tssb/notebooks/dataset_generation.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "31468881",
   "metadata": {},
   "source": [
    "# Dataset Generation\n",
    "In this notebook, we generate the 75 time series segmentation datasets from the UCR datasets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "ee051874",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "import pandas as pd\n",
    "\n",
    "pd.set_option('display.max_rows', None)\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.rcParams.update({'font.size': 26})\n",
    "\n",
    "import aeon, os\n",
    "\n",
    "DATA_PATH = os.path.join(os.path.dirname(aeon.__file__), \"datasets/data\")\n",
    "\n",
    "import seaborn as sns\n",
    "\n",
    "sns.set_theme()\n",
    "sns.set_color_codes()\n",
    "\n",
    "import sys\n",
    "\n",
    "sys.path.append(\"../../\")\n",
    "\n",
    "from tqdm import tqdm"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "27b8396c",
   "metadata": {},
   "source": [
    "In order to run this notebook, you must first download the UCR datasets from https://www.cs.ucr.edu/~eamonn/time_series_data_2018 and place them into the DATA_PATH folder."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "222a447f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "'/Users/ermshaua/miniconda3/envs/py3k/lib/python3.8/site-packages/aeon/datasets/data'"
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DATA_PATH"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "ed54bd7a",
   "metadata": {},
   "outputs": [],
   "source": [
    "from tssb.utils import generate_time_series_segmentation_dataset, load_time_series_segmentation_datasets, visualize_time_series\n",
    "from aeon.datasets import load_from_tsv_file"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "574925f2",
   "metadata": {},
   "source": [
    "Let's create an example TS for the ArrowHead dataset and visualize it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "8438e4e0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 1440x360 with 1 Axes>",
      "image/png": "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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "X, y = load_from_tsv_file(os.path.join(DATA_PATH, \"ArrowHead/ArrowHead_TRAIN.tsv\"))\n",
    "ts, cps = generate_time_series_segmentation_dataset(X, y, labels=[0, 1], resample_rate=2)\n",
    "fig, ax = visualize_time_series(ts, \"ArrowHead\", cps_true=cps)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "165cb7d6",
   "metadata": {},
   "source": [
    "Let's now load the properties for the entire TSSB dataset (labels, resample_rates, label_cuts) and re-create it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "20cc872a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "                           Dataset  Label Cut  Resample Rate  \\\n0                            Adiac          0              4   \n1                        ArrowHead          0              4   \n2                             Beef          0              4   \n3                        BeetleFly          0              4   \n4                      BirdChicken          0              4   \n5                              Car          0             16   \n6                              CBF          0              4   \n7                        Chinatown          0              1   \n8            ChlorineConcentration          0              8   \n9                     CinCECGTorso          0              8   \n10                          Coffee          0              8   \n11                       Computers          0             16   \n12                        CricketX          0             16   \n13                        CricketY          0             16   \n14                        CricketZ          0             16   \n15                            Crop          2              2   \n16             DiatomSizeReduction          0              4   \n17    DistalPhalanxOutlineAgeGroup          0              8   \n18                 DistalPhalanxTW          0              4   \n19                   DodgerLoopDay          0              1   \n20                          ECG200          0              4   \n21                     ECGFiveDays          0              4   \n22             EOGHorizontalSignal          1             16   \n23               EOGVerticalSignal          1             16   \n24                         FaceAll          0              4   \n25                        FaceFour          0              1   \n26                        FacesUCR          0              2   \n27                      FiftyWords          0              8   \n28                            Fish          0              8   \n29             FreezerRegularTrain          1              4   \n30                             Ham          2              4   \n31                         Herring          0              4   \n32                        GunPoint          0              4   \n33                         Haptics          0             16   \n34                     InlineSkate          0             16   \n35             InsectWingbeatSound          0             16   \n36                ItalyPowerDemand          0              1   \n37          LargeKitchenAppliances          0             32   \n38                      Lightning2          0             32   \n39                      Lightning7          0              2   \n40                          Mallat          0              8   \n41                            Meat          0              8   \n42                   MedicalImages          0              2   \n43             MelbournePedestrian          1              2   \n44    MiddlePhalanxOutlineAgeGroup          1              2   \n45     MiddlePhalanxOutlineCorrect          2              4   \n46                 MiddlePhalanxTW          0              1   \n47                      MoteStrain          0              1   \n48      NonInvasiveFetalECGThorax1          0             16   \n49      NonInvasiveFetalECGThorax2          0             16   \n50                        OliveOil          0              2   \n51                         OSULeaf          0             16   \n52                           Plane          0              4   \n53  ProximalPhalanxOutlineAgeGroup          0              8   \n54   ProximalPhalanxOutlineCorrect          1              8   \n55               ProximalPhalanxTW          0              4   \n56                       ShapesAll          0              4   \n57                     ShapeletSim          0              2   \n58           SonyAIBORobotSurface1          0              1   \n59           SonyAIBORobotSurface2          0              1   \n60                      Strawberry          2             16   \n61                     SwedishLeaf          0              4   \n62                         Symbols          0              4   \n63                SyntheticControl          0              4   \n64                ToeSegmentation1          0              4   \n65                ToeSegmentation2          0              4   \n66                           Trace          0              4   \n67                      TwoLeadECG          0              4   \n68                             UMD          0              1   \n69          UWaveGestureLibraryAll          0             64   \n70            UWaveGestureLibraryX          0             64   \n71            UWaveGestureLibraryY          0             64   \n72            UWaveGestureLibraryZ          0             64   \n73                    WordSynonyms          0              8   \n74                            Yoga          0              8   \n\n                         Labels  \n0                  [0, 1, 2, 4]  \n1                        [0, 1]  \n2                        [0, 1]  \n3                        [0, 1]  \n4                        [0, 1]  \n5                  [0, 1, 2, 3]  \n6                     [1, 0, 2]  \n7                           [0]  \n8                        [0, 2]  \n9                     [1, 2, 3]  \n10                       [0, 1]  \n11                       [0, 1]  \n12              [0, 1, 2, 3, 4]  \n13              [0, 1, 2, 3, 4]  \n14              [0, 1, 2, 3, 4]  \n15  [0, 3, 6, 1, 4, 7, 2, 5, 8]  \n16                    [1, 2, 3]  \n17                       [0, 1]  \n18                    [0, 2, 4]  \n19                          [0]  \n20                       [0, 1]  \n21                       [0, 1]  \n22                 [4, 6, 4, 7]  \n23          [4, 8, 5, 8, 10, 4]  \n24        [0, 1, 2, 3, 4, 5, 6]  \n25                 [0, 1, 2, 3]  \n26        [0, 1, 2, 3, 4, 5, 6]  \n27                 [0, 1, 2, 3]  \n28        [0, 1, 2, 3, 4, 5, 6]  \n29                 [0, 2, 1, 3]  \n30                 [2, 4, 2, 4]  \n31                          [0]  \n32                       [0, 1]  \n33                    [0, 1, 2]  \n34                    [0, 1, 2]  \n35                 [0, 1, 2, 3]  \n36                       [0, 1]  \n37                    [0, 1, 2]  \n38                       [0, 1]  \n39              [0, 1, 2, 3, 4]  \n40                 [0, 1, 2, 3]  \n41                    [0, 1, 2]  \n42                       [0, 1]  \n43      [0, 6, 1, 8, 18, 7, 19]  \n44              [0, 2, 5, 1, 3]  \n45                 [0, 3, 2, 5]  \n46                          [1]  \n47                       [0, 1]  \n48           [0, 1, 2, 3, 4, 5]  \n49        [0, 1, 2, 3, 4, 5, 6]  \n50                    [0, 1, 3]  \n51                    [0, 1, 5]  \n52        [0, 1, 2, 3, 4, 5, 6]  \n53                    [0, 1, 2]  \n54                 [0, 2, 1, 3]  \n55                 [0, 1, 2, 4]  \n56              [0, 1, 2, 3, 4]  \n57                          [0]  \n58                       [0, 1]  \n59                       [0, 1]  \n60                 [0, 5, 1, 3]  \n61                 [0, 1, 2, 3]  \n62                 [0, 1, 2, 5]  \n63              [0, 1, 2, 3, 4]  \n64                       [0, 1]  \n65                       [0, 1]  \n66                    [1, 2, 3]  \n67                       [0, 1]  \n68                          [0]  \n69              [0, 1, 2, 3, 4]  \n70              [0, 1, 2, 3, 4]  \n71              [0, 1, 2, 3, 4]  \n72              [0, 1, 2, 3, 4]  \n73                    [0, 1, 2]  \n74                       [0, 1]  ",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Dataset</th>\n      <th>Label Cut</th>\n      <th>Resample Rate</th>\n      <th>Labels</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Adiac</td>\n      <td>0</td>\n      <td>4</td>\n      <td>[0, 1, 2, 4]</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>ArrowHead</td>\n      <td>0</td>\n      <td>4</td>\n      <td>[0, 1]</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Beef</td>\n      <td>0</td>\n      <td>4</td>\n      <td>[0, 1]</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>BeetleFly</td>\n      <td>0</td>\n      <td>4</td>\n      <td>[0, 1]</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>BirdChicken</td>\n      <td>0</td>\n      <td>4</td>\n      <td>[0, 1]</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>Car</td>\n      <td>0</td>\n      <td>16</td>\n      <td>[0, 1, 2, 3]</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>CBF</td>\n      <td>0</td>\n      <td>4</td>\n      <td>[1, 0, 2]</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>Chinatown</td>\n      <td>0</td>\n      <td>1</td>\n      <td>[0]</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>ChlorineConcentration</td>\n      <td>0</td>\n      <td>8</td>\n      <td>[0, 2]</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>CinCECGTorso</td>\n      <td>0</td>\n      <td>8</td>\n      <td>[1, 2, 3]</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>Coffee</td>\n      <td>0</td>\n      <td>8</td>\n      <td>[0, 1]</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>Computers</td>\n      <td>0</td>\n      <td>16</td>\n      <td>[0, 1]</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>CricketX</td>\n      <td>0</td>\n      <td>16</td>\n      <td>[0, 1, 2, 3, 4]</td>\n    </tr>\n    <tr>\n      <th>13</th>\n      <td>CricketY</td>\n      <td>0</td>\n      <td>16</td>\n      <td>[0, 1, 2, 3, 4]</td>\n    </tr>\n    <tr>\n      <th>14</th>\n      <td>CricketZ</td>\n      <td>0</td>\n      <td>16</td>\n      <td>[0, 1, 2, 3, 4]</td>\n    </tr>\n    <tr>\n      <th>15</th>\n      <td>Crop</td>\n      <td>2</td>\n      <td>2</td>\n      <td>[0, 3, 6, 1, 4, 7, 2, 5, 8]</td>\n    </tr>\n    <tr>\n      <th>16</th>\n      <td>DiatomSizeReduction</td>\n      <td>0</td>\n      <td>4</td>\n      <td>[1, 2, 3]</td>\n    </tr>\n    <tr>\n      <th>17</th>\n      <td>DistalPhalanxOutlineAgeGroup</td>\n      <td>0</td>\n      <td>8</td>\n      <td>[0, 1]</td>\n    </tr>\n    <tr>\n      <th>18</th>\n      <td>DistalPhalanxTW</td>\n      <td>0</td>\n      <td>4</td>\n      <td>[0, 2, 4]</td>\n    </tr>\n    <tr>\n      <th>19</th>\n      <td>DodgerLoopDay</td>\n      <td>0</td>\n      <td>1</td>\n      <td>[0]</td>\n    </tr>\n    <tr>\n      <th>20</th>\n      <td>ECG200</td>\n      <td>0</td>\n      <td>4</td>\n      <td>[0, 1]</td>\n    </tr>\n    <tr>\n      <th>21</th>\n      <td>ECGFiveDays</td>\n      <td>0</td>\n      <td>4</td>\n      <td>[0, 1]</td>\n    </tr>\n    <tr>\n      <th>22</th>\n      <td>EOGHorizontalSignal</td>\n      <td>1</td>\n      <td>16</td>\n      <td>[4, 6, 4, 7]</td>\n    </tr>\n    <tr>\n      <th>23</th>\n      <td>EOGVerticalSignal</td>\n      <td>1</td>\n      <td>16</td>\n      <td>[4, 8, 5, 8, 10, 4]</td>\n    </tr>\n    <tr>\n      <th>24</th>\n      <td>FaceAll</td>\n      <td>0</td>\n      <td>4</td>\n      <td>[0, 1, 2, 3, 4, 5, 6]</td>\n    </tr>\n    <tr>\n      <th>25</th>\n      <td>FaceFour</td>\n      <td>0</td>\n      <td>1</td>\n      <td>[0, 1, 2, 3]</td>\n    </tr>\n    <tr>\n      <th>26</th>\n      <td>FacesUCR</td>\n      <td>0</td>\n      <td>2</td>\n      <td>[0, 1, 2, 3, 4, 5, 6]</td>\n    </tr>\n    <tr>\n      <th>27</th>\n      <td>FiftyWords</td>\n      <td>0</td>\n      <td>8</td>\n      <td>[0, 1, 2, 3]</td>\n    </tr>\n    <tr>\n      <th>28</th>\n      <td>Fish</td>\n      <td>0</td>\n      <td>8</td>\n      <td>[0, 1, 2, 3, 4, 5, 6]</td>\n    </tr>\n    <tr>\n      <th>29</th>\n      <td>FreezerRegularTrain</td>\n      <td>1</td>\n      <td>4</td>\n      <td>[0, 2, 1, 3]</td>\n    </tr>\n    <tr>\n      <th>30</th>\n      <td>Ham</td>\n      <td>2</td>\n      <td>4</td>\n      <td>[2, 4, 2, 4]</td>\n    </tr>\n    <tr>\n      <th>31</th>\n      <td>Herring</td>\n      <td>0</td>\n      <td>4</td>\n      <td>[0]</td>\n    </tr>\n    <tr>\n      <th>32</th>\n      <td>GunPoint</td>\n      <td>0</td>\n      <td>4</td>\n      <td>[0, 1]</td>\n    </tr>\n    <tr>\n      <th>33</th>\n      <td>Haptics</td>\n      <td>0</td>\n      <td>16</td>\n      <td>[0, 1, 2]</td>\n    </tr>\n    <tr>\n      <th>34</th>\n      <td>InlineSkate</td>\n      <td>0</td>\n      <td>16</td>\n      <td>[0, 1, 2]</td>\n    </tr>\n    <tr>\n      <th>35</th>\n      <td>InsectWingbeatSound</td>\n      <td>0</td>\n      <td>16</td>\n      <td>[0, 1, 2, 3]</td>\n    </tr>\n    <tr>\n      <th>36</th>\n      <td>ItalyPowerDemand</td>\n      <td>0</td>\n      <td>1</td>\n      <td>[0, 1]</td>\n    </tr>\n    <tr>\n      <th>37</th>\n      <td>LargeKitchenAppliances</td>\n      <td>0</td>\n      <td>32</td>\n      <td>[0, 1, 2]</td>\n    </tr>\n    <tr>\n      <th>38</th>\n      <td>Lightning2</td>\n      <td>0</td>\n      <td>32</td>\n      <td>[0, 1]</td>\n    </tr>\n    <tr>\n      <th>39</th>\n      <td>Lightning7</td>\n      <td>0</td>\n      <td>2</td>\n      <td>[0, 1, 2, 3, 4]</td>\n    </tr>\n    <tr>\n      <th>40</th>\n      <td>Mallat</td>\n      <td>0</td>\n      <td>8</td>\n      <td>[0, 1, 2, 3]</td>\n    </tr>\n    <tr>\n      <th>41</th>\n      <td>Meat</td>\n      <td>0</td>\n      <td>8</td>\n      <td>[0, 1, 2]</td>\n    </tr>\n    <tr>\n      <th>42</th>\n      <td>MedicalImages</td>\n      <td>0</td>\n      <td>2</td>\n      <td>[0, 1]</td>\n    </tr>\n    <tr>\n      <th>43</th>\n      <td>MelbournePedestrian</td>\n      <td>1</td>\n      <td>2</td>\n      <td>[0, 6, 1, 8, 18, 7, 19]</td>\n    </tr>\n    <tr>\n      <th>44</th>\n      <td>MiddlePhalanxOutlineAgeGroup</td>\n      <td>1</td>\n      <td>2</td>\n      <td>[0, 2, 5, 1, 3]</td>\n    </tr>\n    <tr>\n      <th>45</th>\n      <td>MiddlePhalanxOutlineCorrect</td>\n      <td>2</td>\n      <td>4</td>\n      <td>[0, 3, 2, 5]</td>\n    </tr>\n    <tr>\n      <th>46</th>\n      <td>MiddlePhalanxTW</td>\n      <td>0</td>\n      <td>1</td>\n      <td>[1]</td>\n    </tr>\n    <tr>\n      <th>47</th>\n      <td>MoteStrain</td>\n      <td>0</td>\n      <td>1</td>\n      <td>[0, 1]</td>\n    </tr>\n    <tr>\n      <th>48</th>\n      <td>NonInvasiveFetalECGThorax1</td>\n      <td>0</td>\n      <td>16</td>\n      <td>[0, 1, 2, 3, 4, 5]</td>\n    </tr>\n    <tr>\n      <th>49</th>\n      <td>NonInvasiveFetalECGThorax2</td>\n      <td>0</td>\n      <td>16</td>\n      <td>[0, 1, 2, 3, 4, 5, 6]</td>\n    </tr>\n    <tr>\n      <th>50</th>\n      <td>OliveOil</td>\n      <td>0</td>\n      <td>2</td>\n      <td>[0, 1, 3]</td>\n    </tr>\n    <tr>\n      <th>51</th>\n      <td>OSULeaf</td>\n      <td>0</td>\n      <td>16</td>\n      <td>[0, 1, 5]</td>\n    </tr>\n    <tr>\n      <th>52</th>\n      <td>Plane</td>\n      <td>0</td>\n      <td>4</td>\n      <td>[0, 1, 2, 3, 4, 5, 6]</td>\n    </tr>\n    <tr>\n      <th>53</th>\n      <td>ProximalPhalanxOutlineAgeGroup</td>\n      <td>0</td>\n      <td>8</td>\n      <td>[0, 1, 2]</td>\n    </tr>\n    <tr>\n      <th>54</th>\n      <td>ProximalPhalanxOutlineCorrect</td>\n      <td>1</td>\n      <td>8</td>\n      <td>[0, 2, 1, 3]</td>\n    </tr>\n    <tr>\n      <th>55</th>\n      <td>ProximalPhalanxTW</td>\n      <td>0</td>\n      <td>4</td>\n      <td>[0, 1, 2, 4]</td>\n    </tr>\n    <tr>\n      <th>56</th>\n      <td>ShapesAll</td>\n      <td>0</td>\n      <td>4</td>\n      <td>[0, 1, 2, 3, 4]</td>\n    </tr>\n    <tr>\n      <th>57</th>\n      <td>ShapeletSim</td>\n      <td>0</td>\n      <td>2</td>\n      <td>[0]</td>\n    </tr>\n    <tr>\n      <th>58</th>\n      <td>SonyAIBORobotSurface1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>[0, 1]</td>\n    </tr>\n    <tr>\n      <th>59</th>\n      <td>SonyAIBORobotSurface2</td>\n      <td>0</td>\n      <td>1</td>\n      <td>[0, 1]</td>\n    </tr>\n    <tr>\n      <th>60</th>\n      <td>Strawberry</td>\n      <td>2</td>\n      <td>16</td>\n      <td>[0, 5, 1, 3]</td>\n    </tr>\n    <tr>\n      <th>61</th>\n      <td>SwedishLeaf</td>\n      <td>0</td>\n      <td>4</td>\n      <td>[0, 1, 2, 3]</td>\n    </tr>\n    <tr>\n      <th>62</th>\n      <td>Symbols</td>\n      <td>0</td>\n      <td>4</td>\n      <td>[0, 1, 2, 5]</td>\n    </tr>\n    <tr>\n      <th>63</th>\n      <td>SyntheticControl</td>\n      <td>0</td>\n      <td>4</td>\n      <td>[0, 1, 2, 3, 4]</td>\n    </tr>\n    <tr>\n      <th>64</th>\n      <td>ToeSegmentation1</td>\n      <td>0</td>\n      <td>4</td>\n      <td>[0, 1]</td>\n    </tr>\n    <tr>\n      <th>65</th>\n      <td>ToeSegmentation2</td>\n      <td>0</td>\n      <td>4</td>\n      <td>[0, 1]</td>\n    </tr>\n    <tr>\n      <th>66</th>\n      <td>Trace</td>\n      <td>0</td>\n      <td>4</td>\n      <td>[1, 2, 3]</td>\n    </tr>\n    <tr>\n      <th>67</th>\n      <td>TwoLeadECG</td>\n      <td>0</td>\n      <td>4</td>\n      <td>[0, 1]</td>\n    </tr>\n    <tr>\n      <th>68</th>\n      <td>UMD</td>\n      <td>0</td>\n      <td>1</td>\n      <td>[0]</td>\n    </tr>\n    <tr>\n      <th>69</th>\n      <td>UWaveGestureLibraryAll</td>\n      <td>0</td>\n      <td>64</td>\n      <td>[0, 1, 2, 3, 4]</td>\n    </tr>\n    <tr>\n      <th>70</th>\n      <td>UWaveGestureLibraryX</td>\n      <td>0</td>\n      <td>64</td>\n      <td>[0, 1, 2, 3, 4]</td>\n    </tr>\n    <tr>\n      <th>71</th>\n      <td>UWaveGestureLibraryY</td>\n      <td>0</td>\n      <td>64</td>\n      <td>[0, 1, 2, 3, 4]</td>\n    </tr>\n    <tr>\n      <th>72</th>\n      <td>UWaveGestureLibraryZ</td>\n      <td>0</td>\n      <td>64</td>\n      <td>[0, 1, 2, 3, 4]</td>\n    </tr>\n    <tr>\n      <th>73</th>\n      <td>WordSynonyms</td>\n      <td>0</td>\n      <td>8</td>\n      <td>[0, 1, 2]</td>\n    </tr>\n    <tr>\n      <th>74</th>\n      <td>Yoga</td>\n      <td>0</td>\n      <td>8</td>\n      <td>[0, 1]</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prop_filename = os.path.join(\"../datasets\", \"properties.txt\")\n",
    "prop_file = []\n",
    "\n",
    "with open(prop_filename, 'r') as file:\n",
    "    for line in file.readlines():\n",
    "        line = line.split(\",\")\n",
    "\n",
    "        ds_name, interpretable, label_cut, resample_rate, labels = line[0], bool(line[1]), int(line[2]), int(\n",
    "            line[3]), line[4:]\n",
    "        labels = [int(l.replace(\"\\n\", \"\")) for l in labels]\n",
    "\n",
    "        prop_file.append((ds_name, label_cut, resample_rate, labels))\n",
    "\n",
    "prop_file = pd.DataFrame.from_records(prop_file, columns=[\"Dataset\", \"Label Cut\", \"Resample Rate\", \"Labels\"])\n",
    "prop_file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "1b50b324",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "75it [00:39,  1.89it/s]\n"
     ]
    },
    {
     "data": {
      "text/plain": "                           dataset  \\\n0                            Adiac   \n1                        ArrowHead   \n2                             Beef   \n3                        BeetleFly   \n4                      BirdChicken   \n5                              Car   \n6                              CBF   \n7                        Chinatown   \n8            ChlorineConcentration   \n9                     CinCECGTorso   \n10                          Coffee   \n11                       Computers   \n12                        CricketX   \n13                        CricketY   \n14                        CricketZ   \n15                            Crop   \n16             DiatomSizeReduction   \n17    DistalPhalanxOutlineAgeGroup   \n18                 DistalPhalanxTW   \n19                   DodgerLoopDay   \n20                          ECG200   \n21                     ECGFiveDays   \n22             EOGHorizontalSignal   \n23               EOGVerticalSignal   \n24                         FaceAll   \n25                        FaceFour   \n26                        FacesUCR   \n27                      FiftyWords   \n28                            Fish   \n29             FreezerRegularTrain   \n30                             Ham   \n31                         Herring   \n32                        GunPoint   \n33                         Haptics   \n34                     InlineSkate   \n35             InsectWingbeatSound   \n36                ItalyPowerDemand   \n37          LargeKitchenAppliances   \n38                      Lightning2   \n39                      Lightning7   \n40                          Mallat   \n41                            Meat   \n42                   MedicalImages   \n43             MelbournePedestrian   \n44    MiddlePhalanxOutlineAgeGroup   \n45     MiddlePhalanxOutlineCorrect   \n46                 MiddlePhalanxTW   \n47                      MoteStrain   \n48      NonInvasiveFetalECGThorax1   \n49      NonInvasiveFetalECGThorax2   \n50                        OliveOil   \n51                         OSULeaf   \n52                           Plane   \n53  ProximalPhalanxOutlineAgeGroup   \n54   ProximalPhalanxOutlineCorrect   \n55               ProximalPhalanxTW   \n56                       ShapesAll   \n57                     ShapeletSim   \n58           SonyAIBORobotSurface1   \n59           SonyAIBORobotSurface2   \n60                      Strawberry   \n61                     SwedishLeaf   \n62                         Symbols   \n63                SyntheticControl   \n64                ToeSegmentation1   \n65                ToeSegmentation2   \n66                           Trace   \n67                      TwoLeadECG   \n68                             UMD   \n69          UWaveGestureLibraryAll   \n70            UWaveGestureLibraryX   \n71            UWaveGestureLibraryY   \n72            UWaveGestureLibraryZ   \n73                    WordSynonyms   \n74                            Yoga   \n\n                                        change_points  \\\n0                                   [572, 1012, 1232]   \n1                                               [753]   \n2                                               [705]   \n3                                              [1280]   \n4                                              [1280]   \n5                                   [577, 1154, 1550]   \n6                                          [384, 704]   \n7                                                  []   \n8                                              [2365]   \n9                                        [2663, 5121]   \n10                                              [500]   \n11                                             [5625]   \n12                            [712, 1293, 1930, 2586]   \n13                            [525, 1162, 1837, 2399]   \n14                            [600, 1106, 1574, 2174]   \n15  [1725, 3450, 5175, 8625, 12075, 15525, 17250, ...   \n16                                         [517, 948]   \n17                                              [300]   \n18                                        [580, 2220]   \n19                                                 []   \n20                                              [744]   \n21                                              [476]   \n22                                 [1406, 2812, 4218]   \n23                     [1406, 2812, 3749, 5155, 6608]   \n24               [1310, 2620, 3930, 5240, 6550, 7860]   \n25                                 [2800, 5600, 6650]   \n26                [851, 1637, 2292, 3733, 4715, 5566]   \n27                                 [1755, 3408, 4521]   \n28               [1504, 2950, 4570, 5785, 7058, 8504]   \n29                                 [2257, 4514, 7900]   \n30                                 [1400, 2935, 4335]   \n31                                                 []   \n32                                              [900]   \n33                                       [1228, 3548]   \n34                                       [1058, 2704]   \n35                                    [320, 640, 960]   \n36                                              [816]   \n37                                       [2812, 5624]   \n38                                              [398]   \n39                           [1276, 2552, 3828, 5742]   \n40                                  [768, 1792, 2560]   \n41                                       [1120, 2240]   \n42                                             [1732]   \n43                [576, 1152, 2016, 2592, 3168, 4032]   \n44                            [880, 2608, 6400, 7720]   \n45                                 [1060, 3000, 4060]   \n46                                                 []   \n47                                              [840]   \n48                    [1687, 4124, 6233, 8108, 10358]   \n49             [1687, 4124, 6233, 8108, 10358, 12420]   \n50                                       [1425, 3705]   \n51                                        [907, 1680]   \n52                [540, 1044, 1368, 1944, 2412, 3060]   \n53                                        [720, 2110]   \n54                                  [776, 3212, 4376]   \n55                                  [320, 2500, 3660]   \n56                           [1280, 2560, 3840, 5120]   \n57                                                 []   \n58                                              [420]   \n59                                              [715]   \n60                                  [804, 2250, 3054]   \n61                                  [928, 2080, 3200]   \n62                                  [796, 1293, 1591]   \n63                            [750, 1500, 2250, 3000]   \n64                                             [1385]   \n65                                             [1543]   \n66                                       [1443, 2955]   \n67                                              [246]   \n68                                                 []   \n69                           [1801, 3395, 4960, 6584]   \n70                            [600, 1131, 1652, 2193]   \n71                            [600, 1131, 1652, 2193]   \n72                            [600, 1131, 1652, 2193]   \n73                                        [202, 2227]   \n74                                             [7295]   \n\n                                          time_series  \n0   [1.6811456620860457, 1.3833939713969476, 1.029...  \n1   [-1.9577206309715156, -1.8580819245996238, -1....  \n2   [-0.3808184611459132, -0.3522382904977104, -0....  \n3   [1.1148051937755514, 0.7744033379924569, 0.477...  \n4   [1.697714230537119, 1.439032088641677, 1.22286...  \n5   [1.4516478867009357, 1.0301743848837297, 0.531...  \n6   [-1.2771304421352372, -0.6898106156588633, -0....  \n7   [0.02593528814386279, -0.43565455927050417, -0...  \n8   [2.4962273725836015, 1.0467432663502947, 0.391...  \n9   [-0.11821460735609512, -0.11532619232132583, -...  \n10  [-0.6443794351448959, -0.9354437755240335, -0....  \n11  [-0.33526472777743305, -0.33526472777743305, -...  \n12  [1.6687284318319828, 1.811383826109306, 0.1561...  \n13  [-0.07426595813016472, 0.12508525068138565, -2...  \n14  [0.5388645545243573, -0.2361270584753817, -0.7...  \n15  [-1.0191252092970968, -0.9359573602315785, -0....  \n16  [1.851102750639204, 1.7507010856005727, 1.5427...  \n17  [-0.09584243891373648, 1.327275810868278, 0.38...  \n18  [-0.7337566012451453, 0.4509155903334242, 1.31...  \n19  [-0.41782778572558976, 0.21606722194238143, -0...  \n20  [0.8030652832607478, 1.9659733771294134, 0.145...  \n21  [0.00208256816105082, -0.023486727616573715, -...  \n22  [-0.9536228832289295, -0.9700887626126122, -0....  \n23  [1.1827171322359449, 1.156045388449738, 1.1415...  \n24  [-0.5613916259170972, -1.3562183276912418, -0....  \n25  [2.5843157174695235, 2.5843157174695235, 1.933...  \n26  [-0.36326079832230385, -0.22943593812562227, -...  \n27  [-0.3445510936413573, 1.4717211154661174, 2.24...  \n28  [1.8710988310016834, 1.5403189614124408, 1.203...  \n29  [-2.213755288036522, -2.2139108716958202, -2.2...  \n30  [-0.6226012538812328, -0.6993223664768211, -0....  \n31  [1.9013595305134667, 1.7835298811241282, 1.596...  \n32  [-0.7804726464099164, -0.7715912873470101, -0....  \n33  [-1.2658667324529853, -1.3841822398502917, -1....  \n34  [-0.3827578498718709, -0.3970526600200499, -0....  \n35  [-0.2151308540915991, -0.2617109672187612, -0....  \n36  [-0.7257992615525017, -1.2087710554478004, -1....  \n37  [-0.09917672952011744, -0.09917672952011744, -...  \n38  [-1.3971021488734972, -1.3971021488734972, -1....  \n39  [-0.7673588003672911, -0.7673588003672911, -0....  \n40  [-1.023484706988249, -1.0666984500902235, -1.1...  \n41  [-0.2628214018844894, -0.2524907606949458, -0....  \n42  [0.4338270721442356, 1.6512774567573048, 1.535...  \n43  [-1.0646761562992557, -1.1179181050849807, -1....  \n44  [-0.4285314573614312, -0.19212287319716795, 0....  \n45  [-0.5058955136625363, 0.41113075807768285, 1.3...  \n46  [-0.744506193868183, -0.6905826018218594, -0.5...  \n47  [0.08219057031117295, 0.05792553419205202, 0.0...  \n48  [1.261310975866223, -3.252779490857797, -1.038...  \n49  [2.7402938736821465, -2.153493336821312, -1.49...  \n50  [-0.6115171637066081, -0.6043750109058261, -0....  \n51  [0.8213176975345222, -0.3567883978547356, 0.74...  \n52  [2.1950435820348337, 1.4094437030305949, 0.570...  \n53  [0.012881916047346897, 1.3695501201604985, -0....  \n54  [-0.017122470917853794, 1.3782563192289519, -0...  \n55  [-0.4847013941931176, 0.5139830867408755, 1.38...  \n56  [1.5126835781485697, 1.3201826969969401, 1.136...  \n57  [-0.3289541445375265, 0.44969699720476713, -0....  \n58  [-0.06121429593488975, -0.34688099821106483, 1...  \n59  [-0.69684014280709, 0.03765969771049354, 0.282...  \n60  [-0.36322072906310704, 0.48827701690184055, 2....  \n61  [1.6144555070237883, 0.593545528975004, -0.025...  \n62  [-1.8038319942786876, -1.734095906088043, -1.6...  \n63  [0.3832716339477059, -0.3277153740360189, 0.28...  \n64  [2.013114937364204, 2.1911780662498623, 1.5117...  \n65  [-0.5834104654918476, -0.9754419494748687, -1....  \n66  [0.7016594790270235, 0.7216236430745416, 0.722...  \n67  [-0.07138479312823673, 0.11764274850132247, 0....  \n68  [0.49398388604596977, 0.4647613435699864, 0.46...  \n69  [-0.889751642689492, 0.7136831255617402, 0.863...  \n70  [-0.8897664855195976, 0.7220051404204639, 0.86...  \n71  [-1.5234667568685771, -0.10679373579085041, 0....  \n72  [-0.5270584527917569, 0.41611149747934384, -0....  \n73  [-1.183580961179512, -0.5140276617370714, -0.4...  \n74  [-0.15580286897050794, -0.33898963706266844, -...  ",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>dataset</th>\n      <th>change_points</th>\n      <th>time_series</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Adiac</td>\n      <td>[572, 1012, 1232]</td>\n      <td>[1.6811456620860457, 1.3833939713969476, 1.029...</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>ArrowHead</td>\n      <td>[753]</td>\n      <td>[-1.9577206309715156, -1.8580819245996238, -1....</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Beef</td>\n      <td>[705]</td>\n      <td>[-0.3808184611459132, -0.3522382904977104, -0....</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>BeetleFly</td>\n      <td>[1280]</td>\n      <td>[1.1148051937755514, 0.7744033379924569, 0.477...</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>BirdChicken</td>\n      <td>[1280]</td>\n      <td>[1.697714230537119, 1.439032088641677, 1.22286...</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>Car</td>\n      <td>[577, 1154, 1550]</td>\n      <td>[1.4516478867009357, 1.0301743848837297, 0.531...</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>CBF</td>\n      <td>[384, 704]</td>\n      <td>[-1.2771304421352372, -0.6898106156588633, -0....</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>Chinatown</td>\n      <td>[]</td>\n      <td>[0.02593528814386279, -0.43565455927050417, -0...</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>ChlorineConcentration</td>\n      <td>[2365]</td>\n      <td>[2.4962273725836015, 1.0467432663502947, 0.391...</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>CinCECGTorso</td>\n      <td>[2663, 5121]</td>\n      <td>[-0.11821460735609512, -0.11532619232132583, -...</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>Coffee</td>\n      <td>[500]</td>\n      <td>[-0.6443794351448959, -0.9354437755240335, -0....</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>Computers</td>\n      <td>[5625]</td>\n      <td>[-0.33526472777743305, -0.33526472777743305, -...</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>CricketX</td>\n      <td>[712, 1293, 1930, 2586]</td>\n      <td>[1.6687284318319828, 1.811383826109306, 0.1561...</td>\n    </tr>\n    <tr>\n      <th>13</th>\n      <td>CricketY</td>\n      <td>[525, 1162, 1837, 2399]</td>\n      <td>[-0.07426595813016472, 0.12508525068138565, -2...</td>\n    </tr>\n    <tr>\n      <th>14</th>\n      <td>CricketZ</td>\n      <td>[600, 1106, 1574, 2174]</td>\n      <td>[0.5388645545243573, -0.2361270584753817, -0.7...</td>\n    </tr>\n    <tr>\n      <th>15</th>\n      <td>Crop</td>\n      <td>[1725, 3450, 5175, 8625, 12075, 15525, 17250, ...</td>\n      <td>[-1.0191252092970968, -0.9359573602315785, -0....</td>\n    </tr>\n    <tr>\n      <th>16</th>\n      <td>DiatomSizeReduction</td>\n      <td>[517, 948]</td>\n      <td>[1.851102750639204, 1.7507010856005727, 1.5427...</td>\n    </tr>\n    <tr>\n      <th>17</th>\n      <td>DistalPhalanxOutlineAgeGroup</td>\n      <td>[300]</td>\n      <td>[-0.09584243891373648, 1.327275810868278, 0.38...</td>\n    </tr>\n    <tr>\n      <th>18</th>\n      <td>DistalPhalanxTW</td>\n      <td>[580, 2220]</td>\n      <td>[-0.7337566012451453, 0.4509155903334242, 1.31...</td>\n    </tr>\n    <tr>\n      <th>19</th>\n      <td>DodgerLoopDay</td>\n      <td>[]</td>\n      <td>[-0.41782778572558976, 0.21606722194238143, -0...</td>\n    </tr>\n    <tr>\n      <th>20</th>\n      <td>ECG200</td>\n      <td>[744]</td>\n      <td>[0.8030652832607478, 1.9659733771294134, 0.145...</td>\n    </tr>\n    <tr>\n      <th>21</th>\n      <td>ECGFiveDays</td>\n      <td>[476]</td>\n      <td>[0.00208256816105082, -0.023486727616573715, -...</td>\n    </tr>\n    <tr>\n      <th>22</th>\n      <td>EOGHorizontalSignal</td>\n      <td>[1406, 2812, 4218]</td>\n      <td>[-0.9536228832289295, -0.9700887626126122, -0....</td>\n    </tr>\n    <tr>\n      <th>23</th>\n      <td>EOGVerticalSignal</td>\n      <td>[1406, 2812, 3749, 5155, 6608]</td>\n      <td>[1.1827171322359449, 1.156045388449738, 1.1415...</td>\n    </tr>\n    <tr>\n      <th>24</th>\n      <td>FaceAll</td>\n      <td>[1310, 2620, 3930, 5240, 6550, 7860]</td>\n      <td>[-0.5613916259170972, -1.3562183276912418, -0....</td>\n    </tr>\n    <tr>\n      <th>25</th>\n      <td>FaceFour</td>\n      <td>[2800, 5600, 6650]</td>\n      <td>[2.5843157174695235, 2.5843157174695235, 1.933...</td>\n    </tr>\n    <tr>\n      <th>26</th>\n      <td>FacesUCR</td>\n      <td>[851, 1637, 2292, 3733, 4715, 5566]</td>\n      <td>[-0.36326079832230385, -0.22943593812562227, -...</td>\n    </tr>\n    <tr>\n      <th>27</th>\n      <td>FiftyWords</td>\n      <td>[1755, 3408, 4521]</td>\n      <td>[-0.3445510936413573, 1.4717211154661174, 2.24...</td>\n    </tr>\n    <tr>\n      <th>28</th>\n      <td>Fish</td>\n      <td>[1504, 2950, 4570, 5785, 7058, 8504]</td>\n      <td>[1.8710988310016834, 1.5403189614124408, 1.203...</td>\n    </tr>\n    <tr>\n      <th>29</th>\n      <td>FreezerRegularTrain</td>\n      <td>[2257, 4514, 7900]</td>\n      <td>[-2.213755288036522, -2.2139108716958202, -2.2...</td>\n    </tr>\n    <tr>\n      <th>30</th>\n      <td>Ham</td>\n      <td>[1400, 2935, 4335]</td>\n      <td>[-0.6226012538812328, -0.6993223664768211, -0....</td>\n    </tr>\n    <tr>\n      <th>31</th>\n      <td>Herring</td>\n      <td>[]</td>\n      <td>[1.9013595305134667, 1.7835298811241282, 1.596...</td>\n    </tr>\n    <tr>\n      <th>32</th>\n      <td>GunPoint</td>\n      <td>[900]</td>\n      <td>[-0.7804726464099164, -0.7715912873470101, -0....</td>\n    </tr>\n    <tr>\n      <th>33</th>\n      <td>Haptics</td>\n      <td>[1228, 3548]</td>\n      <td>[-1.2658667324529853, -1.3841822398502917, -1....</td>\n    </tr>\n    <tr>\n      <th>34</th>\n      <td>InlineSkate</td>\n      <td>[1058, 2704]</td>\n      <td>[-0.3827578498718709, -0.3970526600200499, -0....</td>\n    </tr>\n    <tr>\n      <th>35</th>\n      <td>InsectWingbeatSound</td>\n      <td>[320, 640, 960]</td>\n      <td>[-0.2151308540915991, -0.2617109672187612, -0....</td>\n    </tr>\n    <tr>\n      <th>36</th>\n      <td>ItalyPowerDemand</td>\n      <td>[816]</td>\n      <td>[-0.7257992615525017, -1.2087710554478004, -1....</td>\n    </tr>\n    <tr>\n      <th>37</th>\n      <td>LargeKitchenAppliances</td>\n      <td>[2812, 5624]</td>\n      <td>[-0.09917672952011744, -0.09917672952011744, -...</td>\n    </tr>\n    <tr>\n      <th>38</th>\n      <td>Lightning2</td>\n      <td>[398]</td>\n      <td>[-1.3971021488734972, -1.3971021488734972, -1....</td>\n    </tr>\n    <tr>\n      <th>39</th>\n      <td>Lightning7</td>\n      <td>[1276, 2552, 3828, 5742]</td>\n      <td>[-0.7673588003672911, -0.7673588003672911, -0....</td>\n    </tr>\n    <tr>\n      <th>40</th>\n      <td>Mallat</td>\n      <td>[768, 1792, 2560]</td>\n      <td>[-1.023484706988249, -1.0666984500902235, -1.1...</td>\n    </tr>\n    <tr>\n      <th>41</th>\n      <td>Meat</td>\n      <td>[1120, 2240]</td>\n      <td>[-0.2628214018844894, -0.2524907606949458, -0....</td>\n    </tr>\n    <tr>\n      <th>42</th>\n      <td>MedicalImages</td>\n      <td>[1732]</td>\n      <td>[0.4338270721442356, 1.6512774567573048, 1.535...</td>\n    </tr>\n    <tr>\n      <th>43</th>\n      <td>MelbournePedestrian</td>\n      <td>[576, 1152, 2016, 2592, 3168, 4032]</td>\n      <td>[-1.0646761562992557, -1.1179181050849807, -1....</td>\n    </tr>\n    <tr>\n      <th>44</th>\n      <td>MiddlePhalanxOutlineAgeGroup</td>\n      <td>[880, 2608, 6400, 7720]</td>\n      <td>[-0.4285314573614312, -0.19212287319716795, 0....</td>\n    </tr>\n    <tr>\n      <th>45</th>\n      <td>MiddlePhalanxOutlineCorrect</td>\n      <td>[1060, 3000, 4060]</td>\n      <td>[-0.5058955136625363, 0.41113075807768285, 1.3...</td>\n    </tr>\n    <tr>\n      <th>46</th>\n      <td>MiddlePhalanxTW</td>\n      <td>[]</td>\n      <td>[-0.744506193868183, -0.6905826018218594, -0.5...</td>\n    </tr>\n    <tr>\n      <th>47</th>\n      <td>MoteStrain</td>\n      <td>[840]</td>\n      <td>[0.08219057031117295, 0.05792553419205202, 0.0...</td>\n    </tr>\n    <tr>\n      <th>48</th>\n      <td>NonInvasiveFetalECGThorax1</td>\n      <td>[1687, 4124, 6233, 8108, 10358]</td>\n      <td>[1.261310975866223, -3.252779490857797, -1.038...</td>\n    </tr>\n    <tr>\n      <th>49</th>\n      <td>NonInvasiveFetalECGThorax2</td>\n      <td>[1687, 4124, 6233, 8108, 10358, 12420]</td>\n      <td>[2.7402938736821465, -2.153493336821312, -1.49...</td>\n    </tr>\n    <tr>\n      <th>50</th>\n      <td>OliveOil</td>\n      <td>[1425, 3705]</td>\n      <td>[-0.6115171637066081, -0.6043750109058261, -0....</td>\n    </tr>\n    <tr>\n      <th>51</th>\n      <td>OSULeaf</td>\n      <td>[907, 1680]</td>\n      <td>[0.8213176975345222, -0.3567883978547356, 0.74...</td>\n    </tr>\n    <tr>\n      <th>52</th>\n      <td>Plane</td>\n      <td>[540, 1044, 1368, 1944, 2412, 3060]</td>\n      <td>[2.1950435820348337, 1.4094437030305949, 0.570...</td>\n    </tr>\n    <tr>\n      <th>53</th>\n      <td>ProximalPhalanxOutlineAgeGroup</td>\n      <td>[720, 2110]</td>\n      <td>[0.012881916047346897, 1.3695501201604985, -0....</td>\n    </tr>\n    <tr>\n      <th>54</th>\n      <td>ProximalPhalanxOutlineCorrect</td>\n      <td>[776, 3212, 4376]</td>\n      <td>[-0.017122470917853794, 1.3782563192289519, -0...</td>\n    </tr>\n    <tr>\n      <th>55</th>\n      <td>ProximalPhalanxTW</td>\n      <td>[320, 2500, 3660]</td>\n      <td>[-0.4847013941931176, 0.5139830867408755, 1.38...</td>\n    </tr>\n    <tr>\n      <th>56</th>\n      <td>ShapesAll</td>\n      <td>[1280, 2560, 3840, 5120]</td>\n      <td>[1.5126835781485697, 1.3201826969969401, 1.136...</td>\n    </tr>\n    <tr>\n      <th>57</th>\n      <td>ShapeletSim</td>\n      <td>[]</td>\n      <td>[-0.3289541445375265, 0.44969699720476713, -0....</td>\n    </tr>\n    <tr>\n      <th>58</th>\n      <td>SonyAIBORobotSurface1</td>\n      <td>[420]</td>\n      <td>[-0.06121429593488975, -0.34688099821106483, 1...</td>\n    </tr>\n    <tr>\n      <th>59</th>\n      <td>SonyAIBORobotSurface2</td>\n      <td>[715]</td>\n      <td>[-0.69684014280709, 0.03765969771049354, 0.282...</td>\n    </tr>\n    <tr>\n      <th>60</th>\n      <td>Strawberry</td>\n      <td>[804, 2250, 3054]</td>\n      <td>[-0.36322072906310704, 0.48827701690184055, 2....</td>\n    </tr>\n    <tr>\n      <th>61</th>\n      <td>SwedishLeaf</td>\n      <td>[928, 2080, 3200]</td>\n      <td>[1.6144555070237883, 0.593545528975004, -0.025...</td>\n    </tr>\n    <tr>\n      <th>62</th>\n      <td>Symbols</td>\n      <td>[796, 1293, 1591]</td>\n      <td>[-1.8038319942786876, -1.734095906088043, -1.6...</td>\n    </tr>\n    <tr>\n      <th>63</th>\n      <td>SyntheticControl</td>\n      <td>[750, 1500, 2250, 3000]</td>\n      <td>[0.3832716339477059, -0.3277153740360189, 0.28...</td>\n    </tr>\n    <tr>\n      <th>64</th>\n      <td>ToeSegmentation1</td>\n      <td>[1385]</td>\n      <td>[2.013114937364204, 2.1911780662498623, 1.5117...</td>\n    </tr>\n    <tr>\n      <th>65</th>\n      <td>ToeSegmentation2</td>\n      <td>[1543]</td>\n      <td>[-0.5834104654918476, -0.9754419494748687, -1....</td>\n    </tr>\n    <tr>\n      <th>66</th>\n      <td>Trace</td>\n      <td>[1443, 2955]</td>\n      <td>[0.7016594790270235, 0.7216236430745416, 0.722...</td>\n    </tr>\n    <tr>\n      <th>67</th>\n      <td>TwoLeadECG</td>\n      <td>[246]</td>\n      <td>[-0.07138479312823673, 0.11764274850132247, 0....</td>\n    </tr>\n    <tr>\n      <th>68</th>\n      <td>UMD</td>\n      <td>[]</td>\n      <td>[0.49398388604596977, 0.4647613435699864, 0.46...</td>\n    </tr>\n    <tr>\n      <th>69</th>\n      <td>UWaveGestureLibraryAll</td>\n      <td>[1801, 3395, 4960, 6584]</td>\n      <td>[-0.889751642689492, 0.7136831255617402, 0.863...</td>\n    </tr>\n    <tr>\n      <th>70</th>\n      <td>UWaveGestureLibraryX</td>\n      <td>[600, 1131, 1652, 2193]</td>\n      <td>[-0.8897664855195976, 0.7220051404204639, 0.86...</td>\n    </tr>\n    <tr>\n      <th>71</th>\n      <td>UWaveGestureLibraryY</td>\n      <td>[600, 1131, 1652, 2193]</td>\n      <td>[-1.5234667568685771, -0.10679373579085041, 0....</td>\n    </tr>\n    <tr>\n      <th>72</th>\n      <td>UWaveGestureLibraryZ</td>\n      <td>[600, 1131, 1652, 2193]</td>\n      <td>[-0.5270584527917569, 0.41611149747934384, -0....</td>\n    </tr>\n    <tr>\n      <th>73</th>\n      <td>WordSynonyms</td>\n      <td>[202, 2227]</td>\n      <td>[-1.183580961179512, -0.5140276617370714, -0.4...</td>\n    </tr>\n    <tr>\n      <th>74</th>\n      <td>Yoga</td>\n      <td>[7295]</td>\n      <td>[-0.15580286897050794, -0.33898963706266844, -...</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tssb = []\n",
    "\n",
    "for _, (dataset, label_cut, resample_rate, labels) in tqdm(prop_file.iterrows()):\n",
    "    X, y = load_from_tsv_file(os.path.join(DATA_PATH, f\"{dataset}/{dataset}_TRAIN.tsv\"))\n",
    "    ts, cps = generate_time_series_segmentation_dataset(X, y, labels=labels, resample_rate=resample_rate,\n",
    "                                                        label_cut=label_cut)\n",
    "    tssb.append((dataset, cps, ts))\n",
    "\n",
    "tssb = pd.DataFrame.from_records(tssb, columns=[\"dataset\", \"change_points\", \"time_series\"])\n",
    "tssb"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5f1d1fde",
   "metadata": {},
   "source": [
    "Now, let's load the reference benchmark to compare it to the generated one (they should match)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "7e24d652",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "                           dataset  window_size  \\\n0                            Adiac           10   \n1                        ArrowHead           10   \n2                             Beef           50   \n3                        BeetleFly           10   \n4                      BirdChicken           20   \n5                              Car           20   \n6                              CBF           20   \n7                        Chinatown           10   \n8            ChlorineConcentration           10   \n9                     CinCECGTorso           10   \n10                          Coffee           10   \n11                       Computers           50   \n12                        CricketX           10   \n13                        CricketY           10   \n14                        CricketZ           10   \n15                            Crop           10   \n16             DiatomSizeReduction           20   \n17    DistalPhalanxOutlineAgeGroup           50   \n18                 DistalPhalanxTW           10   \n19                   DodgerLoopDay           20   \n20                          ECG200           20   \n21                     ECGFiveDays           20   \n22             EOGHorizontalSignal           20   \n23               EOGVerticalSignal           20   \n24                         FaceAll           10   \n25                        FaceFour           50   \n26                        FacesUCR           20   \n27                      FiftyWords           10   \n28                            Fish           10   \n29             FreezerRegularTrain           20   \n30                             Ham           10   \n31                         Herring           10   \n32                        GunPoint           10   \n33                         Haptics           10   \n34                     InlineSkate           10   \n35             InsectWingbeatSound           10   \n36                ItalyPowerDemand           20   \n37          LargeKitchenAppliances          100   \n38                      Lightning2           20   \n39                      Lightning7           50   \n40                          Mallat           10   \n41                            Meat           10   \n42                   MedicalImages           10   \n43             MelbournePedestrian           10   \n44    MiddlePhalanxOutlineAgeGroup           10   \n45     MiddlePhalanxOutlineCorrect           10   \n46                 MiddlePhalanxTW           10   \n47                      MoteStrain           50   \n48      NonInvasiveFetalECGThorax1           20   \n49      NonInvasiveFetalECGThorax2           20   \n50                        OliveOil           50   \n51                         OSULeaf           10   \n52                           Plane           10   \n53  ProximalPhalanxOutlineAgeGroup           10   \n54   ProximalPhalanxOutlineCorrect           10   \n55               ProximalPhalanxTW           10   \n56                       ShapesAll           10   \n57                     ShapeletSim           50   \n58           SonyAIBORobotSurface1           20   \n59           SonyAIBORobotSurface2           10   \n60                      Strawberry           10   \n61                     SwedishLeaf           10   \n62                         Symbols           10   \n63                SyntheticControl           10   \n64                ToeSegmentation1           10   \n65                ToeSegmentation2           10   \n66                           Trace           50   \n67                      TwoLeadECG           10   \n68                             UMD           10   \n69          UWaveGestureLibraryAll           10   \n70            UWaveGestureLibraryX           10   \n71            UWaveGestureLibraryY           10   \n72            UWaveGestureLibraryZ           10   \n73                    WordSynonyms           10   \n74                            Yoga           10   \n\n                                        change_points  \\\n0                                   [572, 1012, 1232]   \n1                                               [753]   \n2                                               [705]   \n3                                              [1280]   \n4                                              [1280]   \n5                                   [577, 1154, 1550]   \n6                                          [384, 704]   \n7                                                  []   \n8                                              [2365]   \n9                                        [2663, 5121]   \n10                                              [500]   \n11                                             [5625]   \n12                            [712, 1293, 1930, 2586]   \n13                            [525, 1162, 1837, 2399]   \n14                            [600, 1106, 1574, 2174]   \n15  [1725, 3450, 5175, 8625, 12075, 15525, 17250, ...   \n16                                         [517, 948]   \n17                                              [300]   \n18                                        [580, 2220]   \n19                                                 []   \n20                                              [744]   \n21                                              [476]   \n22                                 [1406, 2812, 4218]   \n23                     [1406, 2812, 3749, 5155, 6608]   \n24               [1310, 2620, 3930, 5240, 6550, 7860]   \n25                                 [2800, 5600, 6650]   \n26                [851, 1637, 2292, 3733, 4715, 5566]   \n27                                 [1755, 3408, 4521]   \n28               [1504, 2950, 4570, 5785, 7058, 8504]   \n29                                 [2257, 4514, 7900]   \n30                                 [1400, 2935, 4335]   \n31                                                 []   \n32                                              [900]   \n33                                       [1228, 3548]   \n34                                       [1058, 2704]   \n35                                    [320, 640, 960]   \n36                                              [816]   \n37                                       [2812, 5624]   \n38                                              [398]   \n39                           [1276, 2552, 3828, 5742]   \n40                                  [768, 1792, 2560]   \n41                                       [1120, 2240]   \n42                                             [1732]   \n43                [576, 1152, 2016, 2592, 3168, 4032]   \n44                            [880, 2608, 6400, 7720]   \n45                                 [1060, 3000, 4060]   \n46                                                 []   \n47                                              [840]   \n48                    [1687, 4124, 6233, 8108, 10358]   \n49             [1687, 4124, 6233, 8108, 10358, 12420]   \n50                                       [1425, 3705]   \n51                                        [907, 1680]   \n52                [540, 1044, 1368, 1944, 2412, 3060]   \n53                                        [720, 2110]   \n54                                  [776, 3212, 4376]   \n55                                  [320, 2500, 3660]   \n56                           [1280, 2560, 3840, 5120]   \n57                                                 []   \n58                                              [420]   \n59                                              [715]   \n60                                  [804, 2250, 3054]   \n61                                  [928, 2080, 3200]   \n62                                  [796, 1293, 1591]   \n63                            [750, 1500, 2250, 3000]   \n64                                             [1385]   \n65                                             [1543]   \n66                                       [1443, 2955]   \n67                                              [246]   \n68                                                 []   \n69                           [1801, 3395, 4960, 6584]   \n70                            [600, 1131, 1652, 2193]   \n71                            [600, 1131, 1652, 2193]   \n72                            [600, 1131, 1652, 2193]   \n73                                        [202, 2227]   \n74                                             [7295]   \n\n                                          time_series  \n0   [1.681146, 1.383394, 1.029389, 0.688386, 0.354...  \n1   [-1.957721, -1.858082, -1.657983, -1.420794, -...  \n2   [-0.380818, -0.352238, -0.295392, -0.225041, -...  \n3   [1.114805, 0.774403, 0.477129, 0.115781, -0.24...  \n4   [1.697714, 1.439032, 1.22287, 0.99708, 0.76479...  \n5   [1.451648, 1.030174, 0.531853, 0.040229, -0.40...  \n6   [-1.27713, -0.689811, -0.901181, -0.937038, -0...  \n7   [0.025935, -0.435655, -0.608168, -0.81565, -1....  \n8   [2.496227, 1.046743, 0.391124, 0.607525, -0.22...  \n9   [-0.118215, -0.115326, -0.111855, -0.1229, -0....  \n10  [-0.644379, -0.935444, -0.98246, -1.069437, -0...  \n11  [-0.335265, -0.335265, -0.335265, -0.335265, -...  \n12  [1.668728, 1.811384, 0.156194, 1.085055, 0.154...  \n13  [-0.074266, 0.125085, -2.299074, 0.691614, 0.2...  \n14  [0.538865, -0.236127, -0.773805, 0.690791, 0.2...  \n15  [-1.019125, -0.935957, -0.830774, -0.742103, -...  \n16  [1.851103, 1.750701, 1.542724, 1.312404, 1.078...  \n17  [-0.095842, 1.327276, 0.384633, -0.255218, 1.4...  \n18  [-0.733757, 0.450916, 1.312294, 1.238173, 0.14...  \n19  [-0.417828, 0.216067, -0.523477, -0.523477, 0....  \n20  [0.803065, 1.965973, 0.145364, -0.268573, -0.3...  \n21  [0.002083, -0.023487, -0.056923, -0.068725, -0...  \n22  [-0.953623, -0.970089, -0.971874, -0.978193, -...  \n23  [1.182717, 1.156045, 1.14153, 1.128699, 1.0830...  \n24  [-0.561392, -1.356218, -0.597665, 1.486103, 1....  \n25  [2.584316, 2.584316, 1.933222, 1.933222, 1.451...  \n26  [-0.363261, -0.229436, -0.477039, -0.591405, -...  \n27  [-0.344551, 1.471721, 2.243461, 1.331494, 0.23...  \n28  [1.871099, 1.540319, 1.203093, 0.870156, 0.538...  \n29  [-2.213755, -2.213911, -2.213822, -2.213715, -...  \n30  [-0.622601, -0.699322, -0.699322, -0.717133, -...  \n31  [1.90136, 1.78353, 1.59648, 1.369286, 1.158101...  \n32  [-0.780473, -0.771591, -0.767739, -0.779923, -...  \n33  [-1.265867, -1.384182, -1.313314, -1.106834, -...  \n34  [-0.382758, -0.397053, -0.445415, -0.507809, -...  \n35  [-0.215131, -0.261711, -0.37249, -0.365237, 0....  \n36  [-0.725799, -1.208771, -1.40196, -1.627347, -1...  \n37  [-0.099177, -0.099177, -0.099177, -0.099177, -...  \n38  [-1.397102, -1.397102, -1.397102, -0.568111, 0...  \n39  [-0.767359, -0.767359, -0.767359, -0.767359, -...  \n40  [-1.023485, -1.066698, -1.125013, -1.146245, -...  \n41  [-0.262821, -0.252491, -0.189973, -0.152557, -...  \n42  [0.433827, 1.651277, 1.535373, 2.368258, 2.465...  \n43  [-1.064676, -1.117918, -1.109614, -0.871247, -...  \n44  [-0.428531, -0.192123, 0.278944, 0.76281, 1.12...  \n45  [-0.505896, 0.411131, 1.32881, 1.356801, 0.464...  \n46  [-0.744506, -0.690583, -0.55563, -0.29238, 0.0...  \n47  [0.082191, 0.057926, 0.019425, -0.018106, -0.0...  \n48  [1.261311, -3.252779, -1.038552, -0.038799, -0...  \n49  [2.740294, -2.153493, -1.492175, -0.381289, -0...  \n50  [-0.611517, -0.604375, -0.590556, -0.574301, -...  \n51  [0.821318, -0.356788, 0.745552, -0.117068, -0....  \n52  [2.195044, 1.409444, 0.570219, -0.17921, -0.87...  \n53  [0.012882, 1.36955, -0.034655, 0.157485, 1.561...  \n54  [-0.017122, 1.378256, -0.041994, 0.151372, 1.5...  \n55  [-0.484701, 0.513983, 1.386599, 1.248457, 0.44...  \n56  [1.512684, 1.320183, 1.136966, 0.960463, 0.786...  \n57  [-0.328954, 0.449697, -0.69761, -0.775463, -0....  \n58  [-0.061214, -0.346881, 1.938453, 3.652453, 1.9...  \n59  [-0.69684, 0.03766, 0.282493, 0.282493, 0.5273...  \n60  [-0.363221, 0.488277, 2.717473, 1.375118, 0.10...  \n61  [1.614456, 0.593546, -0.025966, -0.24674, -0.4...  \n62  [-1.803832, -1.734096, -1.661634, -1.583883, -...  \n63  [0.383272, -0.327715, 0.282826, 1.086384, -0.4...  \n64  [2.013115, 2.191178, 1.511739, 0.470604, 0.011...  \n65  [-0.58341, -0.975442, -1.224916, -0.935348, -0...  \n66  [0.701659, 0.721624, 0.722078, 0.720484, 0.749...  \n67  [-0.071385, 0.117643, 0.155448, -0.011526, -0....  \n68  [0.493984, 0.464761, 0.461762, 0.383778, 0.411...  \n69  [-0.889752, 0.713683, 0.863088, -0.554829, -0....  \n70  [-0.889766, 0.722005, 0.869297, -0.553167, -0....  \n71  [-1.523467, -0.106794, 0.190361, 0.979247, 0.3...  \n72  [-0.527058, 0.416111, -0.524027, 0.41141, 0.12...  \n73  [-1.183581, -0.514028, -0.480539, -0.394855, -...  \n74  [-0.155803, -0.33899, -0.564977, -0.667323, -0...  ",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>dataset</th>\n      <th>window_size</th>\n      <th>change_points</th>\n      <th>time_series</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Adiac</td>\n      <td>10</td>\n      <td>[572, 1012, 1232]</td>\n      <td>[1.681146, 1.383394, 1.029389, 0.688386, 0.354...</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>ArrowHead</td>\n      <td>10</td>\n      <td>[753]</td>\n      <td>[-1.957721, -1.858082, -1.657983, -1.420794, -...</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Beef</td>\n      <td>50</td>\n      <td>[705]</td>\n      <td>[-0.380818, -0.352238, -0.295392, -0.225041, -...</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>BeetleFly</td>\n      <td>10</td>\n      <td>[1280]</td>\n      <td>[1.114805, 0.774403, 0.477129, 0.115781, -0.24...</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>BirdChicken</td>\n      <td>20</td>\n      <td>[1280]</td>\n      <td>[1.697714, 1.439032, 1.22287, 0.99708, 0.76479...</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>Car</td>\n      <td>20</td>\n      <td>[577, 1154, 1550]</td>\n      <td>[1.451648, 1.030174, 0.531853, 0.040229, -0.40...</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>CBF</td>\n      <td>20</td>\n      <td>[384, 704]</td>\n      <td>[-1.27713, -0.689811, -0.901181, -0.937038, -0...</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>Chinatown</td>\n      <td>10</td>\n      <td>[]</td>\n      <td>[0.025935, -0.435655, -0.608168, -0.81565, -1....</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>ChlorineConcentration</td>\n      <td>10</td>\n      <td>[2365]</td>\n      <td>[2.496227, 1.046743, 0.391124, 0.607525, -0.22...</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>CinCECGTorso</td>\n      <td>10</td>\n      <td>[2663, 5121]</td>\n      <td>[-0.118215, -0.115326, -0.111855, -0.1229, -0....</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>Coffee</td>\n      <td>10</td>\n      <td>[500]</td>\n      <td>[-0.644379, -0.935444, -0.98246, -1.069437, -0...</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>Computers</td>\n      <td>50</td>\n      <td>[5625]</td>\n      <td>[-0.335265, -0.335265, -0.335265, -0.335265, -...</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>CricketX</td>\n      <td>10</td>\n      <td>[712, 1293, 1930, 2586]</td>\n      <td>[1.668728, 1.811384, 0.156194, 1.085055, 0.154...</td>\n    </tr>\n    <tr>\n      <th>13</th>\n      <td>CricketY</td>\n      <td>10</td>\n      <td>[525, 1162, 1837, 2399]</td>\n      <td>[-0.074266, 0.125085, -2.299074, 0.691614, 0.2...</td>\n    </tr>\n    <tr>\n      <th>14</th>\n      <td>CricketZ</td>\n      <td>10</td>\n      <td>[600, 1106, 1574, 2174]</td>\n      <td>[0.538865, -0.236127, -0.773805, 0.690791, 0.2...</td>\n    </tr>\n    <tr>\n      <th>15</th>\n      <td>Crop</td>\n      <td>10</td>\n      <td>[1725, 3450, 5175, 8625, 12075, 15525, 17250, ...</td>\n      <td>[-1.019125, -0.935957, -0.830774, -0.742103, -...</td>\n    </tr>\n    <tr>\n      <th>16</th>\n      <td>DiatomSizeReduction</td>\n      <td>20</td>\n      <td>[517, 948]</td>\n      <td>[1.851103, 1.750701, 1.542724, 1.312404, 1.078...</td>\n    </tr>\n    <tr>\n      <th>17</th>\n      <td>DistalPhalanxOutlineAgeGroup</td>\n      <td>50</td>\n      <td>[300]</td>\n      <td>[-0.095842, 1.327276, 0.384633, -0.255218, 1.4...</td>\n    </tr>\n    <tr>\n      <th>18</th>\n      <td>DistalPhalanxTW</td>\n      <td>10</td>\n      <td>[580, 2220]</td>\n      <td>[-0.733757, 0.450916, 1.312294, 1.238173, 0.14...</td>\n    </tr>\n    <tr>\n      <th>19</th>\n      <td>DodgerLoopDay</td>\n      <td>20</td>\n      <td>[]</td>\n      <td>[-0.417828, 0.216067, -0.523477, -0.523477, 0....</td>\n    </tr>\n    <tr>\n      <th>20</th>\n      <td>ECG200</td>\n      <td>20</td>\n      <td>[744]</td>\n      <td>[0.803065, 1.965973, 0.145364, -0.268573, -0.3...</td>\n    </tr>\n    <tr>\n      <th>21</th>\n      <td>ECGFiveDays</td>\n      <td>20</td>\n      <td>[476]</td>\n      <td>[0.002083, -0.023487, -0.056923, -0.068725, -0...</td>\n    </tr>\n    <tr>\n      <th>22</th>\n      <td>EOGHorizontalSignal</td>\n      <td>20</td>\n      <td>[1406, 2812, 4218]</td>\n      <td>[-0.953623, -0.970089, -0.971874, -0.978193, -...</td>\n    </tr>\n    <tr>\n      <th>23</th>\n      <td>EOGVerticalSignal</td>\n      <td>20</td>\n      <td>[1406, 2812, 3749, 5155, 6608]</td>\n      <td>[1.182717, 1.156045, 1.14153, 1.128699, 1.0830...</td>\n    </tr>\n    <tr>\n      <th>24</th>\n      <td>FaceAll</td>\n      <td>10</td>\n      <td>[1310, 2620, 3930, 5240, 6550, 7860]</td>\n      <td>[-0.561392, -1.356218, -0.597665, 1.486103, 1....</td>\n    </tr>\n    <tr>\n      <th>25</th>\n      <td>FaceFour</td>\n      <td>50</td>\n      <td>[2800, 5600, 6650]</td>\n      <td>[2.584316, 2.584316, 1.933222, 1.933222, 1.451...</td>\n    </tr>\n    <tr>\n      <th>26</th>\n      <td>FacesUCR</td>\n      <td>20</td>\n      <td>[851, 1637, 2292, 3733, 4715, 5566]</td>\n      <td>[-0.363261, -0.229436, -0.477039, -0.591405, -...</td>\n    </tr>\n    <tr>\n      <th>27</th>\n      <td>FiftyWords</td>\n      <td>10</td>\n      <td>[1755, 3408, 4521]</td>\n      <td>[-0.344551, 1.471721, 2.243461, 1.331494, 0.23...</td>\n    </tr>\n    <tr>\n      <th>28</th>\n      <td>Fish</td>\n      <td>10</td>\n      <td>[1504, 2950, 4570, 5785, 7058, 8504]</td>\n      <td>[1.871099, 1.540319, 1.203093, 0.870156, 0.538...</td>\n    </tr>\n    <tr>\n      <th>29</th>\n      <td>FreezerRegularTrain</td>\n      <td>20</td>\n      <td>[2257, 4514, 7900]</td>\n      <td>[-2.213755, -2.213911, -2.213822, -2.213715, -...</td>\n    </tr>\n    <tr>\n      <th>30</th>\n      <td>Ham</td>\n      <td>10</td>\n      <td>[1400, 2935, 4335]</td>\n      <td>[-0.622601, -0.699322, -0.699322, -0.717133, -...</td>\n    </tr>\n    <tr>\n      <th>31</th>\n      <td>Herring</td>\n      <td>10</td>\n      <td>[]</td>\n      <td>[1.90136, 1.78353, 1.59648, 1.369286, 1.158101...</td>\n    </tr>\n    <tr>\n      <th>32</th>\n      <td>GunPoint</td>\n      <td>10</td>\n      <td>[900]</td>\n      <td>[-0.780473, -0.771591, -0.767739, -0.779923, -...</td>\n    </tr>\n    <tr>\n      <th>33</th>\n      <td>Haptics</td>\n      <td>10</td>\n      <td>[1228, 3548]</td>\n      <td>[-1.265867, -1.384182, -1.313314, -1.106834, -...</td>\n    </tr>\n    <tr>\n      <th>34</th>\n      <td>InlineSkate</td>\n      <td>10</td>\n      <td>[1058, 2704]</td>\n      <td>[-0.382758, -0.397053, -0.445415, -0.507809, -...</td>\n    </tr>\n    <tr>\n      <th>35</th>\n      <td>InsectWingbeatSound</td>\n      <td>10</td>\n      <td>[320, 640, 960]</td>\n      <td>[-0.215131, -0.261711, -0.37249, -0.365237, 0....</td>\n    </tr>\n    <tr>\n      <th>36</th>\n      <td>ItalyPowerDemand</td>\n      <td>20</td>\n      <td>[816]</td>\n      <td>[-0.725799, -1.208771, -1.40196, -1.627347, -1...</td>\n    </tr>\n    <tr>\n      <th>37</th>\n      <td>LargeKitchenAppliances</td>\n      <td>100</td>\n      <td>[2812, 5624]</td>\n      <td>[-0.099177, -0.099177, -0.099177, -0.099177, -...</td>\n    </tr>\n    <tr>\n      <th>38</th>\n      <td>Lightning2</td>\n      <td>20</td>\n      <td>[398]</td>\n      <td>[-1.397102, -1.397102, -1.397102, -0.568111, 0...</td>\n    </tr>\n    <tr>\n      <th>39</th>\n      <td>Lightning7</td>\n      <td>50</td>\n      <td>[1276, 2552, 3828, 5742]</td>\n      <td>[-0.767359, -0.767359, -0.767359, -0.767359, -...</td>\n    </tr>\n    <tr>\n      <th>40</th>\n      <td>Mallat</td>\n      <td>10</td>\n      <td>[768, 1792, 2560]</td>\n      <td>[-1.023485, -1.066698, -1.125013, -1.146245, -...</td>\n    </tr>\n    <tr>\n      <th>41</th>\n      <td>Meat</td>\n      <td>10</td>\n      <td>[1120, 2240]</td>\n      <td>[-0.262821, -0.252491, -0.189973, -0.152557, -...</td>\n    </tr>\n    <tr>\n      <th>42</th>\n      <td>MedicalImages</td>\n      <td>10</td>\n      <td>[1732]</td>\n      <td>[0.433827, 1.651277, 1.535373, 2.368258, 2.465...</td>\n    </tr>\n    <tr>\n      <th>43</th>\n      <td>MelbournePedestrian</td>\n      <td>10</td>\n      <td>[576, 1152, 2016, 2592, 3168, 4032]</td>\n      <td>[-1.064676, -1.117918, -1.109614, -0.871247, -...</td>\n    </tr>\n    <tr>\n      <th>44</th>\n      <td>MiddlePhalanxOutlineAgeGroup</td>\n      <td>10</td>\n      <td>[880, 2608, 6400, 7720]</td>\n      <td>[-0.428531, -0.192123, 0.278944, 0.76281, 1.12...</td>\n    </tr>\n    <tr>\n      <th>45</th>\n      <td>MiddlePhalanxOutlineCorrect</td>\n      <td>10</td>\n      <td>[1060, 3000, 4060]</td>\n      <td>[-0.505896, 0.411131, 1.32881, 1.356801, 0.464...</td>\n    </tr>\n    <tr>\n      <th>46</th>\n      <td>MiddlePhalanxTW</td>\n      <td>10</td>\n      <td>[]</td>\n      <td>[-0.744506, -0.690583, -0.55563, -0.29238, 0.0...</td>\n    </tr>\n    <tr>\n      <th>47</th>\n      <td>MoteStrain</td>\n      <td>50</td>\n      <td>[840]</td>\n      <td>[0.082191, 0.057926, 0.019425, -0.018106, -0.0...</td>\n    </tr>\n    <tr>\n      <th>48</th>\n      <td>NonInvasiveFetalECGThorax1</td>\n      <td>20</td>\n      <td>[1687, 4124, 6233, 8108, 10358]</td>\n      <td>[1.261311, -3.252779, -1.038552, -0.038799, -0...</td>\n    </tr>\n    <tr>\n      <th>49</th>\n      <td>NonInvasiveFetalECGThorax2</td>\n      <td>20</td>\n      <td>[1687, 4124, 6233, 8108, 10358, 12420]</td>\n      <td>[2.740294, -2.153493, -1.492175, -0.381289, -0...</td>\n    </tr>\n    <tr>\n      <th>50</th>\n      <td>OliveOil</td>\n      <td>50</td>\n      <td>[1425, 3705]</td>\n      <td>[-0.611517, -0.604375, -0.590556, -0.574301, -...</td>\n    </tr>\n    <tr>\n      <th>51</th>\n      <td>OSULeaf</td>\n      <td>10</td>\n      <td>[907, 1680]</td>\n      <td>[0.821318, -0.356788, 0.745552, -0.117068, -0....</td>\n    </tr>\n    <tr>\n      <th>52</th>\n      <td>Plane</td>\n      <td>10</td>\n      <td>[540, 1044, 1368, 1944, 2412, 3060]</td>\n      <td>[2.195044, 1.409444, 0.570219, -0.17921, -0.87...</td>\n    </tr>\n    <tr>\n      <th>53</th>\n      <td>ProximalPhalanxOutlineAgeGroup</td>\n      <td>10</td>\n      <td>[720, 2110]</td>\n      <td>[0.012882, 1.36955, -0.034655, 0.157485, 1.561...</td>\n    </tr>\n    <tr>\n      <th>54</th>\n      <td>ProximalPhalanxOutlineCorrect</td>\n      <td>10</td>\n      <td>[776, 3212, 4376]</td>\n      <td>[-0.017122, 1.378256, -0.041994, 0.151372, 1.5...</td>\n    </tr>\n    <tr>\n      <th>55</th>\n      <td>ProximalPhalanxTW</td>\n      <td>10</td>\n      <td>[320, 2500, 3660]</td>\n      <td>[-0.484701, 0.513983, 1.386599, 1.248457, 0.44...</td>\n    </tr>\n    <tr>\n      <th>56</th>\n      <td>ShapesAll</td>\n      <td>10</td>\n      <td>[1280, 2560, 3840, 5120]</td>\n      <td>[1.512684, 1.320183, 1.136966, 0.960463, 0.786...</td>\n    </tr>\n    <tr>\n      <th>57</th>\n      <td>ShapeletSim</td>\n      <td>50</td>\n      <td>[]</td>\n      <td>[-0.328954, 0.449697, -0.69761, -0.775463, -0....</td>\n    </tr>\n    <tr>\n      <th>58</th>\n      <td>SonyAIBORobotSurface1</td>\n      <td>20</td>\n      <td>[420]</td>\n      <td>[-0.061214, -0.346881, 1.938453, 3.652453, 1.9...</td>\n    </tr>\n    <tr>\n      <th>59</th>\n      <td>SonyAIBORobotSurface2</td>\n      <td>10</td>\n      <td>[715]</td>\n      <td>[-0.69684, 0.03766, 0.282493, 0.282493, 0.5273...</td>\n    </tr>\n    <tr>\n      <th>60</th>\n      <td>Strawberry</td>\n      <td>10</td>\n      <td>[804, 2250, 3054]</td>\n      <td>[-0.363221, 0.488277, 2.717473, 1.375118, 0.10...</td>\n    </tr>\n    <tr>\n      <th>61</th>\n      <td>SwedishLeaf</td>\n      <td>10</td>\n      <td>[928, 2080, 3200]</td>\n      <td>[1.614456, 0.593546, -0.025966, -0.24674, -0.4...</td>\n    </tr>\n    <tr>\n      <th>62</th>\n      <td>Symbols</td>\n      <td>10</td>\n      <td>[796, 1293, 1591]</td>\n      <td>[-1.803832, -1.734096, -1.661634, -1.583883, -...</td>\n    </tr>\n    <tr>\n      <th>63</th>\n      <td>SyntheticControl</td>\n      <td>10</td>\n      <td>[750, 1500, 2250, 3000]</td>\n      <td>[0.383272, -0.327715, 0.282826, 1.086384, -0.4...</td>\n    </tr>\n    <tr>\n      <th>64</th>\n      <td>ToeSegmentation1</td>\n      <td>10</td>\n      <td>[1385]</td>\n      <td>[2.013115, 2.191178, 1.511739, 0.470604, 0.011...</td>\n    </tr>\n    <tr>\n      <th>65</th>\n      <td>ToeSegmentation2</td>\n      <td>10</td>\n      <td>[1543]</td>\n      <td>[-0.58341, -0.975442, -1.224916, -0.935348, -0...</td>\n    </tr>\n    <tr>\n      <th>66</th>\n      <td>Trace</td>\n      <td>50</td>\n      <td>[1443, 2955]</td>\n      <td>[0.701659, 0.721624, 0.722078, 0.720484, 0.749...</td>\n    </tr>\n    <tr>\n      <th>67</th>\n      <td>TwoLeadECG</td>\n      <td>10</td>\n      <td>[246]</td>\n      <td>[-0.071385, 0.117643, 0.155448, -0.011526, -0....</td>\n    </tr>\n    <tr>\n      <th>68</th>\n      <td>UMD</td>\n      <td>10</td>\n      <td>[]</td>\n      <td>[0.493984, 0.464761, 0.461762, 0.383778, 0.411...</td>\n    </tr>\n    <tr>\n      <th>69</th>\n      <td>UWaveGestureLibraryAll</td>\n      <td>10</td>\n      <td>[1801, 3395, 4960, 6584]</td>\n      <td>[-0.889752, 0.713683, 0.863088, -0.554829, -0....</td>\n    </tr>\n    <tr>\n      <th>70</th>\n      <td>UWaveGestureLibraryX</td>\n      <td>10</td>\n      <td>[600, 1131, 1652, 2193]</td>\n      <td>[-0.889766, 0.722005, 0.869297, -0.553167, -0....</td>\n    </tr>\n    <tr>\n      <th>71</th>\n      <td>UWaveGestureLibraryY</td>\n      <td>10</td>\n      <td>[600, 1131, 1652, 2193]</td>\n      <td>[-1.523467, -0.106794, 0.190361, 0.979247, 0.3...</td>\n    </tr>\n    <tr>\n      <th>72</th>\n      <td>UWaveGestureLibraryZ</td>\n      <td>10</td>\n      <td>[600, 1131, 1652, 2193]</td>\n      <td>[-0.527058, 0.416111, -0.524027, 0.41141, 0.12...</td>\n    </tr>\n    <tr>\n      <th>73</th>\n      <td>WordSynonyms</td>\n      <td>10</td>\n      <td>[202, 2227]</td>\n      <td>[-1.183581, -0.514028, -0.480539, -0.394855, -...</td>\n    </tr>\n    <tr>\n      <th>74</th>\n      <td>Yoga</td>\n      <td>10</td>\n      <td>[7295]</td>\n      <td>[-0.155803, -0.33899, -0.564977, -0.667323, -0...</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tssb_ref = load_time_series_segmentation_datasets()\n",
    "tssb_ref"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d8625ccb",
   "metadata": {},
   "source": [
    "Lastly, let's assert that the generated and reference benchmark datasets match."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "b74332ae",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 75/75 [00:00<00:00, 2879.96it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset successfully re-created!\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "for idx in tqdm(range(tssb.shape[0])):\n",
    "    assert np.array_equal(tssb.change_points.iloc[idx], tssb_ref.change_points.iloc[idx])\n",
    "    assert np.allclose(tssb.time_series.iloc[idx], tssb_ref.time_series.iloc[idx], rtol=1e-03, atol=1e-05)\n",
    "\n",
    "print(\"Dataset successfully re-created!\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}