random_forest_watch_accel.ipynb

{
 "cells": [
  {
   "cell_type": "code",
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   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from scipy.io import arff\n",
    "import os\n",
    "from sklearn import preprocessing\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "import scipy\n",
    "from sklearn.utils import shuffle\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import precision_score\n",
    "from sklearn.metrics import recall_score, f1_score\n",
    "import xgboost as xgb\n",
    "from hyperopt import tpe, hp, fmin, STATUS_OK,Trials\n",
    "from hyperopt.pyll.base import scope\n",
    "from sklearn import metrics\n",
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "pd.set_option('display.max_columns', None)\n",
    "pd.set_option('display.max_rows', None)\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "for file at wisdm-dataset/arff_files/watch/accel/data_1600_accel_watch.arffshape is: (327, 93)\n",
      "for file at wisdm-dataset/arff_files/watch/accel/data_1601_accel_watch.arffshape is: (324, 93)\n",
      "for file at wisdm-dataset/arff_files/watch/accel/data_1602_accel_watch.arffshape is: (324, 93)\n",
      "for file at wisdm-dataset/arff_files/watch/accel/data_1603_accel_watch.arffshape is: (324, 93)\n",
      "for file at wisdm-dataset/arff_files/watch/accel/data_1604_accel_watch.arffshape is: (324, 93)\n",
      "for file at wisdm-dataset/arff_files/watch/accel/data_1605_accel_watch.arffshape is: (330, 93)\n",
      "for file at wisdm-dataset/arff_files/watch/accel/data_1606_accel_watch.arffshape is: (324, 93)\n",
      "for file at wisdm-dataset/arff_files/watch/accel/data_1607_accel_watch.arffshape is: (329, 93)\n",
      "for file at wisdm-dataset/arff_files/watch/accel/data_1608_accel_watch.arffshape is: (340, 93)\n",
      "for file at wisdm-dataset/arff_files/watch/accel/data_1609_accel_watch.arffshape is: (348, 93)\n",
      "for file at wisdm-dataset/arff_files/watch/accel/data_1610_accel_watch.arffshape is: (324, 93)\n",
      "for file at wisdm-dataset/arff_files/watch/accel/data_1611_accel_watch.arffshape is: (324, 93)\n",
      "for file at wisdm-dataset/arff_files/watch/accel/data_1612_accel_watch.arffshape is: (326, 93)\n",
      "for file at wisdm-dataset/arff_files/watch/accel/data_1613_accel_watch.arffshape is: (324, 93)\n",
      "for file at wisdm-dataset/arff_files/watch/accel/data_1615_accel_watch.arffshape is: (324, 93)\n",
      "for file at wisdm-dataset/arff_files/watch/accel/data_1616_accel_watch.arffshape is: (306, 93)\n",
      "for file at wisdm-dataset/arff_files/watch/accel/data_1617_accel_watch.arffshape is: (335, 93)\n",
      "for file at wisdm-dataset/arff_files/watch/accel/data_1618_accel_watch.arffshape is: (306, 93)\n",
      "for file at wisdm-dataset/arff_files/watch/accel/data_1619_accel_watch.arffshape is: (324, 93)\n",
      "for file at wisdm-dataset/arff_files/watch/accel/data_1620_accel_watch.arffshape is: (335, 93)\n",
      "for file at wisdm-dataset/arff_files/watch/accel/data_1621_accel_watch.arffshape is: (356, 93)\n",
      "for file at wisdm-dataset/arff_files/watch/accel/data_1622_accel_watch.arffshape is: (324, 93)\n",
      "for file at wisdm-dataset/arff_files/watch/accel/data_1623_accel_watch.arffshape is: (324, 93)\n",
      "for file at wisdm-dataset/arff_files/watch/accel/data_1624_accel_watch.arffshape is: (356, 93)\n",
      "for file at wisdm-dataset/arff_files/watch/accel/data_1625_accel_watch.arffshape is: (333, 93)\n",
      "for file at wisdm-dataset/arff_files/watch/accel/data_1626_accel_watch.arffshape is: (336, 93)\n",
      "for file at wisdm-dataset/arff_files/watch/accel/data_1627_accel_watch.arffshape is: (324, 93)\n",
      "for file at wisdm-dataset/arff_files/watch/accel/data_1628_accel_watch.arffshape is: (351, 93)\n",
      "for file at wisdm-dataset/arff_files/watch/accel/data_1629_accel_watch.arffshape is: (324, 93)\n",
      "for file at wisdm-dataset/arff_files/watch/accel/data_1630_accel_watch.arffshape is: (324, 93)\n",
      "for file at wisdm-dataset/arff_files/watch/accel/data_1631_accel_watch.arffshape is: (324, 93)\n",
      "for file at wisdm-dataset/arff_files/watch/accel/data_1632_accel_watch.arffshape is: (324, 93)\n",
      "for file at wisdm-dataset/arff_files/watch/accel/data_1633_accel_watch.arffshape is: (324, 93)\n",
      "for file at wisdm-dataset/arff_files/watch/accel/data_1634_accel_watch.arffshape is: (324, 93)\n",
      "for file at wisdm-dataset/arff_files/watch/accel/data_1635_accel_watch.arffshape is: (324, 93)\n",
      "for file at wisdm-dataset/arff_files/watch/accel/data_1636_accel_watch.arffshape is: (324, 93)\n",
      "for file at wisdm-dataset/arff_files/watch/accel/data_1637_accel_watch.arffshape is: (759, 93)\n",
      "for file at wisdm-dataset/arff_files/watch/accel/data_1638_accel_watch.arffshape is: (803, 93)\n",
      "for file at wisdm-dataset/arff_files/watch/accel/data_1639_accel_watch.arffshape is: (759, 93)\n",
      "for file at wisdm-dataset/arff_files/watch/accel/data_1640_accel_watch.arffshape is: (803, 93)\n",
      "for file at wisdm-dataset/arff_files/watch/accel/data_1641_accel_watch.arffshape is: (324, 93)\n",
      "for file at wisdm-dataset/arff_files/watch/accel/data_1642_accel_watch.arffshape is: (300, 93)\n",
      "for file at wisdm-dataset/arff_files/watch/accel/data_1643_accel_watch.arffshape is: (324, 93)\n",
      "for file at wisdm-dataset/arff_files/watch/accel/data_1644_accel_watch.arffshape is: (351, 93)\n",
      "for file at wisdm-dataset/arff_files/watch/accel/data_1645_accel_watch.arffshape is: (338, 93)\n",
      "for file at wisdm-dataset/arff_files/watch/accel/data_1646_accel_watch.arffshape is: (324, 93)\n",
      "for file at wisdm-dataset/arff_files/watch/accel/data_1647_accel_watch.arffshape is: (327, 93)\n",
      "for file at wisdm-dataset/arff_files/watch/accel/data_1648_accel_watch.arffshape is: (324, 93)\n",
      "for file at wisdm-dataset/arff_files/watch/accel/data_1649_accel_watch.arffshape is: (325, 93)\n",
      "for file at wisdm-dataset/arff_files/watch/accel/data_1650_accel_watch.arffshape is: (332, 93)\n",
      "(18211, 93)\n",
      "(18211, 91)\n",
      "(18211,)\n"
     ]
    }
   ],
   "source": [
    "watch_accel_df = pd.DataFrame()\n",
    "filenames = os.listdir('wisdm-dataset/arff_files/watch/accel')\n",
    "filenames.pop(0)\n",
    "for file in filenames:\n",
    "    path = \"wisdm-dataset/arff_files/watch/accel/\" + file\n",
    "    data = arff.loadarff(path)\n",
    "    df = pd.DataFrame(data[0])\n",
    "    print(\"for file at \" + path + \"shape is: \" + str(df.shape))\n",
    "    frames = [df, watch_accel_df]\n",
    "    watch_accel_df = pd.concat(frames, sort = False)\n",
    "    \n",
    "print(watch_accel_df.shape)\n",
    "    \n",
    "watch_accel_old_column_names = watch_accel_df.columns\n",
    "\n",
    "watch_accel_new_column_names = []\n",
    "for name in watch_accel_old_column_names:\n",
    "    watch_accel_new_column_names.append(name.replace('\"', ''))\n",
    "    \n",
    "replacement_dict = dict(zip(watch_accel_old_column_names, watch_accel_new_column_names))\n",
    "watch_accel_df = watch_accel_df.rename(columns = replacement_dict)\n",
    "\n",
    "labelencoder = LabelEncoder()\n",
    "watch_accel_df['ACTIVITY'] = labelencoder.fit_transform(watch_accel_df['ACTIVITY'])\n",
    "\n",
    "watch_accel_df.pop('class')\n",
    "\n",
    "watch_accel_df_shuffle = shuffle(watch_accel_df)\n",
    "\n",
    "watch_accel_data = watch_accel_df_shuffle.to_numpy()\n",
    "\n",
    "X_watch_accel = watch_accel_data[:,1:]\n",
    "Y_watch_accel = watch_accel_data[:,0]\n",
    "print(X_watch_accel.shape)\n",
    "print(Y_watch_accel.shape)\n",
    "\n",
    "x_train, x_test, y_train, y_test = train_test_split(X_watch_accel, Y_watch_accel, test_size = 0.2)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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