model_testing.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import keras\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, Conv2D , MaxPool2D , Flatten , Dropout , BatchNormalization\n",
    "from keras.preprocessing.image import ImageDataGenerator\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import classification_report,confusion_matrix\n",
    "from keras.callbacks import ReduceLROnPlateau\n",
    "import os\n",
    "import numpy as np # linear algebra\n",
    "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
    "import cv2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "labels = ['PNEUMONIA', 'NORMAL']\n",
    "img_size = 150\n",
    "def get_data(data_dir):\n",
    "    data = [] \n",
    "    for label in labels: \n",
    "        path = os.path.join(data_dir, label)\n",
    "        class_num = labels.index(label)\n",
    "        for img in os.listdir(path):\n",
    "            try:\n",
    "                img_arr = cv2.imread(os.path.join(path, img), cv2.IMREAD_GRAYSCALE)\n",
    "                resized_arr = cv2.resize(img_arr, (img_size, img_size)) # Reshaping images to preferred size\n",
    "                data.append([resized_arr, class_num])\n",
    "            except Exception as e:\n",
    "                print(e)\n",
    "    return np.array(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "test1 = get_data('chest_xray/test')\n",
    "test2= get_data('chest_xray/test2')\n",
    "test3 = get_data('chest_xray/test3')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='count'>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "graph = []\n",
    "for i in test1:\n",
    "    if(i[1] == 0):\n",
    "        graph.append(\"Pneumonia\")\n",
    "    else:\n",
    "        graph.append(\"Normal\")\n",
    "sns.set_style('darkgrid')\n",
    "sns.countplot(graph)      \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_test = []\n",
    "y_test = []\n",
    "\n",
    "for feature, label in test1:\n",
    "    x_test.append(feature)\n",
    "    y_test.append(label)\n",
    "    \n",
    "x_test = np.array(x_test) / 255\n",
    "x_test = x_test.reshape(-1, img_size, img_size, 1)\n",
    "y_test = np.array(y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "reconstructed_model = keras.models.load_model(\"models/best_model1\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions2 = reconstructed_model.predict_classes(x_test)\n",
    "predictions2 = predictions2.reshape(1,-1)[0]\n",
    "predictions2[:15]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                     precision    recall  f1-score   support\n",
      "\n",
      "Pneumonia (Class 0)       0.89      0.99      0.94       390\n",
      "   Normal (Class 1)       0.98      0.79      0.88       234\n",
      "\n",
      "           accuracy                           0.92       624\n",
      "          macro avg       0.94      0.89      0.91       624\n",
      "       weighted avg       0.92      0.92      0.91       624\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, predictions2, target_names = ['Pneumonia (Class 0)','Normal (Class 1)']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm2= confusion_matrix(y_test,predictions2)\n",
    "cm2 = pd.DataFrame(cm2 , index = ['0','1'] , columns = ['0','1'])\n",
    "plt.figure(figsize = (10,10))\n",
    "sns.heatmap(cm2,cmap= \"Blues\", linecolor = 'black' , linewidth = 1 , annot = True, fmt='',xticklabels = labels,yticklabels = labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our test1 dataset did really well!!! Let's see the rest of the other datasets we got"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_test = []\n",
    "y_test = []\n",
    "\n",
    "for feature, label in test2:\n",
    "    x_test.append(feature)\n",
    "    y_test.append(label)\n",
    "    \n",
    "x_test = np.array(x_test) / 255\n",
    "x_test = x_test.reshape(-1, img_size, img_size, 1)\n",
    "y_test = np.array(y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions2 = reconstructed_model.predict_classes(x_test)\n",
    "predictions2 = predictions2.reshape(1,-1)[0]\n",
    "predictions2[:15]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                     precision    recall  f1-score   support\n",
      "\n",
      "Pneumonia (Class 0)       0.58      0.12      0.19        94\n",
      "   Normal (Class 1)       0.51      0.91      0.65        94\n",
      "\n",
      "           accuracy                           0.52       188\n",
      "          macro avg       0.54      0.52      0.42       188\n",
      "       weighted avg       0.54      0.52      0.42       188\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, predictions2, target_names = ['Pneumonia (Class 0)','Normal (Class 1)']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm2= confusion_matrix(y_test,predictions2)\n",
    "cm2 = pd.DataFrame(cm2 , index = ['0','1'] , columns = ['0','1'])\n",
    "plt.figure(figsize = (10,10))\n",
    "sns.heatmap(cm2,cmap= \"Blues\", linecolor = 'black' , linewidth = 1 , annot = True, fmt='',xticklabels = labels,yticklabels = labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our model did really bad in these datasets! Oh no! It couldbe because of the way the x-rays look like, they not as clear and are more fuzzy!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "reconstructed_model = keras.models.load_model(\"models/data_augmentation_model\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 0])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions2 = reconstructed_model.predict_classes(x_test)\n",
    "predictions2 = predictions2.reshape(1,-1)[0]\n",
    "predictions2[:15]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                     precision    recall  f1-score   support\n",
      "\n",
      "Pneumonia (Class 0)       0.62      0.45      0.52        94\n",
      "   Normal (Class 1)       0.57      0.72      0.64        94\n",
      "\n",
      "           accuracy                           0.59       188\n",
      "          macro avg       0.59      0.59      0.58       188\n",
      "       weighted avg       0.59      0.59      0.58       188\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, predictions2, target_names = ['Pneumonia (Class 0)','Normal (Class 1)']))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Interesting our data augmentation model did better on this unseen dataset rather than our best model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "reconstructed_model = keras.models.load_model(\"models/new_data_model\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 0])"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions2 = reconstructed_model.predict_classes(x_test)\n",
    "predictions2 = predictions2.reshape(1,-1)[0]\n",
    "predictions2[:15]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                     precision    recall  f1-score   support\n",
      "\n",
      "Pneumonia (Class 0)       0.49      0.31      0.38        94\n",
      "   Normal (Class 1)       0.50      0.68      0.57        94\n",
      "\n",
      "           accuracy                           0.49       188\n",
      "          macro avg       0.49      0.49      0.48       188\n",
      "       weighted avg       0.49      0.49      0.48       188\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, predictions2, target_names = ['Pneumonia (Class 0)','Normal (Class 1)']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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