LBP_MLP_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",
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    "id": "iqI5L27rHjAg"
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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 matplotlib.pyplot as plt\n",
    "import pickle\n",
    "from tqdm.notebook import tqdm\n",
    "from sklearn.model_selection import train_test_split\n",
    "from lbp import get_pixel, lbp_calculated_pixel, show_output\n",
    "import sklearn.metrics as metrics\n",
    "from sklearn.metrics import plot_confusion_matrix\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.neural_network import MLPClassifier"
   ]
  },
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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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   "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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     "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))"
   ]
  },
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   "cell_type": "markdown",
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    "id": "-IK1KAsRF0ou"
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   "source": [
    "## Feature Extraction"
   ]
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      "\n"
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   "source": [
    "height, width, channel = 200, 200, 3\n",
    "\n",
    "# Extract all features from the whole dataset (original + augmented)\n",
    "gray_imgs = []\n",
    "lbp_all = []\n",
    "hist_lbp_all = []\n",
    "\n",
    "for img in tqdm(all_imgs):\n",
    "    \n",
    "    img_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n",
    "    gray_imgs.append(img_gray)\n",
    "\n",
    "    img_lbp = np.zeros((height, width, 3), np.uint8)\n",
    "    \n",
    "    for i in range(0, height):\n",
    "        for j in range(0, width):\n",
    "            img_lbp[i, j] = lbp_calculated_pixel(img_gray, i, j)\n",
    "    lbp_all.append(img_lbp)\n",
    "    hist_lbp = cv2.calcHist([img_lbp], [0], None, [256], [0, 256])\n",
    "    hist_lbp_all.append(hist_lbp)\n",
    "    \n",
    "lbp_all = np.array(lbp_all)\n",
    "hist_lbp_all = np.array(hist_lbp_all)"
   ]
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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_hist_lbp = []\n",
    "hist_lbp = []\n",
    "\n",
    "for j in range(len(hist_lbp_all)):\n",
    "    if(j >= 733):\n",
    "        aug_hist_lbp.append(hist_lbp_all[j])\n",
    "    else:\n",
    "        hist_lbp.append(hist_lbp_all[j])\n",
    "\n",
    "print(len(hist_lbp))\n",
    "print(len(aug_hist_lbp))"
   ]
  },
  {
   "cell_type": "markdown",
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    "id": "2kLF31p6HGit"
   },
   "source": [
    "## First-Level Classification\n",
    "> ### *Classify samples into healthy (0) and ill (1) classes*\n"
   ]
  },
  {
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   "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(hist_lbp):  \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": "TxIw5zFLKlRh"
   },
   "outputs": [],
   "source": [
    "# Create the augmented images' dataset \n",
    "aug_dataset = []\n",
    "\n",
    "for i, aug_features in enumerate(aug_hist_lbp):  \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": 2602,
     "status": "ok",
     "timestamp": 1611317013998,
     "user": {
      "displayName": "KHONG FAN YI",
      "photoUrl": "",
      "userId": "02416044610109286808"
     },
     "user_tz": -480
    },
    "id": "nN84JQ-DKlRi",
    "outputId": "18c46d1e-a02c-4428-de37-02a8a6a98b9d"
   },
   "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": 2567,
     "status": "ok",
     "timestamp": 1611317013999,
     "user": {
      "displayName": "KHONG FAN YI",
      "photoUrl": "",
      "userId": "02416044610109286808"
     },
     "user_tz": -480
    },
    "id": "W-tJJQOdKlRl",
    "outputId": "a906670c-11b1-4646-e8a2-0bd6abc46a07"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1319"
      ]
     },
     "execution_count": 132,
     "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": 1624,
     "status": "ok",
     "timestamp": 1611322136873,
     "user": {
      "displayName": "KHONG FAN YI",
      "photoUrl": "",
      "userId": "02416044610109286808"
     },
     "user_tz": -480
    },
    "id": "po3yHHbqKlRn",
    "outputId": "f67fcd8d-8df6-40b1-e739-26eb3a99d988"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1319, 256)\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": 1996,
     "status": "ok",
     "timestamp": 1611322137272,
     "user": {
      "displayName": "KHONG FAN YI",
      "photoUrl": "",
      "userId": "02416044610109286808"
     },
     "user_tz": -480
    },
    "id": "8LUD8xPJKlRp",
    "outputId": "d2f57099-ad22-4c74-8019-31ca4ce8ff8d",
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(147, 256)\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": "code",
   "execution_count": null,
   "metadata": {
    "id": "B4UckZ6lNR1C"
   },
   "outputs": [],
   "source": [
    "# Normalize the data\n",
    "\n",
    "scaling = StandardScaler()\n",
    "scaling.fit(X_train) \n",
    "X_train = scaling.transform(X_train)\n",
    "X_test = scaling.transform(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "84UEBtn7idcv"
   },
   "source": [
    "### Train Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 3931,
     "status": "ok",
     "timestamp": 1611322139233,
     "user": {
      "displayName": "KHONG FAN YI",
      "photoUrl": "",
      "userId": "02416044610109286808"
     },
     "user_tz": -480
    },
    "id": "Pd6QYAuYKlRr",
    "outputId": "0cafcd47-6ce0-45af-e3f4-cc83f6678f76",
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.9324064254760742 seconds\n"
     ]
    }
   ],
   "source": [
    "mlp = MLPClassifier(solver='adam', \n",
    "                    hidden_layer_sizes=(200,200,100), \n",
    "                    alpha=0.0001,\n",
    "                    early_stopping=True)\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": 3916,
     "status": "ok",
     "timestamp": 1611322139235,
     "user": {
      "displayName": "KHONG FAN YI",
      "photoUrl": "",
      "userId": "02416044610109286808"
     },
     "user_tz": -480
    },
    "id": "3Q4oi5hmKlRs",
    "outputId": "a625fbbc-dc8a-4523-a3a0-eb204ae67d73",
    "scrolled": true
   },
   "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=(200, 200, 100), 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       0.98      0.99      0.98       898\n",
      "           1       0.97      0.95      0.96       421\n",
      "\n",
      "    accuracy                           0.97      1319\n",
      "   macro avg       0.97      0.97      0.97      1319\n",
      "weighted avg       0.97      0.97      0.97      1319\n",
      "\n",
      "\n",
      "Training accuracy: 97.4981046247157\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": 296
    },
    "executionInfo": {
     "elapsed": 4789,
     "status": "ok",
     "timestamp": 1611322140125,
     "user": {
      "displayName": "KHONG FAN YI",
      "photoUrl": "",
      "userId": "02416044610109286808"
     },
     "user_tz": -480
    },
    "id": "m-Cc_354KlRt",
    "outputId": "638f04bb-11b5-4325-8e01-bcc308c66a3b",
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x7f5dae96ce80>"
      ]
     },
     "execution_count": 195,
     "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": 4776,
     "status": "ok",
     "timestamp": 1611322140130,
     "user": {
      "displayName": "KHONG FAN YI",
      "photoUrl": "",
      "userId": "02416044610109286808"
     },
     "user_tz": -480
    },
    "id": "tf_gy_qVKlRu",
    "outputId": "687ddbc3-d7cc-4208-8098-e1ec508eba4c"
   },
   "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=(200, 200, 100), 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       0.96      0.84      0.90       102\n",
      "           1       0.72      0.91      0.80        45\n",
      "\n",
      "    accuracy                           0.86       147\n",
      "   macro avg       0.84      0.88      0.85       147\n",
      "weighted avg       0.88      0.86      0.87       147\n",
      "\n",
      "\n",
      "Testing accuracy: 86.39455782312925\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": 296
    },
    "executionInfo": {
     "elapsed": 4766,
     "status": "ok",
     "timestamp": 1611322140136,
     "user": {
      "displayName": "KHONG FAN YI",
      "photoUrl": "",
      "userId": "02416044610109286808"
     },
     "user_tz": -480
    },
    "id": "aBRowqLvKlRu",
    "outputId": "9946800d-7abe-4b12-b64e-0b4794eaae3a",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x7f5da489a470>"
      ]
     },
     "execution_count": 197,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAATIAAAEGCAYAAADmLRl+AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAYqElEQVR4nO3de9xVZZ338c+Xm5Ny8ADIg+CBUcLIRjREzVKEMqwpdcZx1JqsGMkas5p6JmueV469ah6bfB6nnrEpREYqQ/GU2AFzSFMaDxxEBXSSQBREOQiTHIy479/zx1p3bgj2Wgv2vvdem+/b13rde62997V+N7z4eV3Xug6KCMzMyqxbowMwM9tXTmRmVnpOZGZWek5kZlZ6TmRmVnrdGx1ApQED2+LIo5oqJMuwcuHQRodgBWxhHb+L17QvZbzr7ANjw4b2XJ9dtHD7fRExcV/ul0dTZY0jj+rOA//pfxhlctkBX2l0CFbAnPjyPpexYUM7Dz6S79/pwb1WDNznG+bQVInMzMpA0NHW6CB24kRmZsUEqL25utedyMysEAHq2KdutppzIjOzYgLU0eggduZEZmbFOZGZWakFqMnWmnAiM7PC3LQ0s3ILUHtzVcmcyMysONfIzKzMkuEXrpGZWZkFTVcja67huWZWCop8R2Y50mclLZG0WNIMSb0lDZf0mKRlkm6T1DOrHCcyMysmQDvyHdVIGgpcCYyJiOOBNuAi4OvA9RFxLLARmJQVkhOZmRUXke/I1h04QFJ34EBgDTAeuCN9fzpwXlYhTmRmVpg68h3AQEnzK47JnWVExGrgOuAFkgT238ACYFNEdNbnVgGZawa5s9/MiinW2b8+Isbs7g1JhwDnAsOBTcDtwF4twuhEZmaF1WiK0ruAFRGxDkDSXcDpwMGSuqe1smHA6qyC3LQ0s+I6ch7VvQCcKulASQImAEuBB4AL0s9cCtyTVZATmZkVogC1K9dRTUQ8RtKpvxB4miQfTQG+APydpGXAAOCmrJjctDSz4mo0IDYirgau3uXycmBskXKcyMysmCYc2e9EZmbFNddUSycyMyvOa/abWbkFkNGR39WcyMysOPeRmVmpBe4jM7OyE7iPzMxKL5zIzKzMvEGvmbUEP7U0s1IL3EdmZi3AfWRmVnruIzOzcpNrZGZWcgHhPjIzKz0/tTSzUgvctDSzFtBkTUuv2W9mBaWd/XmOaqVIIyUtqjh+K+kzkg6VdL+k59Kfh2RF5ERmZsV0DojNc1QrJuK/ImJ0RIwG3gZsBe4GrgLmRMQIYE56XpUTmZkVFu3KdRQwAfhNRKwk2bR3enp9OnBe1pfdR2ZmxeXv7B8oaX7F+ZSImLKbz10EzEhfD46INenrl4HBWTdxIjOzYorNtVwfEWOqfUBST+ADwBf/6FYRIWXva+6mpZkVVJvO/grnAAsj4pX0/BVJQwDSn2uzCnAiM7PiatDZX+Fi3mhWAswCLk1fXwrck1WAE5mZFRKR/8giqQ/wbuCuisvXAu+W9BzwrvS8KveRmVlx7bWpA0XEFmDALtc2kDzFzM2JzMyKCQhPUTKzcvMuSi1v6g1vZcb3RyLBcaNe5bobfkmvXu1846sn85MfDaetLfjQx5byscuXNDpUAz7y3bv503N+zWvr+nD12674w/Xxn3iUsy5/nI528fTP3sQd//CeBkbZhPanGpmkicA3gTZgakRkdtqV2csvHci/f/ctzHnsdnof0M4nPjKBe+88hgh4aVUfHpg3k27dYP263o0O1VK/+v6J/OLfTmHSTW/0NY88czmj3/8s15z8SXZs706/QZsbGGFzaramZd2eWkpqA24gGSMyCrhY0qh63a9Z7Gjvxuuvd2fHDrFtW3cGD9nC96eN4jNfWEi39E974KDXGxuk/cFzc49my8YDdro27rJ5/Oy6d7Jje/L/+dfW9W1EaM0rSJa6znN0kXoOvxgLLIuI5RGxHbiVZA5Vy/ofh29l8hVPcerxlzBm5Ifo3387Z4xfzcoV/bn3rmN437jz+fAFE1nxm/6NDtWqGDxiAyNOX8mXHvou//P+mzj6basbHVLTifZuuY6uUs87DQVerDhflV7biaTJkuZLmr9+XXsdw6m/TZt6cv9Pj+JXT85g3rM/YOuWHtx127Fs395Gr147+MmDd3Pxh5/l81ec2ehQrYq27h30OWQb/3TGZO744nv4+C23kVRDDMg/qr8Lm58NHxAbEVMiYkxEjBk4qK3R4eyTuQ8O5YijXmPAwNfp0SOY+P4VLHh8MEMO38LE9z8PwMT3P8+zSwZUL8gaauPq/iy8582AWDF/GNEh+g7c2uiwmkqEch1dpZ6JbDVwRMX5sPRayxo6bDML5x/Gtq1tRMCvfjmUY9+0ibPf9zyPPHw4AI/OHcLwYzY1OFKr5olZb+a4M1cAMPjY9XTv2c7m9Qc2OKomU9spSvusnk8t5wEjJA0nSWAXAZfU8X4Nd+KYdbz3Ayt475l/QVv3Dt7y1g1c8pFneH1bdz49eTxT/+2t9Onze/75Ww81OlRLXfa92xn5zhX0HbiVf152HbO+ehZzp5/IR6f8iGsW/Cs7trcx7W/+HGiup3QN12RPLeuWyCJih6QrgPtIhl9Mi4iWHzz1uS8t4HNfWrDTtV69tnPzzNkNisiqufHDf7nb61M/ekEXR1IeERRdNLHu6jqOLCJ+Cvy0nvcws67Wtf1feXhkv5kV50RmZqXmncbNrCW4RmZmZec+MjMrtyi81VvdNXxkv5mVS1C7kf2SDpZ0h6RnJT0j6TTvNG5mXaN2I/u/CcyOiOOAE4Bn8E7jZlZ3UZsamaSDgDOAmwAiYntEbGIvdhp3IjOz4vKvfjGwc3Wb9JhcUcpwYB3w75KekDQ13VXJO42bWb0VGtlfbafx7sBJwKci4jFJ32SXZqR3Gjez+kjnWuY5MqwCVkXEY+n5HSSJzTuNm1l91eqpZUS8DLwoaWR6aQKwlL3YadxNSzMrrnYDYj8F3CKpJ7Ac+ChJBWumpEnASuDCrEKcyMysINVsrmVELAJ214fmncbNrI6807iZtYLoaK7udScyMyssunDPyjycyMysmMDL+JhZuYWXujazVuBEZmbl50RmZqUW0NHup5ZmVnaZ07i7lhOZmRXkzn4zK7nOSePNxInMzIop076Wkv4fVVrCEXFlXSIys6ZXpilK87ssCjMrkRL1kUXE9MpzSQdGxNb6h2RmTS0gmuypZWb9MN1nbinwbHp+gqRv1z0yM2tKtdzXslbyNHT/BXgPsAEgIp4k2cLJzPZXtdvXsiZyPbWMiBelnYJqr084ZlYGpekjq/CipLcDIakH8GmS3YDNbH8UoqNGTy0lPQ+8RlI52hERYyQdCtwGHA08D1wYERurlZMnmsuBvwWGAi8Bo9NzM9tP1biP7KyIGF2x/+VVwJyIGAHMYZe9Lncns0YWEeuBD+aNyMz2A/VtWp4LjEtfTwceBL5Q7Qt5nlr+iaR7Ja2TtFbSPZL+ZF8jNbNyikiWus5zAAMlza84Ju9aHPBzSQsq3hscEWvS1y8Dg7NiytNH9kPgBuD89PwiYAZwSo7vmlkLKtBsXF/RZNydd0TEakmHAfdLenbn+0RIyhy1lqeP7MCI+H5E7EiPHwC9c3zPzFpUrfrIImJ1+nMtcDcwFnhF0hCA9OfarHL2mMgkHZo+PfiZpKskHS3pKEl/D/w0129rZi0oeWqZ56haitRHUr/O18DZwGJgFnBp+rFLgXuyIqrWtFxA0n7tTKsfr3gvgC9mFW5mLah2uygNBu5Ox6h2B34YEbMlzQNmSpoErAQuzCqo2lzL4bWI1MxaS63WI4uI5cAJu7m+AZhQpKxcI/slHQ+MoqJvLCK+V+RGZtY6SjeyX9LVJGM6RpH0jZ0DzAWcyMz2R9F8O43neWp5AUk17+WI+ChJVfCgukZlZk2sNp39tZSnabktIjok7ZDUn+RR6BF1jsvMmlRZ1+yfL+lg4EaSJ5mbgUfqGpWZNbXSJbKI+GT68juSZgP9I+Kp+oZlZk0rSpTIJJ1U7b2IWFifkMysuZVozX7g/1R5L4DxNY6Fp54YxBEHXVbrYq2OZp3yfKNDsAKeevp3tSmoLNvBRcRZXRmImZVDBF36RDIPb9BrZoU12y5KTmRmVliZ+sjMzHaj+Tr786wQK0kfkvTl9PxISWPrH5qZNasy7mv5beA04OL0/DWSFWPNbD8U0XyJLE/T8pSIOEnSEwARsVFSzzrHZWZNrKO9fE8tfy+pjWTsGJIGAU02993Muk7z9ZHlSWTfIllL+zBJXyNZDeN/1TUqM2teZZqi1CkibpG0gGQpHwHnRYR3GjfbT9V69Yu0xTcfWB0RfyZpOHArMIBkoYq/jojt1crI89TySGArcC/JpgBb0mtmtp+qcWf/p4HKytHXgesj4lhgIzApq4A8PXY/AX6c/pwDLAd+ljdCM2s1tVtYUdIw4H3A1PRcJPO470g/Mh04L6ucPE3Lt+5y45OAT+7h42bW6gIi/6TxgZLmV5xPiYgpFef/Avw90C89HwBsiogd6fkqYGjWTQqP7I+IhZK8y7jZfqpgH9kedxqX9GfA2ohYIGncvsSUZ/ORv6s47QacBLy0Lzc1s3Kr0aTx04EPSHovyQ5t/YFvAgdL6p7WyoYBq7MKytNH1q/i6EXSV3buXgZuZi2gI5TrqCYivhgRwyLiaOAi4BcR8UHgAZJhXlCDncY7H4v2i4jP5/jdzGx/UP9xZF8AbpX0VeAJ4KasL1Rb6rp7ROyQdHoNAzSzkov0qWVNy4x4EHgwfb0cKLQwRbUa2eMk/WGLJM0Cbge2VNz4roKxmlmLKN3IfpJOuA0kYzuCZHR/AE5kZvujYsMvukS1RHZY+sRyMW8ksE5NttCtmXWlMtXI2oC+7JzAOjmRme2nomSrX6yJiK90WSRmVhplSmTNFamZNYeA9hJtBzehy6Iws9Ko9TI+tVBtg95XuzIQMyuPaLI1or0dnJkVVK7OfjOzPxZkzqPsak5kZlZIQM2nKO0rJzIzK8xNSzMruewlerqaE5mZFZLsNN7oKHbmRGZmhZVp0riZ2W65j8zMSi0C2l0jM7Oya7YaWXMNBjGzEsi38UjWk01JvSU9LulJSUskXZNeHy7pMUnLJN0mqWdWRE5kZlZIMmk835Hhd8D4iDgBGA1MlHQq8HXg+og4FtgITMoqyInMzAqLUK6jehkREbE5Pe2RHkGyrP4d6fXpwHlZ8biPzMyKCWhvz91HNlDS/IrzKRExpfMk3XJyAXAscAPwG2BTujkvwCpgaNZNnMjMrJCC65Gtj4gxeywroh0YLelg4G7guL2JyYnMzAqq/RSliNgk6QHgNODgzn11gWHA6qzvu4/MzIrJ2dGf1dkvaVBaE0PSAcC7gWeAB4AL0o9dCtyTFZJrZGZWSFCz9ciGANPTfrJuwMyI+LGkpcCtkr4KPAHclFWQE5mZFVaLSeMR8RRw4m6uLwfGFinLiczMCvMUJTMrNS/jY2YtwQsrmlnpNVuNzMMv6qxbtw5mP3wXN8+c3ehQrJpuHRw39Ucc87/vB2DQ+UsZdcvtnPTLabQd9HqDg2s+NZprWTN1S2SSpklaK2lxve5RBpM+sZhlvz640WFYhsMuWMrrK9/4e9q8eDDLPjeR363p28ComlPn8It9Xf2ilupZI7sZmFjH8pvekMM3M+E9L/LD6SMbHYpV0WPQFvqf+iLrf/ymP1zb9twAtr/cr4FRNbGA9pxHV6lbIouIh4BX61V+GfzjtY/ytS+Pbbr1zW1nw654jNXfORmarAO7WQXKfXSVhveRSZosab6k+RFbGh1OzUyYuJL163vz9KJBjQ7Fquh/2gvs2NSbbb8e2OhQSqUj8h1dpeFPLdMlPaYAtHUb1mTPQvbeyae8wtnnvMD4d8+gV+92+vXbzrdufIArLzur0aFZhb7Hr+Wgt79A/1NW0a1nO219tnP0P/yS5792ZqNDa2rN9g+14YmsVV17zViuvSaZZXHaO17i41c+5STWhF66cQwv3ZisMtN39BoG/9ViJ7EMSWd/o6PYmROZ2W4M+oslDL7oaXocuo03T7ub3z56BC984x2NDqtpdGVHfh51S2SSZgDjSFaIXAVcHRGZs9hb0SNzD+eRuYc3OgzLsHnREDYvGgLAujvfwro739LgiJpXk+Wx+iWyiLi4XmWbWeME0NHoIHbhpqWZFbbf1MjMrHW5RmZmpda5r2UzafiAWDMrn/acRzWSjpD0gKSl6U7jn06vHyrpfknPpT8PyYrHiczMCuns7M9zZNgBfC4iRgGnAn8raRRwFTAnIkYAc9LzqpzIzKywWiSyiFgTEQvT16+R7KA0FDiXZIdx8E7jZlYvBbrIqu403knS0SQbkTwGDI6INelbLwODs27iRGZmhRQcR1Z1p3EASX2BO4HPRMRvpTdWzYiIkJSZN920NLOCIvd/WST1IElit0TEXenlVyQNSd8fAqzNKseJzMwKq9FTS5FsvvtMRPzfirdmkewwDt5p3MzqoYZTlE4H/hp4WtKi9NqXgGuBmZImASuBC7MKciIzs8Iiu9sq/WCVtyLmwh6XkZ1QJB4nMjMrzFOUzKzUvPqFmbWE9iZb/8KJzMwKCcg1tKIrOZGZWWFuWppZ6eXeArSLKm5OZGZWSNLZ76almZWcm5ZmVmpB+KmlmZWfm5ZmVnq5O/u7iBOZmRXizn4zawkeEGtmpeenlmZWan5qaWYtoSPvemRdxInMzApxZ7+ZtYTmSmPefMTM9kIHkevIImmapLWSFldcO1TS/ZKeS38eklWOE5mZFRLADiLXkcPNwMRdrl0FzImIEcCc9LwqJzIzK6h2+1pGxEPAq7tcPheYnr6eDpyXVY77yMyskIKd/QMlza84nxIRUzK+Mzgi1qSvXwYGZ93EiczMilGh4RfrI2LM3t4qIkLKvpmblmZWSOcuSnmOvfSKpCEA6c+1WV9wIjOzwmr11HIPZgGXpq8vBe7J+oKblmZWSDJFqTazLSXNAMaR9KWtAq4GrgVmSpoErAQuzCrHiczMCqvVyP6IuHgPb00oUo4TmZkV5ilKZlZqnmtpZi2hw0tdm1mZuUZmZqUXBL9vsjVincjMrDDXyMys9JzIzKzUgqBdblqaWYkFePMRMyu3ALY3WY1MEc2TWSWtI5lb1WoGAusbHYQV0qp/Z0dFxKB9KUDSbJI/nzzWR8SuK8DWXFMlslYlaf6+rMlkXc9/Z+XiZXzMrPScyMys9JzIukbWGuXWfPx3ViLuIzOz0nONzMxKz4nMzErPiayOJE2U9F+SlknK3C3ZGk/SNElrJS1udCyWnxNZnUhqA24AzgFGARdLGtXYqCyHm4G6D+C02nIiq5+xwLKIWB4R24FbSbaCtyYWEQ8BrzY6DivGiax+hgIvVpyvSq+ZWY05kZlZ6TmR1c9q4IiK82HpNTOrMSey+pkHjJA0XFJP4CKSreDNrMacyOokInYAVwD3Ac8AMyNiSWOjsiySZgCPACMlrZI0qdExWTZPUTKz0nONzMxKz4nMzErPiczMSs+JzMxKz4nMzErPiaxEJLVLWiRpsaTbJR24D2XdLOmC9PXUahPaJY2T9Pa9uMfzkv5ot509Xd/lM5sL3usfJX2+aIzWGpzIymVbRIyOiOOB7cDllW9K2qt9SiPibyJiaZWPjAMKJzKzruJEVl4PA8emtaWHJc0Clkpqk/QNSfMkPSXp4wBK/Gu6Ptp/AId1FiTpQUlj0tcTJS2U9KSkOZKOJkmYn01rg++UNEjSnek95kk6Pf3uAEk/l7RE0lRAWb+EpB9JWpB+Z/Iu712fXp8jaVB67RhJs9PvPCzpuFr8YVq5eafxEkprXucAs9NLJwHHR8SKNBn8d0ScLKkX8CtJPwdOBEaSrI02GFgKTNul3EHAjcAZaVmHRsSrkr4DbI6I69LP/RC4PiLmSjqSZPbCm4GrgbkR8RVJ7wPyjIr/WHqPA4B5ku6MiA1AH2B+RHxW0pfTsq8g2RTk8oh4TtIpwLeB8Xvxx2gtxImsXA6QtCh9/TBwE0mT7/GIWJFePxv4087+L+AgYARwBjAjItqBlyT9Yjflnwo81FlWROxpXa53AaOkP1S4+kvqm97jz9Pv/kTSxhy/05WSzk9fH5HGugHoAG5Lr/8AuCu9x9uB2yvu3SvHPazFOZGVy7aIGF15If0HvaXyEvCpiLhvl8+9t4ZxdANOjYjXdxNLbpLGkSTF0yJiq6QHgd57+Hik992065+BmfvIWs99wCck9QCQ9CZJfYCHgL9K+9CGAGft5ruPAmdIGp5+99D0+mtAv4rP/Rz4VOeJpM7E8hBwSXrtHOCQjFgPAjamSew4khphp25AZ63yEpIm62+BFZL+Mr2HJJ2QcQ/bDziRtZ6pJP1fC9MNNL5LUvO+G3gufe97JCs87CQi1gGTSZpxT/JG0+5e4PzOzn7gSmBM+jBhKW88Pb2GJBEuIWlivpAR62ygu6RngGtJEmmnLcDY9HcYD3wlvf5BYFIa3xK8fLjh1S/MrAW4RmZmpedEZmal50RmZqXnRGZmpedEZmal50RmZqXnRGZmpff/Adg0x3DTKAEKAAAAAElFTkSuQmCC\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": "MceBh834KlRw"
   },
   "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": "x4bG1bFFKlRx"
   },
   "outputs": [],
   "source": [
    "def plot(wrong_case, row, col, gray, labels):\n",
    "    \n",
    "    if (labels == SUBCLASSES):\n",
    "        fsize = 20\n",
    "        top = 5\n",
    "        bottom = 4\n",
    "    else:\n",
    "        fsize = 20\n",
    "        top = 3\n",
    "        bottom = 2.8\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": "TBm7CuVxKlRz"
   },
   "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": "qKNRFTsKo5y9"
   },
   "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": 7610,
     "status": "ok",
     "timestamp": 1611322143079,
     "user": {
      "displayName": "KHONG FAN YI",
      "photoUrl": "",
      "userId": "02416044610109286808"
     },
     "user_tz": -480
    },
    "id": "bKrmeD36o5y-",
    "outputId": "79e8c307-ed27-4a2b-c12a-bbb76d3a58e2",
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train set length:  280\n",
      "Test set length:  120\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": 7595,
     "status": "ok",
     "timestamp": 1611322143080,
     "user": {
      "displayName": "KHONG FAN YI",
      "photoUrl": "",
      "userId": "02416044610109286808"
     },
     "user_tz": -480
    },
    "id": "3M2tthLfo5y_",
    "outputId": "b19fbd46-81c9-4605-f69e-6a8bcb79f233"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(280, 256)\n",
      "(280,)\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": 7576,
     "status": "ok",
     "timestamp": 1611322143082,
     "user": {
      "displayName": "KHONG FAN YI",
      "photoUrl": "",
      "userId": "02416044610109286808"
     },
     "user_tz": -480
    },
    "id": "LzjFH92wo5y_",
    "outputId": "b008cf83-fcab-4c42-88c0-c190ee092339",
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(120, 256)\n",
      "(120,)\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": "JyH_Iu4kTf5a"
   },
   "source": [
    "### Train Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 8286,
     "status": "ok",
     "timestamp": 1611322143820,
     "user": {
      "displayName": "KHONG FAN YI",
      "photoUrl": "",
      "userId": "02416044610109286808"
     },
     "user_tz": -480
    },
    "id": "V1-tpHEuKlR7",
    "outputId": "79b273cb-0986-4c40-f92d-36880f09e187",
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.720468282699585 seconds\n"
     ]
    }
   ],
   "source": [
    "# Train the classifier\n",
    "\n",
    "mlp = MLPClassifier(solver='adam', \n",
    "                    hidden_layer_sizes=(200,200),\n",
    "                    alpha=0.01, \n",
    "                    random_state=None)\n",
    "\n",
    "start_train = time.time() # to record the training time\n",
    "mlp.fit(X_train_ill, y_train_ill)\n",
    "end_train = time.time()\n",
    "print(end_train - start_train, \"seconds\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "AgGMQ7PtbDId"
   },
   "source": [
    "#### Training Performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 8272,
     "status": "ok",
     "timestamp": 1611322143829,
     "user": {
      "displayName": "KHONG FAN YI",
      "photoUrl": "",
      "userId": "02416044610109286808"
     },
     "user_tz": -480
    },
    "id": "6ekZ-tiRKlR7",
    "outputId": "3fa1c912-3b1c-4e2f-bd3d-77b87607a4d3"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classification report for - \n",
      "MLPClassifier(activation='relu', alpha=0.01, batch_size='auto', beta_1=0.9,\n",
      "              beta_2=0.999, early_stopping=False, epsilon=1e-08,\n",
      "              hidden_layer_sizes=(200, 200, 200), 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       1.00      1.00      1.00        86\n",
      "           1       1.00      1.00      1.00       104\n",
      "           2       1.00      1.00      1.00        90\n",
      "\n",
      "    accuracy                           1.00       280\n",
      "   macro avg       1.00      1.00      1.00       280\n",
      "weighted avg       1.00      1.00      1.00       280\n",
      "\n",
      "\n",
      "Training accuracy: 100.0\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": 296
    },
    "executionInfo": {
     "elapsed": 8250,
     "status": "ok",
     "timestamp": 1611322143834,
     "user": {
      "displayName": "KHONG FAN YI",
      "photoUrl": "",
      "userId": "02416044610109286808"
     },
     "user_tz": -480
    },
    "id": "b_yZaSuoKlR7",
    "outputId": "6f9938ee-43a1-4a38-849e-8d3c83018d88",
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x7f5da3a44cc0>"
      ]
     },
     "execution_count": 210,
     "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": "ywBlRJIMbGAu"
   },
   "source": [
    "#### Testing Performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 628,
     "status": "ok",
     "timestamp": 1611322431780,
     "user": {
      "displayName": "KHONG FAN YI",
      "photoUrl": "",
      "userId": "02416044610109286808"
     },
     "user_tz": -480
    },
    "id": "qHkGu1cpKlR8",
    "outputId": "50e7073b-779a-43fb-9834-0734fdfe6a43",
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classification report for - \n",
      "MLPClassifier(activation='relu', alpha=0.01, batch_size='auto', beta_1=0.9,\n",
      "              beta_2=0.999, early_stopping=False, epsilon=1e-08,\n",
      "              hidden_layer_sizes=(200, 200, 200), 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       0.67      0.58      0.62        38\n",
      "           1       0.91      0.95      0.93        41\n",
      "           2       0.66      0.71      0.68        41\n",
      "\n",
      "    accuracy                           0.75       120\n",
      "   macro avg       0.74      0.75      0.74       120\n",
      "weighted avg       0.75      0.75      0.75       120\n",
      "\n",
      "\n",
      "Testing accuracy: 75.0\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": 296
    },
    "executionInfo": {
     "elapsed": 8210,
     "status": "ok",
     "timestamp": 1611322143845,
     "user": {
      "displayName": "KHONG FAN YI",
      "photoUrl": "",
      "userId": "02416044610109286808"
     },
     "user_tz": -480
    },
    "id": "GZ1ax-jrKlR8",
    "outputId": "b19f40e6-d423-4251-832e-e372efb4a055",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x7f5da3a449b0>"
      ]
     },
     "execution_count": 212,
     "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": "QUfKl_ewKlR8",
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "wrong_case = []\n",
    "get_wrong_case(y_pred_ill, y_test_ill)"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [],
   "name": "LBP_MLP_classification.ipynb",
   "provenance": [],
   "toc_visible": true
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
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