scene_recognition_local_global_approach.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Scene Recognition by Combining Local and Global Image Descriptors\n",
    "\n",
    "\n",
    "**Approach** : We make use of global feature descriptors as well as local feature descriptors simultaneously to represent each image, to improve recognition accuracy. Our basic steps are as follows\n",
    "\n",
    "**1. Feature Extraction** : From each image, we first extract DAISY features [1] corresponding to the keypoints detected from the image. We make use of the skimage library (scikit-image) [2] for feature extraction. Corresponding to each keypoint in the image, there would be a DAISY descriptor with dimensions controlled by the parameters step size (distance between descriptor sampling points) and radius (describing size of the scanning area). We also extract a standard HOG descriptor corresponding to the entire image at a different granularity (by making use of the parameters pixels_per_cell, cells_per_block and orientations), effectively allowing us to choose features at different scales.\n",
    "\n",
    "\n",
    "**2. Encoding** : We make use of the local DAISY features corresponding to each keypoints, to perform the encoding. We use the standard \"bag-of-visual-words\" concept here. Concretely, we apply K-means algorithm to quantize DAISY features into 'K' clusters to form \"visual words\". Thus, K becomes the size of our vocabulary. Corresponding to each image, we construct a histogram with 'K' as the dimensionality, based on this vocabulary.\n",
    "\n",
    "\n",
    "**3. Pooling** : We make use of a 2-level pooling scheme. From the DAISY features, we construct a histogram by representing frequency of each visual word in each image. We do this by taking each key point in the image and looking up the cluster_id corresponding to that DAISY descriptor and incrementing the count corresponding to that bin in our histogram. This procedure is effectively a \"sum pooling\". We do an L2 normalization of the resulting histogram to form a \"DAISY histogram feature\". For the 2nd level pooling, we take the HOG global descriptor corresponding to each image and do an L2 normalization followed by a concatenation with the corresponding DAISY histogram feature.\n",
    "\n",
    "\n",
    "**4. Classification** : We make use of the standard SVM classifier with various kernels. We utilize the sckit-learn (sklearn) library [3] for the SVM implementations. We do cross-validation by randomly splitting the dataset into a training and testing set. We construct the \"visual vocabulary\" as well as training feature vectors from the training split. We report the overall accuracy, confusion-matrix as well as standard information-retrieval statistics such as Precision, Recall and F-measure.\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "#### REFERENCES ########\n",
    "\n",
    "[1] Tola, Engin, Vincent Lepetit, and Pascal Fua. \"A fast local descriptor for dense matching.\" Computer Vision and Pattern Recognition, 2008. CVPR 2008. IEEE Conference on. IEEE, 2008.\n",
    "\n",
    "[2] Van der Walt, Stefan, et al. \"scikit-image: image processing in Python.\" PeerJ 2 (2014): e453.\n",
    "\n",
    "[3] Pedregosa, Fabian, et al. \"Scikit-learn: Machine learning in Python.\" Journal of Machine Learning Research 12.Oct (2011): 2825-2830.\n",
    "\n",
    "\n",
    "In case if you find our code to be helpful, please consider citing our paper http://arxiv.org/abs/1702.06850. Wilson, J., Arif, M. (2017). Scene Recognition by Combining Local and Global Image Descriptors. arXiv preprint arXiv:1702.06850."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "### import required libraries ###\n",
    "### file/folder manipulation ###\n",
    "from urllib import urlretrieve\n",
    "from os import listdir\n",
    "from os.path import isfile, join, exists\n",
    "from zipfile import ZipFile\n",
    "##for random split of dataset into a training set and a test set ###\n",
    "from sklearn.model_selection import train_test_split\n",
    "## image processing routines for feature extraction/transformation##\n",
    "from skimage.feature import daisy,hog\n",
    "from skimage import io\n",
    "from skimage.color import rgb2gray\n",
    "import skimage\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from tqdm import tqdm\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn.cluster import MiniBatchKMeans\n",
    "from sklearn import svm\n",
    "\n",
    "from sklearn import metrics\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import classification_report\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Download Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset already downloaded and extracted!\n"
     ]
    }
   ],
   "source": [
    "if not exists('scene_categories/'):\n",
    "    if not exists('scene_categories.zip'):\n",
    "        print 'Downloading scene_categories.zip'\n",
    "        urlretrieve ('http://www-cvr.ai.uiuc.edu/ponce_grp/data/scene_categories/scene_categories.zip', 'scene_categories.zip')\n",
    "        print 'Downloaded scene_categories.zip'\n",
    "    print 'Extracting scene_categories.zip'\n",
    "    zipfile = ZipFile('scene_categories.zip', 'r')\n",
    "    zipfile.extractall('./scene_categories')\n",
    "    zipfile.close()\n",
    "    print 'Extracted scene_categories.zip'\n",
    "else:\n",
    "    print 'Dataset already downloaded and extracted!'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Get all the filenames (including the full path) in a folder as a list."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def get_filenames(path):\n",
    "    onlyfiles = [path+f for f in listdir(path) if isfile(join(path, f))]\n",
    "    return onlyfiles"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Function to extract daisy features as well as hog features from an image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def extract_daisy_and_hog_features_from_image(file_path,daisy_step_size=32,daisy_radius=32,hog_pixels_per_cell=16,hog_cells_per_block=1):\n",
    "    img = io.imread(file_path)\n",
    "    img_gray = rgb2gray(img)\n",
    "    img=skimage.transform.resize(img_gray,(300,250)) ##resize to a suitable dimension, avg size of images in the dataset\n",
    "    #original, histograms=6\n",
    "    descs = daisy(img, step=daisy_step_size, radius=daisy_radius, rings=2, histograms=8,orientations=8, visualize=False)\n",
    "    #calculate daisy feature descriptors\n",
    "    descs_num = descs.shape[0] * descs.shape[1]\n",
    "    daisy_desriptors=descs.reshape(descs_num,descs.shape[2])\n",
    "    hog_desriptor=hog(img, orientations=8, pixels_per_cell=(hog_pixels_per_cell, hog_pixels_per_cell),cells_per_block=(hog_cells_per_block, hog_cells_per_block), visualise=False,feature_vector=True)\n",
    "    return daisy_desriptors,hog_desriptor\n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def plot_file(fname):\n",
    "    img_data=plt.imread(fname)\n",
    "    plt.imshow(rgb2gray(img_data),cmap='Greys_r')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DAISY descriptor size: (1080, 136)\n",
      "HOG descriptor size: (2160,)\n"
     ]
    },
    {
     "data": {
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Oldprnhw7l2lJdkICp+yTuZlMQCcgA/BpI8lkEgcHB3DOoVqt+nbTFaGpTZPJBO12G9vb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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff385f0d310>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "base_path=\"scene_categories/\" ##path where image data is kept\n",
    "## take a descriptor sample to show the shape of arrays ##\n",
    "fname=\"kitchen/image_0001.jpg\"\n",
    "plot_file(base_path+fname)\n",
    "#img_data=plt.imread(base_path+fname)\n",
    "#plt.imshow(rgb2gray(img_data),cmap='Greys_r')\n",
    "daisy_sample,hog_sample=extract_daisy_and_hog_features_from_image(base_path+fname,daisy_step_size=8,daisy_radius=8)\n",
    "print 'DAISY descriptor size:',daisy_sample.shape\n",
    "print 'HOG descriptor size:',hog_sample.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "HOG vector size= 2160\n"
     ]
    }
   ],
   "source": [
    "img_width=300\n",
    "img_height=250\n",
    "hog_pixels_per_cell=16\n",
    "orientations=8\n",
    "print 'HOG vector size=',(img_width/hog_pixels_per_cell)*(img_height/hog_pixels_per_cell)*orientations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Load file names corresponding to each scene category into lists"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MITinsidecity = 0\n",
      "bedroom = 1\n",
      "PARoffice = 2\n",
      "MITmountain = 3\n",
      "MITtallbuilding = 4\n",
      "MIThighway = 5\n",
      "MITcoast = 6\n",
      "livingroom = 7\n",
      "industrial = 8\n",
      "store = 9\n",
      "MITopencountry = 10\n",
      "MITstreet = 11\n",
      "MITforest = 12\n",
      "kitchen = 13\n",
      "CALsuburb = 14\n",
      "total categories: 15\n"
     ]
    }
   ],
   "source": [
    "category_names=listdir(base_path) ##\n",
    "for i in range(len(category_names)):\n",
    "    print category_names[i],'=',i\n",
    "print 'total categories:',len(category_names)\n",
    "dataset_filenames=[] ##list to keep path of all files in the database\n",
    "dataset_labels=[]\n",
    "##category_names.index('store')  list the numeric representation of the category\n",
    "##category_names[0] list the text representation of the category id\n",
    "for category in category_names:\n",
    "    category_filenames=get_filenames(base_path+category+\"/\")##get all the filenames in that category\n",
    "    category_labels=np.ones(len(category_filenames))*category_names.index(category) ##label the category with its index position\n",
    "    dataset_filenames=dataset_filenames+category_filenames\n",
    "    dataset_labels=dataset_labels+list(category_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total dataset size: 4485\n"
     ]
    }
   ],
   "source": [
    "print 'total dataset size:',len(dataset_filenames)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Split into training files and testing files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total files in training split: 1794\n",
      "total files in testing split: 2691\n"
     ]
    }
   ],
   "source": [
    "train_filenames,test_filenames,train_labels,test_labels=train_test_split(dataset_filenames,dataset_labels,test_size=0.6,random_state=42)\n",
    "print 'total files in training split:',len(train_filenames)\n",
    "print 'total files in testing split:',len(test_filenames)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Extract features from training datasplit for downstream processing, takes aprox 12 mins for a standard laptop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1794/1794 [07:00<00:00,  3.48it/s]\n"
     ]
    }
   ],
   "source": [
    "training_data_feature_map={} ##map to store daisy feature as well as hog feature for all training datapoints\n",
    "daisy_descriptor_list=[] ##list to store all daisy descriptors to form our visual vocabulary by clustering\n",
    "counter=0\n",
    "for fname in tqdm(train_filenames):\n",
    "    daisy_features,hog_feature=extract_daisy_and_hog_features_from_image(fname,daisy_step_size=8,daisy_radius=8)\n",
    "    ###extract DAISY features and HOG features from the image and save in a map###\n",
    "    training_data_feature_map[fname]=[daisy_features,hog_feature]\n",
    "    daisy_descriptor_list=daisy_descriptor_list+list(daisy_features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total daisy descriptors: 1937520\n"
     ]
    }
   ],
   "source": [
    "print 'Total daisy descriptors:',len(daisy_descriptor_list)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Now to form \"visual-words\", we cluster DAISY features to form a vocabulary, we form a histogram feature (daisy histogram) corresponding to each feature of 'number_of_clusters' dimensions \n",
    "input: list of DAISY features and number of clusters\n",
    "\n",
    "output: a trained cluster model which will allow to get the cluster id corresponding to any input daisy feature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def cluster_daisy_features(daisy_feature_list,number_of_clusters):\n",
    "    #km=KMeans(n_clusters=number_of_clusters)\n",
    "    km=MiniBatchKMeans(n_clusters=number_of_clusters,batch_size=number_of_clusters*10)\n",
    "    km.fit(daisy_feature_list)\n",
    "    return km"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "### hide warnings ##\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Number of clusters is set as 600#,takes aprox 10 mins to run on standard laptop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "600"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "daisy_cluster_model=cluster_daisy_features(daisy_descriptor_list,600) \n",
    "daisy_cluster_model.n_clusters"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Function to extract hybrid feature from images by pooling daisy histogram and hog descriptor after individual "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def extract_daisy_hog_hybrid_feature_from_image(fname,daisy_cluster_model):\n",
    "    #incase if we have encountered the file during training, the daisy and hog features would already have been computed\n",
    "    if fname in training_data_feature_map:\n",
    "        daisy_features=training_data_feature_map[fname][0]\n",
    "        hog_feature=training_data_feature_map[fname][1]\n",
    "    else:\n",
    "        daisy_features,hog_feature=extract_daisy_and_hog_features_from_image(fname,daisy_step_size=8,daisy_radius=8)\n",
    "        \n",
    "    ##find to which clusters each daisy feature belongs\n",
    "    img_clusters=daisy_cluster_model.predict(daisy_features) \n",
    "    cluster_freq_counts=pd.DataFrame(img_clusters,columns=['cnt'])['cnt'].value_counts()\n",
    "    bovw_vector=np.zeros(daisy_cluster_model.n_clusters) ##feature vector of size as the total number of clusters\n",
    "    for key in cluster_freq_counts.keys():\n",
    "        bovw_vector[key]=cluster_freq_counts[key]\n",
    "\n",
    "    bovw_feature=bovw_vector/np.linalg.norm(bovw_vector)\n",
    "    hog_feature=hog_feature/np.linalg.norm(hog_feature)\n",
    "    return list(bovw_feature)+list(hog_feature)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Training data feature extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1794/1794 [01:02<00:00, 28.77it/s]\n"
     ]
    }
   ],
   "source": [
    "XTRAIN=[]\n",
    "YTRAIN=[]\n",
    "for i in tqdm(range(len(train_filenames))):\n",
    "    XTRAIN.append(extract_daisy_hog_hybrid_feature_from_image(train_filenames[i],daisy_cluster_model))\n",
    "    YTRAIN.append(train_labels[i])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Train an SVM classifier and get accuracy report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=47.7984773925, cache_size=200, class_weight=None, coef0=0.0,\n",
       "  decision_function_shape='ovo', degree=3, gamma=0.68691732428,\n",
       "  kernel='rbf', max_iter=-1, probability=False, random_state=None,\n",
       "  shrinking=True, tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hybrid_classifier=svm.SVC(C=10**1.6794140624999994, gamma=10**-0.1630955304365928, decision_function_shape='ovo') #cross-validated hyper-parameters\n",
    "hybrid_classifier.fit(XTRAIN,YTRAIN)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "true label: 4.0\n",
      "prediction: [ 4.]\n"
     ]
    },
    {
     "data": {
      "image/png": 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6+pBOp3Hx4kXo9XpUq1UMDAwwF2F4eJijCJPJxCVDIg253W5OCyh3F0URCwurfJjBwUEM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\nM4eRF+x51SYrXr6kXfcBjsE8BEJ/MA8iIyICPK9bRcAwuHwO9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RYL8VGhLQYxEtL29bqY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\nDAEBAQX4H1n6Sc6TwEKwAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff386aa4c10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### show a sample classification ###\n",
    "plot_file(test_filenames[3])\n",
    "print 'true label:',test_labels[3]\n",
    "hybrid_feature_vector=extract_daisy_hog_hybrid_feature_from_image(test_filenames[3],daisy_cluster_model)\n",
    "print 'prediction:',hybrid_classifier.predict([hybrid_feature_vector])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Prediction: [ 7.]\n"
     ]
    },
    {
     "data": {
      "image/png": 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LR9ZbAGShRZq3yWTi99DGF1WdDMcTNXBZkFJbpsO85fINBgMWLlyIhoYG3HLL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\nHD16VJJyORwOgSW52dAK4PV0SH5GRgbGx8cRCAREtpihGqvnN4/re2n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FUqusrERPTw98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\ngM1mw/j4OAKBAFpaWuByuZI8jnA4jOLiYoRCITgcDiwuLqK/vx8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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff3906ddb50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### show a sample classification on external file ###\n",
    "external_file_name=\"test/l1.jpg\"\n",
    "plot_file(external_file_name)\n",
    "hybrid_feature_vector=extract_daisy_hog_hybrid_feature_from_image(external_file_name,daisy_cluster_model)\n",
    "print 'Prediction:',hybrid_classifier.predict([hybrid_feature_vector])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 2691/2691 [11:51<00:00,  4.40it/s]\n"
     ]
    }
   ],
   "source": [
    "### testing data feature extraction ###, do only for 200 instances if a quick test is required\n",
    "XTEST=[]\n",
    "YTEST=[]\n",
    "for i in tqdm(range(len(test_filenames))):\n",
    "    XTEST.append(extract_daisy_hog_hybrid_feature_from_image(test_filenames[i],daisy_cluster_model))\n",
    "    YTEST.append(test_labels[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hybrid Classifier Mertics\n",
      "No. of test instances: 2691 2691\n",
      "Overall accuracy: 0.764399851356\n",
      "                 precision    recall  f1-score   support\n",
      "\n",
      "  MITinsidecity       0.74      0.80      0.77       184\n",
      "        bedroom       0.56      0.33      0.41       134\n",
      "      PARoffice       0.71      0.86      0.78       129\n",
      "    MITmountain       0.85      0.83      0.84       217\n",
      "MITtallbuilding       0.84      0.83      0.83       222\n",
      "     MIThighway       0.88      0.86      0.87       157\n",
      "       MITcoast       0.79      0.81      0.80       212\n",
      "     livingroom       0.62      0.55      0.59       190\n",
      "     industrial       0.76      0.56      0.65       201\n",
      "          store       0.59      0.80      0.68       174\n",
      " MITopencountry       0.69      0.80      0.74       225\n",
      "      MITstreet       0.93      0.81      0.87       183\n",
      "      MITforest       0.83      0.93      0.88       178\n",
      "        kitchen       0.61      0.62      0.61       125\n",
      "      CALsuburb       1.00      0.96      0.98       160\n",
      "\n",
      "    avg / total       0.77      0.76      0.76      2691\n",
      "\n",
      "Confusion matrix:\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MITinsidecity</th>\n",
       "      <th>bedroom</th>\n",
       "      <th>PARoffice</th>\n",
       "      <th>MITmountain</th>\n",
       "      <th>MITtallbuilding</th>\n",
       "      <th>MIThighway</th>\n",
       "      <th>MITcoast</th>\n",
       "      <th>livingroom</th>\n",
       "      <th>industrial</th>\n",
       "      <th>store</th>\n",
       "      <th>MITopencountry</th>\n",
       "      <th>MITstreet</th>\n",
       "      <th>MITforest</th>\n",
       "      <th>kitchen</th>\n",
       "      <th>CALsuburb</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>MITinsidecity</th>\n",
       "      <td>148</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bedroom</th>\n",
       "      <td>0</td>\n",
       "      <td>44</td>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>31</td>\n",
       "      <td>8</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PARoffice</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>111</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MITmountain</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>181</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MITtallbuilding</th>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>184</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MIThighway</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>135</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MITcoast</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>172</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>36</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>livingroom</th>\n",
       "      <td>2</td>\n",
       "      <td>18</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>105</td>\n",
       "      <td>5</td>\n",
       "      <td>30</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>industrial</th>\n",
       "      <td>19</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>14</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>113</td>\n",
       "      <td>16</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>store</th>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>140</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MITopencountry</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>179</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MITstreet</th>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>149</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MITforest</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>166</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>kitchen</th>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>77</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CALsuburb</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>153</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 MITinsidecity  bedroom  PARoffice  MITmountain  \\\n",
       "MITinsidecity              148        0          0            0   \n",
       "bedroom                      0       44         16            1   \n",
       "PARoffice                    0        2        111            0   \n",
       "MITmountain                  0        0          0          181   \n",
       "MITtallbuilding              9        0          1            2   \n",
       "MIThighway                   3        0          0            0   \n",
       "MITcoast                     0        0          0            0   \n",
       "livingroom                   2       18         11            1   \n",
       "industrial                  19        3          4            5   \n",
       "store                        7        2          2            1   \n",
       "MITopencountry               0        0          0           13   \n",
       "MITstreet                   10        0          0            3   \n",
       "MITforest                    0        0          0            5   \n",
       "kitchen                      3       10         12            0   \n",
       "CALsuburb                    0        0          0            0   \n",
       "\n",
       "                 MITtallbuilding  MIThighway  MITcoast  livingroom  \\\n",
       "MITinsidecity                  7           2         1           0   \n",
       "bedroom                        1           0         1          31   \n",
       "PARoffice                      0           0         0           6   \n",
       "MITmountain                    1           1         8           0   \n",
       "MITtallbuilding              184           0         1           3   \n",
       "MIThighway                     0         135         4           0   \n",
       "MITcoast                       0           2       172           0   \n",
       "livingroom                     1           0         1         105   \n",
       "industrial                    14           5         7           3   \n",
       "store                          0           0         3           7   \n",
       "MITopencountry                 1           2        19           0   \n",
       "MITstreet                      7           6         0           0   \n",
       "MITforest                      0           0         0           0   \n",
       "kitchen                        1           0         0          11   \n",
       "CALsuburb                      3           0         0           2   \n",
       "\n",
       "                 industrial  store  MITopencountry  MITstreet  MITforest  \\\n",
       "MITinsidecity             4     11               1          6          1   \n",
       "bedroom                   8     12               0          0          0   \n",
       "PARoffice                 0      3               0          0          0   \n",
       "MITmountain               0      1              17          0          8   \n",
       "MITtallbuilding           7      6               1          1          7   \n",
       "MIThighway                2      3               8          1          1   \n",
       "MITcoast                  0      0              36          0          2   \n",
       "livingroom                5     30               1          0          0   \n",
       "industrial              113     16               6          2          2   \n",
       "store                     3    140               2          1          3   \n",
       "MITopencountry            1      0             179          0         10   \n",
       "MITstreet                 3      2               3        149          0   \n",
       "MITforest                 1      1               5          0        166   \n",
       "kitchen                   1     10               0          0          0   \n",
       "CALsuburb                 0      2               0          0          0   \n",
       "\n",
       "                 kitchen  CALsuburb  \n",
       "MITinsidecity          3          0  \n",
       "bedroom               20          0  \n",
       "PARoffice              7          0  \n",
       "MITmountain            0          0  \n",
       "MITtallbuilding        0          0  \n",
       "MIThighway             0          0  \n",
       "MITcoast               0          0  \n",
       "livingroom            15          0  \n",
       "industrial             2          0  \n",
       "store                  3          0  \n",
       "MITopencountry         0          0  \n",
       "MITstreet              0          0  \n",
       "MITforest              0          0  \n",
       "kitchen               77          0  \n",
       "CALsuburb              0        153  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print 'Hybrid Classifier Mertics'\n",
    "print 'No. of test instances:',len(XTEST),len(YTEST)\n",
    "### Accuracy Report ###\n",
    "hybridpred=hybrid_classifier.predict(XTEST)\n",
    "print 'Overall accuracy:',accuracy_score(YTEST,hybridpred)\n",
    "print classification_report(YTEST, hybridpred, target_names=category_names)\n",
    "print 'Confusion matrix:\\n'\n",
    "pd.DataFrame(confusion_matrix(YTEST,hybridpred),\n",
    "             columns=['MITinsidecity', 'bedroom', 'PARoffice', 'MITmountain', 'MITtallbuilding', 'MIThighway', 'MITcoast', 'livingroom', 'industrial', 'store', 'MITopencountry', 'MITstreet', 'MITforest', 'kitchen', 'CALsuburb'],\n",
    "               index=['MITinsidecity', 'bedroom', 'PARoffice', 'MITmountain', 'MITtallbuilding', 'MIThighway', 'MITcoast', 'livingroom', 'industrial', 'store', 'MITopencountry', 'MITstreet', 'MITforest', 'kitchen', 'CALsuburb'])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Linear Classifier Tests:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearSVC(C=1.0, class_weight=None, dual=True, fit_intercept=True,\n",
       "     intercept_scaling=1, loss='squared_hinge', max_iter=1000,\n",
       "     multi_class='ovr', penalty='l2', random_state=None, tol=0.0001,\n",
       "     verbose=0)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#### TRAIN A linear SVM CLASSIFIER and GET ACCURACY REPORT ###\n",
    "hybrid_classifier=svm.LinearSVC()\n",
    "hybrid_classifier.fit(XTRAIN,YTRAIN)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hybrid Classifier Mertics - Linear SVM\n",
      "No. of test instances: 2691 2691\n",
      "Overall accuracy: 0.755109624675\n",
      "                 precision    recall  f1-score   support\n",
      "\n",
      "  MITinsidecity       0.71      0.78      0.74       184\n",
      "        bedroom       0.57      0.42      0.48       134\n",
      "      PARoffice       0.75      0.85      0.80       129\n",
      "    MITmountain       0.85      0.85      0.85       217\n",
      "MITtallbuilding       0.75      0.85      0.80       222\n",
      "     MIThighway       0.87      0.87      0.87       157\n",
      "       MITcoast       0.79      0.80      0.79       212\n",
      "     livingroom       0.63      0.48      0.54       190\n",
      "     industrial       0.82      0.53      0.64       201\n",
      "          store       0.61      0.77      0.68       174\n",
      " MITopencountry       0.69      0.75      0.71       225\n",
      "      MITstreet       0.84      0.83      0.83       183\n",
      "      MITforest       0.80      0.93      0.86       178\n",
      "        kitchen       0.62      0.58      0.60       125\n",
      "      CALsuburb       0.96      0.97      0.97       160\n",
      "\n",
      "    avg / total       0.75      0.76      0.75      2691\n",
      "\n",
      "Confusion matrix:\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MITinsidecity</th>\n",
       "      <th>bedroom</th>\n",
       "      <th>PARoffice</th>\n",
       "      <th>MITmountain</th>\n",
       "      <th>MITtallbuilding</th>\n",
       "      <th>MIThighway</th>\n",
       "      <th>MITcoast</th>\n",
       "      <th>livingroom</th>\n",
       "      <th>industrial</th>\n",
       "      <th>store</th>\n",
       "      <th>MITopencountry</th>\n",
       "      <th>MITstreet</th>\n",
       "      <th>MITforest</th>\n",
       "      <th>kitchen</th>\n",
       "      <th>CALsuburb</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>MITinsidecity</th>\n",
       "      <td>143</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bedroom</th>\n",
       "      <td>0</td>\n",
       "      <td>56</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>28</td>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PARoffice</th>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>110</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MITmountain</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>185</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MITtallbuilding</th>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>189</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MIThighway</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>136</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MITcoast</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>169</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>36</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>livingroom</th>\n",
       "      <td>2</td>\n",
       "      <td>25</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>91</td>\n",
       "      <td>3</td>\n",
       "      <td>30</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>17</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>industrial</th>\n",
       "      <td>18</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>22</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>107</td>\n",
       "      <td>10</td>\n",
       "      <td>9</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>store</th>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>134</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MITopencountry</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>168</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MITstreet</th>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>151</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MITforest</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>165</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>kitchen</th>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>73</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CALsuburb</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>155</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 MITinsidecity  bedroom  PARoffice  MITmountain  \\\n",
       "MITinsidecity              143        0          1            0   \n",
       "bedroom                      0       56         11            1   \n",
       "PARoffice                    0        4        110            0   \n",
       "MITmountain                  1        0          0          185   \n",
       "MITtallbuilding             11        0          1            1   \n",
       "MIThighway                   3        0          0            2   \n",
       "MITcoast                     0        0          0            0   \n",
       "livingroom                   2       25          9            1   \n",
       "industrial                  18        2          4            3   \n",
       "store                        5        1          2            6   \n",
       "MITopencountry               0        1          0           14   \n",
       "MITstreet                   12        0          0            2   \n",
       "MITforest                    0        0          0            3   \n",
       "kitchen                      5        9          8            0   \n",
       "CALsuburb                    0        0          0            0   \n",
       "\n",
       "                 MITtallbuilding  MIThighway  MITcoast  livingroom  \\\n",
       "MITinsidecity                 12           0         1           0   \n",
       "bedroom                        2           0         1          28   \n",
       "PARoffice                      1           0         1           5   \n",
       "MITmountain                    0           1         7           0   \n",
       "MITtallbuilding              189           0         1           1   \n",
       "MIThighway                     0         136         3           0   \n",
       "MITcoast                       0           5       169           0   \n",
       "livingroom                     5           0         0          91   \n",
       "industrial                    22           4         6           1   \n",
       "store                          3           0         3           5   \n",
       "MITopencountry                 0           5        23           0   \n",
       "MITstreet                      8           5         0           1   \n",
       "MITforest                      1           0         0           0   \n",
       "kitchen                        7           0         0          12   \n",
       "CALsuburb                      3           0         0           1   \n",
       "\n",
       "                 industrial  store  MITopencountry  MITstreet  MITforest  \\\n",
       "MITinsidecity             5     12               3          5          1   \n",
       "bedroom                   4      9               0          4          1   \n",
       "PARoffice                 0      3               0          0          0   \n",
       "MITmountain               1      2              10          1          9   \n",
       "MITtallbuilding           2      6               1          2          7   \n",
       "MIThighway                1      0               9          2          1   \n",
       "MITcoast                  0      0              36          0          2   \n",
       "livingroom                3     30               1          3          0   \n",
       "industrial              107     10               9          8          4   \n",
       "store                     4    134               2          1          3   \n",
       "MITopencountry            1      0             168          1         12   \n",
       "MITstreet                 2      1               0        151          1   \n",
       "MITforest                 0      2               6          1        165   \n",
       "kitchen                   1     10               0          0          0   \n",
       "CALsuburb                 0      1               0          0          0   \n",
       "\n",
       "                 kitchen  CALsuburb  \n",
       "MITinsidecity          1          0  \n",
       "bedroom               16          1  \n",
       "PARoffice              5          0  \n",
       "MITmountain            0          0  \n",
       "MITtallbuilding        0          0  \n",
       "MIThighway             0          0  \n",
       "MITcoast               0          0  \n",
       "livingroom            17          3  \n",
       "industrial             2          1  \n",
       "store                  4          1  \n",
       "MITopencountry         0          0  \n",
       "MITstreet              0          0  \n",
       "MITforest              0          0  \n",
       "kitchen               73          0  \n",
       "CALsuburb              0        155  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print 'Hybrid Classifier Mertics - Linear SVM'\n",
    "print 'No. of test instances:',len(XTEST),len(YTEST)\n",
    "### Accuracy Report ###\n",
    "hybridpred=hybrid_classifier.predict(XTEST)\n",
    "print 'Overall accuracy:',accuracy_score(YTEST,hybridpred)\n",
    "print classification_report(YTEST, hybridpred, target_names=category_names)\n",
    "print 'Confusion matrix:\\n'\n",
    "pd.DataFrame(confusion_matrix(YTEST,hybridpred),\n",
    "             columns=['MITinsidecity', 'bedroom', 'PARoffice', 'MITmountain', 'MITtallbuilding', 'MIThighway', 'MITcoast', 'livingroom', 'industrial', 'store', 'MITopencountry', 'MITstreet', 'MITforest', 'kitchen', 'CALsuburb'],\n",
    "               index=['MITinsidecity', 'bedroom', 'PARoffice', 'MITmountain', 'MITtallbuilding', 'MIThighway', 'MITcoast', 'livingroom', 'industrial', 'store', 'MITopencountry', 'MITstreet', 'MITforest', 'kitchen', 'CALsuburb'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### HOG only Tests:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "XTRAIN_HOG=[]\n",
    "for X in XTRAIN:\n",
    "    XTRAIN_HOG.append(X[daisy_cluster_model.n_clusters:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "XTEST_HOG=[]\n",
    "for X in XTEST:\n",
    "    XTEST_HOG.append(X[daisy_cluster_model.n_clusters:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearSVC(C=1.0, class_weight=None, dual=True, fit_intercept=True,\n",
       "     intercept_scaling=1, loss='squared_hinge', max_iter=1000,\n",
       "     multi_class='ovr', penalty='l2', random_state=None, tol=0.0001,\n",
       "     verbose=0)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#### TRAIN HOG only CLASSIFIER and GET ACCURACY REPORT ###\n",
    "hogclassifier=svm.LinearSVC()\n",
    "hogclassifier.fit(XTRAIN_HOG,YTRAIN)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "HOG Only Classifier Mertics\n",
      "No. of test instances: 2691 2691\n",
      "Overall accuracy: 0.586027499071\n",
      "                 precision    recall  f1-score   support\n",
      "\n",
      "  MITinsidecity       0.51      0.62      0.56       184\n",
      "        bedroom       0.39      0.32      0.35       134\n",
      "      PARoffice       0.59      0.49      0.53       129\n",
      "    MITmountain       0.68      0.63      0.65       217\n",
      "MITtallbuilding       0.49      0.62      0.55       222\n",
      "     MIThighway       0.84      0.59      0.69       157\n",
      "       MITcoast       0.65      0.78      0.71       212\n",
      "     livingroom       0.50      0.43      0.46       190\n",
      "     industrial       0.50      0.20      0.29       201\n",
      "          store       0.40      0.45      0.42       174\n",
      " MITopencountry       0.53      0.65      0.58       225\n",
      "      MITstreet       0.72      0.76      0.74       183\n",
      "      MITforest       0.65      0.88      0.75       178\n",
      "        kitchen       0.47      0.36      0.41       125\n",
      "      CALsuburb       0.87      0.84      0.86       160\n",
      "\n",
      "    avg / total       0.59      0.59      0.58      2691\n",
      "\n",
      "Confusion matrix:\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MITinsidecity</th>\n",
       "      <th>bedroom</th>\n",
       "      <th>PARoffice</th>\n",
       "      <th>MITmountain</th>\n",
       "      <th>MITtallbuilding</th>\n",
       "      <th>MIThighway</th>\n",
       "      <th>MITcoast</th>\n",
       "      <th>livingroom</th>\n",
       "      <th>industrial</th>\n",
       "      <th>store</th>\n",
       "      <th>MITopencountry</th>\n",
       "      <th>MITstreet</th>\n",
       "      <th>MITforest</th>\n",
       "      <th>kitchen</th>\n",
       "      <th>CALsuburb</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>MITinsidecity</th>\n",
       "      <td>114</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>35</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bedroom</th>\n",
       "      <td>2</td>\n",
       "      <td>43</td>\n",
       "      <td>12</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>32</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PARoffice</th>\n",
       "      <td>10</td>\n",
       "      <td>6</td>\n",
       "      <td>63</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MITmountain</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>137</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>36</td>\n",
       "      <td>3</td>\n",
       "      <td>24</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MITtallbuilding</th>\n",
       "      <td>32</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>138</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>13</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MIThighway</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>92</td>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>29</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MITcoast</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>166</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>39</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>livingroom</th>\n",
       "      <td>5</td>\n",
       "      <td>39</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>82</td>\n",
       "      <td>4</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>14</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>industrial</th>\n",
       "      <td>28</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>45</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>41</td>\n",
       "      <td>14</td>\n",
       "      <td>8</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>store</th>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>79</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MITopencountry</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>146</td>\n",
       "      <td>4</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MITstreet</th>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>139</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MITforest</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>157</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>kitchen</th>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "      <td>27</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>45</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CALsuburb</th>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>135</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 MITinsidecity  bedroom  PARoffice  MITmountain  \\\n",
       "MITinsidecity              114        1          0            0   \n",
       "bedroom                      2       43         12            4   \n",
       "PARoffice                   10        6         63            1   \n",
       "MITmountain                  1        1          0          137   \n",
       "MITtallbuilding             32        3          6            4   \n",
       "MIThighway                   1        1          0            0   \n",
       "MITcoast                     0        0          0            3   \n",
       "livingroom                   5       39          5            1   \n",
       "industrial                  28        4          5            7   \n",
       "store                       12        3          6           12   \n",
       "MITopencountry               0        1          0           19   \n",
       "MITstreet                    8        0          0            1   \n",
       "MITforest                    0        0          0           13   \n",
       "kitchen                      5        7         10            0   \n",
       "CALsuburb                    4        0          0            0   \n",
       "\n",
       "                 MITtallbuilding  MIThighway  MITcoast  livingroom  \\\n",
       "MITinsidecity                 35           1         4           0   \n",
       "bedroom                        6           0         1          32   \n",
       "PARoffice                     13           0         0           8   \n",
       "MITmountain                    1           2        10           0   \n",
       "MITtallbuilding              138           1         2           1   \n",
       "MIThighway                     1          92        27           0   \n",
       "MITcoast                       0           1       166           0   \n",
       "livingroom                     8           0         0          82   \n",
       "industrial                    45           0         7           8   \n",
       "store                         12           0         6           9   \n",
       "MITopencountry                 0           5        29           0   \n",
       "MITstreet                     12           4         2           3   \n",
       "MITforest                      0           1         0           0   \n",
       "kitchen                        7           1         1          18   \n",
       "CALsuburb                      4           1         0           3   \n",
       "\n",
       "                 industrial  store  MITopencountry  MITstreet  MITforest  \\\n",
       "MITinsidecity             7      7               2          6          1   \n",
       "bedroom                   4     11               1          3          2   \n",
       "PARoffice                 3     11               0          4          0   \n",
       "MITmountain               0      1              36          3         24   \n",
       "MITtallbuilding           8     13               2          4          7   \n",
       "MIThighway                1      1              29          3          1   \n",
       "MITcoast                  0      0              39          0          3   \n",
       "livingroom                4     29               0          0          1   \n",
       "industrial               41     14               8         11         11   \n",
       "store                     9     79               2          4          9   \n",
       "MITopencountry            1      0             146          4         20   \n",
       "MITstreet                 2      4               6        139          2   \n",
       "MITforest                 1      0               5          1        157   \n",
       "kitchen                   1     27               1          1          0   \n",
       "CALsuburb                 0      1               0          9          3   \n",
       "\n",
       "                 kitchen  CALsuburb  \n",
       "MITinsidecity          1          5  \n",
       "bedroom               12          1  \n",
       "PARoffice              9          1  \n",
       "MITmountain            0          1  \n",
       "MITtallbuilding        0          1  \n",
       "MIThighway             0          0  \n",
       "MITcoast               0          0  \n",
       "livingroom            14          2  \n",
       "industrial             5          7  \n",
       "store                 10          1  \n",
       "MITopencountry         0          0  \n",
       "MITstreet              0          0  \n",
       "MITforest              0          0  \n",
       "kitchen               45          1  \n",
       "CALsuburb              0        135  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print 'HOG Only Classifier Mertics'\n",
    "print 'No. of test instances:',len(XTEST_HOG),len(YTEST)\n",
    "### Accuracy Report ###\n",
    "pred=hogclassifier.predict(XTEST_HOG)\n",
    "print 'Overall accuracy:',accuracy_score(YTEST,pred)\n",
    "print classification_report(YTEST, pred, target_names=category_names)\n",
    "print 'Confusion matrix:\\n'\n",
    "pd.DataFrame(confusion_matrix(YTEST,pred),\n",
    "             columns=['MITinsidecity', 'bedroom', 'PARoffice', 'MITmountain', 'MITtallbuilding', 'MIThighway', 'MITcoast', 'livingroom', 'industrial', 'store', 'MITopencountry', 'MITstreet', 'MITforest', 'kitchen', 'CALsuburb'],\n",
    "               index=['MITinsidecity', 'bedroom', 'PARoffice', 'MITmountain', 'MITtallbuilding', 'MIThighway', 'MITcoast', 'livingroom', 'industrial', 'store', 'MITopencountry', 'MITstreet', 'MITforest', 'kitchen', 'CALsuburb'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### DAISY only Tests:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "XTRAIN_DAISY=[]\n",
    "for X in XTRAIN:\n",
    "    XTRAIN_DAISY.append(X[:daisy_cluster_model.n_clusters])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "XTEST_DAISY=[]\n",
    "for X in XTEST:\n",
    "    XTEST_DAISY.append(X[:daisy_cluster_model.n_clusters])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearSVC(C=1.0, class_weight=None, dual=True, fit_intercept=True,\n",
       "     intercept_scaling=1, loss='squared_hinge', max_iter=1000,\n",
       "     multi_class='ovr', penalty='l2', random_state=None, tol=0.0001,\n",
       "     verbose=0)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#### TRAIN DAISY only CLASSIFIER and GET ACCURACY REPORT ###\n",
    "daisyclassifier=svm.LinearSVC()\n",
    "daisyclassifier.fit(XTRAIN_DAISY,YTRAIN)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "HOG Only Classifier Mertics\n",
      "No. of test instances: 2691 2691\n",
      "Overall accuracy: 0.711259754738\n",
      "                 precision    recall  f1-score   support\n",
      "\n",
      "  MITinsidecity       0.64      0.71      0.67       184\n",
      "        bedroom       0.49      0.37      0.42       134\n",
      "      PARoffice       0.72      0.78      0.75       129\n",
      "    MITmountain       0.84      0.82      0.83       217\n",
      "MITtallbuilding       0.75      0.81      0.78       222\n",
      "     MIThighway       0.80      0.80      0.80       157\n",
      "       MITcoast       0.76      0.79      0.77       212\n",
      "     livingroom       0.55      0.39      0.46       190\n",
      "     industrial       0.77      0.44      0.56       201\n",
      "          store       0.55      0.73      0.63       174\n",
      " MITopencountry       0.69      0.74      0.71       225\n",
      "      MITstreet       0.80      0.83      0.81       183\n",
      "      MITforest       0.80      0.92      0.86       178\n",
      "        kitchen       0.47      0.49      0.48       125\n",
      "      CALsuburb       0.89      0.94      0.91       160\n",
      "\n",
      "    avg / total       0.71      0.71      0.70      2691\n",
      "\n",
      "Confusion matrix:\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MITinsidecity</th>\n",
       "      <th>bedroom</th>\n",
       "      <th>PARoffice</th>\n",
       "      <th>MITmountain</th>\n",
       "      <th>MITtallbuilding</th>\n",
       "      <th>MIThighway</th>\n",
       "      <th>MITcoast</th>\n",
       "      <th>livingroom</th>\n",
       "      <th>industrial</th>\n",
       "      <th>store</th>\n",
       "      <th>MITopencountry</th>\n",
       "      <th>MITstreet</th>\n",
       "      <th>MITforest</th>\n",
       "      <th>kitchen</th>\n",
       "      <th>CALsuburb</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>MITinsidecity</th>\n",
       "      <td>130</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>15</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bedroom</th>\n",
       "      <td>1</td>\n",
       "      <td>49</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>25</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>22</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PARoffice</th>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>101</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MITmountain</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>178</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>14</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MITtallbuilding</th>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>180</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MIThighway</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>126</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MITcoast</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>168</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>livingroom</th>\n",
       "      <td>5</td>\n",
       "      <td>19</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>75</td>\n",
       "      <td>4</td>\n",
       "      <td>26</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>26</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>industrial</th>\n",
       "      <td>15</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>17</td>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>88</td>\n",
       "      <td>11</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>store</th>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>127</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MITopencountry</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>166</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MITstreet</th>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>151</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MITforest</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>164</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>kitchen</th>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>61</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CALsuburb</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>150</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 MITinsidecity  bedroom  PARoffice  MITmountain  \\\n",
       "MITinsidecity              130        1          1            1   \n",
       "bedroom                      1       49         12            1   \n",
       "PARoffice                    0        6        101            0   \n",
       "MITmountain                  1        0          0          178   \n",
       "MITtallbuilding             10        0          1            0   \n",
       "MIThighway                   2        0          0            3   \n",
       "MITcoast                     0        0          0            2   \n",
       "livingroom                   5       19         12            1   \n",
       "industrial                  15        9          1            4   \n",
       "store                        9        1          3            4   \n",
       "MITopencountry               1        1          0           13   \n",
       "MITstreet                   14        0          0            2   \n",
       "MITforest                    0        0          0            3   \n",
       "kitchen                     14       14          9            0   \n",
       "CALsuburb                    0        0          0            0   \n",
       "\n",
       "                 MITtallbuilding  MIThighway  MITcoast  livingroom  \\\n",
       "MITinsidecity                 11           2         3           0   \n",
       "bedroom                        5           0         1          25   \n",
       "PARoffice                      2           0         1           9   \n",
       "MITmountain                    1           0         7           0   \n",
       "MITtallbuilding              180           0         1           5   \n",
       "MIThighway                     3         126         6           0   \n",
       "MITcoast                       0           8       168           0   \n",
       "livingroom                    12           0         2          75   \n",
       "industrial                    17           9         7           8   \n",
       "store                          2           0         3           4   \n",
       "MITopencountry                 0           7        23           0   \n",
       "MITstreet                      4           5         0           1   \n",
       "MITforest                      0           0         0           0   \n",
       "kitchen                        2           0         0          10   \n",
       "CALsuburb                      2           0         0           0   \n",
       "\n",
       "                 industrial  store  MITopencountry  MITstreet  MITforest  \\\n",
       "MITinsidecity             3     15               6          8          1   \n",
       "bedroom                   4     10               0          2          1   \n",
       "PARoffice                 2      3               0          0          0   \n",
       "MITmountain               1      3              14          2          9   \n",
       "MITtallbuilding           4      7               1          2          9   \n",
       "MIThighway                0      0              11          4          1   \n",
       "MITcoast                  1      0              31          0          2   \n",
       "livingroom                4     26               1          1          0   \n",
       "industrial               88     11               4         10          3   \n",
       "store                     6    127               1          5          4   \n",
       "MITopencountry            1      0             166          1         11   \n",
       "MITstreet                 0      4               1        151          0   \n",
       "MITforest                 0      4               6          1        164   \n",
       "kitchen                   0     14               0          1          0   \n",
       "CALsuburb                 1      6               0          1          0   \n",
       "\n",
       "                 kitchen  CALsuburb  \n",
       "MITinsidecity          2          0  \n",
       "bedroom               22          1  \n",
       "PARoffice              5          0  \n",
       "MITmountain            0          1  \n",
       "MITtallbuilding        0          2  \n",
       "MIThighway             1          0  \n",
       "MITcoast               0          0  \n",
       "livingroom            26          6  \n",
       "industrial             9          6  \n",
       "store                  3          2  \n",
       "MITopencountry         0          1  \n",
       "MITstreet              1          0  \n",
       "MITforest              0          0  \n",
       "kitchen               61          0  \n",
       "CALsuburb              0        150  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print 'HOG Only Classifier Mertics'\n",
    "print 'No. of test instances:',len(XTEST_DAISY),len(YTEST)\n",
    "### Accuracy Report ###\n",
    "pred = daisyclassifier.predict(XTEST_DAISY)\n",
    "print 'Overall accuracy:',accuracy_score(YTEST,pred)\n",
    "print classification_report(YTEST, pred, target_names=category_names)\n",
    "print 'Confusion matrix:\\n'\n",
    "pd.DataFrame(confusion_matrix(YTEST,pred),\n",
    "             columns=['MITinsidecity', 'bedroom', 'PARoffice', 'MITmountain', 'MITtallbuilding', 'MIThighway', 'MITcoast', 'livingroom', 'industrial', 'store', 'MITopencountry', 'MITstreet', 'MITforest', 'kitchen', 'CALsuburb'],\n",
    "               index=['MITinsidecity', 'bedroom', 'PARoffice', 'MITmountain', 'MITtallbuilding', 'MIThighway', 'MITcoast', 'livingroom', 'industrial', 'store', 'MITopencountry', 'MITstreet', 'MITforest', 'kitchen', 'CALsuburb'])"
   ]
  }
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