{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"pygments_lexer":"ipython3","nbconvert_exporter":"python","version":"3.6.4","file_extension":".py","codemirror_mode":{"name":"ipython","version":3},"name":"python","mimetype":"text/x-python"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"import json\nimport math\nimport os\n\nimport cv2\nfrom PIL import Image\nimport numpy as np\nfrom keras import layers\nfrom keras.applications import DenseNet121\nfrom keras.callbacks import Callback, ModelCheckpoint\nfrom keras.preprocessing.image import ImageDataGenerator\nfrom keras.models import Sequential\nfrom keras.optimizers import Adam\nimport matplotlib.pyplot as plt\nimport pandas as pd\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.metrics import cohen_kappa_score, accuracy_score\nimport scipy\nimport tensorflow as tf\nfrom tqdm import tqdm\n\n%matplotlib inline","metadata":{"_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","execution":{"iopub.status.busy":"2022-05-05T07:22:45.39709Z","iopub.execute_input":"2022-05-05T07:22:45.397648Z","iopub.status.idle":"2022-05-05T07:22:48.709617Z","shell.execute_reply.started":"2022-05-05T07:22:45.397569Z","shell.execute_reply":"2022-05-05T07:22:48.708459Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Set random seed for reproducibility.","metadata":{}},{"cell_type":"code","source":"np.random.seed(2019)\ntf.set_random_seed(2019)","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Loading & Exploration","metadata":{}},{"cell_type":"code","source":"train_df = pd.read_csv('../input/aptos2019-blindness-detection/train.csv')\ntest_df = pd.read_csv('../input/aptos2019-blindness-detection/test.csv')\nprint(train_df.shape)\nprint(test_df.shape)\ntrain_df.head()","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"train_df['diagnosis'].hist()\ntrain_df['diagnosis'].value_counts()","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### Displaying some Sample Images","metadata":{}},{"cell_type":"code","source":"def display_samples(df, columns=4, rows=3):\n    fig=plt.figure(figsize=(5*columns, 4*rows))\n\n    for i in range(columns*rows):\n        image_path = df.loc[i,'id_code']\n        image_id = df.loc[i,'diagnosis']\n        img = cv2.imread(f'../input/aptos2019-blindness-detection/train_images/{image_path}.png')\n        img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\n        \n        fig.add_subplot(rows, columns, i+1)\n        plt.title(image_id)\n        plt.imshow(img)\n    \n    plt.tight_layout()\n\ndisplay_samples(train_df)","metadata":{"_kg_hide-input":true,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Resize Images\n\nWe will resize the images to 224x224, then create a single numpy array to hold the data.","metadata":{}},{"cell_type":"code","source":"def get_pad_width(im, new_shape, is_rgb=True):\n    pad_diff = new_shape - im.shape[0], new_shape - im.shape[1]\n    t, b = math.floor(pad_diff[0]/2), math.ceil(pad_diff[0]/2)\n    l, r = math.floor(pad_diff[1]/2), math.ceil(pad_diff[1]/2)\n    if is_rgb:\n        pad_width = ((t,b), (l,r), (0, 0))\n    else:\n        pad_width = ((t,b), (l,r))\n    return pad_width\n\ndef preprocess_image(image_path, desired_size=224):\n    im = Image.open(image_path)\n    im = im.resize((desired_size, )*2, resample=Image.LANCZOS)\n    \n    return im","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"N = train_df.shape[0]\nx_train = np.empty((N, 224, 224, 3), dtype=np.uint8)\n\nfor i, image_id in enumerate(tqdm(train_df['id_code'])):\n    x_train[i, :, :, :] = preprocess_image(\n        f'../input/aptos2019-blindness-detection/train_images/{image_id}.png'\n    )","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"N = test_df.shape[0]\nx_test = np.empty((N, 224, 224, 3), dtype=np.uint8)\n\nfor i, image_id in enumerate(tqdm(test_df['id_code'])):\n    x_test[i, :, :, :] = preprocess_image(\n        f'../input/aptos2019-blindness-detection/test_images/{image_id}.png'\n    )","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"y_train = pd.get_dummies(train_df['diagnosis']).values\n\nprint(x_train.shape)\nprint(y_train.shape)\nprint(x_test.shape)","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Creating multilabels\n\nInstead of predicting a single label, we will change our target to be a multilabel problem; i.e., if the target is a certain class, then it encompasses all the classes before it. E.g. encoding a class 4 retinopathy would usually be `[0, 0, 0, 1]`, but in our case we will predict `[1, 1, 1, 1]`. For more details, please check out [Lex's kernel](https://www.kaggle.com/lextoumbourou/blindness-detection-resnet34-ordinal-targets).","metadata":{}},{"cell_type":"code","source":"y_train_multi = np.empty(y_train.shape, dtype=y_train.dtype)\ny_train_multi[:, 4] = y_train[:, 4]\n\nfor i in range(3, -1, -1):\n    y_train_multi[:, i] = np.logical_or(y_train[:, i], y_train_multi[:, i+1])\n\nprint(\"Original y_train:\", y_train.sum(axis=0))\nprint(\"Multilabel version:\", y_train_multi.sum(axis=0))","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Now we can split it into a training and validation set.","metadata":{}},{"cell_type":"code","source":"x_train, x_val, y_train, y_val = train_test_split(\n    x_train, y_train_multi, \n    test_size=0.15, \n    random_state=2019\n)","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Mixup & Data Generator\n\nPlease Note: Although I show how to construct Mixup, **it is currently unused**. Please see notice at the top of the kernel.","metadata":{}},{"cell_type":"code","source":"class MixupGenerator():\n    def __init__(self, X_train, y_train, batch_size=32, alpha=0.2, shuffle=True, datagen=None):\n        self.X_train = X_train\n        self.y_train = y_train\n        self.batch_size = batch_size\n        self.alpha = alpha\n        self.shuffle = shuffle\n        self.sample_num = len(X_train)\n        self.datagen = datagen\n\n    def __call__(self):\n        while True:\n            indexes = self.__get_exploration_order()\n            itr_num = int(len(indexes) // (self.batch_size * 2))\n\n            for i in range(itr_num):\n                batch_ids = indexes[i * self.batch_size * 2:(i + 1) * self.batch_size * 2]\n                X, y = self.__data_generation(batch_ids)\n\n                yield X, y\n\n    def __get_exploration_order(self):\n        indexes = np.arange(self.sample_num)\n\n        if self.shuffle:\n            np.random.shuffle(indexes)\n\n        return indexes\n\n    def __data_generation(self, batch_ids):\n        _, h, w, c = self.X_train.shape\n        l = np.random.beta(self.alpha, self.alpha, self.batch_size)\n        X_l = l.reshape(self.batch_size, 1, 1, 1)\n        y_l = l.reshape(self.batch_size, 1)\n\n        X1 = self.X_train[batch_ids[:self.batch_size]]\n        X2 = self.X_train[batch_ids[self.batch_size:]]\n        X = X1 * X_l + X2 * (1 - X_l)\n\n        if self.datagen:\n            for i in range(self.batch_size):\n                X[i] = self.datagen.random_transform(X[i])\n                X[i] = self.datagen.standardize(X[i])\n\n        if isinstance(self.y_train, list):\n            y = []\n\n            for y_train_ in self.y_train:\n                y1 = y_train_[batch_ids[:self.batch_size]]\n                y2 = y_train_[batch_ids[self.batch_size:]]\n                y.append(y1 * y_l + y2 * (1 - y_l))\n        else:\n            y1 = self.y_train[batch_ids[:self.batch_size]]\n            y2 = self.y_train[batch_ids[self.batch_size:]]\n            y = y1 * y_l + y2 * (1 - y_l)\n\n        return X, y","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"BATCH_SIZE = 32\n\ndef create_datagen():\n    return ImageDataGenerator(\n        zoom_range=0.15,  # set range for random zoom\n        # set mode for filling points outside the input boundaries\n        fill_mode='constant',\n        cval=0.,  # value used for fill_mode = \"constant\"\n        horizontal_flip=True,  # randomly flip images\n        vertical_flip=True,  # randomly flip images\n    )\n\n# Using original generator\ndata_generator = create_datagen().flow(x_train, y_train, batch_size=BATCH_SIZE, seed=2019)\n# Using Mixup\nmixup_generator = MixupGenerator(x_train, y_train, batch_size=BATCH_SIZE, alpha=0.2, datagen=create_datagen())()","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"true_labels = np.array([1, 0, 1, 1, 0, 1])\npred_labels = np.array([1, 0, 0, 0, 0, 1])","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"accuracy_score(true_labels, pred_labels) ","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"cohen_kappa_score(true_labels, pred_labels)","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### What is the weighted kappa?\n\nThe wikipedia page offer a very concise explanation: \n> The weighted kappa allows disagreements to be weighted differently and is especially useful when **codes are ordered**. Three matrices are involved, the matrix of observed scores, the matrix of expected scores based on chance agreement, and the weight matrix. Weight matrix cells located on the diagonal (upper-left to bottom-right) represent agreement and thus contain zeros. Off-diagonal cells contain weights indicating the seriousness of that disagreement.\n\nSimply put, if two scores disagree, then the penalty will depend on how far they are apart. That means that our score will be higher if (a) the real value is 4 but the model predicts a 3, and the score will be lower if (b) the model instead predicts a 0. This metric makes sense for this competition, since the labels 0-4 indicates how severe the illness is. Intuitively, a model that predicts a severe retinopathy (3) when it is in reality a proliferative retinopathy (4) is probably better than a model that predicts a mild retinopathy (1).","metadata":{}},{"cell_type":"markdown","source":"### Creating keras callback for QWK","metadata":{}},{"cell_type":"code","source":"class Metrics(Callback):\n    def on_train_begin(self, logs={}):\n        self.val_kappas = []\n\n    def on_epoch_end(self, epoch, logs={}):\n        X_val, y_val = self.validation_data[:2]\n        y_val = y_val.sum(axis=1) - 1\n        \n        y_pred = self.model.predict(X_val) > 0.5\n        y_pred = y_pred.astype(int).sum(axis=1) - 1\n\n        _val_kappa = cohen_kappa_score(\n            y_val,\n            y_pred, \n            weights='quadratic'\n        )\n\n        self.val_kappas.append(_val_kappa)\n\n        print(f\"val_kappa: {_val_kappa:.4f}\")\n        \n        if _val_kappa == max(self.val_kappas):\n            print(\"Validation Kappa has improved. Saving model.\")\n            self.model.save('model.h5')\n\n        return","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Model: DenseNet-121","metadata":{}},{"cell_type":"code","source":"densenet = DenseNet121(\n    weights='../input/densenet-keras/DenseNet-BC-121-32-no-top.h5',\n    include_top=False,\n    input_shape=(224,224,3)\n)\ndensenet.summary()","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"def build_model():\n    model = Sequential()\n    model.add(densenet)\n    model.add(layers.GlobalAveragePooling2D())\n    model.add(layers.Dropout(0.5))\n    model.add(layers.Dense(5, activation='sigmoid'))\n    \n    model.compile(\n        loss='binary_crossentropy',\n        optimizer=Adam(lr=0.00005),\n        metrics=['accuracy']\n    )\n    \n    return model","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"model = build_model()\nmodel.summary()","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"kappa_metrics = Metrics()\n\nhistory = model.fit_generator(\n    data_generator,\n    steps_per_epoch=x_train.shape[0] / BATCH_SIZE,\n    epochs=2,\n    validation_data=(x_val, y_val),\n    callbacks=[kappa_metrics]\n)","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Training & Evaluation","metadata":{}},{"cell_type":"code","source":"kappa_metrics = Metrics()\n\nhistory = model.fit_generator(\n    data_generator,\n    steps_per_epoch=x_train.shape[0] / BATCH_SIZE,\n    epochs=2,\n    validation_data=(x_val, y_val),\n    callbacks=[kappa_metrics]\n)","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"with open('history.json', 'w') as f:\n    json.dump(history.history, f)\n\nhistory_df = pd.DataFrame(history.history)\nhistory_df[['loss', 'val_loss']].plot()\nhistory_df[['acc', 'val_acc']].plot()","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"plt.plot(kappa_metrics.val_kappas)","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Find best threshold\n\nPlease Note: Although I show how to construct a threshold optimizer, **it is currently unused**. Please see notice at the top of the kernel.","metadata":{}},{"cell_type":"code","source":"model.load_weights('model.h5')\ny_val_pred = model.predict(x_val)\n\ndef compute_score_inv(threshold):\n    y1 = y_val_pred > threshold\n    y1 = y1.astype(int).sum(axis=1) - 1\n    y2 = y_val.sum(axis=1) - 1\n    score = cohen_kappa_score(y1, y2, weights='quadratic')\n    \n    return 1 - score\n\nsimplex = scipy.optimize.minimize(\n    compute_score_inv, 0.5, method='nelder-mead'\n)\n\nbest_threshold = simplex['x'][0]","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Submit","metadata":{}},{"cell_type":"code","source":"y_test = model.predict(x_test) > 0.5\ny_test = y_test.astype(int).sum(axis=1) - 1\n\ntest_df['diagnosis'] = y_test\ntest_df.to_csv('submission.csv',index=False)","metadata":{"execution":{"iopub.status.busy":"2022-05-05T07:53:08.909905Z","iopub.execute_input":"2022-05-05T07:53:08.910262Z","iopub.status.idle":"2022-05-05T07:53:09.240359Z","shell.execute_reply.started":"2022-05-05T07:53:08.910212Z","shell.execute_reply":"2022-05-05T07:53:09.238666Z"},"trusted":true},"execution_count":null,"outputs":[]}]}