PCA_KNN_classification.ipynb

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     "text": [
      "Mounted at /content/drive\n"
     ]
    }
   ],
   "source": [
    "from google.colab import drive\n",
    "drive.mount('/content/drive')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "iqI5L27rHjAg"
   },
   "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 matplotlib.pyplot as plt\n",
    "import pickle\n",
    "from tqdm.notebook import tqdm\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.decomposition import PCA\n",
    "import sklearn.metrics as metrics\n",
    "from sklearn.metrics import plot_confusion_matrix\n",
    "from sklearn.neighbors import KNeighborsClassifier"
   ]
  },
  {
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   "source": [
    "# Define labels of the classes and subclasses\n",
    "\n",
    "CLASSES = [\"healthy\", \"ill\"]\n",
    "SUBCLASSES = [\"fever\", \"sore throat\", \"running nose\"]"
   ]
  },
  {
   "cell_type": "markdown",
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   "source": [
    "##Load Dataset"
   ]
  },
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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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   "source": [
    "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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   "cell_type": "markdown",
   "metadata": {
    "id": "2kLF31p6HGit"
   },
   "source": [
    "## First-Level Classification\n",
    "> ### *Classify samples into healthy (0) and ill (1) classes*\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
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   "outputs": [],
   "source": [
    "# Create partial dataset of original images (will add in the augmented set after spliting)\n",
    "dataset = []\n",
    "\n",
    "for i, img in enumerate(img_array):  \n",
    "    try:\n",
    "        img_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n",
    "        dataset.append([img_gray, class_num_list1[i], subcls_num_list1[i]])\n",
    "    except Exception as e: \n",
    "        print(\"error\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
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   "outputs": [],
   "source": [
    "# Create the augmented images' dataset \n",
    "aug_dataset = []\n",
    "\n",
    "for i, aug in enumerate(aug_array):  \n",
    "    try:\n",
    "        aug_gray = cv2.cvtColor(aug, cv2.COLOR_BGR2GRAY)\n",
    "        aug_dataset.append([aug_gray, class_num_list2[i], subcls_num_list2[i]])\n",
    "    except Exception as e: \n",
    "        print(\"error\")"
   ]
  },
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    "executionInfo": {
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     "timestamp": 1611338749833,
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   "outputs": [
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     "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))"
   ]
  },
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     "execution_count": 10,
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   "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)"
   ]
  },
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     "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 in train_set:\n",
    "    X_train.append(features)\n",
    "    y_train.append(label)\n",
    "    subcls_num.append(subcls)\n",
    "    gray_img1.append(features) \n",
    "\n",
    "#X_train = np.array(X_train).reshape(-1, 200, 200, 1)\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": 108,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 1461,
     "status": "ok",
     "timestamp": 1611340662818,
     "user": {
      "displayName": "KHONG FAN YI",
      "photoUrl": "",
      "userId": "02416044610109286808"
     },
     "user_tz": -480
    },
    "id": "m8kNRZrX5OwO",
    "outputId": "c4859b4b-4970-48d3-8fca-e80e92644d70"
   },
   "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 in test_set:\n",
    "    X_test.append(features)\n",
    "    y_test.append(label)\n",
    "    gray_img2.append(features)\n",
    "\n",
    "#X_test = np.array(X_test).reshape(-1, 200, 200, 1)\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": "-IK1KAsRF0ou"
   },
   "source": [
    "### Feature Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {
    "executionInfo": {
     "elapsed": 7912,
     "status": "ok",
     "timestamp": 1611340669285,
     "user": {
      "displayName": "KHONG FAN YI",
      "photoUrl": "",
      "userId": "02416044610109286808"
     },
     "user_tz": -480
    },
    "id": "wXb4-XExdd8H",
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# Apply PCA to extract features\n",
    "n_components = 30\n",
    "\n",
    "pca = PCA(n_components=n_components, svd_solver='randomized',\n",
    "          whiten=True).fit(X_train)\n",
    "X_train = pca.transform(X_train)\n",
    "X_test = pca.transform(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "84UEBtn7idcv"
   },
   "source": [
    "### Train Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 7907,
     "status": "ok",
     "timestamp": 1611340669293,
     "user": {
      "displayName": "KHONG FAN YI",
      "photoUrl": "",
      "userId": "02416044610109286808"
     },
     "user_tz": -480
    },
    "id": "yw-8CAJS5OwW",
    "outputId": "83dee3c5-f4a7-43c6-a8d7-e1aec3a73c32"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.010081768035888672 seconds\n"
     ]
    }
   ],
   "source": [
    "# Train the classifier\n",
    "\n",
    "knn = KNeighborsClassifier(n_neighbors=1, n_jobs=2, weights='distance')\n",
    "\n",
    "start_train = time.time() # to record the training time\n",
    "knn.fit(X_train, y_train)\n",
    "end_train = time.time()\n",
    "print(end_train - start_train, \"seconds\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "EN0feoe9o5yx"
   },
   "source": [
    "#### Training Performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 7895,
     "status": "ok",
     "timestamp": 1611340669294,
     "user": {
      "displayName": "KHONG FAN YI",
      "photoUrl": "",
      "userId": "02416044610109286808"
     },
     "user_tz": -480
    },
    "id": "5flMCsPGcS-C",
    "outputId": "7e1752d8-78d4-4d9d-c8a5-64afcb75af49",
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classification report for - \n",
      "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
      "                     metric_params=None, n_jobs=2, n_neighbors=1, p=2,\n",
      "                     weights='distance'):\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00       899\n",
      "           1       1.00      1.00      1.00       420\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 = knn.predict(X_train)\n",
    "\n",
    "ill_pred = y_pred\n",
    "\n",
    "print(\"Classification report for - \\n{}:\\n{}\\n\".format(\n",
    "    knn, 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": 112,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 295
    },
    "executionInfo": {
     "elapsed": 8683,
     "status": "ok",
     "timestamp": 1611340670097,
     "user": {
      "displayName": "KHONG FAN YI",
      "photoUrl": "",
      "userId": "02416044610109286808"
     },
     "user_tz": -480
    },
    "id": "zBZGN1w9cS-D",
    "outputId": "7cd4aff0-162e-4ec9-ef62-a35548ef1a7c"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x7fbd13a630b8>"
      ]
     },
     "execution_count": 112,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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vAP8jw2fNrHByZdUaLGlqq/3JETG51f4vSHqIO6b7OwHLI2J9ut869+mGvKgRsV7S6+n5r7V38SzLJb2r9X66ysjp7ZxuZkUXENkn27ebF1XSh4DFETFN0mGlql5ruWcyRMTjkvYvR2XMrPqVcEXfg4EPSzqGpHfYD7gIGCCpW9qKa537tCUv6nxJ3UhSJyzp6AJZks58rdVuHTAOWJjzi5hZgZRisn1EfJs0v3LagvtGRHxK0nUkuWCuZsu8qKeQrCg+Ebg7zbTVriwtuB1bvV5Pck/uhuxfw8yKprm8z8F9C7ha0o+A6cBl6fHLgD9ImgUsBU7srKAOA1z6gO+OEfGNrtXXzAqjDEuWR8S9wL3p6znAFvPdI+JN4ON5yu1oyfJu6UjFwblqamaFFvlGUSuqoxbcoyT322ZIuhm4Dljd8mZE3FjmuplZlaqVqVpZ7sH1IhmpOIKNz8MF4ABntj3K95hIRXUU4HZOR1CfZmNga1EjCxabWTkUoQVXD+zApoGthQOc2XYqamg1kY4CXGNEnLfNamJmNaMIAa42voGZbVsBTQUYRR2/zWphZjWjhFO1yq6jxM9Lt2VFzKx2RI2s6e20gWaWUzEGGczMthRln4taMg5wZpZLQCGmapmZtcldVDMrKLmLambFlGS2r3QtsqmNjrSZVZVoVqatI5J6SXpU0hOSnpH0g/T4tsuLama2uRKlDXwLOCIi9gL2BiZIOoBtmRfVzKy1CGhqVqat43IiImJVuts93YIS5kV1gDOz3HK04AZLmtpqm9S6HEn1kmYAi4E7gdlkzIsKtORFbZcHGcwsp1yjqO3mRQWIiCZgb0kDgJuAd5agghu4BWdmuSST7bNtmcuMWA7cAxxImhc1fautvKhkzYvqAGdmuZVikEHSkLTlhqTewAeA50gC3cT0tLbyokIJ86KamW0U0NRUkgd9G4Dfp+lJ64BrI+Kvkp5lW+RFNTPbXKnWg4uIJ4H3tHG8/HlRzcza5qlaZlZUNTRVywHOzHIJvB6cmRWYW3BmVlidTcOqFg5wZpZLLS2X5ABnZrn5HpyZFZZbcMZhR87jBxc8RH19cNXvx3LxhXtXukrFVtfMmEtuZd1rfZj7nU3zlg8+/hkGHTOTaKpj/fKezP/ZwaxbvEOXLle/41uM/M599Bi6irX/2oFXfvh+mlb1ZMARcxhy4tOgoHlNdxZcdABvzhnUpWtVm1oJcGWbiyrpckmLJT1drmtUs7q6Zn7083/wmeMncPh7J3LcxNmMGbus0tUqtMEffY43X+nf5ntvzBrEzNM/xMxJH+b1B0bRMGla5nL77rWI4d98cIvjQ058ilXTG3jhsx9j1fSGJKgBaxftwJyvfZCZpx3H4j++m+FnP7R1X6hKtTwmkmWrtHJOtv8dMKGM5Ve1vfd9lblz+vHK3H6sW1fPX27YjaOOfbnS1Sqs7oNXs+P+81k6ZUyb769+ooF4K+mwrHluCN0Hr97w3pATnmb3i//KmMk3M/TkGZmv2f+geSy7YzcAlt2xG/0PfiUp/9mdaVrVc+O1hqxut4yaFNCUcau0sgW4iLifZELsdqmhYTWN8zd2gRYt7EvDLgX7H72KNJz+GIt+uy9kaDUMmjCTlY8layjusM8CegxbwawvH8vML/w7vd+xhL7vWpTpmt0GvsH6pX0AWL+0N90GvrHFOQOPnsnKR4fn+CbVL1DmrdIqfg8uXeFzEoAYUOHaWC3acf95rF/eizdm7kTfvToOTgPGz6b32CXM+VrSudhxn4XsuM9CxvzmFgDqeq+nx7CVrH7qbez+q1tR9ybqeq+nfse3GPObmwFovHQfVk0dtlnJWy4P1HevRgZNmMXss4vXkWmugtZZFhUPcBExGZgMUF83vEZ+bZ1rbOxLw/BVG/bftstqGhf2rWCNiqvvnovpd+A8+u03H/Voor7POkac8wDzzn/fJuftMG4hO3/yKWZ//YPEuvrkoGDxVe9i6a1jtyh31hnHJuXvtYiBR81i/k8P2eT99ct6023QGtYv7UO3QWtoWt5rw3u9Ri9l+Nf/yUvfPpKmFb0omlr5i+oFL8vkiWlDGP32FYwYtYLu3Zs47vjZ3DllZKWrVUiLLtuH50/6OM9/eiKv/Pj9rJrRsEVw67X7EoZ99SHmfu8Impb33nB85dRdGDRhFnW91gHQbafV1A/YsqvZlhUPjWDgUbMBGHjUbF7/5wgAuu+8ilHfv5d557+PtQvaHvSoZckgQ7at0iregiuqpqY6vvvNg7jypr9RVx9c84exvPh8sR4VqHZDT5nOGy/uxIqHRtIwaRp1vdcz6rv3ArBucV/mfm88q6YNY9nI19ntV1MAaH6jO/N+csgmQbA9i6/ek5HfuY9BE2aydnHymAjA0E8/SX2/txh25sMARFMds778ofJ8yQopxQCCpBHAFcBQkrg5OSIukjQIuAbYFZgLnBARy9IMWhcBxwBrgM9GxOMdXqOTFX+7UvmrgMOAwcC/gHMj4rKOPlNfNzz69PhyWepj5TFlv7WVroLlcNr0/+L5lQu7dPe/QbvFKfrPTOdeECdOay/pjKQGoCEiHpe0IzCNJEXgZ4GlEXG+pHOAgRHxLUnHAGeQBLj9gYsiYv+Orl+2FlxEnFSuss2scgJoLkU5EY1AY/p6paTnSFIDHkfSOIIkD+q9wLfS41ekeRgeljRAUkNaTpt8D87McouMG53kRW0haVeS5csfAYa2ClqLSLqw0Covaqp1ztQ2+R6cmeWWowXXYV5UAEk7ADcAX42IFa2T1UdESNrq+2huwZlZLqXMiyqpO0lwuzIibkwP/yu9P9dyn25xenxDXtRU65ypbXKAM7PcmjJuHUlHRS8DnouI/9vqrdb5TzfPi3qyEgcAr3d0/w3cRTWznEo1yAAcDHwGeEpSyyTg/wWcD1wr6VTgZeCE9L0pJCOos0geE/lcZxdwgDOz3Eo0ivogtDthdfzmB9LR01zPkTnAmVluVTBJIRMHODPLpYRd1LJzgDOznIKokTacA5yZ5dbZCGm1cIAzs1zcRTWzQouskwsq3JN1gDOz3NyCM7NCchfVzAqtqdJ9z4wc4Mwsl2QpJAc4Mysod1HNrLAyJ633KKqZ1ZJkkMFdVDMrKHdRzayQgqiZUVSv6GtmuTUTmbbOSLpc0mJJT7c6NkjSnZJmpj8Hpscl6ZeSZkl6UtK4zsp3gDOz3ELZtgx+B0zY7Ng5wF0RMQa4K90HOBoYk26TgF93VrgDnJnl0jLIUIoWXETcDyzd7PBxJPlQSX9+pNXxKyLxMDCgJTlNexzgzCy3yPjfVnJeVDOrnByjqIMlTW21PzkiJmf9cFfzojrAmVkuOUdRO0383IZ/SWqIiEbnRTWzba5ZkWnbSs6LamaVUcqZDJKuAg4j6crOB87FeVHNrJJK9ZhvRJzUzlvOi2pmleG5qGZWSAGsd4Azs2JyXlQzKygvl2RmxSW68gjINuUAZ2a5OKuWmRWau6hmVkjJVK3aaMM5wJlZbm7BmVlhOcCZWSH5MREzK7TmrHlRK8wBzsxycQvOzAorCNZ5FNXMisotODMrrFoJcF6y3MxyCYImNWfaOiNpgqQX0mTO53T6gZzcgjOzXALyJJ1pl6R64GLgAyQpAB+TdHNEPNvlwlMOcGaWSwBrM7TOMtgPmBURcwAkXU2S3LlkAU7JMufVQdKrJEkmimYw8FqlK2G5FPXPbFREDOlKAZJuI/n9ZNELeLPV/oa8qJImAhMi4vPp/meA/SPiK12pX2tV1YLr6i++WkmauhW5Ia2C/GfWvoiYUOk6ZOVBBjOrlNyJnPNygDOzSnkMGCNptKQewIkkyZ1Lpqq6qAU2udIVsNz8Z1ZmEbFe0leA24F64PKIeKaU16iqQQYzs1JyF9XMCssBzswKywGujMo9DcVKT9LlkhZLerrSdbGuc4Ark1bTUI4G9gBOkrRHZWtlGfwOqJnnvKxjDnDls2EaSkSsBVqmoVgVi4j7gaWVroeVhgNc+QwD5rXan58eM7NtxAHOzArLAa58yj4Nxcw65gBXPmWfhmJmHXOAK5OIWA+0TEN5Dri21NNQrPQkXQU8BIyVNF/SqZWuk209T9Uys8JyC87MCssBzswKywHOzArLAc7MCssBzswKywGuhkhqkjRD0tOSrpPUpwtl/S7NaoSkSztaCEDSYZIO2oprzJW0Rfal9o5vds6qnNf6vqRv5K2jFZsDXG15IyL2jog9gbXAF1u/KWmrlqCPiM93kmz3MCB3gDOrNAe42vUAsHvaunpA0s3As5LqJf1U0mOSnpT0BQAl/l+6Pt3fgZ1bCpJ0r6R909cTJD0u6QlJd0nalSSQnp22Ht8naYikG9JrPCbp4PSzO0m6Q9Izki4F1NmXkPRnSdPSz0za7L0L0+N3SRqSHttN0m3pZx6Q9M5S/DKtmJx0pgalLbWjgdvSQ+OAPSPipTRIvB4R75XUE/iHpDuA9wBjSdamG0qSPfzyzcodAvwWODQta1BELJX0G2BVRPwsPe9PwIUR8aCkkSSzNf4NOBd4MCLOk3QskGUWwP9Mr9EbeEzSDRGxBOgLTI2IsyV9Ly37KyTJYL4YETMl7Q9cAhyxFb9G2w44wNWW3pJmpK8fAC4j6To+GhEvpcePAt7dcn8N6A+MAQ4FroqIJmChpLvbKP8A4P6WsiKivXXRjgT2kDY00PpJ2iG9xsfSz94qaVmG73SmpI+mr0ekdV0CNAPXpMf/CNyYXuMg4LpW1+6Z4Rq2nXKAqy1vRMTerQ+kf9FXtz4EnBERt2923jElrEcdcEBEvNlGXTKTdBhJsDwwItZIuhfo1c7pkV53+ea/A7P2+B5c8dwOfElSdwBJ75DUF7gf+ER6j64BOLyNzz4MHCppdPrZQenxlcCOrc67AzijZUdSS8C5H/hkeuxoYGAnde0PLEuD2ztJWpAt6oCWVugnSbq+K4CXJH08vYYk7dXJNWw75gBXPJeS3F97PE2c8l8kLfWbgJnpe1eQrJixiYh4FZhE0h18go1dxFuAj7YMMgBnAvumgxjPsnE09wckAfIZkq7qK53U9Tagm6TngPNJAmyL1cB+6Xc4AjgvPf4p4NS0fs/gZeCtA15NxMwKyy04MyssBzgzKywHODMrLAc4MyssBzgzKywHODMrLAc4Myus/wbXteH924NMLwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_confusion_matrix(knn, X_train, y_train, cmap='plasma')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "d5qV4luLo5yz"
   },
   "source": [
    "#### Testing Performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 8665,
     "status": "ok",
     "timestamp": 1611340670099,
     "user": {
      "displayName": "KHONG FAN YI",
      "photoUrl": "",
      "userId": "02416044610109286808"
     },
     "user_tz": -480
    },
    "id": "5AzTPuIncS-E",
    "outputId": "c38e72de-7292-408d-fa60-a9855dd9c03b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classification report for - \n",
      "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
      "                     metric_params=None, n_jobs=2, n_neighbors=1, p=2,\n",
      "                     weights='distance'):\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.91      0.95      0.93       101\n",
      "           1       0.88      0.78      0.83        46\n",
      "\n",
      "    accuracy                           0.90       147\n",
      "   macro avg       0.89      0.87      0.88       147\n",
      "weighted avg       0.90      0.90      0.90       147\n",
      "\n",
      "\n",
      "Testing accuracy: 89.79591836734694\n"
     ]
    }
   ],
   "source": [
    "y_pred = knn.predict(X_test)\n",
    "\n",
    "print(\"Classification report for - \\n{}:\\n{}\\n\".format(\n",
    "    knn, 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": 114,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 295
    },
    "executionInfo": {
     "elapsed": 8650,
     "status": "ok",
     "timestamp": 1611340670100,
     "user": {
      "displayName": "KHONG FAN YI",
      "photoUrl": "",
      "userId": "02416044610109286808"
     },
     "user_tz": -480
    },
    "id": "x0VzOzcRcS-F",
    "outputId": "89492d39-6a35-4983-ea6b-b8d5458136ca",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x7fbd12b50c18>"
      ]
     },
     "execution_count": 114,
     "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(knn, X_test, y_test, cmap='plasma')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {
    "executionInfo": {
     "elapsed": 8639,
     "status": "ok",
     "timestamp": 1611340670103,
     "user": {
      "displayName": "KHONG FAN YI",
      "photoUrl": "",
      "userId": "02416044610109286808"
     },
     "user_tz": -480
    },
    "id": "5Pbb-Mm9dd8L"
   },
   "outputs": [],
   "source": [
    "def get_wrong_case(pred_result, test_result):\n",
    "    \n",
    "    #get wrongly classified samples\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": 149,
   "metadata": {
    "executionInfo": {
     "elapsed": 910,
     "status": "ok",
     "timestamp": 1611341255683,
     "user": {
      "displayName": "KHONG FAN YI",
      "photoUrl": "",
      "userId": "02416044610109286808"
     },
     "user_tz": -480
    },
    "id": "ZYpCERhsdd8L"
   },
   "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 = 2.3\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": 117,
   "metadata": {
    "executionInfo": {
     "elapsed": 9085,
     "status": "ok",
     "timestamp": 1611340670566,
     "user": {
      "displayName": "KHONG FAN YI",
      "photoUrl": "",
      "userId": "02416044610109286808"
     },
     "user_tz": -480
    },
    "id": "QljJDCD1dd8M"
   },
   "outputs": [],
   "source": [
    "wrong_case = []\n",
    "get_wrong_case(y_pred, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "v8np4hKTo5y4"
   },
   "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": 119,
   "metadata": {
    "executionInfo": {
     "elapsed": 11968,
     "status": "ok",
     "timestamp": 1611340673475,
     "user": {
      "displayName": "KHONG FAN YI",
      "photoUrl": "",
      "userId": "02416044610109286808"
     },
     "user_tz": -480
    },
    "id": "nrBA0NQzdd8O"
   },
   "outputs": [],
   "source": [
    "ill_case_index = []\n",
    "ill_dataset = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {
    "executionInfo": {
     "elapsed": 11962,
     "status": "ok",
     "timestamp": 1611340673477,
     "user": {
      "displayName": "KHONG FAN YI",
      "photoUrl": "",
      "userId": "02416044610109286808"
     },
     "user_tz": -480
    },
    "id": "j1WjQ8N4dd8P",
    "scrolled": true
   },
   "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": 121,
   "metadata": {
    "executionInfo": {
     "elapsed": 11954,
     "status": "ok",
     "timestamp": 1611340673479,
     "user": {
      "displayName": "KHONG FAN YI",
      "photoUrl": "",
      "userId": "02416044610109286808"
     },
     "user_tz": -480
    },
    "id": "1eV_TObZdd8Q",
    "scrolled": true
   },
   "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]]) # subcls_num[] contains subclass labels (e.g 0, 1, 2)\n",
    "    \n",
    "ill_dataset = np.array(ill_dataset, dtype=object)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 1100,
     "status": "ok",
     "timestamp": 1611340731884,
     "user": {
      "displayName": "KHONG FAN YI",
      "photoUrl": "",
      "userId": "02416044610109286808"
     },
     "user_tz": -480
    },
    "id": "M6m_e-cAdd8U",
    "outputId": "84926edc-5d8c-4166-a264-72b2311bb3e6",
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train set length:  294\n",
      "Test set length:  126\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": 133,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 1837,
     "status": "ok",
     "timestamp": 1611340732646,
     "user": {
      "displayName": "KHONG FAN YI",
      "photoUrl": "",
      "userId": "02416044610109286808"
     },
     "user_tz": -480
    },
    "id": "TUfvP3iMdd8V",
    "outputId": "1328fc0b-19eb-4e4b-c394-6e0d145fff95"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(294, 30)\n",
      "(294,)\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)\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": 134,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 1827,
     "status": "ok",
     "timestamp": 1611340732648,
     "user": {
      "displayName": "KHONG FAN YI",
      "photoUrl": "",
      "userId": "02416044610109286808"
     },
     "user_tz": -480
    },
    "id": "d2AdlGZMdd8W",
    "outputId": "dfd7e5eb-bbf0-4aa4-b358-20bbe988dc62"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(126, 30)\n",
      "(126,)\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)\n",
    "y_test_ill = np.array(y_test_ill)\n",
    "\n",
    "print(X_test_ill.shape)\n",
    "print(y_test_ill.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "HZwp1-eSi04s"
   },
   "source": [
    "### Train Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 1818,
     "status": "ok",
     "timestamp": 1611340732651,
     "user": {
      "displayName": "KHONG FAN YI",
      "photoUrl": "",
      "userId": "02416044610109286808"
     },
     "user_tz": -480
    },
    "id": "sO_ygpoBdd8Y",
    "outputId": "6a83e01b-e896-4f36-f647-30b54bac52df"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0020716190338134766 seconds\n"
     ]
    }
   ],
   "source": [
    "knn = KNeighborsClassifier(n_neighbors=1)\n",
    "\n",
    "start_train_ill = time.time()\n",
    "knn.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": "iflaNAaZIRLQ"
   },
   "source": [
    "#### Training Performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 1804,
     "status": "ok",
     "timestamp": 1611340732654,
     "user": {
      "displayName": "KHONG FAN YI",
      "photoUrl": "",
      "userId": "02416044610109286808"
     },
     "user_tz": -480
    },
    "id": "tUXmJWoRcS-M",
    "outputId": "974ea140-2a32-4479-a0ff-2f4ea9ef37f8",
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classification report for - \n",
      "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
      "                     metric_params=None, n_jobs=None, n_neighbors=1, p=2,\n",
      "                     weights='uniform'):\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00        96\n",
      "           1       1.00      1.00      1.00        97\n",
      "           2       1.00      1.00      1.00       101\n",
      "\n",
      "    accuracy                           1.00       294\n",
      "   macro avg       1.00      1.00      1.00       294\n",
      "weighted avg       1.00      1.00      1.00       294\n",
      "\n",
      "\n",
      "Training accuracy: 100.0\n"
     ]
    }
   ],
   "source": [
    "y_pred_ill = knn.predict(X_train_ill)\n",
    "\n",
    "print(\"Classification report for - \\n{}:\\n{}\\n\".format(\n",
    "    knn, 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": 137,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 297
    },
    "executionInfo": {
     "elapsed": 1792,
     "status": "ok",
     "timestamp": 1611340732658,
     "user": {
      "displayName": "KHONG FAN YI",
      "photoUrl": "",
      "userId": "02416044610109286808"
     },
     "user_tz": -480
    },
    "id": "xnyct16DcS-M",
    "outputId": "116f717b-e7f2-4f13-9df0-e7a24ef2323a",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x7fbd12da2c88>"
      ]
     },
     "execution_count": 137,
     "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(knn, X_train_ill, y_train_ill, cmap='viridis')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "P9WK2WiqIXdl"
   },
   "source": [
    "#### Testing Performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 1778,
     "status": "ok",
     "timestamp": 1611340732660,
     "user": {
      "displayName": "KHONG FAN YI",
      "photoUrl": "",
      "userId": "02416044610109286808"
     },
     "user_tz": -480
    },
    "id": "bzoPpMUMcS-N",
    "outputId": "e41707fa-a6db-4068-c854-d271ad9d911b",
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classification report for - \n",
      "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
      "                     metric_params=None, n_jobs=None, n_neighbors=1, p=2,\n",
      "                     weights='uniform'):\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.79      0.57      0.67        47\n",
      "           1       0.80      0.94      0.86        51\n",
      "           2       0.53      0.61      0.57        28\n",
      "\n",
      "    accuracy                           0.73       126\n",
      "   macro avg       0.71      0.71      0.70       126\n",
      "weighted avg       0.74      0.73      0.72       126\n",
      "\n",
      "\n",
      "Testing accuracy: 73.01587301587301\n"
     ]
    }
   ],
   "source": [
    "y_pred_ill = knn.predict(X_test_ill)\n",
    "\n",
    "print(\"Classification report for - \\n{}:\\n{}\\n\".format(\n",
    "    knn, 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": 139,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 295
    },
    "executionInfo": {
     "elapsed": 2211,
     "status": "ok",
     "timestamp": 1611340733110,
     "user": {
      "displayName": "KHONG FAN YI",
      "photoUrl": "",
      "userId": "02416044610109286808"
     },
     "user_tz": -480
    },
    "id": "XPfD2ihBcS-O",
    "outputId": "7308c0db-3a83-4169-9de2-1af001cfc2de",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x7fbd11c4fe80>"
      ]
     },
     "execution_count": 139,
     "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(knn, X_test_ill, y_test_ill, cmap='viridis')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {
    "executionInfo": {
     "elapsed": 2201,
     "status": "ok",
     "timestamp": 1611340733114,
     "user": {
      "displayName": "KHONG FAN YI",
      "photoUrl": "",
      "userId": "02416044610109286808"
     },
     "user_tz": -480
    },
    "id": "ICP4o56RcS-O"
   },
   "outputs": [],
   "source": [
    "wrong_case = []\n",
    "get_wrong_case(y_pred_ill, y_test_ill)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 961,
     "output_embedded_package_id": "1NYHepdiuOIgLgJxOm317Yd1G3V0YE9kb"
    },
    "executionInfo": {
     "elapsed": 11356,
     "status": "ok",
     "timestamp": 1611341269647,
     "user": {
      "displayName": "KHONG FAN YI",
      "photoUrl": "",
      "userId": "02416044610109286808"
     },
     "user_tz": -480
    },
    "id": "Esb9_-CXcS-O",
    "outputId": "6a0a28fa-03af-4540-ad42-9c8843b968c9"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Output hidden; open in https://colab.research.google.com to view."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "col = 8\n",
    "row = len(wrong_case) / col + len(wrong_case) % 8\n",
    "\n",
    "plot(wrong_case, row, col, gray_img_ill2, SUBCLASSES)"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [],
   "name": "PCA_KNN_classification.ipynb",
   "provenance": []
  },
  "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": "3.8.5"
  },
  "widgets": {
   "application/vnd.jupyter.widget-state+json": {
    "000c881d197043499f48178d235cd33c": {
     "model_module": "@jupyter-widgets/base",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
      "align_items": null,
      "align_self": null,
      "border": null,
      "bottom": null,
      "display": null,
      "flex": null,
      "flex_flow": null,
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