Matrix_Profile_Randomforest_ADS.ipynb

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  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "source": [
        "!pip install tsfresh"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZdeGu5WjvA6_",
        "outputId": "9fcc1829-54ac-432a-8c44-7a9720e35a22"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting tsfresh\n",
            "  Downloading tsfresh-0.20.0-py2.py3-none-any.whl (98 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m98.2/98.2 kB\u001b[0m \u001b[31m7.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: scipy>=1.2.0 in /usr/local/lib/python3.10/dist-packages (from tsfresh) (1.10.1)\n",
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            "Collecting stumpy>=1.7.2\n",
            "  Downloading stumpy-1.11.1-py3-none-any.whl (136 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m136.2/136.2 kB\u001b[0m \u001b[31m9.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: distributed>=2.11.0 in /usr/local/lib/python3.10/dist-packages (from tsfresh) (2022.12.1)\n",
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            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas>=0.25.0->tsfresh) (2022.7.1)\n",
            "Requirement already satisfied: python-dateutil>=2.8.1 in /usr/local/lib/python3.10/dist-packages (from pandas>=0.25.0->tsfresh) (2.8.2)\n",
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            "Requirement already satisfied: charset-normalizer~=2.0.0 in /usr/local/lib/python3.10/dist-packages (from requests>=2.9.1->tsfresh) (2.0.12)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests>=2.9.1->tsfresh) (3.4)\n",
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            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn>=0.22.0->tsfresh) (3.1.0)\n",
            "Requirement already satisfied: numba>=0.54 in /usr/local/lib/python3.10/dist-packages (from stumpy>=1.7.2->tsfresh) (0.56.4)\n",
            "Requirement already satisfied: llvmlite<0.40,>=0.39.0dev0 in /usr/local/lib/python3.10/dist-packages (from numba>=0.54->stumpy>=1.7.2->tsfresh) (0.39.1)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.10/dist-packages (from numba>=0.54->stumpy>=1.7.2->tsfresh) (67.7.2)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->distributed>=2.11.0->tsfresh) (2.1.2)\n",
            "Installing collected packages: stumpy, tsfresh\n",
            "Successfully installed stumpy-1.11.1 tsfresh-0.20.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install --upgrade matrixprofile"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "52C41ivewjdK",
        "outputId": "8af2d0d5-bec1-4be7-aebe-0374b904baaa"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting matrixprofile\n",
            "  Using cached matrixprofile-1.1.10.tar.gz (331 kB)\n",
            "  \u001b[1;31merror\u001b[0m: \u001b[1msubprocess-exited-with-error\u001b[0m\n",
            "  \n",
            "  \u001b[31m×\u001b[0m \u001b[32mpython setup.py egg_info\u001b[0m did not run successfully.\n",
            "  \u001b[31m│\u001b[0m exit code: \u001b[1;36m1\u001b[0m\n",
            "  \u001b[31m╰─>\u001b[0m See above for output.\n",
            "  \n",
            "  \u001b[1;35mnote\u001b[0m: This error originates from a subprocess, and is likely not a problem with pip.\n",
            "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25herror\n",
            "\u001b[1;31merror\u001b[0m: \u001b[1mmetadata-generation-failed\u001b[0m\n",
            "\n",
            "\u001b[31m×\u001b[0m Encountered error while generating package metadata.\n",
            "\u001b[31m╰─>\u001b[0m See above for output.\n",
            "\n",
            "\u001b[1;35mnote\u001b[0m: This is an issue with the package mentioned above, not pip.\n",
            "\u001b[1;36mhint\u001b[0m: See above for details.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install matrixprofile-ts"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YL108rNpx_qI",
        "outputId": "afcf8c8b-47c3-4b4f-cebc-490d2d986cc8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting matrixprofile-ts\n",
            "  Downloading matrixprofile_ts-0.0.9-py2.py3-none-any.whl (24 kB)\n",
            "Requirement already satisfied: numpy>=1.11.3 in /usr/local/lib/python3.10/dist-packages (from matrixprofile-ts) (1.22.4)\n",
            "Installing collected packages: matrixprofile-ts\n",
            "Successfully installed matrixprofile-ts-0.0.9\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BV8hpy_Tywrn",
        "outputId": "788ad032-9636-432a-dfaa-130c81f0246c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "7u8-HTeAsY7x"
      },
      "outputs": [],
      "source": [
        "import os\n",
        "TBI_Pos = '/content/drive/MyDrive/TBI-Pos'\n",
        "TBI_Neg = '/content/drive/MyDrive/TBI-Neg'"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "from matrixprofile import *\n",
        "from tsfresh import extract_features\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.metrics import classification_report\n",
        "from sklearn.model_selection import train_test_split\n",
        "from torch.utils.data import ConcatDataset"
      ],
      "metadata": {
        "id": "Bmat_A7CsoCI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install matrixprofilets"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vnF_w26PthcD",
        "outputId": "1fee5609-e694-48a8-ad8a-8af45dd1d013"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "\u001b[31mERROR: Could not find a version that satisfies the requirement matrixprofilets (from versions: none)\u001b[0m\u001b[31m\n",
            "\u001b[0m\u001b[31mERROR: No matching distribution found for matrixprofilets\u001b[0m\u001b[31m\n",
            "\u001b[0m"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from matrixprofile import matrixProfile\n",
        "from stumpy import *\n",
        "import stumpy\n",
        "from stumpy import stump\n"
      ],
      "metadata": {
        "id": "kSqVi_Pey-O_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from matrixprofile import matrixProfile as mp\n"
      ],
      "metadata": {
        "id": "TgCFVHYSzKcL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "max_length = 0\n",
        "\n",
        "# Load TBI-Neg data\n",
        "neg_data = []\n",
        "neg_labels = []\n",
        "for filename in os.listdir(TBI_Neg):\n",
        "    filepath = os.path.join(TBI_Neg, filename)\n",
        "    df = pd.read_csv(filepath)\n",
        "    mp = matrixProfile.stomp(df.values.flatten(), m=5) \n",
        "    print(mp)\n",
        "    if len(mp) > max_length:\n",
        "        max_length = len(mp)\n",
        "    neg_data.append(mp)\n",
        "\n",
        "    neg_labels.append(0)\n",
        "    print(neg_data, neg_labels)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BjrH16bV6rhW",
        "outputId": "6d8c9814-3d15-42bd-b342-8f892de96ab2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "[(array([0.08503459, 0.07085338, 0.10566037, ..., 0.05481083, 0.06774273,\n",
            "       0.04282805]), array([ 26032.,  99545., 100742., ...,  57477.,  19032.,  34479.]))] [0]\n",
            "(array([0.03957864, 0.09551832, 0.05164751, ..., 0.11211419, 0.0565897 ,\n",
            "       0.05443408]), array([103897., 103898.,  82822., ...,  97047.,  48980.,  28746.]))\n",
            "[(array([0.08503459, 0.07085338, 0.10566037, ..., 0.05481083, 0.06774273,\n",
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            "[(array([0.08503459, 0.07085338, 0.10566037, ..., 0.05481083, 0.06774273,\n",
            "       0.04282805]), array([ 26032.,  99545., 100742., ...,  57477.,  19032.,  34479.])), (array([0.03957864, 0.09551832, 0.05164751, ..., 0.11211419, 0.0565897 ,\n",
            "       0.05443408]), array([103897., 103898.,  82822., ...,  97047.,  48980.,  28746.])), (array([0.0675242 , 0.10916261, 0.07049579, ..., 0.11052128, 0.10444297,\n",
            "       0.03897176]), array([ 82343.,  22134.,  15938., ..., 101296., 104439.,   1791.]))] [0, 0, 0]\n",
            "(array([0.0291342 , 0.10130007, 0.06590627, ..., 0.0318609 , 0.1205308 ,\n",
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            "       0.04282805]), array([ 26032.,  99545., 100742., ...,  57477.,  19032.,  34479.])), (array([0.03957864, 0.09551832, 0.05164751, ..., 0.11211419, 0.0565897 ,\n",
            "       0.05443408]), array([103897., 103898.,  82822., ...,  97047.,  48980.,  28746.])), (array([0.0675242 , 0.10916261, 0.07049579, ..., 0.11052128, 0.10444297,\n",
            "       0.03897176]), array([ 82343.,  22134.,  15938., ..., 101296., 104439.,   1791.])), (array([0.0291342 , 0.10130007, 0.06590627, ..., 0.0318609 , 0.1205308 ,\n",
            "       0.07982905]), array([62905., 11861., 15379., ..., 57680., 42800., 44395.]))] [0, 0, 0, 0]\n",
            "(array([0.06074312, 0.04447182, 0.06329072, ..., 0.1054663 , 0.08234856,\n",
            "       0.09035403]), array([83137., 83138., 81238., ..., 84823., 18095., 76960.]))\n",
            "[(array([0.08503459, 0.07085338, 0.10566037, ..., 0.05481083, 0.06774273,\n",
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            "       0.05443408]), array([103897., 103898.,  82822., ...,  97047.,  48980.,  28746.])), (array([0.0675242 , 0.10916261, 0.07049579, ..., 0.11052128, 0.10444297,\n",
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            "       0.07982905]), array([62905., 11861., 15379., ..., 57680., 42800., 44395.])), (array([0.06074312, 0.04447182, 0.06329072, ..., 0.1054663 , 0.08234856,\n",
            "       0.09035403]), array([83137., 83138., 81238., ..., 84823., 18095., 76960.])), (array([0.0517515 , 0.03277594, 0.09433007, ..., 0.04163346, 0.09816528,\n",
            "       0.06025693]), array([ 73420., 103624.,  10262., ...,  65475.,  86786.,  33074.]))] [0, 0, 0, 0, 0, 0]\n",
            "(array([0.06611323, 0.07720659, 0.08481377, ..., 0.02206824, 0.03949751,\n",
            "       0.04818042]), array([72280., 51571., 84507., ..., 44393., 31260., 31261.]))\n",
            "[(array([0.08503459, 0.07085338, 0.10566037, ..., 0.05481083, 0.06774273,\n",
            "       0.04282805]), array([ 26032.,  99545., 100742., ...,  57477.,  19032.,  34479.])), (array([0.03957864, 0.09551832, 0.05164751, ..., 0.11211419, 0.0565897 ,\n",
            "       0.05443408]), array([103897., 103898.,  82822., ...,  97047.,  48980.,  28746.])), (array([0.0675242 , 0.10916261, 0.07049579, ..., 0.11052128, 0.10444297,\n",
            "       0.03897176]), array([ 82343.,  22134.,  15938., ..., 101296., 104439.,   1791.])), (array([0.0291342 , 0.10130007, 0.06590627, ..., 0.0318609 , 0.1205308 ,\n",
            "       0.07982905]), array([62905., 11861., 15379., ..., 57680., 42800., 44395.])), (array([0.06074312, 0.04447182, 0.06329072, ..., 0.1054663 , 0.08234856,\n",
            "       0.09035403]), array([83137., 83138., 81238., ..., 84823., 18095., 76960.])), (array([0.0517515 , 0.03277594, 0.09433007, ..., 0.04163346, 0.09816528,\n",
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            "(array([0.05817001, 0.08008925, 0.0998926 , ..., 0.10458197, 0.04514156,\n",
            "       0.05684487]), array([32291., 33077., 94811., ..., 38419.,  8126., 71804.]))\n",
            "[(array([0.08503459, 0.07085338, 0.10566037, ..., 0.05481083, 0.06774273,\n",
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            "       0.07982905]), array([62905., 11861., 15379., ..., 57680., 42800., 44395.])), (array([0.06074312, 0.04447182, 0.06329072, ..., 0.1054663 , 0.08234856,\n",
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            "       0.06025693]), array([ 73420., 103624.,  10262., ...,  65475.,  86786.,  33074.])), (array([0.06611323, 0.07720659, 0.08481377, ..., 0.02206824, 0.03949751,\n",
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            "(array([0.07450976, 0.02206231, 0.05245011, ..., 0.06273232, 0.07457895,\n",
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            "       0.09035403]), array([83137., 83138., 81238., ..., 84823., 18095., 76960.])), (array([0.0517515 , 0.03277594, 0.09433007, ..., 0.04163346, 0.09816528,\n",
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            "(array([0.06819813, 0.05128526, 0.04755771, ..., 0.04274569, 0.03052147,\n",
            "       0.08141146]), array([66350., 96495.,  4146., ..., 34542., 57340., 92871.]))\n",
            "[(array([0.08503459, 0.07085338, 0.10566037, ..., 0.05481083, 0.06774273,\n",
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            "       0.09035403]), array([83137., 83138., 81238., ..., 84823., 18095., 76960.])), (array([0.0517515 , 0.03277594, 0.09433007, ..., 0.04163346, 0.09816528,\n",
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            "       0.02810589]), array([ 92491., 103462.,  15959., ...,  96935.,   1804.,  68425.])), (array([0.06819813, 0.05128526, 0.04755771, ..., 0.04274569, 0.03052147,\n",
            "       0.08141146]), array([66350., 96495.,  4146., ..., 34542., 57340., 92871.]))] [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
            "(array([0.06673372, 0.0312287 , 0.06364817, ..., 0.06653477, 0.08127757,\n",
            "       0.06854005]), array([109062.,  88924.,  75217., ...,  20193.,   3069.,  17683.]))\n",
            "[(array([0.08503459, 0.07085338, 0.10566037, ..., 0.05481083, 0.06774273,\n",
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            "       0.09035403]), array([83137., 83138., 81238., ..., 84823., 18095., 76960.])), (array([0.0517515 , 0.03277594, 0.09433007, ..., 0.04163346, 0.09816528,\n",
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            "(array([0.04289399, 0.09317125, 0.04663721, ..., 0.08902477, 0.09695831,\n",
            "       0.05276325]), array([77198., 92352., 58143., ..., 11108., 41062., 22153.]))\n",
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            "(array([0.03103199, 0.07434262, 0.04214692, ..., 0.0517162 , 0.09455342,\n",
            "       0.01447381]), array([ 12470.,  25055.,  29532., ...,  63870.,  23958., 104158.]))\n",
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            "(array([0.06496429, 0.07179326, 0.09303618, ..., 0.04509134, 0.05992138,\n",
            "       0.05193619]), array([52890., 80293., 40207., ..., 49529., 47976., 11862.]))\n",
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            "       0.02810589]), array([ 92491., 103462.,  15959., ...,  96935.,   1804.,  68425.])), (array([0.06819813, 0.05128526, 0.04755771, ..., 0.04274569, 0.03052147,\n",
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            "(array([0.07763801, 0.05320277, 0.0546571 , ..., 0.0711058 , 0.09372432,\n",
            "       0.01563697]), array([27420., 91370., 68798., ..., 96577., 72247., 55283.]))\n",
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            "       0.07982905]), array([62905., 11861., 15379., ..., 57680., 42800., 44395.])), (array([0.06074312, 0.04447182, 0.06329072, ..., 0.1054663 , 0.08234856,\n",
            "       0.09035403]), array([83137., 83138., 81238., ..., 84823., 18095., 76960.])), (array([0.0517515 , 0.03277594, 0.09433007, ..., 0.04163346, 0.09816528,\n",
            "       0.06025693]), array([ 73420., 103624.,  10262., ...,  65475.,  86786.,  33074.])), (array([0.06611323, 0.07720659, 0.08481377, ..., 0.02206824, 0.03949751,\n",
            "       0.04818042]), array([72280., 51571., 84507., ..., 44393., 31260., 31261.])), (array([0.05817001, 0.08008925, 0.0998926 , ..., 0.10458197, 0.04514156,\n",
            "       0.05684487]), array([32291., 33077., 94811., ..., 38419.,  8126., 71804.])), (array([0.07450976, 0.02206231, 0.05245011, ..., 0.06273232, 0.07457895,\n",
            "       0.02810589]), array([ 92491., 103462.,  15959., ...,  96935.,   1804.,  68425.])), (array([0.06819813, 0.05128526, 0.04755771, ..., 0.04274569, 0.03052147,\n",
            "       0.08141146]), array([66350., 96495.,  4146., ..., 34542., 57340., 92871.])), (array([0.06673372, 0.0312287 , 0.06364817, ..., 0.06653477, 0.08127757,\n",
            "       0.06854005]), array([109062.,  88924.,  75217., ...,  20193.,   3069.,  17683.])), (array([0.04289399, 0.09317125, 0.04663721, ..., 0.08902477, 0.09695831,\n",
            "       0.05276325]), array([77198., 92352., 58143., ..., 11108., 41062., 22153.])), (array([0.03103199, 0.07434262, 0.04214692, ..., 0.0517162 , 0.09455342,\n",
            "       0.01447381]), array([ 12470.,  25055.,  29532., ...,  63870.,  23958., 104158.])), (array([0.06496429, 0.07179326, 0.09303618, ..., 0.04509134, 0.05992138,\n",
            "       0.05193619]), array([52890., 80293., 40207., ..., 49529., 47976., 11862.])), (array([0.07763801, 0.05320277, 0.0546571 , ..., 0.0711058 , 0.09372432,\n",
            "       0.01563697]), array([27420., 91370., 68798., ..., 96577., 72247., 55283.]))] [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
            "(array([0.0776094 , 0.07899485, 0.04123632, ..., 0.01724848, 0.07550667,\n",
            "       0.06395713]), array([ 91502.,  10101.,  69099., ..., 106525.,  19333.,  92795.]))\n",
            "[(array([0.08503459, 0.07085338, 0.10566037, ..., 0.05481083, 0.06774273,\n",
            "       0.04282805]), array([ 26032.,  99545., 100742., ...,  57477.,  19032.,  34479.])), (array([0.03957864, 0.09551832, 0.05164751, ..., 0.11211419, 0.0565897 ,\n",
            "       0.05443408]), array([103897., 103898.,  82822., ...,  97047.,  48980.,  28746.])), (array([0.0675242 , 0.10916261, 0.07049579, ..., 0.11052128, 0.10444297,\n",
            "       0.03897176]), array([ 82343.,  22134.,  15938., ..., 101296., 104439.,   1791.])), (array([0.0291342 , 0.10130007, 0.06590627, ..., 0.0318609 , 0.1205308 ,\n",
            "       0.07982905]), array([62905., 11861., 15379., ..., 57680., 42800., 44395.])), (array([0.06074312, 0.04447182, 0.06329072, ..., 0.1054663 , 0.08234856,\n",
            "       0.09035403]), array([83137., 83138., 81238., ..., 84823., 18095., 76960.])), (array([0.0517515 , 0.03277594, 0.09433007, ..., 0.04163346, 0.09816528,\n",
            "       0.06025693]), array([ 73420., 103624.,  10262., ...,  65475.,  86786.,  33074.])), (array([0.06611323, 0.07720659, 0.08481377, ..., 0.02206824, 0.03949751,\n",
            "       0.04818042]), array([72280., 51571., 84507., ..., 44393., 31260., 31261.])), (array([0.05817001, 0.08008925, 0.0998926 , ..., 0.10458197, 0.04514156,\n",
            "       0.05684487]), array([32291., 33077., 94811., ..., 38419.,  8126., 71804.])), (array([0.07450976, 0.02206231, 0.05245011, ..., 0.06273232, 0.07457895,\n",
            "       0.02810589]), array([ 92491., 103462.,  15959., ...,  96935.,   1804.,  68425.])), (array([0.06819813, 0.05128526, 0.04755771, ..., 0.04274569, 0.03052147,\n",
            "       0.08141146]), array([66350., 96495.,  4146., ..., 34542., 57340., 92871.])), (array([0.06673372, 0.0312287 , 0.06364817, ..., 0.06653477, 0.08127757,\n",
            "       0.06854005]), array([109062.,  88924.,  75217., ...,  20193.,   3069.,  17683.])), (array([0.04289399, 0.09317125, 0.04663721, ..., 0.08902477, 0.09695831,\n",
            "       0.05276325]), array([77198., 92352., 58143., ..., 11108., 41062., 22153.])), (array([0.03103199, 0.07434262, 0.04214692, ..., 0.0517162 , 0.09455342,\n",
            "       0.01447381]), array([ 12470.,  25055.,  29532., ...,  63870.,  23958., 104158.])), (array([0.06496429, 0.07179326, 0.09303618, ..., 0.04509134, 0.05992138,\n",
            "       0.05193619]), array([52890., 80293., 40207., ..., 49529., 47976., 11862.])), (array([0.07763801, 0.05320277, 0.0546571 , ..., 0.0711058 , 0.09372432,\n",
            "       0.01563697]), array([27420., 91370., 68798., ..., 96577., 72247., 55283.])), (array([0.0776094 , 0.07899485, 0.04123632, ..., 0.01724848, 0.07550667,\n",
            "       0.06395713]), array([ 91502.,  10101.,  69099., ..., 106525.,  19333.,  92795.]))] [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "max_length =0\n",
        "\n",
        "pos_data = []\n",
        "pos_labels = []\n",
        "for filename in os.listdir(TBI_Pos):\n",
        "    filepath = os.path.join(TBI_Pos, filename)\n",
        "    df = pd.read_csv(filepath)\n",
        "    mp = matrixProfile.stomp(df.values.flatten(), m=5) \n",
        "    if len(mp) > max_length:\n",
        "        max_length = len(mp)\n",
        "    pos_data.append(mp)\n",
        "    pos_labels.append(1)\n",
        "    print(pos_data,pos_labels)"
      ],
      "metadata": {
        "id": "JqM0an6E2Lm6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a7183c51-8c92-41cf-f79b-6f04d681c260"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[(array([0.01772407, 0.0788895 , 0.0697209 , ..., 0.06057048, 0.07950313,\n",
            "       0.10134118]), array([46174., 73007., 22121., ...,   153., 35927., 77537.]))] [1]\n",
            "[(array([0.01772407, 0.0788895 , 0.0697209 , ..., 0.06057048, 0.07950313,\n",
            "       0.10134118]), array([46174., 73007., 22121., ...,   153., 35927., 77537.])), (array([0.06939524, 0.01754455, 0.09902212, ..., 0.09007474, 0.02757584,\n",
            "       0.0492817 ]), array([97244., 21431., 34701., ..., 63236., 37669., 86672.]))] [1, 1]\n",
            "[(array([0.01772407, 0.0788895 , 0.0697209 , ..., 0.06057048, 0.07950313,\n",
            "       0.10134118]), array([46174., 73007., 22121., ...,   153., 35927., 77537.])), (array([0.06939524, 0.01754455, 0.09902212, ..., 0.09007474, 0.02757584,\n",
            "       0.0492817 ]), array([97244., 21431., 34701., ..., 63236., 37669., 86672.])), (array([0.07507163, 0.05566132, 0.06410326, ..., 0.0563021 , 0.05026278,\n",
            "       0.09156955]), array([   175.,  37307., 102320., ...,  24792.,  28230.,  22648.]))] [1, 1, 1]\n",
            "[(array([0.01772407, 0.0788895 , 0.0697209 , ..., 0.06057048, 0.07950313,\n",
            "       0.10134118]), array([46174., 73007., 22121., ...,   153., 35927., 77537.])), (array([0.06939524, 0.01754455, 0.09902212, ..., 0.09007474, 0.02757584,\n",
            "       0.0492817 ]), array([97244., 21431., 34701., ..., 63236., 37669., 86672.])), (array([0.07507163, 0.05566132, 0.06410326, ..., 0.0563021 , 0.05026278,\n",
            "       0.09156955]), array([   175.,  37307., 102320., ...,  24792.,  28230.,  22648.])), (array([0.0899384 , 0.04514997, 0.09188482, ..., 0.07577026, 0.09424309,\n",
            "       0.09389879]), array([110229.,  16455.,  89774., ...,  53454.,  53471.,  84536.]))] [1, 1, 1, 1]\n",
            "[(array([0.01772407, 0.0788895 , 0.0697209 , ..., 0.06057048, 0.07950313,\n",
            "       0.10134118]), array([46174., 73007., 22121., ...,   153., 35927., 77537.])), (array([0.06939524, 0.01754455, 0.09902212, ..., 0.09007474, 0.02757584,\n",
            "       0.0492817 ]), array([97244., 21431., 34701., ..., 63236., 37669., 86672.])), (array([0.07507163, 0.05566132, 0.06410326, ..., 0.0563021 , 0.05026278,\n",
            "       0.09156955]), array([   175.,  37307., 102320., ...,  24792.,  28230.,  22648.])), (array([0.0899384 , 0.04514997, 0.09188482, ..., 0.07577026, 0.09424309,\n",
            "       0.09389879]), array([110229.,  16455.,  89774., ...,  53454.,  53471.,  84536.])), (array([0.07279531, 0.070121  , 0.06604118, ..., 0.11312807, 0.03222526,\n",
            "       0.08484046]), array([ 9901., 34229.,  1960., ..., 73450., 35472.,   217.]))] [1, 1, 1, 1, 1]\n",
            "[(array([0.01772407, 0.0788895 , 0.0697209 , ..., 0.06057048, 0.07950313,\n",
            "       0.10134118]), array([46174., 73007., 22121., ...,   153., 35927., 77537.])), (array([0.06939524, 0.01754455, 0.09902212, ..., 0.09007474, 0.02757584,\n",
            "       0.0492817 ]), array([97244., 21431., 34701., ..., 63236., 37669., 86672.])), (array([0.07507163, 0.05566132, 0.06410326, ..., 0.0563021 , 0.05026278,\n",
            "       0.09156955]), array([   175.,  37307., 102320., ...,  24792.,  28230.,  22648.])), (array([0.0899384 , 0.04514997, 0.09188482, ..., 0.07577026, 0.09424309,\n",
            "       0.09389879]), array([110229.,  16455.,  89774., ...,  53454.,  53471.,  84536.])), (array([0.07279531, 0.070121  , 0.06604118, ..., 0.11312807, 0.03222526,\n",
            "       0.08484046]), array([ 9901., 34229.,  1960., ..., 73450., 35472.,   217.])), (array([0.01942706, 0.07146839, 0.06442254, ..., 0.07989334, 0.05786621,\n",
            "       0.07649292]), array([96929., 82399.,  6593., ..., 99468., 33943., 43858.]))] [1, 1, 1, 1, 1, 1]\n",
            "[(array([0.01772407, 0.0788895 , 0.0697209 , ..., 0.06057048, 0.07950313,\n",
            "       0.10134118]), array([46174., 73007., 22121., ...,   153., 35927., 77537.])), (array([0.06939524, 0.01754455, 0.09902212, ..., 0.09007474, 0.02757584,\n",
            "       0.0492817 ]), array([97244., 21431., 34701., ..., 63236., 37669., 86672.])), (array([0.07507163, 0.05566132, 0.06410326, ..., 0.0563021 , 0.05026278,\n",
            "       0.09156955]), array([   175.,  37307., 102320., ...,  24792.,  28230.,  22648.])), (array([0.0899384 , 0.04514997, 0.09188482, ..., 0.07577026, 0.09424309,\n",
            "       0.09389879]), array([110229.,  16455.,  89774., ...,  53454.,  53471.,  84536.])), (array([0.07279531, 0.070121  , 0.06604118, ..., 0.11312807, 0.03222526,\n",
            "       0.08484046]), array([ 9901., 34229.,  1960., ..., 73450., 35472.,   217.])), (array([0.01942706, 0.07146839, 0.06442254, ..., 0.07989334, 0.05786621,\n",
            "       0.07649292]), array([96929., 82399.,  6593., ..., 99468., 33943., 43858.])), (array([0.06569089, 0.06415199, 0.09581566, ..., 0.09464246, 0.07951439,\n",
            "       0.05556984]), array([47522., 94288., 20707., ..., 75079., 96118., 11531.]))] [1, 1, 1, 1, 1, 1, 1]\n",
            "[(array([0.01772407, 0.0788895 , 0.0697209 , ..., 0.06057048, 0.07950313,\n",
            "       0.10134118]), array([46174., 73007., 22121., ...,   153., 35927., 77537.])), (array([0.06939524, 0.01754455, 0.09902212, ..., 0.09007474, 0.02757584,\n",
            "       0.0492817 ]), array([97244., 21431., 34701., ..., 63236., 37669., 86672.])), (array([0.07507163, 0.05566132, 0.06410326, ..., 0.0563021 , 0.05026278,\n",
            "       0.09156955]), array([   175.,  37307., 102320., ...,  24792.,  28230.,  22648.])), (array([0.0899384 , 0.04514997, 0.09188482, ..., 0.07577026, 0.09424309,\n",
            "       0.09389879]), array([110229.,  16455.,  89774., ...,  53454.,  53471.,  84536.])), (array([0.07279531, 0.070121  , 0.06604118, ..., 0.11312807, 0.03222526,\n",
            "       0.08484046]), array([ 9901., 34229.,  1960., ..., 73450., 35472.,   217.])), (array([0.01942706, 0.07146839, 0.06442254, ..., 0.07989334, 0.05786621,\n",
            "       0.07649292]), array([96929., 82399.,  6593., ..., 99468., 33943., 43858.])), (array([0.06569089, 0.06415199, 0.09581566, ..., 0.09464246, 0.07951439,\n",
            "       0.05556984]), array([47522., 94288., 20707., ..., 75079., 96118., 11531.])), (array([0.08106031, 0.04931641, 0.04324288, ..., 0.11333978, 0.0300625 ,\n",
            "       0.03360956]), array([103921.,  16951.,  33977., ...,  95164.,  42069.,  86045.]))] [1, 1, 1, 1, 1, 1, 1, 1]\n",
            "[(array([0.01772407, 0.0788895 , 0.0697209 , ..., 0.06057048, 0.07950313,\n",
            "       0.10134118]), array([46174., 73007., 22121., ...,   153., 35927., 77537.])), (array([0.06939524, 0.01754455, 0.09902212, ..., 0.09007474, 0.02757584,\n",
            "       0.0492817 ]), array([97244., 21431., 34701., ..., 63236., 37669., 86672.])), (array([0.07507163, 0.05566132, 0.06410326, ..., 0.0563021 , 0.05026278,\n",
            "       0.09156955]), array([   175.,  37307., 102320., ...,  24792.,  28230.,  22648.])), (array([0.0899384 , 0.04514997, 0.09188482, ..., 0.07577026, 0.09424309,\n",
            "       0.09389879]), array([110229.,  16455.,  89774., ...,  53454.,  53471.,  84536.])), (array([0.07279531, 0.070121  , 0.06604118, ..., 0.11312807, 0.03222526,\n",
            "       0.08484046]), array([ 9901., 34229.,  1960., ..., 73450., 35472.,   217.])), (array([0.01942706, 0.07146839, 0.06442254, ..., 0.07989334, 0.05786621,\n",
            "       0.07649292]), array([96929., 82399.,  6593., ..., 99468., 33943., 43858.])), (array([0.06569089, 0.06415199, 0.09581566, ..., 0.09464246, 0.07951439,\n",
            "       0.05556984]), array([47522., 94288., 20707., ..., 75079., 96118., 11531.])), (array([0.08106031, 0.04931641, 0.04324288, ..., 0.11333978, 0.0300625 ,\n",
            "       0.03360956]), array([103921.,  16951.,  33977., ...,  95164.,  42069.,  86045.])), (array([0.04822506, 0.04961116, 0.09221956, ..., 0.09699763, 0.05974574,\n",
            "       0.07544182]), array([38402., 50317., 34951., ..., 70550., 55628., 13219.]))] [1, 1, 1, 1, 1, 1, 1, 1, 1]\n",
            "[(array([0.01772407, 0.0788895 , 0.0697209 , ..., 0.06057048, 0.07950313,\n",
            "       0.10134118]), array([46174., 73007., 22121., ...,   153., 35927., 77537.])), (array([0.06939524, 0.01754455, 0.09902212, ..., 0.09007474, 0.02757584,\n",
            "       0.0492817 ]), array([97244., 21431., 34701., ..., 63236., 37669., 86672.])), (array([0.07507163, 0.05566132, 0.06410326, ..., 0.0563021 , 0.05026278,\n",
            "       0.09156955]), array([   175.,  37307., 102320., ...,  24792.,  28230.,  22648.])), (array([0.0899384 , 0.04514997, 0.09188482, ..., 0.07577026, 0.09424309,\n",
            "       0.09389879]), array([110229.,  16455.,  89774., ...,  53454.,  53471.,  84536.])), (array([0.07279531, 0.070121  , 0.06604118, ..., 0.11312807, 0.03222526,\n",
            "       0.08484046]), array([ 9901., 34229.,  1960., ..., 73450., 35472.,   217.])), (array([0.01942706, 0.07146839, 0.06442254, ..., 0.07989334, 0.05786621,\n",
            "       0.07649292]), array([96929., 82399.,  6593., ..., 99468., 33943., 43858.])), (array([0.06569089, 0.06415199, 0.09581566, ..., 0.09464246, 0.07951439,\n",
            "       0.05556984]), array([47522., 94288., 20707., ..., 75079., 96118., 11531.])), (array([0.08106031, 0.04931641, 0.04324288, ..., 0.11333978, 0.0300625 ,\n",
            "       0.03360956]), array([103921.,  16951.,  33977., ...,  95164.,  42069.,  86045.])), (array([0.04822506, 0.04961116, 0.09221956, ..., 0.09699763, 0.05974574,\n",
            "       0.07544182]), array([38402., 50317., 34951., ..., 70550., 55628., 13219.])), (array([0.07385165, 0.08991476, 0.05393786, ..., 0.04683164, 0.06807562,\n",
            "       0.07763692]), array([57961., 33220., 33221., ..., 21917., 23994.,  7553.]))] [1, 1, 1, 1, 1, 1, 1, 1, 1, 1]\n",
            "[(array([0.01772407, 0.0788895 , 0.0697209 , ..., 0.06057048, 0.07950313,\n",
            "       0.10134118]), array([46174., 73007., 22121., ...,   153., 35927., 77537.])), (array([0.06939524, 0.01754455, 0.09902212, ..., 0.09007474, 0.02757584,\n",
            "       0.0492817 ]), array([97244., 21431., 34701., ..., 63236., 37669., 86672.])), (array([0.07507163, 0.05566132, 0.06410326, ..., 0.0563021 , 0.05026278,\n",
            "       0.09156955]), array([   175.,  37307., 102320., ...,  24792.,  28230.,  22648.])), (array([0.0899384 , 0.04514997, 0.09188482, ..., 0.07577026, 0.09424309,\n",
            "       0.09389879]), array([110229.,  16455.,  89774., ...,  53454.,  53471.,  84536.])), (array([0.07279531, 0.070121  , 0.06604118, ..., 0.11312807, 0.03222526,\n",
            "       0.08484046]), array([ 9901., 34229.,  1960., ..., 73450., 35472.,   217.])), (array([0.01942706, 0.07146839, 0.06442254, ..., 0.07989334, 0.05786621,\n",
            "       0.07649292]), array([96929., 82399.,  6593., ..., 99468., 33943., 43858.])), (array([0.06569089, 0.06415199, 0.09581566, ..., 0.09464246, 0.07951439,\n",
            "       0.05556984]), array([47522., 94288., 20707., ..., 75079., 96118., 11531.])), (array([0.08106031, 0.04931641, 0.04324288, ..., 0.11333978, 0.0300625 ,\n",
            "       0.03360956]), array([103921.,  16951.,  33977., ...,  95164.,  42069.,  86045.])), (array([0.04822506, 0.04961116, 0.09221956, ..., 0.09699763, 0.05974574,\n",
            "       0.07544182]), array([38402., 50317., 34951., ..., 70550., 55628., 13219.])), (array([0.07385165, 0.08991476, 0.05393786, ..., 0.04683164, 0.06807562,\n",
            "       0.07763692]), array([57961., 33220., 33221., ..., 21917., 23994.,  7553.])), (array([0.06348865, 0.10808947, 0.07472507, ..., 0.07497165, 0.05224794,\n",
            "       0.10642957]), array([22889., 16664., 48460., ..., 56518., 40057., 84839.]))] [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]\n",
            "[(array([0.01772407, 0.0788895 , 0.0697209 , ..., 0.06057048, 0.07950313,\n",
            "       0.10134118]), array([46174., 73007., 22121., ...,   153., 35927., 77537.])), (array([0.06939524, 0.01754455, 0.09902212, ..., 0.09007474, 0.02757584,\n",
            "       0.0492817 ]), array([97244., 21431., 34701., ..., 63236., 37669., 86672.])), (array([0.07507163, 0.05566132, 0.06410326, ..., 0.0563021 , 0.05026278,\n",
            "       0.09156955]), array([   175.,  37307., 102320., ...,  24792.,  28230.,  22648.])), (array([0.0899384 , 0.04514997, 0.09188482, ..., 0.07577026, 0.09424309,\n",
            "       0.09389879]), array([110229.,  16455.,  89774., ...,  53454.,  53471.,  84536.])), (array([0.07279531, 0.070121  , 0.06604118, ..., 0.11312807, 0.03222526,\n",
            "       0.08484046]), array([ 9901., 34229.,  1960., ..., 73450., 35472.,   217.])), (array([0.01942706, 0.07146839, 0.06442254, ..., 0.07989334, 0.05786621,\n",
            "       0.07649292]), array([96929., 82399.,  6593., ..., 99468., 33943., 43858.])), (array([0.06569089, 0.06415199, 0.09581566, ..., 0.09464246, 0.07951439,\n",
            "       0.05556984]), array([47522., 94288., 20707., ..., 75079., 96118., 11531.])), (array([0.08106031, 0.04931641, 0.04324288, ..., 0.11333978, 0.0300625 ,\n",
            "       0.03360956]), array([103921.,  16951.,  33977., ...,  95164.,  42069.,  86045.])), (array([0.04822506, 0.04961116, 0.09221956, ..., 0.09699763, 0.05974574,\n",
            "       0.07544182]), array([38402., 50317., 34951., ..., 70550., 55628., 13219.])), (array([0.07385165, 0.08991476, 0.05393786, ..., 0.04683164, 0.06807562,\n",
            "       0.07763692]), array([57961., 33220., 33221., ..., 21917., 23994.,  7553.])), (array([0.06348865, 0.10808947, 0.07472507, ..., 0.07497165, 0.05224794,\n",
            "       0.10642957]), array([22889., 16664., 48460., ..., 56518., 40057., 84839.])), (array([0.06629278, 0.06296081, 0.03929225, ..., 0.07299227, 0.11861994,\n",
            "       0.05291957]), array([92222., 46818.,  7023., ..., 89663., 77279., 31034.]))] [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]\n",
            "[(array([0.01772407, 0.0788895 , 0.0697209 , ..., 0.06057048, 0.07950313,\n",
            "       0.10134118]), array([46174., 73007., 22121., ...,   153., 35927., 77537.])), (array([0.06939524, 0.01754455, 0.09902212, ..., 0.09007474, 0.02757584,\n",
            "       0.0492817 ]), array([97244., 21431., 34701., ..., 63236., 37669., 86672.])), (array([0.07507163, 0.05566132, 0.06410326, ..., 0.0563021 , 0.05026278,\n",
            "       0.09156955]), array([   175.,  37307., 102320., ...,  24792.,  28230.,  22648.])), (array([0.0899384 , 0.04514997, 0.09188482, ..., 0.07577026, 0.09424309,\n",
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            "       0.07763692]), array([57961., 33220., 33221., ..., 21917., 23994.,  7553.])), (array([0.06348865, 0.10808947, 0.07472507, ..., 0.07497165, 0.05224794,\n",
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            "       0.07763692]), array([57961., 33220., 33221., ..., 21917., 23994.,  7553.])), (array([0.06348865, 0.10808947, 0.07472507, ..., 0.07497165, 0.05224794,\n",
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            "       0.0400139 ]), array([ 2877.,  9069., 35202., ..., 44252., 28728.,  1949.])), (array([0.07656382, 0.1010027 , 0.07774071, ..., 0.05228973, 0.03347848,\n",
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            "       0.08386837]), array([  420., 97413., 22419., ..., 69205., 84033.,  8320.]))] [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]\n",
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            "       0.09389879]), array([110229.,  16455.,  89774., ...,  53454.,  53471.,  84536.])), (array([0.07279531, 0.070121  , 0.06604118, ..., 0.11312807, 0.03222526,\n",
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            "       0.06506269]), array([ 34064.,  60401.,  91054., ..., 101663.,  42684.,  22373.])), (array([0.10859008, 0.06318163, 0.07822172, ..., 0.05270477, 0.04422345,\n",
            "       0.09493962]), array([101246.,  99890.,  45176., ...,  79448., 105544.,  35913.])), (array([0.06253405, 0.08511453, 0.03011066, ..., 0.06371269, 0.03982977,\n",
            "       0.0400139 ]), array([ 2877.,  9069., 35202., ..., 44252., 28728.,  1949.])), (array([0.07656382, 0.1010027 , 0.07774071, ..., 0.05228973, 0.03347848,\n",
            "       0.07886389]), array([ 37993.,  70426.,  18720., ...,  72890.,  31869., 100056.])), (array([0.07965397, 0.08218597, 0.02746243, ..., 0.04201302, 0.03431869,\n",
            "       0.0520777 ]), array([  261., 12089., 17133., ..., 56648., 57986.,  9421.])), (array([0.07349499, 0.10500651, 0.10233754, ..., 0.08847256, 0.08313937,\n",
            "       0.08386837]), array([  420., 97413., 22419., ..., 69205., 84033.,  8320.])), (array([0.08516258, 0.04958244, 0.03357655, ..., 0.06493691, 0.04150495,\n",
            "       0.11266774]), array([80513., 19039., 96569., ...,  6853., 93469., 14689.]))] [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]\n",
            "[(array([0.01772407, 0.0788895 , 0.0697209 , ..., 0.06057048, 0.07950313,\n",
            "       0.10134118]), array([46174., 73007., 22121., ...,   153., 35927., 77537.])), (array([0.06939524, 0.01754455, 0.09902212, ..., 0.09007474, 0.02757584,\n",
            "       0.0492817 ]), array([97244., 21431., 34701., ..., 63236., 37669., 86672.])), (array([0.07507163, 0.05566132, 0.06410326, ..., 0.0563021 , 0.05026278,\n",
            "       0.09156955]), array([   175.,  37307., 102320., ...,  24792.,  28230.,  22648.])), (array([0.0899384 , 0.04514997, 0.09188482, ..., 0.07577026, 0.09424309,\n",
            "       0.09389879]), array([110229.,  16455.,  89774., ...,  53454.,  53471.,  84536.])), (array([0.07279531, 0.070121  , 0.06604118, ..., 0.11312807, 0.03222526,\n",
            "       0.08484046]), array([ 9901., 34229.,  1960., ..., 73450., 35472.,   217.])), (array([0.01942706, 0.07146839, 0.06442254, ..., 0.07989334, 0.05786621,\n",
            "       0.07649292]), array([96929., 82399.,  6593., ..., 99468., 33943., 43858.])), (array([0.06569089, 0.06415199, 0.09581566, ..., 0.09464246, 0.07951439,\n",
            "       0.05556984]), array([47522., 94288., 20707., ..., 75079., 96118., 11531.])), (array([0.08106031, 0.04931641, 0.04324288, ..., 0.11333978, 0.0300625 ,\n",
            "       0.03360956]), array([103921.,  16951.,  33977., ...,  95164.,  42069.,  86045.])), (array([0.04822506, 0.04961116, 0.09221956, ..., 0.09699763, 0.05974574,\n",
            "       0.07544182]), array([38402., 50317., 34951., ..., 70550., 55628., 13219.])), (array([0.07385165, 0.08991476, 0.05393786, ..., 0.04683164, 0.06807562,\n",
            "       0.07763692]), array([57961., 33220., 33221., ..., 21917., 23994.,  7553.])), (array([0.06348865, 0.10808947, 0.07472507, ..., 0.07497165, 0.05224794,\n",
            "       0.10642957]), array([22889., 16664., 48460., ..., 56518., 40057., 84839.])), (array([0.06629278, 0.06296081, 0.03929225, ..., 0.07299227, 0.11861994,\n",
            "       0.05291957]), array([92222., 46818.,  7023., ..., 89663., 77279., 31034.])), (array([0.07875016, 0.07347536, 0.03147538, ..., 0.05755184, 0.06303197,\n",
            "       0.06506269]), array([ 34064.,  60401.,  91054., ..., 101663.,  42684.,  22373.])), (array([0.10859008, 0.06318163, 0.07822172, ..., 0.05270477, 0.04422345,\n",
            "       0.09493962]), array([101246.,  99890.,  45176., ...,  79448., 105544.,  35913.])), (array([0.06253405, 0.08511453, 0.03011066, ..., 0.06371269, 0.03982977,\n",
            "       0.0400139 ]), array([ 2877.,  9069., 35202., ..., 44252., 28728.,  1949.])), (array([0.07656382, 0.1010027 , 0.07774071, ..., 0.05228973, 0.03347848,\n",
            "       0.07886389]), array([ 37993.,  70426.,  18720., ...,  72890.,  31869., 100056.])), (array([0.07965397, 0.08218597, 0.02746243, ..., 0.04201302, 0.03431869,\n",
            "       0.0520777 ]), array([  261., 12089., 17133., ..., 56648., 57986.,  9421.])), (array([0.07349499, 0.10500651, 0.10233754, ..., 0.08847256, 0.08313937,\n",
            "       0.08386837]), array([  420., 97413., 22419., ..., 69205., 84033.,  8320.])), (array([0.08516258, 0.04958244, 0.03357655, ..., 0.06493691, 0.04150495,\n",
            "       0.11266774]), array([80513., 19039., 96569., ...,  6853., 93469., 14689.])), (array([0.09013279, 0.06115517, 0.03184919, ..., 0.09726749, 0.07137856,\n",
            "       0.04439637]), array([66344., 97373., 46352., ..., 57518., 53199., 55612.]))] [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "data = np.concatenate([neg_data, pos_data], axis=0)\n",
        "labels = np.concatenate([neg_labels, pos_labels], axis=0)"
      ],
      "metadata": {
        "id": "bM5jSa8vyk47"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print('total data:',len(data))\n",
        "print('total predictions',len(labels))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FDW17xraytTM",
        "outputId": "ec0bed39-74a9-4b82-f366-1474413c7acc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "total data: 36\n",
            "total predictions 36\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_data, test_data, train_labels, test_labels = train_test_split(data, labels, test_size=0.2, random_state=42)\n",
        "train_data_reshaped = np.array(train_data).reshape(len(train_data), -1)\n",
        "test_data_reshaped = np.array(test_data).reshape(len(test_data), -1)\n"
      ],
      "metadata": {
        "id": "HddzsxZKy_1n"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print('training data is:', len(train_data))\n",
        "print('testing data is:',len(test_data))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YpYLTqLxzL7S",
        "outputId": "f656c0c4-4128-4b2b-84c2-856272ae6f0a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "training data is: 28\n",
            "testing data is: 8\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "rfc = RandomForestClassifier(n_estimators=100, random_state=42)\n",
        "rfc.fit(train_data_reshaped, train_labels)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "pwQweeu0zYpT",
        "outputId": "b9a957e9-4604-455c-ff39-f55c3aae45e7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier(random_state=42)"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(random_state=42)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pred_labels = rfc.predict(test_data_reshaped)\n",
        "score = rfc.score(test_data_reshaped, test_labels)\n",
        "print('Accuracy:', score)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "r8EtRZ7x0DC5",
        "outputId": "7ff1908a-a0e1-49e2-c6f0-6f0ddb9dcb03"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 0.625\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import accuracy_score, confusion_matrix, recall_score, f1_score, mean_squared_error"
      ],
      "metadata": {
        "id": "qni3HBBB03Fj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "accuracy = accuracy_score(test_labels, pred_labels)\n",
        "recall = recall_score(test_labels, pred_labels)\n",
        "f1 = f1_score(test_labels, pred_labels)\n",
        "conf_mat = confusion_matrix(test_labels, pred_labels)\n",
        "mse = mean_squared_error(test_labels, pred_labels)\n",
        "test_err_rate = 1 - accuracy\n",
        "\n",
        "# Print the evaluation metrics\n",
        "print(f\"Accuracy: {accuracy:.4f}\")\n",
        "print(f\"Recall: {recall:.4f}\")\n",
        "print(f\"F1 Score: {f1:.4f}\")\n",
        "print(\"Confusion Matrix:\")\n",
        "print(conf_mat)\n",
        "print(f\"Mean Squared Error: {mse:.4f}\")\n",
        "print(f\"Test Error Rate: {test_err_rate:.4f}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VRLPEdCB0zBb",
        "outputId": "767628e3-6d21-49b9-9492-9f0e5b5c10f6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 0.6250\n",
            "Recall: 0.6667\n",
            "F1 Score: 0.7273\n",
            "Confusion Matrix:\n",
            "[[1 1]\n",
            " [2 4]]\n",
            "Mean Squared Error: 0.3750\n",
            "Test Error Rate: 0.3750\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import classification_report\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ],
      "metadata": {
        "id": "FQOjFpI0MQ9J"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(test_labels, lw=3, alpha=0.3, label='Truth')\n",
        "plt.plot(pred_labels, '--', label='Predictions')\n",
        "plt.legend(loc='best')\n",
        "print(classification_report(test_labels, pred_labels))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 586
        },
        "id": "5MfyFW_BMNxZ",
        "outputId": "ed15fa3d-56a5-448b-a006-b67c511dca1c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.33      0.50      0.40         2\n",
            "           1       0.80      0.67      0.73         6\n",
            "\n",
            "    accuracy                           0.62         8\n",
            "   macro avg       0.57      0.58      0.56         8\n",
            "weighted avg       0.68      0.62      0.65         8\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "3-qGvkpR11O_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sns.heatmap(conf_mat, annot=True, cmap=\"Blues\")\n",
        "plt.title(\"Confusion Matrix\")\n",
        "plt.xlabel(\"Predicted Labels\")\n",
        "plt.ylabel(\"True Labels\")\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "__MR6OKI1ots",
        "outputId": "3d3e61d1-fecd-4600-d0ac-6b4de3763f26"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from joblib import dump, load"
      ],
      "metadata": {
        "id": "3W62cA8R2my8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dump(rfc, 'rfc_model.joblib')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vtjXirYk2od1",
        "outputId": "1d3ddeca-3e39-48ae-cbe6-afaa3238c049"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['rfc_model.joblib']"
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "rf = load('rfc_model.joblib')"
      ],
      "metadata": {
        "id": "W-XgXyaa2zjJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data = pd.read_csv('/content/drive/MyDrive/205-DOD-tts_all.csv')"
      ],
      "metadata": {
        "id": "T8BrHFHe25VW"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}