevance_table = relevance_table[relevance_table.relevant]\n",
    "relevance_table.sort_values(\"p_value\", inplace=True)\n",
    "relevance_table\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "extracted_features = extracted_features[relevance_table.reset_index(drop=True).iloc[:n_features, 0].values]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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>cpu__sum_values</th>\n",
       "      <th>cpu__fft_coefficient__attr_\"real\"__coeff_15</th>\n",
       "      <th>cpu__fft_coefficient__attr_\"real\"__coeff_3</th>\n",
       "      <th>cpu__fft_aggregated__aggtype_\"centroid\"</th>\n",
       "      <th>cpu__fft_coefficient__attr_\"imag\"__coeff_3</th>\n",
       "      <th>cpu__fft_coefficient__attr_\"imag\"__coeff_4</th>\n",
       "      <th>cpu__fft_coefficient__attr_\"imag\"__coeff_5</th>\n",
       "      <th>cpu__change_quantiles__f_agg_\"mean\"__isabs_True__qh_1.0__ql_0.8</th>\n",
       "      <th>cpu__fft_coefficient__attr_\"imag\"__coeff_8</th>\n",
       "      <th>cpu__fft_coefficient__attr_\"imag\"__coeff_9</th>\n",
       "      <th>...</th>\n",
       "      <th>cpu__fft_aggregated__aggtype_\"kurtosis\"</th>\n",
       "      <th>cpu__friedrich_coefficients__coeff_3__m_3__r_30</th>\n",
       "      <th>cpu__linear_trend__attr_\"stderr\"</th>\n",
       "      <th>cpu__agg_linear_trend__attr_\"rvalue\"__chunk_len_5__f_agg_\"var\"</th>\n",
       "      <th>cpu__agg_linear_trend__attr_\"rvalue\"__chunk_len_50__f_agg_\"min\"</th>\n",
       "      <th>cpu__agg_linear_trend__attr_\"intercept\"__chunk_len_5__f_agg_\"var\"</th>\n",
       "      <th>cpu__agg_linear_trend__attr_\"intercept\"__chunk_len_10__f_agg_\"var\"</th>\n",
       "      <th>cpu__cwt_coefficients__coeff_12__w_5__widths_(2, 5, 10, 20)</th>\n",
       "      <th>cpu__agg_linear_trend__attr_\"intercept\"__chunk_len_50__f_agg_\"min\"</th>\n",
       "      <th>cpu__agg_linear_trend__attr_\"slope\"__chunk_len_50__f_agg_\"min\"</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">1</th>\n",
       "      <th>57300</th>\n",
       "      <td>2075.200004</td>\n",
       "      <td>-20.279996</td>\n",
       "      <td>-50.406326</td>\n",
       "      <td>4.528378</td>\n",
       "      <td>20.935206</td>\n",
       "      <td>18.730944</td>\n",
       "      <td>13.171162</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-14.430635</td>\n",
       "      <td>-10.006636</td>\n",
       "      <td>...</td>\n",
       "      <td>5.054988</td>\n",
       "      <td>227.954523</td>\n",
       "      <td>0.034253</td>\n",
       "      <td>0.198270</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>10.873964</td>\n",
       "      <td>16.214386</td>\n",
       "      <td>23.446304</td>\n",
       "      <td>27.08</td>\n",
       "      <td>-5.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57600</th>\n",
       "      <td>2061.060004</td>\n",
       "      <td>-2.599996</td>\n",
       "      <td>-67.856539</td>\n",
       "      <td>4.230416</td>\n",
       "      <td>-0.035348</td>\n",
       "      <td>19.646036</td>\n",
       "      <td>25.738698</td>\n",
       "      <td>5.340001</td>\n",
       "      <td>-0.327835</td>\n",
       "      <td>-6.212950</td>\n",
       "      <td>...</td>\n",
       "      <td>5.455482</td>\n",
       "      <td>265.154965</td>\n",
       "      <td>0.033267</td>\n",
       "      <td>0.699360</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>6.770711</td>\n",
       "      <td>15.355349</td>\n",
       "      <td>23.848532</td>\n",
       "      <td>27.08</td>\n",
       "      <td>-5.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57900</th>\n",
       "      <td>2055.940004</td>\n",
       "      <td>34.419996</td>\n",
       "      <td>-69.393889</td>\n",
       "      <td>4.240063</td>\n",
       "      <td>-22.584608</td>\n",
       "      <td>15.081658</td>\n",
       "      <td>28.849570</td>\n",
       "      <td>5.340001</td>\n",
       "      <td>10.187005</td>\n",
       "      <td>-1.439292</td>\n",
       "      <td>...</td>\n",
       "      <td>5.411539</td>\n",
       "      <td>283.642287</td>\n",
       "      <td>0.033356</td>\n",
       "      <td>0.361474</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>8.277701</td>\n",
       "      <td>17.816524</td>\n",
       "      <td>21.531122</td>\n",
       "      <td>27.08</td>\n",
       "      <td>-5.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58200</th>\n",
       "      <td>2053.760004</td>\n",
       "      <td>7.719996</td>\n",
       "      <td>-61.091786</td>\n",
       "      <td>4.237564</td>\n",
       "      <td>-43.596787</td>\n",
       "      <td>7.022839</td>\n",
       "      <td>23.140224</td>\n",
       "      <td>5.340001</td>\n",
       "      <td>12.340653</td>\n",
       "      <td>2.757304</td>\n",
       "      <td>...</td>\n",
       "      <td>5.420643</td>\n",
       "      <td>286.216286</td>\n",
       "      <td>0.033223</td>\n",
       "      <td>0.244684</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>10.182519</td>\n",
       "      <td>17.804211</td>\n",
       "      <td>17.479078</td>\n",
       "      <td>24.60</td>\n",
       "      <td>-2.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58500</th>\n",
       "      <td>2049.680004</td>\n",
       "      <td>-32.239996</td>\n",
       "      <td>-48.509904</td>\n",
       "      <td>4.258986</td>\n",
       "      <td>-61.602198</td>\n",
       "      <td>-3.909779</td>\n",
       "      <td>9.190473</td>\n",
       "      <td>5.340001</td>\n",
       "      <td>3.295982</td>\n",
       "      <td>1.379908</td>\n",
       "      <td>...</td>\n",
       "      <td>5.419782</td>\n",
       "      <td>299.516139</td>\n",
       "      <td>0.033170</td>\n",
       "      <td>0.237401</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>10.584338</td>\n",
       "      <td>15.307700</td>\n",
       "      <td>12.588025</td>\n",
       "      <td>24.60</td>\n",
       "      <td>-2.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">1313</th>\n",
       "      <th>81000</th>\n",
       "      <td>1655.259997</td>\n",
       "      <td>18.359999</td>\n",
       "      <td>-78.930134</td>\n",
       "      <td>4.008382</td>\n",
       "      <td>-142.406780</td>\n",
       "      <td>-48.378235</td>\n",
       "      <td>-29.295466</td>\n",
       "      <td>1.523333</td>\n",
       "      <td>-22.318559</td>\n",
       "      <td>28.792966</td>\n",
       "      <td>...</td>\n",
       "      <td>6.025132</td>\n",
       "      <td>-13.769569</td>\n",
       "      <td>0.043467</td>\n",
       "      <td>-0.027765</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>12.084924</td>\n",
       "      <td>10.532967</td>\n",
       "      <td>-2.446502</td>\n",
       "      <td>18.82</td>\n",
       "      <td>-3.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81300</th>\n",
       "      <td>1638.439997</td>\n",
       "      <td>8.780002</td>\n",
       "      <td>-47.057674</td>\n",
       "      <td>3.934021</td>\n",
       "      <td>-165.025315</td>\n",
       "      <td>-32.745803</td>\n",
       "      <td>-34.861073</td>\n",
       "      <td>1.523333</td>\n",
       "      <td>-10.113507</td>\n",
       "      <td>13.237732</td>\n",
       "      <td>...</td>\n",
       "      <td>6.121457</td>\n",
       "      <td>-34.368532</td>\n",
       "      <td>0.044735</td>\n",
       "      <td>-0.014615</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>13.594078</td>\n",
       "      <td>12.231789</td>\n",
       "      <td>-3.758251</td>\n",
       "      <td>18.82</td>\n",
       "      <td>-5.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81600</th>\n",
       "      <td>1625.239998</td>\n",
       "      <td>-1.539999</td>\n",
       "      <td>-6.312826</td>\n",
       "      <td>3.966966</td>\n",
       "      <td>-175.569046</td>\n",
       "      <td>-16.820247</td>\n",
       "      <td>-37.685683</td>\n",
       "      <td>1.523333</td>\n",
       "      <td>-1.025466</td>\n",
       "      <td>-23.910103</td>\n",
       "      <td>...</td>\n",
       "      <td>6.021644</td>\n",
       "      <td>-18.298198</td>\n",
       "      <td>0.045524</td>\n",
       "      <td>0.159105</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>9.695271</td>\n",
       "      <td>12.513629</td>\n",
       "      <td>-4.993084</td>\n",
       "      <td>18.82</td>\n",
       "      <td>-5.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81900</th>\n",
       "      <td>1609.359998</td>\n",
       "      <td>4.419998</td>\n",
       "      <td>33.147187</td>\n",
       "      <td>3.889931</td>\n",
       "      <td>-173.834046</td>\n",
       "      <td>-4.445296</td>\n",
       "      <td>-38.352445</td>\n",
       "      <td>1.523333</td>\n",
       "      <td>-3.059975</td>\n",
       "      <td>-54.192934</td>\n",
       "      <td>...</td>\n",
       "      <td>6.365107</td>\n",
       "      <td>-29.119766</td>\n",
       "      <td>0.046439</td>\n",
       "      <td>0.237369</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>6.271000</td>\n",
       "      <td>9.639206</td>\n",
       "      <td>-5.719998</td>\n",
       "      <td>18.82</td>\n",
       "      <td>-5.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82200</th>\n",
       "      <td>1595.759998</td>\n",
       "      <td>17.419999</td>\n",
       "      <td>72.308154</td>\n",
       "      <td>3.908793</td>\n",
       "      <td>-159.285589</td>\n",
       "      <td>3.166669</td>\n",
       "      <td>-35.542700</td>\n",
       "      <td>1.523333</td>\n",
       "      <td>-13.176349</td>\n",
       "      <td>-50.800142</td>\n",
       "      <td>...</td>\n",
       "      <td>6.268827</td>\n",
       "      <td>-18.235536</td>\n",
       "      <td>0.047010</td>\n",
       "      <td>-0.235288</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>13.204389</td>\n",
       "      <td>16.854960</td>\n",
       "      <td>-6.226254</td>\n",
       "      <td>18.82</td>\n",
       "      <td>-5.40</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>109363 rows × 106 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            cpu__sum_values  cpu__fft_coefficient__attr_\"real\"__coeff_15  \\\n",
       "1    57300      2075.200004                                   -20.279996   \n",
       "     57600      2061.060004                                    -2.599996   \n",
       "     57900      2055.940004                                    34.419996   \n",
       "     58200      2053.760004                                     7.719996   \n",
       "     58500      2049.680004                                   -32.239996   \n",
       "...                     ...                                          ...   \n",
       "1313 81000      1655.259997                                    18.359999   \n",
       "     81300      1638.439997                                     8.780002   \n",
       "     81600      1625.239998                                    -1.539999   \n",
       "     81900      1609.359998                                     4.419998   \n",
       "     82200      1595.759998                                    17.419999   \n",
       "\n",
       "            cpu__fft_coefficient__attr_\"real\"__coeff_3  \\\n",
       "1    57300                                  -50.406326   \n",
       "     57600                                  -67.856539   \n",
       "     57900                                  -69.393889   \n",
       "     58200                                  -61.091786   \n",
       "     58500                                  -48.509904   \n",
       "...                                                ...   \n",
       "1313 81000                                  -78.930134   \n",
       "     81300                                  -47.057674   \n",
       "     81600                                   -6.312826   \n",
       "     81900                                   33.147187   \n",
       "     82200                                   72.308154   \n",
       "\n",
       "            cpu__fft_aggregated__aggtype_\"centroid\"  \\\n",
       "1    57300                                 4.528378   \n",
       "     57600                                 4.230416   \n",
       "     57900                                 4.240063   \n",
       "     58200                                 4.237564   \n",
       "     58500                                 4.258986   \n",
       "...                                             ...   \n",
       "1313 81000                                 4.008382   \n",
       "     81300                                 3.934021   \n",
       "     81600                                 3.966966   \n",
       "     81900                                 3.889931   \n",
       "     82200                                 3.908793   \n",
       "\n",
       "            cpu__fft_coefficient__attr_\"imag\"__coeff_3  \\\n",
       "1    57300                                   20.935206   \n",
       "     57600                                   -0.035348   \n",
       "     57900                                  -22.584608   \n",
       "     58200                                  -43.596787   \n",
       "     58500                                  -61.602198   \n",
       "...                                                ...   \n",
       "1313 81000                                 -142.406780   \n",
       "     81300                                 -165.025315   \n",
       "     81600                                 -175.569046   \n",
       "     81900                                 -173.834046   \n",
       "     82200                                 -159.285589   \n",
       "\n",
       "            cpu__fft_coefficient__attr_\"imag\"__coeff_4  \\\n",
       "1    57300                                   18.730944   \n",
       "     57600                                   19.646036   \n",
       "     57900                                   15.081658   \n",
       "     58200                                    7.022839   \n",
       "     58500                                   -3.909779   \n",
       "...                                                ...   \n",
       "1313 81000                                  -48.378235   \n",
       "     81300                                  -32.745803   \n",
       "     81600                                  -16.820247   \n",
       "     81900                                   -4.445296   \n",
       "     82200                                    3.166669   \n",
       "\n",
       "            cpu__fft_coefficient__attr_\"imag\"__coeff_5  \\\n",
       "1    57300                                   13.171162   \n",
       "     57600                                   25.738698   \n",
       "     57900                                   28.849570   \n",
       "     58200                                   23.140224   \n",
       "     58500                                    9.190473   \n",
       "...                                                ...   \n",
       "1313 81000                                  -29.295466   \n",
       "     81300                                  -34.861073   \n",
       "     81600                                  -37.685683   \n",
       "     81900                                  -38.352445   \n",
       "     82200                                  -35.542700   \n",
       "\n",
       "            cpu__change_quantiles__f_agg_\"mean\"__isabs_True__qh_1.0__ql_0.8  \\\n",
       "1    57300                                           0.000000                 \n",
       "     57600                                           5.340001                 \n",
       "     57900                                           5.340001                 \n",
       "     58200                                           5.340001                 \n",
       "     58500                                           5.340001                 \n",
       "...                                                       ...                 \n",
       "1313 81000                                           1.523333                 \n",
       "     81300                                           1.523333                 \n",
       "     81600                                           1.523333                 \n",
       "     81900                                           1.523333                 \n",
       "     82200                                           1.523333                 \n",
       "\n",
       "            cpu__fft_coefficient__attr_\"imag\"__coeff_8  \\\n",
       "1    57300                                  -14.430635   \n",
       "     57600                                   -0.327835   \n",
       "     57900                                   10.187005   \n",
       "     58200                                   12.340653   \n",
       "     58500                                    3.295982   \n",
       "...                                                ...   \n",
       "1313 81000                                  -22.318559   \n",
       "     81300                                  -10.113507   \n",
       "     81600                                   -1.025466   \n",
       "     81900                                   -3.059975   \n",
       "     82200                                  -13.176349   \n",
       "\n",
       "            cpu__fft_coefficient__attr_\"imag\"__coeff_9  ...  \\\n",
       "1    57300                                  -10.006636  ...   \n",
       "     57600                                   -6.212950  ...   \n",
       "     57900                                   -1.439292  ...   \n",
       "     58200                                    2.757304  ...   \n",
       "     58500                                    1.379908  ...   \n",
       "...                                                ...  ...   \n",
       "1313 81000                                   28.792966  ...   \n",
       "     81300                                   13.237732  ...   \n",
       "     81600                                  -23.910103  ...   \n",
       "     81900                                  -54.192934  ...   \n",
       "     82200                                  -50.800142  ...   \n",
       "\n",
       "            cpu__fft_aggregated__aggtype_\"kurtosis\"  \\\n",
       "1    57300                                 5.054988   \n",
       "     57600                                 5.455482   \n",
       "     57900                                 5.411539   \n",
       "     58200                                 5.420643   \n",
       "     58500                                 5.419782   \n",
       "...                                             ...   \n",
       "1313 81000                                 6.025132   \n",
       "     81300                                 6.121457   \n",
       "     81600                                 6.021644   \n",
       "     81900                                 6.365107   \n",
       "     82200                                 6.268827   \n",
       "\n",
       "            cpu__friedrich_coefficients__coeff_3__m_3__r_30  \\\n",
       "1    57300                                       227.954523   \n",
       "     57600                                       265.154965   \n",
       "     57900                                       283.642287   \n",
       "     58200                                       286.216286   \n",
       "     58500                                       299.516139   \n",
       "...                                                     ...   \n",
       "1313 81000                                       -13.769569   \n",
       "     81300                                       -34.368532   \n",
       "     81600                                       -18.298198   \n",
       "     81900                                       -29.119766   \n",
       "     82200                                       -18.235536   \n",
       "\n",
       "            cpu__linear_trend__attr_\"stderr\"  \\\n",
       "1    57300                          0.034253   \n",
       "     57600                          0.033267   \n",
       "     57900                          0.033356   \n",
       "     58200                          0.033223   \n",
       "     58500                          0.033170   \n",
       "...                                      ...   \n",
       "1313 81000                          0.043467   \n",
       "     81300                          0.044735   \n",
       "     81600                          0.045524   \n",
       "     81900                          0.046439   \n",
       "     82200                          0.047010   \n",
       "\n",
       "            cpu__agg_linear_trend__attr_\"rvalue\"__chunk_len_5__f_agg_\"var\"  \\\n",
       "1    57300                                           0.198270                \n",
       "     57600                                           0.699360                \n",
       "     57900                                           0.361474                \n",
       "     58200                                           0.244684                \n",
       "     58500                                           0.237401                \n",
       "...                                                       ...                \n",
       "1313 81000                                          -0.027765                \n",
       "     81300                                          -0.014615                \n",
       "     81600                                           0.159105                \n",
       "     81900                                           0.237369                \n",
       "     82200                                          -0.235288                \n",
       "\n",
       "            cpu__agg_linear_trend__attr_\"rvalue\"__chunk_len_50__f_agg_\"min\"  \\\n",
       "1    57300                                               -1.0                 \n",
       "     57600                                               -1.0                 \n",
       "     57900                                               -1.0                 \n",
       "     58200                                               -1.0                 \n",
       "     58500                                               -1.0                 \n",
       "...                                                       ...                 \n",
       "1313 81000                                               -1.0                 \n",
       "     81300                                               -1.0                 \n",
       "     81600                                               -1.0                 \n",
       "     81900                                               -1.0                 \n",
       "     82200                                               -1.0                 \n",
       "\n",
       "            cpu__agg_linear_trend__attr_\"intercept\"__chunk_len_5__f_agg_\"var\"  \\\n",
       "1    57300                                          10.873964                   \n",
       "     57600                                           6.770711                   \n",
       "     57900                                           8.277701                   \n",
       "     58200                                          10.182519                   \n",
       "     58500                                          10.584338                   \n",
       "...                                                       ...                   \n",
       "1313 81000                                          12.084924                   \n",
       "     81300                                          13.594078                   \n",
       "     81600                                           9.695271                   \n",
       "     81900                                           6.271000                   \n",
       "     82200                                          13.204389                   \n",
       "\n",
       "            cpu__agg_linear_trend__attr_\"intercept\"__chunk_len_10__f_agg_\"var\"  \\\n",
       "1    57300                                          16.214386                    \n",
       "     57600                                          15.355349                    \n",
       "     57900                                          17.816524                    \n",
       "     58200                                          17.804211                    \n",
       "     58500                                          15.307700                    \n",
       "...                                                       ...                    \n",
       "1313 81000                                          10.532967                    \n",
       "     81300                                          12.231789                    \n",
       "     81600                                          12.513629                    \n",
       "     81900                                           9.639206                    \n",
       "     82200                                          16.854960                    \n",
       "\n",
       "            cpu__cwt_coefficients__coeff_12__w_5__widths_(2, 5, 10, 20)  \\\n",
       "1    57300                                          23.446304             \n",
       "     57600                                          23.848532             \n",
       "     57900                                          21.531122             \n",
       "     58200                                          17.479078             \n",
       "     58500                                          12.588025             \n",
       "...                                                       ...             \n",
       "1313 81000                                          -2.446502             \n",
       "     81300                                          -3.758251             \n",
       "     81600                                          -4.993084             \n",
       "     81900                                          -5.719998             \n",
       "     82200                                          -6.226254             \n",
       "\n",
       "            cpu__agg_linear_trend__attr_\"intercept\"__chunk_len_50__f_agg_\"min\"  \\\n",
       "1    57300                                              27.08                    \n",
       "     57600                                              27.08                    \n",
       "     57900                                              27.08                    \n",
       "     58200                                              24.60                    \n",
       "     58500                                              24.60                    \n",
       "...                                                       ...                    \n",
       "1313 81000                                              18.82                    \n",
       "     81300                                              18.82                    \n",
       "     81600                                              18.82                    \n",
       "     81900                                              18.82                    \n",
       "     82200                                              18.82                    \n",
       "\n",
       "            cpu__agg_linear_trend__attr_\"slope\"__chunk_len_50__f_agg_\"min\"  \n",
       "1    57300                                              -5.44               \n",
       "     57600                                              -5.44               \n",
       "     57900                                              -5.44               \n",
       "     58200                                              -2.96               \n",
       "     58500                                              -2.96               \n",
       "...                                                       ...               \n",
       "1313 81000                                              -3.70               \n",
       "     81300                                              -5.06               \n",
       "     81600                                              -5.06               \n",
       "     81900                                              -5.40               \n",
       "     82200                                              -5.40               \n",
       "\n",
       "[109363 rows x 106 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "extracted_features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_14406/382924147.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\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",
      "  extracted_features.rename(columns={\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>machine_id</th>\n",
       "      <th>time</th>\n",
       "      <th>cpu__sum_values</th>\n",
       "      <th>cpu__fft_coefficient__attr_\"real\"__coeff_15</th>\n",
       "      <th>cpu__fft_coefficient__attr_\"real\"__coeff_3</th>\n",
       "      <th>cpu__fft_aggregated__aggtype_\"centroid\"</th>\n",
       "      <th>cpu__fft_coefficient__attr_\"imag\"__coeff_3</th>\n",
       "      <th>cpu__fft_coefficient__attr_\"imag\"__coeff_4</th>\n",
       "      <th>cpu__fft_coefficient__attr_\"imag\"__coeff_5</th>\n",
       "      <th>cpu__change_quantiles__f_agg_\"mean\"__isabs_True__qh_1.0__ql_0.8</th>\n",
       "      <th>...</th>\n",
       "      <th>cpu__fft_aggregated__aggtype_\"kurtosis\"</th>\n",
       "      <th>cpu__friedrich_coefficients__coeff_3__m_3__r_30</th>\n",
       "      <th>cpu__linear_trend__attr_\"stderr\"</th>\n",
       "      <th>cpu__agg_linear_trend__attr_\"rvalue\"__chunk_len_5__f_agg_\"var\"</th>\n",
       "      <th>cpu__agg_linear_trend__attr_\"rvalue\"__chunk_len_50__f_agg_\"min\"</th>\n",
       "      <th>cpu__agg_linear_trend__attr_\"intercept\"__chunk_len_5__f_agg_\"var\"</th>\n",
       "      <th>cpu__agg_linear_trend__attr_\"intercept\"__chunk_len_10__f_agg_\"var\"</th>\n",
       "      <th>cpu__cwt_coefficients__coeff_12__w_5__widths_(2, 5, 10, 20)</th>\n",
       "      <th>cpu__agg_linear_trend__attr_\"intercept\"__chunk_len_50__f_agg_\"min\"</th>\n",
       "      <th>cpu__agg_linear_trend__attr_\"slope\"__chunk_len_50__f_agg_\"min\"</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>57300</td>\n",
       "      <td>2075.200004</td>\n",
       "      <td>-20.279996</td>\n",
       "      <td>-50.406326</td>\n",
       "      <td>4.528378</td>\n",
       "      <td>20.935206</td>\n",
       "      <td>18.730944</td>\n",
       "      <td>13.171162</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>5.054988</td>\n",
       "      <td>227.954523</td>\n",
       "      <td>0.034253</td>\n",
       "      <td>0.198270</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>10.873964</td>\n",
       "      <td>16.214386</td>\n",
       "      <td>23.446304</td>\n",
       "      <td>27.08</td>\n",
       "      <td>-5.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>57600</td>\n",
       "      <td>2061.060004</td>\n",
       "      <td>-2.599996</td>\n",
       "      <td>-67.856539</td>\n",
       "      <td>4.230416</td>\n",
       "      <td>-0.035348</td>\n",
       "      <td>19.646036</td>\n",
       "      <td>25.738698</td>\n",
       "      <td>5.340001</td>\n",
       "      <td>...</td>\n",
       "      <td>5.455482</td>\n",
       "      <td>265.154965</td>\n",
       "      <td>0.033267</td>\n",
       "      <td>0.699360</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>6.770711</td>\n",
       "      <td>15.355349</td>\n",
       "      <td>23.848532</td>\n",
       "      <td>27.08</td>\n",
       "      <td>-5.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>57900</td>\n",
       "      <td>2055.940004</td>\n",
       "      <td>34.419996</td>\n",
       "      <td>-69.393889</td>\n",
       "      <td>4.240063</td>\n",
       "      <td>-22.584608</td>\n",
       "      <td>15.081658</td>\n",
       "      <td>28.849570</td>\n",
       "      <td>5.340001</td>\n",
       "      <td>...</td>\n",
       "      <td>5.411539</td>\n",
       "      <td>283.642287</td>\n",
       "      <td>0.033356</td>\n",
       "      <td>0.361474</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>8.277701</td>\n",
       "      <td>17.816524</td>\n",
       "      <td>21.531122</td>\n",
       "      <td>27.08</td>\n",
       "      <td>-5.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>58200</td>\n",
       "      <td>2053.760004</td>\n",
       "      <td>7.719996</td>\n",
       "      <td>-61.091786</td>\n",
       "      <td>4.237564</td>\n",
       "      <td>-43.596787</td>\n",
       "      <td>7.022839</td>\n",
       "      <td>23.140224</td>\n",
       "      <td>5.340001</td>\n",
       "      <td>...</td>\n",
       "      <td>5.420643</td>\n",
       "      <td>286.216286</td>\n",
       "      <td>0.033223</td>\n",
       "      <td>0.244684</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>10.182519</td>\n",
       "      <td>17.804211</td>\n",
       "      <td>17.479078</td>\n",
       "      <td>24.60</td>\n",
       "      <td>-2.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>58500</td>\n",
       "      <td>2049.680004</td>\n",
       "      <td>-32.239996</td>\n",
       "      <td>-48.509904</td>\n",
       "      <td>4.258986</td>\n",
       "      <td>-61.602198</td>\n",
       "      <td>-3.909779</td>\n",
       "      <td>9.190473</td>\n",
       "      <td>5.340001</td>\n",
       "      <td>...</td>\n",
       "      <td>5.419782</td>\n",
       "      <td>299.516139</td>\n",
       "      <td>0.033170</td>\n",
       "      <td>0.237401</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>10.584338</td>\n",
       "      <td>15.307700</td>\n",
       "      <td>12.588025</td>\n",
       "      <td>24.60</td>\n",
       "      <td>-2.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109358</th>\n",
       "      <td>1313</td>\n",
       "      <td>81000</td>\n",
       "      <td>1655.259997</td>\n",
       "      <td>18.359999</td>\n",
       "      <td>-78.930134</td>\n",
       "      <td>4.008382</td>\n",
       "      <td>-142.406780</td>\n",
       "      <td>-48.378235</td>\n",
       "      <td>-29.295466</td>\n",
       "      <td>1.523333</td>\n",
       "      <td>...</td>\n",
       "      <td>6.025132</td>\n",
       "      <td>-13.769569</td>\n",
       "      <td>0.043467</td>\n",
       "      <td>-0.027765</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>12.084924</td>\n",
       "      <td>10.532967</td>\n",
       "      <td>-2.446502</td>\n",
       "      <td>18.82</td>\n",
       "      <td>-3.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109359</th>\n",
       "      <td>1313</td>\n",
       "      <td>81300</td>\n",
       "      <td>1638.439997</td>\n",
       "      <td>8.780002</td>\n",
       "      <td>-47.057674</td>\n",
       "      <td>3.934021</td>\n",
       "      <td>-165.025315</td>\n",
       "      <td>-32.745803</td>\n",
       "      <td>-34.861073</td>\n",
       "      <td>1.523333</td>\n",
       "      <td>...</td>\n",
       "      <td>6.121457</td>\n",
       "      <td>-34.368532</td>\n",
       "      <td>0.044735</td>\n",
       "      <td>-0.014615</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>13.594078</td>\n",
       "      <td>12.231789</td>\n",
       "      <td>-3.758251</td>\n",
       "      <td>18.82</td>\n",
       "      <td>-5.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109360</th>\n",
       "      <td>1313</td>\n",
       "      <td>81600</td>\n",
       "      <td>1625.239998</td>\n",
       "      <td>-1.539999</td>\n",
       "      <td>-6.312826</td>\n",
       "      <td>3.966966</td>\n",
       "      <td>-175.569046</td>\n",
       "      <td>-16.820247</td>\n",
       "      <td>-37.685683</td>\n",
       "      <td>1.523333</td>\n",
       "      <td>...</td>\n",
       "      <td>6.021644</td>\n",
       "      <td>-18.298198</td>\n",
       "      <td>0.045524</td>\n",
       "      <td>0.159105</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>9.695271</td>\n",
       "      <td>12.513629</td>\n",
       "      <td>-4.993084</td>\n",
       "      <td>18.82</td>\n",
       "      <td>-5.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109361</th>\n",
       "      <td>1313</td>\n",
       "      <td>81900</td>\n",
       "      <td>1609.359998</td>\n",
       "      <td>4.419998</td>\n",
       "      <td>33.147187</td>\n",
       "      <td>3.889931</td>\n",
       "      <td>-173.834046</td>\n",
       "      <td>-4.445296</td>\n",
       "      <td>-38.352445</td>\n",
       "      <td>1.523333</td>\n",
       "      <td>...</td>\n",
       "      <td>6.365107</td>\n",
       "      <td>-29.119766</td>\n",
       "      <td>0.046439</td>\n",
       "      <td>0.237369</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>6.271000</td>\n",
       "      <td>9.639206</td>\n",
       "      <td>-5.719998</td>\n",
       "      <td>18.82</td>\n",
       "      <td>-5.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109362</th>\n",
       "      <td>1313</td>\n",
       "      <td>82200</td>\n",
       "      <td>1595.759998</td>\n",
       "      <td>17.419999</td>\n",
       "      <td>72.308154</td>\n",
       "      <td>3.908793</td>\n",
       "      <td>-159.285589</td>\n",
       "      <td>3.166669</td>\n",
       "      <td>-35.542700</td>\n",
       "      <td>1.523333</td>\n",
       "      <td>...</td>\n",
       "      <td>6.268827</td>\n",
       "      <td>-18.235536</td>\n",
       "      <td>0.047010</td>\n",
       "      <td>-0.235288</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>13.204389</td>\n",
       "      <td>16.854960</td>\n",
       "      <td>-6.226254</td>\n",
       "      <td>18.82</td>\n",
       "      <td>-5.40</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>109363 rows × 108 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        machine_id   time  cpu__sum_values  \\\n",
       "0                1  57300      2075.200004   \n",
       "1                1  57600      2061.060004   \n",
       "2                1  57900      2055.940004   \n",
       "3                1  58200      2053.760004   \n",
       "4                1  58500      2049.680004   \n",
       "...            ...    ...              ...   \n",
       "109358        1313  81000      1655.259997   \n",
       "109359        1313  81300      1638.439997   \n",
       "109360        1313  81600      1625.239998   \n",
       "109361        1313  81900      1609.359998   \n",
       "109362        1313  82200      1595.759998   \n",
       "\n",
       "        cpu__fft_coefficient__attr_\"real\"__coeff_15  \\\n",
       "0                                        -20.279996   \n",
       "1                                         -2.599996   \n",
       "2                                         34.419996   \n",
       "3                                          7.719996   \n",
       "4                                        -32.239996   \n",
       "...                                             ...   \n",
       "109358                                    18.359999   \n",
       "109359                                     8.780002   \n",
       "109360                                    -1.539999   \n",
       "109361                                     4.419998   \n",
       "109362                                    17.419999   \n",
       "\n",
       "        cpu__fft_coefficient__attr_\"real\"__coeff_3  \\\n",
       "0                                       -50.406326   \n",
       "1                                       -67.856539   \n",
       "2                                       -69.393889   \n",
       "3                                       -61.091786   \n",
       "4                                       -48.509904   \n",
       "...                                            ...   \n",
       "109358                                  -78.930134   \n",
       "109359                                  -47.057674   \n",
       "109360                                   -6.312826   \n",
       "109361                                   33.147187   \n",
       "109362                                   72.308154   \n",
       "\n",
       "        cpu__fft_aggregated__aggtype_\"centroid\"  \\\n",
       "0                                      4.528378   \n",
       "1                                      4.230416   \n",
       "2                                      4.240063   \n",
       "3                                      4.237564   \n",
       "4                                      4.258986   \n",
       "...                                         ...   \n",
       "109358                                 4.008382   \n",
       "109359                                 3.934021   \n",
       "109360                                 3.966966   \n",
       "109361                                 3.889931   \n",
       "109362                                 3.908793   \n",
       "\n",
       "        cpu__fft_coefficient__attr_\"imag\"__coeff_3  \\\n",
       "0                                        20.935206   \n",
       "1                                        -0.035348   \n",
       "2                                       -22.584608   \n",
       "3                                       -43.596787   \n",
       "4                                       -61.602198   \n",
       "...                                            ...   \n",
       "109358                                 -142.406780   \n",
       "109359                                 -165.025315   \n",
       "109360                                 -175.569046   \n",
       "109361                                 -173.834046   \n",
       "109362                                 -159.285589   \n",
       "\n",
       "        cpu__fft_coefficient__attr_\"imag\"__coeff_4  \\\n",
       "0                                        18.730944   \n",
       "1                                        19.646036   \n",
       "2                                        15.081658   \n",
       "3                                         7.022839   \n",
       "4                                        -3.909779   \n",
       "...                                            ...   \n",
       "109358                                  -48.378235   \n",
       "109359                                  -32.745803   \n",
       "109360                                  -16.820247   \n",
       "109361                                   -4.445296   \n",
       "109362                                    3.166669   \n",
       "\n",
       "        cpu__fft_coefficient__attr_\"imag\"__coeff_5  \\\n",
       "0                                        13.171162   \n",
       "1                                        25.738698   \n",
       "2                                        28.849570   \n",
       "3                                        23.140224   \n",
       "4                                         9.190473   \n",
       "...                                            ...   \n",
       "109358                                  -29.295466   \n",
       "109359                                  -34.861073   \n",
       "109360                                  -37.685683   \n",
       "109361                                  -38.352445   \n",
       "109362                                  -35.542700   \n",
       "\n",
       "        cpu__change_quantiles__f_agg_\"mean\"__isabs_True__qh_1.0__ql_0.8  ...  \\\n",
       "0                                                0.000000                ...   \n",
       "1                                                5.340001                ...   \n",
       "2                                                5.340001                ...   \n",
       "3                                                5.340001                ...   \n",
       "4                                                5.340001                ...   \n",
       "...                                                   ...                ...   \n",
       "109358                                           1.523333                ...   \n",
       "109359                                           1.523333                ...   \n",
       "109360                                           1.523333                ...   \n",
       "109361                                           1.523333                ...   \n",
       "109362                                           1.523333                ...   \n",
       "\n",
       "        cpu__fft_aggregated__aggtype_\"kurtosis\"  \\\n",
       "0                                      5.054988   \n",
       "1                                      5.455482   \n",
       "2                                      5.411539   \n",
       "3                                      5.420643   \n",
       "4                                      5.419782   \n",
       "...                                         ...   \n",
       "109358                                 6.025132   \n",
       "109359                                 6.121457   \n",
       "109360                                 6.021644   \n",
       "109361                                 6.365107   \n",
       "109362                                 6.268827   \n",
       "\n",
       "        cpu__friedrich_coefficients__coeff_3__m_3__r_30  \\\n",
       "0                                            227.954523   \n",
       "1                                            265.154965   \n",
       "2                                            283.642287   \n",
       "3                                            286.216286   \n",
       "4                                            299.516139   \n",
       "...                                                 ...   \n",
       "109358                                       -13.769569   \n",
       "109359                                       -34.368532   \n",
       "109360                                       -18.298198   \n",
       "109361                                       -29.119766   \n",
       "109362                                       -18.235536   \n",
       "\n",
       "        cpu__linear_trend__attr_\"stderr\"  \\\n",
       "0                               0.034253   \n",
       "1                               0.033267   \n",
       "2                               0.033356   \n",
       "3                               0.033223   \n",
       "4                               0.033170   \n",
       "...                                  ...   \n",
       "109358                          0.043467   \n",
       "109359                          0.044735   \n",
       "109360                          0.045524   \n",
       "109361                          0.046439   \n",
       "109362                          0.047010   \n",
       "\n",
       "        cpu__agg_linear_trend__attr_\"rvalue\"__chunk_len_5__f_agg_\"var\"  \\\n",
       "0                                                0.198270                \n",
       "1                                                0.699360                \n",
       "2                                                0.361474                \n",
       "3                                                0.244684                \n",
       "4                                                0.237401                \n",
       "...                                                   ...                \n",
       "109358                                          -0.027765                \n",
       "109359                                          -0.014615                \n",
       "109360                                           0.159105                \n",
       "109361                                           0.237369                \n",
       "109362                                          -0.235288                \n",
       "\n",
       "        cpu__agg_linear_trend__attr_\"rvalue\"__chunk_len_50__f_agg_\"min\"  \\\n",
       "0                                                    -1.0                 \n",
       "1                                                    -1.0                 \n",
       "2                                                    -1.0                 \n",
       "3                                                    -1.0                 \n",
       "4                                                    -1.0                 \n",
       "...                                                   ...                 \n",
       "109358                                               -1.0                 \n",
       "109359                                               -1.0                 \n",
       "109360                                               -1.0                 \n",
       "109361                                               -1.0                 \n",
       "109362                                               -1.0                 \n",
       "\n",
       "        cpu__agg_linear_trend__attr_\"intercept\"__chunk_len_5__f_agg_\"var\"  \\\n",
       "0                                               10.873964                   \n",
       "1                                                6.770711                   \n",
       "2                                                8.277701                   \n",
       "3                                               10.182519                   \n",
       "4                                               10.584338                   \n",
       "...                                                   ...                   \n",
       "109358                                          12.084924                   \n",
       "109359                                          13.594078                   \n",
       "109360                                           9.695271                   \n",
       "109361                                           6.271000                   \n",
       "109362                                          13.204389                   \n",
       "\n",
       "        cpu__agg_linear_trend__attr_\"intercept\"__chunk_len_10__f_agg_\"var\"  \\\n",
       "0                                               16.214386                    \n",
       "1                                               15.355349                    \n",
       "2                                               17.816524                    \n",
       "3                                               17.804211                    \n",
       "4                                               15.307700                    \n",
       "...                                                   ...                    \n",
       "109358                                          10.532967                    \n",
       "109359                                          12.231789                    \n",
       "109360                                          12.513629                    \n",
       "109361                                           9.639206                    \n",
       "109362                                          16.854960                    \n",
       "\n",
       "        cpu__cwt_coefficients__coeff_12__w_5__widths_(2, 5, 10, 20)  \\\n",
       "0                                               23.446304             \n",
       "1                                               23.848532             \n",
       "2                                               21.531122             \n",
       "3                                               17.479078             \n",
       "4                                               12.588025             \n",
       "...                                                   ...             \n",
       "109358                                          -2.446502             \n",
       "109359                                          -3.758251             \n",
       "109360                                          -4.993084             \n",
       "109361                                          -5.719998             \n",
       "109362                                          -6.226254             \n",
       "\n",
       "        cpu__agg_linear_trend__attr_\"intercept\"__chunk_len_50__f_agg_\"min\"  \\\n",
       "0                                                   27.08                    \n",
       "1                                                   27.08                    \n",
       "2                                                   27.08                    \n",
       "3                                                   24.60                    \n",
       "4                                                   24.60                    \n",
       "...                                                   ...                    \n",
       "109358                                              18.82                    \n",
       "109359                                              18.82                    \n",
       "109360                                              18.82                    \n",
       "109361                                              18.82                    \n",
       "109362                                              18.82                    \n",
       "\n",
       "        cpu__agg_linear_trend__attr_\"slope\"__chunk_len_50__f_agg_\"min\"  \n",
       "0                                                   -5.44               \n",
       "1                                                   -5.44               \n",
       "2                                                   -5.44               \n",
       "3                                                   -2.96               \n",
       "4                                                   -2.96               \n",
       "...                                                   ...               \n",
       "109358                                              -3.70               \n",
       "109359                                              -5.06               \n",
       "109360                                              -5.06               \n",
       "109361                                              -5.40               \n",
       "109362                                              -5.40               \n",
       "\n",
       "[109363 rows x 108 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "extracted_features.reset_index(inplace=True)\n",
    "extracted_features.rename(columns={\n",
    "    'level_0': 'machine_id',\n",
    "    'level_1': 'time',\n",
    "}, inplace=True)\n",
    "extracted_features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "extracted_features.to_csv('extracted_features_60_106.csv', index=None)\n",
    "y.to_csv('labels_60_106.csv', index=None)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.9.16 ('py39')",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.16"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "17f19b2e755769a5519b38b6367878bc0c8e8eee85e91277104d2410a6f820df"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}