{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.6.6","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"gpu","dataSources":[{"sourceId":14774,"databundleVersionId":875431,"sourceType":"competition"},{"sourceId":418031,"sourceType":"datasetVersion","datasetId":131128}],"dockerImageVersionId":28450,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":true}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"# Comparing DenseNet121, EfficientNetB5, InceptionV3, ResNet50 and VGG16 (Part 1/6)\n## Trained on APTOS19 + EyePACS15 ; Validation on Messidor-2 for generalization\n\n#### Quadratic Weighted Kappa (QWK):\nQuadratic Weighted Kappa (QWK, the greek letter $\\kappa$), also known as Cohen's Kappa, is the official evaluation metric in both 2015 and 2019 competitions. \nAccording to the [wikipedia article](https://en.wikipedia.org/wiki/Cohen%27s_kappa), we have\n> The definition of $\\kappa$ is:\n> $$\\kappa \\equiv \\frac{p_o - p_e}{1 - p_e}$$\n> where $p_o$ is the relative observed agreement among raters (identical to accuracy), and $p_e$ is the hypothetical probability of chance agreement, using the observed data to calculate the probabilities of each observer randomly seeing each category.\n\nThis metric is used because if we just using accuracy as the metric, it will give spurious results (because the data is unbalanced). The QWK is more fit to the problem.\n\n#### Ordinal Regression (OR):\nWe also treat this as an Ordinal Regression Problem based on this litterature: \n- Mean absolute error as training metric instead of accuracy;  [This](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9955015/)\n- Mean Squared Error as loss function instead of Cross Entropy;  [This](https://ieeexplore.ieee.org/iel7/9342628/9342629/09342711.pdf) \nand [this (Presentation video)](https://www.youtube.com/watch?v=IvhzwhjFyFQ)\n> In DR classification, the disease classes are not independent, meaning that the severity of the disease is not necessarily limited to a specific set of discrete classes. Therefore, an ordinal regression model is more appropriate because it can predict a continuous severity score, allowing for a more precise prediction of disease severity.\n---\n\n#### Note#1: \nUnlike Binary Regression, we cannot use ROC, AUC, Spe, Sen nor CM :(\nTherefore we will attempt to use these plots to compare the models: \n- Learning curve: training and validation loss (e.g., MAE or MSE) as a function of the number of epochs. By comparing the learning curves of your models, you can assess which one is better at minimizing the loss function and which one is less prone to overfitting.\n- Scatter plot: A scatter plot shows the predicted severity level vs. the actual severity level for each sample in the validation set. By comparing the scatter plots of your models, you can assess which one has better predictions and how consistent the predictions are across the severity levels.\n- Box plot: A box plot shows the distribution of the predicted severity levels across different severity levels in the validation set. By comparing the box plots of your models, you can assess how well each model predicts the different severity levels and whether there are any systematic biases in the predictions.\n- Cumulative distribution function (CDF) plot: A CDF plot shows the cumulative distribution of the predicted severity levels across different severity levels in the validation set. By comparing the CDF plots of your models, you can assess how well each model predicts the different severity levels and how the predictions are distributed across the severity levels.\n\n#### Note#2:\nAlthough ROC curves are not appropriate for ordinal regression, you can use Cumulative Net Reclassification Improvement (NRI) or Receiver Operating Characteristic Integrated with Decision Analytic (ROC-DA) curves that are appropriate for ordinal regression. These curves allow you to evaluate how well each model performs at classifying samples across different severity levels.\n\n### Credits\n#### This series of notebooks is heavily inspired by FEDERICO RAIMONDI *brilliant* kernels (DenseNet and EfficientNet).\nPlease check his [Inference Kernel](https://www.kaggle.com/raimonds1993/aptos19-densenet-inference-old-new-data/data?scriptVersionId=17252732)!\n\nThank you!","metadata":{}},{"cell_type":"markdown","source":"# EfficientNetB5 Notebook\n","metadata":{}},{"cell_type":"code","source":"# To have reproducible results and compare them\nnr_seed = 2023\nimport numpy as np \nnp.random.seed(nr_seed)\nimport tensorflow as tf\ntf.set_random_seed(nr_seed)","metadata":{"execution":{"iopub.status.busy":"2025-04-05T19:21:54.388376Z","iopub.execute_input":"2025-04-05T19:21:54.388641Z","iopub.status.idle":"2025-04-05T19:21:55.587819Z","shell.execute_reply.started":"2025-04-05T19:21:54.388584Z","shell.execute_reply":"2025-04-05T19:21:55.587241Z"},"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"!pip install keras_efficientnets","metadata":{"execution":{"iopub.status.busy":"2025-04-05T19:21:55.590344Z","iopub.execute_input":"2025-04-05T19:21:55.590654Z","iopub.status.idle":"2025-04-05T19:22:01.805084Z","shell.execute_reply.started":"2025-04-05T19:21:55.590591Z","shell.execute_reply":"2025-04-05T19:22:01.804008Z"},"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# Libraries\nimport json\nimport math\nimport os\n\n\nimport scipy as sp\nfrom functools import partial\nfrom collections import Counter\nimport json\n\nimport cv2\nfrom PIL import Image\nimport numpy as np\nfrom keras import backend as K\nfrom keras import layers\nfrom keras_efficientnets import *\nfrom keras.callbacks import Callback, ModelCheckpoint\nfrom keras.preprocessing.image import ImageDataGenerator\nfrom keras.models import Sequential\nfrom keras.optimizers import Adam\nfrom tensorflow.keras.utils import plot_model\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\nfrom tqdm import tqdm, tqdm_notebook\nimport gc\nimport warnings\nwarnings.filterwarnings(\"ignore\")\n\n%matplotlib inline","metadata":{"_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_kg_hide-input":true,"execution":{"iopub.status.busy":"2025-04-05T19:22:01.807992Z","iopub.execute_input":"2025-04-05T19:22:01.808314Z","iopub.status.idle":"2025-04-05T19:22:02.584909Z","shell.execute_reply.started":"2025-04-05T19:22:01.808252Z","shell.execute_reply":"2025-04-05T19:22:02.584156Z"},"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# Image size\nim_size = 224\n# Batch size\nBATCH_SIZE = 32","metadata":{"execution":{"iopub.status.busy":"2025-04-05T19:22:02.586468Z","iopub.execute_input":"2025-04-05T19:22:02.586761Z","iopub.status.idle":"2025-04-05T19:22:02.59071Z","shell.execute_reply.started":"2025-04-05T19:22:02.586705Z","shell.execute_reply":"2025-04-05T19:22:02.589792Z"},"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"# Loading & Merging","metadata":{}},{"cell_type":"code","source":"new_train = pd.read_csv('../input/aptos2019-blindness-detection/train.csv')\nold_train = pd.read_csv('../input/diabetic-retinopathy-resized/trainLabels.csv')\nprint(new_train.shape)\nprint(old_train.shape)","metadata":{"execution":{"iopub.status.busy":"2025-04-05T19:22:02.592202Z","iopub.execute_input":"2025-04-05T19:22:02.592489Z","iopub.status.idle":"2025-04-05T19:22:02.650508Z","shell.execute_reply.started":"2025-04-05T19:22:02.592437Z","shell.execute_reply":"2025-04-05T19:22:02.649769Z"},"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"old_train = old_train[['image','level']]\nold_train.columns = new_train.columns\nold_train.diagnosis.value_counts()\n\n# path columns\nnew_train['id_code'] = '../input/aptos2019-blindness-detection/train_images/' + new_train['id_code'].astype(str) + '.png'\nold_train['id_code'] = '../input/diabetic-retinopathy-resized/resized_train/resized_train/' + old_train['id_code'].astype(str) + '.jpeg'\n\ntrain_df = old_train.copy()\nval_df = new_train.copy()\ntrain_df.head()","metadata":{"execution":{"iopub.status.busy":"2025-04-05T19:22:02.651589Z","iopub.execute_input":"2025-04-05T19:22:02.651831Z","iopub.status.idle":"2025-04-05T19:22:02.842723Z","shell.execute_reply.started":"2025-04-05T19:22:02.651787Z","shell.execute_reply":"2025-04-05T19:22:02.842042Z"},"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# Let's shuffle the datasets\ntrain_df = train_df.sample(frac=1).reset_index(drop=True)\nval_df = val_df.sample(frac=1).reset_index(drop=True)\nprint(train_df.shape)\nprint(val_df.shape)","metadata":{"execution":{"iopub.status.busy":"2025-04-05T19:22:02.84405Z","iopub.execute_input":"2025-04-05T19:22:02.8443Z","iopub.status.idle":"2025-04-05T19:22:02.854961Z","shell.execute_reply.started":"2025-04-05T19:22:02.844255Z","shell.execute_reply":"2025-04-05T19:22:02.854138Z"},"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"## Train - Valid split","metadata":{}},{"cell_type":"code","source":"# Not used in version 5 train_df, val_df = train_test_split(train_df, shuffle=True, stratify=train_df.diagnosis, test_size=0.2, random_state=2019)","metadata":{"execution":{"iopub.status.busy":"2025-04-05T19:22:02.856111Z","iopub.execute_input":"2025-04-05T19:22:02.856341Z","iopub.status.idle":"2025-04-05T19:22:02.862877Z","shell.execute_reply.started":"2025-04-05T19:22:02.856298Z","shell.execute_reply":"2025-04-05T19:22:02.862302Z"},"trusted":true},"outputs":[],"execution_count":null},{"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'{image_path}')\n        img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\n        #img = crop_image_from_gray(img)\n        img = cv2.resize(img, (im_size,im_size))\n        img = cv2.addWeighted(img,4,cv2.GaussianBlur(img, (0,0), im_size/40) ,-4 ,128)\n        \n        fig.add_subplot(rows, columns, i+1)\n        plt.title(image_id)\n        plt.imshow(img)\n    \n    plt.tight_layout()\n\n# display train images\ndisplay_samples(train_df)","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2025-04-05T19:22:02.864304Z","iopub.execute_input":"2025-04-05T19:22:02.864541Z","iopub.status.idle":"2025-04-05T19:22:06.358888Z","shell.execute_reply.started":"2025-04-05T19:22:02.864495Z","shell.execute_reply":"2025-04-05T19:22:06.357667Z"},"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"# Processing Images","metadata":{}},{"cell_type":"markdown","source":"Crop function: https://www.kaggle.com/ratthachat/aptos-updated-preprocessing-ben-s-cropping ","metadata":{}},{"cell_type":"code","source":"def crop_image1(img,tol=7):\n    # img is image data\n    # tol  is tolerance\n        \n    mask = img>tol\n    return img[np.ix_(mask.any(1),mask.any(0))]\n\ndef crop_image_from_gray(img,tol=7):\n    if img.ndim ==2:\n        mask = img>tol\n        return img[np.ix_(mask.any(1),mask.any(0))]\n    elif img.ndim==3:\n        gray_img = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)\n        mask = gray_img>tol\n        \n        check_shape = img[:,:,0][np.ix_(mask.any(1),mask.any(0))].shape[0]\n        if (check_shape == 0): # image is too dark so that we crop out everything,\n            return img # return original image\n        else:\n            img1=img[:,:,0][np.ix_(mask.any(1),mask.any(0))]\n            img2=img[:,:,1][np.ix_(mask.any(1),mask.any(0))]\n            img3=img[:,:,2][np.ix_(mask.any(1),mask.any(0))]\n            img = np.stack([img1,img2,img3],axis=-1)\n        return img\n\ndef preprocess_image(image_path, desired_size=224):\n    img = cv2.imread(image_path)\n    img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\n    img = crop_image_from_gray(img)\n    img = cv2.resize(img, (desired_size,desired_size))\n    img = cv2.addWeighted(img,4,cv2.GaussianBlur(img, (0,0), desired_size/30) ,-4 ,128)\n    \n    return img\n\ndef preprocess_image_old(image_path, desired_size=224):\n    img = cv2.imread(image_path)\n    img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\n    #img = crop_image_from_gray(img)\n    img = cv2.resize(img, (desired_size,desired_size))\n    img = cv2.addWeighted(img,4,cv2.GaussianBlur(img, (0,0), desired_size/40) ,-4 ,128)\n    \n    return img","metadata":{"execution":{"iopub.status.busy":"2025-04-05T19:22:06.360365Z","iopub.execute_input":"2025-04-05T19:22:06.36065Z","iopub.status.idle":"2025-04-05T19:22:06.377894Z","shell.execute_reply.started":"2025-04-05T19:22:06.360602Z","shell.execute_reply":"2025-04-05T19:22:06.376892Z"},"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# validation set\nN = val_df.shape[0]\nx_val = np.empty((N, im_size, im_size, 3), dtype=np.uint8)\n\nfor i, image_id in enumerate(tqdm_notebook(val_df['id_code'])):\n    x_val[i, :, :, :] = preprocess_image(\n        f'{image_id}',\n        desired_size = im_size\n    )","metadata":{"execution":{"iopub.status.busy":"2025-04-05T19:22:06.381793Z","iopub.execute_input":"2025-04-05T19:22:06.382103Z"},"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"y_train = train_df['diagnosis'].values\ny_val = val_df['diagnosis'].values\n\nprint(y_train.shape)\nprint(x_val.shape)\nprint(y_val.shape)","metadata":{"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# delete the uneeded df\ndel new_train\ndel old_train\ndel val_df\ngc.collect()","metadata":{"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"# Creating keras callback for QWK\n\n---\n\nI had to change this function, in order to consider the best kappa score among all the buckets.","metadata":{}},{"cell_type":"code","source":"class Metrics(Callback):\n\n    def on_epoch_end(self, epoch, logs={}):\n        X_val, y_val = self.validation_data[:2]\n        \n        y_pred = self.model.predict(X_val)\n        \n        coef = [0.5, 1.5, 2.5, 3.5]\n\n        for i, pred in enumerate(y_pred):\n            if pred < coef[0]:\n                y_pred[i] = 0\n            elif pred >= coef[0] and pred < coef[1]:\n                y_pred[i] = 1\n            elif pred >= coef[1] and pred < coef[2]:\n                y_pred[i] = 2\n            elif pred >= coef[2] and pred < coef[3]:\n                y_pred[i] = 3\n            else:\n                y_pred[i] = 4\n\n        _val_kappa = cohen_kappa_score(\n            y_val,\n            y_pred, \n            weights='quadratic'\n        )\n        self.val_kappas.append(_val_kappa)\n        print(f\"val_kappa: {_val_kappa:.4f}\")\n\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},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"# Data Generator","metadata":{}},{"cell_type":"code","source":"def create_datagen():\n    return ImageDataGenerator(\n        horizontal_flip = True,\n        vertical_flip = True,\n        rotation_range = 160,\n        zoom_range=0.35\n    )","metadata":{"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"# Model: EfficientNetB5\n> The core idea about Efficient Nets is the use of compound scaling - using a weighted scale of three inter-connected hyper parameters of the model - Resolution of the input, Depth of the Network and Width of the 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"}}},{"cell_type":"code","source":"effnet = EfficientNetB5(\n    input_shape=(im_size,im_size,3),\n    weights='imagenet',\n    include_top=False\n)","metadata":{"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"def build_model():\n    model = Sequential()\n    model.add(effnet)\n    model.add(layers.Dropout(0.25))\n    model.add(layers.Dense(2048))\n    model.add(layers.LeakyReLU())\n    model.add(layers.GlobalAveragePooling2D())\n    model.add(layers.Dropout(0.5))\n    model.add(layers.Dense(1, activation='linear'))\n    \n    model.compile(\n        loss='mean_squared_error',\n        optimizer=Adam(lr=0.0001,decay=1e-5),\n        metrics=['mae']\n    )\n    \n    return model","metadata":{"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"model = build_model()\nmodel.summary()","metadata":{"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"plot_model(\n    model, show_shapes=True\n)","metadata":{"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"# Training & Evaluation","metadata":{}},{"cell_type":"code","source":"num_bucket = 8\ndiv = round(train_df.shape[0]/num_bucket)\ndiv","metadata":{"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"results = pd.DataFrame({\n                        'val_loss': [0.0],\n                        'val_mean_absolute_error': [0.0],\n                        'loss': [0.0], \n                        'mean_absolute_error': [0.0],\n                        'bucket': [0.0]\n                        })","metadata":{"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# Epochs\nepochs = [10,10,10,15,15,20,20,25]\nkappa_metrics = Metrics()\nkappa_metrics.val_kappas = []","metadata":{"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"for i in range(0,num_bucket):\n    if i != (num_bucket-1):\n        print(\"Bucket Nr: {}\".format(i))\n        \n        N = train_df.iloc[i*div:(1+i)*div].shape[0]\n        x_train = np.empty((N, im_size, im_size, 3), dtype=np.uint8)\n        for j, image_id in enumerate(tqdm_notebook(train_df.iloc[i*div:(1+i)*div,0])):\n            x_train[j, :, :, :] = preprocess_image_old(f'{image_id}', desired_size = im_size)\n\n        data_generator = create_datagen().flow(x_train, y_train[i*div:(1+i)*div], batch_size=BATCH_SIZE)\n        history = model.fit_generator(\n                        data_generator,\n                        steps_per_epoch=x_train.shape[0] / BATCH_SIZE,\n                        epochs=epochs[i],\n                        validation_data=(x_val, y_val),\n                        callbacks=[kappa_metrics]\n                        )\n        \n        dic = history.history\n        df_model = pd.DataFrame(dic)\n        df_model['bucket'] = i\n    else:\n        print(\"Bucket Nr: {}\".format(i))\n        \n        N = train_df.iloc[i*div:].shape[0]\n        x_train = np.empty((N, im_size, im_size, 3), dtype=np.uint8)\n        for j, image_id in enumerate(tqdm_notebook(train_df.iloc[i*div:,0])):\n            x_train[j, :, :, :] = preprocess_image_old(f'{image_id}', desired_size = im_size)\n        data_generator = create_datagen().flow(x_train, y_train[i*div:], batch_size=BATCH_SIZE)\n        \n        history = model.fit_generator(\n                        data_generator,\n                        steps_per_epoch=x_train.shape[0] / BATCH_SIZE,\n                        epochs=epochs[i],\n                        validation_data=(x_val, y_val),\n                        callbacks=[kappa_metrics]\n                        )\n        \n        dic = history.history\n        df_model = pd.DataFrame(dic)\n        df_model['bucket'] = i\n\n    results = results.append(df_model)\n    \n    del data_generator\n    del x_train\n    gc.collect()\n    \n    print('-'*40)","metadata":{"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"results = results.iloc[1:]\nresults['kappa'] = kappa_metrics.val_kappas\nresults = results.reset_index()\nresults = results.rename(index=str, columns={\"index\": \"epoch\"})\nprint(max(results.kappa))","metadata":{"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"results[['loss', 'val_loss']].plot()\nresults[['mean_absolute_error', 'val_mean_absolute_error']].plot()\nresults[['kappa']].plot()\nresults.to_csv('model_results.csv',index=False)","metadata":{"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"#https://www.kaggle.com/abhishek/optimizer-for-quadratic-weighted-kappa\nclass OptimizedRounder(object):\n    def __init__(self):\n        self.coef_ = 0\n\n    def _kappa_loss(self, coef, X, y):\n        X_p = np.copy(X)\n        for i, pred in enumerate(X_p):\n            if pred < coef[0]:\n                X_p[i] = 0\n            elif pred >= coef[0] and pred < coef[1]:\n                X_p[i] = 1\n            elif pred >= coef[1] and pred < coef[2]:\n                X_p[i] = 2\n            elif pred >= coef[2] and pred < coef[3]:\n                X_p[i] = 3\n            else:\n                X_p[i] = 4\n\n        ll = cohen_kappa_score(y, X_p, weights='quadratic')\n        return -ll\n\n    def fit(self, X, y):\n        loss_partial = partial(self._kappa_loss, X=X, y=y)\n        initial_coef = [0.5, 1.5, 2.5, 3.5]\n        self.coef_ = sp.optimize.minimize(loss_partial, initial_coef, method='nelder-mead')\n        print(-loss_partial(self.coef_['x']))\n\n    def predict(self, X, coef):\n        X_p = np.copy(X)\n        for i, pred in enumerate(X_p):\n            if pred < coef[0]:\n                X_p[i] = 0\n            elif pred >= coef[0] and pred < coef[1]:\n                X_p[i] = 1\n            elif pred >= coef[1] and pred < coef[2]:\n                X_p[i] = 2\n            elif pred >= coef[2] and pred < coef[3]:\n                X_p[i] = 3\n            else:\n                X_p[i] = 4\n        return X_p\n\n    def coefficients(self):\n        return self.coef_['x']","metadata":{"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"model.load_weights('model.h5')\ny_val_pred = model.predict(x_val)\n\noptR = OptimizedRounder()\noptR.fit(y_val_pred, y_val)\ncoefficients = optR.coefficients()\nprint(f'Coefficients: {coefficients}')\ny_val_pred = optR.predict(y_val_pred, coefficients)\n\nscore = cohen_kappa_score(y_val_pred, y_val, weights='quadratic')\n\nprint('Optimized Validation QWK score: {}'.format(score))\nprint('Not Optimized Validation QWK score: {}'.format(max(results.kappa)))","metadata":{"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"model.save(\"efficientnetb5_dr.keras\", save_format=\"keras\")\n","metadata":{"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"accuracy = accuracy_score(y_val, y_val_pred_rounded)\n\nprint('Accuracy:', accuracy)\n","metadata":{"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"from sklearn.metrics import confusion_matrix, roc_curve, auc\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nimport statsmodels.api as sm\n\n# Load the model and predict on the validation data\nmodel.load_weights('model.h5')\ny_val_pred = model.predict(x_val)\n\n# Instantiate an OptimizedRounder object and fit on the validation data\noptR = OptimizedRounder()\noptR.fit(y_val_pred, y_val)\ncoefficients = optR.coefficients()\nprint(f'Coefficients: {coefficients}')\n\n# Get the predicted labels using the optimized coefficients\ny_val_pred_rounded = optR.predict(y_val_pred, coefficients)\n\n","metadata":{"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# Compute confusion matrix\ncm = confusion_matrix(y_val, y_val_pred_rounded)\n\n# Plot confusion matrix\nfig, ax = plt.subplots(figsize=(8, 6))\nsns.heatmap(cm, annot=True, cmap='Blues', fmt='g', ax=ax)\n\n# Add labels, title, and axis ticks\nax.set_xlabel('Predicted labels')\nax.set_ylabel('True labels')\nax.set_title('Confusion Matrix')\nax.xaxis.set_ticklabels(['No-DR', 'Mild NPDR', 'Moderate NPDR', 'Severe NPDR', 'PDR'])\nax.yaxis.set_ticklabels(['No-DR', 'Mild NPDR', 'Moderate NPDR', 'Severe NPDR', 'PDR'])\nplt.show()","metadata":{"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"from sklearn.metrics import confusion_matrix, classification_report\n\n# assuming y_val and y_val_pred_rounded are the true and predicted labels respectively\ncm = confusion_matrix(y_val, y_val_pred_rounded)\n\n# Calculate precision, recall, and accuracy\nreport = classification_report(y_val, y_val_pred_rounded, target_names=['No-DR', 'Mild NPDR', 'Moderate NPDR', 'Severe NPDR', 'PDR'])\n\n# Print confusion matrix, precision, recall, and accuracy\nprint('Confusion Matrix:\\n', cm)\nprint('Classification Report:\\n', report)\n","metadata":{"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"The QWK score of 0.8725 indicates that the model is making accurate predictions and is performing well overall. However, the classification report shows that the model is struggling to accurately predict some of the individual classes, particularly \"Mild NPDR\", \"Severe NPDR\", and \"PDR\". This suggests that the model may be biased towards predicting certain classes more accurately than others, and that further analysis and improvements may be needed to improve its performance on these classes.\n\n\n\n","metadata":{}},{"cell_type":"code","source":"# Map 0 to 'No DR' and 1,2,3,4 to 'DR' in y_true\ny_true_binary = np.where(y_val == 0, 0, 1)\ny_val_pred_rounded_binary = np.where(y_val_pred_rounded == 0, 0, 1)\n\n# Compute ROC curve and AUC\nfpr, tpr, thresholds = roc_curve(y_true_binary, y_val_pred_rounded_binary, pos_label=1)\nroc_auc = auc(fpr, tpr)\n\nplt.plot(fpr, tpr, color='darkorange', lw=2, label='ROC curve (area = %0.2f)' % roc_auc)\nplt.plot([0, 1], [0, 1], color='navy', lw=2, linestyle='--')\nplt.xlim([0.0, 1.0])\nplt.ylim([0.0, 1.05])\nplt.xlabel('False Positive Rate')\nplt.ylabel('True Positive Rate')\nplt.title('Receiver operating characteristic curve')\nplt.legend(loc=\"lower right\")\nplt.show()","metadata":{"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"acc = accuracy_score(y_true_binary, y_val_pred_rounded_binary)\n\nprint('Accuracy for binary classification:', acc)\n","metadata":{"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# Compute confusion matrix\ncm = confusion_matrix(y_true_binary, y_val_pred_rounded_binary)\n\n# Calculate specificity and sensitivity\ntn, fp, fn, tp = cm.ravel()\nspecificity = tn / (tn + fp)\nsensitivity = tp / (tp + fn)\n\n# Display confusion matrix with percentages\nfig, ax = plt.subplots(figsize=(8, 6))\nsns.heatmap(cm, annot=True, cmap='Blues', fmt='g', ax=ax)\n\n# Add labels, title, and axis ticks\nax.set_xlabel('Predicted labels')\nax.set_ylabel('True labels')\nax.set_title('Confusion Matrix (Specificity={:.2f}, Sensitivity={:.2f})'.format(specificity, sensitivity))\nax.xaxis.set_ticklabels(['No-DR', 'DR'])\nax.yaxis.set_ticklabels(['No-DR', 'DR'])\nplt.show()","metadata":{"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"report_binary = classification_report(y_true_binary, y_val_pred_rounded_binary, target_names=['No-DR', 'DR'])\nprint('Classification Report:\\n', report_binary)\n","metadata":{"trusted":true},"outputs":[],"execution_count":null}]}