autoencoder_Conv1D.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from keras.models import Model\n",
    "from keras.layers import Input, LSTM, RepeatVector\n",
    "from keras.preprocessing.sequence import pad_sequences\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "import tensorflow as tf\n",
    "from keras.utils import pad_sequences\n",
    "import keras\n",
    "from keras import layers\n",
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "import draft.data_cleaning as dc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "person_0 = pd.read_json(r'datasets/december/liza.json', orient='records', lines=True) # liza\n",
    "person_1 = pd.read_json(r'datasets/december/sleep_data_Adham.json', lines=True) # adham\n",
    "person_2 = pd.read_json(r'datasets/december/sleep_data_Miriam.json', lines=True) # miriam\n",
    "person_3 = pd.read_json(r'datasets/december/sleep_data_Syahid.json', lines=True) # syahid\n",
    "person_4 = pd.read_json(r'datasets/december/sleep_data_florian.json', lines=True)  #  florian\n",
    "person_5 = pd.read_json(r'datasets/december/sleep_data_Shado.json', lines=True) # shado\n",
    "person_6 = pd.read_json(r'datasets/december/sleep_data_Alina.json', lines=True) # alina\n",
    "\n",
    "# labels dataframe from excel\n",
    "labels_df = pd.read_excel(r'datasets\\sleep_data.xlsx', sheet_name=None) # dict of all label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[    temp_id  ... restingHeartRate\n",
       " 0         0  ...              NaN\n",
       " 1         0  ...              NaN\n",
       " 2         0  ...              NaN\n",
       " 3         0  ...              NaN\n",
       " 4         0  ...              NaN\n",
       " 5         0  ...              NaN\n",
       " 6         0  ...              NaN\n",
       " 7         0  ...              NaN\n",
       " 8         0  ...             69.0\n",
       " 9         0  ...             75.0\n",
       " 10        0  ...             73.0\n",
       " 11        0  ...             67.0\n",
       " 12        0  ...             69.0\n",
       " 13        0  ...             66.0\n",
       " 14        0  ...             69.0\n",
       " 15        0  ...             70.0\n",
       " 16        0  ...             65.0\n",
       " 17        0  ...             65.0\n",
       " 18        0  ...             62.0\n",
       " 19        0  ...             63.0\n",
       " 20        0  ...             55.0\n",
       " 21        0  ...             72.0\n",
       " 22        0  ...             67.0\n",
       " 23        0  ...             64.0\n",
       " 24        0  ...             68.0\n",
       " 25        0  ...              NaN\n",
       " 26        0  ...              NaN\n",
       " 27        0  ...              NaN\n",
       " 28        0  ...              NaN\n",
       " 29        0  ...              NaN\n",
       " 30        0  ...              NaN\n",
       " 31        0  ...              NaN\n",
       " \n",
       " [32 rows x 16 columns],\n",
       "     temp_id  ... bodyBatteryChange\n",
       " 0         1  ...               NaN\n",
       " 1         1  ...              82.0\n",
       " 2         1  ...              60.0\n",
       " 3         1  ...               NaN\n",
       " 4         1  ...               NaN\n",
       " 5         1  ...              69.0\n",
       " 6         1  ...              60.0\n",
       " 7         1  ...              53.0\n",
       " 8         1  ...              70.0\n",
       " 9         1  ...              52.0\n",
       " 10        1  ...              71.0\n",
       " 11        1  ...              51.0\n",
       " 12        1  ...              59.0\n",
       " 13        1  ...              68.0\n",
       " 14        1  ...              54.0\n",
       " 15        1  ...              58.0\n",
       " 16        1  ...              76.0\n",
       " 17        1  ...               0.0\n",
       " 18        1  ...              56.0\n",
       " 19        1  ...              86.0\n",
       " 20        1  ...              56.0\n",
       " 21        1  ...               NaN\n",
       " 22        1  ...              50.0\n",
       " 23        1  ...              55.0\n",
       " 24        1  ...              42.0\n",
       " 25        1  ...               NaN\n",
       " 26        1  ...              81.0\n",
       " 27        1  ...              77.0\n",
       " 28        1  ...              28.0\n",
       " 29        1  ...               NaN\n",
       " 30        1  ...               NaN\n",
       " 31        1  ...              59.0\n",
       " \n",
       " [32 rows x 16 columns],\n",
       "     temp_id  ... bodyBatteryChange\n",
       " 0         2  ...               NaN\n",
       " 1         2  ...               NaN\n",
       " 2         2  ...              61.0\n",
       " 3         2  ...               NaN\n",
       " 4         2  ...              56.0\n",
       " 5         2  ...              71.0\n",
       " 6         2  ...              61.0\n",
       " 7         2  ...               NaN\n",
       " 8         2  ...              70.0\n",
       " 9         2  ...              63.0\n",
       " 10        2  ...              64.0\n",
       " 11        2  ...              57.0\n",
       " 12        2  ...              67.0\n",
       " 13        2  ...              60.0\n",
       " 14        2  ...              53.0\n",
       " 15        2  ...               NaN\n",
       " 16        2  ...              64.0\n",
       " 17        2  ...              47.0\n",
       " 18        2  ...              51.0\n",
       " 19        2  ...              73.0\n",
       " 20        2  ...               NaN\n",
       " 21        2  ...              39.0\n",
       " 22        2  ...               NaN\n",
       " 23        2  ...               NaN\n",
       " 24        2  ...               NaN\n",
       " 25        2  ...               NaN\n",
       " 26        2  ...               NaN\n",
       " 27        2  ...               NaN\n",
       " 28        2  ...               NaN\n",
       " 29        2  ...               NaN\n",
       " 30        2  ...               NaN\n",
       " 31        2  ...               NaN\n",
       " \n",
       " [32 rows x 16 columns],\n",
       "     temp_id  ... restingHeartRate\n",
       " 0         3  ...             55.0\n",
       " 1         3  ...             50.0\n",
       " 2         3  ...             54.0\n",
       " 3         3  ...             56.0\n",
       " 4         3  ...             58.0\n",
       " ..      ...  ...              ...\n",
       " 56        3  ...             53.0\n",
       " 57        3  ...              NaN\n",
       " 58        3  ...              NaN\n",
       " 59        3  ...              NaN\n",
       " 60        3  ...              NaN\n",
       " \n",
       " [61 rows x 15 columns],\n",
       "     temp_id  ... bodyBatteryChange\n",
       " 0         4  ...               NaN\n",
       " 1         4  ...               NaN\n",
       " 2         4  ...              66.0\n",
       " 3         4  ...              62.0\n",
       " 4         4  ...              55.0\n",
       " 5         4  ...              69.0\n",
       " 6         4  ...               NaN\n",
       " 7         4  ...              65.0\n",
       " 8         4  ...              61.0\n",
       " 9         4  ...              76.0\n",
       " 10        4  ...              41.0\n",
       " 11        4  ...              48.0\n",
       " 12        4  ...              60.0\n",
       " 13        4  ...              68.0\n",
       " 14        4  ...              69.0\n",
       " 15        4  ...              59.0\n",
       " 16        4  ...              86.0\n",
       " 17        4  ...              48.0\n",
       " 18        4  ...              53.0\n",
       " 19        4  ...               NaN\n",
       " 20        4  ...              21.0\n",
       " 21        4  ...              64.0\n",
       " 22        4  ...              64.0\n",
       " 23        4  ...               NaN\n",
       " 24        4  ...              63.0\n",
       " 25        4  ...              44.0\n",
       " 26        4  ...              31.0\n",
       " 27        4  ...               NaN\n",
       " 28        4  ...               NaN\n",
       " 29        4  ...               NaN\n",
       " 30        4  ...               NaN\n",
       " 31        4  ...               NaN\n",
       " \n",
       " [32 rows x 18 columns],\n",
       "     temp_id  ... bodyBatteryChange\n",
       " 0         5  ...               NaN\n",
       " 1         5  ...               NaN\n",
       " 2         5  ...               NaN\n",
       " 3         5  ...               NaN\n",
       " 4         5  ...               NaN\n",
       " 5         5  ...               NaN\n",
       " 6         5  ...               NaN\n",
       " 7         5  ...               NaN\n",
       " 8         5  ...               NaN\n",
       " 9         5  ...               NaN\n",
       " 10        5  ...              47.0\n",
       " 11        5  ...              53.0\n",
       " 12        5  ...              50.0\n",
       " 13        5  ...              30.0\n",
       " 14        5  ...              35.0\n",
       " 15        5  ...              43.0\n",
       " 16        5  ...              20.0\n",
       " 17        5  ...              57.0\n",
       " 18        5  ...              30.0\n",
       " 19        5  ...               6.0\n",
       " 20        5  ...               6.0\n",
       " 21        5  ...              17.0\n",
       " 22        5  ...              37.0\n",
       " 23        5  ...               NaN\n",
       " 24        5  ...               NaN\n",
       " 25        5  ...               NaN\n",
       " 26        5  ...               NaN\n",
       " 27        5  ...               NaN\n",
       " 28        5  ...               NaN\n",
       " 29        5  ...               NaN\n",
       " 30        5  ...               NaN\n",
       " 31        5  ...               NaN\n",
       " \n",
       " [32 rows x 16 columns],\n",
       "     temp_id  ... bodyBatteryChange\n",
       " 0         6  ...               NaN\n",
       " 1         6  ...               NaN\n",
       " 2         6  ...               NaN\n",
       " 3         6  ...               NaN\n",
       " 4         6  ...               NaN\n",
       " 5         6  ...               NaN\n",
       " 6         6  ...               NaN\n",
       " 7         6  ...               NaN\n",
       " 8         6  ...               NaN\n",
       " 9         6  ...               NaN\n",
       " 10        6  ...               NaN\n",
       " 11        6  ...               NaN\n",
       " 12        6  ...              43.0\n",
       " 13        6  ...              37.0\n",
       " 14        6  ...              20.0\n",
       " 15        6  ...               NaN\n",
       " 16        6  ...              61.0\n",
       " 17        6  ...              69.0\n",
       " 18        6  ...              51.0\n",
       " 19        6  ...              59.0\n",
       " 20        6  ...              64.0\n",
       " 21        6  ...              64.0\n",
       " 22        6  ...              68.0\n",
       " 23        6  ...              42.0\n",
       " 24        6  ...              44.0\n",
       " 25        6  ...              48.0\n",
       " 26        6  ...              55.0\n",
       " 27        6  ...              16.0\n",
       " 28        6  ...               NaN\n",
       " 29        6  ...               9.0\n",
       " 30        6  ...              43.0\n",
       " 31        6  ...              40.0\n",
       " \n",
       " [32 rows x 16 columns]]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "people_df = [person_0, person_1, person_2, person_3, person_4, person_5, person_6]\n",
    "\n",
    "for num, df in enumerate(people_df):\n",
    "    df.insert(0, \"temp_id\", num)\n",
    "\n",
    "people_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "time_series_list = []\n",
    "\n",
    "for num, df in enumerate(people_df):\n",
    "    # Extrahieren der Zeitreihendaten (Annahme: gibt einen DataFrame zurück)\n",
    "    temp_df = dc.main_interpolation(df)\n",
    "\n",
    "    # Weisen Sie die eindeutige ID dem DataFrame zu\n",
    "    temp_df['temp_id'] = num\n",
    "\n",
    "    # Fügen Sie den aktualisierten DataFrame der neuen Liste hinzu\n",
    "    time_series_list.append(temp_df)\n",
    "\n",
    "len(time_series_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sleepLevel_value</th>\n",
       "      <th>respirationValue</th>\n",
       "      <th>sleepRestlessMoments_value</th>\n",
       "      <th>hrvData_value</th>\n",
       "      <th>sleepStress_value</th>\n",
       "      <th>sleepBodyBattery_value</th>\n",
       "      <th>sleepHeartRate_value</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>startGMT</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  <tbody>\n",
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       "      <th>2023-12-26 00:21:00</th>\n",
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       "    <tr>\n",
       "      <th>2023-12-26 00:23:00</th>\n",
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       "      <th>2023-12-26 00:24:00</th>\n",
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       "      <th>2023-12-26 00:25:00</th>\n",
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       "      <th>2023-12-26 07:36:00</th>\n",
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       "      <td>0.315789</td>\n",
       "      <td>0.456522</td>\n",
       "      <td>0.326923</td>\n",
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       "    <tr>\n",
       "      <th>2023-12-26 07:37:00</th>\n",
       "      <td>0.333333</td>\n",
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       "      <th>2023-12-26 07:38:00</th>\n",
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       "<p>440 rows × 7 columns</p>\n",
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      ],
      "text/plain": [
       "                     sleepLevel_value  ...  sleepHeartRate_value\n",
       "startGMT                               ...                      \n",
       "2023-12-26 00:21:00          0.333333  ...              0.326923\n",
       "2023-12-26 00:22:00          0.333333  ...              0.288462\n",
       "2023-12-26 00:23:00          0.000000  ...              0.298077\n",
       "2023-12-26 00:24:00          0.000000  ...              0.307692\n",
       "2023-12-26 00:25:00          0.000000  ...              0.307692\n",
       "...                               ...  ...                   ...\n",
       "2023-12-26 07:36:00          0.333333  ...              0.326923\n",
       "2023-12-26 07:37:00          0.333333  ...              0.317308\n",
       "2023-12-26 07:38:00          0.333333  ...              0.307692\n",
       "2023-12-26 07:39:00          0.333333  ...              0.307692\n",
       "2023-12-26 07:40:00          0.333333  ...              0.307692\n",
       "\n",
       "[440 rows x 7 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(time_series_list[4][20])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(542, 7)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "time_series_list[0][1].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Person 1:\n",
      "  Nacht 0: 626 Zeilen\n",
      "  Nacht 1: 542 Zeilen\n",
      "  Nacht 2: 428 Zeilen\n",
      "  Nacht 3: 359 Zeilen\n",
      "  Nacht 4: 520 Zeilen\n",
      "  Nacht 5: 413 Zeilen\n",
      "  Nacht 6: 548 Zeilen\n",
      "  Nacht 7: 667 Zeilen\n",
      "  Nacht 8: 428 Zeilen\n",
      "  Nacht 9: 522 Zeilen\n",
      "  Nacht 10: 396 Zeilen\n",
      "  Nacht 11: 541 Zeilen\n",
      "  Nacht 12: 437 Zeilen\n",
      "  Nacht 13: 415 Zeilen\n",
      "  Nacht 14: 552 Zeilen\n",
      "Person 2:\n",
      "  Nacht 0: 476 Zeilen\n",
      "  Nacht 1: 355 Zeilen\n",
      "  Nacht 2: 344 Zeilen\n",
      "  Nacht 3: 454 Zeilen\n",
      "  Nacht 4: 313 Zeilen\n",
      "  Nacht 5: 529 Zeilen\n",
      "  Nacht 6: 254 Zeilen\n",
      "  Nacht 7: 320 Zeilen\n",
      "  Nacht 8: 600 Zeilen\n",
      "  Nacht 9: 301 Zeilen\n",
      "  Nacht 10: 584 Zeilen\n",
      "  Nacht 11: 384 Zeilen\n",
      "  Nacht 12: 478 Zeilen\n",
      "  Nacht 13: 478 Zeilen\n",
      "  Nacht 14: 434 Zeilen\n",
      "  Nacht 15: 324 Zeilen\n",
      "  Nacht 16: 562 Zeilen\n",
      "  Nacht 17: 505 Zeilen\n",
      "  Nacht 18: 394 Zeilen\n",
      "Person 3:\n",
      "  Nacht 0: 579 Zeilen\n",
      "  Nacht 1: 313 Zeilen\n",
      "  Nacht 2: 495 Zeilen\n",
      "  Nacht 3: 455 Zeilen\n",
      "  Nacht 4: 547 Zeilen\n",
      "  Nacht 5: 481 Zeilen\n",
      "  Nacht 6: 458 Zeilen\n",
      "  Nacht 7: 524 Zeilen\n",
      "  Nacht 8: 314 Zeilen\n",
      "  Nacht 9: 328 Zeilen\n",
      "  Nacht 10: 487 Zeilen\n",
      "  Nacht 11: 347 Zeilen\n",
      "  Nacht 12: 407 Zeilen\n",
      "  Nacht 13: 435 Zeilen\n",
      "  Nacht 14: 310 Zeilen\n",
      "Person 4:\n",
      "  Nacht 0: 646 Zeilen\n",
      "  Nacht 1: 463 Zeilen\n",
      "  Nacht 2: 534 Zeilen\n",
      "  Nacht 3: 701 Zeilen\n",
      "  Nacht 4: 438 Zeilen\n",
      "  Nacht 5: 371 Zeilen\n",
      "  Nacht 6: 603 Zeilen\n",
      "  Nacht 7: 367 Zeilen\n",
      "  Nacht 8: 542 Zeilen\n",
      "  Nacht 9: 212 Zeilen\n",
      "  Nacht 10: 411 Zeilen\n",
      "  Nacht 11: 384 Zeilen\n",
      "  Nacht 12: 315 Zeilen\n",
      "  Nacht 13: 460 Zeilen\n",
      "  Nacht 14: 516 Zeilen\n",
      "  Nacht 15: 405 Zeilen\n",
      "  Nacht 16: 453 Zeilen\n",
      "  Nacht 17: 994 Zeilen\n",
      "  Nacht 18: 470 Zeilen\n",
      "  Nacht 19: 695 Zeilen\n",
      "  Nacht 20: 322 Zeilen\n",
      "Person 5:\n",
      "  Nacht 0: 491 Zeilen\n",
      "  Nacht 1: 428 Zeilen\n",
      "  Nacht 2: 426 Zeilen\n",
      "  Nacht 3: 422 Zeilen\n",
      "  Nacht 4: 411 Zeilen\n",
      "  Nacht 5: 408 Zeilen\n",
      "  Nacht 6: 690 Zeilen\n",
      "  Nacht 7: 451 Zeilen\n",
      "  Nacht 8: 416 Zeilen\n",
      "  Nacht 9: 420 Zeilen\n",
      "  Nacht 10: 407 Zeilen\n",
      "  Nacht 11: 417 Zeilen\n",
      "  Nacht 12: 646 Zeilen\n",
      "  Nacht 13: 503 Zeilen\n",
      "  Nacht 14: 398 Zeilen\n",
      "  Nacht 15: 394 Zeilen\n",
      "  Nacht 16: 460 Zeilen\n",
      "  Nacht 17: 499 Zeilen\n",
      "  Nacht 18: 598 Zeilen\n",
      "  Nacht 19: 505 Zeilen\n",
      "  Nacht 20: 440 Zeilen\n",
      "Person 6:\n",
      "  Nacht 0: 499 Zeilen\n",
      "  Nacht 1: 388 Zeilen\n",
      "  Nacht 2: 401 Zeilen\n",
      "  Nacht 3: 512 Zeilen\n",
      "  Nacht 4: 368 Zeilen\n",
      "  Nacht 5: 519 Zeilen\n",
      "  Nacht 6: 479 Zeilen\n",
      "  Nacht 7: 617 Zeilen\n",
      "  Nacht 8: 328 Zeilen\n",
      "  Nacht 9: 337 Zeilen\n",
      "  Nacht 10: 270 Zeilen\n",
      "  Nacht 11: 233 Zeilen\n",
      "  Nacht 12: 361 Zeilen\n",
      "Person 7:\n",
      "  Nacht 0: 423 Zeilen\n",
      "  Nacht 1: 445 Zeilen\n",
      "  Nacht 2: 415 Zeilen\n",
      "  Nacht 3: 573 Zeilen\n",
      "  Nacht 4: 517 Zeilen\n",
      "  Nacht 5: 388 Zeilen\n",
      "  Nacht 6: 434 Zeilen\n",
      "  Nacht 7: 460 Zeilen\n",
      "  Nacht 8: 478 Zeilen\n",
      "  Nacht 9: 462 Zeilen\n",
      "  Nacht 10: 493 Zeilen\n",
      "  Nacht 11: 490 Zeilen\n",
      "  Nacht 12: 511 Zeilen\n",
      "  Nacht 13: 481 Zeilen\n",
      "  Nacht 14: 250 Zeilen\n",
      "  Nacht 15: 253 Zeilen\n",
      "  Nacht 16: 573 Zeilen\n",
      "  Nacht 17: 399 Zeilen\n",
      "122\n"
     ]
    }
   ],
   "source": [
    "# find total nights\n",
    "total_nights = 0\n",
    "# Durchlaufen jeder Person in der Liste\n",
    "for person_index, person_data in enumerate(time_series_list):\n",
    "    print(f\"Person {person_index + 1}:\")\n",
    "\n",
    "    # Überprüfen, ob person_data ein Dictionary ist\n",
    "    if isinstance(person_data, dict):\n",
    "        # Durchlaufen jedes Nacht-DataFrames der Person\n",
    "        for night, df in person_data.items():\n",
    "            # Überprüfen, ob der Wert ein DataFrame ist\n",
    "            if isinstance(df, pd.DataFrame):\n",
    "                print(f\"  Nacht {night}: {len(df)} Zeilen\")\n",
    "                total_nights +=1\n",
    "\n",
    "print(total_nights)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "994"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def calculate_maximum_length(list_):\n",
    "    max_length = 0  # Initialize max_length to store the maximum number of rows\n",
    "\n",
    "    # Iterate through each person in the list\n",
    "    for person_index, person_data in enumerate(time_series_list):\n",
    "        # Iterate through each night DataFrame of the person\n",
    "        for night, df in person_data.items():\n",
    "            # Check if the value is a DataFrame\n",
    "            if isinstance(df, pd.DataFrame):\n",
    "                # Update max_length if this night has more rows\n",
    "                if len(df) > max_length:\n",
    "                    max_length = len(df)\n",
    "\n",
    "    # After completing the iteration, max_length will hold the number of rows of the longest night\n",
    "    return max_length\n",
    "\n",
    "max_length = calculate_maximum_length(time_series_list)\n",
    "max_length"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "996"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"This step is necessary because we are using convolutional layers that halve the dimensions after every layer. \n",
    "If any halve produces a .5, the ceiling will be taken. This causes the Upsampling to reproduce different dimensions that our original input. \"\"\"\n",
    "\n",
    "# Determine how many layers are in our Conv1D network. (how many times the data will be halved)\n",
    "n_layers = 6\n",
    "\n",
    "while True:\n",
    "    if max_length % n_layers != 0:\n",
    "        max_length += 1\n",
    "    else:\n",
    "        break\n",
    "\n",
    "max_length"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(996, 7)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# List to store normalized and padded dataframes\n",
    "normalized_padded_dfs = []\n",
    "\n",
    "for person_data in time_series_list:\n",
    "    for night,df in person_data.items():\n",
    "        if isinstance(df, pd.DataFrame):\n",
    "            # Pad them to ensure  they have the same length (needed for training/testing split)\n",
    "            df_padded = pad_sequences([df.values],maxlen=max_length,dtype='float32', padding='post')\n",
    "            # Add the padded dataframe to the list of normalized & padded dataframes\n",
    "            normalized_padded_dfs.append((df_padded[0]))\n",
    "\n",
    "normalized_padded_dfs[0].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(97, 996, 7)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Convert the list to an array\n",
    "data = np.array(normalized_padded_dfs)\n",
    "\n",
    "# Split into training and test\n",
    "X_train, X_test = train_test_split(data, test_size=0.2,random_state=42)\n",
    "\n",
    "# Define input dimensions\n",
    "n_features = 7\n",
    "\n",
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.layers import MaxPooling1D, UpSampling1D"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_41\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " conv1d_96 (Conv1D)          (None, 498, 64)           3200      \n",
      "                                                                 \n",
      " max_pooling1d_92 (MaxPooli  (None, 249, 64)           0         \n",
      " ng1D)                                                           \n",
      "                                                                 \n",
      " conv1d_97 (Conv1D)          (None, 125, 32)           14368     \n",
      "                                                                 \n",
      " max_pooling1d_93 (MaxPooli  (None, 62, 32)            0         \n",
      " ng1D)                                                           \n",
      "                                                                 \n",
      " flatten_37 (Flatten)        (None, 1984)              0         \n",
      "                                                                 \n",
      " dense_98 (Dense)            (None, 500)               992500    \n",
      "                                                                 \n",
      " dense_99 (Dense)            (None, 100)               50100     \n",
      "                                                                 \n",
      " dense_100 (Dense)           (None, 500)               50500     \n",
      "                                                                 \n",
      " dense_101 (Dense)           (None, 1984)              993984    \n",
      "                                                                 \n",
      " reshape_29 (Reshape)        (None, 62, 32)            0         \n",
      "                                                                 \n",
      " conv1d_transpose_53 (Conv1  (None, 124, 32)           7200      \n",
      " DTranspose)                                                     \n",
      "                                                                 \n",
      " up_sampling1d_28 (UpSampli  (None, 248, 32)           0         \n",
      " ng1D)                                                           \n",
      "                                                                 \n",
      " conv1d_transpose_54 (Conv1  (None, 496, 64)           14400     \n",
      " DTranspose)                                                     \n",
      "                                                                 \n",
      " up_sampling1d_29 (UpSampli  (None, 992, 64)           0         \n",
      " ng1D)                                                           \n",
      "                                                                 \n",
      " conv1d_transpose_55 (Conv1  (None, 992, 7)            3143      \n",
      " DTranspose)                                                     \n",
      "                                                                 \n",
      " zero_padding1d (ZeroPaddin  (None, 996, 7)            0         \n",
      " g1D)                                                            \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 2129395 (8.12 MB)\n",
      "Trainable params: 2129395 (8.12 MB)\n",
      "Non-trainable params: 0 (0.00 Byte)\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# Create model\n",
    "model = keras.Sequential(\n",
    "    [\n",
    "        layers.Input(shape=(max_length,n_features)),\n",
    "        layers.Conv1D(\n",
    "            filters=64,\n",
    "            kernel_size=7,\n",
    "            padding=\"same\",\n",
    "            strides=2,\n",
    "            activation=\"relu\",\n",
    "        ),\n",
    "        #layers.Dropout(rate=0.2),\n",
    "        # maxpooling\n",
    "        layers.MaxPooling1D(\n",
    "            pool_size=2, \n",
    "            strides=2, \n",
    "            padding=\"valid\",\n",
    "        ),\n",
    "        layers.Conv1D(\n",
    "            filters=32,\n",
    "            kernel_size=7,\n",
    "            padding=\"same\",\n",
    "            strides=2,\n",
    "            activation=\"relu\",\n",
    "        ),\n",
    "        # #layers.Dropout(rate=0.2),\n",
    "        # # maxpooling\n",
    "        layers.MaxPooling1D(\n",
    "            pool_size=2, \n",
    "            strides=2, \n",
    "            padding=\"valid\",\n",
    "        ),\n",
    "        # layers.Conv1D(\n",
    "        #     filters=16,\n",
    "        #     kernel_size=7,\n",
    "        #     padding=\"same\",\n",
    "        #     strides=2,\n",
    "        #     activation=\"relu\",\n",
    "        # ),\n",
    "        # # maxpooling\n",
    "        # layers.MaxPooling1D(\n",
    "        #     pool_size=2, \n",
    "        #     strides=2, \n",
    "        #     padding=\"valid\",\n",
    "        # ),\n",
    "        layers.Flatten(),  # Flatten the output\n",
    "        layers.Dense(500, activation='relu'),\n",
    "        layers.Dense(100, activation='relu'),\n",
    "        layers.Dense(500, activation='relu'),\n",
    "        layers.Dense(1984, activation='relu'),\n",
    "        layers.Reshape((62, 32)),  # Reshape to match the original shape\n",
    "        # layers.Conv1DTranspose(\n",
    "        #     filters=16,\n",
    "        #     kernel_size=7,\n",
    "        #     padding=\"same\",\n",
    "        #     strides=2,\n",
    "        #     activation=\"relu\",\n",
    "        # ),\n",
    "        # # #layers.Dropout(rate=0.2),\n",
    "        # # # upsampling layer\n",
    "        #layers.UpSampling1D(size=2),\n",
    "        layers.Conv1DTranspose(\n",
    "            filters=32,\n",
    "            kernel_size=7,\n",
    "            padding=\"same\",\n",
    "            strides=2,\n",
    "            activation=\"relu\",\n",
    "        ),\n",
    "        # # #layers.Dropout(rate=0.2),\n",
    "        # # # upsampling layer\n",
    "        layers.UpSampling1D(size=2),\n",
    "        layers.Conv1DTranspose(\n",
    "            filters=64,\n",
    "            kernel_size=7,\n",
    "            padding=\"same\",\n",
    "            strides=2,\n",
    "            activation=\"relu\",\n",
    "        ),\n",
    "        layers.UpSampling1D(size=2),\n",
    "        layers.Conv1DTranspose(filters=n_features, kernel_size=7, padding=\"same\"),\n",
    "        layers.ZeroPadding1D(padding=2)\n",
    "        # # Cropping layer to adjust the output shape exactly to 1002\n",
    "        #layers.Cropping1D(cropping=((2, 2))),  # Trim 3 units from start and end\n",
    "    ]\n",
    ")\n",
    "\n",
    "# Define function to save embeddings\n",
    "def save_embeddings(model, x_data, save_path='embeddings.npy'):\n",
    "    embeddings = model.predict(x_data)\n",
    "    np.save(save_path, embeddings)\n",
    "    print(f'Embeddings saved to {save_path}')\n",
    "\n",
    " # Compile model and visualize layers\n",
    "model.compile(optimizer=keras.optimizers.Adam(learning_rate=0.001), loss=\"mse\")\n",
    "# Print model architecture\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/250\n",
      "WARNING:tensorflow:From c:\\Users\\Asus\\anaconda3\\envs\\dig_health\\lib\\site-packages\\keras\\src\\utils\\tf_utils.py:492: The name tf.ragged.RaggedTensorValue is deprecated. Please use tf.compat.v1.ragged.RaggedTensorValue instead.\n",
      "\n",
      "3/3 [==============================] - ETA: 0s - loss: 0.0919"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Asus\\anaconda3\\envs\\dig_health\\lib\\site-packages\\keras\\src\\engine\\training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.\n",
      "  saving_api.save_model(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3/3 [==============================] - 16s 773ms/step - loss: 0.0919 - val_loss: 0.0532\n",
      "Epoch 2/250\n",
      "3/3 [==============================] - 1s 234ms/step - loss: 0.0505 - val_loss: 0.0320\n",
      "Epoch 3/250\n",
      "3/3 [==============================] - 0s 189ms/step - loss: 0.0328 - val_loss: 0.0286\n",
      "Epoch 4/250\n",
      "3/3 [==============================] - 0s 167ms/step - loss: 0.0303 - val_loss: 0.0263\n",
      "Epoch 5/250\n",
      "3/3 [==============================] - 0s 201ms/step - loss: 0.0277 - val_loss: 0.0246\n",
      "Epoch 6/250\n",
      "3/3 [==============================] - 1s 226ms/step - loss: 0.0259 - val_loss: 0.0228\n",
      "Epoch 7/250\n",
      "3/3 [==============================] - 1s 230ms/step - loss: 0.0238 - val_loss: 0.0211\n",
      "Epoch 8/250\n",
      "3/3 [==============================] - 0s 175ms/step - loss: 0.0219 - val_loss: 0.0198\n",
      "Epoch 9/250\n",
      "3/3 [==============================] - 1s 215ms/step - loss: 0.0207 - val_loss: 0.0186\n",
      "Epoch 10/250\n",
      "3/3 [==============================] - 0s 214ms/step - loss: 0.0193 - val_loss: 0.0172\n",
      "Epoch 11/250\n",
      "3/3 [==============================] - 0s 205ms/step - loss: 0.0184 - val_loss: 0.0166\n",
      "Epoch 12/250\n",
      "3/3 [==============================] - 0s 174ms/step - loss: 0.0176 - val_loss: 0.0162\n",
      "Epoch 13/250\n",
      "3/3 [==============================] - 1s 190ms/step - loss: 0.0171 - val_loss: 0.0154\n",
      "Epoch 14/250\n",
      "3/3 [==============================] - 0s 207ms/step - loss: 0.0165 - val_loss: 0.0152\n",
      "Epoch 15/250\n",
      "3/3 [==============================] - 1s 217ms/step - loss: 0.0161 - val_loss: 0.0150\n",
      "Epoch 16/250\n",
      "3/3 [==============================] - 1s 228ms/step - loss: 0.0160 - val_loss: 0.0147\n",
      "Epoch 17/250\n",
      "3/3 [==============================] - 1s 221ms/step - loss: 0.0156 - val_loss: 0.0143\n",
      "Epoch 18/250\n",
      "3/3 [==============================] - 1s 241ms/step - loss: 0.0154 - val_loss: 0.0136\n",
      "Epoch 19/250\n",
      "3/3 [==============================] - 0s 120ms/step - loss: 0.0151 - val_loss: 0.0137\n",
      "Epoch 20/250\n",
      "3/3 [==============================] - 0s 114ms/step - loss: 0.0148 - val_loss: 0.0137\n",
      "Epoch 21/250\n",
      "3/3 [==============================] - 1s 213ms/step - loss: 0.0146 - val_loss: 0.0135\n",
      "Epoch 22/250\n",
      "3/3 [==============================] - 1s 229ms/step - loss: 0.0143 - val_loss: 0.0131\n",
      "Epoch 23/250\n",
      "3/3 [==============================] - 1s 215ms/step - loss: 0.0142 - val_loss: 0.0129\n",
      "Epoch 24/250\n",
      "3/3 [==============================] - 1s 216ms/step - loss: 0.0140 - val_loss: 0.0128\n",
      "Epoch 25/250\n",
      "3/3 [==============================] - 1s 219ms/step - loss: 0.0138 - val_loss: 0.0128\n",
      "Epoch 26/250\n",
      "3/3 [==============================] - 1s 250ms/step - loss: 0.0136 - val_loss: 0.0126\n",
      "Epoch 27/250\n",
      "3/3 [==============================] - 1s 238ms/step - loss: 0.0134 - val_loss: 0.0125\n",
      "Epoch 28/250\n",
      "3/3 [==============================] - 1s 223ms/step - loss: 0.0131 - val_loss: 0.0122\n",
      "Epoch 29/250\n",
      "3/3 [==============================] - 1s 248ms/step - loss: 0.0128 - val_loss: 0.0120\n",
      "Epoch 30/250\n",
      "3/3 [==============================] - 1s 228ms/step - loss: 0.0124 - val_loss: 0.0115\n",
      "Epoch 31/250\n",
      "3/3 [==============================] - 1s 225ms/step - loss: 0.0121 - val_loss: 0.0114\n",
      "Epoch 32/250\n",
      "3/3 [==============================] - 0s 110ms/step - loss: 0.0118 - val_loss: 0.0115\n",
      "Epoch 33/250\n",
      "3/3 [==============================] - 1s 242ms/step - loss: 0.0116 - val_loss: 0.0113\n",
      "Epoch 34/250\n",
      "3/3 [==============================] - 1s 214ms/step - loss: 0.0115 - val_loss: 0.0109\n",
      "Epoch 35/250\n",
      "3/3 [==============================] - 0s 121ms/step - loss: 0.0113 - val_loss: 0.0110\n",
      "Epoch 36/250\n",
      "3/3 [==============================] - 1s 221ms/step - loss: 0.0113 - val_loss: 0.0108\n",
      "Epoch 37/250\n",
      "3/3 [==============================] - 0s 155ms/step - loss: 0.0111 - val_loss: 0.0108\n",
      "Epoch 38/250\n",
      "3/3 [==============================] - 1s 234ms/step - loss: 0.0110 - val_loss: 0.0107\n",
      "Epoch 39/250\n",
      "3/3 [==============================] - 1s 237ms/step - loss: 0.0108 - val_loss: 0.0105\n",
      "Epoch 40/250\n",
      "3/3 [==============================] - 1s 222ms/step - loss: 0.0107 - val_loss: 0.0105\n",
      "Epoch 41/250\n",
      "3/3 [==============================] - 0s 199ms/step - loss: 0.0106 - val_loss: 0.0104\n",
      "Epoch 42/250\n",
      "3/3 [==============================] - 0s 150ms/step - loss: 0.0105 - val_loss: 0.0104\n",
      "Epoch 43/250\n",
      "3/3 [==============================] - 1s 248ms/step - loss: 0.0104 - val_loss: 0.0103\n",
      "Epoch 44/250\n",
      "3/3 [==============================] - 0s 157ms/step - loss: 0.0104 - val_loss: 0.0104\n",
      "Epoch 45/250\n",
      "3/3 [==============================] - 1s 215ms/step - loss: 0.0103 - val_loss: 0.0103\n",
      "Epoch 46/250\n",
      "3/3 [==============================] - 1s 213ms/step - loss: 0.0102 - val_loss: 0.0103\n",
      "Epoch 47/250\n",
      "3/3 [==============================] - 1s 236ms/step - loss: 0.0102 - val_loss: 0.0102\n",
      "Epoch 48/250\n",
      "3/3 [==============================] - 0s 113ms/step - loss: 0.0101 - val_loss: 0.0103\n",
      "Epoch 49/250\n",
      "3/3 [==============================] - 1s 240ms/step - loss: 0.0101 - val_loss: 0.0102\n",
      "Epoch 50/250\n",
      "3/3 [==============================] - 0s 134ms/step - loss: 0.0100 - val_loss: 0.0102\n",
      "Epoch 51/250\n",
      "3/3 [==============================] - 0s 161ms/step - loss: 0.0099 - val_loss: 0.0102\n",
      "Epoch 52/250\n",
      "3/3 [==============================] - 0s 112ms/step - loss: 0.0098 - val_loss: 0.0102\n",
      "Epoch 53/250\n",
      "3/3 [==============================] - 0s 123ms/step - loss: 0.0098 - val_loss: 0.0103\n",
      "Epoch 54/250\n",
      "3/3 [==============================] - 1s 218ms/step - loss: 0.0098 - val_loss: 0.0102\n",
      "Epoch 55/250\n",
      "3/3 [==============================] - 0s 109ms/step - loss: 0.0096 - val_loss: 0.0102\n",
      "Epoch 56/250\n",
      "3/3 [==============================] - 0s 143ms/step - loss: 0.0096 - val_loss: 0.0104\n",
      "Epoch 57/250\n",
      "3/3 [==============================] - 1s 218ms/step - loss: 0.0095 - val_loss: 0.0101\n",
      "Epoch 58/250\n",
      "3/3 [==============================] - 0s 169ms/step - loss: 0.0094 - val_loss: 0.0102\n",
      "Epoch 59/250\n",
      "3/3 [==============================] - 1s 224ms/step - loss: 0.0093 - val_loss: 0.0101\n",
      "Epoch 60/250\n",
      "3/3 [==============================] - 0s 124ms/step - loss: 0.0092 - val_loss: 0.0102\n",
      "Epoch 61/250\n",
      "3/3 [==============================] - 0s 122ms/step - loss: 0.0092 - val_loss: 0.0101\n",
      "Epoch 62/250\n",
      "3/3 [==============================] - 0s 123ms/step - loss: 0.0091 - val_loss: 0.0102\n",
      "Epoch 63/250\n",
      "3/3 [==============================] - 0s 110ms/step - loss: 0.0091 - val_loss: 0.0103\n",
      "Epoch 64/250\n",
      "3/3 [==============================] - 0s 120ms/step - loss: 0.0091 - val_loss: 0.0104\n",
      "Epoch 65/250\n",
      "3/3 [==============================] - 0s 143ms/step - loss: 0.0090 - val_loss: 0.0102\n",
      "Epoch 66/250\n",
      "3/3 [==============================] - 0s 108ms/step - loss: 0.0089 - val_loss: 0.0103\n",
      "Epoch 67/250\n",
      "3/3 [==============================] - 0s 146ms/step - loss: 0.0089 - val_loss: 0.0104\n",
      "Epoch 68/250\n",
      "3/3 [==============================] - 0s 160ms/step - loss: 0.0088 - val_loss: 0.0104\n",
      "Epoch 69/250\n",
      "3/3 [==============================] - 0s 158ms/step - loss: 0.0086 - val_loss: 0.0104\n",
      "Epoch 70/250\n",
      "3/3 [==============================] - 0s 107ms/step - loss: 0.0085 - val_loss: 0.0104\n",
      "Epoch 71/250\n",
      "3/3 [==============================] - 0s 166ms/step - loss: 0.0084 - val_loss: 0.0107\n",
      "Epoch 72/250\n",
      "3/3 [==============================] - 0s 128ms/step - loss: 0.0084 - val_loss: 0.0104\n",
      "Epoch 73/250\n",
      "3/3 [==============================] - 0s 112ms/step - loss: 0.0081 - val_loss: 0.0103\n",
      "Epoch 74/250\n",
      "3/3 [==============================] - 0s 142ms/step - loss: 0.0079 - val_loss: 0.0104\n",
      "Epoch 75/250\n",
      "3/3 [==============================] - 0s 128ms/step - loss: 0.0078 - val_loss: 0.0104\n",
      "Epoch 76/250\n",
      "3/3 [==============================] - 0s 118ms/step - loss: 0.0076 - val_loss: 0.0103\n",
      "Epoch 77/250\n",
      "3/3 [==============================] - 0s 144ms/step - loss: 0.0074 - val_loss: 0.0103\n",
      "Epoch 78/250\n",
      "3/3 [==============================] - 0s 164ms/step - loss: 0.0073 - val_loss: 0.0106\n",
      "Epoch 79/250\n",
      "3/3 [==============================] - 0s 131ms/step - loss: 0.0072 - val_loss: 0.0104\n",
      "Epoch 80/250\n",
      "3/3 [==============================] - 0s 135ms/step - loss: 0.0070 - val_loss: 0.0103\n",
      "Epoch 81/250\n",
      "3/3 [==============================] - 0s 148ms/step - loss: 0.0068 - val_loss: 0.0102\n",
      "Epoch 82/250\n",
      "3/3 [==============================] - 0s 139ms/step - loss: 0.0066 - val_loss: 0.0101\n",
      "Epoch 83/250\n",
      "3/3 [==============================] - 0s 121ms/step - loss: 0.0065 - val_loss: 0.0102\n",
      "Epoch 84/250\n",
      "3/3 [==============================] - 0s 167ms/step - loss: 0.0063 - val_loss: 0.0103\n",
      "Epoch 85/250\n",
      "3/3 [==============================] - 0s 116ms/step - loss: 0.0061 - val_loss: 0.0102\n",
      "Epoch 86/250\n",
      "3/3 [==============================] - 0s 117ms/step - loss: 0.0059 - val_loss: 0.0103\n",
      "Epoch 87/250\n",
      "3/3 [==============================] - 0s 149ms/step - loss: 0.0058 - val_loss: 0.0103\n",
      "Epoch 88/250\n",
      "3/3 [==============================] - 0s 115ms/step - loss: 0.0057 - val_loss: 0.0107\n",
      "Epoch 89/250\n",
      "3/3 [==============================] - 0s 118ms/step - loss: 0.0057 - val_loss: 0.0103\n",
      "Epoch 90/250\n",
      "3/3 [==============================] - 1s 237ms/step - loss: 0.0055 - val_loss: 0.0100\n",
      "Epoch 91/250\n",
      "3/3 [==============================] - 0s 123ms/step - loss: 0.0053 - val_loss: 0.0102\n",
      "Epoch 92/250\n",
      "3/3 [==============================] - 0s 138ms/step - loss: 0.0052 - val_loss: 0.0102\n",
      "Epoch 93/250\n",
      "3/3 [==============================] - 0s 112ms/step - loss: 0.0051 - val_loss: 0.0100\n",
      "Epoch 94/250\n",
      "3/3 [==============================] - 0s 138ms/step - loss: 0.0050 - val_loss: 0.0101\n",
      "Epoch 95/250\n",
      "3/3 [==============================] - 0s 113ms/step - loss: 0.0049 - val_loss: 0.0103\n",
      "Epoch 96/250\n",
      "3/3 [==============================] - 0s 112ms/step - loss: 0.0049 - val_loss: 0.0104\n",
      "Epoch 97/250\n",
      "3/3 [==============================] - 0s 137ms/step - loss: 0.0050 - val_loss: 0.0103\n",
      "Epoch 98/250\n",
      "3/3 [==============================] - 0s 108ms/step - loss: 0.0050 - val_loss: 0.0107\n",
      "Epoch 99/250\n",
      "3/3 [==============================] - 0s 161ms/step - loss: 0.0050 - val_loss: 0.0105\n",
      "Epoch 100/250\n",
      "3/3 [==============================] - 0s 113ms/step - loss: 0.0049 - val_loss: 0.0106\n",
      "Epoch 101/250\n",
      "3/3 [==============================] - 0s 139ms/step - loss: 0.0048 - val_loss: 0.0105\n",
      "Epoch 102/250\n",
      "3/3 [==============================] - 0s 147ms/step - loss: 0.0049 - val_loss: 0.0110\n",
      "Epoch 103/250\n",
      "3/3 [==============================] - 0s 105ms/step - loss: 0.0054 - val_loss: 0.0105\n",
      "Epoch 104/250\n",
      "3/3 [==============================] - 0s 145ms/step - loss: 0.0048 - val_loss: 0.0107\n",
      "Epoch 105/250\n",
      "3/3 [==============================] - 0s 135ms/step - loss: 0.0049 - val_loss: 0.0112\n",
      "Epoch 106/250\n",
      "3/3 [==============================] - 0s 104ms/step - loss: 0.0049 - val_loss: 0.0108\n",
      "Epoch 107/250\n",
      "3/3 [==============================] - 0s 147ms/step - loss: 0.0047 - val_loss: 0.0107\n",
      "Epoch 108/250\n",
      "3/3 [==============================] - 0s 101ms/step - loss: 0.0048 - val_loss: 0.0105\n",
      "Epoch 109/250\n",
      "3/3 [==============================] - 0s 118ms/step - loss: 0.0046 - val_loss: 0.0107\n",
      "Epoch 110/250\n",
      "3/3 [==============================] - 0s 156ms/step - loss: 0.0046 - val_loss: 0.0103\n",
      "Epoch 111/250\n",
      "3/3 [==============================] - 0s 108ms/step - loss: 0.0046 - val_loss: 0.0109\n",
      "Epoch 112/250\n",
      "3/3 [==============================] - 0s 130ms/step - loss: 0.0050 - val_loss: 0.0118\n",
      "Epoch 113/250\n",
      "3/3 [==============================] - 0s 160ms/step - loss: 0.0047 - val_loss: 0.0104\n",
      "Epoch 114/250\n",
      "3/3 [==============================] - 0s 114ms/step - loss: 0.0046 - val_loss: 0.0108\n",
      "Epoch 115/250\n",
      "3/3 [==============================] - 0s 154ms/step - loss: 0.0048 - val_loss: 0.0110\n",
      "Epoch 116/250\n",
      "3/3 [==============================] - 0s 151ms/step - loss: 0.0046 - val_loss: 0.0108\n",
      "Epoch 117/250\n",
      "3/3 [==============================] - 0s 112ms/step - loss: 0.0045 - val_loss: 0.0106\n",
      "Epoch 118/250\n",
      "3/3 [==============================] - 0s 116ms/step - loss: 0.0043 - val_loss: 0.0101\n",
      "Epoch 119/250\n",
      "3/3 [==============================] - 0s 153ms/step - loss: 0.0041 - val_loss: 0.0111\n",
      "Epoch 120/250\n",
      "3/3 [==============================] - 0s 112ms/step - loss: 0.0042 - val_loss: 0.0106\n",
      "Epoch 121/250\n",
      "3/3 [==============================] - 0s 129ms/step - loss: 0.0039 - val_loss: 0.0104\n",
      "Epoch 122/250\n",
      "3/3 [==============================] - 0s 191ms/step - loss: 0.0038 - val_loss: 0.0102\n",
      "Epoch 123/250\n",
      "3/3 [==============================] - 0s 151ms/step - loss: 0.0038 - val_loss: 0.0110\n",
      "Epoch 124/250\n",
      "3/3 [==============================] - 0s 108ms/step - loss: 0.0038 - val_loss: 0.0104\n",
      "Epoch 125/250\n",
      "3/3 [==============================] - 0s 145ms/step - loss: 0.0037 - val_loss: 0.0103\n",
      "Epoch 126/250\n",
      "3/3 [==============================] - 0s 169ms/step - loss: 0.0036 - val_loss: 0.0106\n",
      "Epoch 127/250\n",
      "3/3 [==============================] - 0s 104ms/step - loss: 0.0036 - val_loss: 0.0105\n",
      "Epoch 128/250\n",
      "3/3 [==============================] - 0s 120ms/step - loss: 0.0035 - val_loss: 0.0101\n",
      "Epoch 129/250\n",
      "3/3 [==============================] - 0s 151ms/step - loss: 0.0034 - val_loss: 0.0102\n",
      "Epoch 130/250\n",
      "3/3 [==============================] - 0s 113ms/step - loss: 0.0034 - val_loss: 0.0104\n",
      "Epoch 131/250\n",
      "3/3 [==============================] - 0s 120ms/step - loss: 0.0033 - val_loss: 0.0103\n",
      "Epoch 132/250\n",
      "3/3 [==============================] - 0s 152ms/step - loss: 0.0033 - val_loss: 0.0102\n",
      "Epoch 133/250\n",
      "3/3 [==============================] - 0s 115ms/step - loss: 0.0032 - val_loss: 0.0103\n",
      "Epoch 134/250\n",
      "3/3 [==============================] - 0s 125ms/step - loss: 0.0032 - val_loss: 0.0105\n",
      "Epoch 135/250\n",
      "3/3 [==============================] - 0s 147ms/step - loss: 0.0032 - val_loss: 0.0104\n",
      "Epoch 136/250\n",
      "3/3 [==============================] - 0s 115ms/step - loss: 0.0032 - val_loss: 0.0102\n",
      "Epoch 137/250\n",
      "3/3 [==============================] - 0s 113ms/step - loss: 0.0031 - val_loss: 0.0103\n",
      "Epoch 138/250\n",
      "3/3 [==============================] - 0s 147ms/step - loss: 0.0031 - val_loss: 0.0104\n",
      "Epoch 139/250\n",
      "3/3 [==============================] - 0s 115ms/step - loss: 0.0031 - val_loss: 0.0103\n",
      "Epoch 140/250\n",
      "3/3 [==============================] - 0s 120ms/step - loss: 0.0030 - val_loss: 0.0103\n",
      "Epoch 141/250\n",
      "3/3 [==============================] - 0s 118ms/step - loss: 0.0030 - val_loss: 0.0102\n",
      "Epoch 142/250\n",
      "3/3 [==============================] - 0s 138ms/step - loss: 0.0030 - val_loss: 0.0104\n",
      "Epoch 143/250\n",
      "3/3 [==============================] - 0s 113ms/step - loss: 0.0030 - val_loss: 0.0105\n",
      "Epoch 144/250\n",
      "3/3 [==============================] - 0s 114ms/step - loss: 0.0030 - val_loss: 0.0104\n",
      "Epoch 145/250\n",
      "3/3 [==============================] - 0s 105ms/step - loss: 0.0029 - val_loss: 0.0104\n",
      "Epoch 146/250\n",
      "3/3 [==============================] - 0s 117ms/step - loss: 0.0029 - val_loss: 0.0103\n",
      "Epoch 147/250\n",
      "3/3 [==============================] - 0s 138ms/step - loss: 0.0029 - val_loss: 0.0104\n",
      "Epoch 148/250\n",
      "3/3 [==============================] - 0s 130ms/step - loss: 0.0029 - val_loss: 0.0105\n",
      "Epoch 149/250\n",
      "3/3 [==============================] - 0s 154ms/step - loss: 0.0028 - val_loss: 0.0105\n",
      "Epoch 150/250\n",
      "3/3 [==============================] - 0s 110ms/step - loss: 0.0028 - val_loss: 0.0104\n",
      "Epoch 151/250\n",
      "3/3 [==============================] - 0s 119ms/step - loss: 0.0028 - val_loss: 0.0104\n",
      "Epoch 152/250\n",
      "3/3 [==============================] - 0s 149ms/step - loss: 0.0028 - val_loss: 0.0103\n",
      "Epoch 153/250\n",
      "3/3 [==============================] - 0s 131ms/step - loss: 0.0028 - val_loss: 0.0105\n",
      "Epoch 154/250\n",
      "3/3 [==============================] - 0s 115ms/step - loss: 0.0028 - val_loss: 0.0106\n",
      "Epoch 155/250\n",
      "3/3 [==============================] - 0s 145ms/step - loss: 0.0028 - val_loss: 0.0105\n",
      "Epoch 156/250\n",
      "3/3 [==============================] - 0s 114ms/step - loss: 0.0027 - val_loss: 0.0103\n",
      "Epoch 157/250\n",
      "3/3 [==============================] - 0s 116ms/step - loss: 0.0027 - val_loss: 0.0105\n",
      "Epoch 158/250\n",
      "3/3 [==============================] - 0s 145ms/step - loss: 0.0027 - val_loss: 0.0105\n",
      "Epoch 159/250\n",
      "3/3 [==============================] - 0s 111ms/step - loss: 0.0027 - val_loss: 0.0106\n",
      "Epoch 160/250\n",
      "3/3 [==============================] - 0s 136ms/step - loss: 0.0027 - val_loss: 0.0106\n",
      "Epoch 161/250\n",
      "3/3 [==============================] - 0s 154ms/step - loss: 0.0027 - val_loss: 0.0106\n",
      "Epoch 162/250\n",
      "3/3 [==============================] - 0s 108ms/step - loss: 0.0027 - val_loss: 0.0106\n",
      "Epoch 163/250\n",
      "3/3 [==============================] - 0s 122ms/step - loss: 0.0026 - val_loss: 0.0104\n",
      "Epoch 164/250\n",
      "3/3 [==============================] - 0s 152ms/step - loss: 0.0026 - val_loss: 0.0105\n",
      "Epoch 165/250\n",
      "3/3 [==============================] - 0s 111ms/step - loss: 0.0026 - val_loss: 0.0105\n",
      "Epoch 166/250\n",
      "3/3 [==============================] - 0s 124ms/step - loss: 0.0026 - val_loss: 0.0107\n",
      "Epoch 167/250\n",
      "3/3 [==============================] - 0s 154ms/step - loss: 0.0026 - val_loss: 0.0109\n",
      "Epoch 168/250\n",
      "3/3 [==============================] - 0s 114ms/step - loss: 0.0026 - val_loss: 0.0107\n",
      "Epoch 169/250\n",
      "3/3 [==============================] - 0s 125ms/step - loss: 0.0026 - val_loss: 0.0106\n",
      "Epoch 170/250\n",
      "3/3 [==============================] - 0s 146ms/step - loss: 0.0026 - val_loss: 0.0105\n",
      "Epoch 171/250\n",
      "3/3 [==============================] - 0s 111ms/step - loss: 0.0026 - val_loss: 0.0104\n",
      "Epoch 172/250\n",
      "3/3 [==============================] - 0s 143ms/step - loss: 0.0026 - val_loss: 0.0105\n",
      "Epoch 173/250\n",
      "3/3 [==============================] - 0s 148ms/step - loss: 0.0026 - val_loss: 0.0106\n",
      "Epoch 174/250\n",
      "3/3 [==============================] - 0s 120ms/step - loss: 0.0026 - val_loss: 0.0106\n",
      "Epoch 175/250\n",
      "3/3 [==============================] - 0s 145ms/step - loss: 0.0026 - val_loss: 0.0110\n",
      "Epoch 176/250\n",
      "3/3 [==============================] - 0s 158ms/step - loss: 0.0026 - val_loss: 0.0108\n",
      "Epoch 177/250\n",
      "3/3 [==============================] - 0s 117ms/step - loss: 0.0026 - val_loss: 0.0106\n",
      "Epoch 178/250\n",
      "3/3 [==============================] - 0s 154ms/step - loss: 0.0025 - val_loss: 0.0106\n",
      "Epoch 179/250\n",
      "3/3 [==============================] - 0s 119ms/step - loss: 0.0025 - val_loss: 0.0108\n",
      "Epoch 180/250\n",
      "3/3 [==============================] - 0s 124ms/step - loss: 0.0025 - val_loss: 0.0107\n",
      "Epoch 181/250\n",
      "3/3 [==============================] - 0s 151ms/step - loss: 0.0025 - val_loss: 0.0108\n",
      "Epoch 182/250\n",
      "3/3 [==============================] - 0s 117ms/step - loss: 0.0025 - val_loss: 0.0106\n",
      "Epoch 183/250\n",
      "3/3 [==============================] - 0s 178ms/step - loss: 0.0025 - val_loss: 0.0106\n",
      "Epoch 184/250\n",
      "3/3 [==============================] - 0s 114ms/step - loss: 0.0025 - val_loss: 0.0108\n",
      "Epoch 185/250\n",
      "3/3 [==============================] - 0s 161ms/step - loss: 0.0025 - val_loss: 0.0110\n",
      "Epoch 186/250\n",
      "3/3 [==============================] - 0s 120ms/step - loss: 0.0025 - val_loss: 0.0115\n",
      "Epoch 187/250\n",
      "3/3 [==============================] - 0s 133ms/step - loss: 0.0026 - val_loss: 0.0117\n",
      "Epoch 188/250\n",
      "3/3 [==============================] - 0s 132ms/step - loss: 0.0029 - val_loss: 0.0113\n",
      "Epoch 189/250\n",
      "3/3 [==============================] - 0s 147ms/step - loss: 0.0030 - val_loss: 0.0110\n",
      "Epoch 190/250\n",
      "3/3 [==============================] - 0s 178ms/step - loss: 0.0028 - val_loss: 0.0107\n",
      "Epoch 191/250\n",
      "3/3 [==============================] - 0s 116ms/step - loss: 0.0028 - val_loss: 0.0105\n",
      "Epoch 192/250\n",
      "3/3 [==============================] - 0s 150ms/step - loss: 0.0028 - val_loss: 0.0114\n",
      "Epoch 193/250\n",
      "3/3 [==============================] - 0s 156ms/step - loss: 0.0030 - val_loss: 0.0120\n"
     ]
    }
   ],
   "source": [
    "# Define checkpoints and early stopping\n",
    "checkpoint = keras.callbacks.ModelCheckpoint('autoencoder_Conv1D.h5', save_best_only=True)\n",
    "early_stopping = keras.callbacks.EarlyStopping(monitor='loss', patience=10, restore_best_weights=True)\n",
    "\n",
    "# Train the autoencoder\n",
    "history = model.fit(\n",
    "    X_train,\n",
    "    X_train,\n",
    "    epochs=250,\n",
    "    batch_size=32,\n",
    "    validation_split=0.15,\n",
    "    callbacks=[\n",
    "        checkpoint,\n",
    "        early_stopping\n",
    "    ],\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Autoencoder visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 1s 925ms/step\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "\n",
    "X_test_recon = model.predict(X_test)\n",
    "\n",
    "# 1st day in X_test, all values for the last feature\n",
    "plt.plot(X_test[1,:-1,5])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 1st day in X_test_recon, all values for the second last feature\n",
    "plt.plot(X_test_recon[1,:-1,5])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 226ms/step\n",
      "Embeddings saved to X_test_embeddings.npy\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "encoder_model = keras.models.Model(inputs= model.input, outputs=model.get_layer(index=6).output)\n",
    "save_embeddings(encoder_model,X_test,save_path=\"X_test_embeddings.npy\")\n",
    "X_test_embeddings = np.load(\"X_test_embeddings.npy\")\n",
    "plt.plot(X_test[2][:-1])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(25, 996, 7)"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(X_test_embeddings[2][:-1])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(25, 2000)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test_embeddings.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### create the embeddings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model_1\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " input_45 (InputLayer)       [(None, 996, 7)]          0         \n",
      "                                                                 \n",
      " conv1d_96 (Conv1D)          (None, 498, 64)           3200      \n",
      "                                                                 \n",
      " max_pooling1d_92 (MaxPooli  (None, 249, 64)           0         \n",
      " ng1D)                                                           \n",
      "                                                                 \n",
      " conv1d_97 (Conv1D)          (None, 125, 32)           14368     \n",
      "                                                                 \n",
      " max_pooling1d_93 (MaxPooli  (None, 62, 32)            0         \n",
      " ng1D)                                                           \n",
      "                                                                 \n",
      " flatten_37 (Flatten)        (None, 1984)              0         \n",
      "                                                                 \n",
      " dense_98 (Dense)            (None, 500)               992500    \n",
      "                                                                 \n",
      " dense_99 (Dense)            (None, 100)               50100     \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 1060168 (4.04 MB)\n",
      "Trainable params: 1060168 (4.04 MB)\n",
      "Non-trainable params: 0 (0.00 Byte)\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# Recreate the exact same model, including its weights and the optimizer\n",
    "autoencoder= tf.keras.models.load_model('autoencoder_Conv1D.h5')\n",
    "\n",
    "# get only the encoder layer\n",
    "encoder = keras.models.Model(inputs= autoencoder.input, outputs=autoencoder.get_layer(index=6).output)\n",
    "\n",
    "# Show the model architecture\n",
    "encoder.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 49ms/step\n",
      "1/1 [==============================] - 0s 43ms/step\n",
      "1/1 [==============================] - 0s 48ms/step\n",
      "1/1 [==============================] - 0s 48ms/step\n",
      "1/1 [==============================] - 0s 44ms/step\n",
      "1/1 [==============================] - 0s 69ms/step\n",
      "1/1 [==============================] - 0s 47ms/step\n",
      "1/1 [==============================] - 0s 48ms/step\n",
      "1/1 [==============================] - 0s 39ms/step\n",
      "1/1 [==============================] - 0s 42ms/step\n",
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      "1/1 [==============================] - 0s 52ms/step\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "\n",
    "# Create embeddings folder if it doesn't exist\n",
    "if not os.path.exists(f\"datasets/embeddings\"):\n",
    "    os.makedirs(\"datasets/embeddings\")\n",
    "\n",
    "# Assuming encoder and max_length are defined elsewhere\n",
    "nights_embeddings = {}\n",
    "for person_data in time_series_list:\n",
    "    for key, value in person_data.items():\n",
    "        if isinstance(value, pd.DataFrame):\n",
    "            # Pad the DataFrame to ensure consistent shape\n",
    "            df_padded = pad_sequences([value.values], maxlen=max_length, dtype='float32', padding='post')\n",
    "            # Predict embeddings\n",
    "            embeddings = encoder.predict(df_padded)\n",
    "            # Store embeddings\n",
    "            nights_embeddings[key] = embeddings.flatten()  # Flatten embeddings if necessary\n",
    "\n",
    "        if key == 'temp_id':\n",
    "            # Convert the embeddings dictionary to a DataFrame\n",
    "            embeddings_df = pd.DataFrame.from_dict(nights_embeddings, orient='index')  # Consider 'orient' based on your data structure\n",
    "            \n",
    "            # Generate new column names based on the number of features in each embedding\n",
    "            new_column_names = [f'embedding_{i}' for i in range(embeddings_df.shape[1])]\n",
    "\n",
    "            # Rename the DataFrame columns\n",
    "            embeddings_df.columns = new_column_names\n",
    "\n",
    "            # Save the DataFrame as a pickle file\n",
    "            file_name = f\"datasets/embeddings_100/embeddings_{value}.pkl\"\n",
    "            embeddings_df.to_pickle(file_name)\n",
    "\n",
    "            # Reset the embeddings dictionary for the next person\n",
    "            nights_embeddings = {}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>embedding_0</th>\n",
       "      <th>embedding_1</th>\n",
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       "      <th>embedding_1995</th>\n",
       "      <th>embedding_1996</th>\n",
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       "      <th>embedding_1998</th>\n",
       "      <th>embedding_1999</th>\n",
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       "      <th>0</th>\n",
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       "      <td>0.024127</td>\n",
       "      <td>0.188463</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.178814</td>\n",
       "      <td>0.175025</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.182342</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.148534</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.060753</td>\n",
       "      <td>0.239468</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.029974</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.196661</td>\n",
       "      <td>0.100379</td>\n",
       "      <td>0.065028</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.215623</td>\n",
       "      <td>0.270127</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.167461</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.148534</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.060753</td>\n",
       "      <td>0.239468</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.029974</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.044378</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.157962</td>\n",
       "      <td>0.111345</td>\n",
       "      <td>0.084004</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.158381</td>\n",
       "      <td>0.187640</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.232503</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.148534</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.060753</td>\n",
       "      <td>0.239468</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.029974</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.044378</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.245487</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.107861</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.150378</td>\n",
       "      <td>0.210004</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.158773</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.148534</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.060753</td>\n",
       "      <td>0.239468</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.029974</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.044378</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.256926</td>\n",
       "      <td>0.123688</td>\n",
       "      <td>0.173867</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.229670</td>\n",
       "      <td>0.255786</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.200450</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.148534</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.060753</td>\n",
       "      <td>0.239468</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.029974</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.044378</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.041300</td>\n",
       "      <td>0.048306</td>\n",
       "      <td>0.174873</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.037738</td>\n",
       "      <td>0.294980</td>\n",
       "      <td>0.038254</td>\n",
       "      <td>0.341009</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.148534</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.060753</td>\n",
       "      <td>0.239468</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.029974</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.044378</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.177880</td>\n",
       "      <td>0.144757</td>\n",
       "      <td>0.151616</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.151120</td>\n",
       "      <td>0.264503</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.312307</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.148534</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.060753</td>\n",
       "      <td>0.239468</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.029974</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.044378</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.442522</td>\n",
       "      <td>0.266434</td>\n",
       "      <td>0.284811</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.252510</td>\n",
       "      <td>0.047168</td>\n",
       "      <td>0.007921</td>\n",
       "      <td>0.204480</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.148534</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.060753</td>\n",
       "      <td>0.239468</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.029974</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.044378</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.178148</td>\n",
       "      <td>0.130888</td>\n",
       "      <td>0.120792</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.162852</td>\n",
       "      <td>0.295574</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.308893</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.148534</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.060753</td>\n",
       "      <td>0.239468</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.029974</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.044378</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.015297</td>\n",
       "      <td>0.164914</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.094665</td>\n",
       "      <td>0.510414</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.297661</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.148534</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.060753</td>\n",
       "      <td>0.239468</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.029974</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.044378</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.055178</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.185620</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.085594</td>\n",
       "      <td>0.491848</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.374648</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.148534</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.060753</td>\n",
       "      <td>0.239468</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.029974</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.044378</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.095463</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.194850</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.108117</td>\n",
       "      <td>0.363250</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.302769</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.148534</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.060753</td>\n",
       "      <td>0.239468</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.029974</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.044378</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.032896</td>\n",
       "      <td>0.121179</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.008742</td>\n",
       "      <td>0.416652</td>\n",
       "      <td>0.027859</td>\n",
       "      <td>0.316229</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.148534</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.060753</td>\n",
       "      <td>0.239468</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.029974</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.044378</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.122178</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.041282</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.090868</td>\n",
       "      <td>0.326674</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.292701</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.148534</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.060753</td>\n",
       "      <td>0.239468</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.029974</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.044378</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.136938</td>\n",
       "      <td>0.058866</td>\n",
       "      <td>0.074304</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.125743</td>\n",
       "      <td>0.252913</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.331171</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.148534</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.060753</td>\n",
       "      <td>0.239468</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.029974</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.044378</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>15 rows × 2000 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    embedding_0  embedding_1  embedding_2  embedding_3  embedding_4  \\\n",
       "0      0.279488     0.024127     0.188463          0.0          0.0   \n",
       "1      0.196661     0.100379     0.065028          0.0          0.0   \n",
       "2      0.157962     0.111345     0.084004          0.0          0.0   \n",
       "3      0.245487     0.000000     0.107861          0.0          0.0   \n",
       "4      0.256926     0.123688     0.173867          0.0          0.0   \n",
       "5      0.041300     0.048306     0.174873          0.0          0.0   \n",
       "6      0.177880     0.144757     0.151616          0.0          0.0   \n",
       "7      0.442522     0.266434     0.284811          0.0          0.0   \n",
       "8      0.178148     0.130888     0.120792          0.0          0.0   \n",
       "9      0.000000     0.015297     0.164914          0.0          0.0   \n",
       "10     0.055178     0.000000     0.185620          0.0          0.0   \n",
       "11     0.095463     0.000000     0.194850          0.0          0.0   \n",
       "12     0.000000     0.032896     0.121179          0.0          0.0   \n",
       "13     0.122178     0.000000     0.041282          0.0          0.0   \n",
       "14     0.136938     0.058866     0.074304          0.0          0.0   \n",
       "\n",
       "    embedding_5  embedding_6  embedding_7  embedding_8  embedding_9  ...  \\\n",
       "0      0.178814     0.175025     0.000000     0.182342          0.0  ...   \n",
       "1      0.215623     0.270127     0.000000     0.167461          0.0  ...   \n",
       "2      0.158381     0.187640     0.000000     0.232503          0.0  ...   \n",
       "3      0.150378     0.210004     0.000000     0.158773          0.0  ...   \n",
       "4      0.229670     0.255786     0.000000     0.200450          0.0  ...   \n",
       "5      0.037738     0.294980     0.038254     0.341009          0.0  ...   \n",
       "6      0.151120     0.264503     0.000000     0.312307          0.0  ...   \n",
       "7      0.252510     0.047168     0.007921     0.204480          0.0  ...   \n",
       "8      0.162852     0.295574     0.000000     0.308893          0.0  ...   \n",
       "9      0.094665     0.510414     0.000000     0.297661          0.0  ...   \n",
       "10     0.085594     0.491848     0.000000     0.374648          0.0  ...   \n",
       "11     0.108117     0.363250     0.000000     0.302769          0.0  ...   \n",
       "12     0.008742     0.416652     0.027859     0.316229          0.0  ...   \n",
       "13     0.090868     0.326674     0.000000     0.292701          0.0  ...   \n",
       "14     0.125743     0.252913     0.000000     0.331171          0.0  ...   \n",
       "\n",
       "    embedding_1990  embedding_1991  embedding_1992  embedding_1993  \\\n",
       "0         0.148534             0.0        0.060753        0.239468   \n",
       "1         0.148534             0.0        0.060753        0.239468   \n",
       "2         0.148534             0.0        0.060753        0.239468   \n",
       "3         0.148534             0.0        0.060753        0.239468   \n",
       "4         0.148534             0.0        0.060753        0.239468   \n",
       "5         0.148534             0.0        0.060753        0.239468   \n",
       "6         0.148534             0.0        0.060753        0.239468   \n",
       "7         0.148534             0.0        0.060753        0.239468   \n",
       "8         0.148534             0.0        0.060753        0.239468   \n",
       "9         0.148534             0.0        0.060753        0.239468   \n",
       "10        0.148534             0.0        0.060753        0.239468   \n",
       "11        0.148534             0.0        0.060753        0.239468   \n",
       "12        0.148534             0.0        0.060753        0.239468   \n",
       "13        0.148534             0.0        0.060753        0.239468   \n",
       "14        0.148534             0.0        0.060753        0.239468   \n",
       "\n",
       "    embedding_1994  embedding_1995  embedding_1996  embedding_1997  \\\n",
       "0              0.0             0.0        0.029974             0.0   \n",
       "1              0.0             0.0        0.029974             0.0   \n",
       "2              0.0             0.0        0.029974             0.0   \n",
       "3              0.0             0.0        0.029974             0.0   \n",
       "4              0.0             0.0        0.029974             0.0   \n",
       "5              0.0             0.0        0.029974             0.0   \n",
       "6              0.0             0.0        0.029974             0.0   \n",
       "7              0.0             0.0        0.029974             0.0   \n",
       "8              0.0             0.0        0.029974             0.0   \n",
       "9              0.0             0.0        0.029974             0.0   \n",
       "10             0.0             0.0        0.029974             0.0   \n",
       "11             0.0             0.0        0.029974             0.0   \n",
       "12             0.0             0.0        0.029974             0.0   \n",
       "13             0.0             0.0        0.029974             0.0   \n",
       "14             0.0             0.0        0.029974             0.0   \n",
       "\n",
       "    embedding_1998  embedding_1999  \n",
       "0         0.044378             0.0  \n",
       "1         0.044378             0.0  \n",
       "2         0.044378             0.0  \n",
       "3         0.044378             0.0  \n",
       "4         0.044378             0.0  \n",
       "5         0.044378             0.0  \n",
       "6         0.044378             0.0  \n",
       "7         0.044378             0.0  \n",
       "8         0.044378             0.0  \n",
       "9         0.044378             0.0  \n",
       "10        0.044378             0.0  \n",
       "11        0.044378             0.0  \n",
       "12        0.044378             0.0  \n",
       "13        0.044378             0.0  \n",
       "14        0.044378             0.0  \n",
       "\n",
       "[15 rows x 2000 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_pickle(f'datasets/embeddings/embeddings_2.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
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       "      <th>embedding_0</th>\n",
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       "      <th>embedding_68</th>\n",
       "      <th>embedding_69</th>\n",
       "      <th>embedding_70</th>\n",
       "      <th>embedding_71</th>\n",
       "      <th>embedding_72</th>\n",
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       "      <th>embedding_91</th>\n",
       "      <th>embedding_92</th>\n",
       "      <th>embedding_93</th>\n",
       "      <th>embedding_94</th>\n",
       "      <th>embedding_95</th>\n",
       "      <th>embedding_96</th>\n",
       "      <th>embedding_97</th>\n",
       "      <th>embedding_98</th>\n",
       "      <th>embedding_99</th>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.076594</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.305132</td>\n",
       "      <td>0.334921</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.082947</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.055165</td>\n",
       "      <td>0.185296</td>\n",
       "      <td>0.655709</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.705889</td>\n",
       "      <td>0.603604</td>\n",
       "      <td>0.690209</td>\n",
       "      <td>0.437562</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
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       "      <td>0.000000</td>\n",
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       "      <td>0.313462</td>\n",
       "      <td>1.310468</td>\n",
       "      <td>0.378350</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.878168</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.067873</td>\n",
       "      <td>0.243520</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.305268</td>\n",
       "      <td>0.003269</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.460203</td>\n",
       "      <td>0.122737</td>\n",
       "      <td>0.058696</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.051708</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.409121</td>\n",
       "      <td>1.287694</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.027517</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.575170</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.725976</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.453548</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.418613</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.268847</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.046733</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.342454</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.249058</td>\n",
       "      <td>0.030029</td>\n",
       "      <td>1.834397</td>\n",
       "      <td>1.049233</td>\n",
       "      <td>0.535155</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.655863</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.153449</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.890663</td>\n",
       "      <td>0.478218</td>\n",
       "      <td>1.216865</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.695574</td>\n",
       "      <td>0.301547</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.707192</td>\n",
       "      <td>0.047411</td>\n",
       "      <td>0.504583</td>\n",
       "      <td>1.524743</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.140609</td>\n",
       "      <td>1.050606</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.524762</td>\n",
       "      <td>1.736273</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.066537</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.352228</td>\n",
       "      <td>1.030094</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.701919</td>\n",
       "      <td>0.060610</td>\n",
       "      <td>2.878913</td>\n",
       "      <td>0.903881</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.662134</td>\n",
       "      <td>1.262172</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.735326</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.445203</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.120911</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.129308</td>\n",
       "      <td>0.832636</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.804222</td>\n",
       "      <td>0.250638</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.646462</td>\n",
       "      <td>1.719377</td>\n",
       "      <td>1.161095</td>\n",
       "      <td>0.812382</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.171707</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.753724</td>\n",
       "      <td>1.724168</td>\n",
       "      <td>0.503853</td>\n",
       "      <td>2.067103</td>\n",
       "      <td>0.205955</td>\n",
       "      <td>1.497191</td>\n",
       "      <td>1.219232</td>\n",
       "      <td>2.119099</td>\n",
       "      <td>0.894304</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.003114</td>\n",
       "      <td>2.009717</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.754188</td>\n",
       "      <td>0.109085</td>\n",
       "      <td>0.420757</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.346976</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.938523</td>\n",
       "      <td>0.341951</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.289119</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.366142</td>\n",
       "      <td>1.537384</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.922273</td>\n",
       "      <td>0.356229</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.792403</td>\n",
       "      <td>1.506002</td>\n",
       "      <td>2.056849</td>\n",
       "      <td>0.540259</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.235758</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.089618</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.956322</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.364046</td>\n",
       "      <td>0.107496</td>\n",
       "      <td>1.106335</td>\n",
       "      <td>1.076006</td>\n",
       "      <td>0.757943</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.501940</td>\n",
       "      <td>0.175318</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.089513</td>\n",
       "      <td>0.558101</td>\n",
       "      <td>1.437981</td>\n",
       "      <td>1.403809</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.720056</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.284558</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.212498</td>\n",
       "      <td>0.343985</td>\n",
       "      <td>1.394968</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.282488</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.007664</td>\n",
       "      <td>0.288410</td>\n",
       "      <td>0.853894</td>\n",
       "      <td>1.528975</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.050122</td>\n",
       "      <td>0.753390</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.133288</td>\n",
       "      <td>1.123656</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.773845</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.979678</td>\n",
       "      <td>0.567538</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.751005</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.769719</td>\n",
       "      <td>0.622038</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.646834</td>\n",
       "      <td>1.593600</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.125768</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.120823</td>\n",
       "      <td>0.638770</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.375852</td>\n",
       "      <td>0.383542</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.370733</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.412426</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.630195</td>\n",
       "      <td>0.328643</td>\n",
       "      <td>0.932655</td>\n",
       "      <td>0.957347</td>\n",
       "      <td>0.697391</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.074331</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.048954</td>\n",
       "      <td>0.448325</td>\n",
       "      <td>0.121146</td>\n",
       "      <td>1.669024</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.381431</td>\n",
       "      <td>0.524930</td>\n",
       "      <td>0.639926</td>\n",
       "      <td>0.165349</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.783764</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.261578</td>\n",
       "      <td>0.281051</td>\n",
       "      <td>1.030169</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.124054</td>\n",
       "      <td>1.150351</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.915746</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.539148</td>\n",
       "      <td>0.693897</td>\n",
       "      <td>0.181063</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.323224</td>\n",
       "      <td>0.165894</td>\n",
       "      <td>2.881894</td>\n",
       "      <td>0.689250</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.648050</td>\n",
       "      <td>1.061134</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.561292</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.476110</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.556649</td>\n",
       "      <td>0.312188</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.027486</td>\n",
       "      <td>0.815707</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.075337</td>\n",
       "      <td>0.005391</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.636947</td>\n",
       "      <td>0.791580</td>\n",
       "      <td>1.243837</td>\n",
       "      <td>0.896040</td>\n",
       "      <td>0.367109</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.135357</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.106675</td>\n",
       "      <td>0.905805</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.350069</td>\n",
       "      <td>1.071449</td>\n",
       "      <td>0.555989</td>\n",
       "      <td>1.299861</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.030081</td>\n",
       "      <td>1.005101</td>\n",
       "      <td>1.135137</td>\n",
       "      <td>0.498746</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.283047</td>\n",
       "      <td>1.889423</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.870326</td>\n",
       "      <td>0.084268</td>\n",
       "      <td>0.667535</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.345786</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.802414</td>\n",
       "      <td>0.670674</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.015051</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.545213</td>\n",
       "      <td>0.962141</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.758751</td>\n",
       "      <td>0.096877</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.982421</td>\n",
       "      <td>0.626123</td>\n",
       "      <td>2.446175</td>\n",
       "      <td>0.393784</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1.256096</td>\n",
       "      <td>0.704142</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.347815</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.558079</td>\n",
       "      <td>0.401372</td>\n",
       "      <td>0.236088</td>\n",
       "      <td>2.605564</td>\n",
       "      <td>0.891311</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.034028</td>\n",
       "      <td>0.395833</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.248787</td>\n",
       "      <td>1.174781</td>\n",
       "      <td>1.450959</td>\n",
       "      <td>1.974368</td>\n",
       "      <td>0.120547</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.557401</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.669303</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.765166</td>\n",
       "      <td>1.051791</td>\n",
       "      <td>2.129766</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.200032</td>\n",
       "      <td>0.854131</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.328714</td>\n",
       "      <td>0.563556</td>\n",
       "      <td>0.374522</td>\n",
       "      <td>1.834372</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.488151</td>\n",
       "      <td>1.024632</td>\n",
       "      <td>0.412917</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.616049</td>\n",
       "      <td>1.370135</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.772971</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.898327</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.015361</td>\n",
       "      <td>0.781610</td>\n",
       "      <td>2.722872</td>\n",
       "      <td>1.585140</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.121963</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.167906</td>\n",
       "      <td>0.506173</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.369913</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.611330</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.954973</td>\n",
       "      <td>0.958260</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.536860</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.493448</td>\n",
       "      <td>0.189896</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.675888</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.587947</td>\n",
       "      <td>0.059866</td>\n",
       "      <td>0.660052</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.815211</td>\n",
       "      <td>0.320178</td>\n",
       "      <td>1.590587</td>\n",
       "      <td>0.387133</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.102263</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.723669</td>\n",
       "      <td>1.278582</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.816644</td>\n",
       "      <td>0.970939</td>\n",
       "      <td>0.570511</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.659027</td>\n",
       "      <td>0.366792</td>\n",
       "      <td>0.344931</td>\n",
       "      <td>1.071543</td>\n",
       "      <td>0.137118</td>\n",
       "      <td>0.260721</td>\n",
       "      <td>1.024365</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.225015</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.052564</td>\n",
       "      <td>0.349322</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.179214</td>\n",
       "      <td>0.170310</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.372418</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.936230</td>\n",
       "      <td>0.900667</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.009318</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.765787</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.706347</td>\n",
       "      <td>0.449344</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.640133</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.577309</td>\n",
       "      <td>1.435024</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.245262</td>\n",
       "      <td>0.304051</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.129221</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.279587</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.992728</td>\n",
       "      <td>0.033273</td>\n",
       "      <td>0.675387</td>\n",
       "      <td>0.915912</td>\n",
       "      <td>0.065783</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.164684</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.407413</td>\n",
       "      <td>0.028768</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.371234</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.237650</td>\n",
       "      <td>0.125353</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.689497</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.411128</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.795876</td>\n",
       "      <td>0.975785</td>\n",
       "      <td>0.328821</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.094157</td>\n",
       "      <td>0.039240</td>\n",
       "      <td>0.256431</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.514260</td>\n",
       "      <td>0.489736</td>\n",
       "      <td>1.072498</td>\n",
       "      <td>0.237109</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.743630</td>\n",
       "      <td>0.572817</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.845528</td>\n",
       "      <td>2.328147</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.658246</td>\n",
       "      <td>0.909298</td>\n",
       "      <td>1.154417</td>\n",
       "      <td>0.375416</td>\n",
       "      <td>1.711684</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.803540</td>\n",
       "      <td>0.356923</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.492083</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.151302</td>\n",
       "      <td>0.872132</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.225394</td>\n",
       "      <td>0.404758</td>\n",
       "      <td>0.697826</td>\n",
       "      <td>1.094962</td>\n",
       "      <td>0.189745</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.138797</td>\n",
       "      <td>0.073166</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.227107</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.087723</td>\n",
       "      <td>0.652730</td>\n",
       "      <td>0.878421</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.114418</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.104555</td>\n",
       "      <td>0.268216</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.911877</td>\n",
       "      <td>0.569318</td>\n",
       "      <td>1.192228</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.440306</td>\n",
       "      <td>0.224864</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.808206</td>\n",
       "      <td>0.696228</td>\n",
       "      <td>0.840806</td>\n",
       "      <td>1.178128</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.193499</td>\n",
       "      <td>0.109004</td>\n",
       "      <td>1.473330</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.232887</td>\n",
       "      <td>0.849092</td>\n",
       "      <td>1.390820</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.910398</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.175924</td>\n",
       "      <td>0.693632</td>\n",
       "      <td>0.217711</td>\n",
       "      <td>1.034625</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.288116</td>\n",
       "      <td>0.838732</td>\n",
       "      <td>2.404708</td>\n",
       "      <td>0.941304</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.369224</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.028234</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.225926</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.893857</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.511512</td>\n",
       "      <td>0.664134</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.233726</td>\n",
       "      <td>1.055265</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.052252</td>\n",
       "      <td>0.128886</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.823636</td>\n",
       "      <td>0.175388</td>\n",
       "      <td>0.767238</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.640002</td>\n",
       "      <td>0.842596</td>\n",
       "      <td>0.496681</td>\n",
       "      <td>0.380298</td>\n",
       "      <td>0.194865</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.594278</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.380743</td>\n",
       "      <td>0.201473</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.606374</td>\n",
       "      <td>1.114638</td>\n",
       "      <td>0.839134</td>\n",
       "      <td>0.026435</td>\n",
       "      <td>0.478860</td>\n",
       "      <td>0.627659</td>\n",
       "      <td>0.568870</td>\n",
       "      <td>1.295834</td>\n",
       "      <td>0.732721</td>\n",
       "      <td>0.460471</td>\n",
       "      <td>0.455536</td>\n",
       "      <td>0.426676</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.887271</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.558756</td>\n",
       "      <td>0.907652</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.363608</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.844617</td>\n",
       "      <td>0.004230</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.719107</td>\n",
       "      <td>0.259965</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.439376</td>\n",
       "      <td>1.354299</td>\n",
       "      <td>0.063959</td>\n",
       "      <td>0.947750</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1.115811</td>\n",
       "      <td>0.185002</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.472150</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.313593</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.713522</td>\n",
       "      <td>0.426093</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.337890</td>\n",
       "      <td>0.516333</td>\n",
       "      <td>0.136985</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.241213</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.203817</td>\n",
       "      <td>0.125132</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.393144</td>\n",
       "      <td>0.336285</td>\n",
       "      <td>0.758721</td>\n",
       "      <td>0.631810</td>\n",
       "      <td>0.044444</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.607812</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.128985</td>\n",
       "      <td>0.864673</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.690966</td>\n",
       "      <td>0.601206</td>\n",
       "      <td>0.142815</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.143811</td>\n",
       "      <td>0.771548</td>\n",
       "      <td>0.425870</td>\n",
       "      <td>0.468962</td>\n",
       "      <td>0.746465</td>\n",
       "      <td>1.130329</td>\n",
       "      <td>0.556305</td>\n",
       "      <td>1.181723</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.441832</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.134760</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.509469</td>\n",
       "      <td>1.237756</td>\n",
       "      <td>1.300722</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.472812</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.382515</td>\n",
       "      <td>1.417976</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.820915</td>\n",
       "      <td>1.523370</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.228219</td>\n",
       "      <td>1.345084</td>\n",
       "      <td>2.549045</td>\n",
       "      <td>0.430819</td>\n",
       "      <td>0.819383</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1.113517</td>\n",
       "      <td>0.630451</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.659275</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.424807</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.670361</td>\n",
       "      <td>0.370606</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.626014</td>\n",
       "      <td>0.412021</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.398427</td>\n",
       "      <td>0.282233</td>\n",
       "      <td>1.257424</td>\n",
       "      <td>0.546407</td>\n",
       "      <td>0.114591</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.165610</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.108813</td>\n",
       "      <td>1.045741</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.657636</td>\n",
       "      <td>0.561803</td>\n",
       "      <td>0.520701</td>\n",
       "      <td>1.016441</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.900436</td>\n",
       "      <td>0.284554</td>\n",
       "      <td>0.864142</td>\n",
       "      <td>0.513327</td>\n",
       "      <td>0.578340</td>\n",
       "      <td>0.554110</td>\n",
       "      <td>1.302444</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.515221</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.159075</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.615489</td>\n",
       "      <td>0.740866</td>\n",
       "      <td>1.428063</td>\n",
       "      <td>0.520231</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.124566</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.846702</td>\n",
       "      <td>0.946042</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.054685</td>\n",
       "      <td>1.188546</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.215999</td>\n",
       "      <td>1.239755</td>\n",
       "      <td>2.854148</td>\n",
       "      <td>0.932608</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1.314248</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.420561</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.922892</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.813967</td>\n",
       "      <td>0.462734</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.262758</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.041737</td>\n",
       "      <td>0.027422</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.187884</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.104582</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.433536</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.681647</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.162255</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.384687</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.063986</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.976068</td>\n",
       "      <td>0.987077</td>\n",
       "      <td>0.576738</td>\n",
       "      <td>0.382313</td>\n",
       "      <td>1.187727</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.060603</td>\n",
       "      <td>1.027312</td>\n",
       "      <td>0.244555</td>\n",
       "      <td>1.512707</td>\n",
       "      <td>1.252503</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.384132</td>\n",
       "      <td>0.463006</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.305008</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.406722</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.565486</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.373683</td>\n",
       "      <td>1.190596</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.383701</td>\n",
       "      <td>1.760761</td>\n",
       "      <td>0.739230</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.301657</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.945267</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>15 rows × 100 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    embedding_0  embedding_1  ...  embedding_98  embedding_99\n",
       "0      1.325100     0.000000  ...           0.0           0.0\n",
       "1      0.221480     0.874906  ...           0.0           0.0\n",
       "2      0.725976     0.716229  ...           0.0           0.0\n",
       "3      0.662134     1.262172  ...           0.0           0.0\n",
       "4      1.235758     0.000000  ...           0.0           0.0\n",
       "5      0.646834     1.593600  ...           0.0           0.0\n",
       "6      0.648050     1.061134  ...           0.0           0.0\n",
       "7      1.256096     0.704142  ...           0.0           0.0\n",
       "8      0.167906     0.506173  ...           0.0           0.0\n",
       "9      0.706347     0.449344  ...           0.0           0.0\n",
       "10     0.803540     0.356923  ...           0.0           0.0\n",
       "11     0.000000     0.369224  ...           0.0           0.0\n",
       "12     1.115811     0.185002  ...           0.0           0.0\n",
       "13     1.113517     0.630451  ...           0.0           0.0\n",
       "14     1.314248     0.000000  ...           0.0           0.0\n",
       "\n",
       "[15 rows x 100 columns]"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_pickle(f'datasets/embeddings_100/embeddings_2.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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