SML_Project.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "# Import"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import cv2\n",
    "import copy\n",
    "import csv\n",
    "import random\n",
    "import pickle\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import itertools\n",
    "from scipy.stats import randint\n",
    "from itertools import cycle\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.pyplot import figure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "hidden": true,
    "id": "n-ExnNAGwCyE"
   },
   "outputs": [],
   "source": [
    "from sklearn import preprocessing\n",
    "from sklearn.manifold import TSNE\n",
    "from sklearn.decomposition import TruncatedSVD\n",
    "from sklearn import metrics\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn import preprocessing\n",
    "from scipy.sparse import csr_matrix\n",
    "from scipy import stats\n",
    "from my_ml_lib import DataManipulationTools, MetricTools, PlotTools\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.model_selection import RandomizedSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "# Import different classifiers\n",
    "from sklearn import svm\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "from xgboost import XGBClassifier\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.ensemble import BaggingClassifier, RandomForestClassifier, AdaBoostClassifier\n",
    "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis as LDA\n",
    "from sklearn.decomposition import PCA\n",
    "from skimage.feature import hog, local_binary_pattern"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "# Functions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "## Write CSV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "def write_csv(file, a1, a2, a3, a4, a5, a6, a7, name):\n",
    "    with open(file, mode='w') as csv_file:\n",
    "        csv_writer = csv.writer(csv_file, delimiter=',', quotechar='\"', quoting=csv.QUOTE_MINIMAL)\n",
    "        csv_writer.writerow(name)\n",
    "        for i in range(20):\n",
    "            if a2[i] == None:\n",
    "                a2[i] = 'None'\n",
    "            if a4[i] == None:\n",
    "                a4[i] = 'None'\n",
    "            csv_writer.writerow([a1[i], a2[i], a3[i], a4[i], a5[i], a6[i], a7[i]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "## Read dataset:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "def read_images(path):\n",
    "    images = []\n",
    "    labels = []\n",
    "    num1 = 32\n",
    "    num2 = 32\n",
    "    for file_name in os.listdir(path):\n",
    "        file_path = path + '/' + file_name\n",
    "        for img_name in os.listdir(file_path):\n",
    "            if not img_name.startswith('.'):\n",
    "                if img_name.endswith('.png'):\n",
    "                    img = cv2.imread(file_path + '/' + img_name)\n",
    "                    new_img = cv2.resize(img, (num2, num1))\n",
    "                    images.append(new_img)\n",
    "                    if file_name == 'Parasitized':\n",
    "                        label = 0\n",
    "                    else:\n",
    "                        label = 1\n",
    "                    labels.append(label)\n",
    "    \n",
    "    return np.array(images), np.array(labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "## Save / Load Extracted Features:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "def save_feature(feature, name):\n",
    "    # saving all our feature vectors in pickled file\n",
    "    with open('cache/' + name + '.pkl', 'wb') as fp:\n",
    "        pickle.dump(csr_matrix(feature), fp)\n",
    "    \n",
    "    print(f'Feature saved with name cache/{name}.pkl')\n",
    "\n",
    "def load_feature(feature_name):\n",
    "    return pickle.load(open(feature_name, 'rb')).A"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "## Save / Load Trained Model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "def save_model(model):\n",
    "    filename = input('Enter model file name:')\n",
    "    pickle.dump(model, open('models/'+filename + '.pkl', 'wb'))\n",
    "    print(f'Successfully saved model in models/{filename}.pkl')\n",
    "\n",
    "def load_model(model_name):\n",
    "    return pickle.load(open(model_name, 'rb'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "## Features:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "### Flattened Image:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "def get_flattened(images, color=cv2.COLOR_RGB2GRAY, name='flattened', save=False):\n",
    "    \"\"\"\n",
    "    color: default RGB2GRAY, if None is passed then color is used as it is.\n",
    "    \"\"\"\n",
    "    color_images = []\n",
    "    if color is not None:\n",
    "        for img in images:\n",
    "            color_images.append(cv2.cvtColor(img, color))\n",
    "    else:\n",
    "        color_images = images\n",
    "    \n",
    "    count = len(color_images)\n",
    "    \n",
    "    result = np.array(color_images).reshape(count, -1)\n",
    "    \n",
    "    if save:\n",
    "        save_feature(result, name)\n",
    "        \n",
    "    return result"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "### Color Histograms:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "def get_color_hist(images, name='color_hist', save=False):\n",
    "    histograms = []\n",
    "    for img in images:\n",
    "        histograms.append(cv2.calcHist([img], [0, 1, 2],None, [8, 8, 8], [0, 256, 0, 256, 0, 256]).flatten())\n",
    "    \n",
    "    result = np.array(histograms)\n",
    "    \n",
    "    if save:\n",
    "        save_feature(result, name)\n",
    "        \n",
    "    return result"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "### HOG based Features:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 71
    },
    "colab_type": "code",
    "hidden": true,
    "id": "9kmT_2iDfO9e",
    "outputId": "2280667a-c1a0-4261-a85e-665bcf18b77c"
   },
   "outputs": [],
   "source": [
    "def get_hog(images, name='hog', save=False):\n",
    "    result = np.array([hog(img, block_norm='L2') for img in images])\n",
    "    \n",
    "    if save:\n",
    "        save_feature(result, name)\n",
    "        \n",
    "    return result"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "### KAZE based features:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "def get_kaze(images, name='kaze', save=False):\n",
    "    # KAZE descriptor for 1 image\n",
    "    def get_image_kaze(image, vector_size=32):\n",
    "        alg = cv2.KAZE_create()\n",
    "        kps = alg.detect(image)\n",
    "        kps = sorted(kps, key=lambda x: -x.response)[:vector_size]\n",
    "        \n",
    "        # Making descriptor of same size\n",
    "        # Descriptor vector size is 64\n",
    "        needed_size = (vector_size * 64)\n",
    "        if len(kps) == 0:\n",
    "            return np.zeros(needed_size)\n",
    "        \n",
    "        kps, dsc = alg.compute(image, kps)\n",
    "        dsc = dsc.flatten()\n",
    "        \n",
    "        if dsc.size < needed_size:\n",
    "            # if we have less than 32 descriptors then just adding zeros at the\n",
    "            # end of our feature vector\n",
    "            dsc = np.concatenate([dsc, np.zeros(needed_size - dsc.size)])\n",
    "        return dsc\n",
    "    \n",
    "    # KAZE descriptor for all images\n",
    "    features = []\n",
    "    for i, img in enumerate(images):\n",
    "        dsc = get_image_kaze(img)\n",
    "        features.append(dsc)\n",
    "    \n",
    "    result = np.array(features)\n",
    "    \n",
    "    if save:\n",
    "        save_feature(result, name)\n",
    "        \n",
    "    return result"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "### SIFT based Features:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "def get_sift(images, name='sift', save=False):\n",
    "    # SIFT descriptor for 1 image\n",
    "    def get_image_sift(image, vector_size=15):\n",
    "        alg = cv2.xfeatures2d.SIFT_create()\n",
    "        kps = alg.detect(image, None)\n",
    "        kps = sorted(kps, key=lambda x: -x.response)[:vector_size]\n",
    "        \n",
    "        # Making descriptor of same size\n",
    "        # Descriptor vector size is 128\n",
    "        needed_size = (vector_size * 128)\n",
    "        if len(kps) == 0:\n",
    "            return np.zeros(needed_size)\n",
    "        \n",
    "        kps, dsc = alg.compute(image, kps)\n",
    "        dsc = dsc.flatten()\n",
    "        if dsc.size < needed_size:\n",
    "            # if we have less than 32 descriptors then just adding zeros at the\n",
    "            # end of our feature vector\n",
    "            dsc = np.concatenate([dsc, np.zeros(needed_size - dsc.size)])\n",
    "            \n",
    "        return dsc\n",
    "    \n",
    "    # SIFT descriptor for all images\n",
    "    features = []\n",
    "    for i, img in enumerate(images):\n",
    "        dsc = get_image_sift(img)\n",
    "        features.append(dsc)\n",
    "\n",
    "    result = np.array(features)\n",
    "    \n",
    "    if save:\n",
    "        save_feature(result, name)\n",
    "        \n",
    "    return result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "def return_sift_kp(image):\n",
    "    alg = cv2.xfeatures2d.SIFT_create()\n",
    "    kps = alg.detect(image, None)\n",
    "    kps = sorted(kps, key=lambda x: -x.response)[:15]\n",
    "\n",
    "    # Making descriptor of same size\n",
    "    # Descriptor vector size is 128\n",
    "    needed_size = (15 * 128)\n",
    "    if len(kps) == 0:\n",
    "        dsc = np.zeros(needed_size)\n",
    "    else:\n",
    "        kps, dsc = alg.compute(image, kps)\n",
    "        dsc = dsc.flatten()\n",
    "        if dsc.size < needed_size:\n",
    "            # if we have less than 32 descriptors then just adding zeros at the\n",
    "            # end of our feature vector\n",
    "            dsc = np.concatenate([dsc, np.zeros(needed_size - dsc.size)])\n",
    "    return kps"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "### SURF based Features:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "def get_surf(images, name='surf', save=False):\n",
    "    # SURF descriptor for 1 image\n",
    "    def get_image_surf(image, vector_size=4):\n",
    "        alg = cv2.xfeatures2d.SURF_create()\n",
    "        kps = alg.detect(image, None)\n",
    "        kps = sorted(kps, key=lambda x: -x.response)[:vector_size]\n",
    "        \n",
    "        # Making descriptor of same size\n",
    "        # Descriptor vector size is 64\n",
    "        needed_size = (vector_size * 64)\n",
    "        if len(kps) == 0:\n",
    "            return np.zeros(needed_size)\n",
    "        \n",
    "        kps, dsc = alg.compute(image, kps)\n",
    "        dsc = dsc.flatten()\n",
    "        if dsc.size < needed_size:\n",
    "            # if we have less than 32 descriptors then just adding zeros at the\n",
    "            # end of our feature vector\n",
    "            dsc = np.concatenate([dsc, np.zeros(needed_size - dsc.size)])\n",
    "            \n",
    "        return dsc\n",
    "    \n",
    "    # SURF descriptor for all images\n",
    "    features = []\n",
    "    for i, img in enumerate(images):\n",
    "        dsc = get_image_surf(img)\n",
    "        features.append(dsc)\n",
    "    \n",
    "    result = np.array(features)\n",
    "    \n",
    "    if save:\n",
    "        save_feature(result, name)\n",
    "        \n",
    "    return result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "def return_surf_kp(image):\n",
    "    alg = cv2.xfeatures2d.SURF_create()\n",
    "    kps = alg.detect(image, None)\n",
    "    kps = sorted(kps, key=lambda x: -x.response)[:4]\n",
    "\n",
    "    # Making descriptor of same size\n",
    "    # Descriptor vector size is 64\n",
    "    needed_size = (15 * 64)\n",
    "    if len(kps) == 0:\n",
    "        dsc = np.zeros(needed_size)\n",
    "    else:\n",
    "        kps, dsc = alg.compute(image, kps)\n",
    "        dsc = dsc.flatten()\n",
    "        if dsc.size < needed_size:\n",
    "            # if we have less than 32 descriptors then just adding zeros at the\n",
    "            # end of our feature vector\n",
    "            dsc = np.concatenate([dsc, np.zeros(needed_size - dsc.size)])\n",
    "    return kps"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "### LBP Feature:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "def get_lbp(images, name='lbp', save=False):\n",
    "    result = np.array([local_binary_pattern(cv2.cvtColor(img, cv2.COLOR_RGB2GRAY), 10, 3).flatten() for img in images])\n",
    "    \n",
    "    if save:\n",
    "        save_feature(result, name)\n",
    "        \n",
    "    return result"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "### Combine Features:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "def combine_features(features, horizontal=True):\n",
    "    \"\"\"\n",
    "    Array of features [f1, f2, f3] where each fi is a feature set \n",
    "    eg. f1=rgb_flat, f2=SIFT, etc.\n",
    "    \"\"\"\n",
    "    if horizontal:\n",
    "        return np.hstack(features)\n",
    "    else:\n",
    "        return np.vstack(features)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "### Normalize features:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "def norm_features_minmax(train, test):\n",
    "    min_max_scaler = preprocessing.MinMaxScaler()\n",
    "    norm_train = min_max_scaler.fit_transform(train)\n",
    "    norm_test = min_max_scaler.transform(test)\n",
    "    \n",
    "    return norm_train, norm_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "def norm_features_zscore(train, test):\n",
    "    min_max_scaler = preprocessing.StandardScaler()\n",
    "    norm_train = min_max_scaler.fit_transform(train)\n",
    "    norm_test = min_max_scaler.transform(test)\n",
    "    \n",
    "    return norm_train, norm_test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "## Classifiers:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "def train_model(train_x, train_y, model_name='NB', validation=None):\n",
    "    \"\"\"\n",
    "    Possible model names: ['NB', 'SVM', 'XGB', 'MLP', 'ADA', 'BAG', 'RF']\n",
    "    default = 'NB'\n",
    "    \n",
    "    validation: (val_x, val_y) tupple for validation accuracy score.\n",
    "    \n",
    "    return: trained model\n",
    "    \"\"\"\n",
    "    model = None\n",
    "    if model_name == 'SVM':\n",
    "        model = svm.SVC(gamma='scale', probability=True)\n",
    "    elif model_name == 'XGB':\n",
    "        model = XGBClassifier(n_estimators=200, max_depth=5, n_jobs=2)\n",
    "#         model = XGBClassifier()\n",
    "    elif model_name == 'MLP':\n",
    "        model = MLPClassifier(hidden_layer_sizes=(100,100,100), max_iter=800, alpha=0.0001,\n",
    "                     solver='sgd', verbose=10, tol=0.000000001)\n",
    "    elif model_name == 'ADA':\n",
    "        model = AdaBoostClassifier(n_estimators=50)\n",
    "    elif model_name == 'BAG':\n",
    "        model = BaggingClassifier(n_jobs=2, n_estimators=50)\n",
    "    elif model_name == 'RF':\n",
    "        model = RandomForestClassifier(n_estimators=200, max_depth=10)\n",
    "    elif model_name == 'KNN':\n",
    "        model = KNeighborsClassifier(n_neighbors=5, weights='distance', algorithm='auto', leaf_size=30, p=2, metric='minkowski', metric_params=None, n_jobs=None)\n",
    "    else:\n",
    "        model = GaussianNB()\n",
    "    \n",
    "    model.fit(train_x, train_y)\n",
    "    \n",
    "    if validation is not None:\n",
    "        y_hat = model.predict(validation[0])\n",
    "        acc = metrics.accuracy_score(validation[1], y_hat)\n",
    "        print(f\"Validation Accuracy in '{model_name}' = {acc}\")\n",
    "        cm = metrics.confusion_matrix(validation[1], y_hat)\n",
    "        print(cm)\n",
    "        recall = cm[0][0] / (cm[0][0] + cm[0][1])\n",
    "        precision = cm[0][0] / (cm[0][0] + cm[1][0])\n",
    "        f1 = 2*(precision*recall)/(precision+recall)\n",
    "        print(f\"Recall in '{model_name}' = {recall}\")\n",
    "        print(f\"Precision in '{model_name}' = {precision}\")\n",
    "        print(f\"F1 Score in '{model_name}' = {f1}\")\n",
    "               \n",
    "    return model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "## ROC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "def plot_roc(fpr, tpr, class_name, area):\n",
    "    figure(num=None, figsize=(12, 6), dpi=80, facecolor='w', edgecolor='k')\n",
    "    \n",
    "    plt.plot(fpr,tpr)\n",
    "\n",
    "    plt.xlabel('False Positive Rate')\n",
    "    plt.ylabel('True Positive Rate')\n",
    "\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 333,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "def plot_combine_roc(test_y, prob):\n",
    "    n_classes = 7\n",
    "    name_arr = ['RF', 'BAG', 'ADA', 'NB', 'SVM', 'XGB', 'KNN']\n",
    "    fpr = dict()\n",
    "    tpr = dict()\n",
    "    roc_auc = dict()\n",
    "    for i in range(n_classes):\n",
    "        fpr[i], tpr[i], _ = metrics.roc_curve(test_y, prob[:, i])\n",
    "        roc_auc[i] = metrics.roc_auc_score(test_y,  prob[:, i])\n",
    "\n",
    "    figure(num=None, figsize=(12, 6), dpi=80, facecolor='w', edgecolor='k')\n",
    "    colors = cycle(['darkorange', 'cornflowerblue', 'red', 'purple', 'pink', 'violet', 'green'])\n",
    "    for i, color in zip(range(n_classes), colors):\n",
    "        plt.plot(fpr[i], tpr[i], color=color,\n",
    "                 label='ROC curve for ' + name_arr[i] + ' class (area = {1:0.2f})'\n",
    "                 ''.format(i, roc_auc[i]))\n",
    "\n",
    "    plt.xlabel('False Positive Rate')\n",
    "    plt.ylabel('True Positive Rate')\n",
    "    plt.legend()\n",
    "#     plt.savefig('output/roc_lda_on_pca_uninfected.png')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "## CM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "def confusion_mat(cm, classes, title='Confusion matrix', cmap=plt.cm.Blues, figsize=(7,7), path=None, filename=None):\n",
    "        \"\"\"\n",
    "        cm: confusion matrix to be plotted.\n",
    "        classes: array of labels or class names.\n",
    "        title: title of the confusion matrix.\n",
    "        cmap: color of the plot matrix.\n",
    "        figsize: tupple (width, height) representiong size of the plot.\n",
    "        path: destination where the plot image will be saved.\n",
    "        filename: name to save the file with on the specified path. (if None, title is used)\n",
    "        \n",
    "        # Source: https://scikit-learn.org/stable/auto_examples/model_selection/plot_confusion_matrix.html\n",
    "        \"\"\"\n",
    "        cm = cm.astype(np.int64)\n",
    "        plt.figure(figsize=figsize)\n",
    "        plt.imshow(cm, interpolation='nearest', cmap=cmap)\n",
    "        plt.title(title)\n",
    "        plt.colorbar()\n",
    "        tick_marks = np.arange(len(classes))\n",
    "        plt.xticks(tick_marks, classes, rotation=45)\n",
    "        plt.yticks(tick_marks, classes)\n",
    "\n",
    "        fmt = 'd'\n",
    "        thresh = cm.max() / 2.\n",
    "        for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
    "            plt.text(j, i, format(cm[i, j], fmt),\n",
    "                     horizontalalignment=\"center\",\n",
    "                     color=\"white\" if cm[i, j] > thresh else \"black\")\n",
    "        plt.grid(False)\n",
    "        plt.ylabel('True label')\n",
    "        plt.xlabel('Predicted label')\n",
    "        plt.tight_layout()\n",
    "        \n",
    "        if path:\n",
    "            if filename is None:\n",
    "                plt.savefig(path + title + '.png')\n",
    "            else:\n",
    "                plt.savefig(path + filename + '.png')\n",
    "        plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "## PCA projected data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "def pca_projection(mat, name_arr):\n",
    "    plt.figure(figsize=(15,20))\n",
    "    for i, img in enumerate(mat, start=1):\n",
    "        plt.subplot(4, 2, i)\n",
    "        y = np.var(mat[i-1], axis=0)\n",
    "        x = list(range(1, len(y)+1))\n",
    "        plt.plot(x, y, '--o')\n",
    "        plt.ylabel('Variance')\n",
    "        plt.xlabel('Data Projected on Eigen Vector Number')\n",
    "        plt.xticks(x)\n",
    "        plt.title(name_arr[i-1])\n",
    "    plt.savefig('output/pca/subplot.jpg')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "def draw_key_points(image, kp):\n",
    "    img = cv2.drawKeypoints(image, kp, None, flags=cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS)\n",
    "    return img"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Main calls"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "## Read and split data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "### Read and Store"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "full_data_x, full_data_y = read_images('cell_images')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((27558, 32, 32, 3), (27558,))"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "full_data_x.shape, full_data_y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(32, 32, 3)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "full_data_x[2].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "data_x, test_x, data_y, test_y = train_test_split(full_data_x, full_data_y, test_size=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((22046, 32, 32, 3), (5512, 32, 32, 3), (22046,), (5512,))"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_x.shape, test_x.shape, data_y.shape, test_y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "train_imgs, val_imgs, train_y, val_y = train_test_split(data_x, data_y, test_size=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((17636, 32, 32, 3), (4410, 32, 32, 3), (17636,), (4410,))"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_imgs.shape, val_imgs.shape, train_y.shape, val_y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "np.save('data/train_imgs.npy', train_imgs)\n",
    "np.save('data/train_y.npy', train_y)\n",
    "np.save('data/val_imgs.npy', val_imgs)\n",
    "np.save('data/val_y.npy', val_y)\n",
    "\n",
    "np.save('data/data_x.npy', data_x)\n",
    "np.save('data/data_y.npy', data_y)\n",
    "np.save('data/test_x.npy', test_x)\n",
    "np.save('data/test_y.npy', test_y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "### Load"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "train_imgs = np.load('data/train_imgs.npy')\n",
    "train_y = np.load('data/train_y.npy')\n",
    "val_imgs = np.load('data/val_imgs.npy')\n",
    "val_y = np.load('data/val_y.npy')\n",
    "\n",
    "data_x = np.load('data/data_x.npy')\n",
    "data_y = np.load('data/data_y.npy')\n",
    "test_x = np.load('data/test_x.npy')\n",
    "test_y = np.load('data/test_y.npy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((22046, 32, 32, 3), (5512, 32, 32, 3), (22046,), (5512,))"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_x.shape, test_x.shape, data_y.shape, test_y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((17636, 32, 32, 3), (4410, 32, 32, 3), (17636,), (4410,))"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_imgs.shape, val_imgs.shape, train_y.shape, val_y.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "## Training:\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "### Training & Validation set features:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "#### Data visualization:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 346,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "infected_img = train_imgs[train_y == 0][8]\n",
    "uninfected_img = train_imgs[train_y == 1][4]\n",
    "classes = []\n",
    "classes.append(infected_img)\n",
    "classes.append(uninfected_img)\n",
    "class_label= []\n",
    "class_label.append('infected image')\n",
    "class_label.append('uninfected image')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 347,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "vis_img = np.zeros((2, 32, 32))\n",
    "lbp_img = np.zeros((2, 32, 32))\n",
    "sift = np.zeros((2, 32, 32, 3), dtype='uint8')\n",
    "surf = np.zeros((2, 32, 32, 3), dtype='uint8')\n",
    "\n",
    "for i in range(2):\n",
    "    temp1, vis_img[i] = hog(classes[i], block_norm='L2', visualize=True)\n",
    "    lbp_img[i] = local_binary_pattern(cv2.cvtColor(classes[i], cv2.COLOR_RGB2GRAY), 10, 3)\n",
    "    sift[i] = draw_key_points(cv2.cvtColor(classes[i], cv2.COLOR_RGB2GRAY), return_sift_kp(classes[i]))\n",
    "    surf[i] = draw_key_points(cv2.cvtColor(classes[i], cv2.COLOR_RGB2GRAY), return_surf_kp(classes[i]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 348,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "data_vis = []\n",
    "for i in range(len(classes)):\n",
    "    data_vis.append(classes[i])\n",
    "for i in range(len(classes)):\n",
    "    data_vis.append(vis_img[i])\n",
    "for i in range(len(classes)):\n",
    "    data_vis.append(lbp_img[i])\n",
    "for i in range(len(classes)):\n",
    "    data_vis.append(sift[i])\n",
    "for i in range(len(classes)):\n",
    "    data_vis.append(surf[i])\n",
    "\n",
    "img_name_arr = []\n",
    "for i in range(len(classes)):\n",
    "    img_name_arr.append(class_label[i])\n",
    "for i in range(len(classes)):\n",
    "    img_name_arr.append('HOG')\n",
    "for i in range(len(classes)):\n",
    "    img_name_arr.append('LBP')\n",
    "for i in range(len(classes)):\n",
    "    img_name_arr.append('SIFT')\n",
    "for i in range(len(classes)):\n",
    "    img_name_arr.append('SURF')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 351,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x720 with 10 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(4,10))\n",
    "for i, img in enumerate(data_vis, start=1):\n",
    "    plt.subplot(5, 2, i)\n",
    "    plt.title(img_name_arr[i-1])\n",
    "    frame1 = plt.gca()\n",
    "    for tick in frame1.axes.get_xticklines():\n",
    "        tick.set_visible(False)\n",
    "    for tick in frame1.axes.get_yticklines():\n",
    "        tick.set_visible(False)\n",
    "    for xlabel_i in frame1.axes.get_xticklabels():\n",
    "        xlabel_i.set_visible(False)\n",
    "    for xlabel_i in frame1.axes.get_yticklabels():\n",
    "        xlabel_i.set_visible(False)\n",
    "    plt.imshow(data_vis[i-1], cmap='gray')\n",
    "plt.savefig('output/feature_visualization.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "#### HOG Features:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 71
    },
    "colab_type": "code",
    "hidden": true,
    "id": "9kmT_2iDfO9e",
    "outputId": "2280667a-c1a0-4261-a85e-665bcf18b77c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Feature saved with name cache/hog_train.pkl\n",
      "Feature saved with name cache/hog_val.pkl\n"
     ]
    }
   ],
   "source": [
    "hog_train = get_hog(train_imgs, name='hog_train', save=True)\n",
    "hog_val = get_hog(val_imgs, name='hog_val', save=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "hog_train = load_feature('cache/hog_train.pkl')\n",
    "hog_val = load_feature('cache/hog_val.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((17636, 324), (4410, 324))"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hog_train.shape, hog_val.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "#### LBP Features:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 71
    },
    "colab_type": "code",
    "hidden": true,
    "id": "9kmT_2iDfO9e",
    "outputId": "2280667a-c1a0-4261-a85e-665bcf18b77c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Feature saved with name cache/lbp_train.pkl\n",
      "Feature saved with name cache/lbp_val.pkl\n"
     ]
    }
   ],
   "source": [
    "lbp_train = get_lbp(train_imgs, name='lbp_train', save=True)\n",
    "lbp_val = get_lbp(val_imgs, name='lbp_val', save=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "lbp_train = load_feature('cache/lbp_train.pkl')\n",
    "lbp_val = load_feature('cache/lbp_val.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((17636, 1024), (4410, 1024))"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lbp_train.shape, lbp_val.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "#### KAZE Features:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 71
    },
    "colab_type": "code",
    "hidden": true,
    "id": "9kmT_2iDfO9e",
    "outputId": "2280667a-c1a0-4261-a85e-665bcf18b77c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Feature saved with name cache/kaze_train.pkl\n",
      "Feature saved with name cache/kaze_val.pkl\n"
     ]
    }
   ],
   "source": [
    "kaze_train = get_kaze(train_imgs, name='kaze_train', save=True)\n",
    "kaze_val = get_kaze(val_imgs, name='kaze_val', save=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "kaze_train = load_feature('cache/kaze_train.pkl')\n",
    "kaze_val = load_feature('cache/kaze_val.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((17636, 2048), (4410, 2048))"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kaze_train.shape, kaze_val.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "#### SIFT Features:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Feature saved with name cache/sift_train.pkl\n",
      "Feature saved with name cache/sift_val.pkl\n"
     ]
    }
   ],
   "source": [
    "sift_train = get_sift(train_imgs, name='sift_train', save=True)\n",
    "sift_val = get_sift(val_imgs, name='sift_val', save=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "sift_train = load_feature('cache/sift_train.pkl')\n",
    "sift_val = load_feature('cache/sift_val.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((17636, 1920), (4410, 1920))"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sift_train.shape, sift_val.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "#### SURF Features:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Feature saved with name cache/surf_train.pkl\n",
      "Feature saved with name cache/surf_val.pkl\n"
     ]
    }
   ],
   "source": [
    "surf_train = get_surf(train_imgs, name='surf_train', save=True)\n",
    "surf_val = get_surf(val_imgs, name='surf_val', save=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "surf_train = load_feature('cache/surf_train.pkl')\n",
    "surf_val = load_feature('cache/surf_val.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((17636, 256), (4410, 256))"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "surf_train.shape, surf_val.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "#### Flattened Image"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "##### RGB:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Feature saved with name cache/flat_rgb_train.pkl\n",
      "Feature saved with name cache/flat_rgb_val.pkl\n"
     ]
    }
   ],
   "source": [
    "flat_rgb_train = get_flattened(train_imgs, None, name='flat_rgb_train', save=True)\n",
    "flat_rgb_val = get_flattened(val_imgs, None, name='flat_rgb_val', save=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "flat_rgb_train = load_feature('cache/flat_rgb_train.pkl')\n",
    "flat_rgb_val = load_feature('cache/flat_rgb_val.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((17636, 3072), (4410, 3072))"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "flat_rgb_train.shape, flat_rgb_val.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "##### GRAY:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Feature saved with name cache/flat_gray_train.pkl\n",
      "Feature saved with name cache/flat_gray_val.pkl\n"
     ]
    }
   ],
   "source": [
    "flat_gray_train = get_flattened(train_imgs, name='flat_gray_train', save=True)\n",
    "flat_gray_val = get_flattened(val_imgs, name='flat_gray_val', save=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "flat_gray_train = load_feature('cache/flat_gray_train.pkl')\n",
    "flat_gray_val = load_feature('cache/flat_gray_val.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((17636, 1024), (4410, 1024))"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "flat_gray_train.shape, flat_gray_val.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "#### Color Histogram:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Feature saved with name cache/hist_train.pkl\n",
      "Feature saved with name cache/hist_val.pkl\n"
     ]
    }
   ],
   "source": [
    "hist_train = get_color_hist(train_imgs, name='hist_train', save=True)\n",
    "hist_val = get_color_hist(val_imgs, name='hist_val', save=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "hist_train = load_feature('cache/hist_train.pkl')\n",
    "hist_val = load_feature('cache/hist_val.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((17636, 512), (4410, 512))"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hist_train.shape, hist_val.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "### Reduce Features by PCA:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "#### HOG Features:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "# norm_hog_train, norm_hog_val = norm_features_minmax(hog_train, hog_val)\n",
    "norm_hog_train, norm_hog_val = norm_features_zscore(hog_train, hog_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 71
    },
    "colab_type": "code",
    "hidden": true,
    "id": "9kmT_2iDfO9e",
    "outputId": "2280667a-c1a0-4261-a85e-665bcf18b77c"
   },
   "outputs": [],
   "source": [
    "pca = PCA(n_components=10)\n",
    "pca_hog_train = pca.fit_transform(norm_hog_train)\n",
    "pca_hog_val = pca.transform(norm_hog_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "np.save('cache/pca_hog_train.npy', pca_hog_train)\n",
    "np.save('cache/pca_hog_val.npy', pca_hog_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "pca_hog_train = np.load('cache/pca_hog_train.npy')\n",
    "pca_hog_val = np.load('cache/pca_hog_val.npy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((17636, 10), (4410, 10))"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca_hog_train.shape, pca_hog_val.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "#### LBP Features:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "# norm_lbp_train, norm_lbp_val = norm_features_minmax(lbp_train, lbp_val)\n",
    "norm_lbp_train, norm_lbp_val = norm_features_zscore(lbp_train, lbp_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 71
    },
    "colab_type": "code",
    "hidden": true,
    "id": "9kmT_2iDfO9e",
    "outputId": "2280667a-c1a0-4261-a85e-665bcf18b77c"
   },
   "outputs": [],
   "source": [
    "pca = PCA(n_components=10)\n",
    "pca_lbp_train = pca.fit_transform(norm_lbp_train)\n",
    "pca_lbp_val = pca.transform(norm_lbp_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "np.save('cache/pca_lbp_train.npy', pca_lbp_train)\n",
    "np.save('cache/pca_lbp_val.npy', pca_lbp_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "pca_lbp_train = np.load('cache/pca_lbp_train.npy')\n",
    "pca_lbp_val = np.load('cache/pca_lbp_val.npy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((17636, 10), (4410, 10))"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca_lbp_train.shape, pca_lbp_val.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "#### KAZE Features:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "# norm_kaze_train, norm_kaze_val = norm_features_minmax(kaze_train, kaze_val)\n",
    "norm_kaze_train, norm_kaze_val = norm_features_zscore(kaze_train, kaze_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 71
    },
    "colab_type": "code",
    "hidden": true,
    "id": "9kmT_2iDfO9e",
    "outputId": "2280667a-c1a0-4261-a85e-665bcf18b77c"
   },
   "outputs": [],
   "source": [
    "pca = PCA(n_components=10)\n",
    "pca_kaze_train = pca.fit_transform(norm_kaze_train)\n",
    "pca_kaze_val = pca.transform(norm_kaze_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "np.save('cache/pca_kaze_train.npy', pca_kaze_train)\n",
    "np.save('cache/pca_kaze_val.npy', pca_kaze_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "pca_kaze_train = np.load('cache/pca_kaze_train.npy')\n",
    "pca_kaze_val = np.load('cache/pca_kaze_val.npy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((17636, 10), (4410, 10))"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca_kaze_train.shape, pca_kaze_val.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "#### SIFT Features:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "# norm_sift_train, norm_sift_val = norm_features_minmax(sift_train, sift_val)\n",
    "norm_sift_train, norm_sift_val = norm_features_zscore(sift_train, sift_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "pca = PCA(n_components=5)\n",
    "pca_sift_train = pca.fit_transform(norm_sift_train)\n",
    "pca_sift_val = pca.transform(norm_sift_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "np.save('cache/pca_sift_train.npy', pca_sift_train)\n",
    "np.save('cache/pca_sift_val.npy', pca_sift_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "pca_sift_train = np.load('cache/pca_sift_train.npy')\n",
    "pca_sift_val = np.load('cache/pca_sift_val.npy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((17636, 5), (4410, 5))"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca_sift_train.shape, pca_sift_val.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "#### SURF Features:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "# norm_surf_train, norm_surf_val = norm_features_minmax(surf_train, surf_val)\n",
    "norm_surf_train, norm_surf_val = norm_features_zscore(surf_train, surf_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "pca = PCA(n_components=5)\n",
    "pca_surf_train = pca.fit_transform(norm_surf_train)\n",
    "pca_surf_val = pca.transform(norm_surf_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "np.save('cache/pca_surf_train.npy', pca_surf_train)\n",
    "np.save('cache/pca_surf_val.npy', pca_surf_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "pca_surf_train = np.load('cache/pca_surf_train.npy')\n",
    "pca_surf_val = np.load('cache/pca_surf_val.npy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((17636, 5), (4410, 5))"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca_surf_train.shape, pca_surf_val.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "#### Flattened Image"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "##### RGB:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/vrutti/.conda/envs/temp/lib/python3.6/site-packages/sklearn/utils/validation.py:595: DataConversionWarning: Data with input dtype uint8 was converted to float64 by StandardScaler.\n",
      "  warnings.warn(msg, DataConversionWarning)\n",
      "/home/vrutti/.conda/envs/temp/lib/python3.6/site-packages/sklearn/utils/validation.py:595: DataConversionWarning: Data with input dtype uint8 was converted to float64 by StandardScaler.\n",
      "  warnings.warn(msg, DataConversionWarning)\n",
      "/home/vrutti/.conda/envs/temp/lib/python3.6/site-packages/sklearn/utils/validation.py:595: DataConversionWarning: Data with input dtype uint8 was converted to float64 by StandardScaler.\n",
      "  warnings.warn(msg, DataConversionWarning)\n"
     ]
    }
   ],
   "source": [
    "# norm_flat_rgb_train, norm_flat_rgb_val = norm_features_minmax(flat_rgb_train, flat_rgb_val)\n",
    "norm_flat_rgb_train, norm_flat_rgb_val = norm_features_zscore(flat_rgb_train, flat_rgb_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "pca = PCA(n_components=10)\n",
    "pca_flat_rgb_train = pca.fit_transform(norm_flat_rgb_train)\n",
    "pca_flat_rgb_val = pca.transform(norm_flat_rgb_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "np.save('cache/pca_flat_rgb_train.npy', pca_flat_rgb_train)\n",
    "np.save('cache/pca_flat_rgb_val.npy', pca_flat_rgb_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "pca_flat_rgb_train = np.load('cache/pca_flat_rgb_train.npy')\n",
    "pca_flat_rgb_val = np.load('cache/pca_flat_rgb_val.npy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((17636, 10), (4410, 10))"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca_flat_rgb_train.shape, pca_flat_rgb_val.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "##### GRAY:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/vrutti/.conda/envs/temp/lib/python3.6/site-packages/sklearn/utils/validation.py:595: DataConversionWarning: Data with input dtype uint8 was converted to float64 by StandardScaler.\n",
      "  warnings.warn(msg, DataConversionWarning)\n",
      "/home/vrutti/.conda/envs/temp/lib/python3.6/site-packages/sklearn/utils/validation.py:595: DataConversionWarning: Data with input dtype uint8 was converted to float64 by StandardScaler.\n",
      "  warnings.warn(msg, DataConversionWarning)\n",
      "/home/vrutti/.conda/envs/temp/lib/python3.6/site-packages/sklearn/utils/validation.py:595: DataConversionWarning: Data with input dtype uint8 was converted to float64 by StandardScaler.\n",
      "  warnings.warn(msg, DataConversionWarning)\n"
     ]
    }
   ],
   "source": [
    "# norm_flat_gray_train, norm_flat_gray_val = norm_features_minmax(flat_gray_train, flat_gray_val)\n",
    "norm_flat_gray_train, norm_flat_gray_val = norm_features_zscore(flat_gray_train, flat_gray_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "pca = PCA(n_components=6)\n",
    "pca_flat_gray_train = pca.fit_transform(norm_flat_gray_train)\n",
    "pca_flat_gray_val = pca.transform(norm_flat_gray_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "np.save('cache/pca_flat_gray_train.npy', pca_flat_gray_train)\n",
    "np.save('cache/pca_flat_gray_val.npy', pca_flat_gray_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "pca_flat_gray_train = np.load('cache/pca_flat_gray_train.npy')\n",
    "pca_flat_gray_val = np.load('cache/pca_flat_gray_val.npy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((17636, 6), (4410, 6))"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca_flat_gray_train.shape, pca_flat_gray_val.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "#### Color Histogram:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "# norm_hist_train, norm_hist_val = norm_features_minmax(hist_train, hist_val)\n",
    "norm_hist_train, norm_hist_val = norm_features_zscore(hist_train, hist_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "pca = PCA(n_components=10)\n",
    "pca_hist_train = pca.fit_transform(norm_hist_train)\n",
    "pca_hist_val = pca.transform(norm_hist_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "np.save('cache/pca_hist_train.npy', pca_hist_train)\n",
    "np.save('cache/pca_hist_val.npy', pca_hist_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "pca_hist_train = np.load('cache/pca_hist_train.npy')\n",
    "pca_hist_val = np.load('cache/pca_hist_val.npy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((17636, 10), (4410, 10))"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca_hist_train.shape, pca_hist_val.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "### Feature PCA variance comparison:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8"
      ]
     },
     "execution_count": 220,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca_array = []\n",
    "pca_array.append(pca_hog_train)\n",
    "pca_array.append(pca_lbp_train)\n",
    "pca_array.append(pca_sift_train)\n",
    "pca_array.append(pca_surf_train)\n",
    "pca_array.append(pca_kaze_train)\n",
    "pca_array.append(pca_hist_train)\n",
    "pca_array.append(pca_flat_gray_train)\n",
    "pca_array.append(pca_flat_rgb_train)\n",
    "len(pca_array)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {
    "hidden": true,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1440 with 8 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pca_projection(pca_array, ['HOG', 'LBP', 'SIFT', 'SURF', 'KAZE', 'Color Histogram', 'Flatten GRAY', 'Flatten RGB'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "### Reduce Features by LDA:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "#### HOG Features:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 71
    },
    "colab_type": "code",
    "hidden": true,
    "id": "9kmT_2iDfO9e",
    "outputId": "2280667a-c1a0-4261-a85e-665bcf18b77c"
   },
   "outputs": [],
   "source": [
    "lda = LDA()\n",
    "lda_hog_train = lda.fit_transform(norm_hog_train, train_y)\n",
    "lda_hog_val = lda.transform(norm_hog_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((17636, 1), (4410, 1))"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lda_hog_train.shape, lda_hog_val.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "np.save('cache/lda_hog_train.npy', lda_hog_train)\n",
    "np.save('cache/lda_hog_val.npy', lda_hog_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "lda_hog_train = np.load('cache/lda_hog_train.npy')\n",
    "lda_hog_val = np.load('cache/lda_hog_val.npy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((17636, 1), (4410, 1))"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lda_hog_train.shape, lda_hog_val.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "#### LBP Features:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 71
    },
    "colab_type": "code",
    "hidden": true,
    "id": "9kmT_2iDfO9e",
    "outputId": "2280667a-c1a0-4261-a85e-665bcf18b77c"
   },
   "outputs": [],
   "source": [
    "lda = LDA()\n",
    "lda_lbp_train = lda.fit_transform(norm_lbp_train, train_y)\n",
    "lda_lbp_val = lda.transform(norm_lbp_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "np.save('cache/lda_lbp_train.npy', lda_lbp_train)\n",
    "np.save('cache/lda_lbp_val.npy', lda_lbp_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "lda_lbp_train = np.load('cache/lda_lbp_train.npy')\n",
    "lda_lbp_val = np.load('cache/lda_lbp_val.npy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((17636, 1), (4410, 1))"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lda_lbp_train.shape, lda_lbp_val.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "#### SIFT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 71
    },
    "colab_type": "code",
    "hidden": true,
    "id": "9kmT_2iDfO9e",
    "outputId": "2280667a-c1a0-4261-a85e-665bcf18b77c"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/vrutti/.conda/envs/temp/lib/python3.6/site-packages/sklearn/discriminant_analysis.py:388: UserWarning: Variables are collinear.\n",
      "  warnings.warn(\"Variables are collinear.\")\n"
     ]
    }
   ],
   "source": [
    "lda = LDA()\n",
    "lda_sift_train = lda.fit_transform(norm_sift_train, train_y)\n",
    "lda_sift_val = lda.transform(norm_sift_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "np.save('cache/lda_sift_train.npy', lda_sift_train)\n",
    "np.save('cache/lda_sift_val.npy', lda_sift_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "lda_sift_train = np.load('cache/lda_sift_train.npy')\n",
    "lda_sift_val = np.load('cache/lda_sift_val.npy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((17636, 1), (4410, 1))"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lda_sift_train.shape, lda_sift_val.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "#### Color Histogram:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/vrutti/.conda/envs/temp/lib/python3.6/site-packages/sklearn/discriminant_analysis.py:388: UserWarning: Variables are collinear.\n",
      "  warnings.warn(\"Variables are collinear.\")\n"
     ]
    }
   ],
   "source": [
    "lda = LDA()\n",
    "lda_hist_train = lda.fit_transform(norm_hist_train, train_y)\n",
    "lda_hist_val = lda.transform(norm_hist_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "np.save('cache/lda_hist_train.npy', lda_hist_train)\n",
    "np.save('cache/lda_hist_val.npy', lda_hist_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "lda_hist_train = np.load('cache/lda_hist_train.npy')\n",
    "lda_hist_val = np.load('cache/lda_hist_val.npy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((17636, 1), (4410, 1))"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lda_hist_train.shape, lda_hist_val.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "### Combine Features normal:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "features_train = None\n",
    "features_val = None\n",
    "evs = []\n",
    "for ft, fv in zip([hog_train, hist_train, surf_train, flat_rgb_train, lbp_train, kaze_train, sift_train], \n",
    "                  [hog_val, hist_val, surf_val, flat_rgb_val, lbp_val, kaze_val, sift_val]):    \n",
    "#     scaler = preprocessing.StandardScaler()\n",
    "#     ft = scaler.fit_transform(ft)\n",
    "#     fv = scaler.transform(fv)\n",
    "#     ft = preprocessing.normalize(ft)\n",
    "#     fv = preprocessing.normalize(fv)\n",
    "    if features_train is None:\n",
    "        features_train = ft\n",
    "        features_val = fv\n",
    "    else:\n",
    "        features_train = combine_features([features_train, ft])\n",
    "        features_val = combine_features([features_val, fv])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "features_train.shape, features_val.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "### Combine Features PCA:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "features_train = None\n",
    "features_val = None\n",
    "evs = []\n",
    "for ft, fv in zip([pca_hog_train, pca_lbp_train, pca_sift_train, pca_flat_rgb_train, pca_hist_train], \n",
    "                  [pca_hog_val, pca_lbp_val, pca_sift_val, pca_flat_rgb_val, pca_hist_val]):    \n",
    "#     scaler = preprocessing.StandardScaler()\n",
    "#     ft = scaler.fit_transform(ft)\n",
    "#     fv = scaler.transform(fv)\n",
    "#     ft = preprocessing.normalize(ft)\n",
    "#     fv = preprocessing.normalize(fv)\n",
    "    if features_train is None:\n",
    "        features_train = ft\n",
    "        features_val = fv\n",
    "    else:\n",
    "        features_train = combine_features([features_train, ft])\n",
    "        features_val = combine_features([features_val, fv])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((17636, 45), (4410, 45))"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features_train.shape, features_val.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "### Combine Features LDA:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "features_train_lda = None\n",
    "features_val_lda = None\n",
    "evs = []\n",
    "for ft, fv in zip([lda_hog_train, lda_hist_train, lda_lbp_train, lda_sift_train], \n",
    "                  [lda_hog_val, lda_hist_val, lda_lbp_val, lda_sift_val]):    \n",
    "#     scaler = preprocessing.StandardScaler()\n",
    "#     ft = scaler.fit_transform(ft)\n",
    "#     fv = scaler.transform(fv)\n",
    "#     ft = preprocessing.normalize(ft)\n",
    "#     fv = preprocessing.normalize(fv)\n",
    "    if features_train_lda is None:\n",
    "        features_train_lda = ft\n",
    "        features_val_lda = fv\n",
    "    else:\n",
    "        features_train_lda = combine_features([features_train_lda, ft])\n",
    "        features_val_lda = combine_features([features_val_lda, fv])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((17636, 4), (4410, 4))"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features_train_lda.shape, features_val_lda.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "### LDA on PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "lda = LDA()\n",
    "lda_on_pca_train = lda.fit_transform(features_train, train_y)\n",
    "lda_on_pca_val = lda.transform(features_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "np.save('cache/lda_on_pca_train.npy', lda_on_pca_train)\n",
    "np.save('cache/lda_on_pca_val.npy', lda_on_pca_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "lda_on_pca_train = np.load('cache/lda_on_pca_train.npy')\n",
    "lda_on_pca_val = np.load('cache/lda_on_pca_val.npy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((17636, 1), (4410, 1))"
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lda_on_pca_train.shape, lda_on_pca_val.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "### Combine Features:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 330,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "features_t = pca_hist_train\n",
    "features_v = pca_hist_val"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 331,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((17636, 10), (4410, 10))"
      ]
     },
     "execution_count": 331,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features_t.shape, features_v.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "### Classification:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "### PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation Accuracy in 'RF' = 0.8857142857142857\n",
      "[[1954  257]\n",
      " [ 247 1952]]\n",
      "Recall in 'RF' = 0.8837630031659882\n",
      "Precision in 'RF' = 0.8877782825988187\n",
      "F1 Score in 'RF' = 0.8857660924750681\n"
     ]
    }
   ],
   "source": [
    "model1 = train_model(features_train, train_y, model_name='RF', validation=(features_val, val_y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 318,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "prob1 = model1.predict_proba(features_val)\n",
    "prob10 = prob1[:,0].reshape(prob1.shape[0], 1)\n",
    "prob11 = prob1[:,1].reshape(prob1.shape[0], 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation Accuracy in 'BAG' = 0.8913832199546485\n",
      "[[1981  230]\n",
      " [ 249 1950]]\n",
      "Recall in 'BAG' = 0.8959746720940751\n",
      "Precision in 'BAG' = 0.888340807174888\n",
      "F1 Score in 'BAG' = 0.8921414095924342\n"
     ]
    }
   ],
   "source": [
    "model2 = train_model(features_train, train_y, model_name='BAG', validation=(features_val, val_y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 308,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "prob2 = model2.predict_proba(features_val)\n",
    "prob20 = prob2[:,0].reshape(prob2.shape[0], 1)\n",
    "prob21 = prob2[:,1].reshape(prob2.shape[0], 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation Accuracy in 'ADA' = 0.8748299319727891\n",
      "[[1910  301]\n",
      " [ 251 1948]]\n",
      "Recall in 'ADA' = 0.8638625056535504\n",
      "Precision in 'ADA' = 0.8838500694123091\n",
      "F1 Score in 'ADA' = 0.8737419945105215\n"
     ]
    }
   ],
   "source": [
    "model3 = train_model(features_train, train_y, model_name='ADA', validation=(features_val, val_y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 309,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "prob3 = model3.predict_proba(features_val)\n",
    "prob30 = prob3[:,0].reshape(prob3.shape[0], 1)\n",
    "prob31 = prob3[:,1].reshape(prob3.shape[0], 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation Accuracy in 'NB' = 0.6668934240362812\n",
      "[[ 934 1277]\n",
      " [ 192 2007]]\n",
      "Recall in 'NB' = 0.4224332881049299\n",
      "Precision in 'NB' = 0.8294849023090586\n",
      "F1 Score in 'NB' = 0.5597842373389272\n"
     ]
    }
   ],
   "source": [
    "model4 = train_model(features_train, train_y, model_name='NB', validation=(features_val, val_y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 310,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "prob4 = model4.predict_proba(features_val)\n",
    "prob40 = prob4[:,0].reshape(prob4.shape[0], 1)\n",
    "prob41 = prob4[:,1].reshape(prob4.shape[0], 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation Accuracy in 'SVM' = 0.8702947845804989\n",
      "[[1883  328]\n",
      " [ 244 1955]]\n",
      "Recall in 'SVM' = 0.8516508367254636\n",
      "Precision in 'SVM' = 0.8852844381758345\n",
      "F1 Score in 'SVM' = 0.8681420009220839\n"
     ]
    }
   ],
   "source": [
    "model5 = train_model(features_train, train_y, model_name='SVM', validation=(features_val, val_y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 311,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "prob5 = model5.predict_proba(features_val)\n",
    "prob50 = prob5[:,0].reshape(prob5.shape[0], 1)\n",
    "prob51 = prob5[:,1].reshape(prob5.shape[0], 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation Accuracy in 'XGB' = 0.9149659863945578\n",
      "[[2003  208]\n",
      " [ 167 2032]]\n",
      "Recall in 'XGB' = 0.905924920850294\n",
      "Precision in 'XGB' = 0.9230414746543779\n",
      "F1 Score in 'XGB' = 0.9144031043140836\n"
     ]
    }
   ],
   "source": [
    "model6 = train_model(np.array(features_train), train_y, model_name='XGB', validation=(features_val, val_y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 312,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "prob6 = model6.predict_proba(features_val)\n",
    "prob60 = prob6[:,0].reshape(prob6.shape[0], 1)\n",
    "prob61 = prob6[:,1].reshape(prob6.shape[0], 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation Accuracy in 'KNN' = 0.7764172335600907\n",
      "[[1598  613]\n",
      " [ 373 1826]]\n",
      "Recall in 'KNN' = 0.7227498869289914\n",
      "Precision in 'KNN' = 0.8107559614408929\n",
      "F1 Score in 'KNN' = 0.7642276422764227\n"
     ]
    }
   ],
   "source": [
    "model7 = train_model(np.array(features_train), train_y, model_name='KNN', validation=(features_val, val_y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 313,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "prob7 = model7.predict_proba(features_val)\n",
    "prob70 = prob7[:,0].reshape(prob7.shape[0], 1)\n",
    "prob71 = prob7[:,1].reshape(prob7.shape[0], 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 322,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 960x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "prob = np.concatenate((prob10, prob20, prob30, prob40, prob50, prob60, prob70), axis=1)\n",
    "plot_combine_roc(val_y, prob)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 324,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 960x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "prob = np.concatenate((prob11, prob21, prob31, prob41, prob51, prob61, prob71), axis=1)\n",
    "plot_combine_roc(val_y, prob)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "### LDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation Accuracy in 'RF' = 0.8648526077097506\n",
      "[[1903  308]\n",
      " [ 288 1911]]\n",
      "Recall in 'RF' = 0.8606965174129353\n",
      "Precision in 'RF' = 0.8685531720675491\n",
      "F1 Score in 'RF' = 0.8646069968196274\n"
     ]
    }
   ],
   "source": [
    "model8 = train_model(features_train_lda, train_y, model_name='RF', validation=(features_val_lda, val_y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 287,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "prob8 = model8.predict_proba(features_val_lda)\n",
    "prob80 = prob8[:,0].reshape(prob8.shape[0], 1)\n",
    "prob81 = prob8[:,1].reshape(prob8.shape[0], 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation Accuracy in 'BAG' = 0.8546485260770975\n",
      "[[1877  334]\n",
      " [ 307 1892]]\n",
      "Recall in 'BAG' = 0.8489371325192221\n",
      "Precision in 'BAG' = 0.8594322344322345\n",
      "F1 Score in 'BAG' = 0.8541524459613197\n"
     ]
    }
   ],
   "source": [
    "model9 = train_model(features_train_lda, train_y, model_name='BAG', validation=(features_val_lda, val_y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 288,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "prob9 = model9.predict_proba(features_val_lda)\n",
    "prob90 = prob9[:,0].reshape(prob9.shape[0], 1)\n",
    "prob91 = prob9[:,1].reshape(prob9.shape[0], 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation Accuracy in 'ADA' = 0.8650793650793651\n",
      "[[1887  324]\n",
      " [ 271 1928]]\n",
      "Recall in 'ADA' = 0.8534599728629579\n",
      "Precision in 'ADA' = 0.8744207599629287\n",
      "F1 Score in 'ADA' = 0.8638132295719845\n"
     ]
    }
   ],
   "source": [
    "model10 = train_model(features_train_lda, train_y, model_name='ADA', validation=(features_val_lda, val_y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 289,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "prob10 = model10.predict_proba(features_val_lda)\n",
    "prob100 = prob10[:,0].reshape(prob10.shape[0], 1)\n",
    "prob101 = prob10[:,1].reshape(prob10.shape[0], 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation Accuracy in 'NB' = 0.8514739229024944\n",
      "[[1810  401]\n",
      " [ 254 1945]]\n",
      "Recall in 'NB' = 0.8186341022161918\n",
      "Precision in 'NB' = 0.876937984496124\n",
      "F1 Score in 'NB' = 0.8467836257309942\n"
     ]
    }
   ],
   "source": [
    "model11 = train_model(features_train_lda, train_y, model_name='NB', validation=(features_val_lda, val_y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 290,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "prob11 = model11.predict_proba(features_val_lda)\n",
    "prob110 = prob11[:,0].reshape(prob11.shape[0], 1)\n",
    "prob111 = prob11[:,1].reshape(prob11.shape[0], 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation Accuracy in 'SVM' = 0.8682539682539683\n",
      "[[1941  270]\n",
      " [ 311 1888]]\n",
      "Recall in 'SVM' = 0.8778833107191316\n",
      "Precision in 'SVM' = 0.8619005328596803\n",
      "F1 Score in 'SVM' = 0.8698185077302263\n"
     ]
    }
   ],
   "source": [
    "model12 = train_model(features_train_lda, train_y, model_name='SVM', validation=(features_val_lda, val_y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 291,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "prob12 = model12.predict_proba(features_val_lda)\n",
    "prob120 = prob12[:,0].reshape(prob12.shape[0], 1)\n",
    "prob121 = prob12[:,1].reshape(prob12.shape[0], 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation Accuracy in 'XGB' = 0.8607709750566893\n",
      "[[1883  328]\n",
      " [ 286 1913]]\n",
      "Recall in 'XGB' = 0.8516508367254636\n",
      "Precision in 'XGB' = 0.8681420009220839\n",
      "F1 Score in 'XGB' = 0.8598173515981735\n"
     ]
    }
   ],
   "source": [
    "model13 = train_model(features_train_lda, train_y, model_name='XGB', validation=(features_val_lda, val_y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 292,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "prob13 = model13.predict_proba(features_val_lda)\n",
    "prob130 = prob13[:,0].reshape(prob13.shape[0], 1)\n",
    "prob131 = prob13[:,1].reshape(prob13.shape[0], 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation Accuracy in 'KNN' = 0.8507936507936508\n",
      "[[1884  327]\n",
      " [ 331 1868]]\n",
      "Recall in 'KNN' = 0.8521031207598372\n",
      "Precision in 'KNN' = 0.8505643340857788\n",
      "F1 Score in 'KNN' = 0.851333032083145\n"
     ]
    }
   ],
   "source": [
    "model14 = train_model(features_train_lda, train_y, model_name='KNN', validation=(features_val_lda, val_y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 293,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "prob14 = model14.predict_proba(features_val_lda)\n",
    "prob140 = prob14[:,0].reshape(prob14.shape[0], 1)\n",
    "prob141 = prob14[:,1].reshape(prob14.shape[0], 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 328,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 960x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "prob = np.concatenate((prob80, prob90, prob100, prob110, prob120, prob130, prob140), axis=1)\n",
    "plot_combine_roc(val_y, prob)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 326,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 960x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "prob = np.concatenate((prob81, prob91, prob101, prob111, prob121, prob131, prob141), axis=1)\n",
    "plot_combine_roc(val_y, prob)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "### LDA on PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation Accuracy in 'RF' = 0.8360544217687075\n",
      "[[1903  308]\n",
      " [ 415 1784]]\n",
      "Recall in 'RF' = 0.8606965174129353\n",
      "Precision in 'RF' = 0.8209663503019845\n",
      "F1 Score in 'RF' = 0.8403621108412455\n"
     ]
    }
   ],
   "source": [
    "model15 = train_model(lda_on_pca_train, train_y, model_name='RF', validation=(lda_on_pca_val, val_y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 297,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "prob15 = model15.predict_proba(lda_on_pca_val)\n",
    "prob150 = prob15[:,0].reshape(prob15.shape[0], 1)\n",
    "prob151 = prob15[:,1].reshape(prob15.shape[0], 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation Accuracy in 'BAG' = 0.7691609977324263\n",
      "[[1695  516]\n",
      " [ 502 1697]]\n",
      "Recall in 'BAG' = 0.7666214382632293\n",
      "Precision in 'BAG' = 0.7715065999089668\n",
      "F1 Score in 'BAG' = 0.7690562613430127\n"
     ]
    }
   ],
   "source": [
    "model16 = train_model(lda_on_pca_train, train_y, model_name='BAG', validation=(lda_on_pca_val, val_y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 298,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "prob16 = model16.predict_proba(lda_on_pca_val)\n",
    "prob160 = prob16[:,0].reshape(prob16.shape[0], 1)\n",
    "prob161 = prob16[:,1].reshape(prob16.shape[0], 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation Accuracy in 'ADA' = 0.8380952380952381\n",
      "[[1917  294]\n",
      " [ 420 1779]]\n",
      "Recall in 'ADA' = 0.8670284938941656\n",
      "Precision in 'ADA' = 0.8202824133504493\n",
      "F1 Score in 'ADA' = 0.8430079155672824\n"
     ]
    }
   ],
   "source": [
    "model17 = train_model(lda_on_pca_train, train_y, model_name='ADA', validation=(lda_on_pca_val, val_y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 299,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "prob17 = model17.predict_proba(lda_on_pca_val)\n",
    "prob170 = prob17[:,0].reshape(prob17.shape[0], 1)\n",
    "prob171 = prob17[:,1].reshape(prob17.shape[0], 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation Accuracy in 'NB' = 0.8217687074829932\n",
      "[[1675  536]\n",
      " [ 250 1949]]\n",
      "Recall in 'NB' = 0.7575757575757576\n",
      "Precision in 'NB' = 0.8701298701298701\n",
      "F1 Score in 'NB' = 0.8099613152804642\n"
     ]
    }
   ],
   "source": [
    "model18 = train_model(lda_on_pca_train, train_y, model_name='NB', validation=(lda_on_pca_val, val_y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 300,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "prob18 = model18.predict_proba(lda_on_pca_val)\n",
    "prob180 = prob18[:,0].reshape(prob18.shape[0], 1)\n",
    "prob181 = prob18[:,1].reshape(prob18.shape[0], 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation Accuracy in 'SVM' = 0.8365079365079365\n",
      "[[1880  331]\n",
      " [ 390 1809]]\n",
      "Recall in 'SVM' = 0.8502939846223428\n",
      "Precision in 'SVM' = 0.8281938325991189\n",
      "F1 Score in 'SVM' = 0.8390984155322474\n"
     ]
    }
   ],
   "source": [
    "model19 = train_model(lda_on_pca_train, train_y, model_name='SVM', validation=(lda_on_pca_val, val_y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 301,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "prob19 = model19.predict_proba(lda_on_pca_val)\n",
    "prob190 = prob19[:,0].reshape(prob19.shape[0], 1)\n",
    "prob191 = prob19[:,1].reshape(prob19.shape[0], 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation Accuracy in 'XGB' = 0.8383219954648526\n",
      "[[1904  307]\n",
      " [ 406 1793]]\n",
      "Recall in 'XGB' = 0.8611488014473089\n",
      "Precision in 'XGB' = 0.8242424242424242\n",
      "F1 Score in 'XGB' = 0.8422915284229152\n"
     ]
    }
   ],
   "source": [
    "model20 = train_model(lda_on_pca_train, train_y, model_name='XGB', validation=(lda_on_pca_val, val_y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 302,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "prob20 = model20.predict_proba(lda_on_pca_val)\n",
    "prob200 = prob20[:,0].reshape(prob20.shape[0], 1)\n",
    "prob201 = prob20[:,1].reshape(prob20.shape[0], 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation Accuracy in 'KNN' = 0.7934240362811792\n",
      "[[1756  455]\n",
      " [ 456 1743]]\n",
      "Recall in 'KNN' = 0.7942107643600181\n",
      "Precision in 'KNN' = 0.7938517179023508\n",
      "F1 Score in 'KNN' = 0.7940312005426181\n"
     ]
    }
   ],
   "source": [
    "model21 = train_model(lda_on_pca_train, train_y, model_name='KNN', validation=(lda_on_pca_val, val_y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 303,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "prob21 = model21.predict_proba(lda_on_pca_val)\n",
    "prob210 = prob21[:,0].reshape(prob21.shape[0], 1)\n",
    "prob211 = prob21[:,1].reshape(prob21.shape[0], 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 330,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 960x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "prob = np.concatenate((prob150, prob160, prob170, prob180, prob190, prob200, prob210), axis=1)\n",
    "plot_combine_roc(val_y, prob)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 332,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 960x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "prob = np.concatenate((prob151, prob161, prob171, prob181, prob191, prob201, prob211), axis=1)\n",
    "plot_combine_roc(val_y, prob)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "## Testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "hog_comp_train = combine_features([hog_train, hog_val], horizontal=False)\n",
    "lbp_comp_train = combine_features([lbp_train, lbp_val], horizontal=False)\n",
    "kaze_comp_train = combine_features([kaze_train, kaze_val], horizontal=False)\n",
    "sift_comp_train = combine_features([sift_train, sift_val], horizontal=False)\n",
    "surf_comp_train = combine_features([surf_train, surf_val], horizontal=False)\n",
    "flat_rgb_comp_train = combine_features([flat_rgb_train, flat_rgb_val], horizontal=False)\n",
    "flat_gray_comp_train = combine_features([flat_gray_train, flat_gray_val], horizontal=False)\n",
    "hist_comp_train = combine_features([hist_train, hist_val], horizontal=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "data_y = np.hstack([train_y, val_y])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "pcas_array = []\n",
    "\n",
    "pca = PCA(n_components=10)\n",
    "pca_hog_comp_train = pca.fit_transform(hog_comp_train)\n",
    "pcas_array.append(pca)\n",
    "\n",
    "pca = PCA(n_components=30)\n",
    "pca_lbp_comp_train = pca.fit_transform(lbp_comp_train)\n",
    "pcas_array.append(pca)\n",
    "\n",
    "pca = PCA(n_components=20)\n",
    "pca_kaze_comp_train = pca.fit_transform(kaze_comp_train)\n",
    "pcas_array.append(pca)\n",
    "\n",
    "pca = PCA(n_components=15)\n",
    "pca_sift_comp_train = pca.fit_transform(sift_comp_train)\n",
    "pcas_array.append(pca)\n",
    "\n",
    "pca = PCA(n_components=5)\n",
    "pca_surf_comp_train = pca.fit_transform(surf_comp_train)\n",
    "pcas_array.append(pca)\n",
    "\n",
    "pca = PCA(n_components=20)\n",
    "pca_flat_rgb_comp_train = pca.fit_transform(flat_rgb_comp_train)\n",
    "pcas_array.append(pca)\n",
    "\n",
    "pca = PCA(n_components=20)\n",
    "pca_flat_gray_comp_train = pca.fit_transform(flat_gray_comp_train)\n",
    "pcas_array.append(pca)\n",
    "\n",
    "pca = PCA(n_components=10)\n",
    "pca_hist_comp_train = pca.fit_transform(hist_comp_train)\n",
    "pcas_array.append(pca)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "reduced_train_features=[pca_hog_comp_train,\n",
    "                        pca_lbp_comp_train,\n",
    "                        pca_kaze_comp_train,\n",
    "                        pca_sift_comp_train,\n",
    "                        pca_surf_comp_train,\n",
    "                        pca_flat_rgb_comp_train,\n",
    "                        pca_flat_gray_comp_train,\n",
    "                        pca_hist_comp_train]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 71
    },
    "colab_type": "code",
    "hidden": true,
    "id": "jXCXxmjAGEdk",
    "outputId": "097763d9-c695-4479-8c76-a6b1351cc363"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Feature saved with name cache/hog_test.pkl\n",
      "Feature saved with name cache/lbp_test.pkl\n",
      "Feature saved with name cache/kaze_test.pkl\n",
      "Feature saved with name cache/sift_test.pkl\n",
      "Feature saved with name cache/surf_test.pkl\n",
      "Feature saved with name cache/flat_rgb_test.pkl\n",
      "Feature saved with name cache/flat_gray_test.pkl\n",
      "Feature saved with name cache/hist_test.pkl\n"
     ]
    }
   ],
   "source": [
    "hog_test = get_hog(test_x, name='hog_test', save=True)\n",
    "lbp_test = get_lbp(test_x, name='lbp_test', save=True)\n",
    "kaze_test = get_kaze(test_x, name='kaze_test', save=True)\n",
    "sift_test = get_sift(test_x, name='sift_test', save=True)\n",
    "surf_test = get_surf(test_x, name='surf_test', save=True)\n",
    "flat_rgb_test = get_flattened(test_x, None, name='flat_rgb_test', save=True)\n",
    "flat_gray_test = get_flattened(test_x, name='flat_gray_test', save=True)\n",
    "hist_test = get_color_hist(test_x, name='hist_test', save=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "test_features= [hog_test,\n",
    "                lbp_test,\n",
    "                kaze_test,\n",
    "                sift_test,\n",
    "                surf_test,\n",
    "                flat_rgb_test,\n",
    "                flat_gray_test,\n",
    "                hist_test]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "features_comp_train = None\n",
    "features_test = None\n",
    "comp_pcas = []\n",
    "\n",
    "for i, (f_train, f_test) in enumerate(zip(reduced_train_features, \n",
    "                                          test_features)):\n",
    "    if i == 6:\n",
    "        continue\n",
    "    f_test = pcas_array[i].transform(f_test)\n",
    "    \n",
    "    if features_comp_train is None:\n",
    "        features_comp_train = f_train\n",
    "        features_test = f_test\n",
    "    else:\n",
    "        features_comp_train = combine_features([features_comp_train, f_train])\n",
    "        features_test = combine_features([features_test, f_test])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((22046, 110), (5512, 110))"
      ]
     },
     "execution_count": 201,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features_comp_train.shape, features_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "comp_model = train_model(features_comp_train, data_y, model_name='XGB')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "hidden": true,
    "id": "eXTpn1HECr5X"
   },
   "outputs": [],
   "source": [
    "y_hat = comp_model.predict(features_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8394412191582002\n",
      "[[2322  438]\n",
      " [ 447 2305]]\n",
      "0.838569880823402 0.841304347826087 0.8399348887683126\n"
     ]
    }
   ],
   "source": [
    "acc = metrics.accuracy_score(test_y, y_hat)\n",
    "print(acc)\n",
    "cm = metrics.confusion_matrix(test_y, y_hat)\n",
    "print(cm)\n",
    "precision = cm[0][0] / (cm[0][0] + cm[1][0])\n",
    "recall = cm[0][0] / (cm[0][0] + cm[0][1])\n",
    "f1 = 2*(recall * precision) / (recall + precision)\n",
    "print(precision, recall, f1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "## Randomized and gridsearch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "clf = RandomForestClassifier(n_estimators=200)\n",
    "\n",
    "param_dist = {\"max_depth\": [3, None],\n",
    "              \"class_weight\" : [\"balanced\", \"balanced_subsample\", None],\n",
    "              \"min_samples_split\": randint(2, 11),\n",
    "              \"bootstrap\": [True, False],\n",
    "              \"criterion\": [\"gini\", \"entropy\"]}\n",
    "\n",
    "random_search = RandomizedSearchCV(clf, param_distributions=param_dist, n_iter=20, cv=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {
    "hidden": true,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomizedSearchCV(cv=5, error_score='raise-deprecating',\n",
       "          estimator=RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
       "            max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
       "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "            min_samples_leaf=1, min_samples_split=2,\n",
       "            min_weight_fraction_leaf=0.0, n_estimators=200, n_jobs=None,\n",
       "            oob_score=False, random_state=None, verbose=0,\n",
       "            warm_start=False),\n",
       "          fit_params=None, iid='warn', n_iter=20, n_jobs=None,\n",
       "          param_distributions={'max_depth': [3, None], 'class_weight': ['balanced', 'balanced_subsample', None], 'min_samples_split': <scipy.stats._distn_infrastructure.rv_frozen object at 0x7f5ce6129eb8>, 'bootstrap': [True, False], 'criterion': ['gini', 'entropy']},\n",
       "          pre_dispatch='2*n_jobs', random_state=None, refit=True,\n",
       "          return_train_score='warn', scoring=None, verbose=0)"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "random_search.fit(features_train_lda, train_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_keys(['mean_fit_time', 'std_fit_time', 'mean_score_time', 'std_score_time', 'param_bootstrap', 'param_class_weight', 'param_criterion', 'param_max_depth', 'param_min_samples_split', 'params', 'split0_test_score', 'split1_test_score', 'split2_test_score', 'split3_test_score', 'split4_test_score', 'mean_test_score', 'std_test_score', 'rank_test_score', 'split0_train_score', 'split1_train_score', 'split2_train_score', 'split3_train_score', 'split4_train_score', 'mean_train_score', 'std_train_score'])\n"
     ]
    }
   ],
   "source": [
    "print(random_search.cv_results_.keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/vrutti/.conda/envs/temp/lib/python3.6/site-packages/sklearn/utils/deprecation.py:125: FutureWarning: You are accessing a training score ('mean_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n"
     ]
    }
   ],
   "source": [
    "a1 = random_search.cv_results_['param_bootstrap']\n",
    "a2 = random_search.cv_results_['param_class_weight']\n",
    "a3 = random_search.cv_results_['param_criterion']\n",
    "a4 = random_search.cv_results_['param_max_depth']\n",
    "a5 = random_search.cv_results_['param_min_samples_split']\n",
    "\n",
    "a6 = random_search.cv_results_['mean_train_score']\n",
    "a7 = random_search.cv_results_['mean_test_score']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'bootstrap': True, 'class_weight': None, 'criterion': 'gini', 'max_depth': None, 'min_samples_split': 7}\n"
     ]
    }
   ],
   "source": [
    "print(random_search.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['bootstrap', 'class_weight', 'criterion', 'max_depth', 'min_samples_split', 'mean_train_score', 'mean_test_score']\n"
     ]
    }
   ],
   "source": [
    "arr = []\n",
    "for key in random_search.best_params_.keys():\n",
    "    arr.append(key)\n",
    "    \n",
    "arr.append('mean_train_score')\n",
    "arr.append('mean_test_score')\n",
    "print(arr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "write_csv('output/random_forest.csv', a1, a2, a3, a4, a5, a6, a7, arr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8682539682539683"
      ]
     },
     "execution_count": 173,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y1 = random_search.predict(features_val)\n",
    "metrics.accuracy_score(y1, val_y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "### CM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 368,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "y_hat1 = model1.predict(features_val)\n",
    "y_hat2 = model2.predict(features_val)\n",
    "y_hat3 = model3.predict(features_val)\n",
    "y_hat4 = model4.predict(features_val)\n",
    "y_hat5 = model5.predict(features_val)\n",
    "y_hat6 = model6.predict(features_val)\n",
    "y_hat7 = model7.predict(features_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 369,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "cm1 = MetricTools.confusion_matrix(val_y, y_hat1, 2)\n",
    "cm2 = MetricTools.confusion_matrix(val_y, y_hat2, 2)\n",
    "cm3 = MetricTools.confusion_matrix(val_y, y_hat3, 2)\n",
    "cm4 = MetricTools.confusion_matrix(val_y, y_hat4, 2)\n",
    "cm5 = MetricTools.confusion_matrix(val_y, y_hat5, 2)\n",
    "cm6 = MetricTools.confusion_matrix(val_y, y_hat6, 2)\n",
    "cm7 = MetricTools.confusion_matrix(val_y, y_hat7, 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 370,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1794.  417.]\n",
      " [ 415. 1784.]]\n",
      "[[1823.  388.]\n",
      " [ 414. 1785.]]\n",
      "[[1764.  447.]\n",
      " [ 394. 1805.]]\n",
      "[[1537.  674.]\n",
      " [ 428. 1771.]]\n",
      "[[1821.  390.]\n",
      " [ 339. 1860.]]\n",
      "[[1840.  371.]\n",
      " [ 351. 1848.]]\n",
      "[[1572.  639.]\n",
      " [ 568. 1631.]]\n"
     ]
    }
   ],
   "source": [
    "print(cm1)\n",
    "print(cm2)\n",
    "print(cm3)\n",
    "print(cm4)\n",
    "print(cm5)\n",
    "print(cm6)\n",
    "print(cm7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "PlotTools.confusion_matrix(cm1, list(range(2)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "### Model Stacking:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((17636, 50), (4410, 50))"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features_train1 = None\n",
    "features_val1 = None\n",
    "evs = []\n",
    "for ft, fv in zip([pca_hist_train, pca_hog_train, pca_lbp_train], \n",
    "                  [pca_hist_val, pca_hog_val, pca_lbp_val]):    \n",
    "#     scaler = preprocessing.StandardScaler()\n",
    "#     ft = scaler.fit_transform(ft)\n",
    "#     fv = scaler.transform(fv)\n",
    "#     ft = preprocessing.normalize(ft)\n",
    "#     fv = preprocessing.normalize(fv)\n",
    "    if features_train1 is None:\n",
    "        features_train1 = ft\n",
    "        features_val1 = fv\n",
    "    else:\n",
    "        features_train1 = combine_features([features_train1, ft])\n",
    "        features_val1 = combine_features([features_val1, fv])\n",
    "features_train1.shape, features_val1.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation Accuracy in 'XGB' = 0.8260770975056689\n"
     ]
    }
   ],
   "source": [
    "model1 = train_model(features_train1, train_y, model_name='XGB', validation=(features_val1, val_y))\n",
    "prob1 = model1.predict_proba(features_val1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((17636, 50), (4410, 50))"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features_train2 = None\n",
    "features_val2 = None\n",
    "evs = []\n",
    "for ft, fv in zip([pca_hist_train, pca_flat_gray_train, pca_flat_rgb_train], \n",
    "                  [pca_hist_val, pca_flat_gray_val, pca_flat_rgb_val]):    \n",
    "#     scaler = preprocessing.StandardScaler()\n",
    "#     ft = scaler.fit_transform(ft)\n",
    "#     fv = scaler.transform(fv)\n",
    "#     ft = preprocessing.normalize(ft)\n",
    "#     fv = preprocessing.normalize(fv)\n",
    "    if features_train2 is None:\n",
    "        features_train2 = ft\n",
    "        features_val2 = fv\n",
    "    else:\n",
    "        features_train2 = combine_features([features_train2, ft])\n",
    "        features_val2 = combine_features([features_val2, fv])\n",
    "features_train2.shape, features_val2.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation Accuracy in 'XGB' = 0.7816326530612245\n"
     ]
    }
   ],
   "source": [
    "model2 = train_model(features_train2, train_y, model_name='XGB', validation=(features_val2, val_y))\n",
    "prob2 = model2.predict_proba(features_val2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((17636, 38), (4410, 38))"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features_train3 = None\n",
    "features_val3 = None\n",
    "evs = []\n",
    "for ft, fv in zip([pca_kaze_train, pca_sift_train, pca_surf_train], \n",
    "                  [pca_kaze_val, pca_sift_val, pca_surf_val]):    \n",
    "#     scaler = preprocessing.StandardScaler()\n",
    "#     ft = scaler.fit_transform(ft)\n",
    "#     fv = scaler.transform(fv)\n",
    "#     ft = preprocessing.normalize(ft)\n",
    "#     fv = preprocessing.normalize(fv)\n",
    "    if features_train3 is None:\n",
    "        features_train3 = ft\n",
    "        features_val3 = fv\n",
    "    else:\n",
    "        features_train3 = combine_features([features_train3, ft])\n",
    "        features_val3 = combine_features([features_val3, fv])\n",
    "features_train3.shape, features_val3.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation Accuracy in 'XGB' = 0.7639455782312925\n"
     ]
    }
   ],
   "source": [
    "model3 = train_model(features_train3, train_y, model_name='XGB', validation=(features_val3, val_y))\n",
    "prob3 = model3.predict_proba(features_val3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "prob1 = model1.predict_proba(features_val)\n",
    "prob2 = model2.predict_proba(features_val)\n",
    "prob3 = model3.predict_proba(features_val)\n",
    "prob4 = model4.predict_proba(features_val)\n",
    "#prob5 = model5.score(features_val, )\n",
    "prob6 = model6.predict_proba(features_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "prob = (prob1 + prob2 + prob3 + prob4 + prob6) / 5\n",
    "y_hat = np.argmax(prob, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7839002267573696"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics.accuracy_score(val_y, y_hat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "y_hat = model.predict(features_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "cm = MetricTools.confusion_matrix(val_y, y_hat, 20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "PlotTools.confusion_matrix(cm, list(range(20)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "PlotTools.confusion_matrix(cm, list(range(20)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "collapsed": true,
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f27d492df60>"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(train_imgs[train_y == 14][3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}