BP_Prediction_(Sarah's_Version).ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/sarahmialiu/BP_Prediction/blob/main/BP_Prediction_(Sarah's_Version).ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xwawsDvwZzZt"
      },
      "source": [
        "# Import Statements"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "2RY-A_Zvhtp_"
      },
      "outputs": [],
      "source": [
        "# Import all the necessary functions here\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "import torchvision\n",
        "import torchvision.transforms as transforms\n",
        "\n",
        "import pandas as pd\n",
        "import dask.dataframe as dd\n",
        "\n",
        "import numpy as np\n",
        "\n",
        "import os\n",
        "import tensorflow as tf\n",
        "from tensorflow.keras.regularizers import L2\n",
        "from tensorflow import keras\n",
        "from tensorflow.keras import layers\n",
        "\n",
        "os.environ[\"TF_CPP_MIN_LOG_LEVEL\"] = \"2\"\n",
        "\n",
        "from tensorflow.keras.callbacks import ModelCheckpoint\n",
        "from tensorflow.keras import layers, regularizers\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.optimizers import Adam"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KD-uTRBlZ8RI"
      },
      "source": [
        "# Importing and Prepping Data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2nxzUB8ch5IS",
        "outputId": "3e6450c4-aec9-40a7-8423-88e73dc60955"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/gdrive\n"
          ]
        }
      ],
      "source": [
        "# This gives google colab access to Google Drive\n",
        "from google.colab import drive\n",
        "drive.mount('/content/gdrive')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "iYKdlV72iFEn"
      },
      "outputs": [],
      "source": [
        "# Pathing to file directory\n",
        "pwd_lying = \"/content/gdrive/MyDrive/Body position prediction/lying data\"\n",
        "pwd_sitting = \"/content/gdrive/MyDrive/Body position prediction/sitting data\"\n",
        "pwd_standing = \"/content/gdrive/MyDrive/Body position prediction/standing data\"\n",
        "#!cd \"$pwd\" && ls\n",
        "\n",
        "# Note: For you to connect to the shared folder, you first have to add a\n",
        "# shortcut from the shared folder to your personal folder before you can path\n",
        "# your way into it\n",
        "data_files_lying = !ls gdrive/MyDrive/'Body position prediction'/'lying data'\n",
        "data_files_sitting = !ls gdrive/MyDrive/'Body position prediction'/'sitting data'\n",
        "data_files_standing = !ls gdrive/MyDrive/'Body position prediction'/'standing data'"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sDlzSLklgc83",
        "outputId": "3d7c33a7-ed5c-4831-da07-997131e92e8a"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['Ainsley_Standing_30_Minutes_2.csv',\n",
              " 'Bowen_Standing_30_Minutes.csv',\n",
              " 'Ainsley_Standing_30_Minutes.csv',\n",
              " 'Sarah_Standing_10_Minutes.csv',\n",
              " 'Bowen_Standing_10_Minutes2.csv',\n",
              " 'Sarah_Standing_11_Minutes.csv',\n",
              " 'Bowen_Standing_10_Minutes.csv',\n",
              " 'Sarah_Standing_20_Minutes_1.csv',\n",
              " 'Bowen_Standing_15_Minutes_Walking.csv',\n",
              " 'Sarah_Standing_20_Minutes_2.csv']"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ],
      "source": [
        "from numpy.lib.shape_base import split\n",
        "new_data_files_lying = []\n",
        "new_data_files_sitting = []\n",
        "new_data_files_standing = []\n",
        "\n",
        "# I'll need to split the names up in the list since it's grouping 2 at a time\n",
        "for i in data_files_lying:\n",
        "  new_data_files_lying.extend(i.split())\n",
        "for i in data_files_sitting:\n",
        "  new_data_files_sitting.extend(i.split())\n",
        "for i in data_files_standing:\n",
        "  new_data_files_standing.extend(i.split())\n",
        "\n",
        "new_data_files_standing"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cXnnYz7dWWpA",
        "outputId": "8e63af04-fac5-41af-a563-7c2b5b4dcf26"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Ainsley_Lying_30_Minutes_2.csv\n",
            "Bowen_Lying_20_Minutes.csv\n",
            "Sarah_Lying_20_Minutes_3.csv\n",
            "Ainsley_Lying_30_Minutes.csv\n",
            "Bowen_Lying_21_Minutes.csv\n",
            "Sarah_Lying_20_Minutes.csv\n",
            "Bowen_Lying_20_Minutes_2.csv\n",
            "Sarah_Lying_20_Minutes_2.csv\n",
            "Ainsley_Sitting_30_Minutes_2.csv\n",
            "Bowen_Sitting_11_Minutes_2.csv\n",
            "Sarah_Sitting_20_Minutes_2.csv\n",
            "Ainsley_Sitting_30_Minutes.csv\n",
            "Bowen_Sitting_11_Minutes.csv\n",
            "Sarah_Sitting_20_Minutes_3.csv\n",
            "Bowen_Sitting_10_Minutes.csv\n",
            "Bowen_Sitting_40_Minutes.csv\n",
            "Sarah_Sitting_20_Minutes.csv\n",
            "Ainsley_Standing_30_Minutes_2.csv\n",
            "Bowen_Standing_30_Minutes.csv\n",
            "Ainsley_Standing_30_Minutes.csv\n",
            "Sarah_Standing_10_Minutes.csv\n",
            "Bowen_Standing_10_Minutes2.csv\n",
            "Sarah_Standing_11_Minutes.csv\n",
            "Bowen_Standing_10_Minutes.csv\n",
            "Sarah_Standing_20_Minutes_1.csv\n",
            "Bowen_Standing_15_Minutes_Walking.csv\n",
            "Sarah_Standing_20_Minutes_2.csv\n"
          ]
        }
      ],
      "source": [
        "raw_lying_data = pd.DataFrame()\n",
        "raw_sitting_data = pd.DataFrame()\n",
        "raw_standing_data = pd.DataFrame()\n",
        "\n",
        "\n",
        "for i in range(len(new_data_files_lying)):\n",
        "  print(new_data_files_lying[i])\n",
        "  df = pd.read_csv(pwd_lying + \"/\" + new_data_files_lying[i], header=1)\n",
        "  raw_lying_data = pd.concat([raw_lying_data, df])\n",
        "\n",
        "for i in range(len(new_data_files_sitting)):\n",
        "  print(new_data_files_sitting[i])\n",
        "  df = pd.read_csv(pwd_sitting + \"/\" + new_data_files_sitting[i], header=1)\n",
        "  raw_sitting_data = pd.concat([raw_sitting_data, df])\n",
        "\n",
        "for i in range(len(new_data_files_standing)):\n",
        "  print(new_data_files_standing[i])\n",
        "  df = pd.read_csv(pwd_standing + \"/\" + new_data_files_standing[i], header=1)\n",
        "  raw_standing_data = pd.concat([raw_standing_data, df])\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "sFPC9ktTgA1s",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2fc0ec48-c260-4a14-fb97-bb9582077d1a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-6-31a3e4792fcd>:6: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  ly['body postion'] = 1\n",
            "<ipython-input-6-31a3e4792fcd>:7: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  sit['body postion'] = 2\n",
            "<ipython-input-6-31a3e4792fcd>:8: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  stand['body postion'] = 3\n"
          ]
        }
      ],
      "source": [
        "# Now, I'm filtering the table since I only want time and the accelerometer data\n",
        "ly = raw_lying_data[[\"timestamp\", \"green\", \"green2\", \"ir\", \"red\", \"acceleration_x\", \"acceleration_y\", \"acceleration_z\"]]\n",
        "sit = raw_sitting_data[[\"timestamp\", \"green\", \"green2\", \"ir\", \"red\", \"acceleration_x\", \"acceleration_y\", \"acceleration_z\"]]\n",
        "stand = raw_standing_data[[\"timestamp\", \"green\", \"green2\", \"ir\", \"red\", \"acceleration_x\", \"acceleration_y\", \"acceleration_z\"]]\n",
        "\n",
        "ly['body postion'] = 1\n",
        "sit['body postion'] = 2\n",
        "stand['body postion'] = 3"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn import preprocessing\n",
        "\n",
        "min_max_scaler = preprocessing.MinMaxScaler()\n",
        "\n",
        "ppg_sitting_values = sit[[\"green\", \"green2\", \"ir\", \"red\"]].values\n",
        "ppg_lying_values = ly[[\"green\", \"green2\", \"ir\", \"red\"]].values\n",
        "ppg_standing_values = stand[[\"green\", \"green2\", \"ir\", \"red\"]].values #returns a numpy array\n",
        "accel_sitting_values = sit[[\"acceleration_x\", \"acceleration_y\", \"acceleration_z\"]].values\n",
        "accel_standing_values = stand[[\"acceleration_x\", \"acceleration_y\", \"acceleration_z\"]].values\n",
        "accel_lying_values = ly[[\"acceleration_x\", \"acceleration_y\", \"acceleration_z\"]].values\n",
        "\n",
        "\n",
        "sitting_values_norm = min_max_scaler.fit_transform(ppg_sitting_values)\n",
        "lying_values_norm = min_max_scaler.fit_transform(ppg_lying_values)\n",
        "standing_values_norm = min_max_scaler.fit_transform(ppg_standing_values)\n",
        "\n",
        "sitting_values = np.concatenate((sitting_values_norm, accel_sitting_values), axis=1)\n",
        "standing_values = np.concatenate((standing_values_norm, accel_standing_values), axis=1)\n",
        "lying_values = np.concatenate((lying_values_norm, accel_lying_values), axis=1)\n",
        "\n",
        "\n",
        "sitting = pd.DataFrame(sitting_values)\n",
        "lying = pd.DataFrame(lying_values)\n",
        "standing = pd.DataFrame(standing_values)"
      ],
      "metadata": {
        "id": "xDf6J-IK-iiA"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ppg_sitting_values"
      ],
      "metadata": {
        "id": "RBO2obCiD-m3",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3dc9189e-a1cc-4cf7-c84e-2b1f6c7d520b"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
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          "metadata": {},
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    {
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      "source": [
        "sitting"
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        "id": "i_zrc20I-2QT",
        "colab": {
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          "height": 424
        },
        "outputId": "2b1b4ec8-9eba-4208-e2b6-576c99c09628"
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      "execution_count": 9,
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              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-7b768837-04b4-4645-bee5-abd0dd575a0f button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-7b768837-04b4-4645-bee5-abd0dd575a0f');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-14a2dd99-3557-4899-8e20-3858446a9564\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-14a2dd99-3557-4899-8e20-3858446a9564')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-14a2dd99-3557-4899-8e20-3858446a9564 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "sitting"
            }
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "mLEvBW-HB34h"
      },
      "outputs": [],
      "source": [
        "# Put it in a tensor\n",
        "\n",
        "tt_lying = torch.tensor(lying[[0,1,2,3,4,5,6]].values)\n",
        "tt_sitting = torch.tensor(sitting[[0,1,2,3,4,5,6]].values)\n",
        "tt_standing = torch.tensor(standing[[0,1,2,3,4,5,6]].values)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "BVuXFkh_Sdnf"
      },
      "outputs": [],
      "source": [
        "unfolded_tt_lying = tt_lying.unfold(0, 1000, 50)\n",
        "unfolded_tt_sitting = tt_sitting.unfold(0, 1000, 50)\n",
        "unfolded_tt_standing = tt_standing.unfold(0, 1000, 50)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sJK_fPKFgQks"
      },
      "source": [
        "# Creating Training and Testing Datasets"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "x1uJEfLoTRKs",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "fe4920ff-413b-4a5f-a3fa-ab69192d524c"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(torch.Size([12776, 7, 1000]), torch.Size([3193, 7, 1000]))"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ],
      "source": [
        "train_tt_accels = torch.empty(0, 7, 1000)\n",
        "test_tt_accels = torch.empty(0, 7, 1000)\n",
        "\n",
        "train_label = []\n",
        "test_label = []\n",
        "\n",
        "unfolded_tt_accels = [unfolded_tt_lying, unfolded_tt_sitting, unfolded_tt_standing]\n",
        "\n",
        "for i in range(len(unfolded_tt_accels)):\n",
        "  train_dataset, test_dataset = torch.utils.data.random_split(unfolded_tt_accels[i], [0.8,.2])\n",
        "\n",
        "  train = torch.stack([t for t in train_dataset])\n",
        "  test = torch.stack([t for t in test_dataset])\n",
        "\n",
        "  train_tt_accels = torch.cat((train_tt_accels, train), 0)\n",
        "  test_tt_accels = torch.cat((test_tt_accels, test), 0)\n",
        "\n",
        "  for j in range(len(train)):\n",
        "    train_label.append(i)\n",
        "  for j in range(len(test)):\n",
        "    test_label.append(i)\n",
        "\n",
        "train_tt_accels.shape, test_tt_accels.shape"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0K98YFLfQKhi"
      },
      "source": [
        "# ML Model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "4WT2HrPfjeOP",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "27a4f92e-1e8c-4cd3-bcec-da8383b785fc"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/20\n",
            "399/400 [============================>.] - ETA: 0s - loss: 1.0876 - accuracy: 0.5250\n",
            "Epoch 1: val_accuracy improved from -inf to 0.71156, saving model to best_model.h5\n",
            "400/400 [==============================] - 15s 25ms/step - loss: 1.0873 - accuracy: 0.5251 - val_loss: 0.7984 - val_accuracy: 0.7116\n",
            "Epoch 2/20\n",
            "399/400 [============================>.] - ETA: 0s - loss: 0.7571 - accuracy: 0.7047\n",
            "Epoch 2: val_accuracy improved from 0.71156 to 0.78766, saving model to best_model.h5\n",
            "400/400 [==============================] - 9s 23ms/step - loss: 0.7570 - accuracy: 0.7047 - val_loss: 0.5984 - val_accuracy: 0.7877\n",
            "Epoch 3/20\n",
            "399/400 [============================>.] - ETA: 0s - loss: 0.5975 - accuracy: 0.7890\n",
            "Epoch 3: val_accuracy improved from 0.78766 to 0.80520, saving model to best_model.h5\n",
            "400/400 [==============================] - 8s 21ms/step - loss: 0.5979 - accuracy: 0.7889 - val_loss: 0.5520 - val_accuracy: 0.8052\n",
            "Epoch 4/20\n",
            "400/400 [==============================] - ETA: 0s - loss: 0.5609 - accuracy: 0.8003\n",
            "Epoch 4: val_accuracy improved from 0.80520 to 0.85500, saving model to best_model.h5\n",
            "400/400 [==============================] - 10s 24ms/step - loss: 0.5609 - accuracy: 0.8003 - val_loss: 0.4120 - val_accuracy: 0.8550\n",
            "Epoch 5/20\n",
            "400/400 [==============================] - ETA: 0s - loss: 0.4938 - accuracy: 0.8289\n",
            "Epoch 5: val_accuracy did not improve from 0.85500\n",
            "400/400 [==============================] - 9s 23ms/step - loss: 0.4938 - accuracy: 0.8289 - val_loss: 0.4137 - val_accuracy: 0.8418\n",
            "Epoch 6/20\n",
            "400/400 [==============================] - ETA: 0s - loss: 0.4970 - accuracy: 0.8282\n",
            "Epoch 6: val_accuracy did not improve from 0.85500\n",
            "400/400 [==============================] - 9s 23ms/step - loss: 0.4970 - accuracy: 0.8282 - val_loss: 1.2167 - val_accuracy: 0.6608\n",
            "Epoch 7/20\n",
            "400/400 [==============================] - ETA: 0s - loss: 0.5199 - accuracy: 0.8055\n",
            "Epoch 7: val_accuracy did not improve from 0.85500\n",
            "400/400 [==============================] - 14s 35ms/step - loss: 0.5199 - accuracy: 0.8055 - val_loss: 0.3735 - val_accuracy: 0.8537\n",
            "Epoch 8/20\n",
            "399/400 [============================>.] - ETA: 0s - loss: 0.4067 - accuracy: 0.8613\n",
            "Epoch 8: val_accuracy improved from 0.85500 to 0.88350, saving model to best_model.h5\n",
            "400/400 [==============================] - 10s 25ms/step - loss: 0.4069 - accuracy: 0.8612 - val_loss: 0.3084 - val_accuracy: 0.8835\n",
            "Epoch 9/20\n",
            "398/400 [============================>.] - ETA: 0s - loss: 0.3733 - accuracy: 0.8712\n",
            "Epoch 9: val_accuracy did not improve from 0.88350\n",
            "400/400 [==============================] - 8s 19ms/step - loss: 0.3738 - accuracy: 0.8709 - val_loss: 0.3222 - val_accuracy: 0.8829\n",
            "Epoch 10/20\n",
            "399/400 [============================>.] - ETA: 0s - loss: 0.3781 - accuracy: 0.8617\n",
            "Epoch 10: val_accuracy did not improve from 0.88350\n",
            "400/400 [==============================] - 10s 25ms/step - loss: 0.3780 - accuracy: 0.8618 - val_loss: 0.3271 - val_accuracy: 0.8785\n",
            "Epoch 11/20\n",
            "398/400 [============================>.] - ETA: 0s - loss: 0.4137 - accuracy: 0.8489\n",
            "Epoch 11: val_accuracy did not improve from 0.88350\n",
            "400/400 [==============================] - 9s 22ms/step - loss: 0.4135 - accuracy: 0.8491 - val_loss: 0.3897 - val_accuracy: 0.8578\n",
            "Epoch 12/20\n",
            "400/400 [==============================] - ETA: 0s - loss: 0.3890 - accuracy: 0.8609\n",
            "Epoch 12: val_accuracy improved from 0.88350 to 0.88443, saving model to best_model.h5\n",
            "400/400 [==============================] - 9s 23ms/step - loss: 0.3890 - accuracy: 0.8609 - val_loss: 0.3005 - val_accuracy: 0.8844\n",
            "Epoch 13/20\n",
            "399/400 [============================>.] - ETA: 0s - loss: 0.3542 - accuracy: 0.8784\n",
            "Epoch 13: val_accuracy improved from 0.88443 to 0.89634, saving model to best_model.h5\n",
            "400/400 [==============================] - 9s 23ms/step - loss: 0.3541 - accuracy: 0.8784 - val_loss: 0.2781 - val_accuracy: 0.8963\n",
            "Epoch 14/20\n",
            "399/400 [============================>.] - ETA: 0s - loss: 0.3342 - accuracy: 0.8825\n",
            "Epoch 14: val_accuracy did not improve from 0.89634\n",
            "400/400 [==============================] - 9s 22ms/step - loss: 0.3343 - accuracy: 0.8824 - val_loss: 0.3003 - val_accuracy: 0.8841\n",
            "Epoch 15/20\n",
            "400/400 [==============================] - ETA: 0s - loss: 0.3318 - accuracy: 0.8825\n",
            "Epoch 15: val_accuracy did not improve from 0.89634\n",
            "400/400 [==============================] - 9s 23ms/step - loss: 0.3318 - accuracy: 0.8825 - val_loss: 0.2918 - val_accuracy: 0.8929\n",
            "Epoch 16/20\n",
            "399/400 [============================>.] - ETA: 0s - loss: 0.3331 - accuracy: 0.8843\n",
            "Epoch 16: val_accuracy did not improve from 0.89634\n",
            "400/400 [==============================] - 11s 27ms/step - loss: 0.3336 - accuracy: 0.8842 - val_loss: 0.2806 - val_accuracy: 0.8951\n",
            "Epoch 17/20\n",
            "400/400 [==============================] - ETA: 0s - loss: 0.3435 - accuracy: 0.8745\n",
            "Epoch 17: val_accuracy improved from 0.89634 to 0.90291, saving model to best_model.h5\n",
            "400/400 [==============================] - 9s 22ms/step - loss: 0.3435 - accuracy: 0.8745 - val_loss: 0.2741 - val_accuracy: 0.9029\n",
            "Epoch 18/20\n",
            "397/400 [============================>.] - ETA: 0s - loss: 0.3052 - accuracy: 0.8911\n",
            "Epoch 18: val_accuracy improved from 0.90291 to 0.90980, saving model to best_model.h5\n",
            "400/400 [==============================] - 9s 23ms/step - loss: 0.3055 - accuracy: 0.8909 - val_loss: 0.2537 - val_accuracy: 0.9098\n",
            "Epoch 19/20\n",
            "398/400 [============================>.] - ETA: 0s - loss: 0.2926 - accuracy: 0.8963\n",
            "Epoch 19: val_accuracy did not improve from 0.90980\n",
            "400/400 [==============================] - 10s 25ms/step - loss: 0.2936 - accuracy: 0.8961 - val_loss: 0.2631 - val_accuracy: 0.8901\n",
            "Epoch 20/20\n",
            "399/400 [============================>.] - ETA: 0s - loss: 0.2943 - accuracy: 0.8929\n",
            "Epoch 20: val_accuracy did not improve from 0.90980\n",
            "400/400 [==============================] - 9s 23ms/step - loss: 0.2942 - accuracy: 0.8929 - val_loss: 0.2710 - val_accuracy: 0.8832\n"
          ]
        }
      ],
      "source": [
        "def build_model():\n",
        "    model = Sequential()\n",
        "    model.add(layers.LSTM(32, activation='relu', return_sequences=True, input_shape=(7, 1000)))\n",
        "    model.add(layers.LSTM(32, activation='relu'))\n",
        "\n",
        "    model.add(layers.Dense(32, activation='relu', kernel_regularizer=regularizers.l2(0.001)))\n",
        "    model.add(layers.Dropout(0.3))\n",
        "    model.add(layers.BatchNormalization())\n",
        "\n",
        "    model.add(layers.Dense(16, activation='relu', kernel_regularizer=regularizers.l2(0.001)))\n",
        "    model.add(layers.Dropout(0.3))\n",
        "    model.add(layers.BatchNormalization())\n",
        "\n",
        "    model.add(layers.Dense(3, activation='softmax'))\n",
        "\n",
        "    model.compile(\n",
        "        optimizer=Adam(learning_rate=0.001),\n",
        "        loss='sparse_categorical_crossentropy',\n",
        "        metrics=['accuracy']\n",
        "    )\n",
        "    return model\n",
        "\n",
        "\n",
        "# Define your training and validation data\n",
        "x_train = train_tt_accels.numpy()\n",
        "y_train = np.array(train_label)\n",
        "x_val = test_tt_accels.numpy()\n",
        "y_val = np.array(test_label)\n",
        "\n",
        "x_train.shape\n",
        "\n",
        "# Build the model\n",
        "model = build_model()\n",
        "\n",
        "# Set up ModelCheckpoint callback to save the best model\n",
        "checkpoint = ModelCheckpoint(\"best_model.h5\", monitor='val_accuracy', save_best_only=True, mode='max', verbose=1)\n",
        "\n",
        "# Train the model with callbacks\n",
        "history = model.fit(x_train, y_train, epochs=20, validation_data=(x_val, y_val), callbacks=[checkpoint])\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Evaluate the best model\n",
        "best_model = build_model()  # Rebuild the model\n",
        "best_model.load_weights(\"best_model.h5\")  # Load the best weights\n",
        "loss, accuracy = best_model.evaluate(x_val, y_val)\n",
        "print(f\"Validation loss of the best model: {loss}, Validation accuracy of the best model: {accuracy}\")"
      ],
      "metadata": {
        "id": "_RDDlVFMFO97",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c574f50f-4967-4b69-832f-160ddfe5ce77"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "100/100 [==============================] - 2s 8ms/step - loss: 0.1993 - accuracy: 0.9251\n",
            "Validation loss of the best model: 0.199269101023674, Validation accuracy of the best model: 0.9251487851142883\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.save('my_model.keras')"
      ],
      "metadata": {
        "id": "pqZbLgtmHf5X"
      },
      "execution_count": 15,
      "outputs": []
    }
  ],
  "metadata": {
    "colab": {
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}