Experiments.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "XTrain = np.loadtxt('ActivityLog.csv', delimiter=',', dtype='str')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1171, 25)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "XTrain.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "XTrain = XTrain[28:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1143, 25)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "XTrain.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([['-0.670082734', '-0.670025414', '-0.792625194', '-0.94234768',\n",
       "        '-2.045734651', '-1.870624671', '-2.466389799', '-0.800681604',\n",
       "        '-0.410635809', '0.68467463', '0.430518847', '0.089264115',\n",
       "        '-0.399635138', '-0.766293883', '-1.043289532', '-0.769320455',\n",
       "        '-0.397893954', '0.855069631', '-0.10460162', '-0.300502041',\n",
       "        '-0.828248297', '0.20641181', '-0.73878926', '-0.00519707', '3'],\n",
       "       ['-0.861629091', '-1.067786329', '-1.004662542', '-1.192019706',\n",
       "        '-1.691731914', '-1.873449733', '-2.483530608', '-1.100105432',\n",
       "        '-0.33402968', '0.660998979', '0.204621014', '0.145373889',\n",
       "        '-0.447505849', '-1.196392244', '-1.196684941', '-0.899659644',\n",
       "        '-1.194254625', '-0.134052804', '-0.158409253', '-0.321210473',\n",
       "        '-1.284549258', '0.388558613', '-0.727133125', '-0.028941119', '3'],\n",
       "       ['-1.120216684', '-1.243872472', '-1.088129179', '-1.321977776',\n",
       "        '-1.568389836', '-1.84344819', '-2.466832437', '-1.230669109',\n",
       "        '-0.312126644', '0.660937209', '0.20757578', '0.058901833',\n",
       "        '-0.769545176', '-1.247878471', '-1.2653933', '-1.060588654',\n",
       "        '-1.274839619', '-0.126441017', '-0.207582735', '-0.256538507',\n",
       "        '-1.379049173', '0.374665264', '-0.731673663', '-0.029146589', '3'],\n",
       "       ['-1.148948641', '-1.275784493', '-1.098497718', '-1.334028255',\n",
       "        '-1.570453467', '-1.812269966', '-2.46548721', '-1.217212644',\n",
       "        '-0.315851316', '0.660844827', '0.208659934', '0.082455705',\n",
       "        '-0.76084141', '-1.257924564', '-1.285366659', '-1.077878547',\n",
       "        '-1.355908685', '-0.080027048', '-0.234463146', '-0.23790762',\n",
       "        '-1.422561476', '0.379111016', '-0.732445475', '-0.029891371', '3'],\n",
       "       ['-1.152779564', '-1.275784493', '-1.097460658', '-1.330953992',\n",
       "        '-1.569404654', '-1.810911269', '-2.467587493', '-1.191752728',\n",
       "        '-0.315808274', '0.660838819', '0.208655279', '0.107454964',\n",
       "        '-0.46491338', '-1.259180326', '-1.286964528', '-1.080538532',\n",
       "        '-1.358766742', '-0.077899848', '-0.241133513', '-0.246843297',\n",
       "        '-1.428392499', '0.379116523', '-0.732428236', '-0.030026413', '3']],\n",
       "      dtype='<U36')"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "XTrain[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "YTrain = XTrain[:,-1]\n",
    "YTrain = YTrain.astype(np.float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1143,) [ 3.  3.  3.  3.  3.]\n"
     ]
    }
   ],
   "source": [
    "print(YTrain.shape, YTrain[:5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1143, 24)\n"
     ]
    }
   ],
   "source": [
    "XTrain = XTrain[:,:-1]\n",
    "print(XTrain.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[['-0.670082734' '-0.670025414' '-0.792625194' '-0.94234768' '-2.045734651'\n",
      "  '-1.870624671' '-2.466389799' '-0.800681604' '-0.410635809' '0.68467463'\n",
      "  '0.430518847' '0.089264115' '-0.399635138' '-0.766293883' '-1.043289532'\n",
      "  '-0.769320455' '-0.397893954' '0.855069631' '-0.10460162' '-0.300502041'\n",
      "  '-0.828248297' '0.20641181' '-0.73878926' '-0.00519707']\n",
      " ['-0.861629091' '-1.067786329' '-1.004662542' '-1.192019706'\n",
      "  '-1.691731914' '-1.873449733' '-2.483530608' '-1.100105432' '-0.33402968'\n",
      "  '0.660998979' '0.204621014' '0.145373889' '-0.447505849' '-1.196392244'\n",
      "  '-1.196684941' '-0.899659644' '-1.194254625' '-0.134052804'\n",
      "  '-0.158409253' '-0.321210473' '-1.284549258' '0.388558613' '-0.727133125'\n",
      "  '-0.028941119']\n",
      " ['-1.120216684' '-1.243872472' '-1.088129179' '-1.321977776'\n",
      "  '-1.568389836' '-1.84344819' '-2.466832437' '-1.230669109' '-0.312126644'\n",
      "  '0.660937209' '0.20757578' '0.058901833' '-0.769545176' '-1.247878471'\n",
      "  '-1.2653933' '-1.060588654' '-1.274839619' '-0.126441017' '-0.207582735'\n",
      "  '-0.256538507' '-1.379049173' '0.374665264' '-0.731673663' '-0.029146589']\n",
      " ['-1.148948641' '-1.275784493' '-1.098497718' '-1.334028255'\n",
      "  '-1.570453467' '-1.812269966' '-2.46548721' '-1.217212644' '-0.315851316'\n",
      "  '0.660844827' '0.208659934' '0.082455705' '-0.76084141' '-1.257924564'\n",
      "  '-1.285366659' '-1.077878547' '-1.355908685' '-0.080027048'\n",
      "  '-0.234463146' '-0.23790762' '-1.422561476' '0.379111016' '-0.732445475'\n",
      "  '-0.029891371']\n",
      " ['-1.152779564' '-1.275784493' '-1.097460658' '-1.330953992'\n",
      "  '-1.569404654' '-1.810911269' '-2.467587493' '-1.191752728'\n",
      "  '-0.315808274' '0.660838819' '0.208655279' '0.107454964' '-0.46491338'\n",
      "  '-1.259180326' '-1.286964528' '-1.080538532' '-1.358766742'\n",
      "  '-0.077899848' '-0.241133513' '-0.246843297' '-1.428392499' '0.379116523'\n",
      "  '-0.732428236' '-0.030026413']]\n"
     ]
    }
   ],
   "source": [
    "print(XTrain[:5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1143, 24)\n",
      "[[-0.67008273 -0.67002541 -0.79262519 -0.94234768 -2.04573465 -1.87062467\n",
      "  -2.4663898  -0.8006816  -0.41063581  0.68467463  0.43051885  0.08926412\n",
      "  -0.39963514 -0.76629388 -1.04328953 -0.76932046 -0.39789395  0.85506963\n",
      "  -0.10460162 -0.30050204 -0.8282483   0.20641181 -0.73878926 -0.00519707]\n",
      " [-0.86162909 -1.06778633 -1.00466254 -1.19201971 -1.69173191 -1.87344973\n",
      "  -2.48353061 -1.10010543 -0.33402968  0.66099898  0.20462101  0.14537389\n",
      "  -0.44750585 -1.19639224 -1.19668494 -0.89965964 -1.19425462 -0.1340528\n",
      "  -0.15840925 -0.32121047 -1.28454926  0.38855861 -0.72713313 -0.02894112]\n",
      " [-1.12021668 -1.24387247 -1.08812918 -1.32197778 -1.56838984 -1.84344819\n",
      "  -2.46683244 -1.23066911 -0.31212664  0.66093721  0.20757578  0.05890183\n",
      "  -0.76954518 -1.24787847 -1.2653933  -1.06058865 -1.27483962 -0.12644102\n",
      "  -0.20758273 -0.25653851 -1.37904917  0.37466526 -0.73167366 -0.02914659]\n",
      " [-1.14894864 -1.27578449 -1.09849772 -1.33402825 -1.57045347 -1.81226997\n",
      "  -2.46548721 -1.21721264 -0.31585132  0.66084483  0.20865993  0.08245571\n",
      "  -0.76084141 -1.25792456 -1.28536666 -1.07787855 -1.35590868 -0.08002705\n",
      "  -0.23446315 -0.23790762 -1.42256148  0.37911102 -0.73244548 -0.02989137]]\n"
     ]
    }
   ],
   "source": [
    "XTrain = XTrain.astype(np.float)\n",
    "print(XTrain.shape)\n",
    "print(XTrain[:4])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Multi Class SVM\n",
    "from sklearn import svm\n",
    "def classifyMultiClassSVMClassifier(XTrain, XTest, YTrain, YTest, params):\n",
    "    ker = params['kernel']\n",
    "    YPred = svm.SVC(kernel=ker).fit(XTrain, YTrain).predict(XTest)\n",
    "    diff = YPred - YTest\n",
    "    score = diff[diff == 0].size\n",
    "    return (100.0 * score)/(YPred.size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Random Forest Classifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "# def classifyRandomForestClassifier(XTrain, XTest, YTrain, YTest,trees=100,crit='gini'):\n",
    "def classifyRandomForestClassifier(XTrain, XTest, YTrain, YTest, params):\n",
    "    trees = params['trees']\n",
    "    crit = params['criterion']\n",
    "    clf = RandomForestClassifier(n_estimators=trees,criterion=crit)\n",
    "    clf.fit(XTrain, YTrain)\n",
    "    YPred = clf.predict(XTest)\n",
    "    diff = YPred - YTest\n",
    "    score = diff[diff == 0].size\n",
    "    return (100.0 * score)/(YPred.size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#K Nearest Neighbours Classifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "def classifyKNNClassifier(XTrain, XTest, YTrain, YTest, params):\n",
    "    neighbours = params['neighbours']\n",
    "    neigh = KNeighborsClassifier(n_neighbors=neighbours)\n",
    "    YPred = neigh.fit(XTrain, YTrain).predict(XTest)\n",
    "    diff = YPred - YTest\n",
    "    score = diff[diff == 0].size\n",
    "    return (100.0 * score)/(YPred.size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/nithishr/virtual_envs/activity_detection/lib/python3.5/site-packages/sklearn/cross_validation.py:44: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n",
      "  \"This module will be removed in 0.20.\", DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.cross_validation import StratifiedKFold\n",
    "def stratifiedKFoldVal(XTrain, YTrain, classify, params):\n",
    "    n_folds = 5\n",
    "    score = 0.0\n",
    "    skf = StratifiedKFold(YTrain, n_folds)\n",
    "    for train_index, test_index in skf:\n",
    "        X_train, X_test = XTrain[train_index], XTrain[test_index]\n",
    "        y_train, y_test = YTrain[train_index], YTrain[test_index]\n",
    "        score += classify(X_train, X_test,  y_train, y_test, params)\n",
    "    return score/n_folds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "could not convert string to float: 'elevator_down'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-13-3538586217b1>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0msklearn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpreprocessing\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mOneHotEncoder\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0menc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mOneHotEncoder\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0menc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mYTrain\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/virtual_envs/activity_detection/lib/python3.5/site-packages/sklearn/preprocessing/data.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y)\u001b[0m\n\u001b[1;32m   1842\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1843\u001b[0m         \"\"\"\n\u001b[0;32m-> 1844\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit_transform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1845\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1846\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/virtual_envs/activity_detection/lib/python3.5/site-packages/sklearn/preprocessing/data.py\u001b[0m in \u001b[0;36mfit_transform\u001b[0;34m(self, X, y)\u001b[0m\n\u001b[1;32m   1900\u001b[0m         \"\"\"\n\u001b[1;32m   1901\u001b[0m         return _transform_selected(X, self._fit_transform,\n\u001b[0;32m-> 1902\u001b[0;31m                                    self.categorical_features, copy=True)\n\u001b[0m\u001b[1;32m   1903\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1904\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_transform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/virtual_envs/activity_detection/lib/python3.5/site-packages/sklearn/preprocessing/data.py\u001b[0m in \u001b[0;36m_transform_selected\u001b[0;34m(X, transform, selected, copy)\u001b[0m\n\u001b[1;32m   1695\u001b[0m     \u001b[0mX\u001b[0m \u001b[0;34m:\u001b[0m \u001b[0marray\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0msparse\u001b[0m \u001b[0mmatrix\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mshape\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn_samples\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_features_new\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1696\u001b[0m     \"\"\"\n\u001b[0;32m-> 1697\u001b[0;31m     \u001b[0mX\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcheck_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maccept_sparse\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'csc'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mFLOAT_DTYPES\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1698\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1699\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mselected\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msix\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstring_types\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mselected\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"all\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/virtual_envs/activity_detection/lib/python3.5/site-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36mcheck_array\u001b[0;34m(array, accept_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, warn_on_dtype, estimator)\u001b[0m\n\u001b[1;32m    380\u001b[0m                                       force_all_finite)\n\u001b[1;32m    381\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 382\u001b[0;31m         \u001b[0marray\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morder\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0morder\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    383\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    384\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mensure_2d\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: could not convert string to float: 'elevator_down'"
     ]
    }
   ],
   "source": [
    "from sklearn.preprocessing import OneHotEncoder\n",
    "enc = OneHotEncoder()\n",
    "enc.fit(YTrain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Turning arround' 'Walking' 'elevator_down' 'elevator_up' 'sitting'\n",
      " 'standing' 'walking' 'walking_downstairs' 'walking_upstairs']\n"
     ]
    }
   ],
   "source": [
    "from sklearn import preprocessing\n",
    "lb = preprocessing.LabelBinarizer()\n",
    "lb.fit(YTrain)\n",
    "print(lb.classes_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "YTrainOHE = lb.transform(YTrain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1143, 9)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "YTrainOHE.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0 0 0 0 1 0 0 0 0]\n",
      " [0 0 0 0 1 0 0 0 0]\n",
      " [0 0 0 0 1 0 0 0 0]\n",
      " [0 0 0 0 1 0 0 0 0]\n",
      " [0 0 0 0 1 0 0 0 0]]\n"
     ]
    }
   ],
   "source": [
    "print(YTrainOHE[:5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "72.75828175005157\n"
     ]
    }
   ],
   "source": [
    "params = {'kernel':'linear'}\n",
    "score = stratifiedKFoldVal(XTrain, YTrain, classifyMultiClassSVMClassifier, params)\n",
    "print(score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "84.67034859354649\n"
     ]
    }
   ],
   "source": [
    "params = {'trees':200, 'criterion':'entropy'}\n",
    "score = stratifiedKFoldVal(XTrain, YTrain, classifyRandomForestClassifier, params)\n",
    "print(score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[67.40372437081258, 68.1684428586681, 67.74167995828665, 69.496612960855, 69.74564297250147]\n"
     ]
    }
   ],
   "source": [
    "selectNeighbourScores = []\n",
    "\n",
    "params = {'neighbours':2}\n",
    "score = stratifiedKFoldVal(XTrain, YTrain, classifyKNNClassifier, params)\n",
    "# print score\n",
    "selectNeighbourScores.append(score)\n",
    "\n",
    "params = {'neighbours':3}\n",
    "score = stratifiedKFoldVal(XTrain, YTrain, classifyKNNClassifier, params)\n",
    "# print score\n",
    "selectNeighbourScores.append(score)\n",
    "\n",
    "params = {'neighbours':4}\n",
    "score = stratifiedKFoldVal(XTrain, YTrain, classifyKNNClassifier, params)\n",
    "# print score\n",
    "selectNeighbourScores.append(score)\n",
    "\n",
    "params = {'neighbours':5}\n",
    "score = stratifiedKFoldVal(XTrain, YTrain, classifyKNNClassifier, params)\n",
    "# print score\n",
    "selectNeighbourScores.append(score)\n",
    "\n",
    "params = {'neighbours':10}\n",
    "score = stratifiedKFoldVal(XTrain, YTrain, classifyKNNClassifier, params)\n",
    "# print score\n",
    "selectNeighbourScores.append(score)\n",
    "\n",
    "print(selectNeighbourScores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(859, 24) (284, 24) (859, 9) (284, 9)\n"
     ]
    }
   ],
   "source": [
    "XTrain1 = XTrain[:859]\n",
    "XTest1 = XTrain[859:]\n",
    "YTrain1 = YTrainOHE[:859]\n",
    "YTest1 = YTrainOHE[859:]\n",
    "print(XTrain1.shape, XTest1.shape, YTrain1.shape, YTest1.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "bad input shape (859, 9)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-23-c04621bd557e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mYPred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msvm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mSVC\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkernel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'linear'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mXTrain1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mYTtrain1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mXTest1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/virtual_envs/activity_detection/lib/python3.5/site-packages/sklearn/svm/base.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y, sample_weight)\u001b[0m\n\u001b[1;32m    149\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_sparse\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msparse\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mcallable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkernel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    150\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 151\u001b[0;31m         \u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcheck_X_y\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfloat64\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morder\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'C'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maccept_sparse\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'csr'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    152\u001b[0m         \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_validate_targets\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    153\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/virtual_envs/activity_detection/lib/python3.5/site-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36mcheck_X_y\u001b[0;34m(X, y, accept_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, multi_output, ensure_min_samples, ensure_min_features, y_numeric, warn_on_dtype, estimator)\u001b[0m\n\u001b[1;32m    524\u001b[0m                         dtype=None)\n\u001b[1;32m    525\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 526\u001b[0;31m         \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcolumn_or_1d\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwarn\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    527\u001b[0m         \u001b[0m_assert_all_finite\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    528\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0my_numeric\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkind\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'O'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/virtual_envs/activity_detection/lib/python3.5/site-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36mcolumn_or_1d\u001b[0;34m(y, warn)\u001b[0m\n\u001b[1;32m    560\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mravel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    561\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 562\u001b[0;31m     \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"bad input shape {0}\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    563\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    564\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: bad input shape (859, 9)"
     ]
    }
   ],
   "source": [
    "YPred = svm.SVC(kernel='linear').fit(XTrain1, YTrain1).predict(XTest1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'numpy.ndarray'>\n",
      "<class 'numpy.ndarray'>\n"
     ]
    }
   ],
   "source": [
    "print(type(XTrain1))\n",
    "print(type(YTrain1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'numpy.str_' object has no attribute 'items'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-27-71638fa50d44>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0msklearn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfeature_extraction\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mDictVectorizer\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mv\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mDictVectorizer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msparse\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mY\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mv\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit_transform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mYTrain\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mY\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mY\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/virtual_envs/activity_detection/lib/python3.5/site-packages/sklearn/feature_extraction/dict_vectorizer.py\u001b[0m in \u001b[0;36mfit_transform\u001b[0;34m(self, X, y)\u001b[0m\n\u001b[1;32m    229\u001b[0m             \u001b[0mFeature\u001b[0m \u001b[0mvectors\u001b[0m\u001b[0;34m;\u001b[0m \u001b[0malways\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0md\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    230\u001b[0m         \"\"\"\n\u001b[0;32m--> 231\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_transform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfitting\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    232\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    233\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0minverse_transform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdict_type\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdict\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/virtual_envs/activity_detection/lib/python3.5/site-packages/sklearn/feature_extraction/dict_vectorizer.py\u001b[0m in \u001b[0;36m_transform\u001b[0;34m(self, X, fitting)\u001b[0m\n\u001b[1;32m    165\u001b[0m         \u001b[0;31m# same time\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    166\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 167\u001b[0;31m             \u001b[0;32mfor\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mv\u001b[0m \u001b[0;32min\u001b[0m \u001b[0msix\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miteritems\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    168\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mv\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msix\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstring_types\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    169\u001b[0m                     \u001b[0mf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"%s%s%s\"\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mseparator\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mv\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/virtual_envs/activity_detection/lib/python3.5/site-packages/sklearn/externals/six.py\u001b[0m in \u001b[0;36miteritems\u001b[0;34m(d, **kw)\u001b[0m\n\u001b[1;32m    437\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0miteritems\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0md\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkw\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    438\u001b[0m     \u001b[0;34m\"\"\"Return an iterator over the (key, value) pairs of a dictionary.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 439\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0miter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0md\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_iteritems\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mkw\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    440\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    441\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0miterlists\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0md\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkw\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mAttributeError\u001b[0m: 'numpy.str_' object has no attribute 'items'"
     ]
    }
   ],
   "source": [
    "from sklearn.feature_extraction import DictVectorizer\n",
    "v = DictVectorizer(sparse=False)\n",
    "YTrain_Test = {'Turning arround' 'Walking' 'elevator_down' 'elevator_up' 'sitting'\n",
    " 'standing' 'walking' 'walking_downstairs' 'walking_upstairs']\n",
    "Y = v.fit_transform(\n",
    "print(Y, Y.shape)"
   ]
  }
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