LDA_MLP_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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    "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",
    "import sklearn.metrics as metrics\n",
    "from tqdm.notebook import tqdm\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n",
    "from sklearn.metrics import plot_confusion_matrix\n",
    "from sklearn.neural_network import MLPClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "YzUuI4AIcS9r"
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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",
   "metadata": {
    "id": "ciqkz1NoIE9L"
   },
   "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": null,
   "metadata": {
    "id": "MEuw9582cS91"
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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": null,
   "metadata": {
    "id": "_XSiQmhxcS93"
   },
   "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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   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 1384,
     "status": "ok",
     "timestamp": 1611328744243,
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   "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": {
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    },
    "executionInfo": {
     "elapsed": 1935,
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     "timestamp": 1611328744815,
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "1319"
      ]
     },
     "execution_count": 105,
     "metadata": {
      "tags": []
     },
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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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   "metadata": {
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    },
    "executionInfo": {
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     "status": "ok",
     "timestamp": 1611328744818,
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     "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 img_gray, label, subcls in train_set:\n",
    "    X_train.append(img_gray)\n",
    "    y_train.append(label)\n",
    "    subcls_num.append(subcls)\n",
    "    gray_img1.append(img_gray)\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)"
   ]
  },
  {
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     "text": [
      "(147, 40000)\n",
      "(147,)\n"
     ]
    }
   ],
   "source": [
    "# Create testing set\n",
    "\n",
    "X_test = []\n",
    "y_test = []\n",
    "gray_img2 = []\n",
    "\n",
    "for img_gray, label, subcls in test_set:\n",
    "    X_test.append(img_gray)\n",
    "    y_test.append(label)\n",
    "    gray_img2.append(img_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": "-IK1KAsRF0ou"
   },
   "source": [
    "### Feature Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 34826,
     "status": "ok",
     "timestamp": 1611328777759,
     "user": {
      "displayName": "KHONG FAN YI",
      "photoUrl": "",
      "userId": "02416044610109286808"
     },
     "user_tz": -480
    },
    "id": "78SSEbJhcS9_",
    "outputId": "6ad92fb3-b585-427c-ea5b-804d282ce938",
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1319, 1)\n",
      "(147, 1)\n"
     ]
    }
   ],
   "source": [
    "# Apply LDA to extract features\n",
    "lda = LinearDiscriminantAnalysis(n_components = 1)\n",
    "X_train = lda.fit_transform(X_train, y_train)\n",
    "X_test = lda.transform(X_test)\n",
    "print(X_train.shape)\n",
    "print(X_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "84UEBtn7idcv"
   },
   "source": [
    "### Train Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 36469,
     "status": "ok",
     "timestamp": 1611328779416,
     "user": {
      "displayName": "KHONG FAN YI",
      "photoUrl": "",
      "userId": "02416044610109286808"
     },
     "user_tz": -480
    },
    "id": "TahkdbINcS-A",
    "outputId": "ec8f28ab-252c-4a82-9cc4-7512474d120b",
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.1484034061431885 seconds\n"
     ]
    }
   ],
   "source": [
    "# Train the classifier\n",
    "\n",
    "mlp = MLPClassifier(solver='adam', \n",
    "                    hidden_layer_sizes=(500,500),\n",
    "                    alpha=0.1, \n",
    "                    early_stopping=True,\n",
    "                    random_state=None)\n",
    "\n",
    "start_train = time.time() # to record the training time\n",
    "mlp.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": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 37123,
     "status": "ok",
     "timestamp": 1611328780086,
     "user": {
      "displayName": "KHONG FAN YI",
      "photoUrl": "",
      "userId": "02416044610109286808"
     },
     "user_tz": -480
    },
    "id": "5flMCsPGcS-C",
    "outputId": "49c79eae-e669-46b4-da45-34bcb07a43ef",
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classification report for - \n",
      "MLPClassifier(activation='relu', alpha=0.1, batch_size='auto', beta_1=0.9,\n",
      "              beta_2=0.999, early_stopping=True, epsilon=1e-08,\n",
      "              hidden_layer_sizes=(500, 500), learning_rate='constant',\n",
      "              learning_rate_init=0.001, max_fun=15000, max_iter=200,\n",
      "              momentum=0.9, n_iter_no_change=10, nesterovs_momentum=True,\n",
      "              power_t=0.5, random_state=None, shuffle=True, solver='adam',\n",
      "              tol=0.0001, validation_fraction=0.1, verbose=False,\n",
      "              warm_start=False):\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.99      0.98      0.98       897\n",
      "           1       0.95      0.98      0.96       422\n",
      "\n",
      "    accuracy                           0.98      1319\n",
      "   macro avg       0.97      0.98      0.97      1319\n",
      "weighted avg       0.98      0.98      0.98      1319\n",
      "\n",
      "\n",
      "Training accuracy: 97.64973464746019\n"
     ]
    }
   ],
   "source": [
    "y_pred = mlp.predict(X_train)\n",
    "\n",
    "ill_pred = y_pred\n",
    "\n",
    "print(\"Classification report for - \\n{}:\\n{}\\n\".format(\n",
    "    mlp, 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": 295
    },
    "executionInfo": {
     "elapsed": 37743,
     "status": "ok",
     "timestamp": 1611328780729,
     "user": {
      "displayName": "KHONG FAN YI",
      "photoUrl": "",
      "userId": "02416044610109286808"
     },
     "user_tz": -480
    },
    "id": "zBZGN1w9cS-D",
    "outputId": "85250bcb-7aeb-4c47-c81f-216040fefe9d"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x7f24c188ab38>"
      ]
     },
     "execution_count": 111,
     "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(mlp, X_train, y_train, cmap='plasma')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "d5qV4luLo5yz"
   },
   "source": [
    "#### Testing Performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 37724,
     "status": "ok",
     "timestamp": 1611328780730,
     "user": {
      "displayName": "KHONG FAN YI",
      "photoUrl": "",
      "userId": "02416044610109286808"
     },
     "user_tz": -480
    },
    "id": "5AzTPuIncS-E",
    "outputId": "d3a999c9-ed2c-41a1-ef92-7b8de18b23f0"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classification report for - \n",
      "MLPClassifier(activation='relu', alpha=0.1, batch_size='auto', beta_1=0.9,\n",
      "              beta_2=0.999, early_stopping=True, epsilon=1e-08,\n",
      "              hidden_layer_sizes=(500, 500), learning_rate='constant',\n",
      "              learning_rate_init=0.001, max_fun=15000, max_iter=200,\n",
      "              momentum=0.9, n_iter_no_change=10, nesterovs_momentum=True,\n",
      "              power_t=0.5, random_state=None, shuffle=True, solver='adam',\n",
      "              tol=0.0001, validation_fraction=0.1, verbose=False,\n",
      "              warm_start=False):\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.85      0.89      0.87       103\n",
      "           1       0.72      0.64      0.67        44\n",
      "\n",
      "    accuracy                           0.82       147\n",
      "   macro avg       0.78      0.76      0.77       147\n",
      "weighted avg       0.81      0.82      0.81       147\n",
      "\n",
      "\n",
      "Testing accuracy: 81.63265306122449\n"
     ]
    }
   ],
   "source": [
    "y_pred = mlp.predict(X_test)\n",
    "\n",
    "print(\"Classification report for - \\n{}:\\n{}\\n\".format(\n",
    "    mlp, 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": 295
    },
    "executionInfo": {
     "elapsed": 37701,
     "status": "ok",
     "timestamp": 1611328780733,
     "user": {
      "displayName": "KHONG FAN YI",
      "photoUrl": "",
      "userId": "02416044610109286808"
     },
     "user_tz": -480
    },
    "id": "x0VzOzcRcS-F",
    "outputId": "dae2f728-a844-4c67-917b-dd3c2ceee08e",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x7f24c188a080>"
      ]
     },
     "execution_count": 113,
     "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(mlp, X_test, y_test, cmap='plasma')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "DdxcQY3IcS-G"
   },
   "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": "8ukQNmNXcS-G"
   },
   "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\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": "_Xxjc79ycS-H"
   },
   "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": null,
   "metadata": {
    "id": "hQ0SP3lqo5y7"
   },
   "outputs": [],
   "source": [
    "ill_case_index = []\n",
    "ill_dataset = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "Bieq4CR4o5y8"
   },
   "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": "znoPXebZJMcQ"
   },
   "outputs": [],
   "source": [
    "# Create dataset for second-level classification\n",
    "\n",
    "for a in ill_case_index:\n",
    "    ill_dataset.append([gray_img1[a], subcls_num[a], gray_img1[a]])  # gray_img1 contains raw grayscale images\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": 45007,
     "status": "ok",
     "timestamp": 1611328788148,
     "user": {
      "displayName": "KHONG FAN YI",
      "photoUrl": "",
      "userId": "02416044610109286808"
     },
     "user_tz": -480
    },
    "id": "zCjbIBSOcS-J",
    "outputId": "d92fcb3c-7734-41f7-e5f6-5dd0f61a0dd6"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train set length:  288\n",
      "Test set length:  124\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": 44994,
     "status": "ok",
     "timestamp": 1611328788150,
     "user": {
      "displayName": "KHONG FAN YI",
      "photoUrl": "",
      "userId": "02416044610109286808"
     },
     "user_tz": -480
    },
    "id": "4bhiqhtGcS-J",
    "outputId": "1049e410-debd-423b-9d72-32ca48449ef2"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(288, 40000)\n",
      "(288,)\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": 44980,
     "status": "ok",
     "timestamp": 1611328788152,
     "user": {
      "displayName": "KHONG FAN YI",
      "photoUrl": "",
      "userId": "02416044610109286808"
     },
     "user_tz": -480
    },
    "id": "z_XFBmQucS-K",
    "outputId": "74b9d13c-3e7e-4334-9d5d-e2b8d08458ea"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(124, 40000)\n",
      "(124,)\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": "markdown",
   "metadata": {
    "id": "JyJRfbO_Jq-d"
   },
   "source": [
    "### Feature Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "vo1k_gPWA43D"
   },
   "outputs": [],
   "source": [
    "# Apply LDA on classified ill images to extract features again\n",
    "lda = LinearDiscriminantAnalysis(n_components = 2)\n",
    "X_train_ill = lda.fit_transform(X_train_ill, y_train_ill)\n",
    "X_test_ill = lda.transform(X_test_ill)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "HZwp1-eSi04s"
   },
   "source": [
    "### Train Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 47013,
     "status": "ok",
     "timestamp": 1611328790215,
     "user": {
      "displayName": "KHONG FAN YI",
      "photoUrl": "",
      "userId": "02416044610109286808"
     },
     "user_tz": -480
    },
    "id": "ePJOIx87cS-L",
    "outputId": "c19a381c-ade6-48b3-a453-b7e4bdee83a8"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.492948055267334 seconds\n"
     ]
    }
   ],
   "source": [
    "# Train the classifier\n",
    "\n",
    "mlp = MLPClassifier(solver='adam', \n",
    "                    hidden_layer_sizes=(500,500),\n",
    "                    early_stopping=True,\n",
    "                    random_state=None)\n",
    "\n",
    "start_train_ill = time.time()\n",
    "mlp.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": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 46996,
     "status": "ok",
     "timestamp": 1611328790217,
     "user": {
      "displayName": "KHONG FAN YI",
      "photoUrl": "",
      "userId": "02416044610109286808"
     },
     "user_tz": -480
    },
    "id": "tUXmJWoRcS-M",
    "outputId": "d86b60ff-a6da-4d15-e1ff-a06535f0591d",
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classification report for - \n",
      "MLPClassifier(activation='relu', alpha=0.0001, batch_size='auto', beta_1=0.9,\n",
      "              beta_2=0.999, early_stopping=True, epsilon=1e-08,\n",
      "              hidden_layer_sizes=(500, 500), learning_rate='constant',\n",
      "              learning_rate_init=0.001, max_fun=15000, max_iter=200,\n",
      "              momentum=0.9, n_iter_no_change=10, nesterovs_momentum=True,\n",
      "              power_t=0.5, random_state=None, shuffle=True, solver='adam',\n",
      "              tol=0.0001, validation_fraction=0.1, verbose=False,\n",
      "              warm_start=False):\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.91      0.83      0.87       103\n",
      "           1       0.91      0.99      0.95        88\n",
      "           2       0.87      0.87      0.87        97\n",
      "\n",
      "    accuracy                           0.89       288\n",
      "   macro avg       0.89      0.90      0.89       288\n",
      "weighted avg       0.89      0.89      0.89       288\n",
      "\n",
      "\n",
      "Training accuracy: 89.23611111111111\n"
     ]
    }
   ],
   "source": [
    "y_pred_ill = mlp.predict(X_train_ill)\n",
    "\n",
    "print(\"Classification report for - \\n{}:\\n{}\\n\".format(\n",
    "    mlp, 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": 295
    },
    "executionInfo": {
     "elapsed": 46988,
     "status": "ok",
     "timestamp": 1611328790230,
     "user": {
      "displayName": "KHONG FAN YI",
      "photoUrl": "",
      "userId": "02416044610109286808"
     },
     "user_tz": -480
    },
    "id": "xnyct16DcS-M",
    "outputId": "84803518-3e9e-42ee-9c29-b202d8b87e81",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x7f24c3c7c358>"
      ]
     },
     "execution_count": 127,
     "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(mlp, X_train_ill, y_train_ill, cmap='viridis')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "P9WK2WiqIXdl"
   },
   "source": [
    "#### Testing Performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 46976,
     "status": "ok",
     "timestamp": 1611328790238,
     "user": {
      "displayName": "KHONG FAN YI",
      "photoUrl": "",
      "userId": "02416044610109286808"
     },
     "user_tz": -480
    },
    "id": "bzoPpMUMcS-N",
    "outputId": "0267473b-1b74-4bf4-e0dc-27478f9a82ee",
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classification report for - \n",
      "MLPClassifier(activation='relu', alpha=0.0001, batch_size='auto', beta_1=0.9,\n",
      "              beta_2=0.999, early_stopping=True, epsilon=1e-08,\n",
      "              hidden_layer_sizes=(500, 500), learning_rate='constant',\n",
      "              learning_rate_init=0.001, max_fun=15000, max_iter=200,\n",
      "              momentum=0.9, n_iter_no_change=10, nesterovs_momentum=True,\n",
      "              power_t=0.5, random_state=None, shuffle=True, solver='adam',\n",
      "              tol=0.0001, validation_fraction=0.1, verbose=False,\n",
      "              warm_start=False):\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.54      0.45      0.49        42\n",
      "           1       0.72      0.93      0.82        45\n",
      "           2       0.52      0.43      0.47        37\n",
      "\n",
      "    accuracy                           0.62       124\n",
      "   macro avg       0.59      0.61      0.59       124\n",
      "weighted avg       0.60      0.62      0.60       124\n",
      "\n",
      "\n",
      "Testing accuracy: 62.096774193548384\n"
     ]
    }
   ],
   "source": [
    "y_pred_ill = mlp.predict(X_test_ill)\n",
    "\n",
    "print(\"Classification report for - \\n{}:\\n{}\\n\".format(\n",
    "    mlp, 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": 295
    },
    "executionInfo": {
     "elapsed": 46957,
     "status": "ok",
     "timestamp": 1611328790240,
     "user": {
      "displayName": "KHONG FAN YI",
      "photoUrl": "",
      "userId": "02416044610109286808"
     },
     "user_tz": -480
    },
    "id": "XPfD2ihBcS-O",
    "outputId": "9632aa5c-3be5-484f-cbed-7188e67c6689",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x7f24c3c7ce10>"
      ]
     },
     "execution_count": 129,
     "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(mlp, X_test_ill, y_test_ill, cmap='viridis')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "ICP4o56RcS-O"
   },
   "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": 986,
     "output_embedded_package_id": "1js_lf4FrOW5gJrsNd7vm7xCfVFxUNoVq"
    },
    "executionInfo": {
     "elapsed": 16185,
     "status": "ok",
     "timestamp": 1611329077709,
     "user": {
      "displayName": "KHONG FAN YI",
      "photoUrl": "",
      "userId": "02416044610109286808"
     },
     "user_tz": -480
    },
    "id": "Esb9_-CXcS-O",
    "outputId": "fccd9d70-bf6f-4f18-f652-272636e4b742"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Output hidden; open in https://colab.research.google.com to view."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "col = 9\n",
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    "\n",
    "plot(wrong_case, row, col, gray_img_ill2, SUBCLASSES)"
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