parkinsons-detection.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# parkinsons-detection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import os\n",
    "import seaborn as sns\n",
    "from time import time\n",
    "from math import sqrt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Timing decorator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def timeit(fn):\n",
    "    def wrapper(*args, **kwargs):\n",
    "        start=time()\n",
    "        res=fn(*args, **kwargs)\n",
    "        print(fn.__name__, \"took\", time()-start, \"seconds.\")\n",
    "        return res\n",
    "    return wrapper"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data paths"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "control_data_path=os.path.join('data', 'control')\n",
    "parkinson_data_path=os.path.join('data', 'parkinson')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "control_file_list=[os.path.join(control_data_path, x) for x in os.listdir(control_data_path)]\n",
    "parkinson_file_list=[os.path.join(parkinson_data_path, x) for x in os.listdir(parkinson_data_path)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Features\n",
    "1. No of strokes\n",
    "2. Stroke speed\n",
    "3. Velocity\n",
    "4. Acceleration\n",
    "5. Jerk\n",
    "6. Horizontal velocity/acceleration/jerk\n",
    "7. Vertical velocity/acceleration/jerk\n",
    "8. Number of changes in velocity direction\n",
    "9. Number of changes in acceleration direction\n",
    "10. Relative NCV\n",
    "11. Relative NCA\n",
    "12. in-air time\n",
    "13. on-surface time\n",
    "14. normalized in-air time\n",
    "15. normalized on-surface time\n",
    "16. in-air/on-surface ratio"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "header_row=[\"X\", \"Y\", \"Z\", \"Pressure\" , \"GripAngle\" , \"Timestamp\" , \"Test_ID\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#@timeit\n",
    "def get_no_strokes(df):\n",
    "    pressure_data=df['Pressure'].as_matrix()\n",
    "    on_surface = (pressure_data>600).astype(int)\n",
    "    return ((np.roll(on_surface, 1) - on_surface) != 0).astype(int).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#@timeit\n",
    "def get_speed(df):\n",
    "    total_dist=0\n",
    "    duration=df['Timestamp'].as_matrix()[-1]\n",
    "    coords=df[['X', 'Y', 'Z']].as_matrix()\n",
    "    for i in range(10, df.shape[0]):\n",
    "        temp=np.linalg.norm(coords[i, :]-coords[i-10, :])\n",
    "        total_dist+=temp\n",
    "    speed=total_dist/duration\n",
    "    return speed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#@timeit\n",
    "def get_in_air_time(data):\n",
    "    data=data['Pressure'].as_matrix()\n",
    "    return (data<600).astype(int).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#@timeit\n",
    "def get_on_surface_time(data):\n",
    "    data=data['Pressure'].as_matrix()\n",
    "    return (data>600).astype(int).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def find_velocity(f):\n",
    "    '''\n",
    "    change in direction and its position\n",
    "    \n",
    "    '''\n",
    "    data_pat=f\n",
    "    Vel = []\n",
    "    horz_Vel = []\n",
    "    horz_vel_mag = []\n",
    "    vert_vel_mag = []\n",
    "    vert_Vel = []\n",
    "    magnitude = []\n",
    "    timestamp_diff =  []\n",
    "    \n",
    "    t = 0\n",
    "    for i in range(len(data_pat)-2):\n",
    "        if t+10 <= len(data_pat)-1:\n",
    "            Vel.append(((data_pat['X'].as_matrix()[t+10] - data_pat['X'].as_matrix()[t])/(data_pat['Timestamp'].as_matrix()[t+10]-data_pat['Timestamp'].as_matrix()[t]) , (data_pat['Y'].as_matrix()[t+10]-data_pat['Y'].as_matrix()[t])/(data_pat['Timestamp'].as_matrix()[t+10]-data_pat['Timestamp'].as_matrix()[t])))\n",
    "            horz_Vel.append((data_pat['X'].as_matrix()[t+10] - data_pat['X'].as_matrix()[t])/(data_pat['Timestamp'].as_matrix()[t+10]-data_pat['Timestamp'].as_matrix()[t]))\n",
    "            \n",
    "            vert_Vel.append((data_pat['Y'].as_matrix()[t+10] - data_pat['Y'].as_matrix()[t])/(data_pat['Timestamp'].as_matrix()[t+10]-data_pat['Timestamp'].as_matrix()[t]))\n",
    "            magnitude.append(sqrt(((data_pat['X'].as_matrix()[t+10]-data_pat['X'].as_matrix()[t])/(data_pat['Timestamp'].as_matrix()[t+10]-data_pat['Timestamp'].as_matrix()[t]))**2 + (((data_pat['Y'].as_matrix()[t+10]-data_pat['Y'].as_matrix()[t])/(data_pat['Timestamp'].as_matrix()[t+10]-data_pat['Timestamp'].as_matrix()[t]))**2)))\n",
    "            timestamp_diff.append(data_pat['Timestamp'].as_matrix()[t+10]-data_pat['Timestamp'].as_matrix()[t])\n",
    "            horz_vel_mag.append(abs(horz_Vel[len(horz_Vel)-1]))\n",
    "            vert_vel_mag.append(abs(vert_Vel[len(vert_Vel)-1]))\n",
    "            t = t+10\n",
    "        else:\n",
    "            break\n",
    "    magnitude_vel = np.mean(magnitude)  \n",
    "    magnitude_horz_vel = np.mean(horz_vel_mag)\n",
    "    magnitude_vert_vel = np.mean(vert_vel_mag)\n",
    "    return Vel,magnitude,timestamp_diff,horz_Vel,vert_Vel,magnitude_vel,magnitude_horz_vel,magnitude_vert_vel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def find_acceleration(f):\n",
    "    '''\n",
    "    change in direction and its velocity\n",
    "   \n",
    "    '''\n",
    "    Vel,magnitude,timestamp_diff,horz_Vel,vert_Vel,magnitude_vel,magnitude_horz_vel,magnitude_vert_vel = find_velocity(f)\n",
    "    accl = []\n",
    "    horz_Accl =  []\n",
    "    vert_Accl = []\n",
    "    magnitude = []\n",
    "    horz_acc_mag = []\n",
    "    vert_acc_mag = []\n",
    "    for i in range(len(Vel)-2):\n",
    "        accl.append(((Vel[i+1][0]-Vel[i][0])/timestamp_diff[i] , (Vel[i+1][1]-Vel[i][1])/timestamp_diff[i]))\n",
    "        horz_Accl.append((horz_Vel[i+1]-horz_Vel[i])/timestamp_diff[i])\n",
    "        vert_Accl.append((vert_Vel[i+1]-vert_Vel[i])/timestamp_diff[i])\n",
    "        horz_acc_mag.append(abs(horz_Accl[len(horz_Accl)-1]))\n",
    "        vert_acc_mag.append(abs(vert_Accl[len(vert_Accl)-1]))\n",
    "        magnitude.append(sqrt(((Vel[i+1][0]-Vel[i][0])/timestamp_diff[i])**2 + ((Vel[i+1][1]-Vel[i][1])/timestamp_diff[i])**2))\n",
    "    \n",
    "    magnitude_acc = np.mean(magnitude)  \n",
    "    magnitude_horz_acc = np.mean(horz_acc_mag)\n",
    "    magnitude_vert_acc = np.mean(vert_acc_mag)\n",
    "    return accl,magnitude,horz_Accl,vert_Accl,timestamp_diff,magnitude_acc,magnitude_horz_acc,magnitude_vert_acc\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def find_jerk(f):\n",
    "    accl,magnitude,horz_Accl,vert_Accl,timestamp_diff,magnitude_acc,magnitude_horz_acc,magnitude_vert_acc = find_acceleration(f)\n",
    "    jerk = []\n",
    "    hrz_jerk = []\n",
    "    vert_jerk = []\n",
    "    magnitude = []\n",
    "    horz_jerk_mag = []\n",
    "    vert_jerk_mag = []\n",
    "    \n",
    "    for i in range(len(accl)-2):\n",
    "        jerk.append(((accl[i+1][0]-accl[i][0])/timestamp_diff[i] , (accl[i+1][1]-accl[i][1])/timestamp_diff[i]))\n",
    "        hrz_jerk.append((horz_Accl[i+1]-horz_Accl[i])/timestamp_diff[i])\n",
    "        vert_jerk.append((vert_Accl[i+1]-vert_Accl[i])/timestamp_diff[i])\n",
    "        horz_jerk_mag.append(abs(hrz_jerk[len(hrz_jerk)-1]))\n",
    "        vert_jerk_mag.append(abs(vert_jerk[len(vert_jerk)-1]))\n",
    "        magnitude.append(sqrt(((accl[i+1][0]-accl[i][0])/timestamp_diff[i])**2 + ((accl[i+1][1]-accl[i][1])/timestamp_diff[i])**2))\n",
    "        \n",
    "    magnitude_jerk = np.mean(magnitude)  \n",
    "    magnitude_horz_jerk = np.mean(horz_jerk_mag)\n",
    "    magnitude_vert_jerk = np.mean(vert_jerk_mag)\n",
    "    return jerk,magnitude,hrz_jerk,vert_jerk,timestamp_diff,magnitude_jerk,magnitude_horz_jerk,magnitude_vert_jerk\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def NCV_per_halfcircle(f):\n",
    "    data_pat=f\n",
    "    Vel = []\n",
    "    ncv = []\n",
    "    temp_ncv = 0\n",
    "    basex = data_pat['X'].as_matrix()[0]\n",
    "    for i in range(len(data_pat)-2):\n",
    "        if data_pat['X'].as_matrix()[i] == basex:\n",
    "            ncv.append(temp_ncv)\n",
    "            temp_ncv = 0\n",
    "            continue\n",
    "            \n",
    "        Vel.append(((data_pat['X'].as_matrix()[i+1] - data_pat['X'].as_matrix()[i])/(data_pat['Timestamp'].as_matrix()[i+1]-data_pat['Timestamp'].as_matrix()[i]) , (data_pat['Y'].as_matrix()[i+1]-data_pat['Y'].as_matrix()[i])/(data_pat['Timestamp'].as_matrix()[i+1]-data_pat['Timestamp'].as_matrix()[i])))\n",
    "        if Vel[len(Vel)-1] != (0,0):\n",
    "            temp_ncv+=1\n",
    "    ncv.append(temp_ncv)\n",
    "    #ncv = list(filter((2).__ne__, ncv))\n",
    "    ncv_Val = np.sum(ncv)/np.count_nonzero(ncv)\n",
    "    return ncv,ncv_Val"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def NCA_per_halfcircle(f):\n",
    "    data_pat=f\n",
    "    Vel,magnitude,timestamp_diff,horz_Vel,vert_Vel,magnitude_vel,magnitude_horz_vel,magnitude_vert_vel = find_velocity(f)\n",
    "    accl = []\n",
    "    nca = []\n",
    "    temp_nca = 0\n",
    "    basex = data_pat['X'].as_matrix()[0]\n",
    "    for i in range(len(Vel)-2):\n",
    "        if data_pat['X'].as_matrix()[i] == basex:\n",
    "            nca.append(temp_nca)\n",
    "            #print ('tempNCa::',temp_nca)\n",
    "            temp_nca = 0\n",
    "            continue\n",
    "            \n",
    "        accl.append(((Vel[i+1][0]-Vel[i][0])/timestamp_diff[i] , (Vel[i+1][1]-Vel[i][1])/timestamp_diff[i]))\n",
    "        if accl[len(accl)-1] != (0,0):\n",
    "            temp_nca+=1\n",
    "    nca.append(temp_nca)\n",
    "    nca = list(filter((2).__ne__, nca))\n",
    "    nca_Val = np.sum(nca)/np.count_nonzero(nca)\n",
    "    return nca,nca_Val"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {},
   "outputs": [],
   "source": [
    "#@timeit\n",
    "def get_features(f, parkinson_target):\n",
    "    global header_row\n",
    "    df=pd.read_csv(f, sep=';', header=None, names=header_row)\n",
    "    \n",
    "    df_static=df[df[\"Test_ID\"]==0]    # static test\n",
    "    df_dynamic=df[df[\"Test_ID\"]==1]    # dynamic test\n",
    "    df_stcp=df[df[\"Test_ID\"]==2]    # STCP\n",
    "    #df_static_dynamic=pd.concat([df_static, df_dynamic])\n",
    "    \n",
    "    initial_timestamp=df['Timestamp'][0]\n",
    "    df['Timestamp']=df['Timestamp']- initial_timestamp # offset timestamps\n",
    "    \n",
    "    duration_static = df_static['Timestamp'].as_matrix()[-1] if df_static.shape[0] else 1\n",
    "    duration_dynamic = df_dynamic['Timestamp'].as_matrix()[-1] if df_dynamic.shape[0] else 1\n",
    "    duration_STCP = df_stcp['Timestamp'].as_matrix()[-1] if df_stcp.shape[0] else 1\n",
    "\n",
    "    \n",
    "    data_point=[]\n",
    "    data_point.append(get_no_strokes(df_static) if df_static.shape[0] else 0) # no. of strokes for static test\n",
    "    data_point.append(get_no_strokes(df_dynamic) if df_dynamic.shape[0] else 0) # no. of strokes for dynamic test\n",
    "    data_point.append(get_speed(df_static) if df_static.shape[0] else 0) # speed for static test\n",
    "    data_point.append(get_speed(df_dynamic) if df_dynamic.shape[0] else 0) # speed for dynamic test\n",
    "\n",
    "    Vel,magnitude,timestamp_diff,horz_Vel,vert_Vel,magnitude_vel,magnitude_horz_vel,magnitude_vert_vel = find_velocity(df_static) if df_static.shape[0] else (0,0,0,0,0,0,0,0) # magnitudes of velocity, horizontal velocity and vertical velocity for static test\n",
    "    data_point.extend([magnitude_vel, magnitude_horz_vel,magnitude_vert_vel])\n",
    "    Vel,magnitude,timestamp_diff,horz_Vel,vert_Vel,magnitude_vel,magnitude_horz_vel,magnitude_vert_vel = find_velocity(df_dynamic) if df_dynamic.shape[0] else (0,0,0,0,0,0,0,0) # magnitudes of velocity, horizontal velocity and vertical velocity for dynamic test\n",
    "    data_point.extend([magnitude_vel, magnitude_horz_vel,magnitude_vert_vel])\n",
    "    \n",
    "    accl,magnitude,horz_Accl,vert_Accl,timestamp_diff,magnitude_acc,magnitude_horz_acc,magnitude_vert_acc=find_acceleration(df_static) if df_static.shape[0] else (0,0,0,0,0,0,0,0)# magnitudes of acceleration, horizontal acceleration and vertical acceleration for static test        \n",
    "    data_point.extend([magnitude_acc,magnitude_horz_acc,magnitude_vert_acc])\n",
    "    accl,magnitude,horz_Accl,vert_Accl,timestamp_diff,magnitude_acc,magnitude_horz_acc,magnitude_vert_acc=find_acceleration(df_dynamic) if df_dynamic.shape[0] else (0,0,0,0,0,0,0,0)# magnitudes of acceleration, horizontal acceleration and vertical acceleration for dynamic test        \n",
    "    data_point.extend([magnitude_acc,magnitude_horz_acc,magnitude_vert_acc])\n",
    "    \n",
    "    jerk,magnitude,hrz_jerk,vert_jerk,timestamp_diff,magnitude_jerk,magnitude_horz_jerk,magnitude_vert_jerk=find_jerk(df_static) if df_static.shape[0] else (0,0,0,0,0,0,0,0) # magnitudes of jerk, horizontal jerk and vertical jerk for static test\n",
    "    data_point.extend([magnitude_jerk,magnitude_horz_jerk,magnitude_vert_jerk])\n",
    "    jerk,magnitude,hrz_jerk,vert_jerk,timestamp_diff,magnitude_jerk,magnitude_horz_jerk,magnitude_vert_jerk=find_jerk(df_dynamic) if df_dynamic.shape[0] else (0,0,0,0,0,0,0,0) # magnitudes of jerk, horizontal jerk and vertical jerk for dynamic test\n",
    "    data_point.extend([magnitude_jerk,magnitude_horz_jerk,magnitude_vert_jerk])\n",
    "    \n",
    "    ncv,ncv_Val=NCV_per_halfcircle(df_static) if df_static.shape[0] else (0,0) # NCV for static test\n",
    "    data_point.append(ncv_Val)\n",
    "    ncv,ncv_Val=NCV_per_halfcircle(df_dynamic) if df_dynamic.shape[0] else (0,0) # NCV for dynamic test\n",
    "    data_point.append(ncv_Val)\n",
    "    \n",
    "    nca,nca_Val=NCA_per_halfcircle(df_static) if df_static.shape[0] else (0,0) # NCA for static test\n",
    "    data_point.append(nca_Val)\n",
    "    nca,nca_Val=NCA_per_halfcircle(df_dynamic) if df_dynamic.shape[0] else (0,0) # NCA for dynamic test\n",
    "    data_point.append(nca_Val)\n",
    "    \n",
    "    data_point.append(get_in_air_time(df_stcp) if df_stcp.shape[0] else 0) # in air time for STCP\n",
    "    data_point.append(get_on_surface_time(df_static) if df_static.shape[0] else 0) # on surface time for static test\n",
    "    data_point.append(get_on_surface_time(df_dynamic) if df_dynamic.shape[0] else 0) # on surface time for dynamic test\n",
    "    \n",
    "    data_point.append(parkinson_target)    # traget. 1 for parkinson. 0 for control.\n",
    "    \n",
    "    return data_point"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {},
   "outputs": [],
   "source": [
    "temp_feat=get_features(parkinson_file_list[35], 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[30, 72, 1.3588757917667431e-05, 2.6236510831179708e-05, 0.12890441251529997, 0.080471233196279121, 0.08363708822986228, 0.19229349111079519, 0.11555023022027296, 0.12880277922214939, 0.00091615395232794758, 0.00051882221335077254, 0.00066556220080457216, 0.0010669055486579792, 0.00061047275261607225, 0.00075630335675646069, 2.2116391163746176e-05, 1.2543602500530039e-05, 1.6466372203471851e-05, 2.2504617627053959e-05, 1.3394439474233898e-05, 1.5692010468146771e-05, 230.14285714285714, 269.88888888888891, 85.5, 86.666666666666671, 850, 524, 1230, 1]\n"
     ]
    }
   ],
   "source": [
    "print(temp_feat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "raw=[]\n",
    "for x in parkinson_file_list:\n",
    "    raw.append(get_features(x, 1))\n",
    "for x in control_file_list:\n",
    "    raw.append(get_features(x, 0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "raw=np.array(raw)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "metadata": {},
   "outputs": [],
   "source": [
    "features_headers=['no_strokes_st', 'no_strokes_dy', 'speed_st', 'speed_dy', 'magnitude_vel_st' , 'magnitude_horz_vel_st' , 'magnitude_vert_vel_st', 'magnitude_vel_dy', 'magnitude_horz_vel_dy' , 'magnitude_vert_vel_dy', 'magnitude_acc_st' , 'magnitude_horz_acc_st' , 'magnitude_vert_acc_st','magnitude_acc_dy' , 'magnitude_horz_acc_dy' , 'magnitude_vert_acc_dy', 'magnitude_jerk_st', 'magnitude_horz_jerk_st' , 'magnitude_vert_jerk_st', 'magnitude_jerk_dy', 'magnitude_horz_jerk_dy' , 'magnitude_vert_jerk_dy', 'ncv_st', 'ncv_dy', 'nca_st', 'nca_dy', 'in_air_stcp','on_surface_st', 'on_surface_dy', 'target']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "metadata": {},
   "outputs": [],
   "source": [
    "data=pd.DataFrame(raw, columns=features_headers)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
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       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>no_strokes_st</th>\n",
       "      <th>no_strokes_dy</th>\n",
       "      <th>speed_st</th>\n",
       "      <th>speed_dy</th>\n",
       "      <th>magnitude_vel_st</th>\n",
       "      <th>magnitude_horz_vel_st</th>\n",
       "      <th>magnitude_vert_vel_st</th>\n",
       "      <th>magnitude_vel_dy</th>\n",
       "      <th>magnitude_horz_vel_dy</th>\n",
       "      <th>magnitude_vert_vel_dy</th>\n",
       "      <th>...</th>\n",
       "      <th>magnitude_horz_jerk_dy</th>\n",
       "      <th>magnitude_vert_jerk_dy</th>\n",
       "      <th>ncv_st</th>\n",
       "      <th>ncv_dy</th>\n",
       "      <th>nca_st</th>\n",
       "      <th>nca_dy</th>\n",
       "      <th>in_air_stcp</th>\n",
       "      <th>on_surface_st</th>\n",
       "      <th>on_surface_dy</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.001592</td>\n",
       "      <td>0.001560</td>\n",
       "      <td>0.046842</td>\n",
       "      <td>0.029474</td>\n",
       "      <td>0.029731</td>\n",
       "      <td>0.042719</td>\n",
       "      <td>0.026828</td>\n",
       "      <td>0.026851</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>330.571429</td>\n",
       "      <td>324.571429</td>\n",
       "      <td>275.500000</td>\n",
       "      <td>282.000000</td>\n",
       "      <td>1536.0</td>\n",
       "      <td>5867.0</td>\n",
       "      <td>6558.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.001387</td>\n",
       "      <td>0.001339</td>\n",
       "      <td>0.088510</td>\n",
       "      <td>0.055965</td>\n",
       "      <td>0.055433</td>\n",
       "      <td>0.114583</td>\n",
       "      <td>0.073060</td>\n",
       "      <td>0.070357</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000006</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>224.222222</td>\n",
       "      <td>227.125000</td>\n",
       "      <td>71.500000</td>\n",
       "      <td>73.666667</td>\n",
       "      <td>1536.0</td>\n",
       "      <td>3029.0</td>\n",
       "      <td>2312.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>48.0</td>\n",
       "      <td>176.0</td>\n",
       "      <td>0.001201</td>\n",
       "      <td>0.001194</td>\n",
       "      <td>0.200499</td>\n",
       "      <td>0.124995</td>\n",
       "      <td>0.126018</td>\n",
       "      <td>0.175576</td>\n",
       "      <td>0.111122</td>\n",
       "      <td>0.109489</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000007</td>\n",
       "      <td>0.000006</td>\n",
       "      <td>206.833333</td>\n",
       "      <td>276.400000</td>\n",
       "      <td>29.333333</td>\n",
       "      <td>74.500000</td>\n",
       "      <td>1536.0</td>\n",
       "      <td>572.0</td>\n",
       "      <td>575.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>6.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.001159</td>\n",
       "      <td>0.001157</td>\n",
       "      <td>0.150016</td>\n",
       "      <td>0.095055</td>\n",
       "      <td>0.095358</td>\n",
       "      <td>0.136387</td>\n",
       "      <td>0.090064</td>\n",
       "      <td>0.085038</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000007</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>189.125000</td>\n",
       "      <td>234.571429</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>58.333333</td>\n",
       "      <td>1536.0</td>\n",
       "      <td>835.0</td>\n",
       "      <td>363.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.001152</td>\n",
       "      <td>0.001140</td>\n",
       "      <td>0.103493</td>\n",
       "      <td>0.066596</td>\n",
       "      <td>0.064488</td>\n",
       "      <td>0.106868</td>\n",
       "      <td>0.068777</td>\n",
       "      <td>0.067774</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>0.000006</td>\n",
       "      <td>277.285714</td>\n",
       "      <td>266.285714</td>\n",
       "      <td>81.333333</td>\n",
       "      <td>117.000000</td>\n",
       "      <td>1536.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    no_strokes_st  no_strokes_dy  speed_st  speed_dy  magnitude_vel_st  \\\n",
       "72            2.0            2.0  0.001592  0.001560          0.046842   \n",
       "73            2.0            2.0  0.001387  0.001339          0.088510   \n",
       "74           48.0          176.0  0.001201  0.001194          0.200499   \n",
       "75            6.0           10.0  0.001159  0.001157          0.150016   \n",
       "76            0.0            0.0  0.001152  0.001140          0.103493   \n",
       "\n",
       "    magnitude_horz_vel_st  magnitude_vert_vel_st  magnitude_vel_dy  \\\n",
       "72               0.029474               0.029731          0.042719   \n",
       "73               0.055965               0.055433          0.114583   \n",
       "74               0.124995               0.126018          0.175576   \n",
       "75               0.095055               0.095358          0.136387   \n",
       "76               0.066596               0.064488          0.106868   \n",
       "\n",
       "    magnitude_horz_vel_dy  magnitude_vert_vel_dy   ...    \\\n",
       "72               0.026828               0.026851   ...     \n",
       "73               0.073060               0.070357   ...     \n",
       "74               0.111122               0.109489   ...     \n",
       "75               0.090064               0.085038   ...     \n",
       "76               0.068777               0.067774   ...     \n",
       "\n",
       "    magnitude_horz_jerk_dy  magnitude_vert_jerk_dy      ncv_st      ncv_dy  \\\n",
       "72                0.000004                0.000004  330.571429  324.571429   \n",
       "73                0.000006                0.000005  224.222222  227.125000   \n",
       "74                0.000007                0.000006  206.833333  276.400000   \n",
       "75                0.000007                0.000005  189.125000  234.571429   \n",
       "76                0.000005                0.000006  277.285714  266.285714   \n",
       "\n",
       "        nca_st      nca_dy  in_air_stcp  on_surface_st  on_surface_dy  target  \n",
       "72  275.500000  282.000000       1536.0         5867.0         6558.0     0.0  \n",
       "73   71.500000   73.666667       1536.0         3029.0         2312.0     0.0  \n",
       "74   29.333333   74.500000       1536.0          572.0          575.0     0.0  \n",
       "75   14.000000   58.333333       1536.0          835.0          363.0     0.0  \n",
       "76   81.333333  117.000000       1536.0            0.0            0.0     0.0  \n",
       "\n",
       "[5 rows x 30 columns]"
      ]
     },
     "execution_count": 225,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data.to_csv('data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data shape (77, 30)\n"
     ]
    }
   ],
   "source": [
    "print('data shape', data.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train shape (72, 30)\n"
     ]
    }
   ],
   "source": [
    "pos=data[data['target']==1]\n",
    "neg=data[data['target']==0]\n",
    "\n",
    "train_pos=pos.head(pos.shape[0]-5)\n",
    "train_neg=neg.head(pos.shape[0]-5)\n",
    "train=pd.concat([train_pos, train_neg])\n",
    "print('train shape', train.shape)\n",
    "\n",
    "test_pos=pos.tail(5)\n",
    "test_neg=neg.tail(5)\n",
    "test=pd.concat([test_pos, test_neg])\n",
    "\n",
    "\n",
    "train_y=train['target']\n",
    "train_x=train.drop(['target'], axis=1)\n",
    "test_y=test['target']\n",
    "test_x=test.drop(['target'], axis=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 306,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def accuracy(prediction,actual):\n",
    "    correct = 0\n",
    "    not_correct = 0\n",
    "    for i in range(len(prediction)):\n",
    "        if prediction[i] == actual[i]:\n",
    "            correct+=1\n",
    "        else:\n",
    "            not_correct+=1\n",
    "    return (correct*100)/(correct+not_correct)\n",
    "\n",
    "\n",
    "def metrics(prediction,actual):\n",
    "    tp = 0\n",
    "    tn = 0\n",
    "    fp = 0\n",
    "    fn = 0\n",
    "    for i in range(len(prediction)):\n",
    "        if prediction[i] == actual[i] and actual[i]==1:\n",
    "            tp+=1\n",
    "        if prediction[i] == actual[i] and actual[i]==0:\n",
    "            tn+=1\n",
    "        if prediction[i] != actual[i] and actual[i]==0:\n",
    "            fp+=1\n",
    "        if prediction[i] != actual[i] and actual[i]==1:\n",
    "            fn+=1\n",
    "    metrics = {'Precision':(tp/(tp+fp+tn+fn)),'Recall':(tp/(tp+fn)),'F1':(2*(tp/(tp+fp+tn+fn))*(tp/(tp+fn)))/((tp/(tp+fp+tn+fn))+(tp/(tp+fn)))}\n",
    "    return (metrics)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 307,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 310,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy: 70.0 %\n",
      "{'Recall': 0.625, 'F1': 0.5555555555555556, 'Precision': 0.5}\n"
     ]
    }
   ],
   "source": [
    "clf=LogisticRegression()\n",
    "clf.fit(train_x, train_y)\n",
    "preds=clf.predict(test_x)\n",
    "print('accuracy:',accuracy(test_y.tolist(), preds.tolist()), '%')\n",
    "print(metrics(test_y.tolist(), preds.tolist()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 311,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy: 100.0 %\n",
      "{'Recall': 1.0, 'F1': 0.6666666666666666, 'Precision': 0.5}\n"
     ]
    }
   ],
   "source": [
    "clf=RandomForestClassifier()\n",
    "clf.fit(train_x, train_y)\n",
    "preds=clf.predict(test_x)\n",
    "print('accuracy:',accuracy(test_y.tolist(), preds.tolist()), '%')\n",
    "print(metrics(test_y.tolist(), preds.tolist()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Support Vector Machine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 312,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy: 100.0 %\n",
      "{'Recall': 1.0, 'F1': 0.6666666666666666, 'Precision': 0.5}\n"
     ]
    }
   ],
   "source": [
    "clf=SVC()\n",
    "clf.fit(train_x, train_y)\n",
    "preds=clf.predict(test_x)\n",
    "print('accuracy:',accuracy(test_y.tolist(), preds.tolist()), '%')\n",
    "print(metrics(test_y.tolist(), preds.tolist()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Decision Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 313,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy: 100.0 %\n",
      "{'Recall': 1.0, 'F1': 0.6666666666666666, 'Precision': 0.5}\n"
     ]
    }
   ],
   "source": [
    "clf=DecisionTreeClassifier()\n",
    "clf.fit(train_x, train_y)\n",
    "preds=clf.predict(test_x)\n",
    "print('accuracy:',accuracy(test_y.tolist(), preds.tolist()), '%')\n",
    "print(metrics(test_y.tolist(), preds.tolist()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### K-Nearest Neighbors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 314,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy: 60.0 %\n",
      "{'Recall': 0.5714285714285714, 'F1': 0.47058823529411764, 'Precision': 0.4}\n"
     ]
    }
   ],
   "source": [
    "clf=KNeighborsClassifier()\n",
    "clf.fit(train_x, train_y)\n",
    "preds=clf.predict(test_x)\n",
    "print('accuracy:',accuracy(test_y.tolist(), preds.tolist()), '%')\n",
    "print(metrics(test_y.tolist(), preds.tolist()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Some Plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 285,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def plot(f, plot_func, t_id=0, x=None, y=None):\n",
    "    global header_row\n",
    "    df=pd.read_csv(f, sep=';', header=None, names=header_row)\n",
    "    df=df[df[\"Test_ID\"]==t_id]\n",
    "    initial_timestamp=df['Timestamp'][0]\n",
    "    df['Timestamp']=df['Timestamp']- initial_timestamp\n",
    "    plot_func(data=df, x=x, y=y, fit_reg=False, scatter_kws={\"s\": 0.5})\n",
    "    print(metrics(test_y.tolist(), preds.tolist()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Pressure (Parkinsons)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 286,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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co/qlOhwRkcPS1q+4/zKz//CnypD9iMRihKKOkX3yVXsQkQ7vYPog8oCImTXg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\nkRYpQYiISIuUIEREpEX/D2Ck4MWCjrbrAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1e1d56995f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(f=parkinson_file_list[35],  plot_func=sns.regplot, t_id=0, x='Timestamp', y='Pressure')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Pressure (Control)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 287,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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c7e6l7l5aXFx8yHkUEZHkuqU3lLvvAF4ATgEGmllLl90RwPowvR4YCRCWFwLb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\nTX3weed3AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1e1d59b0278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(control_file_list[1], plot_func=sns.regplot, t_id=0, x='Timestamp', y='Pressure')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "plot(f=parkinson_file_list[35],  plot_func=sns.barplot, t_id=0, x='Timestamp', y='Pressure')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "plot(control_file_list[1], plot_func=sns.regplot, t_id=0, x='Timestamp', y='Pressure')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}