Facial-Expression-Data-Visualization.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Facial Expression Recognition\n",
    "\n",
    "*author: Jamell Dacon (daconjam@msu.edu)*\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/jamelldacon/anaconda3/lib/python3.7/site-packages/theano/configdefaults.py:560: UserWarning: DeprecationWarning: there is no c++ compiler.This is deprecated and with Theano 0.11 a c++ compiler will be mandatory\n",
      "  warnings.warn(\"DeprecationWarning: there is no c++ compiler.\"\n",
      "WARNING (theano.configdefaults): g++ not detected ! Theano will be unable to execute optimized C-implementations (for both CPU and GPU) and will default to Python implementations. Performance will be severely degraded. To remove this warning, set Theano flags cxx to an empty string.\n",
      "WARNING (theano.tensor.blas): Using NumPy C-API based implementation for BLAS functions.\n",
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "# Load in models and packages necessary for datasets\n",
    "# Importing individual libraries make it easy for us to use them without having to call the parent libraries\n",
    "\n",
    "import math\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import seaborn as sns\n",
    "import theano\n",
    "\n",
    "import csv\n",
    "import scipy.misc\n",
    "import scipy\n",
    "from scipy import ndimage\n",
    "from keras.preprocessing.image import ImageDataGenerator"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data: Attribute Information\n",
    "\n",
    "\n",
    "Facial Expression labels (target labels)\n",
    "\n",
    "0: - 4593 images- Angry <br>\n",
    "1: - 547 images- Disgust<br>\n",
    "2: - 5121 images- Fear<br>\n",
    "3: - 8989 images- Happy<br>\n",
    "4: - 6077 images- Sad<br>\n",
    "5: - 4002 images- Surprise<br>\n",
    "6: - 6198 images- Neutral<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Importing the file\n"
     ]
    },
    {
     "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>emotion</th>\n",
       "      <th>pixels</th>\n",
       "      <th>Usage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>70 80 82 72 58 58 60 63 54 58 60 48 89 115 121...</td>\n",
       "      <td>Training</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>151 150 147 155 148 133 111 140 170 174 182 15...</td>\n",
       "      <td>Training</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>231 212 156 164 174 138 161 173 182 200 106 38...</td>\n",
       "      <td>Training</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>24 32 36 30 32 23 19 20 30 41 21 22 32 34 21 1...</td>\n",
       "      <td>Training</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6</td>\n",
       "      <td>4 0 0 0 0 0 0 0 0 0 0 0 3 15 23 28 48 50 58 84...</td>\n",
       "      <td>Training</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   emotion                                             pixels     Usage\n",
       "0        0  70 80 82 72 58 58 60 63 54 58 60 48 89 115 121...  Training\n",
       "1        0  151 150 147 155 148 133 111 140 170 174 182 15...  Training\n",
       "2        2  231 212 156 164 174 138 161 173 182 200 106 38...  Training\n",
       "3        4  24 32 36 30 32 23 19 20 30 41 21 22 32 34 21 1...  Training\n",
       "4        6  4 0 0 0 0 0 0 0 0 0 0 0 3 15 23 28 48 50 58 84...  Training"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"Importing the file\")\n",
    "\n",
    "df = pd.read_csv('fer2013.csv')\n",
    "\n",
    "df.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "emotion    0\n",
       "pixels     0\n",
       "Usage      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We first check the df for missing values i.e. null entries\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 35887 entries, 0 to 35886\n",
      "Data columns (total 3 columns):\n",
      "emotion    35887 non-null int64\n",
      "pixels     35887 non-null object\n",
      "Usage      35887 non-null object\n",
      "dtypes: int64(1), object(2)\n",
      "memory usage: 841.2+ KB\n"
     ]
    }
   ],
   "source": [
    "# We can now get the information of each row in the df i.e data types and null entries of each column\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 35887 entries, 0 to 35886\n",
      "Data columns (total 3 columns):\n",
      "emotion    35887 non-null int64\n",
      "pixels     35887 non-null object\n",
      "Usage      35887 non-null object\n",
      "dtypes: int64(1), object(2)\n",
      "memory usage: 841.2+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training:  28709\n",
      "PublicTest:  3589\n",
      "PrivateTest:  3589\n"
     ]
    }
   ],
   "source": [
    "# Here we define the Training (training), PublicTest (validation), and PrivTest (test) sets\n",
    "training, val, test = np.split(df, [int(.8*len(df)), int(.9*len(df))])\n",
    "\n",
    "print('Training: ', len(training))\n",
    "print('PublicTest: ', len(val))\n",
    "print('PrivateTest: ', len(test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data distribution i.e. visualization of each feature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# We can now plot the distribution of emotions throughoout the training,\n",
    "\n",
    "#labels = ['Angry', 'Digust', 'Fear', 'Happy', 'Sad', 'Surprise', 'Neutral']\n",
    "\n",
    "fig = plt.figure(figsize = (10, 6))\n",
    "ax = fig.add_axes([0,0,1,1])\n",
    "ax.set_title(\"Emotions in the Training Data\", fontsize = 20)\n",
    "sns.countplot(x = \"emotion\", data = training)\n",
    "plt.grid()\n",
    "for i in ax.patches:\n",
    "    ax.text(x = i.get_x() + 0.2, y = i.get_height()+1.5, s = str(i.get_height()), fontsize = 20, color = \"grey\")\n",
    "plt.xlabel(\"Facial Expressions\")\n",
    "plt.ylabel(\"Count\", fontsize = 15)\n",
    "plt.tick_params(labelsize = 15)\n",
    "plt.xticks(rotation = 40)\n",
    "plt.show()\n",
    "\n",
    "\n",
    "fig = plt.figure(figsize = (10, 6))\n",
    "ax = fig.add_axes([0,0,1,1])\n",
    "ax.set_title(\"Emotions in Public Test Data\", fontsize = 20)\n",
    "sns.countplot(x = \"emotion\", data = val)\n",
    "plt.grid()\n",
    "for i in ax.patches:\n",
    "    ax.text(x = i.get_x() + 0.27, y = i.get_height()+0.2, s = str(i.get_height()), fontsize = 20, color = \"grey\")\n",
    "plt.xlabel(\"Facial Expressions\")\n",
    "plt.ylabel(\"Count\", fontsize = 15)\n",
    "plt.tick_params(labelsize = 15)\n",
    "plt.xticks(rotation = 40)\n",
    "plt.show()\n",
    "\n",
    "\n",
    "fig = plt.figure(figsize = (10, 6))\n",
    "ax = fig.add_axes([0,0,1,1])\n",
    "ax.set_title(\"Count of each Emotion in Test Data\", fontsize = 20)\n",
    "sns.countplot(x = \"emotion\", data = test)\n",
    "plt.grid()\n",
    "for i in ax.patches:\n",
    "    ax.text(x = i.get_x() + 0.27, y = i.get_height()+0.2, s = str(i.get_height()), fontsize = 20, color = \"grey\")\n",
    "plt.xlabel(\"Facial Expressions\")\n",
    "plt.ylabel(\"Count\", fontsize = 15)\n",
    "plt.tick_params(labelsize = 15)\n",
    "plt.xticks(rotation = 40)\n",
    "plt.show()\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}