Gabor_SVM_classification.ipynb

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     "text": [
      "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
     ]
    }
   ],
   "source": [
    "from google.colab import drive\n",
    "drive.mount('/content/drive')"
   ]
  },
  {
   "cell_type": "markdown",
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   "source": [
    "##Import Packages"
   ]
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   "source": [
    "import time\n",
    "import numpy as np\n",
    "import os\n",
    "import cv2\n",
    "import random\n",
    "import pickle\n",
    "import matplotlib.pyplot as plt\n",
    "import sklearn.metrics as metrics\n",
    "from tqdm.notebook import tqdm\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.svm import SVC, LinearSVC\n",
    "from sklearn.metrics import plot_confusion_matrix\n",
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "markdown",
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   },
   "source": [
    "##Load Dataset"
   ]
  },
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   "source": [
    "# Define labels of the classes and subclasses\n",
    "\n",
    "CLASSES = [\"healthy\", \"ill\"]\n",
    "SUBCLASSES = [\"fever\", \"sore throat\", \"running nose\"]"
   ]
  },
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    "folder = \"/content/drive/MyDrive/FYP/dataset\"\n",
    "img_array = []\n",
    "class_num_list1 = []\n",
    "subcls_num_list1 = [\"None\"]*500   # fill the list with 500 \"None's\" first to represent \"healthy\" samples\n",
    "\n",
    "for c in CLASSES: \n",
    "        \n",
    "    if (c == \"healthy\"):\n",
    "        path = os.path.join(folder,c)  # create path to dataset folder\n",
    "        class_num = 0  # get the CLASSES index, 0 = healthy, 1 = ill\n",
    "        for img in tqdm(os.listdir(path)):  # iterate over each image per folder\n",
    "            try:\n",
    "                img = cv2.imread(os.path.join(path,img))  # read image into a list \n",
    "                img_array.append(img)\n",
    "                class_num_list1.append(class_num) # save a list of the class numbers\n",
    "            except Exception as e: \n",
    "                print(\"error\")\n",
    "                \n",
    "    elif (c == \"ill\"):\n",
    "        for s in SUBCLASSES:\n",
    "            path = os.path.join(folder,c,s) # create path to ill subfolders \n",
    "            class_num = 1\n",
    "            subclass_num = SUBCLASSES.index(s)\n",
    "            for img in tqdm(os.listdir(path)):  # iterate over each image per folder\n",
    "                try:\n",
    "                    img = cv2.imread(os.path.join(path,img))  # read image into a list\n",
    "                    img_array.append(img)\n",
    "                    class_num_list1.append(class_num) \n",
    "                    subcls_num_list1.append(subclass_num) # save a list of the subclass numbers; 0 = fever, 1 = sore throat, 2 = running nose\n",
    "                except Exception as e: \n",
    "                    print(\"error\")"
   ]
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    "folder_aug = \"/content/drive/MyDrive/FYP/augmented images\"\n",
    "aug_array = []\n",
    "class_num_list2 = []\n",
    "subcls_num_list2 = [\"None\"]*500 # fill the list with 500 \"None's\" first to represent \"healthy\" samples\n",
    "\n",
    "for c in CLASSES: \n",
    "        \n",
    "    if (c == \"healthy\"):\n",
    "        path = os.path.join(folder_aug,c)  # create path to dataset folder_aug\n",
    "        class_num = 0  # get the CLASSES index, 0 = healthy, 1 = ill\n",
    "        for img in tqdm(os.listdir(path)):  # iterate over each image per folder_aug\n",
    "            try:\n",
    "                img = cv2.imread(os.path.join(path,img))  # read image into a list \n",
    "                aug_array.append(img)\n",
    "                class_num_list2.append(class_num) # save a list of the class numbers\n",
    "            except Exception as e: \n",
    "                print(\"error\")\n",
    "                \n",
    "    elif (c == \"ill\"):\n",
    "        for s in SUBCLASSES:\n",
    "            path = os.path.join(folder_aug,c,s) # create path to ill subfolder_augs \n",
    "            class_num = 1\n",
    "            subclass_num = SUBCLASSES.index(s)\n",
    "            for img in tqdm(os.listdir(path)):  # iterate over each image per folder_aug\n",
    "                try:\n",
    "                    img = cv2.imread(os.path.join(path,img))  # read image into a list\n",
    "                    aug_array.append(img)\n",
    "                    class_num_list2.append(class_num) \n",
    "                    subcls_num_list2.append(subclass_num) # save a list of the subclass numbers; 0 = fever, 1 = sore throat, 2 = running nose\n",
    "                except Exception as e: \n",
    "                    print(\"error\")"
   ]
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     "text": [
      "1466\n"
     ]
    }
   ],
   "source": [
    "# Combine the original and augmented images into a new list\n",
    "\n",
    "all_imgs = []\n",
    "\n",
    "all_imgs = img_array.copy()\n",
    "\n",
    "for i, img in enumerate(aug_array):\n",
    "    all_imgs.append(img)\n",
    "\n",
    "print(len(all_imgs))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "-IK1KAsRF0ou"
   },
   "source": [
    "## Feature Extraction"
   ]
  },
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    "id": "adzcQf6RKV6T"
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   "source": [
    "# Apply Gabor filter to extract features\n",
    "def build_filters():\n",
    "    filters = []\n",
    "    ksize = 31\n",
    "    for theta in np.arange(0, np.pi, np.pi / 16):\n",
    "        kern = cv2.getGaborKernel((ksize, ksize), 4.0, theta, 10.0, 0.5, 0, ktype=cv2.CV_32F)\n",
    "        kern /= 1.5*kern.sum()\n",
    "        filters.append(kern)\n",
    "    return filters\n",
    " \n",
    "def process(img, filters):\n",
    "    accum = np.zeros_like(img)\n",
    "    for kern in filters:\n",
    "        fimg = cv2.filter2D(img, cv2.CV_8UC3, kern)\n",
    "        np.maximum(accum, fimg, accum)\n",
    "    return accum"
   ]
  },
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      "\n"
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   "source": [
    "# Extract all features from the whole dataset\n",
    "\n",
    "all_gab_features = []\n",
    "\n",
    "for img in tqdm(all_imgs):\n",
    "    img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) # convert color image to grayscale\n",
    "    filters = build_filters()\n",
    "    extracted = process(img, filters)\n",
    "    all_gab_features.append(extracted)"
   ]
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     "text": [
      "733\n",
      "733\n"
     ]
    }
   ],
   "source": [
    "# Separate the features of augmented images from original images\n",
    "# to prevent information leakage during splitting \n",
    "\n",
    "aug_gab_features = []\n",
    "gab_features = []\n",
    "\n",
    "for j in range(len(all_gab_features)):\n",
    "    if(j >= 733):\n",
    "        aug_gab_features.append(all_gab_features[j])\n",
    "    else:\n",
    "        gab_features.append(all_gab_features[j])\n",
    "\n",
    "print(len(gab_features))\n",
    "print(len(aug_gab_features))"
   ]
  },
  {
   "cell_type": "markdown",
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    "id": "7J5IOQKAygCo"
   },
   "source": [
    "## First-Level Classification\n",
    "> ### *Classify samples into healthy (0) and ill (1) classes*\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "WEkhJEdcygCu"
   },
   "outputs": [],
   "source": [
    "# Create partial dataset with features of original images (will add in the augmented set after spliting)\n",
    "dataset = []\n",
    "\n",
    "for i, features in enumerate(gab_features):  \n",
    "    try:\n",
    "        img_gray = cv2.cvtColor(img_array[i], cv2.COLOR_BGR2GRAY)\n",
    "        dataset.append([features, class_num_list1[i], subcls_num_list1[i], img_gray])\n",
    "    except Exception as e: \n",
    "        print(\"error\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "z-b6W-qBygCv"
   },
   "outputs": [],
   "source": [
    "# Create the augmented images' dataset \n",
    "aug_dataset = []\n",
    "\n",
    "for i, aug_features in enumerate(aug_gab_features):  \n",
    "    try:\n",
    "        aug_gray = cv2.cvtColor(aug_array[i], cv2.COLOR_BGR2GRAY)\n",
    "        aug_dataset.append([aug_features, class_num_list2[i], subcls_num_list2[i], aug_gray])\n",
    "    except Exception as e: \n",
    "        print(\"error\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 55748,
     "status": "ok",
     "timestamp": 1611412430646,
     "user": {
      "displayName": "KHONG FAN YI",
      "photoUrl": "",
      "userId": "02416044610109286808"
     },
     "user_tz": -480
    },
    "id": "8ZTjTzlFygCv",
    "outputId": "ff387977-12b9-42a2-d5d4-30c5e44b4ac7"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train set length before data augmentation:  586\n",
      "Test set length:  147\n"
     ]
    }
   ],
   "source": [
    "# Split the original dataset\n",
    "train_set_before, test_set = train_test_split(dataset, test_size=0.2, shuffle=True)\n",
    "\n",
    "print('Train set length before data augmentation: ', len(train_set_before))\n",
    "print('Test set length: ', len(test_set))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 56139,
     "status": "ok",
     "timestamp": 1611412431043,
     "user": {
      "displayName": "KHONG FAN YI",
      "photoUrl": "",
      "userId": "02416044610109286808"
     },
     "user_tz": -480
    },
    "id": "CVST_RV9ygCw",
    "outputId": "ba1d1d7d-2d94-483e-a231-520c2bd85175"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1319"
      ]
     },
     "execution_count": 15,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Add the augmented dataset into the training set, then shuffle\n",
    "train_set = []\n",
    "\n",
    "train_set = train_set_before.copy()\n",
    "\n",
    "for aug_set in aug_dataset:  \n",
    "    if (len(train_set) < 1319):\n",
    "        train_set.append(aug_set)\n",
    "        \n",
    "random.shuffle(train_set)\n",
    "len(train_set)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 56135,
     "status": "ok",
     "timestamp": 1611412431046,
     "user": {
      "displayName": "KHONG FAN YI",
      "photoUrl": "",
      "userId": "02416044610109286808"
     },
     "user_tz": -480
    },
    "id": "piVebWAbygCx",
    "outputId": "1efe3504-37a8-4eac-fc88-1fe873428ae1"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1319, 40000)\n",
      "(1319,)\n"
     ]
    }
   ],
   "source": [
    "# Create training set\n",
    "\n",
    "X_train = []\n",
    "y_train = []\n",
    "gray_img1 = []\n",
    "subcls_num = []\n",
    "\n",
    "for features, label, subcls, gray in train_set:\n",
    "    X_train.append(features)\n",
    "    y_train.append(label)\n",
    "    gray_img1.append(gray)\n",
    "    subcls_num.append(subcls)\n",
    "\n",
    "X_train = np.array(X_train).reshape(len(y_train), -1)\n",
    "y_train = np.array(y_train)\n",
    "\n",
    "print(X_train.shape)\n",
    "print(y_train.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 56126,
     "status": "ok",
     "timestamp": 1611412431048,
     "user": {
      "displayName": "KHONG FAN YI",
      "photoUrl": "",
      "userId": "02416044610109286808"
     },
     "user_tz": -480
    },
    "id": "fh0-oxXpygCx",
    "outputId": "8f218c84-9b9d-4467-b519-340c23ff04f9"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(147, 40000)\n",
      "(147,)\n"
     ]
    }
   ],
   "source": [
    "# Create testing set\n",
    "\n",
    "X_test = []\n",
    "y_test = []\n",
    "gray_img2 = []\n",
    "\n",
    "for features, label, subcls, gray in test_set:\n",
    "    X_test.append(features)\n",
    "    y_test.append(label)\n",
    "    gray_img2.append(gray)\n",
    "\n",
    "X_test = np.array(X_test).reshape(len(y_test), -1)\n",
    "y_test = np.array(y_test)\n",
    "\n",
    "print(X_test.shape)\n",
    "print(y_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "4eO3wQoPygCy"
   },
   "source": [
    "### Train Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 241769,
     "status": "ok",
     "timestamp": 1611412616705,
     "user": {
      "displayName": "KHONG FAN YI",
      "photoUrl": "",
      "userId": "02416044610109286808"
     },
     "user_tz": -480
    },
    "id": "roQhL5F7ygCy",
    "outputId": "63e8c38d-01f9-4167-eb5b-f709cc255eda",
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "185.52421593666077 seconds\n"
     ]
    }
   ],
   "source": [
    "# Train the classifier\n",
    "\n",
    "svm = LinearSVC(C=1, max_iter=8000)   # LinearSVC is faster than SVC\n",
    "\n",
    "start_train = time.time() # to record the training time\n",
    "svm.fit(X_train, y_train)\n",
    "end_train = time.time()\n",
    "print(end_train - start_train, \"seconds\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "5Ugi9ASLygCz"
   },
   "source": [
    "#### Training Performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 241765,
     "status": "ok",
     "timestamp": 1611412616709,
     "user": {
      "displayName": "KHONG FAN YI",
      "photoUrl": "",
      "userId": "02416044610109286808"
     },
     "user_tz": -480
    },
    "id": "NRuEyap2ygCz",
    "outputId": "4ea2d5d8-2b7c-432f-ab77-3f4d51cb3845",
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classification report for - \n",
      "LinearSVC(C=1, class_weight=None, dual=True, fit_intercept=True,\n",
      "          intercept_scaling=1, loss='squared_hinge', max_iter=8000,\n",
      "          multi_class='ovr', penalty='l2', random_state=None, tol=0.0001,\n",
      "          verbose=0):\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00       897\n",
      "           1       1.00      1.00      1.00       422\n",
      "\n",
      "    accuracy                           1.00      1319\n",
      "   macro avg       1.00      1.00      1.00      1319\n",
      "weighted avg       1.00      1.00      1.00      1319\n",
      "\n",
      "\n",
      "Training accuracy: 100.0\n"
     ]
    }
   ],
   "source": [
    "y_pred = svm.predict(X_train)\n",
    "\n",
    "ill_pred = y_pred\n",
    "\n",
    "print(\"Classification report for - \\n{}:\\n{}\\n\".format(\n",
    "    svm, metrics.classification_report(y_train, y_pred)))\n",
    "\n",
    "train_acc = metrics.accuracy_score(y_train, y_pred)*100\n",
    "print(\"Training accuracy: \" + str(train_acc))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 296
    },
    "executionInfo": {
     "elapsed": 242335,
     "status": "ok",
     "timestamp": 1611412617288,
     "user": {
      "displayName": "KHONG FAN YI",
      "photoUrl": "",
      "userId": "02416044610109286808"
     },
     "user_tz": -480
    },
    "id": "2D5esNSOygC0",
    "outputId": "81536bba-44c4-400a-8e06-e59d34c66517",
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x7f053b0239e8>"
      ]
     },
     "execution_count": 20,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_confusion_matrix(svm, X_train, y_train, cmap='plasma')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "x_OuXxwGygC0"
   },
   "source": [
    "#### Testing Performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 242330,
     "status": "ok",
     "timestamp": 1611412617291,
     "user": {
      "displayName": "KHONG FAN YI",
      "photoUrl": "",
      "userId": "02416044610109286808"
     },
     "user_tz": -480
    },
    "id": "k09gFyEnygC0",
    "outputId": "64167105-1b1d-43c3-a7ea-57cd86191750"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classification report for - \n",
      "LinearSVC(C=1, class_weight=None, dual=True, fit_intercept=True,\n",
      "          intercept_scaling=1, loss='squared_hinge', max_iter=8000,\n",
      "          multi_class='ovr', penalty='l2', random_state=None, tol=0.0001,\n",
      "          verbose=0):\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.85      0.86      0.86       103\n",
      "           1       0.67      0.64      0.65        44\n",
      "\n",
      "    accuracy                           0.80       147\n",
      "   macro avg       0.76      0.75      0.75       147\n",
      "weighted avg       0.79      0.80      0.79       147\n",
      "\n",
      "\n",
      "Testing accuracy: 79.59183673469387\n"
     ]
    }
   ],
   "source": [
    "y_pred = svm.predict(X_test)\n",
    "\n",
    "print(\"Classification report for - \\n{}:\\n{}\\n\".format(\n",
    "    svm, metrics.classification_report(y_test, y_pred)))\n",
    "\n",
    "test_acc = metrics.accuracy_score(y_test, y_pred)*100\n",
    "print(\"Testing accuracy: \" + str(test_acc))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 296
    },
    "executionInfo": {
     "elapsed": 242323,
     "status": "ok",
     "timestamp": 1611412617294,
     "user": {
      "displayName": "KHONG FAN YI",
      "photoUrl": "",
      "userId": "02416044610109286808"
     },
     "user_tz": -480
    },
    "id": "ZCaafwoIygC1",
    "outputId": "3552fbb3-6a66-4c3e-f5ca-a6d97ca318d4"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x7f053b0237b8>"
      ]
     },
     "execution_count": 22,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_confusion_matrix(svm, X_test, y_test, cmap='plasma')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "SNqix4fEygC1"
   },
   "outputs": [],
   "source": [
    "# Get wrongly classified samples\n",
    "def get_wrong_case(pred_result, test_result):\n",
    "    \n",
    "    for i in range(len(pred_result)):\n",
    "        predicted = pred_result[i]\n",
    "        actual = test_result[i]\n",
    "        if(actual != predicted):\n",
    "            wrong_case.append([i,predicted,actual])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "zjAVaEpDygC1"
   },
   "outputs": [],
   "source": [
    "def plot(wrong_case, row, col, gray, labels):\n",
    "    \n",
    "    if (labels == SUBCLASSES):\n",
    "        fsize = 18\n",
    "        top = 5\n",
    "        bottom = 4.3\n",
    "    else:\n",
    "        fsize = 20\n",
    "        top = 3\n",
    "        bottom = 2.4\n",
    "        \n",
    "    \n",
    "    plt.figure(figsize=(40, 40))\n",
    "    plt.subplots_adjust(top=top, bottom=bottom)\n",
    "    \n",
    "    for i in range(len(wrong_case)):\n",
    "        plt.subplot(row, col, i+1)\n",
    "        plt.xticks([])\n",
    "        plt.yticks([])\n",
    "        plt.grid(False)\n",
    "        title = str(i+1) + '. Predicted: ' + labels[wrong_case[i][1]] + '\\nActual: ' + labels[wrong_case[i][2]]\n",
    "        plt.title(title, fontdict = {'fontsize' : fsize})\n",
    "        plt.imshow(gray[wrong_case[i][0]], cmap='gray')\n",
    "\n",
    "    plt.show()\n",
    "    \n",
    "    print(\"The number of misclassified images: \")\n",
    "    print(len(wrong_case))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "2QZqTzbCygC2"
   },
   "outputs": [],
   "source": [
    "wrong_case = []\n",
    "get_wrong_case(y_pred, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Ip_hL0ONygC2"
   },
   "source": [
    "## Second-Level Classification\n",
    "> ### *Classify  'ill' cases into fever (0), sore throat (1) and running nose (2) subclasses*\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "56m9pgxfygC2"
   },
   "outputs": [],
   "source": [
    "ill_case_index = []\n",
    "ill_dataset = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "qvE2gzLOygC2"
   },
   "outputs": [],
   "source": [
    "# Get the indexes of samples correctly classified as \"ill\"\n",
    "\n",
    "for i, a in enumerate(ill_pred):\n",
    "    if((ill_pred[i] == 1) and (ill_pred[i] == y_train[i]) and (subcls_num[i] != \"None\")):  # 1 = ill, filter out the cases with \"healthy\" true labels\n",
    "        ill_case_index.append(i) # save the indexes of ill cases"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "66IDFWLwygC3"
   },
   "outputs": [],
   "source": [
    "# Create dataset for second-level classification\n",
    "\n",
    "for a in ill_case_index:\n",
    "    ill_dataset.append([X_train[a], subcls_num[a], gray_img1[a]])\n",
    "    \n",
    "ill_dataset = np.array(ill_dataset, dtype=object)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 245889,
     "status": "ok",
     "timestamp": 1611412620926,
     "user": {
      "displayName": "KHONG FAN YI",
      "photoUrl": "",
      "userId": "02416044610109286808"
     },
     "user_tz": -480
    },
    "id": "z0yh_gokygC3",
    "outputId": "362093f0-915b-4702-b355-d1bedb0ac30d",
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train set length:  295\n",
      "Test set length:  127\n"
     ]
    }
   ],
   "source": [
    "train_ill_set, test_ill_set = train_test_split(ill_dataset, test_size=0.3, shuffle=True)\n",
    "\n",
    "print('Train set length: ', len(train_ill_set))\n",
    "print('Test set length: ', len(test_ill_set))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 245884,
     "status": "ok",
     "timestamp": 1611412620928,
     "user": {
      "displayName": "KHONG FAN YI",
      "photoUrl": "",
      "userId": "02416044610109286808"
     },
     "user_tz": -480
    },
    "id": "MAUo1H7vygC3",
    "outputId": "f3faa907-bb3e-4c15-bbc8-db510d07d5b5"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(295, 40000)\n",
      "(295,)\n"
     ]
    }
   ],
   "source": [
    "X_train_ill = []\n",
    "y_train_ill = []\n",
    "gray_img_ill1 = []\n",
    "\n",
    "for features, label, gray in train_ill_set:\n",
    "    X_train_ill.append(features)\n",
    "    y_train_ill.append(label)\n",
    "    gray_img_ill1.append(gray)\n",
    "\n",
    "X_train_ill = np.array(X_train_ill).reshape(len(y_train_ill), -1)\n",
    "y_train_ill = np.array(y_train_ill)\n",
    "\n",
    "print(X_train_ill.shape)\n",
    "print(y_train_ill.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 245877,
     "status": "ok",
     "timestamp": 1611412620930,
     "user": {
      "displayName": "KHONG FAN YI",
      "photoUrl": "",
      "userId": "02416044610109286808"
     },
     "user_tz": -480
    },
    "id": "qVW99XF7ygC3",
    "outputId": "d109921e-4294-40da-c027-1235a86ef4a7",
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(127, 40000)\n",
      "(127,)\n"
     ]
    }
   ],
   "source": [
    "X_test_ill = []\n",
    "y_test_ill = []\n",
    "gray_img_ill2 = []\n",
    "\n",
    "for features, label, gray in test_ill_set:\n",
    "    X_test_ill.append(features)\n",
    "    y_test_ill.append(label)\n",
    "    gray_img_ill2.append(gray)\n",
    "\n",
    "X_test_ill = np.array(X_test_ill).reshape(len(y_test_ill), -1)\n",
    "y_test_ill = np.array(y_test_ill)\n",
    "\n",
    "print(X_test_ill.shape)\n",
    "print(y_test_ill.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "_7u9YV00ygC4"
   },
   "outputs": [],
   "source": [
    "scaling = StandardScaler()\n",
    "scaling.fit(X_train_ill) \n",
    "X_train_ill = scaling.transform(X_train_ill)\n",
    "X_test_ill = scaling.transform(X_test_ill)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "PoeU5DxyygC4"
   },
   "source": [
    "### Train Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 251463,
     "status": "ok",
     "timestamp": 1611412626531,
     "user": {
      "displayName": "KHONG FAN YI",
      "photoUrl": "",
      "userId": "02416044610109286808"
     },
     "user_tz": -480
    },
    "id": "rqra6YFDygC4",
    "outputId": "3dd75bfb-866a-449c-f3c9-ee54f64d64af",
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6.702991485595703 seconds\n"
     ]
    }
   ],
   "source": [
    "# Train the classifier\n",
    "\n",
    "svm = SVC(gamma=0.00001, C=1, kernel='rbf')\n",
    "\n",
    "start_train_ill = time.time()\n",
    "svm.fit(X_train_ill, y_train_ill)\n",
    "end_train_ill = time.time()\n",
    "print(end_train_ill - start_train_ill, \"seconds\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "swJ8LjgWygC4"
   },
   "source": [
    "#### Training Performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 257468,
     "status": "ok",
     "timestamp": 1611412632543,
     "user": {
      "displayName": "KHONG FAN YI",
      "photoUrl": "",
      "userId": "02416044610109286808"
     },
     "user_tz": -480
    },
    "id": "3ala_g2bygC5",
    "outputId": "01cdc46b-b272-4ad9-e6d0-e00f90727360"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classification report for - \n",
      "SVC(C=1, break_ties=False, cache_size=200, class_weight=None, coef0=0.0,\n",
      "    decision_function_shape='ovr', degree=3, gamma=1e-05, kernel='rbf',\n",
      "    max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
      "    tol=0.001, verbose=False):\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.82      0.89      0.85        99\n",
      "           1       0.91      0.94      0.92        96\n",
      "           2       0.90      0.80      0.85       100\n",
      "\n",
      "    accuracy                           0.87       295\n",
      "   macro avg       0.88      0.88      0.87       295\n",
      "weighted avg       0.88      0.87      0.87       295\n",
      "\n",
      "\n",
      "Training accuracy: 87.45762711864407\n"
     ]
    }
   ],
   "source": [
    "y_pred_ill = svm.predict(X_train_ill)\n",
    "\n",
    "print(\"Classification report for - \\n{}:\\n{}\\n\".format(\n",
    "    svm, metrics.classification_report(y_train_ill, y_pred_ill)))\n",
    "\n",
    "train_ill_acc = metrics.accuracy_score(y_train_ill, y_pred_ill)*100\n",
    "print(\"Training accuracy: \" + str(train_ill_acc))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 301
    },
    "executionInfo": {
     "elapsed": 263316,
     "status": "ok",
     "timestamp": 1611412638398,
     "user": {
      "displayName": "KHONG FAN YI",
      "photoUrl": "",
      "userId": "02416044610109286808"
     },
     "user_tz": -480
    },
    "id": "nQxLihzDygC5",
    "outputId": "b0aa1295-37cd-4576-b002-95e84a64d953",
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x7f0532267cc0>"
      ]
     },
     "execution_count": 36,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_confusion_matrix(svm, X_train_ill, y_train_ill, cmap='viridis')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "XmCjmxIsygC5"
   },
   "source": [
    "#### Testing Performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 265993,
     "status": "ok",
     "timestamp": 1611412641083,
     "user": {
      "displayName": "KHONG FAN YI",
      "photoUrl": "",
      "userId": "02416044610109286808"
     },
     "user_tz": -480
    },
    "id": "wrFfZUFuygC5",
    "outputId": "40a04c4d-83e7-4fa5-9738-6278017e2722"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classification report for - \n",
      "SVC(C=1, break_ties=False, cache_size=200, class_weight=None, coef0=0.0,\n",
      "    decision_function_shape='ovr', degree=3, gamma=1e-05, kernel='rbf',\n",
      "    max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
      "    tol=0.001, verbose=False):\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.56      0.53      0.55        45\n",
      "           1       0.81      0.88      0.84        48\n",
      "           2       0.41      0.38      0.39        34\n",
      "\n",
      "    accuracy                           0.62       127\n",
      "   macro avg       0.59      0.60      0.59       127\n",
      "weighted avg       0.61      0.62      0.62       127\n",
      "\n",
      "\n",
      "Testing accuracy: 62.20472440944882\n"
     ]
    }
   ],
   "source": [
    "y_pred_ill = svm.predict(X_test_ill)\n",
    "\n",
    "print(\"Classification report for - \\n{}:\\n{}\\n\".format(\n",
    "    svm, metrics.classification_report(y_test_ill, y_pred_ill)))\n",
    "\n",
    "test_ill_acc = metrics.accuracy_score(y_test_ill, y_pred_ill)*100\n",
    "print(\"Testing accuracy: \" + str(test_ill_acc))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 296
    },
    "executionInfo": {
     "elapsed": 268605,
     "status": "ok",
     "timestamp": 1611412643703,
     "user": {
      "displayName": "KHONG FAN YI",
      "photoUrl": "",
      "userId": "02416044610109286808"
     },
     "user_tz": -480
    },
    "id": "A11HC6qsygC5",
    "outputId": "b55a83e7-bed9-420a-84de-4319f45a25a7",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x7f0532267e10>"
      ]
     },
     "execution_count": 38,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_confusion_matrix(svm, X_test_ill, y_test_ill, cmap='viridis')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "xB_jBx2MygC6",
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "wrong_case = []\n",
    "get_wrong_case(y_pred_ill, y_test_ill)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 995,
     "output_embedded_package_id": "1YLpZC2pX8K7P72_hOLxmgcqQPB5huco7"
    },
    "executionInfo": {
     "elapsed": 7619,
     "status": "ok",
     "timestamp": 1611413552635,
     "user": {
      "displayName": "KHONG FAN YI",
      "photoUrl": "",
      "userId": "02416044610109286808"
     },
     "user_tz": -480
    },
    "id": "7PDTXprvygC6",
    "outputId": "c4a76704-b50c-4e69-b49c-cde486887071"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Output hidden; open in https://colab.research.google.com to view."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# To visualise misclassified samples\n",
    "col = 8\n",
    "row = len(wrong_case) / col + len(wrong_case) % 8\n",
    "\n",
    "plot(wrong_case, row, col, gray_img_ill2, SUBCLASSES)"
   ]
  }
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