Final_code.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import os\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn import preprocessing\n",
    "from sklearn.model_selection import cross_val_score\n",
    "import math\n",
    "from scipy.stats import iqr\n",
    "from numpy import linalg\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.metrics import accuracy_score\n",
    "from scipy import stats\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "import pickle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "## Features\n",
    "\n",
    "def mean(x):\n",
    "    \"\"\"Calculates mean\"\"\"\n",
    "    mean_x = np.mean(x)\n",
    "    return mean_x\n",
    "\n",
    "def std_dev(x):\n",
    "    \"\"\"Calculates standard deviation\"\"\"\n",
    "    std_x = np.std(x)\n",
    "    return std_x\n",
    "    \n",
    "def mad(x):\n",
    "    \"\"\"Calculates median absolute deviation\"\"\"\n",
    "    mad_x = np.median(np.abs(x - np.median(x)))\n",
    "    return mad_x\n",
    "\n",
    "def minimum(x):\n",
    "    \"\"\"Calculates minimum\"\"\"\n",
    "    return min(x)\n",
    "\n",
    "def maximum(x):\n",
    "    \"\"\"Calculates maximum\"\"\"\n",
    "    return max(x)\n",
    "\n",
    "def energy_measure(x):\n",
    "    \"\"\"Calculates energy measures\"\"\"\n",
    "    em_x = np.mean(np.square(x))\n",
    "    return em_x\n",
    "\n",
    "def inter_quartile_range(x):\n",
    "    \"\"\"Calculates inter-quartile range\"\"\"\n",
    "    iqr_x = np.subtract(*np.percentile(x, [75, 25]))\n",
    "    return iqr_x\n",
    "\n",
    "def sma(x,y,z):\n",
    "    \"\"\"Calculates signal magnitude area\"\"\"   \n",
    "    abs_x = np.absolute(x)\n",
    "    abs_y = np.absolute(y)\n",
    "    abs_z = np.absolute(z)\n",
    "    return np.mean(abs_x+abs_y+abs_z)\n",
    "\n",
    "def skewness(x):\n",
    "    \"\"\"Calculates skewness\"\"\"\n",
    "    skew_x=stats.skew(x)\n",
    "    return skew_x\n",
    "\n",
    "def kurt(x):\n",
    "    \"\"\"Calculates kurtosis\"\"\"\n",
    "    kurt_x = stats.kurtosis(x,fisher=False)\n",
    "    return kurt_x\n",
    "    \n",
    "def max_index(x):\n",
    "    \"\"\"Calculates index of maximum\"\"\"\n",
    "    idx_x = np.argmax(x)\n",
    "    return idx_x\n",
    "\n",
    "def butter_bandpass(lowcut, highcut, fs, order=5):\n",
    "    nyq = 0.5 * fs\n",
    "    low = lowcut / nyq\n",
    "    high = highcut / nyq\n",
    "    b, a = butter(order, [low, high], btype='band')\n",
    "    return b, a\n",
    "\n",
    "def butter_bandpass_filter(data, lowcut, highcut, fs, order=5):\n",
    "    b, a = butter_bandpass(lowcut, highcut, fs, order=order)\n",
    "    y = lfilter(b, a, data)\n",
    "    return y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "macc_cols = ['mad_macc', 'min_macc', 'max_macc', 'em_macc', 'sma_macc', 'label']\n",
    "\n",
    "def process_mag_acc(input_df, columns, output_df):\n",
    "    mag = np.linalg.norm(input_df.iloc[:,1:4], axis = 1)\n",
    "    input_df[6] = mag.tolist()\n",
    "    \n",
    "    i = 0\n",
    "    while i < len(input_df[0]):\n",
    "        max_window = input_df[0][i] + 10000\n",
    "        min_window = input_df[0][i]\n",
    "        if i != 0:                                  #handling overlap\n",
    "            min_window -= 5000\n",
    "            max_window -= 5000\n",
    "            i -= int(count/2)\n",
    "        x = [input_df[6][i]]\n",
    "        l = input_df[5][i]\n",
    "        \n",
    "        count = 0\n",
    "        while min_window < max_window:\n",
    "            i += 1\n",
    "            count += 1\n",
    "            try:\n",
    "                label = input_df[5][i]\n",
    "                if label=='null' or label is None:\n",
    "                    continue\n",
    "                x.append(input_df[6][i])\n",
    "                min_window = input_df[0][i]\n",
    "            except KeyError:\n",
    "                break\n",
    "        \n",
    "        \n",
    "        mad_macc = mad(x)\n",
    "        min_macc = minimum(x)\n",
    "        max_macc = maximum(x)\n",
    "        em_macc = energy_measure(x)\n",
    "        sma_macc = np.mean(np.absolute(x))  \n",
    "        \n",
    "        macc_data = [mad_macc,min_macc, max_macc, em_macc, sma_macc, l]\n",
    "        output_df = output_df.append(pd.Series(macc_data, index=columns), ignore_index=True)\n",
    "\n",
    "    return output_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "maccj_cols = ['mad_macc_jerk', 'min_macc_jerk', 'iqr_macc_jerk', 'sma_macc_jerk']\n",
    "def process_mag_acc_jerk(input_df, columns, output_df):\n",
    "    mag = np.linalg.norm(input_df.iloc[:,1:4], axis = 1)\n",
    "    input_df[6] = mag.tolist()\n",
    "    \n",
    "    i = 0\n",
    "    while i < len(input_df[0]):\n",
    "        max_window = input_df[0][i] + 10000\n",
    "        min_window = input_df[0][i]\n",
    "        if i != 0:                                  #handling overlap\n",
    "            min_window -= 5000\n",
    "            max_window -= 5000\n",
    "            i -= int(count/2)\n",
    "        x = [input_df[6][i]]\n",
    "        l = input_df[5][i]\n",
    "        \n",
    "        count = 0\n",
    "        while min_window < max_window:\n",
    "            i += 1\n",
    "            count += 1\n",
    "            try:\n",
    "                label = input_df[5][i]\n",
    "                if label=='null' or label is None:\n",
    "                    continue\n",
    "                x.append(input_df[6][i])\n",
    "                min_window = input_df[0][i]\n",
    "            except KeyError:\n",
    "                break\n",
    "        mad_macc_jerk = mad(x)\n",
    "        min_macc_jerk = minimum(x)\n",
    "        iqr_macc_jerk = inter_quartile_range(x)\n",
    "        sma_macc_jerk = np.mean(np.absolute(x))\n",
    "        \n",
    "        maccj_data = [mad_macc_jerk, min_macc_jerk, iqr_macc_jerk, sma_macc_jerk]\n",
    "        output_df = output_df.append(pd.Series(maccj_data, index=columns), ignore_index=True)\n",
    "\n",
    "    return output_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "facc_jmag_cols = ['std_mfacc_jerk', 'mad_mfacc_jerk', 'iqr_mfacc_jerk']\n",
    "\n",
    "def process_faccj_mag(input_df, columns, output_df):\n",
    "    mag = np.linalg.norm(input_df.iloc[:,1:4], axis = 1)\n",
    "    input_df[6] = mag.tolist()\n",
    "    i = 0\n",
    "\n",
    "    while i < len(input_df[0]):\n",
    "        max_window = input_df[0][i] + 10000\n",
    "        min_window = input_df[0][i]\n",
    "        if i != 0:                                  #handling overlap\n",
    "            min_window -= 5000\n",
    "            max_window -= 5000\n",
    "            i -= int(count/2)\n",
    "        x = [input_df[6][i]]\n",
    "\n",
    "        count = 0\n",
    "        while min_window < max_window:\n",
    "            i += 1\n",
    "            count += 1\n",
    "            try:\n",
    "                label = input_df[5][i]\n",
    "                if label=='null' or label is None:\n",
    "                    continue\n",
    "                x.append(input_df[6][i])\n",
    "                min_window = input_df[0][i]\n",
    "            except KeyError:\n",
    "                break\n",
    "                \n",
    "        label = (input_df[5][1], )\n",
    "        if label == 'null':\n",
    "            print('--------null')\n",
    "            return\n",
    "        \n",
    "        std_acc = std_dev(x)\n",
    "        iqr_acc = inter_quartile_range(x)\n",
    "        mad_acc = mad(x)\n",
    "\n",
    "        acc_data = [std_acc, mad_acc, iqr_acc]\n",
    "        output_df = output_df.append(pd.Series(acc_data, index=columns), ignore_index=True)\n",
    "\n",
    "    return output_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def hist_activities(df):\n",
    "    df['label'].value_counts().plot(kind='bar', title='Training examples by activity type')\n",
    "    plt.show()\n",
    "    \n",
    "def plot_activity(activity, df):\n",
    "    data = df[df.iloc[:, 5] == activity][[1, 2, 3]][:2000]\n",
    "    axis = data.plot(subplots=True, figsize=(16, 12), \n",
    "                     title=activity)\n",
    "    for ax in axis:\n",
    "        ax.legend(loc='lower left', bbox_to_anchor=(1.0, 0.5))\n",
    "        \n",
    "def plot_act_signals(df):\n",
    "    plot_activity(\"sitting\", df)\n",
    "    plt.ylabel('sitting')\n",
    "    plt.show()\n",
    "    plot_activity(\"walking\", df)\n",
    "    plt.ylabel('walking')\n",
    "    plt.show()\n",
    "    plot_activity(\"standing\", df)\n",
    "    plt.ylabel('standing')\n",
    "    plt.show()\n",
    "    plot_activity(\"laying_down\", df)\n",
    "    plt.ylabel('laying_down')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def calc_jerk(acc, ts):\n",
    "    jk = [0]* len(acc)\n",
    "    for i in range(1,len(acc)):\n",
    "        jk[i-1] = 1000*(acc[i] - acc[i-1])/(ts[i] - ts[i-1])\n",
    "    return jk\n",
    "\n",
    "def fmagnitude(num):\n",
    "    return math.sqrt(num.real**2 + num.imag**2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def get_session_dirs(path):\n",
    "    path = path\n",
    "    dirs = []\n",
    "    dir_list = [x[0] for x in os.walk(path)]\n",
    "    last_dirs = [\"1_android.sensor.accelerometer.data.csv.gz\",\"4_android.sensor.gyroscope.data.csv.gz\"]\n",
    "    dirs = []\n",
    "    for each_dir in dir_list[2:]:\n",
    "\n",
    "        if \"data\" in each_dir:\n",
    "            dirs.append(each_dir)\n",
    "\n",
    "    dirs.sort(key=lambda x:x[-22:])\n",
    "    return dirs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def get_jerk(df):\n",
    "    df[1] = calc_jerk(df[1],df[0])\n",
    "    df[2] = calc_jerk(df[2],df[0])\n",
    "    df[3] = calc_jerk(df[3],df[0])\n",
    "    return df\n",
    "\n",
    "def get_fft(df):\n",
    "    df[1] = np.fft.fft(df[1])\n",
    "    df[2] = np.fft.fft(df[2])\n",
    "    df[3] = np.fft.fft(df[3])\n",
    "    df[1] = [fmagnitude(j) for j in df[1]]\n",
    "    df[2] = [fmagnitude(j) for j in df[2]]\n",
    "    df[3] = [fmagnitude(j) for j in df[3]]\n",
    "    return df\n",
    "\n",
    "def five_point_smoothing(df):\n",
    "    m=np.array(df[1])\n",
    "    m_smooth = np.zeros(len(m))\n",
    "    m_smooth = m;\n",
    "    for i ,val in enumerate(m_smooth[2 : (len(m_smooth) -2)]):\n",
    "        m_smooth[i] = (m_smooth[i-2] + m_smooth[i-1] + m_smooth[i] + m_smooth[i+1] + m_smooth[i+2])/5\n",
    "    df[1] = m_smooth\n",
    "        \n",
    "    m=np.array(df[2])\n",
    "    m_smooth = np.zeros(len(m))\n",
    "    m_smooth = m;\n",
    "    for i ,val in enumerate(m_smooth[2 : (len(m_smooth) -2)]):\n",
    "        m_smooth[i] = (m_smooth[i-2] + m_smooth[i-1] + m_smooth[i] + m_smooth[i+1] + m_smooth[i+2])/5\n",
    "    df[2] = m_smooth\n",
    "    \n",
    "    m = np.array(df[3])\n",
    "    m_smooth = np.zeros(len(m))\n",
    "    m_smooth = m;\n",
    "    for i ,val in enumerate(m_smooth[2 : (len(m_smooth) -2)]):\n",
    "        m_smooth[i] = (m_smooth[i-2] + m_smooth[i-1] + m_smooth[i] + m_smooth[i+1] + m_smooth[i+2])/5\n",
    "    df[3] = m_smooth\n",
    "    \n",
    "    return df\n",
    "\n",
    "def clean_dataset(df):\n",
    "    assert isinstance(df, pd.DataFrame), \"df needs to be a pd.DataFrame\"\n",
    "    df.dropna(inplace=True)\n",
    "    indices_to_keep = ~df.isin([np.nan, np.inf, -np.inf]).any(1)\n",
    "    return df[indices_to_keep]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def process_csv_data(path):\n",
    "    dirs = get_session_dirs(path)\n",
    "    acc_mag = pd.DataFrame(columns = macc_cols)\n",
    "    acc_jerk_mag = pd.DataFrame(columns = maccj_cols)\n",
    "    facc_jerk_mag = pd.DataFrame(columns = facc_jmag_cols)\n",
    "    \n",
    "    acc_gy_df = pd.DataFrame()\n",
    "\n",
    "    for i in dirs: \n",
    "        full_path_acc = os.path.normpath(i + \"/\" + \"1_android.sensor.accelerometer.data.csv.gz\")\n",
    "        \n",
    "        print(\"processing acceleration: \" + full_path_acc)\n",
    "        in_df_ac = pd.read_csv(full_path_acc, compression='gzip', sep=',', header=None)\n",
    "\n",
    "        #cropping\n",
    "        if in_df_ac.shape[0] > 3000:\n",
    "    \n",
    "            in_df_ac = in_df_ac.drop(list(range(1000)), axis = 0)\n",
    "            in_df_ac.index = range(in_df_ac.shape[0])\n",
    "            in_df_ac = in_df_ac.drop(list(range(in_df_ac.shape[0]-1000, in_df_ac.shape[0])), axis = 0)\n",
    "            in_df_ac.index = range(in_df_ac.shape[0])\n",
    "            \n",
    "        #smoothing\n",
    "        in_df_ac = five_point_smoothing(in_df_ac)\n",
    "    \n",
    "        #jerk\n",
    "        in_df_ac_jerk = in_df_ac.copy()\n",
    "        in_df_ac_jerk = get_jerk(in_df_ac_jerk)\n",
    "        \n",
    "        #fft\n",
    "        in_df_fac = in_df_ac.copy()\n",
    "        in_df_fac = get_fft(in_df_fac)\n",
    "        \n",
    "        #fft_jerk\n",
    "        in_df_fac_jerk = in_df_ac_jerk.copy()\n",
    "        in_df_fac_jerk = get_fft(in_df_fac_jerk)\n",
    "\n",
    "        acc_mag = process_mag_acc(in_df_ac, macc_cols, acc_mag)\n",
    "        acc_jerk_mag = process_mag_acc_jerk(in_df_ac_jerk, maccj_cols, acc_jerk_mag)\n",
    "        facc_jerk_mag = process_faccj_mag(in_df_fac_jerk, facc_jmag_cols, facc_jerk_mag)\n",
    "        acc_gy_df = pd.concat([acc_jerk_mag, facc_jerk_mag, acc_mag], axis=1)\n",
    "\n",
    "    print(acc_gy_df.shape)\n",
    "    return acc_gy_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "processing acceleration: Sessions3-full\\14442D57F7E1B88_Tue_Mar_06_18-33_2018_PST\\data\\1_android.sensor.accelerometer.data.csv.gz\n",
      "processing acceleration: Sessions3-full\\14442D57F7E1B88_Wed_Mar_07_11-53_2018_PST\\data\\1_android.sensor.accelerometer.data.csv.gz\n",
      "processing acceleration: Sessions3-full\\14442D57F7E1B88_Wed_Mar_07_13-36_2018_PST\\data\\1_android.sensor.accelerometer.data.csv.gz\n",
      "processing acceleration: Sessions3-full\\14442D57F7E1B88_Wed_Mar_07_13-42_2018_PST\\data\\1_android.sensor.accelerometer.data.csv.gz\n",
      "processing acceleration: Sessions3-full\\14442D57F7E1B88_Wed_Mar_07_13-44_2018_PST\\data\\1_android.sensor.accelerometer.data.csv.gz\n",
      "processing acceleration: Sessions3-full\\14442D57F7E1B88_Wed_Mar_07_13-57_2018_PST\\data\\1_android.sensor.accelerometer.data.csv.gz\n",
      "processing acceleration: Sessions3-full\\14442D57F7E1B88_Thu_Feb_08_00-16_2018_PST\\data\\1_android.sensor.accelerometer.data.csv.gz\n",
      "processing acceleration: Sessions3-full\\14442D57F7E1B88_Thu_Feb_08_00-18_2018_PST\\data\\1_android.sensor.accelerometer.data.csv.gz\n",
      "processing acceleration: Sessions3-full\\14442D57F7E1B88_Thu_Feb_08_00-25_2018_PST\\data\\1_android.sensor.accelerometer.data.csv.gz\n",
      "processing acceleration: Sessions3-full\\14442D57F7E1B88_Thu_Feb_08_00-31_2018_PST\\data\\1_android.sensor.accelerometer.data.csv.gz\n",
      "processing acceleration: Sessions3-full\\14442D57F7E1B88_Thu_Feb_08_00-35_2018_PST\\data\\1_android.sensor.accelerometer.data.csv.gz\n",
      "processing acceleration: Sessions3-full\\14442D57F7E1B88_Thu_Feb_08_00-38_2018_PST\\data\\1_android.sensor.accelerometer.data.csv.gz\n",
      "processing acceleration: Sessions3-full\\14442D57F7E1B88_Thu_Feb_08_00-39_2018_PST\\data\\1_android.sensor.accelerometer.data.csv.gz\n",
      "processing acceleration: Sessions3-full\\14442D57F7E1B88_Thu_Feb_08_07-43_2018_PST\\data\\1_android.sensor.accelerometer.data.csv.gz\n",
      "processing acceleration: Sessions3-full\\14442D57F7E1B88_Thu_Feb_08_07-54_2018_PST\\data\\1_android.sensor.accelerometer.data.csv.gz\n",
      "processing acceleration: Sessions3-full\\14442D57F7E1B88_Thu_Feb_08_08-13_2018_PST\\data\\1_android.sensor.accelerometer.data.csv.gz\n",
      "processing acceleration: Sessions3-full\\14442D57F7E1B88_Thu_Feb_08_14-46_2018_PST\\data\\1_android.sensor.accelerometer.data.csv.gz\n",
      "processing acceleration: Sessions3-full\\14442D57F7E1B88_Thu_Feb_08_19-19_2018_PST\\data\\1_android.sensor.accelerometer.data.csv.gz\n",
      "processing acceleration: Sessions3-full\\14442D57F7E1B88_Fri_Feb_09_09-37_2018_PST\\data\\1_android.sensor.accelerometer.data.csv.gz\n",
      "processing acceleration: Sessions3-full\\14442D57F7E1B88_Fri_Feb_09_09-41_2018_PST\\data\\1_android.sensor.accelerometer.data.csv.gz\n",
      "processing acceleration: Sessions3-full\\14442D57F7E1B88_Fri_Feb_09_09-47_2018_PST\\data\\1_android.sensor.accelerometer.data.csv.gz\n",
      "processing acceleration: Sessions3-full\\14442D57F7E1B88_Fri_Feb_09_09-54_2018_PST\\data\\1_android.sensor.accelerometer.data.csv.gz\n",
      "processing acceleration: Sessions3-full\\14442D57F7E1B88_Fri_Feb_09_11-54_2018_PST\\data\\1_android.sensor.accelerometer.data.csv.gz\n",
      "processing acceleration: Sessions3-full\\14442D57F7E1B88_Sun_Feb_11_18-31_2018_PST\\data\\1_android.sensor.accelerometer.data.csv.gz\n",
      "processing acceleration: Sessions3-full\\14442D57F7E1B88_Mon_Feb_12_01-46_2018_PST\\data\\1_android.sensor.accelerometer.data.csv.gz\n",
      "processing acceleration: Sessions3-full\\14442D57F7E1B88_Mon_Feb_12_22-04_2018_PST\\data\\1_android.sensor.accelerometer.data.csv.gz\n",
      "processing acceleration: Sessions3-full\\14442D57F7E1B88_Tue_Feb_13_00-40_2018_PST\\data\\1_android.sensor.accelerometer.data.csv.gz\n",
      "processing acceleration: Sessions3-full\\14442D57F7E1B88_Tue_Feb_13_17-53_2018_PST\\data\\1_android.sensor.accelerometer.data.csv.gz\n",
      "processing acceleration: Sessions3-full\\14442D57F7E1B88_Tue_Feb_13_18-01_2018_PST\\data\\1_android.sensor.accelerometer.data.csv.gz\n",
      "processing acceleration: Sessions3-full\\14442D57F7E1B88_Tue_Feb_13_18-09_2018_PST\\data\\1_android.sensor.accelerometer.data.csv.gz\n",
      "processing acceleration: Sessions3-full\\14442D57F7E1B88_Fri_Feb_16_14-11_2018_PST\\data\\1_android.sensor.accelerometer.data.csv.gz\n",
      "processing acceleration: Sessions3-full\\14442D57F7E1B88_Fri_Feb_16_14-21_2018_PST\\data\\1_android.sensor.accelerometer.data.csv.gz\n",
      "processing acceleration: Sessions3-full\\14442D57F7E1B88_Fri_Feb_16_14-33_2018_PST\\data\\1_android.sensor.accelerometer.data.csv.gz\n",
      "processing acceleration: Sessions3-full\\14442D57F7E1B88_Fri_Feb_16_17-01_2018_PST\\data\\1_android.sensor.accelerometer.data.csv.gz\n",
      "processing acceleration: Sessions3-full\\14442D57F7E1B88_Fri_Feb_16_18-24_2018_PST\\data\\1_android.sensor.accelerometer.data.csv.gz\n",
      "processing acceleration: Sessions3-full\\14442D57F7E1B88_Mon_Feb_26_13-54_2018_PST\\data\\1_android.sensor.accelerometer.data.csv.gz\n",
      "processing acceleration: Sessions3-full\\14442D57F7E1B88_Mon_Feb_26_15-18_2018_PST\\data\\1_android.sensor.accelerometer.data.csv.gz\n",
      "processing acceleration: Sessions3-full\\14442D57F7E1B88_Mon_Feb_26_16-04_2018_PST\\data\\1_android.sensor.accelerometer.data.csv.gz\n",
      "processing acceleration: Sessions3-full\\14442D57F7E1B88_Mon_Feb_26_18-01_2018_PST\\data\\1_android.sensor.accelerometer.data.csv.gz\n",
      "processing acceleration: Sessions3-full\\14442D57F7E1B88_Mon_Feb_26_18-03_2018_PST\\data\\1_android.sensor.accelerometer.data.csv.gz\n",
      "(3959, 13)\n"
     ]
    }
   ],
   "source": [
    "training_set_df = process_csv_data(\"Sessions/\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYAAAAE9CAYAAAACk7UUAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xd5dbf60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .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>mad_macc_jerk</th>\n",
       "      <th>min_macc_jerk</th>\n",
       "      <th>iqr_macc_jerk</th>\n",
       "      <th>sma_macc_jerk</th>\n",
       "      <th>std_mfacc_jerk</th>\n",
       "      <th>mad_mfacc_jerk</th>\n",
       "      <th>iqr_mfacc_jerk</th>\n",
       "      <th>mad_macc</th>\n",
       "      <th>min_macc</th>\n",
       "      <th>max_macc</th>\n",
       "      <th>em_macc</th>\n",
       "      <th>sma_macc</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>853</th>\n",
       "      <td>0.810269</td>\n",
       "      <td>0.178663</td>\n",
       "      <td>1.676020</td>\n",
       "      <td>2.519056</td>\n",
       "      <td>663.979647</td>\n",
       "      <td>478.818374</td>\n",
       "      <td>956.446390</td>\n",
       "      <td>0.017800</td>\n",
       "      <td>9.126425</td>\n",
       "      <td>9.317504</td>\n",
       "      <td>85.550563</td>\n",
       "      <td>9.249317</td>\n",
       "      <td>laying_down</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2470</th>\n",
       "      <td>0.667521</td>\n",
       "      <td>0.170738</td>\n",
       "      <td>1.349984</td>\n",
       "      <td>2.388494</td>\n",
       "      <td>25947.808736</td>\n",
       "      <td>18686.411029</td>\n",
       "      <td>37605.332780</td>\n",
       "      <td>0.009800</td>\n",
       "      <td>9.624984</td>\n",
       "      <td>9.717329</td>\n",
       "      <td>93.458765</td>\n",
       "      <td>9.667395</td>\n",
       "      <td>laying_down</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>506</th>\n",
       "      <td>16.847106</td>\n",
       "      <td>1.897452</td>\n",
       "      <td>37.165755</td>\n",
       "      <td>46.559281</td>\n",
       "      <td>4569.266788</td>\n",
       "      <td>3076.435502</td>\n",
       "      <td>6166.646767</td>\n",
       "      <td>1.404407</td>\n",
       "      <td>6.839887</td>\n",
       "      <td>18.038666</td>\n",
       "      <td>121.995489</td>\n",
       "      <td>10.832138</td>\n",
       "      <td>walking</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2255</th>\n",
       "      <td>24.443641</td>\n",
       "      <td>0.313032</td>\n",
       "      <td>59.128621</td>\n",
       "      <td>51.657892</td>\n",
       "      <td>5081.327830</td>\n",
       "      <td>3347.761312</td>\n",
       "      <td>6752.391173</td>\n",
       "      <td>0.275047</td>\n",
       "      <td>1.642862</td>\n",
       "      <td>15.477629</td>\n",
       "      <td>96.556101</td>\n",
       "      <td>9.758634</td>\n",
       "      <td>sitting</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2829</th>\n",
       "      <td>3.256233</td>\n",
       "      <td>0.353142</td>\n",
       "      <td>6.977196</td>\n",
       "      <td>8.184784</td>\n",
       "      <td>1301.559871</td>\n",
       "      <td>864.966609</td>\n",
       "      <td>1777.955788</td>\n",
       "      <td>0.054472</td>\n",
       "      <td>9.627217</td>\n",
       "      <td>10.603044</td>\n",
       "      <td>101.129052</td>\n",
       "      <td>10.055586</td>\n",
       "      <td>sitting</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      mad_macc_jerk  min_macc_jerk  iqr_macc_jerk  sma_macc_jerk  \\\n",
       "853        0.810269       0.178663       1.676020       2.519056   \n",
       "2470       0.667521       0.170738       1.349984       2.388494   \n",
       "506       16.847106       1.897452      37.165755      46.559281   \n",
       "2255      24.443641       0.313032      59.128621      51.657892   \n",
       "2829       3.256233       0.353142       6.977196       8.184784   \n",
       "\n",
       "      std_mfacc_jerk  mad_mfacc_jerk  iqr_mfacc_jerk  mad_macc  min_macc  \\\n",
       "853       663.979647      478.818374      956.446390  0.017800  9.126425   \n",
       "2470    25947.808736    18686.411029    37605.332780  0.009800  9.624984   \n",
       "506      4569.266788     3076.435502     6166.646767  1.404407  6.839887   \n",
       "2255     5081.327830     3347.761312     6752.391173  0.275047  1.642862   \n",
       "2829     1301.559871      864.966609     1777.955788  0.054472  9.627217   \n",
       "\n",
       "       max_macc     em_macc   sma_macc        label  \n",
       "853    9.317504   85.550563   9.249317  laying_down  \n",
       "2470   9.717329   93.458765   9.667395  laying_down  \n",
       "506   18.038666  121.995489  10.832138      walking  \n",
       "2255  15.477629   96.556101   9.758634      sitting  \n",
       "2829  10.603044  101.129052  10.055586      sitting  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hist_activities(training_set_df)\n",
    "#plot_act_signals(in_df_ac)\n",
    "training_set_df.to_csv(\"train_feat12.csv\")\n",
    "#training_set_df = pd.read_csv(\"train_feat12.csv\", sep=',', header=None)\n",
    "\n",
    "training_set_df = clean_dataset(training_set_df)\n",
    "\n",
    "combined_training_df = training_set_df\n",
    "combined_training_df = combined_training_df.iloc[np.random.permutation(len(combined_training_df))]\n",
    "combined_training_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "fileobj = open(\"combineddf\",'wb') \n",
    "pickle.dump(combined_training_df, fileobj)\n",
    "fileobj.close()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "f = open(\"combineddf\", 'r')\n",
    "combined_training_df = pickle.load(f)\n",
    "f.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(3959, 12)\n",
      "3959\n",
      "['laying_down' 'laying_down' 'walking' ..., 'standing' 'standing'\n",
      " 'laying_down']\n",
      "(3959L,)\n",
      "3959\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda2\\lib\\site-packages\\sklearn\\utils\\validation.py:444: DataConversionWarning: Data with input dtype object was converted to float64 by StandardScaler.\n",
      "  warnings.warn(msg, DataConversionWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0, 0, 2, 1, 1]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Training set\n",
    "\n",
    "training_data = combined_training_df[facc_jmag_cols+ maccj_cols+ macc_cols].as_matrix()\n",
    "training_targets = training_data[:, -1]\n",
    "training_data = training_data[:, :-1]\n",
    "\n",
    "std_scale = preprocessing.StandardScaler().fit(training_data)\n",
    "training_data_std = std_scale.transform(training_data)\n",
    "\n",
    "print(len(training_data_std), len(training_data_std[0]))\n",
    "print(len(training_targets))\n",
    "\n",
    "cache = {'laying_down' : 0, 'sitting' : 1, 'walking' : 2, 'standing' : 3}\n",
    "training_targets.reshape(len(training_targets))\n",
    "print(training_targets)\n",
    "print(training_targets.shape)\n",
    "\n",
    "training_bin_targets = []\n",
    "for i in training_targets:\n",
    "    training_bin_targets.append(cache[i])\n",
    "print(len(training_bin_targets))\n",
    "\n",
    "training_bin_targets[: 5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "processing acceleration: Test1\\14442D53F81BFEA_Mon_Mar_05_21-54_2018_PST\\data\\1_android.sensor.accelerometer.data.csv.gz\n",
      "processing acceleration: Test1\\14442D53F81BFEA_Mon_Mar_05_22-01_2018_PST\\data\\1_android.sensor.accelerometer.data.csv.gz\n",
      "processing acceleration: Test1\\14442D53F81BFEA_Mon_Mar_05_22-06_2018_PST\\data\\1_android.sensor.accelerometer.data.csv.gz\n",
      "processing acceleration: Test1\\14442D53F81BFEA_Mon_Mar_05_22-14_2018_PST\\data\\1_android.sensor.accelerometer.data.csv.gz\n",
      "(246, 13)\n"
     ]
    }
   ],
   "source": [
    "## Testing set\n",
    "testing_set_df = process_csv_data('Test/')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAE9CAYAAAAbNJn3AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ngKJw/MkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xf04db70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   mad_macc_jerk  min_macc_jerk  iqr_macc_jerk  sma_macc_jerk  std_mfacc_jerk  \\\n",
      "0       6.486658       0.497670      36.551227      37.555970    10359.834832   \n",
      "1      30.078538       0.482442      75.531202      70.526370    10354.024571   \n",
      "2      23.799503       0.482442      56.511297      64.561295    12195.405454   \n",
      "3      14.612629       0.613749      36.838603      33.306843    12219.383768   \n",
      "4       5.031943       0.524822      13.060006      18.466302    12315.352013   \n",
      "\n",
      "   mad_mfacc_jerk  iqr_mfacc_jerk  mad_macc  min_macc   max_macc     em_macc  \\\n",
      "0     7164.478061    14437.441429  0.150934  4.664813  17.564620  101.415949   \n",
      "1     6984.974209    14018.530783  0.526478  4.564220  19.553360  104.117344   \n",
      "2     8281.999556    16924.103055  0.367884  4.564220  19.553360  101.049449   \n",
      "3     8299.069413    16534.115459  0.159578  5.899200  12.998641   99.232530   \n",
      "4     8786.140902    17348.383306  0.062758  6.920704  16.499057   99.736771   \n",
      "\n",
      "    sma_macc    label  \n",
      "0   9.987768  sitting  \n",
      "1  10.058577  sitting  \n",
      "2   9.967217  sitting  \n",
      "3   9.940171  sitting  \n",
      "4   9.974960  sitting  \n"
     ]
    }
   ],
   "source": [
    "hist_activities(testing_set_df)\n",
    "# plot_act_signals(in_df_ac)\n",
    "testing_set_df.to_csv(\"test_feat12.csv\")\n",
    "#testing_set_df = pd.read_csv(\"test_feat12.csv\", sep=',', header=None)\n",
    "\n",
    "testing_set_df = clean_dataset(testing_set_df)\n",
    "\n",
    "combined_testing_df = testing_set_df\n",
    "combined_testing_df = combined_testing_df.iloc[np.random.permutation(len(combined_testing_df))]\n",
    "print testing_set_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(246, 12)\n",
      "246\n",
      "(246L,)\n",
      "246\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[3, 2, 0, 1, 1]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "testing_data = combined_testing_df[facc_jmag_cols+ maccj_cols+ macc_cols].as_matrix()\n",
    "testing_targets = testing_data[:, -1]\n",
    "testing_data = testing_data[:, :-1]\n",
    "\n",
    "std_scale = preprocessing.StandardScaler().fit(testing_data)\n",
    "testing_data_std = std_scale.transform(testing_data)\n",
    "\n",
    "print(len(testing_data_std), len(testing_data_std[0]))\n",
    "print(len(testing_targets))\n",
    "\n",
    "cache = {'laying_down' : 0, 'sitting' : 1, 'walking' : 2, 'standing' : 3}\n",
    "testing_targets.reshape(len(testing_targets))\n",
    "print(testing_targets.shape)\n",
    "\n",
    "testing_bin_targets = []\n",
    "for i in testing_targets:\n",
    "    testing_bin_targets.append(cache[i])\n",
    "print(len(testing_bin_targets))\n",
    "\n",
    "testing_bin_targets[: 5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "fileobj = open(\"combinedtestdf\",'wb') \n",
    "pickle.dump(combined_testing_df, fileobj)\n",
    "fileobj.close()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "f = open(\"combinedtestdf\", 'r')\n",
    "combined_testing_df = pickle.load(f)\n",
    "f.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#model = DecisionTreeClassifier(random_state=0)\n",
    "model = RandomForestClassifier(random_state = 42)\n",
    "#model = SVC()\n",
    "#model = KNeighborsClassifier()\n",
    "#model = GaussianNB()\n",
    "#model = LogisticRegression()\n",
    "#model = MLPClassifier()\n",
    "\n",
    "# Training the model\n",
    "model.fit(training_data, training_bin_targets)\n",
    "\n",
    "# Testing on the testing data\n",
    "predicted_values = model.predict(testing_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.77777778  0.84615385  0.92        0.96        0.6         0.91666667\n",
      "  0.875       0.70833333  0.82608696  0.86956522]\n",
      "0.829958379784\n"
     ]
    }
   ],
   "source": [
    "cvscore = cross_val_score(model, testing_data, testing_bin_targets, cv=10)\n",
    "print(cvscore)\n",
    "print(np.mean(cvscore))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('Score: ', 0.58130081300813008)\n"
     ]
    }
   ],
   "source": [
    "acc_score = accuracy_score(testing_bin_targets, predicted_values)\n",
    "print(\"Score: \", acc_score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "celltoolbar": "Raw Cell Format",
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}