Parkinson's Speech Detection.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "7457b651",
   "metadata": {},
   "outputs": [],
   "source": [
    "import glob\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import parselmouth\n",
    "from parselmouth.praat import call"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "0827f7a9",
   "metadata": {},
   "outputs": [],
   "source": [
    "def measurePitch(voiceID, f0min, f0max, unit):\n",
    "    sound = parselmouth.Sound(voiceID) # read the sound\n",
    "    pitch = call(sound, \"To Pitch\", 0.0, f0min, f0max)\n",
    "    pointProcess = call(sound, \"To PointProcess (periodic, cc)\", f0min, f0max)#create a praat pitch object\n",
    "    localJitter = call(pointProcess, \"Get jitter (local)\", 0, 0, 0.0001, 0.02, 1.3)\n",
    "    localabsoluteJitter = call(pointProcess, \"Get jitter (local, absolute)\", 0, 0, 0.0001, 0.02, 1.3)\n",
    "    rapJitter = call(pointProcess, \"Get jitter (rap)\", 0, 0, 0.0001, 0.02, 1.3)\n",
    "    ppq5Jitter = call(pointProcess, \"Get jitter (ppq5)\", 0, 0, 0.0001, 0.02, 1.3)\n",
    "    localShimmer =  call([sound, pointProcess], \"Get shimmer (local)\", 0, 0, 0.0001, 0.02, 1.3, 1.6)\n",
    "    localdbShimmer = call([sound, pointProcess], \"Get shimmer (local_dB)\", 0, 0, 0.0001, 0.02, 1.3, 1.6)\n",
    "    apq3Shimmer = call([sound, pointProcess], \"Get shimmer (apq3)\", 0, 0, 0.0001, 0.02, 1.3, 1.6)\n",
    "    aqpq5Shimmer = call([sound, pointProcess], \"Get shimmer (apq5)\", 0, 0, 0.0001, 0.02, 1.3, 1.6)\n",
    "    apq11Shimmer =  call([sound, pointProcess], \"Get shimmer (apq11)\", 0, 0, 0.0001, 0.02, 1.3, 1.6)\n",
    "    harmonicity05 = call(sound, \"To Harmonicity (cc)\", 0.01, 500, 0.1, 1.0)\n",
    "    hnr05 = call(harmonicity05, \"Get mean\", 0, 0)\n",
    "    harmonicity15 = call(sound, \"To Harmonicity (cc)\", 0.01, 1500, 0.1, 1.0)\n",
    "    hnr15 = call(harmonicity15, \"Get mean\", 0, 0)\n",
    "    harmonicity25 = call(sound, \"To Harmonicity (cc)\", 0.01, 2500, 0.1, 1.0)\n",
    "    hnr25 = call(harmonicity25, \"Get mean\", 0, 0)\n",
    "    harmonicity35 = call(sound, \"To Harmonicity (cc)\", 0.01, 3500, 0.1, 1.0)\n",
    "    hnr35 = call(harmonicity35, \"Get mean\", 0, 0)\n",
    "    harmonicity38 = call(sound, \"To Harmonicity (cc)\", 0.01, 3800, 0.1, 1.0)\n",
    "    hnr38 = call(harmonicity38, \"Get mean\", 0, 0)\n",
    "    return localJitter, localabsoluteJitter, rapJitter, ppq5Jitter, localShimmer, localdbShimmer, apq3Shimmer, aqpq5Shimmer, apq11Shimmer, hnr05, hnr15 ,hnr25 ,hnr35 ,hnr38\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "52982c46",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "C:\\Users\\user\\projects\\Hackathon\\HackTJ 2022\\Speech\n"
     ]
    }
   ],
   "source": [
    "localJitter_list = [] #measure\n",
    "localabsoluteJitter_list = [] #measure\n",
    "rapJitter_list = [] #measure\n",
    "ppq5Jitter_list = [] #measure\n",
    "localShimmer_list =  [] #measure\n",
    "localdbShimmer_list = [] #measure\n",
    "apq3Shimmer_list = [] #measure\n",
    "aqpq5Shimmer_list = [] #measure\n",
    "apq11Shimmer_list =  [] #measure\n",
    "hnr05_list = [] #measure\n",
    "hnr15_list = [] #measure\n",
    "hnr25_list = [] #measure\n",
    "parkinson_list = [] #Parkinson(1) or healthy(0)\n",
    "file_list = []\n",
    "import os\n",
    "cwd = os.getcwd()\n",
    "print(cwd)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "4c7f3cae",
   "metadata": {},
   "outputs": [],
   "source": [
    "for wave_file in glob.glob(\"audio/SpontaneousDialogue/PD/*.wav\"):\n",
    "    sound = parselmouth.Sound(wave_file)\n",
    "    (localJitter, localabsoluteJitter, rapJitter, ppq5Jitter, localShimmer, localdbShimmer, apq3Shimmer, aqpq5Shimmer, apq11Shimmer, hnr05, hnr15 ,hnr25 ,hnr35 ,hnr38) = measurePitch(sound, 75, 1000, \"Hertz\")\n",
    "    file_list.append(wave_file) # make an ID list\n",
    "    localJitter_list.append(localJitter) # make a mean F0 list\n",
    "    localabsoluteJitter_list.append(localabsoluteJitter) # make a sd F0 list\n",
    "    rapJitter_list.append(rapJitter)\n",
    "    ppq5Jitter_list.append(ppq5Jitter)\n",
    "    localShimmer_list.append(localShimmer)\n",
    "    localdbShimmer_list.append(localdbShimmer)\n",
    "    apq3Shimmer_list.append(apq3Shimmer)\n",
    "    aqpq5Shimmer_list.append(aqpq5Shimmer)\n",
    "    apq11Shimmer_list.append(apq11Shimmer)\n",
    "    hnr05_list.append(hnr05)\n",
    "    hnr15_list.append(hnr15)\n",
    "    hnr25_list.append(hnr25)\n",
    "    parkinson_list.append(1) #1 because parkinson file\n",
    "\n",
    "for wave_file in glob.glob(\"audio/ReadText/PD/*.wav\"):\n",
    "    sound = parselmouth.Sound(wave_file)\n",
    "    (localJitter, localabsoluteJitter, rapJitter, ppq5Jitter, localShimmer, localdbShimmer, apq3Shimmer, aqpq5Shimmer, apq11Shimmer, hnr05, hnr15 ,hnr25 ,hnr35 ,hnr38) = measurePitch(sound, 75, 1000, \"Hertz\")\n",
    "    file_list.append(wave_file) # make an ID list\n",
    "    localJitter_list.append(localJitter) # make a mean F0 list\n",
    "    localabsoluteJitter_list.append(localabsoluteJitter) # make a sd F0 list\n",
    "    rapJitter_list.append(rapJitter)\n",
    "    ppq5Jitter_list.append(ppq5Jitter)\n",
    "    localShimmer_list.append(localShimmer)\n",
    "    localdbShimmer_list.append(localdbShimmer)\n",
    "    apq3Shimmer_list.append(apq3Shimmer)\n",
    "    aqpq5Shimmer_list.append(aqpq5Shimmer)\n",
    "    apq11Shimmer_list.append(apq11Shimmer)\n",
    "    hnr05_list.append(hnr05)\n",
    "    hnr15_list.append(hnr15)\n",
    "    hnr25_list.append(hnr25)\n",
    "    parkinson_list.append(1) #1 because parkinson file\n",
    "\n",
    "for wave_file in glob.glob(\"audio/SpontaneousDialogue/HC/*.wav\"):\n",
    "    sound = parselmouth.Sound(wave_file)\n",
    "    (localJitter, localabsoluteJitter, rapJitter, ppq5Jitter, localShimmer, localdbShimmer, apq3Shimmer, aqpq5Shimmer, apq11Shimmer, hnr05, hnr15 ,hnr25 ,hnr35 ,hnr38) = measurePitch(sound, 75, 1000, \"Hertz\")\n",
    "    file_list.append(wave_file) # make an ID list\n",
    "    localJitter_list.append(localJitter) # make a mean F0 list\n",
    "    localabsoluteJitter_list.append(localabsoluteJitter) # make a sd F0 list\n",
    "    rapJitter_list.append(rapJitter)\n",
    "    ppq5Jitter_list.append(ppq5Jitter)\n",
    "    localShimmer_list.append(localShimmer)\n",
    "    localdbShimmer_list.append(localdbShimmer)\n",
    "    apq3Shimmer_list.append(apq3Shimmer)\n",
    "    aqpq5Shimmer_list.append(aqpq5Shimmer)\n",
    "    apq11Shimmer_list.append(apq11Shimmer)\n",
    "    hnr05_list.append(hnr05)\n",
    "    hnr15_list.append(hnr15)\n",
    "    hnr25_list.append(hnr25)\n",
    "    parkinson_list.append(0) #0 because healthy file\n",
    "\n",
    "for wave_file in glob.glob(\"audio/ReadText/HC/*.wav\"):\n",
    "    sound = parselmouth.Sound(wave_file)\n",
    "    (localJitter, localabsoluteJitter, rapJitter, ppq5Jitter, localShimmer, localdbShimmer, apq3Shimmer, aqpq5Shimmer, apq11Shimmer, hnr05, hnr15 ,hnr25 ,hnr35 ,hnr38) = measurePitch(sound, 75, 1000, \"Hertz\")\n",
    "    file_list.append(wave_file) # make an ID list\n",
    "    localJitter_list.append(localJitter) # make a mean F0 list\n",
    "    localabsoluteJitter_list.append(localabsoluteJitter) # make a sd F0 list\n",
    "    rapJitter_list.append(rapJitter)\n",
    "    ppq5Jitter_list.append(ppq5Jitter)\n",
    "    localShimmer_list.append(localShimmer)\n",
    "    localdbShimmer_list.append(localdbShimmer)\n",
    "    apq3Shimmer_list.append(apq3Shimmer)\n",
    "    aqpq5Shimmer_list.append(aqpq5Shimmer)\n",
    "    apq11Shimmer_list.append(apq11Shimmer)\n",
    "    hnr05_list.append(hnr05)\n",
    "    hnr15_list.append(hnr15)\n",
    "    hnr25_list.append(hnr25)\n",
    "    parkinson_list.append(0) #0 because healthy file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "8af06d04",
   "metadata": {},
   "outputs": [],
   "source": [
    "pred = pd.DataFrame(np.column_stack([parkinson_list,localJitter_list, localabsoluteJitter_list, rapJitter_list, ppq5Jitter_list, localShimmer_list, localdbShimmer_list, apq3Shimmer_list, aqpq5Shimmer_list, apq11Shimmer_list, hnr05_list, hnr15_list, hnr25_list]),\n",
    "                               columns=[\"Parkinson\",\"Jitter_rel\",\"Jitter_abs\",\"Jitter_RAP\",\"Jitter_PPQ\",\"Shim_loc\",\"Shim_dB\",\"Shim_APQ3\",\"Shim_APQ5\",\"Shi_APQ11\", \"hnr05\", \"hnr15\", \"hnr25\"])  #add these lists to pandas in the right order\n",
    "pred\n",
    "\n",
    "\n",
    "# Write out the updated dataframe\n",
    "pred.to_csv(\"processed_results.csv\", index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "92c2a56a",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    .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>Parkinson</th>\n",
       "      <th>Jitter_rel</th>\n",
       "      <th>Jitter_abs</th>\n",
       "      <th>Jitter_RAP</th>\n",
       "      <th>Jitter_PPQ</th>\n",
       "      <th>Shim_loc</th>\n",
       "      <th>Shim_dB</th>\n",
       "      <th>Shim_APQ3</th>\n",
       "      <th>Shim_APQ5</th>\n",
       "      <th>Shi_APQ11</th>\n",
       "      <th>hnr05</th>\n",
       "      <th>hnr15</th>\n",
       "      <th>hnr25</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.019273</td>\n",
       "      <td>0.000149</td>\n",
       "      <td>0.007988</td>\n",
       "      <td>0.008823</td>\n",
       "      <td>0.093380</td>\n",
       "      <td>0.866658</td>\n",
       "      <td>0.035126</td>\n",
       "      <td>0.048810</td>\n",
       "      <td>0.085224</td>\n",
       "      <td>5.031867</td>\n",
       "      <td>5.616350</td>\n",
       "      <td>6.553027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.031410</td>\n",
       "      <td>0.000281</td>\n",
       "      <td>0.013289</td>\n",
       "      <td>0.013440</td>\n",
       "      <td>0.112340</td>\n",
       "      <td>1.044282</td>\n",
       "      <td>0.041351</td>\n",
       "      <td>0.059329</td>\n",
       "      <td>0.106660</td>\n",
       "      <td>4.302138</td>\n",
       "      <td>4.372703</td>\n",
       "      <td>8.602448</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.025405</td>\n",
       "      <td>0.000205</td>\n",
       "      <td>0.011561</td>\n",
       "      <td>0.012323</td>\n",
       "      <td>0.095643</td>\n",
       "      <td>0.946494</td>\n",
       "      <td>0.035971</td>\n",
       "      <td>0.049964</td>\n",
       "      <td>0.094885</td>\n",
       "      <td>3.931486</td>\n",
       "      <td>4.001019</td>\n",
       "      <td>7.723529</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.022578</td>\n",
       "      <td>0.000167</td>\n",
       "      <td>0.007994</td>\n",
       "      <td>0.010027</td>\n",
       "      <td>0.082393</td>\n",
       "      <td>0.836335</td>\n",
       "      <td>0.027471</td>\n",
       "      <td>0.043146</td>\n",
       "      <td>0.081937</td>\n",
       "      <td>1.985262</td>\n",
       "      <td>4.034309</td>\n",
       "      <td>4.729302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.031441</td>\n",
       "      <td>0.000164</td>\n",
       "      <td>0.015739</td>\n",
       "      <td>0.016306</td>\n",
       "      <td>0.149788</td>\n",
       "      <td>1.300907</td>\n",
       "      <td>0.055637</td>\n",
       "      <td>0.077863</td>\n",
       "      <td>0.102166</td>\n",
       "      <td>0.377313</td>\n",
       "      <td>2.473989</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Parkinson  Jitter_rel  Jitter_abs  Jitter_RAP  Jitter_PPQ  Shim_loc  \\\n",
       "0        1.0    0.019273    0.000149    0.007988    0.008823  0.093380   \n",
       "1        1.0    0.031410    0.000281    0.013289    0.013440  0.112340   \n",
       "2        1.0    0.025405    0.000205    0.011561    0.012323  0.095643   \n",
       "3        1.0    0.022578    0.000167    0.007994    0.010027  0.082393   \n",
       "4        1.0    0.031441    0.000164    0.015739    0.016306  0.149788   \n",
       "\n",
       "    Shim_dB  Shim_APQ3  Shim_APQ5  Shi_APQ11     hnr05     hnr15     hnr25  \n",
       "0  0.866658   0.035126   0.048810   0.085224  5.031867  5.616350  6.553027  \n",
       "1  1.044282   0.041351   0.059329   0.106660  4.302138  4.372703  8.602448  \n",
       "2  0.946494   0.035971   0.049964   0.094885  3.931486  4.001019  7.723529  \n",
       "3  0.836335   0.027471   0.043146   0.081937  1.985262  4.034309  4.729302  \n",
       "4  1.300907   0.055637   0.077863   0.102166  0.377313  2.473989       NaN  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "parkinson = pd.read_csv(\"processed_results.csv\")\n",
    "parkinson.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "42747673",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Parkinson</th>\n",
       "      <th>Jitter_rel</th>\n",
       "      <th>Jitter_abs</th>\n",
       "      <th>Jitter_RAP</th>\n",
       "      <th>Jitter_PPQ</th>\n",
       "      <th>Shim_loc</th>\n",
       "      <th>Shim_dB</th>\n",
       "      <th>Shim_APQ3</th>\n",
       "      <th>Shim_APQ5</th>\n",
       "      <th>Shi_APQ11</th>\n",
       "      <th>hnr05</th>\n",
       "      <th>hnr15</th>\n",
       "      <th>hnr25</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.019273</td>\n",
       "      <td>0.000149</td>\n",
       "      <td>0.007988</td>\n",
       "      <td>0.008823</td>\n",
       "      <td>0.093380</td>\n",
       "      <td>0.866658</td>\n",
       "      <td>0.035126</td>\n",
       "      <td>0.048810</td>\n",
       "      <td>0.085224</td>\n",
       "      <td>5.031867</td>\n",
       "      <td>5.616350</td>\n",
       "      <td>6.553027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.031410</td>\n",
       "      <td>0.000281</td>\n",
       "      <td>0.013289</td>\n",
       "      <td>0.013440</td>\n",
       "      <td>0.112340</td>\n",
       "      <td>1.044282</td>\n",
       "      <td>0.041351</td>\n",
       "      <td>0.059329</td>\n",
       "      <td>0.106660</td>\n",
       "      <td>4.302138</td>\n",
       "      <td>4.372703</td>\n",
       "      <td>8.602448</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.025405</td>\n",
       "      <td>0.000205</td>\n",
       "      <td>0.011561</td>\n",
       "      <td>0.012323</td>\n",
       "      <td>0.095643</td>\n",
       "      <td>0.946494</td>\n",
       "      <td>0.035971</td>\n",
       "      <td>0.049964</td>\n",
       "      <td>0.094885</td>\n",
       "      <td>3.931486</td>\n",
       "      <td>4.001019</td>\n",
       "      <td>7.723529</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.022578</td>\n",
       "      <td>0.000167</td>\n",
       "      <td>0.007994</td>\n",
       "      <td>0.010027</td>\n",
       "      <td>0.082393</td>\n",
       "      <td>0.836335</td>\n",
       "      <td>0.027471</td>\n",
       "      <td>0.043146</td>\n",
       "      <td>0.081937</td>\n",
       "      <td>1.985262</td>\n",
       "      <td>4.034309</td>\n",
       "      <td>4.729302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.031441</td>\n",
       "      <td>0.000164</td>\n",
       "      <td>0.015739</td>\n",
       "      <td>0.016306</td>\n",
       "      <td>0.149788</td>\n",
       "      <td>1.300907</td>\n",
       "      <td>0.055637</td>\n",
       "      <td>0.077863</td>\n",
       "      <td>0.102166</td>\n",
       "      <td>0.377313</td>\n",
       "      <td>2.473989</td>\n",
       "      <td>8.594723</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Parkinson  Jitter_rel  Jitter_abs  Jitter_RAP  Jitter_PPQ  Shim_loc  \\\n",
       "0        1.0    0.019273    0.000149    0.007988    0.008823  0.093380   \n",
       "1        1.0    0.031410    0.000281    0.013289    0.013440  0.112340   \n",
       "2        1.0    0.025405    0.000205    0.011561    0.012323  0.095643   \n",
       "3        1.0    0.022578    0.000167    0.007994    0.010027  0.082393   \n",
       "4        1.0    0.031441    0.000164    0.015739    0.016306  0.149788   \n",
       "\n",
       "    Shim_dB  Shim_APQ3  Shim_APQ5  Shi_APQ11     hnr05     hnr15     hnr25  \n",
       "0  0.866658   0.035126   0.048810   0.085224  5.031867  5.616350  6.553027  \n",
       "1  1.044282   0.041351   0.059329   0.106660  4.302138  4.372703  8.602448  \n",
       "2  0.946494   0.035971   0.049964   0.094885  3.931486  4.001019  7.723529  \n",
       "3  0.836335   0.027471   0.043146   0.081937  1.985262  4.034309  4.729302  \n",
       "4  1.300907   0.055637   0.077863   0.102166  0.377313  2.473989  8.594723  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "parkinson['hnr25'].fillna((parkinson['hnr25'].mean()), inplace=True)\n",
    "parkinson['hnr15'].fillna((parkinson['hnr15'].mean()), inplace=True)\n",
    "parkinson.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "cb2ec580",
   "metadata": {},
   "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>Parkinson</th>\n",
       "      <th>Jitter_rel</th>\n",
       "      <th>Jitter_abs</th>\n",
       "      <th>Jitter_RAP</th>\n",
       "      <th>Jitter_PPQ</th>\n",
       "      <th>Shim_loc</th>\n",
       "      <th>Shim_dB</th>\n",
       "      <th>Shim_APQ3</th>\n",
       "      <th>Shim_APQ5</th>\n",
       "      <th>Shi_APQ11</th>\n",
       "      <th>hnr05</th>\n",
       "      <th>hnr15</th>\n",
       "      <th>hnr25</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.019273</td>\n",
       "      <td>0.000149</td>\n",
       "      <td>0.007988</td>\n",
       "      <td>0.008823</td>\n",
       "      <td>0.093380</td>\n",
       "      <td>0.866658</td>\n",
       "      <td>0.035126</td>\n",
       "      <td>0.048810</td>\n",
       "      <td>0.085224</td>\n",
       "      <td>5.031867</td>\n",
       "      <td>5.616350</td>\n",
       "      <td>6.553027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.031410</td>\n",
       "      <td>0.000281</td>\n",
       "      <td>0.013289</td>\n",
       "      <td>0.013440</td>\n",
       "      <td>0.112340</td>\n",
       "      <td>1.044282</td>\n",
       "      <td>0.041351</td>\n",
       "      <td>0.059329</td>\n",
       "      <td>0.106660</td>\n",
       "      <td>4.302138</td>\n",
       "      <td>4.372703</td>\n",
       "      <td>8.602448</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.025405</td>\n",
       "      <td>0.000205</td>\n",
       "      <td>0.011561</td>\n",
       "      <td>0.012323</td>\n",
       "      <td>0.095643</td>\n",
       "      <td>0.946494</td>\n",
       "      <td>0.035971</td>\n",
       "      <td>0.049964</td>\n",
       "      <td>0.094885</td>\n",
       "      <td>3.931486</td>\n",
       "      <td>4.001019</td>\n",
       "      <td>7.723529</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.022578</td>\n",
       "      <td>0.000167</td>\n",
       "      <td>0.007994</td>\n",
       "      <td>0.010027</td>\n",
       "      <td>0.082393</td>\n",
       "      <td>0.836335</td>\n",
       "      <td>0.027471</td>\n",
       "      <td>0.043146</td>\n",
       "      <td>0.081937</td>\n",
       "      <td>1.985262</td>\n",
       "      <td>4.034309</td>\n",
       "      <td>4.729302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.031441</td>\n",
       "      <td>0.000164</td>\n",
       "      <td>0.015739</td>\n",
       "      <td>0.016306</td>\n",
       "      <td>0.149788</td>\n",
       "      <td>1.300907</td>\n",
       "      <td>0.055637</td>\n",
       "      <td>0.077863</td>\n",
       "      <td>0.102166</td>\n",
       "      <td>0.377313</td>\n",
       "      <td>2.473989</td>\n",
       "      <td>8.594723</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.024510</td>\n",
       "      <td>0.000132</td>\n",
       "      <td>0.010953</td>\n",
       "      <td>0.010540</td>\n",
       "      <td>0.079994</td>\n",
       "      <td>0.771243</td>\n",
       "      <td>0.030688</td>\n",
       "      <td>0.039900</td>\n",
       "      <td>0.066022</td>\n",
       "      <td>1.839533</td>\n",
       "      <td>5.360876</td>\n",
       "      <td>7.631712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.022260</td>\n",
       "      <td>0.000111</td>\n",
       "      <td>0.010136</td>\n",
       "      <td>0.010165</td>\n",
       "      <td>0.088066</td>\n",
       "      <td>0.896240</td>\n",
       "      <td>0.032909</td>\n",
       "      <td>0.045445</td>\n",
       "      <td>0.081866</td>\n",
       "      <td>1.518921</td>\n",
       "      <td>3.764296</td>\n",
       "      <td>5.547934</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.017001</td>\n",
       "      <td>0.000079</td>\n",
       "      <td>0.008471</td>\n",
       "      <td>0.008114</td>\n",
       "      <td>0.061106</td>\n",
       "      <td>0.633178</td>\n",
       "      <td>0.023786</td>\n",
       "      <td>0.029054</td>\n",
       "      <td>0.047723</td>\n",
       "      <td>0.731566</td>\n",
       "      <td>1.767502</td>\n",
       "      <td>6.397744</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.020385</td>\n",
       "      <td>0.000106</td>\n",
       "      <td>0.009641</td>\n",
       "      <td>0.009898</td>\n",
       "      <td>0.095524</td>\n",
       "      <td>0.908515</td>\n",
       "      <td>0.041091</td>\n",
       "      <td>0.052293</td>\n",
       "      <td>0.081502</td>\n",
       "      <td>7.693267</td>\n",
       "      <td>8.640795</td>\n",
       "      <td>11.301341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.028030</td>\n",
       "      <td>0.000200</td>\n",
       "      <td>0.011095</td>\n",
       "      <td>0.013643</td>\n",
       "      <td>0.122444</td>\n",
       "      <td>1.142830</td>\n",
       "      <td>0.046892</td>\n",
       "      <td>0.066606</td>\n",
       "      <td>0.117418</td>\n",
       "      <td>3.279126</td>\n",
       "      <td>4.517997</td>\n",
       "      <td>5.558389</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>73 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Parkinson  Jitter_rel  Jitter_abs  Jitter_RAP  Jitter_PPQ  Shim_loc  \\\n",
       "0         1.0    0.019273    0.000149    0.007988    0.008823  0.093380   \n",
       "1         1.0    0.031410    0.000281    0.013289    0.013440  0.112340   \n",
       "2         1.0    0.025405    0.000205    0.011561    0.012323  0.095643   \n",
       "3         1.0    0.022578    0.000167    0.007994    0.010027  0.082393   \n",
       "4         1.0    0.031441    0.000164    0.015739    0.016306  0.149788   \n",
       "..        ...         ...         ...         ...         ...       ...   \n",
       "68        0.0    0.024510    0.000132    0.010953    0.010540  0.079994   \n",
       "69        0.0    0.022260    0.000111    0.010136    0.010165  0.088066   \n",
       "70        0.0    0.017001    0.000079    0.008471    0.008114  0.061106   \n",
       "71        0.0    0.020385    0.000106    0.009641    0.009898  0.095524   \n",
       "72        0.0    0.028030    0.000200    0.011095    0.013643  0.122444   \n",
       "\n",
       "     Shim_dB  Shim_APQ3  Shim_APQ5  Shi_APQ11     hnr05     hnr15      hnr25  \n",
       "0   0.866658   0.035126   0.048810   0.085224  5.031867  5.616350   6.553027  \n",
       "1   1.044282   0.041351   0.059329   0.106660  4.302138  4.372703   8.602448  \n",
       "2   0.946494   0.035971   0.049964   0.094885  3.931486  4.001019   7.723529  \n",
       "3   0.836335   0.027471   0.043146   0.081937  1.985262  4.034309   4.729302  \n",
       "4   1.300907   0.055637   0.077863   0.102166  0.377313  2.473989   8.594723  \n",
       "..       ...        ...        ...        ...       ...       ...        ...  \n",
       "68  0.771243   0.030688   0.039900   0.066022  1.839533  5.360876   7.631712  \n",
       "69  0.896240   0.032909   0.045445   0.081866  1.518921  3.764296   5.547934  \n",
       "70  0.633178   0.023786   0.029054   0.047723  0.731566  1.767502   6.397744  \n",
       "71  0.908515   0.041091   0.052293   0.081502  7.693267  8.640795  11.301341  \n",
       "72  1.142830   0.046892   0.066606   0.117418  3.279126  4.517997   5.558389  \n",
       "\n",
       "[73 rows x 13 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictors=[\"Jitter_rel\",\"Jitter_abs\",\"Jitter_RAP\",\"Jitter_PPQ\",\"Shim_loc\",\"Shim_dB\",\"Shim_APQ3\",\"Shim_APQ5\",\"Shi_APQ11\",\"hnr05\",\"hnr15\", \"hnr25\"]\n",
    "\n",
    "for col in predictors: # Loop through all columns in predictors\n",
    "    if parkinson[col].dtype == 'object':  # check if column's type is object (text)\n",
    "        parkinson[col] = pd.Categorical(parkinson[col]).codes  # convert text to numerical\n",
    "\n",
    "parkinson"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "0d573d6a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train shape (58, 12) (58,)\n",
      "test shape (15, 12) (15,)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(parkinson[predictors], parkinson['Parkinson'], test_size=0.2, random_state=1)\n",
    "\n",
    "print (\"train shape\", X_train.shape, y_train.shape)\n",
    "print (\"test shape\", X_test.shape, y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "4295375c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train accuracy = 0.6551724137931034\n",
      "test accuracy = 0.6\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "clf = LogisticRegression()\n",
    "clf.fit(X_train, y_train)\n",
    "train_score = clf.score(X_train, y_train)\n",
    "test_score = clf.score(X_test, y_test)\n",
    "print ('train accuracy =', train_score)\n",
    "print ('test accuracy =', test_score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "43ed1959",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "pickle.dump(clf, open('model.sav', 'wb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "1236708d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.46666667 0.73333333 0.86666667 0.64285714 0.64285714]\n",
      "0.6704761904761904\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "clf = LogisticRegression()\n",
    "scores = cross_val_score(clf, parkinson[predictors], parkinson[\"Parkinson\"], scoring='accuracy', cv=5)\n",
    "\n",
    "print(scores)\n",
    "print(scores.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "8152e162",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "bfd0ddcc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['trainedModel.sav']"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import joblib\n",
    "joblib.dump(clf, \"trainedModel.sav\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "360d08a1",
   "metadata": {},
   "outputs": [],
   "source": [
    "import joblib\n",
    "import parselmouth\n",
    "from parselmouth.praat import call\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import sklearn\n",
    "\n",
    "\n",
    "def loadModel(PATH):\n",
    "    clf = joblib.load(PATH)\n",
    "    return clf\n",
    "\n",
    "def measurePitch(voiceID, f0min, f0max, unit):\n",
    "    sound = parselmouth.Sound(voiceID) # read the sound\n",
    "    pitch = call(sound, \"To Pitch\", 0.0, f0min, f0max)\n",
    "    pointProcess = call(sound, \"To PointProcess (periodic, cc)\", f0min, f0max)#create a praat pitch object\n",
    "    localJitter = call(pointProcess, \"Get jitter (local)\", 0, 0, 0.0001, 0.02, 1.3)\n",
    "    localabsoluteJitter = call(pointProcess, \"Get jitter (local, absolute)\", 0, 0, 0.0001, 0.02, 1.3)\n",
    "    rapJitter = call(pointProcess, \"Get jitter (rap)\", 0, 0, 0.0001, 0.02, 1.3)\n",
    "    ppq5Jitter = call(pointProcess, \"Get jitter (ppq5)\", 0, 0, 0.0001, 0.02, 1.3)\n",
    "    localShimmer =  call([sound, pointProcess], \"Get shimmer (local)\", 0, 0, 0.0001, 0.02, 1.3, 1.6)\n",
    "    localdbShimmer = call([sound, pointProcess], \"Get shimmer (local_dB)\", 0, 0, 0.0001, 0.02, 1.3, 1.6)\n",
    "    apq3Shimmer = call([sound, pointProcess], \"Get shimmer (apq3)\", 0, 0, 0.0001, 0.02, 1.3, 1.6)\n",
    "    aqpq5Shimmer = call([sound, pointProcess], \"Get shimmer (apq5)\", 0, 0, 0.0001, 0.02, 1.3, 1.6)\n",
    "    apq11Shimmer =  call([sound, pointProcess], \"Get shimmer (apq11)\", 0, 0, 0.0001, 0.02, 1.3, 1.6)\n",
    "    harmonicity05 = call(sound, \"To Harmonicity (cc)\", 0.01, 500, 0.1, 1.0)\n",
    "    hnr05 = call(harmonicity05, \"Get mean\", 0, 0)\n",
    "    harmonicity15 = call(sound, \"To Harmonicity (cc)\", 0.01, 1500, 0.1, 1.0)\n",
    "    hnr15 = call(harmonicity15, \"Get mean\", 0, 0)\n",
    "    harmonicity25 = call(sound, \"To Harmonicity (cc)\", 0.01, 2500, 0.1, 1.0)\n",
    "    hnr25 = call(harmonicity25, \"Get mean\", 0, 0)\n",
    "    harmonicity35 = call(sound, \"To Harmonicity (cc)\", 0.01, 3500, 0.1, 1.0)\n",
    "    hnr35 = call(harmonicity35, \"Get mean\", 0, 0)\n",
    "    harmonicity38 = call(sound, \"To Harmonicity (cc)\", 0.01, 3800, 0.1, 1.0)\n",
    "    hnr38 = call(harmonicity38, \"Get mean\", 0, 0)\n",
    "    return localJitter, localabsoluteJitter, rapJitter, ppq5Jitter, localShimmer, localdbShimmer, apq3Shimmer, aqpq5Shimmer, apq11Shimmer, hnr05, hnr15 ,hnr25 ,hnr35 ,hnr38\n",
    "\n",
    "\n",
    "def predict(clf, wavPath):\n",
    "    file_list = []\n",
    "    localJitter_list = []\n",
    "    localabsoluteJitter_list = []\n",
    "    rapJitter_list = []\n",
    "    ppq5Jitter_list = []\n",
    "    localShimmer_list = []\n",
    "    localdbShimmer_list = []\n",
    "    apq3Shimmer_list = []\n",
    "    aqpq5Shimmer_list = []\n",
    "    apq11Shimmer_list = []\n",
    "    hnr05_list = []\n",
    "    hnr15_list = []\n",
    "    hnr25_list = []\n",
    "    hnr35_list = []\n",
    "    hnr38_list = []\n",
    "\n",
    "    sound = parselmouth.Sound(wavPath)\n",
    "    (localJitter, localabsoluteJitter, rapJitter, ppq5Jitter, localShimmer, localdbShimmer, apq3Shimmer, aqpq5Shimmer,\n",
    "     apq11Shimmer, hnr05, hnr15, hnr25, hnr35, hnr38) = measurePitch(sound, 75, 1000, \"Hertz\")\n",
    "    localJitter_list.append(localJitter)  # make a mean F0 list\n",
    "    localabsoluteJitter_list.append(localabsoluteJitter)  # make a sd F0 list\n",
    "    rapJitter_list.append(rapJitter)\n",
    "    ppq5Jitter_list.append(ppq5Jitter)\n",
    "    localShimmer_list.append(localShimmer)\n",
    "    localdbShimmer_list.append(localdbShimmer)\n",
    "    apq3Shimmer_list.append(apq3Shimmer)\n",
    "    aqpq5Shimmer_list.append(aqpq5Shimmer)\n",
    "    apq11Shimmer_list.append(apq11Shimmer)\n",
    "    hnr05_list.append(hnr05)\n",
    "    hnr15_list.append(hnr15)\n",
    "    hnr25_list.append(hnr25)\n",
    "    hnr35_list.append(hnr35)\n",
    "    hnr38_list.append(hnr38)\n",
    "\n",
    "    toPred = pd.DataFrame(np.column_stack(\n",
    "        [localJitter_list, localabsoluteJitter_list, rapJitter_list, ppq5Jitter_list, localShimmer_list,\n",
    "         localdbShimmer_list, apq3Shimmer_list, aqpq5Shimmer_list, apq11Shimmer_list, hnr05_list, hnr15_list,\n",
    "         hnr25_list]),\n",
    "                         columns=[\"Jitter_rel\", \"Jitter_abs\", \"Jitter_RAP\", \"Jitter_PPQ\", \"Shim_loc\", \"Shim_dB\",\n",
    "                                  \"Shim_APQ3\", \"Shim_APQ5\", \"Shi_APQ11\", \"hnr05\", \"hnr15\",\n",
    "                                  \"hnr25\"])  # add these lists to pandas in the right order\n",
    "\n",
    "    resp = clf.predict(toPred)\n",
    "    resp = str(resp)\n",
    "\n",
    "    if resp == \"[1.]\":\n",
    "        return True\n",
    "    else:\n",
    "        return False"
   ]
  }
 ],
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