FaceDetectionAndExpressionRecognition_ML.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "FaceDetectionAndExpressionRecognition_ML.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "accelerator": "TPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/MaharshSuryawala/Face-Detection-and-Facial-Expression-Recognition/blob/master/FaceDetectionAndExpressionRecognition_ML.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "yovkrQiR_WMa",
        "colab": {}
      },
      "source": [
        "!pip install -U -q PyDrive\n",
        "import os\n",
        "\n",
        "from pydrive.auth import GoogleAuth\n",
        "from pydrive.drive import GoogleDrive\n",
        "from google.colab import auth\n",
        "from oauth2client.client import GoogleCredentials\n",
        "\n",
        "#import google.colab import drive\n",
        "#drive.mount('/gdrive')\n",
        "\n",
        "# 1. Authenticate and create the PyDrive client.\n",
        "auth.authenticate_user()\n",
        "gauth = GoogleAuth()\n",
        "gauth.credentials = GoogleCredentials.get_application_default()\n",
        "drive = GoogleDrive(gauth)\n",
        "\n",
        "# choose a local (colab) directory to store the data.\n",
        "local_download_path = os.path.expanduser('~/ML')\n",
        "\n",
        "try:\n",
        "  os.makedirs(local_download_path)\n",
        "except: pass\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "eIm8DFz4_X0W",
        "colab": {}
      },
      "source": [
        "# 2. Auto-iterate using the query syntax\n",
        "#    https://developers.google.com/drive/v2/web/search-parameters\n",
        "file_list = drive.ListFile(\n",
        "    {'q': \"'12efh4IZ9r54HgrnPULW0qgFk2MjIDR5A' in parents\"}).GetList()\n",
        "\n",
        "for f in file_list:\n",
        "  # 3. Create & download by id.\n",
        "  print('title: %s, id: %s' % (f['title'], f['id']))\n",
        "  fname = os.path.join(local_download_path, f['title'])\n",
        "  print('downloading to {}'.format(fname))\n",
        "  f_ = drive.CreateFile({'id': f['id']})\n",
        "  f_.GetContentFile(fname)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0M_9BWswz7ld",
        "colab_type": "text"
      },
      "source": [
        ""
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "-0ddrRqs_k64",
        "outputId": "1cfc0157-9ca4-4f9c-8851-21db14342d75",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 90
        }
      },
      "source": [
        "import numpy as np\n",
        "from matplotlib import pyplot as plt\n",
        "#py.init_notebook_mode(connected=True)\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd\n",
        "import cv2\n",
        "import numpy as np\n",
        "import math\n",
        "from scipy.stats import norm\n",
        "import time\n",
        "import pandas as pd\n",
        "import statsmodels.api as sm\n",
        "from keras.layers import Dense, Dropout, LSTM, Embedding\n",
        "from keras.models import Sequential\n",
        "from matplotlib import pyplot as plt\n",
        "import plotly.offline as py\n",
        "import plotly.graph_objs as go\n",
        "py.init_notebook_mode(connected=True)\n",
        "from sklearn.decomposition import PCA\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "from sklearn import model_selection\n",
        "from keras.utils.np_utils import to_categorical\n",
        "%matplotlib inline\n",
        "\n",
        "#from PIL import Image\n",
        "#from resizeimage import resizeimage\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/statsmodels/compat/pandas.py:56: FutureWarning: The pandas.core.datetools module is deprecated and will be removed in a future version. Please use the pandas.tseries module instead.\n",
            "  from pandas.core import datetools\n",
            "Using TensorFlow backend.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/vnd.plotly.v1+html": "<script type=\"text/javascript\">window.PlotlyConfig = {MathJaxConfig: 'local'};</script><script type=\"text/javascript\">if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}</script><script>requirejs.config({paths: { 'plotly': ['https://cdn.plot.ly/plotly-latest.min']},});if(!window._Plotly) {require(['plotly'],function(plotly) {window._Plotly=plotly;});}</script>",
            "text/html": [
              "<script type=\"text/javascript\">window.PlotlyConfig = {MathJaxConfig: 'local'};</script><script type=\"text/javascript\">if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}</script><script>requirejs.config({paths: { 'plotly': ['https://cdn.plot.ly/plotly-latest.min']},});if(!window._Plotly) {require(['plotly'],function(plotly) {window._Plotly=plotly;});}</script>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "4Bl9DsAq_rf9",
        "outputId": "0445479a-b288-45c5-98f3-913b3b75f126",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 15540
        }
      },
      "source": [
        "%cd /root/ML\n",
        "\n",
        "data = pd.read_csv('Train_Roll_Number.txt', sep=\" \", header=None)\n",
        "data1 = pd.read_csv('Train_RGB_Sketch.txt', sep=\" \", header=None)\n",
        "data2 = pd.read_csv('Train_Gender.txt', sep=\" \", header=None)\n",
        "data3 = pd.read_csv('Train_Expression.txt', sep=\" \", header=None)\n",
        "\n",
        "data.columns = [\"filename\", \"subject\"]\n",
        "data1.columns = [\"filename\", \"sketch\"]\n",
        "data2.columns = [\"filename\", \"gender\"]\n",
        "data3.columns = [\"filename\", \"expression\"]\n",
        "\n",
        "data['X'] = None\n",
        "X = None\n",
        "\n",
        "set_type = set()\n",
        "set_shape = set()\n",
        "\n",
        "for index, row in data.iterrows():\n",
        "    # print(row[\"filename\"], row[\"X\"], row[\"subject\"])\n",
        "    \n",
        "    im = cv2.imread(row[\"filename\"])\n",
        "    \n",
        "    if row[\"filename\"] == \"1641010_Male_Neutral_Sketch.png\":\n",
        "        NoneType = type(im)\n",
        "    \n",
        "        break\n",
        "\n",
        "count_none = 0\n",
        "  \n",
        "width = 420\n",
        "height = 240\n",
        "\n",
        "for index, row in data.iterrows():\n",
        "    # print(row[\"filename\"], row[\"X\"], row[\"subject\"])\n",
        "    \n",
        "    im = cv2.imread(row[\"filename\"])\n",
        "    \n",
        "    if type(im) != None:\n",
        "        \n",
        "        im = cv2.resize(im, (height, width), interpolation = cv2.INTER_CUBIC)\n",
        "        print(row[\"filename\"])\n",
        "        vec = np.array(np.reshape(im, (-1, 1)))\n",
        "        \n",
        "        data['X'][index] = vec\n",
        "        \n",
        "        \"\"\"\n",
        "        if ((data1['filename'] == row['filename']) & (data1['sketch'] == 0)).any():\n",
        "            if index == 0:\n",
        "                X = vec\n",
        "                y_subject = np.array(data['subject'][index])\n",
        "\n",
        "            else:\n",
        "                X = np.concatenate((X, vec), axis=1)\n",
        "                y_subject = np.concatenate((y_subject, np.array(data['subject'][index])), axis=1)\n",
        "        \"\"\"\n",
        "        \n",
        "    if type(im) == None:\n",
        "        count_none += 1\n",
        "        print(row[\"filename\"])\n",
        "    \n",
        "print(count_none)\n",
        "\n",
        "# Let's print the first ten values for the signal vector\n",
        "print(data.head())"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/root/ML\n",
            "1641002_Male_Anger_RGB.png\n",
            "1641002_Male_Angry_Sketch.png\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:45: SettingWithCopyWarning:\n",
            "\n",
            "\n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
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            "1641002_Male_Disgust_RGB.png\n",
            "1641002_Male_Disgust_Sketch.png\n",
            "1641002_Male_Fear_RGB.png\n",
            "1641002_Male_Fear_Sketch.png\n",
            "1641002_Male_Happy_RGB.png\n",
            "1641002_Male_Happy_Sketch.png\n",
            "1641002_Male_Neutral_RGB.png\n",
            "1641002_Male_Neutral_Sketch.png\n",
            "1641002_Male_Sad_RGB.png\n",
            "1641002_Male_Sad_Sketch.png\n",
            "1641002_Male_Surprise_RGB.png\n",
            "1641002_Male_Surprise_Sketch.png\n",
            "1641003_Male_Disgust_RGB.png\n",
            "1641003_Male_Disgust_Sketch.png\n",
            "1641003_Male_Happy_RGB.png\n",
            "1641003_Male_Happy_Sketch.png\n",
            "1641003_Male_Neutral_RGB.png\n",
            "1641003_Male_Neutral_Sketch.png\n",
            "1641003_Male_Sad_RGB.png\n",
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            "1641003_Male_surprise_RGB.png\n",
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            "1641005_Male_Fear_RGB.png\n",
            "1641005_Male_Fear_Sketch.png\n",
            "1641005_Male_Happy_RGB.png\n",
            "1641005_Male_Happy_Sketch.png\n",
            "1641005_Male_Neutral_RGB.png\n",
            "1641005_Male_Neutral_Sketch.png\n",
            "1641005_Male_Sad_RGB.png\n",
            "1641005_Male_Sad_Sketch.png\n",
            "1641005_Male_Surprise_RGB.png\n",
            "1641005_Male_Surprise_Sketch.png\n",
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            "1641006_Male_Disgust_RGB.png\n",
            "1641006_Male_Disgust_Sketch.png\n",
            "1641006_Male_Fear_RGB.png\n",
            "1641006_Male_Fear_Sketch.png\n",
            "1641006_Male_Happy_RGB.png\n",
            "1641006_Male_Happy_Sketch.png\n",
            "1641006_Male_Neutral_RGB.png\n",
            "1641006_Male_Neutral_Sketch.png\n",
            "1641006_Male_Sad_RGB.png\n",
            "1641006_Male_Sad_Sketch.png\n",
            "1641006_Male_Surprise_RGB.png\n",
            "1641006_Male_Surprise_Sketch.png\n",
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            "1641007_Male_Fear_RGB.png\n",
            "1641007_Male_Fear_Sketch.png\n",
            "1641007_Male_Happy_RGB.png\n",
            "1641007_Male_Happy_Sketch.png\n",
            "1641007_Male_Neutral_RGB.png\n",
            "1641007_Male_Neutral_Sketch.png\n",
            "1641007_Male_Sad_RGB.png\n",
            "1641007_Male_Sad_Sketch.png\n",
            "1641007_Male_Surprise_RGB.png\n",
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            "1641010_Male_Disgust_RGB.png\n",
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            "1641010_Male_Fear_RGB.png\n",
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            "1641010_Male_Happy_RGB.png\n",
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            "1641010_Male_Neutral_RGB.png\n",
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            "1641010_Male_Sad_RGB.png\n",
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            "1641011_Male_Fear_RGB.png\n",
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            "1641011_Male_Happy_RGB.png\n",
            "1641011_Male_Happy_Sketch.png\n",
            "1641011_Male_Neutral_RGB.png\n",
            "1641011_Male_Neutral_Sketch.png\n",
            "1641011_Male_Sad_RGB.png\n",
            "1641011_Male_Sad_Sketch.png\n",
            "1641011_Male_Surprise_RGB.png\n",
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            "1641013_Female_Disgust_Sketch.png\n",
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            "1641013_Female_Fear_Sketch.png\n",
            "1641013_Female_Happy_RGB.png\n",
            "1641013_Female_Happy_Sketch.png\n",
            "1641013_Female_Neutral_RGB.png\n",
            "1641013_Female_Neutral_Sketch.png\n",
            "1641013_Female_Sad_RGB.png\n",
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            "1641015_Male_Neutral_RGB.png\n",
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            "1641016_Female_Happy_RGB.png\n",
            "1641016_Female_Happy_Sketch.png\n",
            "1641016_Female_Neutral_RGB.png\n",
            "1641016_Female_Neutral_Sketch.png\n",
            "1641016_Female_Sad_RGB.png\n",
            "1641016_Female_Sad_Sketch.png\n",
            "1641016_Female_Surprise_RGB.png\n",
            "1641016_Female_Surprise_Sketch.png\n",
            "1641016_Female_fear_RGB.png\n",
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            "1641072_Female_Sad_RGB.png\n",
            "1641072_Female_Sad_sketch.png\n",
            "1641072_Female_Surprise_RGB.png\n",
            "1641072_Female_Surprise_sketch.png\n",
            "1641073_Male_Anger_RGB.png\n",
            "1641073_Male_Anger_Sketch.png\n",
            "1641073_Male_Disgust_RGB.png\n",
            "1641073_Male_Disgust_Sketch.png\n",
            "1641073_Male_Fear_RGB.png\n",
            "1641073_Male_Fear_Sketch.png\n",
            "1641073_Male_Happy_RGB.png\n",
            "1641073_Male_Happy_Sketch.png\n",
            "1641073_Male_Neutral_RGB.png\n",
            "1641073_Male_Neutral_Sketch.png\n",
            "1641073_Male_Sad_RGB.png\n",
            "1641073_Male_Sad_Sketch.png\n",
            "1641073_Male_Surprise_RGB.png\n",
            "1641073_Male_Surprise_Sketch.png\n",
            "1844001_Male_Anger_RGB.png\n",
            "1844001_Male_Anger_Sketch.png\n",
            "1844001_Male_Disgust_RGB.png\n",
            "1844001_Male_Disgust_Sketch.png\n",
            "1844001_Male_Fear_RGB.png\n",
            "1844001_Male_Fear_Sketch.png\n",
            "1844001_Male_Happy_RGB.png\n",
            "1844001_Male_Happy_Sketch.png\n",
            "1844001_Male_Neutral_RGB.png\n",
            "1844001_Male_Neutral_Sketch.png\n",
            "1844001_Male_Sad_RGB.png\n",
            "1844001_Male_Sad_Sketch.png\n",
            "1844001_Male_Surprise_RGB.png\n",
            "1844001_Male_Surprise_Sketch.png\n",
            "1844002_Male_Angry_RGB.png\n",
            "1844002_Male_Angry_Sketch.png\n",
            "1844002_Male_Disgust_RGB.png\n",
            "1844002_Male_Disgust_Sketch.png\n",
            "1844002_Male_Fear_RGB.png\n",
            "1844002_Male_Fear_Sketch.png\n",
            "1844002_Male_Happy_RGB.png\n",
            "1844002_Male_Happy_Sketch.png\n",
            "1844002_Male_Neutral_RGB.png\n",
            "1844002_Male_Neutral_Sketch.png\n",
            "1844002_Male_Sad_RGB.png\n",
            "1844002_Male_Sad_Sketch.png\n",
            "1844002_Male_Surprise_RGB.png\n",
            "1844002_Male_Surprise_Sketch.png\n",
            "1844004_Female_Anger_RGB.png\n",
            "1844004_Female_Anger_Sketch.png\n",
            "1844004_Female_Disgust_RGB.png\n",
            "1844004_Female_Disgust_Sketch.png\n",
            "1844004_Female_Fear_RGB.png\n",
            "1844004_Female_Fear_Sketch.png\n",
            "1844004_Female_Happy_RGB.png\n",
            "1844004_Female_Happy_Sketch.png\n",
            "1844004_Female_Neutral_RGB.png\n",
            "1844004_Female_Neutral_Sketch.png\n",
            "1844004_Female_Sad_RGB.png\n",
            "1844004_Female_Sad_Sketch.png\n",
            "1844004_Female_Surprise_RGB.png\n",
            "1844004_Female_Surprise_Sketch.png\n",
            "1844005_Female_Anger_RGB.png\n",
            "1844005_Female_Anger_Sketch.png\n",
            "1844005_Female_Disgust_RGB.png\n",
            "1844005_Female_Disgust_Sketch.png\n",
            "1844005_Female_Fear_RGB.png\n",
            "1844005_Female_Fear_Sketch.png\n",
            "1844005_Female_Happy_RGB.png\n",
            "1844005_Female_Happy_Sketch.png\n",
            "1844005_Female_Neutral_RGB.png\n",
            "1844005_Female_Neutral_Sketch.png\n",
            "1844005_Female_Sad_RGB.png\n",
            "1844005_Female_Sad_Sketch.png\n",
            "1844005_Female_Surprise_RGB.png\n",
            "1844005_Female_Surprise_Sketch.png\n",
            "1844006_Male_Anger_RGB.png\n",
            "1844006_Male_Anger_Sketch.png\n",
            "1844006_Male_Disgust_RGB.png\n",
            "1844006_Male_Disgust_Sketch.png\n",
            "1844006_Male_Fear_RGB.png\n",
            "1844006_Male_Fear_Sketch.png\n",
            "1844006_Male_Happy_RGB.png\n",
            "1844006_Male_Happy_Sketch.png\n",
            "1844006_Male_Neutral_RGB.png\n",
            "1844006_Male_Neutral_Sketch.png\n",
            "1844006_Male_Sad_RGB.png\n",
            "1844006_Male_Sad_Sketch.png\n",
            "1844006_Male_Surprise_RGB.png\n",
            "1844006_Male_Surprise_Sketch.png\n",
            "1844007_Male_Anger_RGB.png\n",
            "1844007_Male_Anger_Sketch.png\n",
            "1844007_Male_Disgust_RGB.png\n",
            "1844007_Male_Disgust_Sketch.png\n",
            "1844007_Male_Fear_RGB.png\n",
            "1844007_Male_Fear_Sketch.png\n",
            "1844007_Male_Happy_RGB.png\n",
            "1844007_Male_Happy_Sketch.png\n",
            "1844007_Male_Neutral_RGB.png\n",
            "1844007_Male_Neutral_Sketch.png\n",
            "1844007_Male_Sad_RGB.png\n",
            "1844007_Male_Sad_Sketch.png\n",
            "1844007_Male_Surprise_RGB.png\n",
            "1844007_Male_Surprise_Sketch.png\n",
            "1844008_Female_Anger_RGB.png\n",
            "1844008_Female_Anger_Sketch.png\n",
            "1844008_Female_Disgust_RGB.png\n",
            "1844008_Female_Disgust_Sketch.png\n",
            "1844008_Female_Fear_RGB.png\n",
            "1844008_Female_Fear_Sketch.png\n",
            "1844008_Female_Happy_RGB.png\n",
            "1844008_Female_Happy_Sketch.png\n",
            "1844008_Female_Neutral_RGB.png\n",
            "1844008_Female_Neutral_Sketch.png\n",
            "1844008_Female_Sad_RGB.png\n",
            "1844008_Female_Sad_Sketch.png\n",
            "1844008_Female_Surprise_RGB.png\n",
            "1844008_Female_Surprise_Sketch.png\n",
            "1844010_Female_Angry_RGB.png\n",
            "1844010_Female_Angry_Sketch.png\n",
            "1844010_Female_Disgust_RGB.png\n",
            "1844010_Female_Disgust_Sketch.png\n",
            "1844010_Female_Fear_RGB.png\n",
            "1844010_Female_Fear_Sketch.png\n",
            "1844010_Female_Happy_RGB.png\n",
            "1844010_Female_Happy_Sketch.png\n",
            "1844010_Female_Neutral_RGB.png\n",
            "1844010_Female_Neutral_Sketch.png\n",
            "1844010_Female_Sad_RGB.png\n",
            "1844010_Female_Sad_Sketch.png\n",
            "1844010_Female_Surprise_RGB.png\n",
            "1844010_Female_Surprise_Sketch.png\n",
            "1849007_Female_Anger_RGB.png\n",
            "1849007_Female_Anger_Sketch.png\n",
            "1849007_Female_Disgust_RGB.png\n",
            "1849007_Female_Disgust_Sketch.png\n",
            "1849007_Female_Fear_RGB.png\n",
            "1849007_Female_Fear_Sketch.png\n",
            "1849007_Female_Happy_RGB.png\n",
            "1849007_Female_Happy_Sketch.png\n",
            "1849007_Female_Neutral_RGB.png\n",
            "1849007_Female_Neutral_Sketch.png\n",
            "1849007_Female_Sad_RGB.png\n",
            "1849007_Female_Sad_Sketch.png\n",
            "1849007_Female_Surprised_RGB.png\n",
            "1849007_Female_Surprised_Sketch.png\n",
            "1849011_Female_Anger_RGB.png\n",
            "1849011_Female_Anger_Sketch.png\n",
            "1849011_Female_Disgust_RGB.png\n",
            "1849011_Female_Disgust_Sketch.png\n",
            "1849011_Female_Fear_RGB.png\n",
            "1849011_Female_Fear_Sketch.png\n",
            "1849011_Female_Happy_RGB.png\n",
            "1849011_Female_Happy_Sketch.png\n",
            "1849011_Female_Neutral_RGB.png\n",
            "1849011_Female_Neutral_Sketch.png\n",
            "1849011_Female_Sad_RGB.png\n",
            "1849011_Female_Sad_Sketch.png\n",
            "1849011_Female_Surprise_RGB.png\n",
            "1849011_Female_Surprise_Sketch.png\n",
            "1849012_Male_Anger_RGB.png\n",
            "1849012_Male_Anger_Sketch.png\n",
            "1849012_Male_Disgust_RGB.png\n",
            "1849012_Male_Disgust_Sketch.png\n",
            "1849012_Male_Fear_RGB.png\n",
            "1849012_Male_Fear_Sketch.png\n",
            "1849012_Male_Happy_RGB.png\n",
            "1849012_Male_Happy_Sketch.png\n",
            "1849012_Male_Neutral_RGB.png\n",
            "1849012_Male_Neutral_Sketch.png\n",
            "1849012_Male_Sad_RGB.png\n",
            "1849012_Male_Sad_Sketch.png\n",
            "1849012_Male_Surprise_RGB.png\n",
            "1849012_Male_Surprise_Sketch.png\n",
            "1849013_Male_Anger_RGB.png\n",
            "1849013_Male_Anger_Sketch.png\n",
            "1849013_Male_Disgust_RGB.png\n",
            "1849013_Male_Disgust_Sketch.png\n",
            "1849013_Male_Fear_RGB.png\n",
            "1849013_Male_Fear_Sketch.png\n",
            "1849013_Male_Happy_RGB.png\n",
            "1849013_Male_Happy_Sketch.png\n",
            "1849013_Male_Neutral_RGB.png\n",
            "1849013_Male_Neutral_Sketch.png\n",
            "1849013_Male_Sad_RGB.png\n",
            "1849013_Male_Sad_Sketch.png\n",
            "1849013_Male_Surprise_RGB.png\n",
            "1849013_Male_Surprise_Sketch.png\n",
            "201501103_Male_Anger_RGB.png\n",
            "201501103_Male_Anger_Sketch.png\n",
            "201501103_Male_Disgust_RGB.png\n",
            "201501103_Male_Disgust_Sketch.png\n",
            "201501103_Male_Fear_RGB.png\n",
            "201501103_Male_Fear_Sketch.png\n",
            "201501103_Male_Happy_RGB.png\n",
            "201501103_Male_Happy_Sketch.png\n",
            "201501103_Male_Neutral_RGB.png\n",
            "201501103_Male_Neutral_Sketch.png\n",
            "201501103_Male_Sad_RGB.png\n",
            "201501103_Male_Sad_Sketch.png\n",
            "201501103_Male_Surprise_RGB.png\n",
            "201501103_Male_Surprise_Sketch.png\n",
            "0\n",
            "                          filename  subject  \\\n",
            "0       1641002_Male_Anger_RGB.png        0   \n",
            "1    1641002_Male_Angry_Sketch.png        0   \n",
            "2     1641002_Male_Disgust_RGB.png        0   \n",
            "3  1641002_Male_Disgust_Sketch.png        0   \n",
            "4        1641002_Male_Fear_RGB.png        0   \n",
            "\n",
            "                                                   X  \n",
            "0  [[186], [197], [201], [192], [198], [203], [19...  \n",
            "1  [[253], [253], [253], [253], [253], [253], [25...  \n",
            "2  [[185], [197], [201], [186], [199], [201], [18...  \n",
            "3  [[254], [254], [254], [252], [252], [252], [25...  \n",
            "4  [[188], [197], [201], [187], [198], [202], [18...  \n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "NMFrIaFS_vxf",
        "colab": {}
      },
      "source": [
        "data = pd.merge(data, data1, on=['filename'])\n",
        "data = pd.merge(data, data2, on=['filename'])\n",
        "data = pd.merge(data, data3, on=['filename'])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "3G0RCLrC_z6p",
        "outputId": "38b0771f-bd28-41dd-fbed-cebc9088aa25",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1917
        }
      },
      "source": [
        "# Deleting None rows\n",
        "print(data.shape)\n",
        "data = data.dropna()\n",
        "print(data.shape)\n",
        "data\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(860, 3)\n",
            "(860, 3)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>filename</th>\n",
              "      <th>subject</th>\n",
              "      <th>X</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1641002_Male_Anger_RGB.png</td>\n",
              "      <td>0</td>\n",
              "      <td>[[186], [197], [201], [192], [198], [203], [19...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1641002_Male_Angry_Sketch.png</td>\n",
              "      <td>0</td>\n",
              "      <td>[[253], [253], [253], [253], [253], [253], [25...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1641002_Male_Disgust_RGB.png</td>\n",
              "      <td>0</td>\n",
              "      <td>[[185], [197], [201], [186], [199], [201], [18...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1641002_Male_Disgust_Sketch.png</td>\n",
              "      <td>0</td>\n",
              "      <td>[[254], [254], [254], [252], [252], [252], [25...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1641002_Male_Fear_RGB.png</td>\n",
              "      <td>0</td>\n",
              "      <td>[[188], [197], [201], [187], [198], [202], [18...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>1641002_Male_Fear_Sketch.png</td>\n",
              "      <td>0</td>\n",
              "      <td>[[253], [253], [253], [253], [253], [253], [25...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>1641002_Male_Happy_RGB.png</td>\n",
              "      <td>0</td>\n",
              "      <td>[[196], [205], [208], [195], [204], [207], [19...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>1641002_Male_Happy_Sketch.png</td>\n",
              "      <td>0</td>\n",
              "      <td>[[253], [253], [253], [254], [254], [254], [25...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>1641002_Male_Neutral_RGB.png</td>\n",
              "      <td>0</td>\n",
              "      <td>[[194], [202], [202], [187], [197], [197], [19...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>1641002_Male_Neutral_Sketch.png</td>\n",
              "      <td>0</td>\n",
              "      <td>[[253], [253], [253], [252], [252], [252], [25...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>1641002_Male_Sad_RGB.png</td>\n",
              "      <td>0</td>\n",
              "      <td>[[156], [172], [184], [154], [171], [180], [15...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>1641002_Male_Sad_Sketch.png</td>\n",
              "      <td>0</td>\n",
              "      <td>[[252], [252], [252], [253], [253], [253], [25...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>1641002_Male_Surprise_RGB.png</td>\n",
              "      <td>0</td>\n",
              "      <td>[[171], [179], [178], [168], [183], [185], [16...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>1641002_Male_Surprise_Sketch.png</td>\n",
              "      <td>0</td>\n",
              "      <td>[[253], [253], [253], [253], [253], [253], [25...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>1641003_Male_Disgust_RGB.png</td>\n",
              "      <td>1</td>\n",
              "      <td>[[159], [169], [157], [177], [194], [181], [18...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>1641003_Male_Disgust_Sketch.png</td>\n",
              "      <td>1</td>\n",
              "      <td>[[236], [236], [236], [243], [243], [243], [24...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>1641003_Male_Happy_RGB.png</td>\n",
              "      <td>1</td>\n",
              "      <td>[[215], [220], [214], [193], [210], [203], [19...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>1641003_Male_Happy_Sketch.png</td>\n",
              "      <td>1</td>\n",
              "      <td>[[253], [253], [253], [244], [244], [244], [24...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>1641003_Male_Neutral_RGB.png</td>\n",
              "      <td>1</td>\n",
              "      <td>[[213], [217], [211], [220], [226], [215], [19...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>1641003_Male_Neutral_Sketch.png</td>\n",
              "      <td>1</td>\n",
              "      <td>[[250], [250], [250], [251], [251], [251], [24...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>1641003_Male_Sad_RGB.png</td>\n",
              "      <td>1</td>\n",
              "      <td>[[149], [153], [118], [139], [143], [106], [14...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>1641003_Male_Sad_Sketch.png</td>\n",
              "      <td>1</td>\n",
              "      <td>[[235], [235], [235], [243], [243], [243], [24...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>1641003_Male_surprise_RGB.png</td>\n",
              "      <td>1</td>\n",
              "      <td>[[155], [164], [154], [157], [165], [154], [13...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>1641003_Male_surprise_Sketch.png</td>\n",
              "      <td>1</td>\n",
              "      <td>[[223], [223], [223], [237], [237], [237], [20...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>1641005_Male_Anger_RGB.png</td>\n",
              "      <td>2</td>\n",
              "      <td>[[245], [250], [251], [247], [252], [253], [24...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>1641005_Male_Anger_Sketch.png</td>\n",
              "      <td>2</td>\n",
              "      <td>[[252], [252], [252], [252], [252], [252], [25...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>1641005_Male_Disgust_RGB.png</td>\n",
              "      <td>2</td>\n",
              "      <td>[[145], [159], [165], [146], [160], [166], [14...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>1641005_Male_Disgust_Sketch.png</td>\n",
              "      <td>2</td>\n",
              "      <td>[[248], [248], [248], [250], [250], [250], [25...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>1641005_Male_Fear_RGB.png</td>\n",
              "      <td>2</td>\n",
              "      <td>[[141], [163], [159], [142], [164], [160], [14...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>1641005_Male_Fear_Sketch.png</td>\n",
              "      <td>2</td>\n",
              "      <td>[[251], [251], [251], [250], [250], [250], [25...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>830</th>\n",
              "      <td>1849012_Male_Surprise_RGB.png</td>\n",
              "      <td>59</td>\n",
              "      <td>[[236], [239], [237], [236], [239], [237], [23...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>831</th>\n",
              "      <td>1849012_Male_Surprise_Sketch.png</td>\n",
              "      <td>59</td>\n",
              "      <td>[[249], [249], [249], [255], [255], [255], [25...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>832</th>\n",
              "      <td>1849013_Male_Anger_RGB.png</td>\n",
              "      <td>60</td>\n",
              "      <td>[[229], [231], [231], [232], [234], [234], [23...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>833</th>\n",
              "      <td>1849013_Male_Anger_Sketch.png</td>\n",
              "      <td>60</td>\n",
              "      <td>[[249], [249], [249], [255], [255], [255], [25...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>834</th>\n",
              "      <td>1849013_Male_Disgust_RGB.png</td>\n",
              "      <td>60</td>\n",
              "      <td>[[246], [251], [250], [246], [251], [250], [24...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>835</th>\n",
              "      <td>1849013_Male_Disgust_Sketch.png</td>\n",
              "      <td>60</td>\n",
              "      <td>[[249], [249], [249], [255], [255], [255], [25...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>836</th>\n",
              "      <td>1849013_Male_Fear_RGB.png</td>\n",
              "      <td>60</td>\n",
              "      <td>[[234], [236], [236], [234], [236], [236], [23...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>837</th>\n",
              "      <td>1849013_Male_Fear_Sketch.png</td>\n",
              "      <td>60</td>\n",
              "      <td>[[247], [247], [247], [255], [255], [255], [25...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>838</th>\n",
              "      <td>1849013_Male_Happy_RGB.png</td>\n",
              "      <td>60</td>\n",
              "      <td>[[233], [238], [237], [234], [238], [239], [23...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>839</th>\n",
              "      <td>1849013_Male_Happy_Sketch.png</td>\n",
              "      <td>60</td>\n",
              "      <td>[[249], [249], [249], [255], [255], [255], [25...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>840</th>\n",
              "      <td>1849013_Male_Neutral_RGB.png</td>\n",
              "      <td>60</td>\n",
              "      <td>[[238], [238], [238], [238], [238], [238], [23...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>841</th>\n",
              "      <td>1849013_Male_Neutral_Sketch.png</td>\n",
              "      <td>60</td>\n",
              "      <td>[[248], [248], [248], [255], [255], [255], [25...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>842</th>\n",
              "      <td>1849013_Male_Sad_RGB.png</td>\n",
              "      <td>60</td>\n",
              "      <td>[[237], [239], [239], [237], [239], [239], [23...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>843</th>\n",
              "      <td>1849013_Male_Sad_Sketch.png</td>\n",
              "      <td>60</td>\n",
              "      <td>[[248], [248], [248], [253], [253], [253], [25...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>844</th>\n",
              "      <td>1849013_Male_Surprise_RGB.png</td>\n",
              "      <td>60</td>\n",
              "      <td>[[231], [233], [233], [230], [232], [232], [23...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>845</th>\n",
              "      <td>1849013_Male_Surprise_Sketch.png</td>\n",
              "      <td>60</td>\n",
              "      <td>[[251], [251], [251], [255], [255], [255], [25...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>846</th>\n",
              "      <td>201501103_Male_Anger_RGB.png</td>\n",
              "      <td>61</td>\n",
              "      <td>[[205], [213], [230], [195], [203], [220], [19...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>847</th>\n",
              "      <td>201501103_Male_Anger_Sketch.png</td>\n",
              "      <td>61</td>\n",
              "      <td>[[250], [250], [250], [250], [250], [250], [23...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>848</th>\n",
              "      <td>201501103_Male_Disgust_RGB.png</td>\n",
              "      <td>61</td>\n",
              "      <td>[[255], [255], [255], [255], [255], [255], [25...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>849</th>\n",
              "      <td>201501103_Male_Disgust_Sketch.png</td>\n",
              "      <td>61</td>\n",
              "      <td>[[255], [255], [255], [255], [255], [255], [25...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>850</th>\n",
              "      <td>201501103_Male_Fear_RGB.png</td>\n",
              "      <td>61</td>\n",
              "      <td>[[255], [255], [255], [255], [255], [255], [25...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>851</th>\n",
              "      <td>201501103_Male_Fear_Sketch.png</td>\n",
              "      <td>61</td>\n",
              "      <td>[[255], [255], [255], [255], [255], [255], [25...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>852</th>\n",
              "      <td>201501103_Male_Happy_RGB.png</td>\n",
              "      <td>61</td>\n",
              "      <td>[[255], [255], [255], [255], [255], [255], [25...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>853</th>\n",
              "      <td>201501103_Male_Happy_Sketch.png</td>\n",
              "      <td>61</td>\n",
              "      <td>[[255], [255], [255], [255], [255], [255], [25...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>854</th>\n",
              "      <td>201501103_Male_Neutral_RGB.png</td>\n",
              "      <td>61</td>\n",
              "      <td>[[194], [206], [224], [189], [201], [219], [18...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>855</th>\n",
              "      <td>201501103_Male_Neutral_Sketch.png</td>\n",
              "      <td>61</td>\n",
              "      <td>[[244], [244], [244], [247], [247], [247], [24...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>856</th>\n",
              "      <td>201501103_Male_Sad_RGB.png</td>\n",
              "      <td>61</td>\n",
              "      <td>[[198], [204], [223], [201], [207], [226], [20...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>857</th>\n",
              "      <td>201501103_Male_Sad_Sketch.png</td>\n",
              "      <td>61</td>\n",
              "      <td>[[246], [246], [246], [246], [246], [246], [24...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>858</th>\n",
              "      <td>201501103_Male_Surprise_RGB.png</td>\n",
              "      <td>61</td>\n",
              "      <td>[[183], [190], [209], [180], [187], [206], [17...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>859</th>\n",
              "      <td>201501103_Male_Surprise_Sketch.png</td>\n",
              "      <td>61</td>\n",
              "      <td>[[239], [239], [239], [221], [221], [221], [24...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>860 rows × 3 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                               filename  subject  \\\n",
              "0            1641002_Male_Anger_RGB.png        0   \n",
              "1         1641002_Male_Angry_Sketch.png        0   \n",
              "2          1641002_Male_Disgust_RGB.png        0   \n",
              "3       1641002_Male_Disgust_Sketch.png        0   \n",
              "4             1641002_Male_Fear_RGB.png        0   \n",
              "5          1641002_Male_Fear_Sketch.png        0   \n",
              "6            1641002_Male_Happy_RGB.png        0   \n",
              "7         1641002_Male_Happy_Sketch.png        0   \n",
              "8          1641002_Male_Neutral_RGB.png        0   \n",
              "9       1641002_Male_Neutral_Sketch.png        0   \n",
              "10             1641002_Male_Sad_RGB.png        0   \n",
              "11          1641002_Male_Sad_Sketch.png        0   \n",
              "12        1641002_Male_Surprise_RGB.png        0   \n",
              "13     1641002_Male_Surprise_Sketch.png        0   \n",
              "14         1641003_Male_Disgust_RGB.png        1   \n",
              "15      1641003_Male_Disgust_Sketch.png        1   \n",
              "16           1641003_Male_Happy_RGB.png        1   \n",
              "17        1641003_Male_Happy_Sketch.png        1   \n",
              "18         1641003_Male_Neutral_RGB.png        1   \n",
              "19      1641003_Male_Neutral_Sketch.png        1   \n",
              "20             1641003_Male_Sad_RGB.png        1   \n",
              "21          1641003_Male_Sad_Sketch.png        1   \n",
              "22        1641003_Male_surprise_RGB.png        1   \n",
              "23     1641003_Male_surprise_Sketch.png        1   \n",
              "24           1641005_Male_Anger_RGB.png        2   \n",
              "25        1641005_Male_Anger_Sketch.png        2   \n",
              "26         1641005_Male_Disgust_RGB.png        2   \n",
              "27      1641005_Male_Disgust_Sketch.png        2   \n",
              "28            1641005_Male_Fear_RGB.png        2   \n",
              "29         1641005_Male_Fear_Sketch.png        2   \n",
              "..                                  ...      ...   \n",
              "830       1849012_Male_Surprise_RGB.png       59   \n",
              "831    1849012_Male_Surprise_Sketch.png       59   \n",
              "832          1849013_Male_Anger_RGB.png       60   \n",
              "833       1849013_Male_Anger_Sketch.png       60   \n",
              "834        1849013_Male_Disgust_RGB.png       60   \n",
              "835     1849013_Male_Disgust_Sketch.png       60   \n",
              "836           1849013_Male_Fear_RGB.png       60   \n",
              "837        1849013_Male_Fear_Sketch.png       60   \n",
              "838          1849013_Male_Happy_RGB.png       60   \n",
              "839       1849013_Male_Happy_Sketch.png       60   \n",
              "840        1849013_Male_Neutral_RGB.png       60   \n",
              "841     1849013_Male_Neutral_Sketch.png       60   \n",
              "842            1849013_Male_Sad_RGB.png       60   \n",
              "843         1849013_Male_Sad_Sketch.png       60   \n",
              "844       1849013_Male_Surprise_RGB.png       60   \n",
              "845    1849013_Male_Surprise_Sketch.png       60   \n",
              "846        201501103_Male_Anger_RGB.png       61   \n",
              "847     201501103_Male_Anger_Sketch.png       61   \n",
              "848      201501103_Male_Disgust_RGB.png       61   \n",
              "849   201501103_Male_Disgust_Sketch.png       61   \n",
              "850         201501103_Male_Fear_RGB.png       61   \n",
              "851      201501103_Male_Fear_Sketch.png       61   \n",
              "852        201501103_Male_Happy_RGB.png       61   \n",
              "853     201501103_Male_Happy_Sketch.png       61   \n",
              "854      201501103_Male_Neutral_RGB.png       61   \n",
              "855   201501103_Male_Neutral_Sketch.png       61   \n",
              "856          201501103_Male_Sad_RGB.png       61   \n",
              "857       201501103_Male_Sad_Sketch.png       61   \n",
              "858     201501103_Male_Surprise_RGB.png       61   \n",
              "859  201501103_Male_Surprise_Sketch.png       61   \n",
              "\n",
              "                                                     X  \n",
              "0    [[186], [197], [201], [192], [198], [203], [19...  \n",
              "1    [[253], [253], [253], [253], [253], [253], [25...  \n",
              "2    [[185], [197], [201], [186], [199], [201], [18...  \n",
              "3    [[254], [254], [254], [252], [252], [252], [25...  \n",
              "4    [[188], [197], [201], [187], [198], [202], [18...  \n",
              "5    [[253], [253], [253], [253], [253], [253], [25...  \n",
              "6    [[196], [205], [208], [195], [204], [207], [19...  \n",
              "7    [[253], [253], [253], [254], [254], [254], [25...  \n",
              "8    [[194], [202], [202], [187], [197], [197], [19...  \n",
              "9    [[253], [253], [253], [252], [252], [252], [25...  \n",
              "10   [[156], [172], [184], [154], [171], [180], [15...  \n",
              "11   [[252], [252], [252], [253], [253], [253], [25...  \n",
              "12   [[171], [179], [178], [168], [183], [185], [16...  \n",
              "13   [[253], [253], [253], [253], [253], [253], [25...  \n",
              "14   [[159], [169], [157], [177], [194], [181], [18...  \n",
              "15   [[236], [236], [236], [243], [243], [243], [24...  \n",
              "16   [[215], [220], [214], [193], [210], [203], [19...  \n",
              "17   [[253], [253], [253], [244], [244], [244], [24...  \n",
              "18   [[213], [217], [211], [220], [226], [215], [19...  \n",
              "19   [[250], [250], [250], [251], [251], [251], [24...  \n",
              "20   [[149], [153], [118], [139], [143], [106], [14...  \n",
              "21   [[235], [235], [235], [243], [243], [243], [24...  \n",
              "22   [[155], [164], [154], [157], [165], [154], [13...  \n",
              "23   [[223], [223], [223], [237], [237], [237], [20...  \n",
              "24   [[245], [250], [251], [247], [252], [253], [24...  \n",
              "25   [[252], [252], [252], [252], [252], [252], [25...  \n",
              "26   [[145], [159], [165], [146], [160], [166], [14...  \n",
              "27   [[248], [248], [248], [250], [250], [250], [25...  \n",
              "28   [[141], [163], [159], [142], [164], [160], [14...  \n",
              "29   [[251], [251], [251], [250], [250], [250], [25...  \n",
              "..                                                 ...  \n",
              "830  [[236], [239], [237], [236], [239], [237], [23...  \n",
              "831  [[249], [249], [249], [255], [255], [255], [25...  \n",
              "832  [[229], [231], [231], [232], [234], [234], [23...  \n",
              "833  [[249], [249], [249], [255], [255], [255], [25...  \n",
              "834  [[246], [251], [250], [246], [251], [250], [24...  \n",
              "835  [[249], [249], [249], [255], [255], [255], [25...  \n",
              "836  [[234], [236], [236], [234], [236], [236], [23...  \n",
              "837  [[247], [247], [247], [255], [255], [255], [25...  \n",
              "838  [[233], [238], [237], [234], [238], [239], [23...  \n",
              "839  [[249], [249], [249], [255], [255], [255], [25...  \n",
              "840  [[238], [238], [238], [238], [238], [238], [23...  \n",
              "841  [[248], [248], [248], [255], [255], [255], [25...  \n",
              "842  [[237], [239], [239], [237], [239], [239], [23...  \n",
              "843  [[248], [248], [248], [253], [253], [253], [25...  \n",
              "844  [[231], [233], [233], [230], [232], [232], [23...  \n",
              "845  [[251], [251], [251], [255], [255], [255], [25...  \n",
              "846  [[205], [213], [230], [195], [203], [220], [19...  \n",
              "847  [[250], [250], [250], [250], [250], [250], [23...  \n",
              "848  [[255], [255], [255], [255], [255], [255], [25...  \n",
              "849  [[255], [255], [255], [255], [255], [255], [25...  \n",
              "850  [[255], [255], [255], [255], [255], [255], [25...  \n",
              "851  [[255], [255], [255], [255], [255], [255], [25...  \n",
              "852  [[255], [255], [255], [255], [255], [255], [25...  \n",
              "853  [[255], [255], [255], [255], [255], [255], [25...  \n",
              "854  [[194], [206], [224], [189], [201], [219], [18...  \n",
              "855  [[244], [244], [244], [247], [247], [247], [24...  \n",
              "856  [[198], [204], [223], [201], [207], [226], [20...  \n",
              "857  [[246], [246], [246], [246], [246], [246], [24...  \n",
              "858  [[183], [190], [209], [180], [187], [206], [17...  \n",
              "859  [[239], [239], [239], [221], [221], [221], [24...  \n",
              "\n",
              "[860 rows x 3 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "YmjiqfNS_5yS",
        "colab": {}
      },
      "source": [
        "data_nosketch = data[data['sketch'] == 0]\n",
        "data_nosketch"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "LcQ6UFzj_8D4",
        "colab": {}
      },
      "source": [
        "def return_dataset(data, class_name):\n",
        "    for index, row in data.iterrows():\n",
        "        \n",
        "        vec = data['X'][index]\n",
        "      \n",
        "        if index == 0:\n",
        "            X = vec\n",
        "            y = np.reshape(data[class_name][index], (1, 1))\n",
        "\n",
        "        else:\n",
        "            X = np.concatenate((X, vec), axis=1)\n",
        "            y = np.concatenate((y, np.reshape(data[class_name][index], (1, 1))), axis=0)\n",
        "            \n",
        "    return X, y"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "JobqtTlc_8md",
        "colab": {}
      },
      "source": [
        "X, y = return_dataset(data, 'subject')\n",
        "X = X.T\n",
        "\n",
        "# print(X.shape)\n",
        "\n",
        "#train_x, test_x, train_y, test_y = model_selection.train_test_split(X, y, test_size = 0.1, random_state = 0)\n",
        "\n",
        "#X = X.astype(np.float32)\n",
        "#X = X.T\n",
        "#print(type(X))\n",
        "\n",
        "X_train_DT, X_test_DT, y_train_DT, y_test_DT = model_selection.train_test_split(X, data['subject'], test_size = 0.1, random_state = 0)\n",
        "X_train_KNN, X_test_KNN, y_train_KNN, y_test_KNN = model_selection.train_test_split(X, data['subject'], test_size = 0.1, random_state = 0)\n",
        "X_train_SVM, X_test_SVM, y_train_SVM, y_test_SVM = model_selection.train_test_split(X, data['subject'], test_size = 0.1, random_state = 0)\n",
        "X_train_RF, X_test_RF, y_train_RF, y_test_RF = model_selection.train_test_split(X, data['subject'], test_size = 0.1, random_state = 0)\n",
        "X_train_NB, X_test_NB, y_train_NB, y_test_NB = model_selection.train_test_split(X, data['subject'], test_size = 0.1, random_state = 0)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "wYwSHuii__Fc",
        "colab": {}
      },
      "source": [
        "from sklearn.tree import DecisionTreeClassifier\n",
        "classifier_DT = DecisionTreeClassifier(criterion = 'entropy', random_state = 0)\n",
        "classifier_DT.fit(X_train_DT, y_train_DT)\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "7H5mL80fG4XL",
        "colab": {}
      },
      "source": [
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "classifier_KNN = KNeighborsClassifier(n_neighbors = 5, metric = 'minkowski', p = 2)\n",
        "classifier_KNN.fit(X_train_KNN, y_train_KNN)\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "kz1PRCLPr5fI",
        "colab": {}
      },
      "source": [
        "from sklearn.svm import SVC\n",
        "classifier_SVM = SVC(kernel = 'linear', random_state = 0)\n",
        "classifier_SVM.fit(X_train_SVM, y_train_SVM)\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "w8WyNk3osCtW",
        "colab": {}
      },
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "sc = StandardScaler()\n",
        "X_train_NB = sc.fit_transform(X_train_NB)\n",
        "X_test_NB = sc.transform(X_test_NB)\n",
        "\n",
        "from sklearn.naive_bayes import GaussianNB\n",
        "classifier_NB = GaussianNB()\n",
        "classifier_NB.fit(X_train_NB, y_train_NB)\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "CmaUTVD4hcgH",
        "colab": {}
      },
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "classifier_RF = RandomForestClassifier(n_estimators = 10, criterion = 'entropy', random_state = 0)\n",
        "classifier_RF.fit(X_train_RF, y_train_RF)\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "Ax-p76CxnZUA",
        "colab": {}
      },
      "source": [
        "y_pred_DT = classifier.predict(X_test_DT)\n",
        "y_test_DT = y_test_DT.tolist() \n",
        "\n",
        "y_pred_SVM = classifier.predict(X_test_SVM)\n",
        "y_test_SVM = y_test_SVM.tolist() \n",
        "\n",
        "y_pred_RF = classifier.predict(X_test_RF)\n",
        "y_test_RF = y_test_RF.tolist() \n",
        "\n",
        "y_pred_NB = classifier.predict(X_test_NB)\n",
        "y_test_NB = y_test_NB.tolist() \n",
        "\n",
        "y_pred_KNN = classifier.predict(X_test_KNN)\n",
        "y_test_KNN = y_test_KNN.tolist() \n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "vL1rMRJvoOJP",
        "colab": {}
      },
      "source": [
        "y_test_np_DT = np.asarray(y_test_DT)\n",
        "y_test_np_SVM = np.asarray(y_test_SVM)\n",
        "y_test_np_RF = np.asarray(y_test_RF)\n",
        "y_test_np_KNN = np.asarray(y_test_KNN)\n",
        "y_test_np_NB = np.asarray(y_test_NB)\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "s11Jm7LknbhO",
        "colab": {}
      },
      "source": [
        "# Accuracy\n",
        "##############################################\n",
        "disc_NB = y_test_np_NB - y_pred_NB\n",
        "\n",
        "count = 0\n",
        "for i in disc_NB:\n",
        "  if i == 0:\n",
        "    count += 1\n",
        "\n",
        "accuracy_NB = ( (100 * count) / len(y_pred_NB))\n",
        "##############################################\n",
        "disc_RF = y_test_np_RF - y_pred_RF\n",
        "\n",
        "count = 0\n",
        "for i in disc_RF:\n",
        "  if i == 0:\n",
        "    count += 1\n",
        "\n",
        "accuracy_RF = ( (100 * count) / len(y_pred_RF))\n",
        "##############################################\n",
        "disc_KNN = y_test_np_KNN - y_pred_KNN\n",
        "\n",
        "count = 0\n",
        "for i in disc_KNN:\n",
        "  if i == 0:\n",
        "    count += 1\n",
        "\n",
        "accuracy_KNN = ( (100 * count) / len(y_pred_KNN))\n",
        "##############################################\n",
        "disc_SVM = y_test_np_SVM - y_pred_SVM\n",
        "\n",
        "count = 0\n",
        "for i in disc_SVM:\n",
        "  if i == 0:\n",
        "    count += 1\n",
        "\n",
        "accuracy_SVM = ( (100 * count) / len(y_pred_SVM))\n",
        "##############################################\n",
        "disc_DT = y_test_np_DT - y_pred_DT\n",
        "\n",
        "count = 0\n",
        "for i in disc_DT:\n",
        "  if i == 0:\n",
        "    count += 1\n",
        "\n",
        "accuracy_DT = ( (100 * count) / len(y_pred_DT))\n",
        "##############################################\n",
        "\n",
        "\n",
        "\n",
        "print(accuracy_NB)\n",
        "print(accuracy_RF)\n",
        "print(accuracy_DT)\n",
        "print(accuracy_KNN)\n",
        "print(accuracy_SVM)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f60Kwqk2vFpm",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}