Preprocess.ipynb

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      "name": "Preprocess.ipynb",
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  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "hFsGeKGjNPPW"
      },
      "source": [
        "#import dependencies\n",
        "import pandas as pd\n",
        "import glob"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZSj5vw85QJrg"
      },
      "source": [
        "# Data Preprocessing"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JKMyqIPeNPPk",
        "outputId": "0e4b1885-63d5-426a-8962-6cd4a5af1d4a"
      },
      "source": [
        "path = r'TrainingData' # use your path to training data files to import\n",
        "all_x_files = glob.glob(path + \"/subject_00*_*__x.csv\")\n",
        "all_x_time_files = glob.glob(path + \"/subject_00*_*__x_time.csv\")\n",
        "all_y_files = glob.glob(path + \"/subject_00*_*__y.csv\")\n",
        "all_y_time_files = glob.glob(path + \"/subject_00*_*__y_time.csv\")\n",
        "li = []\n",
        "\n",
        "print(sorted(all_x_files))\n",
        "print(\"\\n\")\n",
        "print(sorted(all_x_time_files))\n",
        "print(\"\\n\")\n",
        "print(sorted(all_y_files))\n",
        "print(\"\\n\")\n",
        "print(sorted(all_y_time_files))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['TrainingData/subject_001_01__x.csv', 'TrainingData/subject_001_02__x.csv', 'TrainingData/subject_001_03__x.csv', 'TrainingData/subject_001_04__x.csv', 'TrainingData/subject_001_05__x.csv', 'TrainingData/subject_001_06__x.csv', 'TrainingData/subject_001_07__x.csv', 'TrainingData/subject_001_08__x.csv', 'TrainingData/subject_002_01__x.csv', 'TrainingData/subject_002_02__x.csv', 'TrainingData/subject_002_03__x.csv', 'TrainingData/subject_002_04__x.csv', 'TrainingData/subject_002_05__x.csv', 'TrainingData/subject_003_01__x.csv', 'TrainingData/subject_003_02__x.csv', 'TrainingData/subject_003_03__x.csv', 'TrainingData/subject_004_01__x.csv', 'TrainingData/subject_004_02__x.csv', 'TrainingData/subject_005_01__x.csv', 'TrainingData/subject_005_02__x.csv', 'TrainingData/subject_005_03__x.csv', 'TrainingData/subject_006_01__x.csv', 'TrainingData/subject_006_02__x.csv', 'TrainingData/subject_006_03__x.csv', 'TrainingData/subject_007_01__x.csv', 'TrainingData/subject_007_02__x.csv', 'TrainingData/subject_007_03__x.csv', 'TrainingData/subject_007_04__x.csv', 'TrainingData/subject_008_01__x.csv']\n",
            "\n",
            "\n",
            "['TrainingData/subject_001_01__x_time.csv', 'TrainingData/subject_001_02__x_time.csv', 'TrainingData/subject_001_03__x_time.csv', 'TrainingData/subject_001_04__x_time.csv', 'TrainingData/subject_001_05__x_time.csv', 'TrainingData/subject_001_06__x_time.csv', 'TrainingData/subject_001_07__x_time.csv', 'TrainingData/subject_001_08__x_time.csv', 'TrainingData/subject_002_01__x_time.csv', 'TrainingData/subject_002_02__x_time.csv', 'TrainingData/subject_002_03__x_time.csv', 'TrainingData/subject_002_04__x_time.csv', 'TrainingData/subject_002_05__x_time.csv', 'TrainingData/subject_003_01__x_time.csv', 'TrainingData/subject_003_02__x_time.csv', 'TrainingData/subject_003_03__x_time.csv', 'TrainingData/subject_004_01__x_time.csv', 'TrainingData/subject_004_02__x_time.csv', 'TrainingData/subject_005_01__x_time.csv', 'TrainingData/subject_005_02__x_time.csv', 'TrainingData/subject_005_03__x_time.csv', 'TrainingData/subject_006_01__x_time.csv', 'TrainingData/subject_006_02__x_time.csv', 'TrainingData/subject_006_03__x_time.csv', 'TrainingData/subject_007_01__x_time.csv', 'TrainingData/subject_007_02__x_time.csv', 'TrainingData/subject_007_03__x_time.csv', 'TrainingData/subject_007_04__x_time.csv', 'TrainingData/subject_008_01__x_time.csv']\n",
            "\n",
            "\n",
            "['TrainingData/subject_001_01__y.csv', 'TrainingData/subject_001_02__y.csv', 'TrainingData/subject_001_03__y.csv', 'TrainingData/subject_001_04__y.csv', 'TrainingData/subject_001_05__y.csv', 'TrainingData/subject_001_06__y.csv', 'TrainingData/subject_001_07__y.csv', 'TrainingData/subject_001_08__y.csv', 'TrainingData/subject_002_01__y.csv', 'TrainingData/subject_002_02__y.csv', 'TrainingData/subject_002_03__y.csv', 'TrainingData/subject_002_04__y.csv', 'TrainingData/subject_002_05__y.csv', 'TrainingData/subject_003_01__y.csv', 'TrainingData/subject_003_02__y.csv', 'TrainingData/subject_003_03__y.csv', 'TrainingData/subject_004_01__y.csv', 'TrainingData/subject_004_02__y.csv', 'TrainingData/subject_005_01__y.csv', 'TrainingData/subject_005_02__y.csv', 'TrainingData/subject_005_03__y.csv', 'TrainingData/subject_006_01__y.csv', 'TrainingData/subject_006_02__y.csv', 'TrainingData/subject_006_03__y.csv', 'TrainingData/subject_007_01__y.csv', 'TrainingData/subject_007_02__y.csv', 'TrainingData/subject_007_03__y.csv', 'TrainingData/subject_007_04__y.csv', 'TrainingData/subject_008_01__y.csv']\n",
            "\n",
            "\n",
            "['TrainingData/subject_001_01__y_time.csv', 'TrainingData/subject_001_02__y_time.csv', 'TrainingData/subject_001_03__y_time.csv', 'TrainingData/subject_001_04__y_time.csv', 'TrainingData/subject_001_05__y_time.csv', 'TrainingData/subject_001_06__y_time.csv', 'TrainingData/subject_001_07__y_time.csv', 'TrainingData/subject_001_08__y_time.csv', 'TrainingData/subject_002_01__y_time.csv', 'TrainingData/subject_002_02__y_time.csv', 'TrainingData/subject_002_03__y_time.csv', 'TrainingData/subject_002_04__y_time.csv', 'TrainingData/subject_002_05__y_time.csv', 'TrainingData/subject_003_01__y_time.csv', 'TrainingData/subject_003_02__y_time.csv', 'TrainingData/subject_003_03__y_time.csv', 'TrainingData/subject_004_01__y_time.csv', 'TrainingData/subject_004_02__y_time.csv', 'TrainingData/subject_005_01__y_time.csv', 'TrainingData/subject_005_02__y_time.csv', 'TrainingData/subject_005_03__y_time.csv', 'TrainingData/subject_006_01__y_time.csv', 'TrainingData/subject_006_02__y_time.csv', 'TrainingData/subject_006_03__y_time.csv', 'TrainingData/subject_007_01__y_time.csv', 'TrainingData/subject_007_02__y_time.csv', 'TrainingData/subject_007_03__y_time.csv', 'TrainingData/subject_007_04__y_time.csv', 'TrainingData/subject_008_01__y_time.csv']\n"
          ],
          "name": "stdout"
        }
      ]
    },
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      "cell_type": "code",
      "metadata": {
        "id": "WeQSpkJgNPPk",
        "outputId": "81d16631-2ec0-4070-d62c-35712f30cc4b"
      },
      "source": [
        "gen_files=[]\n",
        "for filename in sorted(all_x_files):\n",
        "    gen= filename[:-7]\n",
        "    #print(filename,gen)\n",
        "    gen_files.append(gen)\n",
        "\n",
        "print(gen_files)\n",
        "subject_list=[]\n",
        "for filename in gen_files:\n",
        "    file=filename.split('/')[1]\n",
        "    print(file) \n",
        "    subject_list.append(file)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['TrainingData/subject_001_01', 'TrainingData/subject_001_02', 'TrainingData/subject_001_03', 'TrainingData/subject_001_04', 'TrainingData/subject_001_05', 'TrainingData/subject_001_06', 'TrainingData/subject_001_07', 'TrainingData/subject_001_08', 'TrainingData/subject_002_01', 'TrainingData/subject_002_02', 'TrainingData/subject_002_03', 'TrainingData/subject_002_04', 'TrainingData/subject_002_05', 'TrainingData/subject_003_01', 'TrainingData/subject_003_02', 'TrainingData/subject_003_03', 'TrainingData/subject_004_01', 'TrainingData/subject_004_02', 'TrainingData/subject_005_01', 'TrainingData/subject_005_02', 'TrainingData/subject_005_03', 'TrainingData/subject_006_01', 'TrainingData/subject_006_02', 'TrainingData/subject_006_03', 'TrainingData/subject_007_01', 'TrainingData/subject_007_02', 'TrainingData/subject_007_03', 'TrainingData/subject_007_04', 'TrainingData/subject_008_01']\n",
            "subject_001_01\n",
            "subject_001_02\n",
            "subject_001_03\n",
            "subject_001_04\n",
            "subject_001_05\n",
            "subject_001_06\n",
            "subject_001_07\n",
            "subject_001_08\n",
            "subject_002_01\n",
            "subject_002_02\n",
            "subject_002_03\n",
            "subject_002_04\n",
            "subject_002_05\n",
            "subject_003_01\n",
            "subject_003_02\n",
            "subject_003_03\n",
            "subject_004_01\n",
            "subject_004_02\n",
            "subject_005_01\n",
            "subject_005_02\n",
            "subject_005_03\n",
            "subject_006_01\n",
            "subject_006_02\n",
            "subject_006_03\n",
            "subject_007_01\n",
            "subject_007_02\n",
            "subject_007_03\n",
            "subject_007_04\n",
            "subject_008_01\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "k2iuszjeNPPl",
        "outputId": "1cc8ca41-df34-45fd-d5ee-7fa5e552874f"
      },
      "source": [
        "subject_list"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['subject_001_01',\n",
              " 'subject_001_02',\n",
              " 'subject_001_03',\n",
              " 'subject_001_04',\n",
              " 'subject_001_05',\n",
              " 'subject_001_06',\n",
              " 'subject_001_07',\n",
              " 'subject_001_08',\n",
              " 'subject_002_01',\n",
              " 'subject_002_02',\n",
              " 'subject_002_03',\n",
              " 'subject_002_04',\n",
              " 'subject_002_05',\n",
              " 'subject_003_01',\n",
              " 'subject_003_02',\n",
              " 'subject_003_03',\n",
              " 'subject_004_01',\n",
              " 'subject_004_02',\n",
              " 'subject_005_01',\n",
              " 'subject_005_02',\n",
              " 'subject_005_03',\n",
              " 'subject_006_01',\n",
              " 'subject_006_02',\n",
              " 'subject_006_03',\n",
              " 'subject_007_01',\n",
              " 'subject_007_02',\n",
              " 'subject_007_03',\n",
              " 'subject_007_04',\n",
              " 'subject_008_01']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 76
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eViCPba_QeR7"
      },
      "source": [
        "## Making individual subject data files\n",
        "\n",
        "Merge the x_meas and x_time file based on the time value\n",
        "Merge the y_time and y_label file based on the time value"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VEVLeuRINPPl"
      },
      "source": [
        "#Creating x meas and time csv and y label and time csv\n",
        "for filename in gen_files:\n",
        "    subject=filename.split('/')[1]\n",
        "    df1=pd.read_csv(filename+'__x.csv',  names =['acc_x', 'acc_y', 'acc_z', 'gyro_x', 'gyro_y', 'gyro_z'])\n",
        "    df2=pd.read_csv(filename+'__x_time.csv', names =['time'])\n",
        "    df1['time']=df2['time']\n",
        "    df1['time']=df1['time'].apply(lambda x:int(x*100)/100)\n",
        "    #df1['time']=df1['time'].apply(lambda x:format{x,\".2f\"})\n",
        "    df1['time']=df1['time'].astype(str)\n",
        "    #df1['time'] = df1['time'].astype(str).str.encode('utf-8')\n",
        "    df3=pd.read_csv(filename+'__y.csv', names =['label'])\n",
        "    df4=pd.read_csv(filename+'__y_time.csv', names =['time'])\n",
        "    df3['time']=df4['time']\n",
        "    df3['time']=df3['time'].astype(str)\n",
        "    #df3['time'] = df3['time'].astype(str).str.encode('utf-8')\n",
        "    df1.to_csv('ProcessData/'+subject+'x_meas_time.csv',index=False)\n",
        "    df3.to_csv('ProcessData/'+subject+'y_label_time.csv',index=False)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wupjwJKzNPPl"
      },
      "source": [
        "## Join x and y data on each subject and then interpolate the labels\n",
        "\n",
        "Merge the x and y_data files.\n",
        "After merging, we will observe that only 1/4th of total dataset is labelled. hence, we use interplolation technique to fill the other labels. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NsP2HLDhNPPm"
      },
      "source": [
        "#Creating each subject final csv and interpolate the data \n",
        "for subject in subject_list:\n",
        "    df1 = pd.read_csv('ProcessData/'+subject+'x_meas_time.csv')\n",
        "    df2 = pd.read_csv('ProcessData/'+subject+'y_label_time.csv')\n",
        "    df3 = pd.merge(df1, df2, on='time', how='outer')\n",
        "    #print(df3)\n",
        "    df3.to_csv('Subject/'+subject+'final.csv',index=False)\n",
        "    df3['label']=df3['label'].interpolate(method='pad')\n",
        "    df4 = df3.loc[df3['time']!=0]\n",
        "    df4.to_csv('Interpolated/'+subject+'_interpolated.csv',index=False)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3kqSxTu1NPPm",
        "outputId": "bc847264-d5e2-4d1f-e72c-28a2badacee4"
      },
      "source": [
        "#checking the shape of the csv created\n",
        "df3 = pd.read_csv('TrainingData/subject_001_01__x_time.csv')\n",
        "df4 = pd.read_csv('TrainingData/subject_001_01__x.csv')\n",
        "df5 = pd.read_csv('TrainingData/subject_001_01__y_time.csv')\n",
        "df6 = pd.read_csv('TrainingData/subject_001_01__y.csv')\n",
        "print(df3.shape, df4.shape, df5.shape, df6.shape)\n",
        "df1 = pd.read_csv('ProcessData/subject_001_01x_meas_time.csv')\n",
        "df2 = pd.read_csv('ProcessData/subject_001_01y_label_time.csv')\n",
        "print(df1.shape)\n",
        "print(df2.shape)\n",
        "df3=pd.read_csv('Subject/subject_001_01final.csv')\n",
        "print(df3.shape)\n",
        "df3=pd.read_csv('Interpolated/subject_001_01_interpolated.csv')\n",
        "print(df3.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(37889, 1) (37889, 6) (9472, 1) (9472, 1)\n",
            "(37890, 7)\n",
            "(9473, 2)\n",
            "(37890, 8)\n",
            "(37889, 8)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8WHyREUONPPn"
      },
      "source": [
        "## Merge all individual subject interpolated csv files to single file\n",
        "\n",
        "The interpolated_combined.csv file will be our master file for training and validation."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AYO6E44xNPPn",
        "outputId": "ca64d857-1509-42aa-c185-2eeb83b18d45"
      },
      "source": [
        "li=[]\n",
        "path = r'Interpolated' # use your path\n",
        "all_files = glob.glob(path + \"/*.csv\")\n",
        "for files in sorted(all_files):\n",
        "    print(files)\n",
        "    df = pd.read_csv(files, names =['acc_x','acc_y','acc_z','gyro_x','gyro_y','gyro_z','time','label'])\n",
        "    li.append(df)\n",
        "    \n",
        "df1 = pd.concat(li, axis=0, ignore_index=True)\n",
        "df2 = df1.loc[df1['acc_x']!='acc_x']\n",
        "print(df1)\n",
        "df2.to_csv('Interpolated_combined.csv',index=False)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Interpolated/subject_001_01_interpolated.csv\n",
            "Interpolated/subject_001_02_interpolated.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/Users/himanshugupta/opt/anaconda3/lib/python3.8/site-packages/IPython/core/interactiveshell.py:3071: DtypeWarning: Columns (0,1,2,3,4,5,6,7) have mixed types.Specify dtype option on import or set low_memory=False.\n",
            "  has_raised = await self.run_ast_nodes(code_ast.body, cell_name,\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Interpolated/subject_001_03_interpolated.csv\n",
            "Interpolated/subject_001_04_interpolated.csv\n",
            "Interpolated/subject_001_05_interpolated.csv\n",
            "Interpolated/subject_001_06_interpolated.csv\n",
            "Interpolated/subject_001_07_interpolated.csv\n",
            "Interpolated/subject_001_08_interpolated.csv\n",
            "Interpolated/subject_002_01_interpolated.csv\n",
            "Interpolated/subject_002_02_interpolated.csv\n",
            "Interpolated/subject_002_03_interpolated.csv\n",
            "Interpolated/subject_002_04_interpolated.csv\n",
            "Interpolated/subject_002_05_interpolated.csv\n",
            "Interpolated/subject_003_01_interpolated.csv\n",
            "Interpolated/subject_003_02_interpolated.csv\n",
            "Interpolated/subject_003_03_interpolated.csv\n",
            "Interpolated/subject_004_01_interpolated.csv\n",
            "Interpolated/subject_004_02_interpolated.csv\n",
            "Interpolated/subject_005_01_interpolated.csv\n",
            "Interpolated/subject_005_02_interpolated.csv\n",
            "Interpolated/subject_005_03_interpolated.csv\n",
            "Interpolated/subject_006_01_interpolated.csv\n",
            "Interpolated/subject_006_02_interpolated.csv\n",
            "Interpolated/subject_006_03_interpolated.csv\n",
            "Interpolated/subject_007_01_interpolated.csv\n",
            "Interpolated/subject_007_02_interpolated.csv\n",
            "Interpolated/subject_007_03_interpolated.csv\n",
            "Interpolated/subject_007_04_interpolated.csv\n",
            "Interpolated/subject_008_01_interpolated.csv\n",
            "                      acc_x              acc_y                acc_z  \\\n",
            "0                     acc_x              acc_y                acc_z   \n",
            "1                   4.18692           8.344455             2.908057   \n",
            "2         4.544637000000002           8.408659                 2.89   \n",
            "3        4.8493080000000015           8.411614             2.900692   \n",
            "4                   4.50919  8.118649000000001             2.847298   \n",
            "...                     ...                ...                  ...   \n",
            "1341641            2.072244           8.908878                 -3.5   \n",
            "1341642            2.085123           8.915123                -3.52   \n",
            "1341643            2.083774               8.91            -3.538981   \n",
            "1341644            2.111447           8.908553            -3.535724   \n",
            "1341645             2.08773                8.9  -3.5188650000000004   \n",
            "\n",
            "               gyro_x        gyro_y        gyro_z     time  label  \n",
            "0              gyro_x        gyro_y        gyro_z     time  label  \n",
            "1         0.005770759  -0.004479538  -0.003345031     0.02    0.0  \n",
            "2         0.007967429    0.02241187   0.001158524     0.05    0.0  \n",
            "3          0.02777778   -0.01067026   -0.01422302     0.07    0.0  \n",
            "4          0.02157696   -0.04549827   -0.02111111      0.1    0.0  \n",
            "...               ...           ...           ...      ...    ...  \n",
            "1341641   0.001350761   0.001190994   0.001031228  1203.32    0.0  \n",
            "1341642   0.001917847  -0.001146575           0.0  1203.34    0.0  \n",
            "1341643  -0.002015218  -0.004099438    0.00104211  1203.37    0.0  \n",
            "1341644  0.0001831479  -0.001672778   0.001855926   1203.4    0.0  \n",
            "1341645  5.186139e-05  -0.001266695           0.0  1203.42    0.0  \n",
            "\n",
            "[1341646 rows x 8 columns]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sYyUaIi4NPPo",
        "outputId": "083c0848-df11-4413-8d92-c5a1b36d722c"
      },
      "source": [
        "# Checking the data imbalance\n",
        "df=pd.read_csv('Interpolated/subject_001_01_interpolated.csv')\n",
        "walking = df[df.label==0].count()[0]\n",
        "downstairs = df[df.label==1].count()[0]\n",
        "upstairs = df[df.label==2].count()[0]\n",
        "grass = df[df.label==3].count()[0]\n",
        "print(walking, downstairs,upstairs, grass)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "25773 2844 3152 6120\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bw9OXUySNPPo",
        "outputId": "d2ba0e68-82ee-4c95-9405-ff8ea6730b01"
      },
      "source": [
        "# The data imbalance graph for subject 1\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "text = [\"standing / walking\", \"going downstairs\",\"going upstairs\", \"walking on grass\"]\n",
        "label = [walking, downstairs,upstairs, grass]\n",
        "plt.style.use('seaborn')\n",
        "plt.figure(figsize=(8,6),dpi=100)\n",
        "for bar in range(0,4):\n",
        "    plt.bar(text[bar],label[bar])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CcwY6hD5NPPo",
        "outputId": "bf30b53f-8051-4789-8678-7fae427117c5"
      },
      "source": [
        "# The data imbalance graph for entire dataset consisting of all subjects\n",
        "df=pd.read_csv('Interpolated_combined.csv')\n",
        "walking_full = df[df.label==0].count()[0]\n",
        "downstairs_full = df[df.label==1].count()[0]\n",
        "upstairs_full = df[df.label==2].count()[0]\n",
        "grass_full = df[df.label==3].count()[0]\n",
        "print(walking_full, downstairs_full,upstairs_full, grass_full)\n",
        "\n",
        "text = [\"standing / walking\", \"going downstairs\",\"going upstairs\", \"walking on grass\"]\n",
        "label = [walking_full, downstairs_full,upstairs_full, grass_full]\n",
        "plt.style.use('seaborn')\n",
        "plt.figure(figsize=(8,6),dpi=100)\n",
        "for bar in range(0,4):\n",
        "    plt.bar(text[bar],label[bar])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "1006897 55216 73068 206436\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_3qeOVsiNPPp",
        "outputId": "4d1055c2-7706-4981-a4eb-415315b9e74f"
      },
      "source": [
        "y_train = df.label\n",
        "x_train= df.drop('label', axis=1)\n",
        "print(y_train)\n",
        "print(\"\\n~~~~~~~\")\n",
        "print(x_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0          0.0\n",
            "1          0.0\n",
            "2          0.0\n",
            "3          0.0\n",
            "4          0.0\n",
            "          ... \n",
            "1341612    0.0\n",
            "1341613    0.0\n",
            "1341614    0.0\n",
            "1341615    0.0\n",
            "1341616    0.0\n",
            "Name: label, Length: 1341617, dtype: float64\n",
            "\n",
            "~~~~~~~\n",
            "            acc_x     acc_y     acc_z    gyro_x    gyro_y    gyro_z     time\n",
            "0        4.186920  8.344455  2.908057  0.005771 -0.004480 -0.003345     0.02\n",
            "1        4.544637  8.408659  2.890000  0.007967  0.022412  0.001159     0.05\n",
            "2        4.849308  8.411614  2.900692  0.027778 -0.010670 -0.014223     0.07\n",
            "3        4.509190  8.118649  2.847298  0.021577 -0.045498 -0.021111     0.10\n",
            "4        4.226515  8.273807  2.851742  0.012534  0.000445 -0.016830     0.12\n",
            "...           ...       ...       ...       ...       ...       ...      ...\n",
            "1341612  2.072244  8.908878 -3.500000  0.001351  0.001191  0.001031  1203.32\n",
            "1341613  2.085123  8.915123 -3.520000  0.001918 -0.001147  0.000000  1203.34\n",
            "1341614  2.083774  8.910000 -3.538981 -0.002015 -0.004099  0.001042  1203.37\n",
            "1341615  2.111447  8.908553 -3.535724  0.000183 -0.001673  0.001856  1203.40\n",
            "1341616  2.087730  8.900000 -3.518865  0.000052 -0.001267  0.000000  1203.42\n",
            "\n",
            "[1341617 rows x 7 columns]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ye0FVQ9SNPPp",
        "outputId": "2d49ed72-b75b-4a73-a027-9c69097b7805"
      },
      "source": [
        "conda install -c conda-forge imbalanced-learn"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting package metadata (current_repodata.json): done\n",
            "Solving environment: failed with initial frozen solve. Retrying with flexible solve.\n",
            "Solving environment: failed with repodata from current_repodata.json, will retry with next repodata source.\n",
            "Collecting package metadata (repodata.json): done\n",
            "Solving environment: done\n",
            "\n",
            "\n",
            "==> WARNING: A newer version of conda exists. <==\n",
            "  current version: 4.8.3\n",
            "  latest version: 4.9.2\n",
            "\n",
            "Please update conda by running\n",
            "\n",
            "    $ conda update -n base -c defaults conda\n",
            "\n",
            "\n",
            "\n",
            "## Package Plan ##\n",
            "\n",
            "  environment location: /Users/himanshugupta/opt/anaconda3\n",
            "\n",
            "  added / updated specs:\n",
            "    - imbalanced-learn\n",
            "\n",
            "\n",
            "The following packages will be downloaded:\n",
            "\n",
            "    package                    |            build\n",
            "    ---------------------------|-----------------\n",
            "    conda-4.9.2                |   py38h50d1736_0         3.1 MB  conda-forge\n",
            "    imbalanced-learn-0.7.0     |             py_1          97 KB  conda-forge\n",
            "    python_abi-3.8             |           1_cp38           4 KB  conda-forge\n",
            "    ------------------------------------------------------------\n",
            "                                           Total:         3.2 MB\n",
            "\n",
            "The following NEW packages will be INSTALLED:\n",
            "\n",
            "  imbalanced-learn   conda-forge/noarch::imbalanced-learn-0.7.0-py_1\n",
            "  python_abi         conda-forge/osx-64::python_abi-3.8-1_cp38\n",
            "\n",
            "The following packages will be UPDATED:\n",
            "\n",
            "  conda                       pkgs/main::conda-4.8.3-py38_0 --> conda-forge::conda-4.9.2-py38h50d1736_0\n",
            "\n",
            "\n",
            "\n",
            "Downloading and Extracting Packages\n",
            "conda-4.9.2          | 3.1 MB    | ##################################### | 100% \n",
            "imbalanced-learn-0.7 | 97 KB     | ##################################### | 100% \n",
            "python_abi-3.8       | 4 KB      | ##################################### | 100% \n",
            "Preparing transaction: done\n",
            "Verifying transaction: done\n",
            "Executing transaction: done\n",
            "\n",
            "Note: you may need to restart the kernel to use updated packages.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZShjzbf4NPPp"
      },
      "source": [
        "## Using SMOTE to balance the minority classes (1.0, 2.0, 3.0)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z-0ajmELNPPp"
      },
      "source": [
        "from imblearn.over_sampling import SMOTE \n",
        "from collections import Counter\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bCdAd5ZfNPPq",
        "outputId": "3df76393-25ad-4343-b086-cb59c5cc20e7"
      },
      "source": [
        "county = Counter(y_train)\n",
        "print(county)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Counter({0.0: 1006897, 3.0: 206436, 2.0: 73068, 1.0: 55216})\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ww9kpLZNNPPq",
        "outputId": "704ea505-9f91-43c8-e797-4e4fc89a262c"
      },
      "source": [
        "sm = SMOTE(random_state=23)\n",
        "x_train_sm, y_train_sm = sm.fit_resample(x_train, y_train)\n",
        "print(len(x_train_sm), len(y_train_sm))\n",
        "\n",
        "county_sm = Counter(y_train_sm)\n",
        "print(county_sm)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "4027588 4027588\n",
            "Counter({0.0: 1006897, 1.0: 1006897, 2.0: 1006897, 3.0: 1006897})\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "O-4TJu9oNPPq"
      },
      "source": [
        "## Split into training and validation dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EUXaBQptNPPq"
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn import metrics\n",
        "import numpy as np"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sw61Y84BNPPr"
      },
      "source": [
        "x_train,x_val,y_train,y_val=train_test_split(x_train_sm, y_train_sm, test_size=0.2,random_state=123)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Yfowpy2HNPPr",
        "outputId": "091daa72-89ff-4772-c165-3b4e6257bbfc"
      },
      "source": [
        "print(len(x_train), len(y_train))\n",
        "print(len(x_val), len(y_val))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "3222070 3222070\n",
            "805518 805518\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DRoMzft5NPPr"
      },
      "source": [
        "## Random Forest"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7-T_PemMNPPr",
        "outputId": "718a1534-c4d4-41d3-970a-46e6a63fb9d4"
      },
      "source": [
        "#Training the Random Forest model on training data\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "\n",
        "Random_F_model = RandomForestClassifier(n_estimators=20, random_state=0)\n",
        "Random_F_model.fit(x_train, y_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier(n_estimators=20, random_state=0)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 194
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "I8EKeIoyNPPs"
      },
      "source": [
        "import pickle\n",
        "\n",
        "saved_model_RF = 'RF_model_smote.sav'\n",
        "pickle.dump(Random_F_model, open(saved_model_RF, 'wb'))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HZY05x1hNPPs"
      },
      "source": [
        "y_pred = Random_F_model.predict(x_val)\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kj3jENutNPPs",
        "outputId": "927ac305-7752-49ba-f269-cddb17c88fe5"
      },
      "source": [
        "#print(type(y_pred))\n",
        "#print (type(y_val))\n",
        "print('F1 Score:', metrics.f1_score(y_val, y_pred, average='macro'))\n",
        "print('Accuracy Score:', metrics.accuracy_score(y_val, y_pred))\n",
        "print('Precision score: ', metrics.precision_score(y_val, y_pred, average='macro'))\n",
        "print('Recall score: ', metrics.recall_score(y_val, y_pred, average='macro'))\n",
        "print('Accuracy Score:', metrics.accuracy_score(y_val, y_pred))\n",
        "print('Confusion Matrix:', metrics.confusion_matrix(y_val, y_pred))\n",
        "print('Mean Absolute Error:', metrics.mean_absolute_error(y_val, y_pred))\n",
        "print('Mean Squared Error:', metrics.mean_squared_error(y_val, y_pred))\n",
        "print('Root Mean Squared Error:', np.sqrt(metrics.mean_squared_error(y_val, y_pred)))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "F1 Score: 0.9097797768751095\n",
            "Accuracy Score: 0.9108052209882337\n",
            "Precision score:  0.9099234360466042\n",
            "Recall score:  0.9107539838492922\n",
            "Accuracy Score: 0.9108052209882337\n",
            "Confusion Matrix: [[161095   7381   6098  26607]\n",
            " [  3229 195674   1023   1360]\n",
            " [  2317    783 197676    951]\n",
            " [ 17306   2417   2376 179225]]\n",
            "Mean Absolute Error: 0.2133608435813973\n",
            "Mean Squared Error: 0.5707234351063539\n",
            "Root Mean Squared Error: 0.7554623982081133\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EGBi13RQNPPs"
      },
      "source": [
        "## KNN"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZawA0P6mNPPt",
        "outputId": "c7d87669-5162-471f-dc88-0455f855661f"
      },
      "source": [
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "KNN_Classifier = KNeighborsClassifier(n_neighbors=10)\n",
        "KNN_Classifier.fit(x_train, y_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "KNeighborsClassifier(n_neighbors=10)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 197
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RhQwpB0CNPPt"
      },
      "source": [
        "import pickle\n",
        "\n",
        "saved_model_KNN = 'KNN_smote.sav'\n",
        "pickle.dump(KNN_Classifier, open(saved_model_KNN, 'wb'))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "txgXyqdGNPPt",
        "outputId": "c6b99ecb-f5dc-4af3-89c6-d83730c13ff9"
      },
      "source": [
        "y_pred_KNN = KNN_Classifier.predict(x_val)\n",
        "print('F1 score: ', metrics.f1_score(y_val, y_pred_KNN, average='macro'))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "F1 score:  0.8787054499267164\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z6R77jKKNPPu",
        "outputId": "94dade44-e027-4482-9d97-e54a37acd8c8"
      },
      "source": [
        "print('F1 Score:', metrics.f1_score(y_val, y_pred_KNN, average='macro'))\n",
        "print('Accuracy Score:', metrics.accuracy_score(y_val, y_pred_KNN))\n",
        "print('Precision score: ', metrics.precision_score(y_val, y_pred_KNN, average='macro'))\n",
        "print('Recall score: ', metrics.recall_score(y_val, y_pred_KNN, average='macro'))\n",
        "print('Accuracy Score:', metrics.accuracy_score(y_val, y_pred_KNN))\n",
        "print('Confusion Matrix:', metrics.confusion_matrix(y_val, y_pred_KNN))\n",
        "print('Mean Absolute Error:', metrics.mean_absolute_error(y_val, y_pred_KNN))\n",
        "print('Mean Squared Error:', metrics.mean_squared_error(y_val, y_pred_KNN))\n",
        "print('Root Mean Squared Error:', np.sqrt(metrics.mean_squared_error(y_val, y_pred_KNN)))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "F1 Score: 0.8787054499267164\n",
            "Accuracy Score: 0.8847909047345931\n",
            "Precision score:  0.8888437453974138\n",
            "Recall score:  0.884697604791054\n",
            "Accuracy Score: 0.8847909047345931\n",
            "Confusion Matrix: [[128531  17204  12804  42642]\n",
            " [   412 200167    270    437]\n",
            " [   381    434 200579    333]\n",
            " [ 10128   4175   3583 183438]]\n",
            "Mean Absolute Error: 0.2683242335987526\n",
            "Mean Squared Error: 0.7055757909817036\n",
            "Root Mean Squared Error: 0.8399855897464573\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nTwH9EtTNPPu"
      },
      "source": [
        "## Random Forest with estimator 40"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tlrFVtqcNPPu",
        "outputId": "6cc1b964-c585-4fe6-b220-54df16070ba9"
      },
      "source": [
        "#Training the Random Forest model on training data\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "\n",
        "Random_F_model_40 = RandomForestClassifier(n_estimators=40, random_state=0)\n",
        "Random_F_model_40.fit(x_train, y_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier(n_estimators=40, random_state=0)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 211
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Q8oFapZANPPu"
      },
      "source": [
        "x_test_009 = pd.read_csv('TestProcessData/subject_009_01final_inner.csv')\n",
        "x_test_010 = pd.read_csv('TestProcessData/subject_010_01final_inner.csv')\n",
        "x_test_011 = pd.read_csv('TestProcessData/subject_011_01final_inner.csv')\n",
        "x_test_012 = pd.read_csv('TestProcessData/subject_012_01final_inner.csv')\n",
        "\n",
        "y_pred_RF_009 = Random_F_model_40.predict(x_test_009)\n",
        "y_pred_RF_010 = Random_F_model_40.predict(x_test_010)\n",
        "y_pred_RF_011 = Random_F_model_40.predict(x_test_011)\n",
        "y_pred_RF_012 = Random_F_model_40.predict(x_test_012)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4b-FIf1nNPPv"
      },
      "source": [
        "np.savetxt(\"TestResults/RF40/subject_009_01__y_prediction.csv\", y_pred_RF_009, delimiter=\",\")\n",
        "np.savetxt(\"TestResults/RF40/subject_010_01__y_prediction.csv\", y_pred_RF_010, delimiter=\",\")\n",
        "np.savetxt(\"TestResults/RF40/subject_011_01__y_prediction.csv\", y_pred_RF_011, delimiter=\",\")\n",
        "np.savetxt(\"TestResults/RF40/subject_012_01__y_prediction.csv\", y_pred_RF_012, delimiter=\",\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rbMB3lCoNPPv"
      },
      "source": [
        "y_pred_RF40 = Random_F_model_40.predict(x_val)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rk98AnDVNPPv",
        "outputId": "64ce18ed-ac91-4bb4-b73e-397a9c3196fb"
      },
      "source": [
        "print('F1 Score:', metrics.f1_score(y_val, y_pred_RF40, average='macro'))\n",
        "print('Accuracy Score:', metrics.accuracy_score(y_val, y_pred_RF40))\n",
        "print('Precision score: ', metrics.precision_score(y_val, y_pred_RF40, average='macro'))\n",
        "print('Recall score: ', metrics.recall_score(y_val, y_pred_RF40, average='macro'))\n",
        "print('Accuracy Score:', metrics.accuracy_score(y_val, y_pred_RF40))\n",
        "print('Confusion Matrix:', metrics.confusion_matrix(y_val, y_pred_RF40))\n",
        "print('Mean Absolute Error:', metrics.mean_absolute_error(y_val, y_pred_RF40))\n",
        "print('Mean Squared Error:', metrics.mean_squared_error(y_val, y_pred_RF40))\n",
        "print('Root Mean Squared Error:', np.sqrt(metrics.mean_squared_error(y_val, y_pred_RF40)))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "F1 Score: 0.915856516937698\n",
            "Accuracy Score: 0.9169279891945308\n",
            "Precision score:  0.9164699744704151\n",
            "Recall score:  0.9168777622074374\n",
            "Accuracy Score: 0.9169279891945308\n",
            "Confusion Matrix: [[161565   7205   6074  26337]\n",
            " [  2723 196315    986   1262]\n",
            " [  1943    658 198177    949]\n",
            " [ 14475   2154   2150 182545]]\n",
            "Mean Absolute Error: 0.19859643111637482\n",
            "Mean Squared Error: 0.5309763406900901\n",
            "Root Mean Squared Error: 0.7286812339357246\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IOunMXd5NPPv"
      },
      "source": [
        "## Random Forest with estimator 60"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0N6ueSgbNPPw",
        "outputId": "c476e47e-50f9-4863-e172-f0d9323901bc"
      },
      "source": [
        "#Training the Random Forest model on training data\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "\n",
        "Random_F_model_60 = RandomForestClassifier(n_estimators=60, random_state=0)\n",
        "Random_F_model_60.fit(x_train, y_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier(n_estimators=60, random_state=0)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 214
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VXlM9m48NPPw"
      },
      "source": [
        "x_test_009 = pd.read_csv('TestProcessData/subject_009_01final_inner.csv')\n",
        "x_test_010 = pd.read_csv('TestProcessData/subject_010_01final_inner.csv')\n",
        "x_test_011 = pd.read_csv('TestProcessData/subject_011_01final_inner.csv')\n",
        "x_test_012 = pd.read_csv('TestProcessData/subject_012_01final_inner.csv')\n",
        "\n",
        "y_pred_RF60_009 = Random_F_model_60.predict(x_test_009)\n",
        "y_pred_RF60_010 = Random_F_model_60.predict(x_test_010)\n",
        "y_pred_RF60_011 = Random_F_model_60.predict(x_test_011)\n",
        "y_pred_RF60_012 = Random_F_model_60.predict(x_test_012)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ecfK8aujNPPw"
      },
      "source": [
        "np.savetxt(\"TestResults/RF60/subject_009_01__y_prediction.csv\", y_pred_RF60_009, delimiter=\",\")\n",
        "np.savetxt(\"TestResults/RF60/subject_010_01__y_prediction.csv\", y_pred_RF60_010, delimiter=\",\")\n",
        "np.savetxt(\"TestResults/RF60/subject_011_01__y_prediction.csv\", y_pred_RF60_011, delimiter=\",\")\n",
        "np.savetxt(\"TestResults/RF60/subject_012_01__y_prediction.csv\", y_pred_RF60_012, delimiter=\",\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6cZvTwOlNPPw"
      },
      "source": [
        "y_pred_RF60 = Random_F_model_60.predict(x_val)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fLFezo-aNPPx",
        "outputId": "653e4f5b-e751-4ebc-f485-064031a9a8e9"
      },
      "source": [
        "print('F1 Score:', metrics.f1_score(y_val, y_pred_RF60, average='macro'))\n",
        "print('Accuracy Score:', metrics.accuracy_score(y_val, y_pred_RF60))\n",
        "print('Precision score: ', metrics.precision_score(y_val, y_pred_RF60, average='macro'))\n",
        "print('Recall score: ', metrics.recall_score(y_val, y_pred_RF60, average='macro'))\n",
        "print('Accuracy Score:', metrics.accuracy_score(y_val, y_pred_RF60))\n",
        "print('Confusion Matrix:', metrics.confusion_matrix(y_val, y_pred_RF60))\n",
        "print('Mean Absolute Error:', metrics.mean_absolute_error(y_val, y_pred_RF60))\n",
        "print('Mean Squared Error:', metrics.mean_squared_error(y_val, y_pred_RF60))\n",
        "print('Root Mean Squared Error:', np.sqrt(metrics.mean_squared_error(y_val, y_pred_RF60)))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "F1 Score: 0.9181688554609746\n",
            "Accuracy Score: 0.9192494767342256\n",
            "Precision score:  0.9189303249886663\n",
            "Recall score:  0.9191997639831198\n",
            "Accuracy Score: 0.9192494767342256\n",
            "Confusion Matrix: [[161905   7143   6082  26051]\n",
            " [  2551 196562    948   1225]\n",
            " [  1860    610 198372    885]\n",
            " [ 13539   2047   2105 183633]]\n",
            "Mean Absolute Error: 0.1929689963476918\n",
            "Mean Squared Error: 0.5157029389783965\n",
            "Root Mean Squared Error: 0.7181245985052988\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qLr634KwNPPx"
      },
      "source": [
        "## Random forest with estimator 100"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FCbsYXi7NPPx",
        "outputId": "4f5bae9d-8804-4eb4-f96d-a3954df8f2a7"
      },
      "source": [
        "## Random Forest with estimator 100 \n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "\n",
        "Random_F_model_100 = RandomForestClassifier(n_estimators=100, random_state=0)\n",
        "Random_F_model_100.fit(x_train, y_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier(random_state=0)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 230
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HfqARXnZNPPx"
      },
      "source": [
        "y_pred_RF100 = Random_F_model_100.predict(x_val)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dHavy1MPNPPy",
        "outputId": "a1ea97fb-c6fd-4aa8-e3d9-f6bce73e2a48"
      },
      "source": [
        "print('F1 Score:', metrics.f1_score(y_val, y_pred_RF100, average='macro'))\n",
        "print('Accuracy Score:', metrics.accuracy_score(y_val, y_pred_RF100))\n",
        "print('Precision score: ', metrics.precision_score(y_val, y_pred_RF100, average='macro'))\n",
        "print('Recall score: ', metrics.recall_score(y_val, y_pred_RF100, average='macro'))\n",
        "print('Accuracy Score:', metrics.accuracy_score(y_val, y_pred_RF100))\n",
        "print('Confusion Matrix:', metrics.confusion_matrix(y_val, y_pred_RF100))\n",
        "print('Mean Absolute Error:', metrics.mean_absolute_error(y_val, y_pred_RF100))\n",
        "print('Mean Squared Error:', metrics.mean_squared_error(y_val, y_pred_RF100))\n",
        "print('Root Mean Squared Error:', np.sqrt(metrics.mean_squared_error(y_val, y_pred_RF100)))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "F1 Score: 0.9189096865720365\n",
            "Accuracy Score: 0.9200104777298583\n",
            "Precision score:  0.919829672079975\n",
            "Recall score:  0.9199607377639372\n",
            "Accuracy Score: 0.9200104777298583\n",
            "Confusion Matrix: [[161759   7136   6092  26194]\n",
            " [  2435 196651    970   1230]\n",
            " [  1767    607 198424    929]\n",
            " [ 12959   2009   2105 184251]]\n",
            "Mean Absolute Error: 0.19097897253692656\n",
            "Mean Squared Error: 0.5101698534359257\n",
            "Root Mean Squared Error: 0.7142617541461433\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WubbDhNuNPPy"
      },
      "source": [
        "x_test_009 = pd.read_csv('TestProcessData/subject_009_01final_inner.csv')\n",
        "x_test_010 = pd.read_csv('TestProcessData/subject_010_01final_inner.csv')\n",
        "x_test_011 = pd.read_csv('TestProcessData/subject_011_01final_inner.csv')\n",
        "x_test_012 = pd.read_csv('TestProcessData/subject_012_01final_inner.csv')\n",
        "\n",
        "y_pred_RF100_009 = Random_F_model_100.predict(x_test_009)\n",
        "y_pred_RF100_010 = Random_F_model_100.predict(x_test_010)\n",
        "y_pred_RF100_011 = Random_F_model_100.predict(x_test_011)\n",
        "y_pred_RF100_012 = Random_F_model_100.predict(x_test_012)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TyLMwkSsNPPy"
      },
      "source": [
        "np.savetxt(\"TestResults/RF100/subject_009_01__y_prediction.csv\", y_pred_RF100_009, delimiter=\",\")\n",
        "np.savetxt(\"TestResults/RF100/subject_010_01__y_prediction.csv\", y_pred_RF100_010, delimiter=\",\")\n",
        "np.savetxt(\"TestResults/RF100/subject_011_01__y_prediction.csv\", y_pred_RF100_011, delimiter=\",\")\n",
        "np.savetxt(\"TestResults/RF100/subject_012_01__y_prediction.csv\", y_pred_RF100_012, delimiter=\",\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0c9BPkVRSE1y"
      },
      "source": [
        "## XGBoost "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zHVuFIvhR_qS",
        "outputId": "435c8d5b-2ceb-496c-f89f-4004820eca9a"
      },
      "source": [
        "from xgboost import XGBClassifier\n",
        "\n",
        "# fit model on training data\n",
        "XGB_model = XGBClassifier(learning_rate=0.1, n_estimators=20)\n",
        "XGB_model.fit(x_train, y_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
              "              colsample_bynode=1, colsample_bytree=1, gamma=0, gpu_id=-1,\n",
              "              importance_type='gain', interaction_constraints='',\n",
              "              learning_rate=0.1, max_delta_step=0, max_depth=6,\n",
              "              min_child_weight=1, missing=nan, monotone_constraints='()',\n",
              "              n_estimators=20, n_jobs=0, num_parallel_tree=1,\n",
              "              objective='multi:softprob', random_state=0, reg_alpha=0,\n",
              "              reg_lambda=1, scale_pos_weight=None, subsample=1,\n",
              "              tree_method='exact', validate_parameters=1, verbosity=None)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "V1dcgNxZR_qT"
      },
      "source": [
        "import pickle\n",
        "\n",
        "saved_model_XGB = 'XGB_smote.sav'\n",
        "pickle.dump(XGB_model, open(saved_model_XGB, 'wb'))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cu0i5vzRR_qT"
      },
      "source": [
        "x_test_009 = pd.read_csv('TestProcessData/subject_009_01final_inner.csv')\n",
        "x_test_010 = pd.read_csv('TestProcessData/subject_010_01final_inner.csv')\n",
        "x_test_011 = pd.read_csv('TestProcessData/subject_011_01final_inner.csv')\n",
        "x_test_012 = pd.read_csv('TestProcessData/subject_012_01final_inner.csv')\n",
        "\n",
        "y_pred_XG_009 = XGB_model.predict(x_test_009)\n",
        "y_pred_XG_010 = XGB_model.predict(x_test_010)\n",
        "y_pred_XG_011 = XGB_model.predict(x_test_011)\n",
        "y_pred_XG_012 = XGB_model.predict(x_test_012)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rT6Szxn4R_qT"
      },
      "source": [
        "np.savetxt(\"TestResults/XGB/subject_009_01__y_prediction.csv\", y_pred_XG_009, delimiter=\",\")\n",
        "np.savetxt(\"TestResults/XGB/subject_010_01__y_prediction.csv\", y_pred_XG_010, delimiter=\",\")\n",
        "np.savetxt(\"TestResults/XGB/subject_011_01__y_prediction.csv\", y_pred_XG_011, delimiter=\",\")\n",
        "np.savetxt(\"TestResults/XGB/subject_012_01__y_prediction.csv\", y_pred_XG_012, delimiter=\",\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yPswi3IkR_qU"
      },
      "source": [
        "y_pred_XGB = XGB_model.predict(x_val)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jsT8VZeFR_qU",
        "outputId": "9444cb02-e70b-47da-bd91-2a4c92f1ecda"
      },
      "source": [
        "print('F1 Score:', metrics.f1_score(y_val, y_pred_XGB, average='macro'))\n",
        "print('Accuracy Score:', metrics.accuracy_score(y_val, y_pred_XGB))\n",
        "print('Precision score: ', metrics.precision_score(y_val, y_pred_XGB, average='macro'))\n",
        "print('Recall score: ', metrics.recall_score(y_val, y_pred_XGB, average='macro'))\n",
        "print('Accuracy Score:', metrics.accuracy_score(y_val, y_pred_XGB))\n",
        "print('Confusion Matrix:', metrics.confusion_matrix(y_val, y_pred_XGB))\n",
        "print('Mean Absolute Error:', metrics.mean_absolute_error(y_val, y_pred_XGB))\n",
        "print('Mean Squared Error:', metrics.mean_squared_error(y_val, y_pred_XGB))\n",
        "print('Root Mean Squared Error:', np.sqrt(metrics.mean_squared_error(y_val, y_pred_XGB)))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "F1 Score: 0.6286275721015606\n",
            "Accuracy Score: 0.6345494452017211\n",
            "Precision score:  0.6342074393716188\n",
            "Recall score:  0.6344190196590909\n",
            "Accuracy Score: 0.6345494452017211\n",
            "Confusion Matrix: [[ 82065  26486  23596  69034]\n",
            " [ 14068 136467  19671  31080]\n",
            " [  8829  10996 165184  16718]\n",
            " [ 37554  17952  18393 127425]]\n",
            "Mean Absolute Error: 0.7312189175164304\n",
            "Mean Squared Error: 1.7274002567292104\n",
            "Root Mean Squared Error: 1.3143059981333154\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}