01-data-analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Import libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "import os\n",
    "os.environ[\"KERAS_BACKEND\"] = \"tensorflow\"\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, Activation, BatchNormalization, Dropout\n",
    "from IPython.display import SVG\n",
    "from keras.utils.vis_utils import model_to_dot\n",
    "import keras\n",
    "from datetime import datetime"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load training dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "train_dataframe = pd.read_csv('dataset/1. istanbul/train_data.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualize some training data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "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>Subject ID</th>\n",
       "      <th>Jitter (local)</th>\n",
       "      <th>Jitter (local, absolute)</th>\n",
       "      <th>Jitter (rap)</th>\n",
       "      <th>Jitter (ppq5)</th>\n",
       "      <th>Jitter (ddp)</th>\n",
       "      <th>Shimmer (local)</th>\n",
       "      <th>Shimmer (local, dB)</th>\n",
       "      <th>Shimmer (apq3)</th>\n",
       "      <th>Shimmer (apq5)</th>\n",
       "      <th>...</th>\n",
       "      <th>Maximum pitch</th>\n",
       "      <th>Number of pulses</th>\n",
       "      <th>Number of periods</th>\n",
       "      <th>Mean period</th>\n",
       "      <th>Standard deviation of period</th>\n",
       "      <th>Fraction of locally unvoiced frames</th>\n",
       "      <th>Number of voice breaks</th>\n",
       "      <th>Degree of voice breaks</th>\n",
       "      <th>UPDRS</th>\n",
       "      <th>Class information</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1.488</td>\n",
       "      <td>0.000090</td>\n",
       "      <td>0.900</td>\n",
       "      <td>0.794</td>\n",
       "      <td>2.699</td>\n",
       "      <td>8.334</td>\n",
       "      <td>0.779</td>\n",
       "      <td>4.517</td>\n",
       "      <td>4.609</td>\n",
       "      <td>...</td>\n",
       "      <td>187.576</td>\n",
       "      <td>160</td>\n",
       "      <td>159</td>\n",
       "      <td>0.006065</td>\n",
       "      <td>0.000416</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>23</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0.728</td>\n",
       "      <td>0.000038</td>\n",
       "      <td>0.353</td>\n",
       "      <td>0.376</td>\n",
       "      <td>1.059</td>\n",
       "      <td>5.864</td>\n",
       "      <td>0.642</td>\n",
       "      <td>2.058</td>\n",
       "      <td>3.180</td>\n",
       "      <td>...</td>\n",
       "      <td>234.505</td>\n",
       "      <td>170</td>\n",
       "      <td>169</td>\n",
       "      <td>0.005181</td>\n",
       "      <td>0.000403</td>\n",
       "      <td>2.247</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>23</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1.220</td>\n",
       "      <td>0.000074</td>\n",
       "      <td>0.732</td>\n",
       "      <td>0.670</td>\n",
       "      <td>2.196</td>\n",
       "      <td>8.719</td>\n",
       "      <td>0.875</td>\n",
       "      <td>4.347</td>\n",
       "      <td>5.166</td>\n",
       "      <td>...</td>\n",
       "      <td>211.442</td>\n",
       "      <td>1431</td>\n",
       "      <td>1427</td>\n",
       "      <td>0.006071</td>\n",
       "      <td>0.000474</td>\n",
       "      <td>10.656</td>\n",
       "      <td>1</td>\n",
       "      <td>0.178</td>\n",
       "      <td>23</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>2.502</td>\n",
       "      <td>0.000123</td>\n",
       "      <td>1.156</td>\n",
       "      <td>1.634</td>\n",
       "      <td>3.469</td>\n",
       "      <td>13.513</td>\n",
       "      <td>1.273</td>\n",
       "      <td>5.263</td>\n",
       "      <td>8.771</td>\n",
       "      <td>...</td>\n",
       "      <td>220.230</td>\n",
       "      <td>94</td>\n",
       "      <td>92</td>\n",
       "      <td>0.004910</td>\n",
       "      <td>0.000320</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>23</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>3.509</td>\n",
       "      <td>0.000167</td>\n",
       "      <td>1.715</td>\n",
       "      <td>1.539</td>\n",
       "      <td>5.145</td>\n",
       "      <td>9.112</td>\n",
       "      <td>1.040</td>\n",
       "      <td>3.102</td>\n",
       "      <td>4.927</td>\n",
       "      <td>...</td>\n",
       "      <td>225.162</td>\n",
       "      <td>117</td>\n",
       "      <td>114</td>\n",
       "      <td>0.004757</td>\n",
       "      <td>0.000380</td>\n",
       "      <td>18.182</td>\n",
       "      <td>1</td>\n",
       "      <td>13.318</td>\n",
       "      <td>23</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 29 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Subject ID  Jitter (local)  Jitter (local, absolute)  Jitter (rap)  \\\n",
       "0           1           1.488                  0.000090         0.900   \n",
       "1           1           0.728                  0.000038         0.353   \n",
       "2           1           1.220                  0.000074         0.732   \n",
       "3           1           2.502                  0.000123         1.156   \n",
       "4           1           3.509                  0.000167         1.715   \n",
       "\n",
       "   Jitter (ppq5)  Jitter (ddp)  Shimmer (local)  Shimmer (local, dB)  \\\n",
       "0          0.794         2.699            8.334                0.779   \n",
       "1          0.376         1.059            5.864                0.642   \n",
       "2          0.670         2.196            8.719                0.875   \n",
       "3          1.634         3.469           13.513                1.273   \n",
       "4          1.539         5.145            9.112                1.040   \n",
       "\n",
       "   Shimmer (apq3)  Shimmer (apq5)        ...          Maximum pitch  \\\n",
       "0           4.517           4.609        ...                187.576   \n",
       "1           2.058           3.180        ...                234.505   \n",
       "2           4.347           5.166        ...                211.442   \n",
       "3           5.263           8.771        ...                220.230   \n",
       "4           3.102           4.927        ...                225.162   \n",
       "\n",
       "   Number of pulses  Number of periods  Mean period  \\\n",
       "0               160                159     0.006065   \n",
       "1               170                169     0.005181   \n",
       "2              1431               1427     0.006071   \n",
       "3                94                 92     0.004910   \n",
       "4               117                114     0.004757   \n",
       "\n",
       "   Standard deviation of period  Fraction of locally unvoiced frames  \\\n",
       "0                      0.000416                                0.000   \n",
       "1                      0.000403                                2.247   \n",
       "2                      0.000474                               10.656   \n",
       "3                      0.000320                                0.000   \n",
       "4                      0.000380                               18.182   \n",
       "\n",
       "   Number of voice breaks  Degree of voice breaks  UPDRS  Class information  \n",
       "0                       0                   0.000     23                  1  \n",
       "1                       0                   0.000     23                  1  \n",
       "2                       1                   0.178     23                  1  \n",
       "3                       0                   0.000     23                  1  \n",
       "4                       1                  13.318     23                  1  \n",
       "\n",
       "[5 rows x 29 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_dataframe.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Prepare training data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1040, 26)\n"
     ]
    }
   ],
   "source": [
    "train_x = train_dataframe.drop(['Subject ID', 'Class information', 'UPDRS'], axis=1)\n",
    "train_x = train_x.as_matrix()\n",
    "print(train_x.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Prepare training labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1040,)\n"
     ]
    }
   ],
   "source": [
    "train_y = train_dataframe['Class information']\n",
    "train_y = train_y.as_matrix()\n",
    "print(train_y.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Prepare test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(168, 26)\n"
     ]
    }
   ],
   "source": [
    "test_dataframe = pd.read_csv('dataset/1. istanbul/test_data.csv')\n",
    "test_x = test_dataframe.drop(['Subject ID', 'Class information'], axis=1)\n",
    "test_x = test_x.as_matrix()\n",
    "print(test_x.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Prepare test labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(168,)\n"
     ]
    }
   ],
   "source": [
    "test_y = test_dataframe['Class information']\n",
    "test_y = test_y.as_matrix()\n",
    "print(test_y.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Prepare model for training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Sequential()\n",
    "model.add(BatchNormalization(input_shape = (train_x.shape[1],)))\n",
    "\n",
    "model.add(Dense(units = 256, activation='relu'))\n",
    "model.add(Dropout(0.5))\n",
    "model.add(BatchNormalization())\n",
    "\n",
    "model.add(Dense(units = 128, activation='relu'))\n",
    "model.add(Dropout(0.5))\n",
    "model.add(BatchNormalization())\n",
    "\n",
    "model.add(Dense(units = 64, activation='relu'))\n",
    "model.add(Dropout(0.5))\n",
    "model.add(BatchNormalization())\n",
    "\n",
    "model.add(Dense(units = 32, activation='relu'))\n",
    "model.add(Dropout(0.5))\n",
    "model.add(BatchNormalization())\n",
    "\n",
    "model.add(Dense(units = 1))\n",
    "model.add(Activation(\"sigmoid\"))\n",
    "\n",
    "model.summary()\n",
    "SVG(model_to_dot(model, show_layer_names=False, show_shapes=True).create(prog='dot', format='svg'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Train the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "date = datetime.now().strftime(\"%Y-%m-%d-%H-%M-%S\")\n",
    "tensorboard_callback = keras.callbacks.TensorBoard(log_dir='Tensorboard/' + date + '/')\n",
    "model.compile(optimizer=keras.optimizers.RMSprop(), loss=keras.losses.binary_crossentropy, metrics=['accuracy'])\n",
    "model.fit(train_x, train_y, validation_data=(test_x, test_y), epochs=5000, batch_size=20, callbacks=[tensorboard_callback])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# The End"
   ]
  }
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