{"cells":[{"cell_type":"code","execution_count":1,"metadata":{"id":"GZGbpCAWQ_Xt","executionInfo":{"status":"ok","timestamp":1737285245430,"user_tz":-60,"elapsed":15404,"user":{"displayName":"Abdul Rehman Javed","userId":"09001954855647296003"}}},"outputs":[],"source":["import tensorflow as tf\n","import pandas as pd\n","from sklearn.preprocessing import LabelEncoder\n","from sklearn.model_selection import train_test_split\n","import numpy as np\n","from sklearn.metrics import confusion_matrix, classification_report, roc_curve, roc_auc_score\n","import matplotlib.pyplot as plt\n","from sklearn.metrics import confusion_matrix, classification_report, roc_curve, roc_auc_score, f1_score\n","from sklearn.metrics import confusion_matrix, classification_report, f1_score, roc_curve, roc_auc_score, accuracy_score\n","from sklearn.preprocessing import LabelEncoder, StandardScaler\n","import seaborn as sns"]},{"cell_type":"code","execution_count":2,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":38390,"status":"ok","timestamp":1737285283817,"user":{"displayName":"Abdul Rehman Javed","userId":"09001954855647296003"},"user_tz":-60},"id":"bU0jbRKDRDzq","outputId":"0139c98d-fb8a-45ff-aa4b-219c46aa9af0"},"outputs":[{"output_type":"stream","name":"stdout","text":["Mounted at /content/drive\n"]}],"source":["from google.colab import drive\n","drive.mount('/content/drive')\n","data_folder_path = '/content/drive/'\n"]},{"cell_type":"code","execution_count":3,"metadata":{"id":"-Xvr6QcWRHpR","executionInfo":{"status":"ok","timestamp":1737285362273,"user_tz":-60,"elapsed":1396,"user":{"displayName":"Abdul Rehman Javed","userId":"09001954855647296003"}}},"outputs":[],"source":["# Load the dataset\n","data = pd.read_csv(\"/content/drive/MyDrive/Work with Nayab/Siezues-GNN_RESULTS/Data/paitent.csv\")  # Replace with the actual file path"]},{"cell_type":"code","execution_count":4,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":532},"executionInfo":{"elapsed":919,"status":"ok","timestamp":1737285364571,"user":{"displayName":"Abdul Rehman Javed","userId":"09001954855647296003"},"user_tz":-60},"id":"AfO8hME9RKCB","outputId":"d38782a2-490a-4351-a5fd-9b2748cb6cdf"},"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}],"source":["# Visualize the label column\n","label_counts = data['label'].value_counts()\n","label_counts.plot(kind='bar')\n","plt.xlabel('Labels')\n","plt.ylabel('Counts')\n","plt.title('Label Distribution')\n","plt.show()"]},{"cell_type":"code","execution_count":5,"metadata":{"id":"t5E2ABJlRMAU","executionInfo":{"status":"ok","timestamp":1737285364945,"user_tz":-60,"elapsed":2,"user":{"displayName":"Abdul Rehman Javed","userId":"09001954855647296003"}}},"outputs":[],"source":["# Separate features and labels\n","X = data.drop(\"label\", axis=1)\n","y = data[\"label\"]"]},{"cell_type":"code","execution_count":6,"metadata":{"id":"rrM3RXftRMmx","executionInfo":{"status":"ok","timestamp":1737285366855,"user_tz":-60,"elapsed":327,"user":{"displayName":"Abdul Rehman Javed","userId":"09001954855647296003"}}},"outputs":[],"source":["# Convert labels to numerical values\n","label_encoder = LabelEncoder()\n","y_encoded = label_encoder.fit_transform(y)"]},{"cell_type":"code","execution_count":7,"metadata":{"id":"APRrePVjROio","executionInfo":{"status":"ok","timestamp":1737285368406,"user_tz":-60,"elapsed":628,"user":{"displayName":"Abdul Rehman Javed","userId":"09001954855647296003"}}},"outputs":[],"source":["# Split the dataset into train and test sets\n","X_train, X_test, y_train, y_test = train_test_split(X, y_encoded, test_size=0.2, random_state=42)"]},{"cell_type":"code","execution_count":8,"metadata":{"id":"bFmNq32bRQlm","executionInfo":{"status":"ok","timestamp":1737285368856,"user_tz":-60,"elapsed":1,"user":{"displayName":"Abdul Rehman Javed","userId":"09001954855647296003"}}},"outputs":[],"source":["# Standardize the feature data\n","scaler = StandardScaler()\n","X_train_scaled = scaler.fit_transform(X_train)\n","X_test_scaled = scaler.transform(X_test)"]},{"cell_type":"code","execution_count":9,"metadata":{"id":"GNq1BWL8RTqE","executionInfo":{"status":"ok","timestamp":1737285371268,"user_tz":-60,"elapsed":1022,"user":{"displayName":"Abdul Rehman Javed","userId":"09001954855647296003"}}},"outputs":[],"source":["# Convert the data to TensorFlow tensors\n","X_train_tensor = tf.constant(X_train_scaled, dtype=tf.float32)\n","y_train_tensor = tf.constant(y_train, dtype=tf.int32)\n","X_test_tensor = tf.constant(X_test_scaled, dtype=tf.float32)\n","y_test_tensor = tf.constant(y_test, dtype=tf.int32)"]},{"cell_type":"code","execution_count":10,"metadata":{"id":"Z36-M2gnRUq3","executionInfo":{"status":"ok","timestamp":1737285372796,"user_tz":-60,"elapsed":2,"user":{"displayName":"Abdul Rehman Javed","userId":"09001954855647296003"}}},"outputs":[],"source":["import tensorflow as tf\n","\n","class SimpleRNNModel(tf.keras.Model):\n","    def __init__(self, num_classes):\n","        super(SimpleRNNModel, self).__init__()\n","        self.rnn_layer = tf.keras.layers.SimpleRNN(32, activation='relu')\n","        self.dense_output = tf.keras.layers.Dense(num_classes, activation='softmax')\n","\n","    def call(self, inputs):\n","        expanded_inputs = tf.expand_dims(inputs, axis=-1)\n","        x = self.rnn_layer(expanded_inputs)\n","        output = self.dense_output(x)\n","        return output"]},{"cell_type":"code","source":["num_classes = len(label_encoder.classes_)\n","model = SimpleRNNModel(num_classes)  # Replace 'GNNModel' with the appropriate LSTM model class\n","loss_object = tf.keras.losses.SparseCategoricalCrossentropy()\n","optimizer = tf.keras.optimizers.Adam()\n","epochs = 50\n","batch_size = 32\n","steps_per_epoch = len(X_train_scaled) // batch_size\n","train_loss_history = []\n","test_loss_history = []\n","train_accuracy_history = []\n","test_accuracy_history = []"],"metadata":{"id":"ygxrlTCQ75D_","executionInfo":{"status":"ok","timestamp":1737285374151,"user_tz":-60,"elapsed":3,"user":{"displayName":"Abdul Rehman Javed","userId":"09001954855647296003"}}},"execution_count":11,"outputs":[]},{"cell_type":"code","execution_count":12,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"FEWU4xeGRpWM","executionInfo":{"status":"ok","timestamp":1737287337993,"user_tz":-60,"elapsed":1913071,"user":{"displayName":"Abdul Rehman Javed","userId":"09001954855647296003"}},"outputId":"0cdf6d35-a468-48e1-85fb-59d98592a609"},"outputs":[{"output_type":"stream","name":"stdout","text":["Epoch 1/50, Val Loss: 0.1825, Val Accuracy: 0.9401\n","Epoch 2/50, Val Loss: 0.1340, Val Accuracy: 0.9575\n","Epoch 3/50, Val Loss: 0.1392, Val Accuracy: 0.9545\n","No improvement in validation loss for 1 epoch(s).\n","Epoch 4/50, Val Loss: 0.1130, Val Accuracy: 0.9637\n","Epoch 5/50, Val Loss: 0.1075, Val Accuracy: 0.9678\n","Epoch 6/50, Val Loss: 0.1120, Val Accuracy: 0.9656\n","No improvement in validation loss for 1 epoch(s).\n","Epoch 7/50, Val Loss: 0.1280, Val Accuracy: 0.9567\n","No improvement in validation loss for 2 epoch(s).\n","Epoch 8/50, Val Loss: 0.1440, Val Accuracy: 0.9504\n","No improvement in validation loss for 3 epoch(s).\n","Epoch 9/50, Val Loss: 0.1850, Val Accuracy: 0.9327\n","No improvement in validation loss for 4 epoch(s).\n","Epoch 10/50, Val Loss: 0.1033, Val Accuracy: 0.9674\n","Epoch 11/50, Val Loss: 0.0934, Val Accuracy: 0.9730\n","Epoch 12/50, Val Loss: 0.1070, Val Accuracy: 0.9674\n","No improvement in validation loss for 1 epoch(s).\n","Epoch 13/50, Val Loss: 0.0867, Val Accuracy: 0.9723\n","Epoch 14/50, Val Loss: 0.0850, Val Accuracy: 0.9748\n","Epoch 15/50, Val Loss: 0.1167, Val Accuracy: 0.9634\n","No improvement in validation loss for 1 epoch(s).\n","Epoch 16/50, Val Loss: 0.0967, Val Accuracy: 0.9700\n","No improvement in validation loss for 2 epoch(s).\n","Epoch 17/50, Val Loss: 0.1098, Val Accuracy: 0.9667\n","No improvement in validation loss for 3 epoch(s).\n","Epoch 18/50, Val Loss: 0.1101, Val Accuracy: 0.9652\n","No improvement in validation loss for 4 epoch(s).\n","Epoch 19/50, Val Loss: 0.0861, Val Accuracy: 0.9734\n","No improvement in validation loss for 5 epoch(s).\n","Early stopping triggered. Stopping training.\n"]}],"source":["# Parameters for Early Stopping\n","patience = 5\n","best_loss = float('inf')\n","wait = 0\n","validation_split = 0.1  # Use a small validation set for faster evaluation\n","\n","# Split a portion of training data for validation\n","val_size = int(len(X_train_tensor) * validation_split)\n","X_val_tensor = X_train_tensor[:val_size]\n","y_val_tensor = y_train_tensor[:val_size]\n","X_train_tensor = X_train_tensor[val_size:]\n","y_train_tensor = y_train_tensor[val_size:]\n","\n","for epoch in range(epochs):\n","    for step in range(steps_per_epoch):\n","        # Mini-batch gradient updates\n","        start = step * batch_size\n","        end = (step + 1) * batch_size\n","        X_batch = X_train_tensor[start:end]\n","        y_batch = y_train_tensor[start:end]\n","\n","        with tf.GradientTape() as tape:\n","            predictions = model(X_batch)\n","            loss = loss_object(y_batch, predictions)\n","\n","        gradients = tape.gradient(loss, model.trainable_variables)\n","        optimizer.apply_gradients(zip(gradients, model.trainable_variables))\n","\n","    # Evaluate on validation data\n","    val_loss = loss_object(y_val_tensor, model(X_val_tensor))\n","    val_predictions = np.argmax(model(X_val_tensor).numpy(), axis=1)\n","    val_accuracy = accuracy_score(y_val_tensor.numpy(), val_predictions)\n","\n","    train_loss_history.append(loss.numpy())\n","    test_loss_history.append(val_loss.numpy())\n","    train_accuracy_history.append(None)  # Skipping full train accuracy for speed\n","    test_accuracy_history.append(val_accuracy)\n","\n","    print(f\"Epoch {epoch + 1}/{epochs}, Val Loss: {val_loss:.4f}, Val Accuracy: {val_accuracy:.4f}\")\n","\n","    # Early stopping check\n","    if val_loss < best_loss:\n","        best_loss = val_loss\n","        wait = 0  # Reset patience counter\n","    else:\n","        wait += 1\n","        print(f\"No improvement in validation loss for {wait} epoch(s).\")\n","        if wait >= patience:\n","            print(\"Early stopping triggered. Stopping training.\")\n","            break"]},{"cell_type":"code","execution_count":13,"metadata":{"id":"h2sB1lkPSB8y","executionInfo":{"status":"ok","timestamp":1737287337993,"user_tz":-60,"elapsed":4,"user":{"displayName":"Abdul Rehman Javed","userId":"09001954855647296003"}}},"outputs":[],"source":["# Evaluation\n","y_pred_train_prob = model(X_train_tensor).numpy()\n","y_pred_test_prob = model(X_test_tensor).numpy()\n","\n","# Convert probabilities to class indices\n","y_pred_train = np.argmax(y_pred_train_prob, axis=1)\n","y_pred_test = np.argmax(y_pred_test_prob, axis=1)\n"]},{"cell_type":"code","execution_count":14,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":4,"status":"ok","timestamp":1737287337994,"user":{"displayName":"Abdul Rehman Javed","userId":"09001954855647296003"},"user_tz":-60},"id":"HXnAbH3KSEs6","outputId":"ca83f7f8-a0ea-4b16-93ea-5c0e19c43e5a"},"outputs":[{"output_type":"stream","name":"stdout","text":["Train F1 Score: 0.9734359219119019\n","Test F1 Score: 0.9743883415698735\n","Train Accuracy: 0.9734484175914508\n","Test Accuracy: 0.9744044977067614\n"]}],"source":["# Evaluation\n","y_pred_train_prob = model(X_train_tensor).numpy() # This is making predictions on the validation training set\n","y_pred_test_prob = model(X_test_tensor).numpy()\n","\n","# Convert probabilities to class indices\n","y_pred_train = np.argmax(y_pred_train_prob, axis=1)\n","y_pred_test = np.argmax(y_pred_test_prob, axis=1)\n","\n","# F1 Score\n","# y_train_tensor contains the labels for the validation training set. We need to call .numpy()\n","# to change it to a numpy array so it's compatible with other arrays for the scoring function\n","f1_train = f1_score(y_train_tensor.numpy(), y_pred_train, average='weighted')\n","f1_test = f1_score(y_test, y_pred_test, average='weighted')\n","\n","# Accuracy\n","train_accuracy = np.mean(np.equal(y_train_tensor.numpy(), y_pred_train))\n","test_accuracy = np.mean(np.equal(y_test, y_pred_test))\n","\n","# Other metrics\n","print(\"Train F1 Score:\", f1_train)\n","print(\"Test F1 Score:\", f1_test)\n","\n","# Accuracy\n","print(\"Train Accuracy:\", train_accuracy)\n","print(\"Test Accuracy:\", test_accuracy)"]},{"cell_type":"code","execution_count":15,"metadata":{"executionInfo":{"elapsed":1072,"status":"ok","timestamp":1737287339063,"user":{"displayName":"Abdul Rehman Javed","userId":"09001954855647296003"},"user_tz":-60},"id":"x4aHisQgSIMf"},"outputs":[],"source":["y_pred_train_prob = model(X_train_tensor).numpy()\n","y_pred_test_prob = model(X_test_tensor).numpy()\n","\n","# Convert probabilities to class indices\n","y_pred_train = np.argmax(y_pred_train_prob, axis=1)\n","y_pred_test = np.argmax(y_pred_test_prob, axis=1)\n","\n","# Use y_train_tensor.numpy() instead of y_train for the training confusion matrix\n","cm_train = confusion_matrix(y_train_tensor.numpy(), y_pred_train)\n","cm_test = confusion_matrix(y_test, y_pred_test)"]},{"cell_type":"code","source":["import matplotlib.pyplot as plt\n","\n","# Plot Loss Curve\n","plt.figure(figsize=(10, 5))\n","plt.plot(train_loss_history, label=\"Training Loss\")\n","plt.plot(test_loss_history, label=\"Validation Loss\", linestyle=\"--\")\n","plt.xlabel(\"Epochs\")\n","plt.ylabel(\"Loss\")\n","plt.title(\"Loss Curve\")\n","plt.legend()\n","plt.grid()\n","plt.show()\n","\n","# Plot Accuracy Curve\n","plt.figure(figsize=(10, 5))\n","plt.plot(train_accuracy_history, label=\"Training Accuracy\")\n","plt.plot(test_accuracy_history, label=\"Validation Accuracy\", linestyle=\"--\")\n","plt.xlabel(\"Epochs\")\n","plt.ylabel(\"Accuracy\")\n","plt.title(\"Accuracy Curve\")\n","plt.legend()\n","plt.grid()\n","plt.show()\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":957},"id":"1KjBcbnnSA5h","executionInfo":{"status":"ok","timestamp":1737287339063,"user_tz":-60,"elapsed":7,"user":{"displayName":"Abdul Rehman Javed","userId":"09001954855647296003"}},"outputId":"99cc3818-59de-48ff-fd05-39f91574926c"},"execution_count":16,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 1000x500 with 1 Axes>"],"image/png":"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\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["<Figure 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\n"},"metadata":{}}]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":564},"executionInfo":{"elapsed":659,"status":"ok","timestamp":1737147672203,"user":{"displayName":"Nayyab 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\n"},"metadata":{}}],"source":["# Loss Curve\n","plt.figure(figsize=(10, 6))\n","plt.plot(train_loss_history, label='Train Loss')\n","plt.plot(test_loss_history, label='Test Loss')\n","plt.xlabel('Epoch')\n","plt.ylabel('Loss')\n","plt.title('Loss Curve')\n","plt.legend()\n","plt.show()"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"HRrZZJqvSMof"},"outputs":[],"source":["y_pred_train_prob = model(X_train_tensor).numpy()\n","y_pred_test_prob = model(X_test_tensor).numpy()\n","\n","# Convert probabilities to class indices\n","y_pred_train = np.argmax(y_pred_train_prob, axis=1)\n","y_pred_test = np.argmax(y_pred_test_prob, axis=1)\n","\n","# Classification Report\n","# Now y_train_tensor and y_pred_train are compatible (assuming y_train_tensor was used for training)\n","report_train = classification_report(y_train_tensor.numpy(), y_pred_train, target_names=label_encoder.classes_)\n","report_test = classification_report(y_test, y_pred_test, target_names=label_encoder.classes_)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":373,"status":"ok","timestamp":1737147718792,"user":{"displayName":"Nayyab Rajpoot","userId":"10723923538147021558"},"user_tz":-300},"id":"yczDjs7TSOsW","outputId":"f40fab0b-8767-4608-e857-1fa55d6c12ed"},"outputs":[{"output_type":"stream","name":"stdout","text":["Train Classification Report:\n","              precision    recall  f1-score   support\n","\n","  noseizures       0.89      1.00      0.94     10916\n","    seizures       1.00      0.87      0.93     10981\n","\n","    accuracy                           0.94     21897\n","   macro avg       0.94      0.94      0.94     21897\n","weighted avg       0.94      0.94      0.94     21897\n","\n"]}],"source":["# Print Confusion Matrix and Classification Report\n","print(\"Train Classification Report:\")\n","print(report_train)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":379,"status":"ok","timestamp":1737147722170,"user":{"displayName":"Nayyab Rajpoot","userId":"10723923538147021558"},"user_tz":-300},"id":"yVg1U66-SQhZ","outputId":"a229e799-1dd7-4aa6-df40-a5df4d375004"},"outputs":[{"output_type":"stream","name":"stdout","text":["Test Classification Report:\n","              precision    recall  f1-score   support\n","\n","  noseizures       0.90      1.00      0.95      3404\n","    seizures       1.00      0.88      0.94      3355\n","\n","    accuracy                           0.94      6759\n","   macro avg       0.95      0.94      0.94      6759\n","weighted avg       0.95      0.94      0.94      6759\n","\n"]}],"source":["print(\"Test Classification Report:\")\n","print(report_test)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":872},"executionInfo":{"elapsed":845,"status":"ok","timestamp":1737147813097,"user":{"displayName":"Nayyab Rajpoot","userId":"10723923538147021558"},"user_tz":-300},"id":"muhRX61CSSSF","outputId":"a6d992d7-550c-4029-eb4f-6ab6f0792571"},"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 1500x1000 with 2 Axes>"],"image/png":"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\n"},"metadata":{}}],"source":["# Plot Confusion Matrix Heatmap for Train Set\n","plt.figure(figsize=(15, 10))\n","sns.heatmap(cm_train, annot=True, fmt=\"d\", cmap=\"Blues\", xticklabels=label_encoder.classes_, yticklabels=label_encoder.classes_)\n","plt.xlabel('Predicted')\n","plt.ylabel('True')\n","plt.title('Confusion Matrix (Train Set)')\n","plt.show()"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":872},"executionInfo":{"elapsed":1184,"status":"ok","timestamp":1737147817952,"user":{"displayName":"Nayyab Rajpoot","userId":"10723923538147021558"},"user_tz":-300},"id":"iRorQnO9SUZt","outputId":"436547c0-f435-4b8c-b4e9-b35d29bce38a"},"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 1500x1000 with 2 Axes>"],"image/png":"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\n"},"metadata":{}}],"source":["# Plot Confusion Matrix Heatmap for Test Set\n","plt.figure(figsize=(15, 10))\n","sns.heatmap(cm_test, annot=True, fmt=\"d\", cmap=\"Blues\", xticklabels=label_encoder.classes_, yticklabels=label_encoder.classes_)\n","plt.xlabel('Predicted')\n","plt.ylabel('True')\n","plt.title('Confusion Matrix (Test Set)')\n","plt.show()"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":356,"status":"ok","timestamp":1737147821870,"user":{"displayName":"Nayyab Rajpoot","userId":"10723923538147021558"},"user_tz":-300},"id":"1K3NQQkjn2jG","outputId":"ad470c6f-44f3-48ec-fb62-bd6fd07620d2"},"outputs":[{"output_type":"stream","name":"stdout","text":["(27033,)\n"]}],"source":["print(y_train.shape)\n"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":600},"executionInfo":{"elapsed":1093,"status":"ok","timestamp":1737147877781,"user":{"displayName":"Nayyab Rajpoot","userId":"10723923538147021558"},"user_tz":-300},"id":"XYIzPwGWn0-t","outputId":"74e2c738-cdb0-428d-b8a2-1f666b2e48c4"},"outputs":[{"output_type":"stream","name":"stdout","text":["ROC AUC Train: 0.9790351406969011\n","ROC AUC Test: 0.9789373770842054\n"]},{"output_type":"display_data","data":{"text/plain":["<Figure size 1000x600 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}],"source":["import matplotlib.pyplot as plt\n","from sklearn.metrics import roc_curve, auc, roc_auc_score\n","import numpy as np\n","\n","# Assuming y_pred_train_prob and y_pred_test_prob are the predicted probabilities for each class\n","# Extract the probabilities for each class\n","y_pred_train_prob = model(X_train_tensor).numpy()\n","y_pred_test_prob = model(X_test_tensor).numpy()\n","\n","# Convert probabilities to class indices\n","y_pred_train = np.argmax(y_pred_train_prob, axis=1)\n","y_pred_test = np.argmax(y_pred_test_prob, axis=1)\n","\n","# Calculate ROC AUC score for each class\n","roc_auc_train = []\n","roc_auc_test = []\n","\n","# Use y_train_tensor.numpy() for training ROC AUC calculation to match the shape of y_pred_train_prob\n","for i in range(num_classes):  # Replace num_classes with the actual number of classes\n","    roc_auc_train.append(roc_auc_score(y_train_tensor.numpy() == i, y_pred_train_prob[:, i]))\n","    roc_auc_test.append(roc_auc_score(y_test == i, y_pred_test_prob[:, i]))\n","\n","# Print or use roc_auc_train and roc_auc_test as needed\n","print(\"ROC AUC Train:\", np.mean(roc_auc_train))\n","print(\"ROC AUC Test:\", np.mean(roc_auc_test))\n","\n","# Plot ROC curves\n","plt.figure(figsize=(10, 6))\n","\n","for i in range(num_classes):\n","    # Use y_train_tensor.numpy() for training ROC curve calculation\n","    fpr_train, tpr_train, _ = roc_curve(y_train_tensor.numpy() == i, y_pred_train_prob[:, i])\n","    roc_auc_train_class = auc(fpr_train, tpr_train)\n","    plt.plot(fpr_train, tpr_train, label=f'Train ROC Curve (Class {i + 1}, AUC = {roc_auc_train_class:.2f})')\n","\n","    fpr_test, tpr_test, _ = roc_curve(y_test == i, y_pred_test_prob[:, i])\n","    roc_auc_test_class = auc(fpr_test, tpr_test)\n","    plt.plot(fpr_test, tpr_test, label=f'Test ROC Curve (Class {i + 1}, AUC = {roc_auc_test_class:.2f})')\n","\n","plt.plot([0, 1], [0, 1], 'k--')\n","plt.xlabel('False Positive Rate')\n","plt.ylabel('True Positive Rate')\n","plt.title('Receiver Operating Characteristic (ROC) Curve for each class')\n","plt.legend()\n","plt.show()"]},{"cell_type":"code","source":["import time\n","import numpy as np\n","\n","# Start time for training predictions\n","start_train_time = time.time()\n","y_pred_train_prob = model(X_train_tensor).numpy()\n","end_train_time = time.time()\n","\n","# Convert probabilities to class indices for training predictions\n","y_pred_train_prob = np.argmax(y_pred_train_prob, axis=1)\n","\n","# Calculate time taken for training predictions\n","train_time = end_train_time - start_train_time\n","print(f\"Time taken for training predictions: {train_time:.4f} seconds\")\n","\n","# Start time for testing predictions\n","start_test_time = time.time()\n","y_pred_test_prob = model(X_test_tensor).numpy()\n","end_test_time = time.time()\n","\n","# Convert probabilities to class indices for testing predictions\n","y_pred_test_prob = np.argmax(y_pred_test_prob, axis=1)\n","\n","# Calculate time taken for testing predictions\n","test_time = end_test_time - start_test_time\n","print(f\"Time taken for testing predictions: {test_time:.4f} seconds\")\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"pdu6a6go7mH_","executionInfo":{"status":"ok","timestamp":1737147882978,"user_tz":-300,"elapsed":726,"user":{"displayName":"Nayyab Rajpoot","userId":"10723923538147021558"}},"outputId":"0cef1d45-a4a4-4168-9cd2-cf04523fafbf"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Time taken for training predictions: 0.2411 seconds\n","Time taken for testing predictions: 0.1234 seconds\n"]}]}],"metadata":{"accelerator":"GPU","colab":{"gpuType":"T4","provenance":[]},"kernelspec":{"display_name":"Python 3","name":"python3"},"language_info":{"name":"python"}},"nbformat":4,"nbformat_minor":0}