HAR-combined (1).ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "ename": "ModuleNotFoundError",
     "evalue": "No module named 'sklearn'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-1-41afc776a89c>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mnumpy\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0msklearn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtree\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mDecisionTreeClassifier\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      5\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0msklearn\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mpreprocessing\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0msklearn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodel_selection\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mcross_val_score\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'sklearn'"
     ]
    }
   ],
   "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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "## Features\n",
    "\n",
    "def mean(x, y, z):\n",
    "    \"\"\"Calculates mean\"\"\"\n",
    "    mean_x = np.mean(x)\n",
    "    mean_y = np.mean(y)\n",
    "    mean_z = np.mean(z)\n",
    "    return mean_x, mean_y, mean_z\n",
    "\n",
    "def std_dev(x, y, z):\n",
    "    \"\"\"Calculates standard deviation\"\"\"\n",
    "    std_x = np.std(x)\n",
    "    std_y = np.std(y)\n",
    "    std_z = np.std(z)\n",
    "    return std_x, std_y, std_z  \n",
    "    \n",
    "def mad(x, y, z):\n",
    "    \"\"\"Calculates median absolute deviation\"\"\"\n",
    "    mad_x = np.median(np.abs(x - np.median(x)))\n",
    "    mad_y = np.median(np.abs(y - np.median(y)))\n",
    "    mad_z = np.median(np.abs(z - np.median(z)))\n",
    "    return mad_x, mad_y, mad_z\n",
    "\n",
    "def minimum(x, y, z):\n",
    "    \"\"\"Calculates minimum\"\"\"\n",
    "    return min(x), min(y), min(z)\n",
    "\n",
    "def maximum(x, y, z):\n",
    "    \"\"\"Calculates maximum\"\"\"\n",
    "    return max(x), max(y), max(z)\n",
    "\n",
    "def energy_measure(x, y, z):\n",
    "    \"\"\"Calculates energy measures\"\"\"\n",
    "    em_x = np.mean(np.square(x))\n",
    "    em_y = np.mean(np.square(y))\n",
    "    em_z = np.mean(np.square(z))\n",
    "    return em_x, em_y, em_z\n",
    "\n",
    "def inter_quartile_range(x, y, z):\n",
    "    \"\"\"Calculates inter-quartile range\"\"\"\n",
    "    iqr_x = np.subtract(*np.percentile(x, [75, 25]))\n",
    "    iqr_y = np.subtract(*np.percentile(y, [75, 25]))\n",
    "    iqr_z = np.subtract(*np.percentile(z, [75, 25]))\n",
    "    return iqr_x, iqr_y, iqr_z\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, y, z):\n",
    "    \"\"\"Calculates skewness\"\"\"\n",
    "    skew_x=stats.skew(x)\n",
    "    skew_y=stats.skew(y)\n",
    "    skew_z=stats.skew(z)\n",
    "    return skew_x, skew_y, skew_z\n",
    "\n",
    "def kurt(x, y, z):\n",
    "    \"\"\"Calculates kurtosis\"\"\"\n",
    "    kurt_x = stats.kurtosis(x,fisher=False)\n",
    "    kurt_y = stats.kurtosis(y,fisher=False)\n",
    "    kurt_z = stats.kurtosis(z,fisher=False)\n",
    "    return kurt_x, kurt_y, kurt_z\n",
    "    \n",
    "def max_index(x, y, z):\n",
    "    \"\"\"Calculates index of maximum\"\"\"\n",
    "    idx_x = np.argmax(x)\n",
    "    idx_y = np.argmax(y)\n",
    "    idx_z = np.argmax(z)\n",
    "    return idx_x, idx_y, idx_z\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",
    "\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\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def process_acc(input_df, columns, output_df, freq_flag):\n",
    "    i = 0\n",
    "    while i < len(input_df[0]):\n",
    "        max_window = input_df[0][i] + 5000\n",
    "        min_window = input_df[0][i]\n",
    "        if i != 0:                                  #handling overlap\n",
    "            min_window -= 2500\n",
    "            max_window -= 2500\n",
    "            i -= int(count/2)\n",
    "        x = [input_df[1][i]]\n",
    "        y = [input_df[2][i]]\n",
    "        z = [input_df[3][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[1][i])\n",
    "                y.append(input_df[2][i])\n",
    "                z.append(input_df[3][i])\n",
    "                min_window = input_df[0][i]\n",
    "            except KeyError:\n",
    "                break\n",
    "\n",
    "        sma_val = (sma(x,y,z), )\n",
    "        \n",
    "        if freq_flag == 0:\n",
    "            acc_data = list(mean(x, y, z)+std_dev(x, y, z)+mad(x, y, z)+minimum(x, y, z)+maximum(x, y, z)+energy_measure(x, y, z)+inter_quartile_range(x, y, z)+sma_val)\n",
    "        \n",
    "        \n",
    "        else:\n",
    "            acc_data = list(mean(x, y, z)+std_dev(x, y, z)+mad(x, y, z)+minimum(x, y, z)+maximum(x, y, z)+energy_measure(x, y, z)+inter_quartile_range(x, y, z)+sma_val+\n",
    "                            skewness(x, y, z)+kurt(x, y, z)+max_index(x, y, z))\n",
    "        \n",
    "        \n",
    "       # acc_data = list(mean(x, y, z)+std_dev(x, y, z)+mad(x, y, z)+minimum(x, y, z)+maximum(x, y, z)+energy_measure(x, y, z)+inter_quartile_range(x, y, z)+sma_val)\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": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def process_gyr(input_df, columns, output_df, freq_flag):\n",
    "\n",
    "    i = 0\n",
    "    while i < len(input_df[0]):\n",
    "        max_window = input_df[0][i] + 5000\n",
    "        min_window = input_df[0][i]\n",
    "        if i != 0:                                                                 #handling overlap\n",
    "            min_window -= 2500\n",
    "            max_window -= 2500\n",
    "            i -= int(count/2)\n",
    "        x = [input_df[1][i]]\n",
    "        y = [input_df[2][i]]\n",
    "        z = [input_df[3][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[1][i])\n",
    "                y.append(input_df[2][i])\n",
    "                z.append(input_df[3][i])\n",
    "                min_window = input_df[0][i]\n",
    "            except KeyError:\n",
    "                break\n",
    "        \n",
    "        l = (l, )  \n",
    "        sma_val = (sma(x,y,z), )\n",
    "        \n",
    "        if freq_flag == 0:\n",
    "            gy_data = list(mean(x, y, z)+std_dev(x, y, z)+mad(x, y, z)+minimum(x, y, z)+maximum(x, y, z)+energy_measure(x, y, z)+inter_quartile_range(x, y, z)+sma_val+l)\n",
    "        \n",
    "        \n",
    "        else:\n",
    "            gy_data = list(mean(x, y, z)+std_dev(x, y, z)+mad(x, y, z)+minimum(x, y, z)+maximum(x, y, z)+energy_measure(x, y, z)+inter_quartile_range(x, y, z)+sma_val+\n",
    "                            skewness(x, y, z)+kurt(x, y, z)+max_index(x, y, z)+l)\n",
    "        \n",
    "        #gy_data = list(mean(x, y, z)+std_dev(x, y, z)+mad(x, y, z)+minimum(x, y, z)+maximum(x, y, z)+energy_measure(x, y, z)+inter_quartile_range(x, y, z)+sma_val+label) \n",
    "        output_df = output_df.append(pd.Series(gy_data, index=columns), ignore_index=True)\n",
    "        \n",
    "    return output_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def process_mag(input_df, columns, output_df, freq_flag):\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] + 5000\n",
    "        min_window = input_df[0][i]\n",
    "        if i != 0:                                  #handling overlap\n",
    "            min_window -= 2500\n",
    "            max_window -= 2500\n",
    "            i -= int(count/2)\n",
    "        x = [input_df[6][i]]\n",
    "        \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",
    "        mean = (np.mean(x),)\n",
    "        std_dev = (np.std(x),)\n",
    "        mad = (np.median(np.abs(x - np.median(x))),)\n",
    "        minimum = (np.min(x),)\n",
    "        maximum = (np.max(x),)\n",
    "        energy_measure = (np.mean(np.square(x)),)\n",
    "        inter_quartile_range = (np.subtract(*np.percentile(x, [75, 25])),)\n",
    "        sma_val = (np.mean(np.absolute(x)),)\n",
    "        \n",
    "        if freq_flag == 0:\n",
    "            acc_data = list(mean+std_dev+mad+minimum+maximum+energy_measure+inter_quartile_range+sma_val)\n",
    "        else:\n",
    "            skew = (stats.skew(x),)\n",
    "            kur = (stats.kurtosis(x,fisher=False),)\n",
    "            m_idx = (np.argmax(x),)\n",
    "            acc_data = list(mean+std_dev+mad+minimum+maximum+energy_measure+inter_quartile_range+sma_val+skew+kur+m_idx)\n",
    "        \n",
    "        #acc_data = list(mean+std_dev+mad+minimum+maximum+energy_measure+inter_quartile_range+sma_val)\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": 3,
   "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": 4,
   "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": 5,
   "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": 6,
   "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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "tacc_cols = ['mean_acc_x', 'mean_acc_y', 'mean_acc_z', \n",
    "               'std_acc_x', 'std_acc_y', 'std_acc_z', \n",
    "               'mad_acc_x', 'mad_acc_y', 'mad_acc_z',\n",
    "               'min_acc_x', 'min_acc_y', 'min_acc_z', \n",
    "               'max_acc_x', 'max_acc_y', 'max_acc_z', \n",
    "               'em_acc_x', 'em_acc_y', 'em_acc_z', \n",
    "               'iqr_acc_x', 'iqr_acc_y', 'iqr_acc_z', \n",
    "               'sma_acc']\n",
    "\n",
    "tacc_jerk_cols = ['mean_accj_x', 'mean_accj_y', 'mean_accj_z', \n",
    "                   'std_accj_x', 'std_accj_y', 'std_accj_z', \n",
    "                   'mad_accj_x', 'mad_accj_y', 'mad_accj_z',\n",
    "                   'min_accj_x', 'min_accj_y', 'min_accj_z', \n",
    "                   'max_accj_x', 'max_accj_y', 'max_accj_z', \n",
    "                   'em_accj_x', 'em_accj_y', 'em_accj_z', \n",
    "                   'iqr_accj_x', 'iqr_accj_y', 'iqr_accj_z', \n",
    "                   'sma_accj']\n",
    "\n",
    "tacc_mag_cols = ['mean_macc', 'std_macc', 'mad_macc', 'min_macc', 'max_macc', 'em_macc', 'iqr_macc', 'sma_macc']\n",
    "\n",
    "tacc_jmag_cols = ['mean_macc_jerk', 'std_macc_jerk', 'mad_macc_jerk', 'min_macc_jerk', 'max_macc_jerk', \n",
    "                     'em_macc_jerk', 'iqr_macc_jerk', 'sma_macc_jerk']\n",
    "\n",
    "facc_cols = ['mean_facc_x', 'mean_facc_y', 'mean_facc_z', \n",
    "               'std_facc_x', 'std_facc_y', 'std_facc_z', \n",
    "               'mad_facc_x', 'mad_facc_y', 'mad_facc_z',\n",
    "               'min_facc_x', 'min_facc_y', 'min_facc_z', \n",
    "               'max_facc_x', 'max_facc_y', 'max_facc_z', \n",
    "               'em_facc_x', 'em_facc_y', 'em_facc_z', \n",
    "               'iqr_facc_x', 'iqr_facc_y', 'iqr_facc_z', \n",
    "               'sma_facc',\n",
    "               'skew_facc_x', 'skew_facc_y', 'skew_facc_z', \n",
    "               'kurt_facc_x', 'kurt_facc_y', 'kurt_facc_z', \n",
    "               'idx_facc_x', 'idx_facc_y', 'idx_facc_z']\n",
    "\n",
    "facc_jerk_cols = ['mean_faccj_x', 'mean_faccj_y', 'mean_faccj_z', \n",
    "                   'std_faccj_x', 'std_faccj_y', 'std_faccj_z', \n",
    "                   'mad_faccj_x', 'mad_faccj_y', 'mad_faccj_z',\n",
    "                   'min_faccj_x', 'min_faccj_y', 'min_faccj_z', \n",
    "                   'max_faccj_x', 'max_faccj_y', 'max_faccj_z', \n",
    "                   'em_faccj_x', 'em_faccj_y', 'em_faccj_z', \n",
    "                   'iqr_faccj_x', 'iqr_faccj_y', 'iqr_faccj_z', \n",
    "                   'sma_faccj',\n",
    "                   'skew_faccj_x', 'skew_faccj_y', 'skew_faccj_z', \n",
    "                   'kurt_faccj_x', 'kurt_faccj_y', 'kurt_faccj_z', \n",
    "                   'idx_faccj_x', 'idx_faccj_y', 'idx_faccj_z']\n",
    "\n",
    "facc_mag_cols = ['mean_mfacc', 'std_mfacc', 'mad_mfacc', 'min_mfacc', 'max_mfacc', 'em_mfacc', 'iqr_mfacc', 'sma_mfacc',\n",
    "                'skew_mfacc', 'kurt_mfacc',  'idx_mfacc']\n",
    "\n",
    "facc_jmag_cols = ['mean_mfacc_jerk', 'std_mfacc_jerk', 'mad_mfacc_jerk', 'min_mfacc_jerk', 'max_mfacc_jerk', \n",
    "                     'em_mfacc_jerk', 'iqr_mfacc_jerk', 'sma_mfacc_jerk', 'skew_mfacc_jerk', 'kurt_mfacc_jerk',  \n",
    "                  'idx_mfacc_jerk']\n",
    "\n",
    "tgy_cols = ['mean_gy_x', 'mean_gy_y', 'mean_gy_z', \n",
    "               'std_gy_x', 'std_gy_y', 'std_gy_z', \n",
    "               'mad_gy_x', 'mad_gy_y', 'mad_gy_z',\n",
    "               'min_gy_x', 'min_gy_y', 'min_gy_z', \n",
    "               'max_gy_x', 'max_gy_y', 'max_gy_z', \n",
    "               'em_gy_x', 'em_gy_y', 'em_gy_z', \n",
    "               'iqr_gy_x', 'iqr_gy_y', 'iqr_gy_z', \n",
    "               'sma_gy', 'label']\n",
    "\n",
    "tgy_jerk_cols = ['mean_gyj_x', 'mean_gyj_y', 'mean_gyj_z', \n",
    "                   'std_gyj_x', 'std_gyj_y', 'std_gyj_z', \n",
    "                   'mad_gyj_x', 'mad_gyj_y', 'mad_gyj_z',\n",
    "                   'min_gyj_x', 'min_gyj_y', 'min_gyj_z', \n",
    "                   'max_gyj_x', 'max_gyj_y', 'max_gyj_z', \n",
    "                   'em_gyj_x', 'em_gyj_y', 'em_gyj_z', \n",
    "                   'iqr_gyj_x', 'iqr_gyj_y', 'iqr_gyj_z', \n",
    "                   'sma_gyj']\n",
    "\n",
    "tgy_mag_cols = ['mean_mgy', 'std_mgy', 'mad_mgy', 'min_mgy', 'max_mgy', 'em_mgy', 'iqr_mgy', 'sma_mgy']\n",
    "\n",
    "tgy_jmag_cols = ['mean_mgy_jerk', 'std_mgy_jerk', 'mad_mgy_jerk', 'min_mgy_jerk', 'max_mgy_jerk', \n",
    "                     'em_mgy_jerk', 'iqr_mgy_jerk', 'sma_mgy_jerk']\n",
    "fgy_cols = ['mean_fgy_x', 'mean_fgy_y', 'mean_fgy_z', \n",
    "               'std_fgy_x', 'std_fgy_y', 'std_fgy_z', \n",
    "               'mad_fgy_x', 'mad_fgy_y', 'mad_fgy_z',\n",
    "               'min_fgy_x', 'min_fgy_y', 'min_fgy_z', \n",
    "               'max_fgy_x', 'max_fgy_y', 'max_fgy_z', \n",
    "               'em_fgy_x', 'em_fgy_y', 'em_fgy_z', \n",
    "               'iqr_fgy_x', 'iqr_fgy_y', 'iqr_fgy_z', \n",
    "               'sma_fgy', \n",
    "               'skew_fgy_x', 'skew_fgy_y', 'skew_fgy_z', \n",
    "               'kurt_fgy_x', 'kurt_fgy_y', 'kurt_fgy_z', \n",
    "               'idx_fgy_x', 'idx_fgy_y', 'idx_fgy_z']\n",
    "\n",
    "fgy_jerk_cols = ['mean_fgyj_x', 'mean_fgyj_y', 'mean_fgyj_z', \n",
    "                   'std_fgyj_x', 'std_fgyj_y', 'std_fgyj_z', \n",
    "                   'mad_fgyj_x', 'mad_fgyj_y', 'mad_fgyj_z',\n",
    "                   'min_fgyj_x', 'min_fgyj_y', 'min_fgyj_z', \n",
    "                   'max_fgyj_x', 'max_fgyj_y', 'max_fgyj_z', \n",
    "                   'em_fgyj_x', 'em_fgyj_y', 'em_fgyj_z', \n",
    "                   'iqr_fgyj_x', 'iqr_fgyj_y', 'iqr_fgyj_z', \n",
    "                   'sma_fgyj',\n",
    "                   'skew_fgyj_x', 'skew_fgyj_y', 'skew_fgyj_z', \n",
    "                   'kurt_fgyj_x', 'kurt_fgyj_y', 'kurt_fgyj_z', \n",
    "                   'idx_fgyj_x', 'idx_fgyj_y', 'idx_fgyj_z']\n",
    "\n",
    "fgy_mag_cols = ['mean_mfgy', 'std_mfgy', 'mad_mfgy', 'min_mfgy', 'max_mfgy', 'em_mfgy', 'iqr_mfgy', 'sma_mfgy',\n",
    "               'skew_mfgy', 'kurt_mfgy',  'idx_mfgy']\n",
    "\n",
    "fgy_jmag_cols = ['mean_mfgy_jerk', 'std_mfgy_jerk', 'mad_mfgy_jerk', 'min_mfgy_jerk', 'max_mfgy_jerk', \n",
    "                     'em_mfgy_jerk', 'iqr_mfgy_jerk', 'sma_mfgy_jerk', 'skew_mfgy_jerk', 'kurt_mfgy_jerk',  'idx_mfgy_jerk']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def process_csv_data(path):\n",
    "    dirs = get_session_dirs(path)\n",
    "\n",
    "    out_acc_df = pd.DataFrame(columns = tacc_cols)\n",
    "\n",
    "    out_acc_jerk_df = pd.DataFrame(columns = tacc_jerk_cols)\n",
    "\n",
    "    acc_mag = pd.DataFrame(columns = tacc_mag_cols)\n",
    "\n",
    "    acc_jerk_mag = pd.DataFrame(columns = tacc_jmag_cols)\n",
    "\n",
    "    out_facc_df = pd.DataFrame(columns = facc_cols)\n",
    "\n",
    "    out_facc_jerk_df = pd.DataFrame(columns = facc_jerk_cols)\n",
    "\n",
    "    facc_mag = pd.DataFrame(columns = facc_mag_cols)\n",
    "\n",
    "    facc_jerk_mag = pd.DataFrame(columns = facc_jmag_cols)\n",
    "\n",
    "    out_gy_df = pd.DataFrame(columns = tgy_cols)\n",
    "\n",
    "    out_gy_jerk_df = pd.DataFrame(columns = tgy_jerk_cols)\n",
    "\n",
    "    gy_mag = pd.DataFrame(columns = tgy_mag_cols)\n",
    "\n",
    "    gy_jerk_mag = pd.DataFrame(columns = tgy_jmag_cols)\n",
    "\n",
    "    out_fgy_df = pd.DataFrame(columns = fgy_cols)\n",
    "\n",
    "    out_fgy_jerk_df = pd.DataFrame(columns = fgy_jerk_cols)\n",
    "\n",
    "    fgy_mag = pd.DataFrame(columns = fgy_mag_cols)\n",
    "\n",
    "    fgy_jerk_mag = pd.DataFrame(columns = fgy_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",
    "        full_path_gy = os.path.normpath(i + \"/\" + \"4_android.sensor.gyroscope.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",
    "            \n",
    "        in_df_ac = five_point_smoothing(in_df_ac)\n",
    "    \n",
    "        in_df_ac_jerk = in_df_ac.copy()\n",
    "        in_df_ac_jerk = get_jerk(in_df_ac_jerk)\n",
    "\n",
    "        in_df_fac = in_df_ac.copy()\n",
    "        in_df_fac = get_fft(in_df_fac)\n",
    "\n",
    "        in_df_fac_jerk = in_df_ac_jerk.copy()\n",
    "        in_df_fac_jerk = get_fft(in_df_fac_jerk)\n",
    "\n",
    "        out_acc_df = process_acc(in_df_ac, tacc_cols, out_acc_df, 0)\n",
    "        out_acc_jerk_df = process_acc(in_df_ac_jerk, tacc_jerk_cols, out_acc_jerk_df, 0)\n",
    "        out_facc_df = process_acc(in_df_fac, facc_cols, out_facc_df, 1)\n",
    "        out_facc_jerk_df = process_acc(in_df_fac_jerk, facc_jerk_cols, out_facc_jerk_df, 1)\n",
    "\n",
    "        acc_mag = process_mag(in_df_ac, tacc_mag_cols, acc_mag, 0)\n",
    "        acc_jerk_mag = process_mag(in_df_ac_jerk, tacc_jmag_cols, acc_jerk_mag, 0)\n",
    "        facc_mag = process_mag(in_df_fac, facc_mag_cols, facc_mag, 1)\n",
    "        facc_jerk_mag = process_mag(in_df_fac_jerk, facc_jmag_cols, facc_jerk_mag, 1)\n",
    "\n",
    "        print(\"processing gyroscope: \" + full_path_gy)\n",
    "        in_df_gy = pd.read_csv(full_path_gy, compression='gzip', sep=',', header=None)\n",
    "\n",
    "        in_df_gy = five_point_smoothing(in_df_gy)\n",
    "        \n",
    "        in_df_gy_jerk = in_df_gy.copy()\n",
    "        in_df_gy_jerk = get_jerk(in_df_gy_jerk)\n",
    "\n",
    "        in_df_fgy = in_df_gy.copy()\n",
    "        in_df_fgy = get_fft(in_df_fgy)\n",
    "\n",
    "        in_df_fgy_jerk = in_df_gy_jerk.copy()\n",
    "        in_df_fgy_jerk = get_fft(in_df_fgy_jerk)\n",
    "\n",
    "        out_gy_df = process_gyr(in_df_gy, tgy_cols, out_gy_df, 0)\n",
    "        out_gy_jerk_df = process_acc(in_df_gy_jerk, tgy_jerk_cols, out_gy_jerk_df, 0)\n",
    "        out_fgy_df = process_acc(in_df_fgy, fgy_cols, out_fgy_df, 1)\n",
    "        out_fgy_jerk_df = process_acc(in_df_fgy_jerk, fgy_jerk_cols, out_fgy_jerk_df, 1)\n",
    "\n",
    "        gy_mag = process_mag(in_df_gy, tgy_mag_cols, gy_mag, 0)\n",
    "        gy_jerk_mag = process_mag(in_df_gy_jerk, tgy_jmag_cols, gy_jerk_mag, 0)\n",
    "        fgy_mag = process_mag(in_df_fgy, fgy_mag_cols, fgy_mag, 1)\n",
    "        fgy_jerk_mag = process_mag(in_df_fgy_jerk, fgy_jmag_cols, fgy_jerk_mag, 1)\n",
    "\n",
    "\n",
    "        #handling mismatch in rows\n",
    "        if out_gy_df.shape[0] < out_acc_df.shape[0]:\n",
    "            out_acc_df = out_acc_df[:out_gy_df.shape[0]]\n",
    "            out_acc_jerk_df = out_acc_jerk_df[:out_gy_df.shape[0]]\n",
    "            out_facc_df = out_facc_df[:out_gy_df.shape[0]]\n",
    "            out_facc_jerk_df = out_facc_jerk_df[:out_gy_df.shape[0]]\n",
    "            acc_mag = acc_mag[:out_gy_df.shape[0]]\n",
    "            acc_jerk_mag = acc_jerk_mag[:out_gy_df.shape[0]]\n",
    "            facc_mag = facc_mag[:out_gy_df.shape[0]]\n",
    "            facc_jerk_mag = facc_jerk_mag[:out_gy_df.shape[0]]\n",
    "\n",
    "        acc_gy_df = pd.concat([out_acc_df, acc_mag, out_acc_jerk_df, acc_jerk_mag, \n",
    "                               out_facc_df, facc_mag, out_facc_jerk_df, facc_jerk_mag,\n",
    "                               out_gy_df, gy_mag, out_gy_jerk_df, gy_jerk_mag, \n",
    "                               out_fgy_df, fgy_mag, out_fgy_jerk_df, fgy_jerk_mag], axis=1)\n",
    "\n",
    "    print(acc_gy_df.shape)\n",
    "    return acc_gy_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(0, 0)\n"
     ]
    }
   ],
   "source": [
    "training_set_df = process_csv_data(\"nullTest/\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a08a53780>"
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     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean_acc_x</th>\n",
       "      <th>mean_acc_y</th>\n",
       "      <th>mean_acc_z</th>\n",
       "      <th>std_acc_x</th>\n",
       "      <th>std_acc_y</th>\n",
       "      <th>std_acc_z</th>\n",
       "      <th>mad_acc_x</th>\n",
       "      <th>mad_acc_y</th>\n",
       "      <th>mad_acc_z</th>\n",
       "      <th>min_acc_x</th>\n",
       "      <th>...</th>\n",
       "      <th>std_mfgy_jerk</th>\n",
       "      <th>mad_mfgy_jerk</th>\n",
       "      <th>min_mfgy_jerk</th>\n",
       "      <th>max_mfgy_jerk</th>\n",
       "      <th>em_mfgy_jerk</th>\n",
       "      <th>iqr_mfgy_jerk</th>\n",
       "      <th>sma_mfgy_jerk</th>\n",
       "      <th>skew_mfgy_jerk</th>\n",
       "      <th>kurt_mfgy_jerk</th>\n",
       "      <th>idx_mfgy_jerk</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>-7.340152</td>\n",
       "      <td>-4.396661</td>\n",
       "      <td>3.316030</td>\n",
       "      <td>0.978043</td>\n",
       "      <td>1.001660</td>\n",
       "      <td>1.265788</td>\n",
       "      <td>0.421281</td>\n",
       "      <td>0.584048</td>\n",
       "      <td>0.497877</td>\n",
       "      <td>-10.967659</td>\n",
       "      <td>...</td>\n",
       "      <td>669.239068</td>\n",
       "      <td>427.803872</td>\n",
       "      <td>499.384249</td>\n",
       "      <td>5018.866304</td>\n",
       "      <td>5.498292e+06</td>\n",
       "      <td>872.310077</td>\n",
       "      <td>2247.312001</td>\n",
       "      <td>0.356066</td>\n",
       "      <td>3.366436</td>\n",
       "      <td>880.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-8.787479</td>\n",
       "      <td>-1.870150</td>\n",
       "      <td>1.780016</td>\n",
       "      <td>1.275642</td>\n",
       "      <td>0.726383</td>\n",
       "      <td>0.893357</td>\n",
       "      <td>0.938307</td>\n",
       "      <td>0.440430</td>\n",
       "      <td>0.555324</td>\n",
       "      <td>-12.212352</td>\n",
       "      <td>...</td>\n",
       "      <td>1106.109354</td>\n",
       "      <td>691.284754</td>\n",
       "      <td>278.225616</td>\n",
       "      <td>7065.332703</td>\n",
       "      <td>8.140896e+06</td>\n",
       "      <td>1449.145825</td>\n",
       "      <td>2630.098555</td>\n",
       "      <td>0.802582</td>\n",
       "      <td>3.526206</td>\n",
       "      <td>218.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>-4.884610</td>\n",
       "      <td>-6.516254</td>\n",
       "      <td>3.546129</td>\n",
       "      <td>2.879534</td>\n",
       "      <td>2.181546</td>\n",
       "      <td>1.554325</td>\n",
       "      <td>1.397886</td>\n",
       "      <td>1.273416</td>\n",
       "      <td>0.497877</td>\n",
       "      <td>-11.484685</td>\n",
       "      <td>...</td>\n",
       "      <td>794.774334</td>\n",
       "      <td>515.484662</td>\n",
       "      <td>387.549911</td>\n",
       "      <td>5378.571649</td>\n",
       "      <td>6.349704e+06</td>\n",
       "      <td>1033.933636</td>\n",
       "      <td>2391.241920</td>\n",
       "      <td>0.552510</td>\n",
       "      <td>3.253821</td>\n",
       "      <td>776.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>-10.121929</td>\n",
       "      <td>-1.946226</td>\n",
       "      <td>1.718196</td>\n",
       "      <td>2.064532</td>\n",
       "      <td>1.457126</td>\n",
       "      <td>1.657922</td>\n",
       "      <td>1.551078</td>\n",
       "      <td>0.957456</td>\n",
       "      <td>1.388311</td>\n",
       "      <td>-15.620895</td>\n",
       "      <td>...</td>\n",
       "      <td>961.427482</td>\n",
       "      <td>590.288158</td>\n",
       "      <td>1130.960961</td>\n",
       "      <td>7703.536303</td>\n",
       "      <td>1.483555e+07</td>\n",
       "      <td>1175.532363</td>\n",
       "      <td>3729.773139</td>\n",
       "      <td>0.339074</td>\n",
       "      <td>3.471433</td>\n",
       "      <td>84.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>-9.269515</td>\n",
       "      <td>-2.919723</td>\n",
       "      <td>1.160694</td>\n",
       "      <td>1.917250</td>\n",
       "      <td>1.940557</td>\n",
       "      <td>1.163286</td>\n",
       "      <td>1.397886</td>\n",
       "      <td>0.957456</td>\n",
       "      <td>0.919158</td>\n",
       "      <td>-15.180466</td>\n",
       "      <td>...</td>\n",
       "      <td>679.279699</td>\n",
       "      <td>462.442301</td>\n",
       "      <td>495.437829</td>\n",
       "      <td>4110.345568</td>\n",
       "      <td>5.462040e+06</td>\n",
       "      <td>948.202176</td>\n",
       "      <td>2236.206309</td>\n",
       "      <td>0.265269</td>\n",
       "      <td>2.691344</td>\n",
       "      <td>273.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 289 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    mean_acc_x  mean_acc_y  mean_acc_z  std_acc_x  std_acc_y  std_acc_z  \\\n",
       "39   -7.340152   -4.396661    3.316030   0.978043   1.001660   1.265788   \n",
       "3    -8.787479   -1.870150    1.780016   1.275642   0.726383   0.893357   \n",
       "41   -4.884610   -6.516254    3.546129   2.879534   2.181546   1.554325   \n",
       "21  -10.121929   -1.946226    1.718196   2.064532   1.457126   1.657922   \n",
       "54   -9.269515   -2.919723    1.160694   1.917250   1.940557   1.163286   \n",
       "\n",
       "    mad_acc_x  mad_acc_y  mad_acc_z  min_acc_x      ...        std_mfgy_jerk  \\\n",
       "39   0.421281   0.584048   0.497877 -10.967659      ...           669.239068   \n",
       "3    0.938307   0.440430   0.555324 -12.212352      ...          1106.109354   \n",
       "41   1.397886   1.273416   0.497877 -11.484685      ...           794.774334   \n",
       "21   1.551078   0.957456   1.388311 -15.620895      ...           961.427482   \n",
       "54   1.397886   0.957456   0.919158 -15.180466      ...           679.279699   \n",
       "\n",
       "    mad_mfgy_jerk  min_mfgy_jerk  max_mfgy_jerk  em_mfgy_jerk  iqr_mfgy_jerk  \\\n",
       "39     427.803872     499.384249    5018.866304  5.498292e+06     872.310077   \n",
       "3      691.284754     278.225616    7065.332703  8.140896e+06    1449.145825   \n",
       "41     515.484662     387.549911    5378.571649  6.349704e+06    1033.933636   \n",
       "21     590.288158    1130.960961    7703.536303  1.483555e+07    1175.532363   \n",
       "54     462.442301     495.437829    4110.345568  5.462040e+06     948.202176   \n",
       "\n",
       "    sma_mfgy_jerk  skew_mfgy_jerk  kurt_mfgy_jerk  idx_mfgy_jerk  \n",
       "39    2247.312001        0.356066        3.366436          880.0  \n",
       "3     2630.098555        0.802582        3.526206          218.0  \n",
       "41    2391.241920        0.552510        3.253821          776.0  \n",
       "21    3729.773139        0.339074        3.471433           84.0  \n",
       "54    2236.206309        0.265269        2.691344          273.0  \n",
       "\n",
       "[5 rows x 289 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hist_activities(training_set_df)\n",
    "# plot_act_signals(in_df_ac)\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": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "64 288\n",
      "64\n",
      "['walking' 'walking' 'walking' 'walking' 'walking' 'walking' 'walking'\n",
      " 'walking' 'walking' 'walking' 'walking' 'walking' 'walking' 'walking'\n",
      " 'walking' 'walking' 'walking' 'walking' 'walking' 'walking' 'walking'\n",
      " 'walking' 'walking' 'walking' 'walking' 'walking' 'walking' 'walking'\n",
      " 'walking' 'walking' 'walking' 'walking' 'walking' 'walking' 'walking'\n",
      " 'walking' 'walking' 'walking' 'walking' 'walking' 'walking' 'walking'\n",
      " 'walking' 'walking' 'walking' 'walking' 'walking' 'walking' 'walking'\n",
      " 'walking' 'walking' 'walking' 'walking' 'walking' 'walking' 'walking'\n",
      " 'walking' 'walking' 'walking' 'walking' 'walking' 'walking' 'walking'\n",
      " 'walking']\n",
      "(64,)\n",
      "64\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gringotts/anaconda3/lib/python3.6/site-packages/sklearn/utils/validation.py:475: DataConversionWarning: Data with input dtype object was converted to float64 by StandardScaler.\n",
      "  warnings.warn(msg, DataConversionWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[2, 2, 2, 2, 2]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Training set\n",
    "\n",
    "training_data = combined_training_df[tacc_cols+tacc_jerk_cols+tacc_mag_cols+tacc_jmag_cols+facc_cols+facc_jerk_cols+facc_mag_cols+facc_jmag_cols+tgy_jerk_cols+tgy_mag_cols+tgy_jmag_cols+fgy_cols+fgy_jerk_cols+fgy_mag_cols+fgy_jmag_cols+tgy_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": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "processing acceleration: nullTest/14442D57F7E1B88_Thu_Feb_08_00-31_2018_PST/data/1_android.sensor.accelerometer.data.csv.gz\n",
      "processing gyroscope: nullTest/14442D57F7E1B88_Thu_Feb_08_00-31_2018_PST/data/4_android.sensor.gyroscope.data.csv.gz\n",
      "processing acceleration: nullTest/14442D57F7E1B88_Thu_Feb_08_00-35_2018_PST/data/1_android.sensor.accelerometer.data.csv.gz\n",
      "processing gyroscope: nullTest/14442D57F7E1B88_Thu_Feb_08_00-35_2018_PST/data/4_android.sensor.gyroscope.data.csv.gz\n",
      "(64, 289)\n"
     ]
    }
   ],
   "source": [
    "## Testing set\n",
    "testing_set_df = process_csv_data('nullTest/')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1007d6cc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean_acc_x</th>\n",
       "      <th>mean_acc_y</th>\n",
       "      <th>mean_acc_z</th>\n",
       "      <th>std_acc_x</th>\n",
       "      <th>std_acc_y</th>\n",
       "      <th>std_acc_z</th>\n",
       "      <th>mad_acc_x</th>\n",
       "      <th>mad_acc_y</th>\n",
       "      <th>mad_acc_z</th>\n",
       "      <th>min_acc_x</th>\n",
       "      <th>...</th>\n",
       "      <th>std_mfgy_jerk</th>\n",
       "      <th>mad_mfgy_jerk</th>\n",
       "      <th>min_mfgy_jerk</th>\n",
       "      <th>max_mfgy_jerk</th>\n",
       "      <th>em_mfgy_jerk</th>\n",
       "      <th>iqr_mfgy_jerk</th>\n",
       "      <th>sma_mfgy_jerk</th>\n",
       "      <th>skew_mfgy_jerk</th>\n",
       "      <th>kurt_mfgy_jerk</th>\n",
       "      <th>idx_mfgy_jerk</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>-4.148736</td>\n",
       "      <td>-4.291143</td>\n",
       "      <td>0.307734</td>\n",
       "      <td>7.238115</td>\n",
       "      <td>7.102772</td>\n",
       "      <td>7.071993</td>\n",
       "      <td>4.978772</td>\n",
       "      <td>3.552162</td>\n",
       "      <td>1.819167</td>\n",
       "      <td>-33.467876</td>\n",
       "      <td>...</td>\n",
       "      <td>772.101333</td>\n",
       "      <td>510.514266</td>\n",
       "      <td>387.549911</td>\n",
       "      <td>5378.571649</td>\n",
       "      <td>6.244450e+06</td>\n",
       "      <td>1030.650643</td>\n",
       "      <td>2376.617168</td>\n",
       "      <td>0.576109</td>\n",
       "      <td>3.353295</td>\n",
       "      <td>354.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>-9.422388</td>\n",
       "      <td>-3.695547</td>\n",
       "      <td>0.555554</td>\n",
       "      <td>1.958629</td>\n",
       "      <td>2.659053</td>\n",
       "      <td>2.302860</td>\n",
       "      <td>1.072351</td>\n",
       "      <td>1.953210</td>\n",
       "      <td>1.282991</td>\n",
       "      <td>-19.852852</td>\n",
       "      <td>...</td>\n",
       "      <td>738.767734</td>\n",
       "      <td>472.202226</td>\n",
       "      <td>489.975780</td>\n",
       "      <td>5643.361166</td>\n",
       "      <td>5.228427e+06</td>\n",
       "      <td>950.759916</td>\n",
       "      <td>2163.943059</td>\n",
       "      <td>0.557547</td>\n",
       "      <td>3.506112</td>\n",
       "      <td>786.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>-5.728522</td>\n",
       "      <td>-2.313053</td>\n",
       "      <td>4.331260</td>\n",
       "      <td>3.863189</td>\n",
       "      <td>1.782906</td>\n",
       "      <td>4.226734</td>\n",
       "      <td>3.657481</td>\n",
       "      <td>1.359587</td>\n",
       "      <td>3.111732</td>\n",
       "      <td>-13.495343</td>\n",
       "      <td>...</td>\n",
       "      <td>745.155593</td>\n",
       "      <td>495.193644</td>\n",
       "      <td>625.838550</td>\n",
       "      <td>4710.856279</td>\n",
       "      <td>5.327522e+06</td>\n",
       "      <td>989.797871</td>\n",
       "      <td>2184.551513</td>\n",
       "      <td>0.615721</td>\n",
       "      <td>3.260893</td>\n",
       "      <td>711.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>-8.769099</td>\n",
       "      <td>-2.412953</td>\n",
       "      <td>1.376248</td>\n",
       "      <td>0.388927</td>\n",
       "      <td>0.626176</td>\n",
       "      <td>0.407912</td>\n",
       "      <td>0.191491</td>\n",
       "      <td>0.421281</td>\n",
       "      <td>0.229790</td>\n",
       "      <td>-10.603826</td>\n",
       "      <td>...</td>\n",
       "      <td>276.467684</td>\n",
       "      <td>166.971369</td>\n",
       "      <td>169.465656</td>\n",
       "      <td>1835.828355</td>\n",
       "      <td>7.367753e+05</td>\n",
       "      <td>346.821047</td>\n",
       "      <td>812.613628</td>\n",
       "      <td>0.740233</td>\n",
       "      <td>3.614898</td>\n",
       "      <td>27.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>-9.126246</td>\n",
       "      <td>-2.564713</td>\n",
       "      <td>1.170793</td>\n",
       "      <td>1.804724</td>\n",
       "      <td>1.103069</td>\n",
       "      <td>1.121335</td>\n",
       "      <td>1.129798</td>\n",
       "      <td>0.689368</td>\n",
       "      <td>0.842561</td>\n",
       "      <td>-17.803896</td>\n",
       "      <td>...</td>\n",
       "      <td>746.550953</td>\n",
       "      <td>511.725677</td>\n",
       "      <td>469.799551</td>\n",
       "      <td>4518.510411</td>\n",
       "      <td>5.831356e+06</td>\n",
       "      <td>1014.518980</td>\n",
       "      <td>2296.522967</td>\n",
       "      <td>0.328401</td>\n",
       "      <td>2.814501</td>\n",
       "      <td>243.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 289 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    mean_acc_x  mean_acc_y  mean_acc_z  std_acc_x  std_acc_y  std_acc_z  \\\n",
       "44   -4.148736   -4.291143    0.307734   7.238115   7.102772   7.071993   \n",
       "35   -9.422388   -3.695547    0.555554   1.958629   2.659053   2.302860   \n",
       "56   -5.728522   -2.313053    4.331260   3.863189   1.782906   4.226734   \n",
       "7    -8.769099   -2.412953    1.376248   0.388927   0.626176   0.407912   \n",
       "53   -9.126246   -2.564713    1.170793   1.804724   1.103069   1.121335   \n",
       "\n",
       "    mad_acc_x  mad_acc_y  mad_acc_z  min_acc_x      ...        std_mfgy_jerk  \\\n",
       "44   4.978772   3.552162   1.819167 -33.467876      ...           772.101333   \n",
       "35   1.072351   1.953210   1.282991 -19.852852      ...           738.767734   \n",
       "56   3.657481   1.359587   3.111732 -13.495343      ...           745.155593   \n",
       "7    0.191491   0.421281   0.229790 -10.603826      ...           276.467684   \n",
       "53   1.129798   0.689368   0.842561 -17.803896      ...           746.550953   \n",
       "\n",
       "    mad_mfgy_jerk  min_mfgy_jerk  max_mfgy_jerk  em_mfgy_jerk  iqr_mfgy_jerk  \\\n",
       "44     510.514266     387.549911    5378.571649  6.244450e+06    1030.650643   \n",
       "35     472.202226     489.975780    5643.361166  5.228427e+06     950.759916   \n",
       "56     495.193644     625.838550    4710.856279  5.327522e+06     989.797871   \n",
       "7      166.971369     169.465656    1835.828355  7.367753e+05     346.821047   \n",
       "53     511.725677     469.799551    4518.510411  5.831356e+06    1014.518980   \n",
       "\n",
       "    sma_mfgy_jerk  skew_mfgy_jerk  kurt_mfgy_jerk  idx_mfgy_jerk  \n",
       "44    2376.617168        0.576109        3.353295          354.0  \n",
       "35    2163.943059        0.557547        3.506112          786.0  \n",
       "56    2184.551513        0.615721        3.260893          711.0  \n",
       "7      812.613628        0.740233        3.614898           27.0  \n",
       "53    2296.522967        0.328401        2.814501          243.0  \n",
       "\n",
       "[5 rows x 289 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hist_activities(testing_set_df)\n",
    "# plot_act_signals(in_df_ac)\n",
    "\n",
    "combined_testing_df = testing_set_df\n",
    "combined_testing_df = combined_testing_df.iloc[np.random.permutation(len(combined_testing_df))]\n",
    "combined_testing_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "64 288\n",
      "64\n",
      "(64,)\n",
      "64\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gringotts/anaconda3/lib/python3.6/site-packages/sklearn/utils/validation.py:475: DataConversionWarning: Data with input dtype object was converted to float64 by StandardScaler.\n",
      "  warnings.warn(msg, DataConversionWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[2, 2, 2, 2, 2]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "testing_data = combined_testing_df[tacc_cols+tacc_jerk_cols+tacc_mag_cols+tacc_jmag_cols+facc_cols+facc_jerk_cols+facc_mag_cols+facc_jmag_cols+tgy_jerk_cols+tgy_mag_cols+tgy_jmag_cols+fgy_cols+fgy_jerk_cols+fgy_mag_cols+fgy_jmag_cols+tgy_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": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model = DecisionTreeClassifier(random_state=0)\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": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Score:  1.0\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": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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