ML Activity Classification.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "86cc25d7",
   "metadata": {},
   "source": [
    "## Lightweight Activity Classification With Fused Sensor Data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e7b56715",
   "metadata": {},
   "source": [
    "We present work in progress on activity classification with lightweight ML models, that can be deployed synchronously in embedded applications. We employ off the shelf accelerometer and barometer sensors."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f8ef023b",
   "metadata": {},
   "source": [
    "![image](untitled.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b303cd13",
   "metadata": {},
   "source": [
    "The visualizations above show general characteristics of the seven target activity classes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "847eb68f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import re\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from scipy.signal import find_peaks\n",
    "from scipy.stats import skew, iqr\n",
    "from scipy.fftpack import fft\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.preprocessing import OrdinalEncoder\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.metrics import multilabel_confusion_matrix\n",
    "from sklearn.preprocessing import StandardScaler  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "7aa5c81e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "2fabb729",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "%config InlineBackend.figure_format='retina'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c5acd256",
   "metadata": {},
   "source": [
    "## Feature Extraction\n",
    "We employ ten time domain features and five frequency domain features for each sensor. The feature names are self-explanatory. We window the data into periods of ten seconds with a window shift of two seconds. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "4d59b5e0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Walking flat surface'}, xlabel='timestamp'>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 277,
       "width": 380
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot(title=\"Walking flat surface\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "52382911",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 277,
       "width": 381
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(nrows=2, ncols=1)\n",
    "window.plot(title=\"Top: First window \\n Bottom: FFT transformed (Frequency Domain)\",ax=axes[0])\n",
    "pd.DataFrame(yf).plot(ax=axes[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "e812eaea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Elevator up'}, xlabel='timestamp'>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 277,
       "width": 381
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot(title=\"Elevator up\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "5012d3ea",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 277,
       "width": 381
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(nrows=2, ncols=1)\n",
    "window.plot(title=\"Top: Second window \\n Bottom: FFT transformed\",ax=axes[0])\n",
    "pd.DataFrame(yf).plot(ax=axes[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "38701d64",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Running'}, xlabel='timestamp'>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 277,
       "width": 369
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot(title=\"Running\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "18db7265",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 277,
       "width": 371
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(nrows=2, ncols=1)\n",
    "window.plot(title=\"Top: First window \\n Bottom: FFT transformed\",ax=axes[0])\n",
    "pd.DataFrame(yf).plot(ax=axes[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "fcc59c39",
   "metadata": {},
   "outputs": [],
   "source": [
    "col_names_accel = ['accel_median', 'accel_std', 'accel_skewness', 'accel_mean_crossing_rate', 'accel_slope',\n",
    "'accel_num_peaks', 'accel_mean_peak_val', 'accel_mean_peak_distance',\n",
    "'accel_interquartile_range', 'accel_percentile25', 'accel_spectral_centroid',\n",
    "'accel_spectral_spread', 'accel_spectral_rolloff_75', 'accel_energy1', 'accel_energy2','activity','id']\n",
    "col_names_pressure = ['pressure_median', 'pressure_std', 'pressure_skewness', 'pressure_mean_crossing_rate', 'pressure_slope',\n",
    "'pressure_num_peaks', 'pressure_mean_peak_val', 'pressure_mean_peak_distance',\n",
    "'pressure_interquartile_range', 'pressure_percentile25', 'pressure_spectral_centroid',\n",
    "'pressure_spectral_spread', 'pressure_spectral_rolloff_75', 'pressure_energy1', 'pressure_energy2','activity','id']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "de275277",
   "metadata": {},
   "outputs": [],
   "source": [
    "def extract_features(df, df_type, activity, subject_id):\n",
    "    '''Windows input data and extracts time and frequency domain features.'''\n",
    "    if df_type == 'accel':\n",
    "        col_name = 'magnitude'\n",
    "    elif df_type == 'pressure':\n",
    "        col_name = 'pressure'\n",
    "    lst = []\n",
    "    #initialize window indices\n",
    "    left_window = df.index[0]\n",
    "    right_window = df.index[0] + pd.to_timedelta('10S')\n",
    "    right_idx = 0\n",
    "    while right_idx < len(df)-1:\n",
    "        #find the closest time in the data to our desired window\n",
    "        left_idx = df.index.get_indexer([left_window], method='nearest')\n",
    "        right_idx = df.index.get_indexer([right_window], method='nearest')\n",
    "        window = df.iloc[left_idx[0]:right_idx[0],:]\n",
    "        \n",
    "        #calculate time domain features\n",
    "        mean = window[col_name].mean()\n",
    "        median = window[col_name].median()\n",
    "        std = window[col_name].std()\n",
    "        skewness = skew(window[col_name])\n",
    "        interquartile_range = iqr(window[col_name])\n",
    "        percentile25 = np.percentile(window[col_name], 25)\n",
    "        mean_crossing_rate = ((window[col_name] > mean).astype(int).diff() == 1).sum() / len(window)\n",
    "        slope, intercept = np.polyfit(np.arange(len(window.index)), window[col_name], 1)\n",
    "        peaks, _ = find_peaks(window[col_name])\n",
    "        num_peaks = len(peaks) \n",
    "        mean_peak_val = np.mean(window[col_name][peaks])\n",
    "        mean_peak_distance = np.mean(np.diff(peaks))\n",
    "        \n",
    "        # calculate frequency domain features\n",
    "        y = np.array(window[col_name])\n",
    "        N = len(y)\n",
    "        T = 1.0 / 100.0\n",
    "        yf = np.abs(fft(y))\n",
    "        yf = 2.0/N * np.abs(yf[:N//2])\n",
    "        f = np.linspace(0.0, 1.0/(2.0*T), N//2)\n",
    "        spectral_centroid = np.sum(f * yf) / np.sum(yf)\n",
    "        spectral_spread = np.sqrt(np.sum(((f - spectral_centroid) ** 2) * yf) / np.sum(yf))\n",
    "        spectral_rolloff_75 = f[np.where(np.cumsum(yf) >= 0.75 * np.sum(yf))[0][0]]\n",
    "        energy1 = np.sum(yf[:N//4])\n",
    "        energy2 = np.sum(yf[N//4:N//2])\n",
    "\n",
    "        lst.append([median, std, skewness, mean_crossing_rate, slope,\n",
    "                          num_peaks, mean_peak_val, mean_peak_distance,\n",
    "                          interquartile_range, percentile25, spectral_centroid, \n",
    "                          spectral_spread, spectral_rolloff_75, energy1, energy2, activity, subject_id])\n",
    "        #move the window\n",
    "        left_window = df.index[left_idx] + pd.to_timedelta('2S')\n",
    "        right_window = df.index[left_idx] + pd.to_timedelta('10S')\n",
    "    return lst\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "504a7f95",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "for directory in [x[0] for x in os.walk(os.curdir) if '\\\\' in x[0]]:\n",
    "    os.chdir(directory)\n",
    "    print('Processing directory %s'%(directory))\n",
    "    subject_id = directory[2:]\n",
    "    empty_file = False\n",
    "    for filename in os.listdir():\n",
    "        if os.path.getsize(filename) < 1:\n",
    "            empty_file = True\n",
    "    if not empty_file:\n",
    "        for filename in os.listdir():\n",
    "            result = re.search(r\"\\d{15}-\\d{4}-\\d{2}-\\d{2}_\\d{2}-\\d{2}-\\d{2}-(.*)-\" + subject_id + r\"-(\\w*).txt\", filename)\n",
    "            if result:\n",
    "                activity = result.group(1)\n",
    "                df_type = result.group(2)\n",
    "                try:\n",
    "                    if df_type == 'accel':\n",
    "                        df = pd.read_csv(filename,header=None,index_col=0,names=['timestamp','X','Y','Z'])\n",
    "                        df.index=pd.to_datetime(df.index,unit='ms')\n",
    "                        df = df.resample('31.25ms').mean().interpolate(method='time')\n",
    "                        df['magnitude'] = np.sqrt(df[\"X\"]**2+df[\"Y\"]**2+df[\"Z\"]**2)\n",
    "                        df.drop([\"X\",\"Y\",\"Z\"],axis=1,inplace=True)\n",
    "                        feature_lst = extract_features(df, df_type, activity, subject_id)\n",
    "                        accel_df = pd.concat([accel_df,pd.DataFrame(feature_lst, columns = col_names_accel)],axis=0)\n",
    "                    elif df_type == 'pressure':\n",
    "                        df = pd.read_csv(filename,header=None,index_col=0,names=['timestamp','pressure'])\n",
    "                        df.index=pd.to_datetime(df.index,unit='ms')\n",
    "                        df = df.resample('31.25ms').mean().interpolate(method='time')\n",
    "                        feature_lst = extract_features(df, df_type, activity, subject_id)\n",
    "                        pressure_df = pd.concat([pressure_df,pd.DataFrame(feature_lst, columns = col_names_pressure)],axis=0)\n",
    "                except:\n",
    "                    print('%s cannot be parsed, skipping.'%(filename))\n",
    "                    continue\n",
    "            \n",
    "    os.chdir('..')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "84642b51",
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.concat([accel_df,pressure_df],axis=1).to_csv('test.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 330,
   "id": "636bfc80",
   "metadata": {},
   "outputs": [],
   "source": [
    "train = pd.read_csv('train.csv',index_col=)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "4327c082",
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "test = pd.read_csv('test.csv',index_col=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 331,
   "id": "20a5052f",
   "metadata": {},
   "outputs": [],
   "source": [
    "label = {'Stationary':1, 'Walking-flat-surface':2, 'Walking-up-stairs':3,\n",
    "       'Walking-down-stairs':4, 'Elevator-up':5, 'Elevator-down':6, 'Running':7}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 333,
   "id": "55f69dfc",
   "metadata": {},
   "outputs": [],
   "source": [
    "train['activity'] = train['activity'].replace(label)\n",
    "test['activity'] = test['activity'].replace(label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "3a217097",
   "metadata": {},
   "outputs": [],
   "source": [
    "train.drop('id',axis=1,inplace=True)\n",
    "test.drop('id',axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "id": "a160a9e2",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_X = train[train.columns[0:30]]\n",
    "train_y = train['activity']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "id": "756c6768",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_X = test[test.columns[0:30]]\n",
    "test_y = test['activity']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "id": "4feb389d",
   "metadata": {},
   "outputs": [],
   "source": [
    "target_names = ['Stationary', 'Walking-flat-surface', 'Walking-up-stairs',\n",
    "       'Walking-down-stairs', 'Elevator-up', 'Elevator-down', 'Running']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6c4c7ed1",
   "metadata": {},
   "source": [
    "## k-nn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "id": "d698394f",
   "metadata": {},
   "outputs": [],
   "source": [
    "neigh = KNeighborsClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "id": "181febc6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-18 {color: black;background-color: white;}#sk-container-id-18 pre{padding: 0;}#sk-container-id-18 div.sk-toggleable {background-color: white;}#sk-container-id-18 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-18 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-18 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-18 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-18 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-18 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-18 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-18 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-18 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-18 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-18 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-18 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-18 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-18 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-18 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-18 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-18 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-18 div.sk-item {position: relative;z-index: 1;}#sk-container-id-18 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-18 div.sk-item::before, #sk-container-id-18 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-18 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-18 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-18 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-18 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-18 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-18 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-18 div.sk-label-container {text-align: center;}#sk-container-id-18 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-18 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-18\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-18\" type=\"checkbox\" checked><label for=\"sk-estimator-id-18\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "KNeighborsClassifier()"
      ]
     },
     "execution_count": 213,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "neigh.fit(train_X.fillna(0), train_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "id": "3bc8c754",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.4662576687116564"
      ]
     },
     "execution_count": 214,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "neigh.score(test_X,test_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "id": "78f6ca7b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                      precision    recall  f1-score   support\n",
      "\n",
      "          Stationary       0.56      0.85      0.68        59\n",
      "Walking-flat-surface       0.07      0.08      0.08        59\n",
      "   Walking-up-stairs       0.00      0.00      0.00        33\n",
      " Walking-down-stairs       0.38      0.57      0.46        60\n",
      "         Elevator-up       0.00      0.00      0.00        30\n",
      "       Elevator-down       0.17      0.05      0.07        22\n",
      "             Running       0.95      0.98      0.97        63\n",
      "\n",
      "            accuracy                           0.47       326\n",
      "           macro avg       0.31      0.36      0.32       326\n",
      "        weighted avg       0.38      0.47      0.41       326\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(test_y, neigh.predict(test_X), target_names=target_names))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "id": "51d4eb40",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[228,  39],\n",
       "        [  9,  50]],\n",
       "\n",
       "       [[202,  65],\n",
       "        [ 54,   5]],\n",
       "\n",
       "       [[288,   5],\n",
       "        [ 33,   0]],\n",
       "\n",
       "       [[211,  55],\n",
       "        [ 26,  34]],\n",
       "\n",
       "       [[294,   2],\n",
       "        [ 30,   0]],\n",
       "\n",
       "       [[299,   5],\n",
       "        [ 21,   1]],\n",
       "\n",
       "       [[260,   3],\n",
       "        [  1,  62]]], dtype=int64)"
      ]
     },
     "execution_count": 228,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "multilabel_confusion_matrix(test_y, neigh.predict(test_X))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b320d5a4",
   "metadata": {},
   "source": [
    "## Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "id": "d1b066a8",
   "metadata": {},
   "outputs": [],
   "source": [
    "clf = RandomForestClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "id": "dfb10cca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-19 {color: black;background-color: white;}#sk-container-id-19 pre{padding: 0;}#sk-container-id-19 div.sk-toggleable {background-color: white;}#sk-container-id-19 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-19 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-19 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-19 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-19 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-19 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-19 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-19 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-19 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-19 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-19 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-19 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-19 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-19 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-19 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-19 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-19 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-19 div.sk-item {position: relative;z-index: 1;}#sk-container-id-19 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-19 div.sk-item::before, #sk-container-id-19 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-19 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-19 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-19 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-19 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-19 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-19 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-19 div.sk-label-container {text-align: center;}#sk-container-id-19 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-19 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-19\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-19\" type=\"checkbox\" checked><label for=\"sk-estimator-id-19\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestClassifier()"
      ]
     },
     "execution_count": 216,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.fit(train_X.fillna(0), train_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "id": "43202c30",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                      precision    recall  f1-score   support\n",
      "\n",
      "          Stationary       0.95      0.97      0.96        59\n",
      "Walking-flat-surface       0.12      0.02      0.03        59\n",
      "   Walking-up-stairs       0.76      0.67      0.71        33\n",
      " Walking-down-stairs       0.79      1.00      0.88        60\n",
      "         Elevator-up       1.00      0.90      0.95        30\n",
      "       Elevator-down       0.92      1.00      0.96        22\n",
      "             Running       0.61      0.98      0.75        63\n",
      "\n",
      "            accuracy                           0.77       326\n",
      "           macro avg       0.74      0.79      0.75       326\n",
      "        weighted avg       0.69      0.77      0.71       326\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(test_y, clf.predict(test_X), target_names=target_names))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "id": "9fe65adf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[264,   3],\n",
       "        [  2,  57]],\n",
       "\n",
       "       [[260,   7],\n",
       "        [ 58,   1]],\n",
       "\n",
       "       [[286,   7],\n",
       "        [ 11,  22]],\n",
       "\n",
       "       [[250,  16],\n",
       "        [  0,  60]],\n",
       "\n",
       "       [[296,   0],\n",
       "        [  3,  27]],\n",
       "\n",
       "       [[302,   2],\n",
       "        [  0,  22]],\n",
       "\n",
       "       [[223,  40],\n",
       "        [  1,  62]]], dtype=int64)"
      ]
     },
     "execution_count": 230,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "multilabel_confusion_matrix(test_y, clf.predict(test_X))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "03daa597",
   "metadata": {},
   "source": [
    "## Time domain features only"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 251,
   "id": "8fb30f0a",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_X_time = test_X.drop(test_X.columns[10:15],axis=1).drop(test_X.columns[25:30],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 248,
   "id": "644bc7ec",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_X_time = train_X.drop(train_X.columns[10:15],axis=1).drop(train_X.columns[25:30],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 249,
   "id": "8e6777bf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-20 {color: black;background-color: white;}#sk-container-id-20 pre{padding: 0;}#sk-container-id-20 div.sk-toggleable {background-color: white;}#sk-container-id-20 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-20 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-20 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-20 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-20 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-20 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-20 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-20 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-20 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-20 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-20 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-20 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-20 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-20 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-20 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-20 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-20 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-20 div.sk-item {position: relative;z-index: 1;}#sk-container-id-20 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-20 div.sk-item::before, #sk-container-id-20 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-20 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-20 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-20 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-20 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-20 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-20 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-20 div.sk-label-container {text-align: center;}#sk-container-id-20 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-20 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-20\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-20\" type=\"checkbox\" checked><label for=\"sk-estimator-id-20\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestClassifier()"
      ]
     },
     "execution_count": 249,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.fit(train_X_time.fillna(0), train_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 252,
   "id": "c9ec5655",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                      precision    recall  f1-score   support\n",
      "\n",
      "          Stationary       0.95      0.97      0.96        59\n",
      "Walking-flat-surface       0.00      0.00      0.00        59\n",
      "   Walking-up-stairs       0.56      0.67      0.61        33\n",
      " Walking-down-stairs       0.79      0.95      0.86        60\n",
      "         Elevator-up       1.00      0.87      0.93        30\n",
      "       Elevator-down       0.88      1.00      0.94        22\n",
      "             Running       0.66      0.98      0.79        63\n",
      "\n",
      "            accuracy                           0.75       326\n",
      "           macro avg       0.69      0.78      0.73       326\n",
      "        weighted avg       0.65      0.75      0.70       326\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(test_y, clf.predict(test_X_time), target_names=target_names))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "id": "5766f109",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[264,   3],\n",
       "        [  2,  57]],\n",
       "\n",
       "       [[257,  10],\n",
       "        [ 59,   0]],\n",
       "\n",
       "       [[276,  17],\n",
       "        [ 11,  22]],\n",
       "\n",
       "       [[251,  15],\n",
       "        [  3,  57]],\n",
       "\n",
       "       [[296,   0],\n",
       "        [  4,  26]],\n",
       "\n",
       "       [[301,   3],\n",
       "        [  0,  22]],\n",
       "\n",
       "       [[231,  32],\n",
       "        [  1,  62]]], dtype=int64)"
      ]
     },
     "execution_count": 253,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "multilabel_confusion_matrix(test_y, clf.predict(test_X_time))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4622e6a6",
   "metadata": {},
   "source": [
    "## Frequency domain features only"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 254,
   "id": "58570544",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_X_freq = test_X.drop(test_X.columns[0:10],axis=1).drop(test_X.columns[15:25],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 255,
   "id": "9d805922",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_X_freq = train_X.drop(train_X.columns[0:10],axis=1).drop(train_X.columns[15:25],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 257,
   "id": "9fa44602",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-21 {color: black;background-color: white;}#sk-container-id-21 pre{padding: 0;}#sk-container-id-21 div.sk-toggleable {background-color: white;}#sk-container-id-21 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-21 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-21 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-21 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-21 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-21 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-21 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-21 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-21 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-21 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-21 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-21 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-21 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-21 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-21 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-21 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-21 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-21 div.sk-item {position: relative;z-index: 1;}#sk-container-id-21 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-21 div.sk-item::before, #sk-container-id-21 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-21 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-21 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-21 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-21 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-21 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-21 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-21 div.sk-label-container {text-align: center;}#sk-container-id-21 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-21 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-21\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-21\" type=\"checkbox\" checked><label for=\"sk-estimator-id-21\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestClassifier()"
      ]
     },
     "execution_count": 257,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.fit(train_X_freq.fillna(0), train_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 258,
   "id": "68368002",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                      precision    recall  f1-score   support\n",
      "\n",
      "          Stationary       0.95      0.92      0.93        59\n",
      "Walking-flat-surface       0.08      0.02      0.03        59\n",
      "   Walking-up-stairs       0.11      0.18      0.13        33\n",
      " Walking-down-stairs       0.25      0.37      0.30        60\n",
      "         Elevator-up       0.28      0.17      0.21        30\n",
      "       Elevator-down       0.42      0.64      0.51        22\n",
      "             Running       1.00      0.98      0.99        63\n",
      "\n",
      "            accuracy                           0.50       326\n",
      "           macro avg       0.44      0.47      0.44       326\n",
      "        weighted avg       0.49      0.50      0.49       326\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(test_y, clf.predict(test_X_freq), target_names=target_names))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 259,
   "id": "334a49bd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[264,   3],\n",
       "        [  5,  54]],\n",
       "\n",
       "       [[255,  12],\n",
       "        [ 58,   1]],\n",
       "\n",
       "       [[243,  50],\n",
       "        [ 27,   6]],\n",
       "\n",
       "       [[201,  65],\n",
       "        [ 38,  22]],\n",
       "\n",
       "       [[283,  13],\n",
       "        [ 25,   5]],\n",
       "\n",
       "       [[285,  19],\n",
       "        [  8,  14]],\n",
       "\n",
       "       [[263,   0],\n",
       "        [  1,  62]]], dtype=int64)"
      ]
     },
     "execution_count": 259,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "multilabel_confusion_matrix(test_y, clf.predict(test_X_freq))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "afe53cd7",
   "metadata": {},
   "source": [
    "The accuracy only lowers by 2% when removing frequency domain features, but over 20% when removing time domain features."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3f918268",
   "metadata": {},
   "source": [
    "## LOOCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fb37896c",
   "metadata": {},
   "outputs": [],
   "source": [
    "#generate .csv files for each subject\n",
    "data_dir = 'data_dir'\n",
    "os.chdir(data_dir)\n",
    "for directory in [x[0] for x in os.walk(os.curdir) if '\\\\' in x[0]]:\n",
    "    accel_df = pd.DataFrame()\n",
    "    pressure_df = pd.DataFrame()\n",
    "    os.chdir(directory)\n",
    "    print('Processing directory %s'%(directory))\n",
    "    subject_id = directory[2:]\n",
    "    empty_file = False\n",
    "    for filename in os.listdir():\n",
    "        if os.path.getsize(filename) < 1:\n",
    "            empty_file = True\n",
    "    if not empty_file:\n",
    "        for filename in os.listdir():\n",
    "            result = re.search(r\"\\d{15}-\\d{4}-\\d{2}-\\d{2}_\\d{2}-\\d{2}-\\d{2}-(.*)-\" + subject_id + r\"-(\\w*).txt\", filename)\n",
    "            if result:\n",
    "                activity = result.group(1)\n",
    "                df_type = result.group(2)\n",
    "                try:\n",
    "                    if df_type == 'accel':\n",
    "                        df = pd.read_csv(filename,header=None,index_col=0,names=['timestamp','X','Y','Z'])\n",
    "                        df.index=pd.to_datetime(df.index,unit='ms')\n",
    "                        df = df.resample('31.25ms').mean().interpolate(method='time')\n",
    "                        df['magnitude'] = np.sqrt(df[\"X\"]**2+df[\"Y\"]**2+df[\"Z\"]**2)\n",
    "                        df.drop([\"X\",\"Y\",\"Z\"],axis=1,inplace=True)\n",
    "                        feature_lst = extract_features(df, df_type, activity, subject_id)\n",
    "                        accel_df = pd.concat([accel_df,pd.DataFrame(feature_lst, columns = col_names_accel)],axis=0)\n",
    "                    elif df_type == 'pressure':\n",
    "                        df = pd.read_csv(filename,header=None,index_col=0,names=['timestamp','pressure'])\n",
    "                        df.index=pd.to_datetime(df.index,unit='ms')\n",
    "                        df = df.resample('31.25ms').mean().interpolate(method='time')\n",
    "                        feature_lst = extract_features(df, df_type, activity, subject_id)\n",
    "                        pressure_df = pd.concat([pressure_df,pd.DataFrame(feature_lst, columns = col_names_pressure)],axis=0)\n",
    "                except:\n",
    "                    print('%s cannot be parsed, skipping.'%(filename))\n",
    "                    continue\n",
    "            \n",
    "    os.chdir('..')\n",
    "    accel_df.reset_index(drop=True,inplace=True)\n",
    "    pressure_df.reset_index(drop=True,inplace=True)\n",
    "    if (len(accel_df) != len(pressure_df)):\n",
    "        #windowed timestamps are out of sync, so we ignore this data\n",
    "        print('Mismatch on %s'%subject_id)\n",
    "    else: \n",
    "        pd.concat([accel_df,pressure_df],axis=1).to_csv('%s.csv'%subject_id)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 318,
   "id": "5cb039e2",
   "metadata": {},
   "outputs": [],
   "source": [
    "#leave one out CV\n",
    "file_list = []\n",
    "y_true = pd.DataFrame()\n",
    "y_pred = pd.DataFrame()\n",
    "data_dir = 'data_dir'\n",
    "os.chdir(data_dir)\n",
    "for filename in os.listdir():\n",
    "    if filename.endswith('csv'):\n",
    "        file_list.append(filename)\n",
    "for x in range(len(file_list)):\n",
    "    train_X = pd.DataFrame()\n",
    "    train_y = pd.DataFrame()\n",
    "    test = file_list[x]\n",
    "    train = [y for y in file_list if y != test]\n",
    "    for file in train:\n",
    "        df = pd.read_csv(file,index_col=0)\n",
    "        train_X = pd.concat([train_X,df[df.columns[0:30]]],axis=0)\n",
    "        train_y = pd.concat([train_y,df['activity'].replace(label)],axis=0,ignore_index=True)\n",
    "    df = pd.read_csv(test,index_col=0)\n",
    "    test_X = df[df.columns[0:30]]\n",
    "    test_y = df['activity'].replace(label)\n",
    "    clf.fit(train_X.fillna(0), train_y)\n",
    "    y_true.append(test_y)\n",
    "    y_true = pd.concat([y_true,test_y],axis=0,ignore_index=True)\n",
    "    y_pred = pd.concat([y_pred,pd.Series(clf.predict(test_X.fillna(0)))],axis=0,ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 325,
   "id": "ef888c21",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                      precision    recall  f1-score   support\n",
      "\n",
      "          Stationary       0.90      0.98      0.94       434\n",
      "Walking-flat-surface       0.92      0.78      0.84       397\n",
      "   Walking-up-stairs       0.82      0.83      0.83       120\n",
      " Walking-down-stairs       0.83      0.83      0.83       196\n",
      "         Elevator-up       0.93      0.92      0.92        99\n",
      "       Elevator-down       0.93      0.94      0.94        85\n",
      "             Running       0.89      0.98      0.93       197\n",
      "\n",
      "            accuracy                           0.89      1528\n",
      "           macro avg       0.89      0.89      0.89      1528\n",
      "        weighted avg       0.89      0.89      0.89      1528\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_true, y_pred, target_names=target_names))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 326,
   "id": "0e4e0c6f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[1047,   47],\n",
       "        [  10,  424]],\n",
       "\n",
       "       [[1104,   27],\n",
       "        [  87,  310]],\n",
       "\n",
       "       [[1386,   22],\n",
       "        [  20,  100]],\n",
       "\n",
       "       [[1299,   33],\n",
       "        [  33,  163]],\n",
       "\n",
       "       [[1422,    7],\n",
       "        [   8,   91]],\n",
       "\n",
       "       [[1437,    6],\n",
       "        [   5,   80]],\n",
       "\n",
       "       [[1306,   25],\n",
       "        [   4,  193]]], dtype=int64)"
      ]
     },
     "execution_count": 326,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "multilabel_confusion_matrix(y_true, y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eeac6438",
   "metadata": {},
   "source": [
    "## LSTM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 525,
   "id": "efb0fbc2",
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = StandardScaler()  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 544,
   "id": "df1348e1",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train = scaler.fit_transform(train_X.fillna(0)).reshape((train_X.to_numpy().shape[0], train_X.to_numpy().shape[1], 1))\n",
    "x_test = scaler.fit_transform(test_X.fillna(0)).reshape((test_X.to_numpy().shape[0], test_X.to_numpy().shape[1], 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 585,
   "id": "7de45523",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "model = keras.Sequential()\n",
    "\n",
    "model.add(\n",
    "    layers.Bidirectional(layers.LSTM(64, return_sequences=True), input_shape=(30,1))\n",
    ")\n",
    "model.add(layers.LSTM(32))\n",
    "model.add(layers.Dense(64))\n",
    "\n",
    "model.compile(\n",
    "    loss=\"sparse_categorical_crossentropy\",\n",
    "    optimizer=keras.optimizers.Adam(learning_rate=1e-4),\n",
    "    metrics=[\"sparse_categorical_accuracy\",\"accuracy\"],\n",
    ")\n",
    "\n",
    "callbacks = [keras.callbacks.EarlyStopping(patience=10, restore_best_weights=True)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 590,
   "id": "4d0171e6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 590,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "keras.utils.plot_model(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 589,
   "id": "639ebdeb",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/200\n",
      "27/27 [==============================] - 0s 17ms/step - loss: 1.1602 - sparse_categorical_accuracy: 0.6498 - accuracy: 0.6498 - val_loss: 1.0923 - val_sparse_categorical_accuracy: 0.7186 - val_accuracy: 0.7186\n",
      "Epoch 2/200\n",
      "27/27 [==============================] - 0s 9ms/step - loss: 1.1333 - sparse_categorical_accuracy: 0.6562 - accuracy: 0.6562 - val_loss: 1.0642 - val_sparse_categorical_accuracy: 0.7535 - val_accuracy: 0.7535\n",
      "Epoch 3/200\n",
      "27/27 [==============================] - 0s 9ms/step - loss: 1.0939 - sparse_categorical_accuracy: 0.6638 - accuracy: 0.6638 - val_loss: 1.0944 - val_sparse_categorical_accuracy: 0.7628 - val_accuracy: 0.7628\n",
      "Epoch 4/200\n",
      "27/27 [==============================] - 0s 9ms/step - loss: 1.0930 - sparse_categorical_accuracy: 0.6661 - accuracy: 0.6661 - val_loss: 1.1678 - val_sparse_categorical_accuracy: 0.7442 - val_accuracy: 0.7442\n",
      "Epoch 5/200\n",
      "27/27 [==============================] - 0s 9ms/step - loss: 1.0952 - sparse_categorical_accuracy: 0.6533 - accuracy: 0.6533 - val_loss: 1.0763 - val_sparse_categorical_accuracy: 0.7209 - val_accuracy: 0.7209\n",
      "Epoch 6/200\n",
      "27/27 [==============================] - 0s 10ms/step - loss: 1.0766 - sparse_categorical_accuracy: 0.6597 - accuracy: 0.6597 - val_loss: 1.1421 - val_sparse_categorical_accuracy: 0.7279 - val_accuracy: 0.7279\n",
      "Epoch 7/200\n",
      "27/27 [==============================] - 0s 9ms/step - loss: 1.0893 - sparse_categorical_accuracy: 0.6632 - accuracy: 0.6632 - val_loss: 1.2291 - val_sparse_categorical_accuracy: 0.7186 - val_accuracy: 0.7186\n",
      "Epoch 8/200\n",
      "27/27 [==============================] - 0s 9ms/step - loss: 1.1028 - sparse_categorical_accuracy: 0.6638 - accuracy: 0.6638 - val_loss: 1.1979 - val_sparse_categorical_accuracy: 0.7302 - val_accuracy: 0.7302\n",
      "Epoch 9/200\n",
      "27/27 [==============================] - 0s 9ms/step - loss: 1.0888 - sparse_categorical_accuracy: 0.6667 - accuracy: 0.6667 - val_loss: 1.0842 - val_sparse_categorical_accuracy: 0.7442 - val_accuracy: 0.7442\n",
      "Epoch 10/200\n",
      "27/27 [==============================] - 0s 10ms/step - loss: 1.1065 - sparse_categorical_accuracy: 0.6696 - accuracy: 0.6696 - val_loss: 1.2125 - val_sparse_categorical_accuracy: 0.7488 - val_accuracy: 0.7488\n",
      "Epoch 11/200\n",
      "27/27 [==============================] - 0s 9ms/step - loss: 1.1003 - sparse_categorical_accuracy: 0.6731 - accuracy: 0.6731 - val_loss: 1.1345 - val_sparse_categorical_accuracy: 0.7465 - val_accuracy: 0.7465\n",
      "Epoch 12/200\n",
      "27/27 [==============================] - 0s 9ms/step - loss: 1.1757 - sparse_categorical_accuracy: 0.6556 - accuracy: 0.6556 - val_loss: 1.0117 - val_sparse_categorical_accuracy: 0.7419 - val_accuracy: 0.7419\n",
      "Epoch 13/200\n",
      "27/27 [==============================] - 0s 9ms/step - loss: 1.0922 - sparse_categorical_accuracy: 0.6533 - accuracy: 0.6533 - val_loss: 1.0691 - val_sparse_categorical_accuracy: 0.7209 - val_accuracy: 0.7209\n",
      "Epoch 14/200\n",
      "27/27 [==============================] - 0s 9ms/step - loss: 1.1416 - sparse_categorical_accuracy: 0.6451 - accuracy: 0.6451 - val_loss: 1.3468 - val_sparse_categorical_accuracy: 0.6512 - val_accuracy: 0.6512\n",
      "Epoch 15/200\n",
      "27/27 [==============================] - 0s 10ms/step - loss: 1.1773 - sparse_categorical_accuracy: 0.6504 - accuracy: 0.6504 - val_loss: 1.2839 - val_sparse_categorical_accuracy: 0.6953 - val_accuracy: 0.6953\n",
      "Epoch 16/200\n",
      "27/27 [==============================] - 0s 9ms/step - loss: 1.1405 - sparse_categorical_accuracy: 0.6539 - accuracy: 0.6539 - val_loss: 1.2497 - val_sparse_categorical_accuracy: 0.7093 - val_accuracy: 0.7093\n",
      "Epoch 17/200\n",
      "27/27 [==============================] - 0s 9ms/step - loss: 1.3074 - sparse_categorical_accuracy: 0.6754 - accuracy: 0.6754 - val_loss: 1.2692 - val_sparse_categorical_accuracy: 0.6837 - val_accuracy: 0.6837\n",
      "Epoch 18/200\n",
      "27/27 [==============================] - 0s 10ms/step - loss: 1.2769 - sparse_categorical_accuracy: 0.6847 - accuracy: 0.6847 - val_loss: 1.1856 - val_sparse_categorical_accuracy: 0.7326 - val_accuracy: 0.7326\n",
      "Epoch 19/200\n",
      "27/27 [==============================] - 0s 10ms/step - loss: 1.2193 - sparse_categorical_accuracy: 0.6975 - accuracy: 0.6975 - val_loss: 1.1877 - val_sparse_categorical_accuracy: 0.7512 - val_accuracy: 0.7512\n",
      "Epoch 20/200\n",
      "27/27 [==============================] - 0s 9ms/step - loss: 1.2308 - sparse_categorical_accuracy: 0.7016 - accuracy: 0.7016 - val_loss: 1.1549 - val_sparse_categorical_accuracy: 0.7628 - val_accuracy: 0.7628\n",
      "Epoch 21/200\n",
      "27/27 [==============================] - 0s 9ms/step - loss: 1.1705 - sparse_categorical_accuracy: 0.6894 - accuracy: 0.6894 - val_loss: 1.0645 - val_sparse_categorical_accuracy: 0.7791 - val_accuracy: 0.7791\n",
      "Epoch 22/200\n",
      "27/27 [==============================] - 0s 9ms/step - loss: 1.1558 - sparse_categorical_accuracy: 0.6690 - accuracy: 0.6690 - val_loss: 1.1160 - val_sparse_categorical_accuracy: 0.7837 - val_accuracy: 0.7837\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x227be267730>"
      ]
     },
     "execution_count": 589,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(\n",
    "    x_train,\n",
    "    y_train.reshape(-1,1),\n",
    "    validation_split=0.2,\n",
    "    epochs=200,\n",
    "    batch_size=64,\n",
    "    callbacks=callbacks,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f6719fc8",
   "metadata": {},
   "source": [
    "Our bidirectional LSTM model achieves 78% validation accuracy. This is comparable to the Random Forest Classifier."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "937ab5f5",
   "metadata": {},
   "source": [
    "## Sensor in hand, instead of pocket"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6c129db9",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_dir = '\\\\inhand'\n",
    "os.chdir(data_dir)\n",
    "accel_df = pd.DataFrame()\n",
    "pressure_df = pd.DataFrame()\n",
    "\n",
    "for directory in [x[0] for x in os.walk(os.curdir) if '\\\\' in x[0]]:\n",
    "    os.chdir(directory)\n",
    "    print('Processing directory %s'%(directory))\n",
    "    subject_id = directory[2:]\n",
    "    empty_file = False\n",
    "    for filename in os.listdir():\n",
    "        if os.path.getsize(filename) < 1:\n",
    "            empty_file = True\n",
    "    if not empty_file:\n",
    "        for filename in os.listdir():\n",
    "            result = re.search(r\"\\d{15}-\\d{4}-\\d{2}-\\d{2}_\\d{2}-\\d{2}-\\d{2}-(.*)-\" + subject_id + r\"-(\\w*).txt\", filename)\n",
    "            if result:\n",
    "                activity = result.group(1)\n",
    "                df_type = result.group(2)\n",
    "                try:\n",
    "                    if df_type == 'accel':\n",
    "                        df = pd.read_csv(filename,header=None,index_col=0,names=['timestamp','X','Y','Z'])\n",
    "                        df.index=pd.to_datetime(df.index,unit='ms')\n",
    "                        df = df.resample('31.25ms').mean().interpolate(method='time')\n",
    "                        df['magnitude'] = np.sqrt(df[\"X\"]**2+df[\"Y\"]**2+df[\"Z\"]**2)\n",
    "                        df.drop([\"X\",\"Y\",\"Z\"],axis=1,inplace=True)\n",
    "                        feature_lst = extract_features(df, df_type, activity, subject_id)\n",
    "                        accel_df = pd.concat([accel_df,pd.DataFrame(feature_lst, columns = col_names_accel)],axis=0)\n",
    "                    elif df_type == 'pressure':\n",
    "                        df = pd.read_csv(filename,header=None,index_col=0,names=['timestamp','pressure'])\n",
    "                        df.index=pd.to_datetime(df.index,unit='ms')\n",
    "                        df = df.resample('31.25ms').mean().interpolate(method='time')\n",
    "                        feature_lst = extract_features(df, df_type, activity, subject_id)\n",
    "                        pressure_df = pd.concat([pressure_df,pd.DataFrame(feature_lst, columns = col_names_pressure)],axis=0)\n",
    "                except:\n",
    "                    print('%s cannot be parsed, skipping.'%(filename))\n",
    "                    continue\n",
    "            \n",
    "    os.chdir('..')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 398,
   "id": "c041ae30",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_X = pd.concat([accel_df,pressure_df],axis=1).drop(['id','activity'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 399,
   "id": "699c9172",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_y = accel_df['activity'].replace(label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 400,
   "id": "9fcd4700",
   "metadata": {},
   "outputs": [],
   "source": [
    "train['activity'] = train['activity'].replace(label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 348,
   "id": "2102095e",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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      ],
      "text/plain": [
       "RandomForestClassifier()"
      ]
     },
     "execution_count": 348,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.fit(train_X.fillna(0), train_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "486c1ffa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                      precision    recall  f1-score   support\n",
      "\n",
      "          Stationary       0.98      0.97      0.97        60\n",
      "Walking-flat-surface       0.66      0.48      0.56        56\n",
      "   Walking-up-stairs       0.64      0.82      0.72        55\n",
      " Walking-down-stairs       0.89      0.93      0.91        55\n",
      "         Elevator-up       1.00      0.96      0.98        25\n",
      "       Elevator-down       0.92      1.00      0.96        23\n",
      "             Running       1.00      0.96      0.98        55\n",
      "\n",
      "            accuracy                           0.85       329\n",
      "           macro avg       0.87      0.87      0.87       329\n",
      "        weighted avg       0.86      0.85      0.85       329\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(test_y, clf.predict(test_X), target_names=target_names))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bd58d50a",
   "metadata": {},
   "source": [
    "The in hand performance is overall 8% higher than the in pocket performance. There is likely more movement in the pocket, while it would be held relatively stably in the hand."
   ]
  }
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