50-oxford-split-data.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Import libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "os.environ[\"KERAS_BACKEND\"] = \"tensorflow\"\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from IPython.display import SVG, display, Markdown\n",
    "from datetime import datetime\n",
    "from sklearn import preprocessing\n",
    "from sklearn.utils import shuffle\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from keras.utils import np_utils\n",
    "from sklearn.model_selection import StratifiedShuffleSplit\n",
    "from sklearn.metrics import accuracy_score\n",
    "import pickle"
   ]
  },
  {
   "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/2. oxford/parkinsons.data')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualize some training data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>MDVP:Fo(Hz)</th>\n",
       "      <th>MDVP:Fhi(Hz)</th>\n",
       "      <th>MDVP:Flo(Hz)</th>\n",
       "      <th>MDVP:Jitter(%)</th>\n",
       "      <th>MDVP:Jitter(Abs)</th>\n",
       "      <th>MDVP:RAP</th>\n",
       "      <th>MDVP:PPQ</th>\n",
       "      <th>Jitter:DDP</th>\n",
       "      <th>MDVP:Shimmer</th>\n",
       "      <th>...</th>\n",
       "      <th>Shimmer:DDA</th>\n",
       "      <th>NHR</th>\n",
       "      <th>HNR</th>\n",
       "      <th>status</th>\n",
       "      <th>RPDE</th>\n",
       "      <th>DFA</th>\n",
       "      <th>spread1</th>\n",
       "      <th>spread2</th>\n",
       "      <th>D2</th>\n",
       "      <th>PPE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>phon_R01_S01_1</td>\n",
       "      <td>119.992</td>\n",
       "      <td>157.302</td>\n",
       "      <td>74.997</td>\n",
       "      <td>0.00784</td>\n",
       "      <td>0.00007</td>\n",
       "      <td>0.00370</td>\n",
       "      <td>0.00554</td>\n",
       "      <td>0.01109</td>\n",
       "      <td>0.04374</td>\n",
       "      <td>...</td>\n",
       "      <td>0.06545</td>\n",
       "      <td>0.02211</td>\n",
       "      <td>21.033</td>\n",
       "      <td>1</td>\n",
       "      <td>0.414783</td>\n",
       "      <td>0.815285</td>\n",
       "      <td>-4.813031</td>\n",
       "      <td>0.266482</td>\n",
       "      <td>2.301442</td>\n",
       "      <td>0.284654</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>phon_R01_S01_2</td>\n",
       "      <td>122.400</td>\n",
       "      <td>148.650</td>\n",
       "      <td>113.819</td>\n",
       "      <td>0.00968</td>\n",
       "      <td>0.00008</td>\n",
       "      <td>0.00465</td>\n",
       "      <td>0.00696</td>\n",
       "      <td>0.01394</td>\n",
       "      <td>0.06134</td>\n",
       "      <td>...</td>\n",
       "      <td>0.09403</td>\n",
       "      <td>0.01929</td>\n",
       "      <td>19.085</td>\n",
       "      <td>1</td>\n",
       "      <td>0.458359</td>\n",
       "      <td>0.819521</td>\n",
       "      <td>-4.075192</td>\n",
       "      <td>0.335590</td>\n",
       "      <td>2.486855</td>\n",
       "      <td>0.368674</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>phon_R01_S01_3</td>\n",
       "      <td>116.682</td>\n",
       "      <td>131.111</td>\n",
       "      <td>111.555</td>\n",
       "      <td>0.01050</td>\n",
       "      <td>0.00009</td>\n",
       "      <td>0.00544</td>\n",
       "      <td>0.00781</td>\n",
       "      <td>0.01633</td>\n",
       "      <td>0.05233</td>\n",
       "      <td>...</td>\n",
       "      <td>0.08270</td>\n",
       "      <td>0.01309</td>\n",
       "      <td>20.651</td>\n",
       "      <td>1</td>\n",
       "      <td>0.429895</td>\n",
       "      <td>0.825288</td>\n",
       "      <td>-4.443179</td>\n",
       "      <td>0.311173</td>\n",
       "      <td>2.342259</td>\n",
       "      <td>0.332634</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>phon_R01_S01_4</td>\n",
       "      <td>116.676</td>\n",
       "      <td>137.871</td>\n",
       "      <td>111.366</td>\n",
       "      <td>0.00997</td>\n",
       "      <td>0.00009</td>\n",
       "      <td>0.00502</td>\n",
       "      <td>0.00698</td>\n",
       "      <td>0.01505</td>\n",
       "      <td>0.05492</td>\n",
       "      <td>...</td>\n",
       "      <td>0.08771</td>\n",
       "      <td>0.01353</td>\n",
       "      <td>20.644</td>\n",
       "      <td>1</td>\n",
       "      <td>0.434969</td>\n",
       "      <td>0.819235</td>\n",
       "      <td>-4.117501</td>\n",
       "      <td>0.334147</td>\n",
       "      <td>2.405554</td>\n",
       "      <td>0.368975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>phon_R01_S01_5</td>\n",
       "      <td>116.014</td>\n",
       "      <td>141.781</td>\n",
       "      <td>110.655</td>\n",
       "      <td>0.01284</td>\n",
       "      <td>0.00011</td>\n",
       "      <td>0.00655</td>\n",
       "      <td>0.00908</td>\n",
       "      <td>0.01966</td>\n",
       "      <td>0.06425</td>\n",
       "      <td>...</td>\n",
       "      <td>0.10470</td>\n",
       "      <td>0.01767</td>\n",
       "      <td>19.649</td>\n",
       "      <td>1</td>\n",
       "      <td>0.417356</td>\n",
       "      <td>0.823484</td>\n",
       "      <td>-3.747787</td>\n",
       "      <td>0.234513</td>\n",
       "      <td>2.332180</td>\n",
       "      <td>0.410335</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             name  MDVP:Fo(Hz)  MDVP:Fhi(Hz)  MDVP:Flo(Hz)  MDVP:Jitter(%)  \\\n",
       "0  phon_R01_S01_1      119.992       157.302        74.997         0.00784   \n",
       "1  phon_R01_S01_2      122.400       148.650       113.819         0.00968   \n",
       "2  phon_R01_S01_3      116.682       131.111       111.555         0.01050   \n",
       "3  phon_R01_S01_4      116.676       137.871       111.366         0.00997   \n",
       "4  phon_R01_S01_5      116.014       141.781       110.655         0.01284   \n",
       "\n",
       "   MDVP:Jitter(Abs)  MDVP:RAP  MDVP:PPQ  Jitter:DDP  MDVP:Shimmer    ...     \\\n",
       "0           0.00007   0.00370   0.00554     0.01109       0.04374    ...      \n",
       "1           0.00008   0.00465   0.00696     0.01394       0.06134    ...      \n",
       "2           0.00009   0.00544   0.00781     0.01633       0.05233    ...      \n",
       "3           0.00009   0.00502   0.00698     0.01505       0.05492    ...      \n",
       "4           0.00011   0.00655   0.00908     0.01966       0.06425    ...      \n",
       "\n",
       "   Shimmer:DDA      NHR     HNR  status      RPDE       DFA   spread1  \\\n",
       "0      0.06545  0.02211  21.033       1  0.414783  0.815285 -4.813031   \n",
       "1      0.09403  0.01929  19.085       1  0.458359  0.819521 -4.075192   \n",
       "2      0.08270  0.01309  20.651       1  0.429895  0.825288 -4.443179   \n",
       "3      0.08771  0.01353  20.644       1  0.434969  0.819235 -4.117501   \n",
       "4      0.10470  0.01767  19.649       1  0.417356  0.823484 -3.747787   \n",
       "\n",
       "    spread2        D2       PPE  \n",
       "0  0.266482  2.301442  0.284654  \n",
       "1  0.335590  2.486855  0.368674  \n",
       "2  0.311173  2.342259  0.332634  \n",
       "3  0.334147  2.405554  0.368975  \n",
       "4  0.234513  2.332180  0.410335  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_dataframe.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Prepare inputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(195, 22)\n"
     ]
    }
   ],
   "source": [
    "x = train_dataframe.drop(['name', 'status'], axis=1)\n",
    "x = x.as_matrix()\n",
    "print(x.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Prepare labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(195,)\n",
      "(195, 2)\n"
     ]
    }
   ],
   "source": [
    "y = train_dataframe['status']\n",
    "y = y.as_matrix()\n",
    "print(y.shape)\n",
    "y = np_utils.to_categorical(y)\n",
    "print(y.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Normalize data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>21</th>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.184308</td>\n",
       "      <td>0.112592</td>\n",
       "      <td>0.054815</td>\n",
       "      <td>0.195680</td>\n",
       "      <td>0.249012</td>\n",
       "      <td>0.145472</td>\n",
       "      <td>0.247588</td>\n",
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       "      <td>0.312215</td>\n",
       "      <td>0.280197</td>\n",
       "      <td>...</td>\n",
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       "      <td>0.332584</td>\n",
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       "      <td>0.511745</td>\n",
       "      <td>0.369155</td>\n",
       "      <td>0.960148</td>\n",
       "      <td>0.569875</td>\n",
       "      <td>0.585765</td>\n",
       "      <td>0.390661</td>\n",
       "      <td>0.497310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.198327</td>\n",
       "      <td>0.094930</td>\n",
       "      <td>0.278323</td>\n",
       "      <td>0.254130</td>\n",
       "      <td>0.288538</td>\n",
       "      <td>0.191233</td>\n",
       "      <td>0.323687</td>\n",
       "      <td>0.191042</td>\n",
       "      <td>0.472887</td>\n",
       "      <td>0.444536</td>\n",
       "      <td>...</td>\n",
       "      <td>0.279424</td>\n",
       "      <td>0.516048</td>\n",
       "      <td>0.059331</td>\n",
       "      <td>0.432577</td>\n",
       "      <td>0.470830</td>\n",
       "      <td>0.977024</td>\n",
       "      <td>0.703277</td>\n",
       "      <td>0.741337</td>\n",
       "      <td>0.473145</td>\n",
       "      <td>0.671326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.165039</td>\n",
       "      <td>0.059128</td>\n",
       "      <td>0.265288</td>\n",
       "      <td>0.280178</td>\n",
       "      <td>0.328063</td>\n",
       "      <td>0.229287</td>\n",
       "      <td>0.369239</td>\n",
       "      <td>0.229411</td>\n",
       "      <td>0.390634</td>\n",
       "      <td>0.326212</td>\n",
       "      <td>...</td>\n",
       "      <td>0.219848</td>\n",
       "      <td>0.443317</td>\n",
       "      <td>0.039596</td>\n",
       "      <td>0.496220</td>\n",
       "      <td>0.404416</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.636745</td>\n",
       "      <td>0.686371</td>\n",
       "      <td>0.408819</td>\n",
       "      <td>0.596682</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.165004</td>\n",
       "      <td>0.072927</td>\n",
       "      <td>0.264200</td>\n",
       "      <td>0.263342</td>\n",
       "      <td>0.328063</td>\n",
       "      <td>0.209056</td>\n",
       "      <td>0.324759</td>\n",
       "      <td>0.208862</td>\n",
       "      <td>0.414278</td>\n",
       "      <td>0.354971</td>\n",
       "      <td>...</td>\n",
       "      <td>0.233785</td>\n",
       "      <td>0.475478</td>\n",
       "      <td>0.040997</td>\n",
       "      <td>0.495936</td>\n",
       "      <td>0.416255</td>\n",
       "      <td>0.975885</td>\n",
       "      <td>0.695627</td>\n",
       "      <td>0.738089</td>\n",
       "      <td>0.436977</td>\n",
       "      <td>0.671949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.161150</td>\n",
       "      <td>0.080909</td>\n",
       "      <td>0.260107</td>\n",
       "      <td>0.354511</td>\n",
       "      <td>0.407115</td>\n",
       "      <td>0.282755</td>\n",
       "      <td>0.437299</td>\n",
       "      <td>0.282870</td>\n",
       "      <td>0.499452</td>\n",
       "      <td>0.410025</td>\n",
       "      <td>...</td>\n",
       "      <td>0.286852</td>\n",
       "      <td>0.584542</td>\n",
       "      <td>0.054174</td>\n",
       "      <td>0.455499</td>\n",
       "      <td>0.375159</td>\n",
       "      <td>0.992813</td>\n",
       "      <td>0.762472</td>\n",
       "      <td>0.513798</td>\n",
       "      <td>0.404336</td>\n",
       "      <td>0.757611</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         0         1         2         3         4         5         6   \\\n",
       "0  0.184308  0.112592  0.054815  0.195680  0.249012  0.145472  0.247588   \n",
       "1  0.198327  0.094930  0.278323  0.254130  0.288538  0.191233  0.323687   \n",
       "2  0.165039  0.059128  0.265288  0.280178  0.328063  0.229287  0.369239   \n",
       "3  0.165004  0.072927  0.264200  0.263342  0.328063  0.209056  0.324759   \n",
       "4  0.161150  0.080909  0.260107  0.354511  0.407115  0.282755  0.437299   \n",
       "\n",
       "         7         8         9     ...           12        13        14  \\\n",
       "0  0.145288  0.312215  0.280197    ...     0.172448  0.332584  0.068307   \n",
       "1  0.191042  0.472887  0.444536    ...     0.279424  0.516048  0.059331   \n",
       "2  0.229411  0.390634  0.326212    ...     0.219848  0.443317  0.039596   \n",
       "3  0.208862  0.414278  0.354971    ...     0.233785  0.475478  0.040997   \n",
       "4  0.282870  0.499452  0.410025    ...     0.286852  0.584542  0.054174   \n",
       "\n",
       "         15        16        17        18        19        20        21  \n",
       "0  0.511745  0.369155  0.960148  0.569875  0.585765  0.390661  0.497310  \n",
       "1  0.432577  0.470830  0.977024  0.703277  0.741337  0.473145  0.671326  \n",
       "2  0.496220  0.404416  1.000000  0.636745  0.686371  0.408819  0.596682  \n",
       "3  0.495936  0.416255  0.975885  0.695627  0.738089  0.436977  0.671949  \n",
       "4  0.455499  0.375159  0.992813  0.762472  0.513798  0.404336  0.757611  \n",
       "\n",
       "[5 rows x 22 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mins = np.min(x, axis=0)\n",
    "maxs = np.max(x, axis=0)\n",
    "rng = maxs - mins\n",
    "x = 1.0 - (((1.0 - 0.0) * (maxs - x)) / rng)\n",
    "pd.DataFrame(x).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Shuffle data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x, y = shuffle(x, y)\n",
    "pd.DataFrame(y).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Split data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x_train: (136, 22)\n",
      "y_train: (136, 2)\n",
      "x_test: (59, 22)\n",
      "y_test: (59, 2)\n"
     ]
    }
   ],
   "source": [
    "sss = StratifiedShuffleSplit(n_splits=1, test_size=0.3, random_state=1)\n",
    "for train_index, test_index in sss.split(x, y):\n",
    "    X_train, X_test = x[train_index], x[test_index]\n",
    "    Y_train, Y_test = y[train_index], y[test_index]\n",
    "    \n",
    "print('x_train: ' + str(X_train.shape))\n",
    "print('y_train: ' + str(Y_train.shape))\n",
    "print('x_test: ' + str(X_test.shape))\n",
    "print('y_test: ' + str(Y_test.shape))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Tuning parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Folder paths to store outputs.\n",
    "root_directory = 'dataset/8. oxford-normalized-30/'\n",
    "pickle.dump(X_train, open(root_directory + 'x_train.p', \"wb\"))\n",
    "pickle.dump(X_test, open(root_directory + 'x_test.p', \"wb\"))\n",
    "pickle.dump(Y_train, open(root_directory + 'y_train.p', \"wb\"))\n",
    "pickle.dump(Y_test, open(root_directory + 'y_test.p', \"wb\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# The End"
   ]
  }
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