{"metadata":{"kernelspec":{"name":"python3","display_name":"Python 3","language":"python"},"language_info":{"name":"python","version":"3.10.16","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"colab":{"provenance":[]}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"import tensorflow as tf\nimport pandas as pd\nfrom sklearn.preprocessing import LabelEncoder\nfrom sklearn.model_selection import train_test_split\nimport numpy as np\nfrom sklearn.metrics import confusion_matrix, classification_report, roc_curve, roc_auc_score\nimport matplotlib.pyplot as plt\nfrom sklearn.metrics import confusion_matrix, classification_report, roc_curve, roc_auc_score, f1_score\nfrom sklearn.metrics import confusion_matrix, classification_report, f1_score, roc_curve, roc_auc_score, accuracy_score\nfrom sklearn.preprocessing import LabelEncoder, StandardScaler\nimport seaborn as sns","metadata":{"id":"8eKA0JEWYzq6","trusted":true,"execution":{"iopub.status.busy":"2025-01-17T19:56:34.255736Z","iopub.execute_input":"2025-01-17T19:56:34.256344Z","iopub.status.idle":"2025-01-17T19:56:55.266276Z","shell.execute_reply.started":"2025-01-17T19:56:34.256314Z","shell.execute_reply":"2025-01-17T19:56:55.265167Z"}},"outputs":[{"name":"stderr","text":"WARNING: Logging before InitGoogle() is written to STDERR\nE0000 00:00:1737143800.454544      10 common_lib.cc:612] Could not set metric server port: INVALID_ARGUMENT: Could not find SliceBuilder port 8471 in any of the 0 ports provided in `tpu_process_addresses`=\"local\"\n=== Source Location Trace: === \nlearning/45eac/tfrc/runtime/common_lib.cc:230\n","output_type":"stream"}],"execution_count":1},{"cell_type":"code","source":"# Load the dataset\ndata = pd.read_csv(\"/kaggle/input/patientdata/paitent.csv\")  # Replace with the actual file path","metadata":{"id":"ZyEyN9NUY9sk","trusted":true,"execution":{"iopub.status.busy":"2025-01-17T19:56:55.267044Z","iopub.execute_input":"2025-01-17T19:56:55.267382Z","iopub.status.idle":"2025-01-17T19:56:55.495319Z","shell.execute_reply.started":"2025-01-17T19:56:55.267359Z","shell.execute_reply":"2025-01-17T19:56:55.494133Z"}},"outputs":[],"execution_count":2},{"cell_type":"code","source":"# Visualize the label column\nlabel_counts = data['label'].value_counts()\nlabel_counts.plot(kind='bar')\nplt.xlabel('Labels')\nplt.ylabel('Counts')\nplt.title('Label Distribution')\nplt.show()","metadata":{"id":"9SL3mHSzY_ga","trusted":true,"execution":{"iopub.status.busy":"2025-01-17T19:56:55.496436Z","iopub.execute_input":"2025-01-17T19:56:55.496710Z","iopub.status.idle":"2025-01-17T19:56:55.727806Z","shell.execute_reply.started":"2025-01-17T19:56:55.496683Z","shell.execute_reply":"2025-01-17T19:56:55.727138Z"}},"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 1 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"},"metadata":{}}],"execution_count":3},{"cell_type":"code","source":"# Separate features and labels\nX = data.drop(\"label\", axis=1)\ny = data[\"label\"]","metadata":{"id":"jnh90eNjZBdZ","trusted":true,"execution":{"iopub.status.busy":"2025-01-17T19:56:55.728874Z","iopub.execute_input":"2025-01-17T19:56:55.729088Z","iopub.status.idle":"2025-01-17T19:56:55.735807Z","shell.execute_reply.started":"2025-01-17T19:56:55.729066Z","shell.execute_reply":"2025-01-17T19:56:55.734972Z"}},"outputs":[],"execution_count":4},{"cell_type":"code","source":"# Convert labels to numerical values\nlabel_encoder = LabelEncoder()\ny_encoded = label_encoder.fit_transform(y)","metadata":{"id":"t2AyEyPqZDOX","trusted":true,"execution":{"iopub.status.busy":"2025-01-17T19:56:55.736464Z","iopub.execute_input":"2025-01-17T19:56:55.736706Z","iopub.status.idle":"2025-01-17T19:56:55.750454Z","shell.execute_reply.started":"2025-01-17T19:56:55.736681Z","shell.execute_reply":"2025-01-17T19:56:55.749782Z"}},"outputs":[],"execution_count":5},{"cell_type":"code","source":"# Split the dataset into train and test sets\nX_train, X_test, y_train, y_test = train_test_split(X, y_encoded, test_size=0.2, random_state=42)","metadata":{"id":"2Jvv9miwZE9F","trusted":true,"execution":{"iopub.status.busy":"2025-01-17T19:56:55.751493Z","iopub.execute_input":"2025-01-17T19:56:55.751692Z","iopub.status.idle":"2025-01-17T19:56:55.766991Z","shell.execute_reply.started":"2025-01-17T19:56:55.751671Z","shell.execute_reply":"2025-01-17T19:56:55.765942Z"}},"outputs":[],"execution_count":6},{"cell_type":"code","source":"# Standardize the feature data\nscaler = StandardScaler()\nX_train_scaled = scaler.fit_transform(X_train)\nX_test_scaled = scaler.transform(X_test)","metadata":{"id":"N_ueeOgzZGql","trusted":true,"execution":{"iopub.status.busy":"2025-01-17T19:56:55.767913Z","iopub.execute_input":"2025-01-17T19:56:55.768292Z","iopub.status.idle":"2025-01-17T19:56:55.785201Z","shell.execute_reply.started":"2025-01-17T19:56:55.768269Z","shell.execute_reply":"2025-01-17T19:56:55.784146Z"}},"outputs":[],"execution_count":7},{"cell_type":"code","source":"# Convert the data to TensorFlow tensors\nX_train_tensor = tf.constant(X_train_scaled, dtype=tf.float32)\ny_train_tensor = tf.constant(y_train, dtype=tf.int32)\nX_test_tensor = tf.constant(X_test_scaled, dtype=tf.float32)\ny_test_tensor = tf.constant(y_test, dtype=tf.int32)","metadata":{"id":"8b52-GJVZIuv","trusted":true,"execution":{"iopub.status.busy":"2025-01-17T19:56:55.785823Z","iopub.execute_input":"2025-01-17T19:56:55.786076Z","iopub.status.idle":"2025-01-17T19:56:59.727265Z","shell.execute_reply.started":"2025-01-17T19:56:55.786049Z","shell.execute_reply":"2025-01-17T19:56:59.726063Z"}},"outputs":[{"name":"stderr","text":"WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\nI0000 00:00:1737143819.689462      10 service.cc:148] XLA service 0x55c694261cb0 initialized for platform TPU (this does not guarantee that XLA will be used). Devices:\nI0000 00:00:1737143819.689498      10 service.cc:156]   StreamExecutor device (0): TPU, 2a886c8\nI0000 00:00:1737143819.689502      10 service.cc:156]   StreamExecutor device (1): TPU, 2a886c8\nI0000 00:00:1737143819.689505      10 service.cc:156]   StreamExecutor device (2): TPU, 2a886c8\nI0000 00:00:1737143819.689509      10 service.cc:156]   StreamExecutor device (3): TPU, 2a886c8\nI0000 00:00:1737143819.689512      10 service.cc:156]   StreamExecutor device (4): TPU, 2a886c8\nI0000 00:00:1737143819.689515      10 service.cc:156]   StreamExecutor device (5): TPU, 2a886c8\nI0000 00:00:1737143819.689517      10 service.cc:156]   StreamExecutor device (6): TPU, 2a886c8\nI0000 00:00:1737143819.689520      10 service.cc:156]   StreamExecutor device (7): TPU, 2a886c8\n","output_type":"stream"}],"execution_count":8},{"cell_type":"code","source":"class LSTMModel(tf.keras.Model):\n    def __init__(self, num_classes):\n        super(LSTMModel, self).__init__()\n        self.lstm_layer = tf.keras.layers.LSTM(16, activation='relu')\n        self.dense_output = tf.keras.layers.Dense(num_classes, activation='softmax')\n\n    def call(self, inputs):\n        # Assuming inputs has shape (batch_size, sequence_length)\n        # Add a dimension for the number of features\n        inputs = tf.expand_dims(inputs, axis=-1)\n\n        x = self.lstm_layer(inputs)\n        output = self.dense_output(x)\n        return output","metadata":{"id":"5VtXOJQqZLDJ","trusted":true,"execution":{"iopub.status.busy":"2025-01-17T19:56:59.727960Z","iopub.execute_input":"2025-01-17T19:56:59.728195Z","iopub.status.idle":"2025-01-17T19:56:59.789925Z","shell.execute_reply.started":"2025-01-17T19:56:59.728171Z","shell.execute_reply":"2025-01-17T19:56:59.788999Z"}},"outputs":[],"execution_count":9},{"cell_type":"code","source":"# Create the LSTM model\nnum_classes = len(label_encoder.classes_)\nmodel = LSTMModel(num_classes)  # Replace 'GNNModel' with the appropriate LSTM model class\nloss_object = tf.keras.losses.SparseCategoricalCrossentropy()\noptimizer = tf.keras.optimizers.Adam()\nepochs = 50\nbatch_size = 32\nsteps_per_epoch = len(X_train_scaled) // batch_size\ntrain_loss_history = []\ntest_loss_history = []\ntrain_accuracy_history = []\ntest_accuracy_history = []","metadata":{"id":"YTyr4yDM2WoM","trusted":true,"execution":{"iopub.status.busy":"2025-01-17T19:56:59.790973Z","iopub.execute_input":"2025-01-17T19:56:59.791219Z","iopub.status.idle":"2025-01-17T19:56:59.809857Z","shell.execute_reply.started":"2025-01-17T19:56:59.791196Z","shell.execute_reply":"2025-01-17T19:56:59.808568Z"}},"outputs":[],"execution_count":10},{"cell_type":"code","source":"for epoch in range(epochs):\n    for step in range(steps_per_epoch):\n        start = step * batch_size\n        end = (step + 1) * batch_size\n        with tf.GradientTape() as tape:\n            predictions = model(X_train_tensor[start:end])\n            loss = loss_object(y_train_tensor[start:end], predictions)\n\n        gradients = tape.gradient(loss, model.trainable_variables)\n        optimizer.apply_gradients(zip(gradients, model.trainable_variables))\n\n    train_loss = loss_object(y_train_tensor, model(X_train_tensor))\n    test_loss = loss_object(y_test_tensor, model(X_test_tensor))\n    train_loss_history.append(train_loss.numpy())\n    test_loss_history.append(test_loss.numpy())\n\n    y_pred_train = np.argmax(model(X_train_tensor).numpy(), axis=1)\n    y_pred_test = np.argmax(model(X_test_tensor).numpy(), axis=1)\n\n    train_accuracy = accuracy_score(y_train, y_pred_train)\n    test_accuracy = accuracy_score(y_test, y_pred_test)\n\n    train_accuracy_history.append(train_accuracy)\n    test_accuracy_history.append(test_accuracy)\n\n    print(f\"Epoch {epoch + 1}/{epochs}, Train Loss: {train_loss:.4f}, Train Accuracy: {train_accuracy:.4f}, Test Loss: {test_loss:.4f}, Test Accuracy: {test_accuracy:.4f}\")\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-01-17T19:56:59.810728Z","iopub.execute_input":"2025-01-17T19:56:59.810936Z","iopub.status.idle":"2025-01-17T21:05:28.202174Z","shell.execute_reply.started":"2025-01-17T19:56:59.810914Z","shell.execute_reply":"2025-01-17T21:05:28.201109Z"}},"outputs":[{"name":"stdout","text":"Epoch 1/50, Train Loss: 0.1791, Train Accuracy: 0.9403, Test Loss: 0.1681, Test Accuracy: 0.9422\nEpoch 2/50, Train Loss: 0.1658, Train Accuracy: 0.9458, Test Loss: 0.1559, Test Accuracy: 0.9470\nEpoch 3/50, Train Loss: 0.1596, Train Accuracy: 0.9478, Test Loss: 0.1493, Test Accuracy: 0.9498\nEpoch 4/50, Train Loss: 0.1551, Train Accuracy: 0.9497, Test Loss: 0.1447, Test Accuracy: 0.9515\nEpoch 5/50, Train Loss: 0.1515, Train Accuracy: 0.9511, Test Loss: 0.1412, Test Accuracy: 0.9525\nEpoch 6/50, Train Loss: 0.1482, Train Accuracy: 0.9524, Test Loss: 0.1379, Test Accuracy: 0.9547\nEpoch 7/50, Train Loss: 0.1439, Train Accuracy: 0.9542, Test Loss: 0.1337, Test Accuracy: 0.9556\nEpoch 8/50, Train Loss: 0.1403, Train Accuracy: 0.9556, Test Loss: 0.1301, Test Accuracy: 0.9569\nEpoch 9/50, Train Loss: 0.1370, Train Accuracy: 0.9565, Test Loss: 0.1267, Test Accuracy: 0.9584\nEpoch 10/50, Train Loss: 0.1376, Train Accuracy: 0.9552, Test Loss: 0.1278, Test Accuracy: 0.9584\nEpoch 11/50, Train Loss: 0.1306, Train Accuracy: 0.9590, Test Loss: 0.1202, Test Accuracy: 0.9611\nEpoch 12/50, Train Loss: 0.1249, Train Accuracy: 0.9610, Test Loss: 0.1146, Test Accuracy: 0.9636\nEpoch 13/50, Train Loss: 0.1215, Train Accuracy: 0.9626, Test Loss: 0.1110, Test Accuracy: 0.9654\nEpoch 14/50, Train Loss: 0.1210, Train Accuracy: 0.9631, Test Loss: 0.1101, Test Accuracy: 0.9657\nEpoch 15/50, Train Loss: 0.1222, Train Accuracy: 0.9628, Test Loss: 0.1112, Test Accuracy: 0.9640\nEpoch 16/50, Train Loss: 0.1218, Train Accuracy: 0.9630, Test Loss: 0.1108, Test Accuracy: 0.9642\nEpoch 17/50, Train Loss: 0.1207, Train Accuracy: 0.9631, Test Loss: 0.1100, Test Accuracy: 0.9654\nEpoch 18/50, Train Loss: 0.1210, Train Accuracy: 0.9628, Test Loss: 0.1102, Test Accuracy: 0.9649\nEpoch 19/50, Train Loss: 0.1183, Train Accuracy: 0.9642, Test Loss: 0.1077, Test Accuracy: 0.9654\nEpoch 20/50, Train Loss: 0.1186, Train Accuracy: 0.9634, Test Loss: 0.1082, Test Accuracy: 0.9657\nEpoch 21/50, Train Loss: 0.1137, Train Accuracy: 0.9653, Test Loss: 0.1036, Test Accuracy: 0.9677\nEpoch 22/50, Train Loss: 0.1129, Train Accuracy: 0.9656, Test Loss: 0.1022, Test Accuracy: 0.9691\nEpoch 23/50, Train Loss: 0.1098, Train Accuracy: 0.9667, Test Loss: 0.1000, Test Accuracy: 0.9704\nEpoch 24/50, Train Loss: 0.1103, Train Accuracy: 0.9667, Test Loss: 0.1005, Test Accuracy: 0.9701\nEpoch 25/50, Train Loss: 0.1059, Train Accuracy: 0.9686, Test Loss: 0.0970, Test Accuracy: 0.9709\nEpoch 26/50, Train Loss: 0.1060, Train Accuracy: 0.9683, Test Loss: 0.0962, Test Accuracy: 0.9716\nEpoch 27/50, Train Loss: 0.1023, Train Accuracy: 0.9691, Test Loss: 0.0943, Test Accuracy: 0.9722\nEpoch 28/50, Train Loss: 0.1007, Train Accuracy: 0.9697, Test Loss: 0.0930, Test Accuracy: 0.9720\nEpoch 29/50, Train Loss: 0.1003, Train Accuracy: 0.9700, Test Loss: 0.0930, Test Accuracy: 0.9725\nEpoch 30/50, Train Loss: 0.1017, Train Accuracy: 0.9699, Test Loss: 0.0919, Test Accuracy: 0.9722\nEpoch 31/50, Train Loss: 0.1028, Train Accuracy: 0.9683, Test Loss: 0.0939, Test Accuracy: 0.9716\nEpoch 32/50, Train Loss: 0.0989, Train Accuracy: 0.9700, Test Loss: 0.0905, Test Accuracy: 0.9731\nEpoch 33/50, Train Loss: 0.0982, Train Accuracy: 0.9704, Test Loss: 0.0899, Test Accuracy: 0.9729\nEpoch 34/50, Train Loss: 0.0964, Train Accuracy: 0.9706, Test Loss: 0.0881, Test Accuracy: 0.9734\nEpoch 35/50, Train Loss: 0.0973, Train Accuracy: 0.9703, Test Loss: 0.0880, Test Accuracy: 0.9732\nEpoch 36/50, Train Loss: 0.0972, Train Accuracy: 0.9701, Test Loss: 0.0868, Test Accuracy: 0.9744\nEpoch 37/50, Train Loss: 0.0947, Train Accuracy: 0.9707, Test Loss: 0.0856, Test Accuracy: 0.9734\nEpoch 38/50, Train Loss: 0.0961, Train Accuracy: 0.9701, Test Loss: 0.0860, Test Accuracy: 0.9743\nEpoch 39/50, Train Loss: 0.0962, Train Accuracy: 0.9702, Test Loss: 0.0860, Test Accuracy: 0.9744\nEpoch 40/50, Train Loss: 0.0943, Train Accuracy: 0.9709, Test Loss: 0.0847, Test Accuracy: 0.9746\nEpoch 41/50, Train Loss: 0.0930, Train Accuracy: 0.9714, Test Loss: 0.0836, Test Accuracy: 0.9746\nEpoch 42/50, Train Loss: 0.0908, Train Accuracy: 0.9719, Test Loss: 0.0825, Test Accuracy: 0.9747\nEpoch 43/50, Train Loss: 0.0909, Train Accuracy: 0.9722, Test Loss: 0.0824, Test Accuracy: 0.9751\nEpoch 44/50, Train Loss: 0.0896, Train Accuracy: 0.9727, Test Loss: 0.0823, Test Accuracy: 0.9757\nEpoch 45/50, Train Loss: 0.0879, Train Accuracy: 0.9730, Test Loss: 0.0803, Test Accuracy: 0.9762\nEpoch 46/50, Train Loss: 0.0892, Train Accuracy: 0.9722, Test Loss: 0.0803, Test Accuracy: 0.9760\nEpoch 47/50, Train Loss: 0.0876, Train Accuracy: 0.9727, Test Loss: 0.0793, Test Accuracy: 0.9768\nEpoch 48/50, Train Loss: 0.0870, Train Accuracy: 0.9727, Test Loss: 0.0792, Test Accuracy: 0.9768\nEpoch 49/50, Train Loss: 0.0863, Train Accuracy: 0.9736, Test Loss: 0.0791, Test Accuracy: 0.9759\nEpoch 50/50, Train Loss: 0.0853, Train Accuracy: 0.9735, Test Loss: 0.0784, Test Accuracy: 0.9763\n","output_type":"stream"}],"execution_count":11},{"cell_type":"code","source":"# Evaluation\ny_pred_train_prob = model(X_train_tensor).numpy()\ny_pred_test_prob = model(X_test_tensor).numpy()\n\n# Convert probabilities to class indices\ny_pred_train = np.argmax(y_pred_train_prob, axis=1)\ny_pred_test = np.argmax(y_pred_test_prob, axis=1)\n","metadata":{"id":"j2BhA8ZoZPgH","trusted":true,"execution":{"iopub.status.busy":"2025-01-17T21:05:28.202926Z","iopub.execute_input":"2025-01-17T21:05:28.203289Z","iopub.status.idle":"2025-01-17T21:05:28.502301Z","shell.execute_reply.started":"2025-01-17T21:05:28.203262Z","shell.execute_reply":"2025-01-17T21:05:28.501474Z"}},"outputs":[],"execution_count":12},{"cell_type":"code","source":"# F1 Score\nf1_train = f1_score(y_train, y_pred_train, average='weighted')\nf1_test = f1_score(y_test, y_pred_test, average='weighted')\n\n# Accuracy\ntrain_accuracy = np.mean(np.equal(y_train, y_pred_train))\ntest_accuracy = np.mean(np.equal(y_test, y_pred_test))\n\n# Other metrics\nprint(\"Train F1 Score:\", f1_train)\nprint(\"Test F1 Score:\", f1_test)\n\n# Accuracy\nprint(\"Train Accuracy:\", train_accuracy)\nprint(\"Test Accuracy:\", test_accuracy)","metadata":{"id":"FhYx6g9JZSI3","trusted":true,"execution":{"iopub.status.busy":"2025-01-17T21:05:28.503459Z","iopub.execute_input":"2025-01-17T21:05:28.503804Z","iopub.status.idle":"2025-01-17T21:05:28.516048Z","shell.execute_reply.started":"2025-01-17T21:05:28.503780Z","shell.execute_reply":"2025-01-17T21:05:28.514687Z"}},"outputs":[{"name":"stdout","text":"Train F1 Score: 0.9735034004517411\nTest F1 Score: 0.976319068309469\nTrain Accuracy: 0.9735138534383901\nTest Accuracy: 0.9763278591507619\n","output_type":"stream"}],"execution_count":13},{"cell_type":"code","source":"# Confusion Matrix\ncm_train = confusion_matrix(y_train, y_pred_train)\ncm_test = confusion_matrix(y_test, y_pred_test)\n\n# Plot Accuracy Curve\nplt.figure(figsize=(10, 6))\nplt.plot(range(epochs), train_accuracy_history, label='Train Accuracy')\nplt.plot(range(epochs), test_accuracy_history, label='Test Accuracy')\nplt.xlabel('Epoch')\nplt.ylabel('Accuracy')\nplt.title('Accuracy Curve')\nplt.legend()\nplt.show()\n\n","metadata":{"id":"odb4C2YWZUcC","trusted":true,"execution":{"iopub.status.busy":"2025-01-17T21:05:28.516971Z","iopub.execute_input":"2025-01-17T21:05:28.517249Z","iopub.status.idle":"2025-01-17T21:05:28.671119Z","shell.execute_reply.started":"2025-01-17T21:05:28.517228Z","shell.execute_reply":"2025-01-17T21:05:28.670457Z"}},"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 1000x600 with 1 Axes>","image/png":"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"},"metadata":{}}],"execution_count":14},{"cell_type":"code","source":"# Loss Curve\nplt.figure(figsize=(10, 6))\nplt.plot(train_loss_history, label='Train Loss')\nplt.plot(test_loss_history, label='Test Loss')\nplt.xlabel('Epoch')\nplt.ylabel('Loss')\nplt.title('Loss Curve')\nplt.legend()\nplt.show()","metadata":{"id":"IBUv-mfeZW16","trusted":true,"execution":{"iopub.status.busy":"2025-01-17T21:05:28.672096Z","iopub.execute_input":"2025-01-17T21:05:28.672286Z","iopub.status.idle":"2025-01-17T21:05:28.811492Z","shell.execute_reply.started":"2025-01-17T21:05:28.672266Z","shell.execute_reply":"2025-01-17T21:05:28.810583Z"}},"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 1000x600 with 1 Axes>","image/png":"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"},"metadata":{}}],"execution_count":15},{"cell_type":"code","source":"# Classification Report\nreport_train = classification_report(y_train, y_pred_train, target_names=label_encoder.classes_)\nreport_test = classification_report(y_test, y_pred_test, target_names=label_encoder.classes_)","metadata":{"id":"QjvF0qWfZZP-","trusted":true,"execution":{"iopub.status.busy":"2025-01-17T21:05:28.812235Z","iopub.execute_input":"2025-01-17T21:05:28.812473Z","iopub.status.idle":"2025-01-17T21:05:28.836855Z","shell.execute_reply.started":"2025-01-17T21:05:28.812451Z","shell.execute_reply":"2025-01-17T21:05:28.835653Z"}},"outputs":[],"execution_count":16},{"cell_type":"code","source":"# Print Confusion Matrix and Classification Report\nprint(\"Train Classification Report:\")\nprint(report_train)","metadata":{"id":"65Pjo0luZZFK","trusted":true,"execution":{"iopub.status.busy":"2025-01-17T21:05:28.837592Z","iopub.execute_input":"2025-01-17T21:05:28.837775Z","iopub.status.idle":"2025-01-17T21:05:28.841182Z","shell.execute_reply.started":"2025-01-17T21:05:28.837756Z","shell.execute_reply":"2025-01-17T21:05:28.840195Z"}},"outputs":[{"name":"stdout","text":"Train Classification Report:\n              precision    recall  f1-score   support\n\n  noseizures       0.95      0.99      0.97     13492\n    seizures       0.99      0.95      0.97     13541\n\n    accuracy                           0.97     27033\n   macro avg       0.97      0.97      0.97     27033\nweighted avg       0.97      0.97      0.97     27033\n\n","output_type":"stream"}],"execution_count":17},{"cell_type":"code","source":"print(\"Test Classification Report:\")\nprint(report_test)","metadata":{"id":"ySiBRP1UZd8f","trusted":true,"execution":{"iopub.status.busy":"2025-01-17T21:05:28.841986Z","iopub.execute_input":"2025-01-17T21:05:28.842256Z","iopub.status.idle":"2025-01-17T21:05:28.850052Z","shell.execute_reply.started":"2025-01-17T21:05:28.842214Z","shell.execute_reply":"2025-01-17T21:05:28.849199Z"}},"outputs":[{"name":"stdout","text":"Test Classification Report:\n              precision    recall  f1-score   support\n\n  noseizures       0.96      0.99      0.98      3404\n    seizures       0.99      0.96      0.98      3355\n\n    accuracy                           0.98      6759\n   macro avg       0.98      0.98      0.98      6759\nweighted avg       0.98      0.98      0.98      6759\n\n","output_type":"stream"}],"execution_count":18},{"cell_type":"code","source":"# Plot Confusion Matrix Heatmap for Train Set\nplt.figure(figsize=(15, 10))\nsns.heatmap(cm_train, annot=True, fmt=\"d\", cmap=\"Blues\", xticklabels=label_encoder.classes_, yticklabels=label_encoder.classes_)\nplt.xlabel('Predicted')\nplt.ylabel('True')\nplt.title('Confusion Matrix (Train Set)')\nplt.show()","metadata":{"id":"ACPd67uAZhDS","trusted":true,"execution":{"iopub.status.busy":"2025-01-17T21:05:28.851150Z","iopub.execute_input":"2025-01-17T21:05:28.851428Z","iopub.status.idle":"2025-01-17T21:05:29.017037Z","shell.execute_reply.started":"2025-01-17T21:05:28.851404Z","shell.execute_reply":"2025-01-17T21:05:29.016053Z"}},"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 1500x1000 with 2 Axes>","image/png":"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Plot Confusion Matrix Heatmap for Test Set\nplt.figure(figsize=(15, 10))\nsns.heatmap(cm_test, annot=True, fmt=\"d\", cmap=\"Blues\", xticklabels=label_encoder.classes_, yticklabels=label_encoder.classes_)\nplt.xlabel('Predicted')\nplt.ylabel('True')\nplt.title('Confusion Matrix (Test Set)')\nplt.show()","metadata":{"id":"MJkWzrDWZjZx","trusted":true,"execution":{"iopub.status.busy":"2025-01-17T21:05:29.017845Z","iopub.execute_input":"2025-01-17T21:05:29.018042Z","iopub.status.idle":"2025-01-17T21:05:29.180405Z","shell.execute_reply.started":"2025-01-17T21:05:29.018022Z","shell.execute_reply":"2025-01-17T21:05:29.179580Z"}},"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 1500x1000 with 2 Axes>","image/png":"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"},"metadata":{}}],"execution_count":20},{"cell_type":"code","source":"import matplotlib.pyplot as plt\nfrom sklearn.metrics import roc_curve, auc, roc_auc_score\nimport 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\ny_pred_train_prob = model(X_train_tensor).numpy()\ny_pred_test_prob = model(X_test_tensor).numpy()\n\n# Convert probabilities to class indices\ny_pred_train = np.argmax(y_pred_train_prob, axis=1)\ny_pred_test = np.argmax(y_pred_test_prob, axis=1)\n\n# Calculate ROC AUC score for each class\nroc_auc_train = []\nroc_auc_test = []\n\nfor i in range(num_classes):  # Replace num_classes with the actual number of classes\n    roc_auc_train.append(roc_auc_score(y_train == 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\nprint(\"ROC AUC Train:\", np.mean(roc_auc_train))\nprint(\"ROC AUC Test:\", np.mean(roc_auc_test))\n\n# Plot ROC curves\nplt.figure(figsize=(10, 6))\n\nfor i in range(num_classes):\n    fpr_train, tpr_train, _ = roc_curve(y_train == 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\nplt.plot([0, 1], [0, 1], 'k--')\nplt.xlabel('False Positive Rate')\nplt.ylabel('True Positive Rate')\nplt.title('Receiver Operating Characteristic (ROC) Curve for each class')\nplt.legend()\nplt.show()\n","metadata":{"id":"6ijUpdJe8QZP","trusted":true,"execution":{"iopub.status.busy":"2025-01-17T21:05:29.181661Z","iopub.execute_input":"2025-01-17T21:05:29.181917Z","iopub.status.idle":"2025-01-17T21:05:29.700792Z","shell.execute_reply.started":"2025-01-17T21:05:29.181894Z","shell.execute_reply":"2025-01-17T21:05:29.699987Z"}},"outputs":[{"name":"stdout","text":"ROC AUC Train: 0.9918749973863568\nROC AUC Test: 0.9928148001562114\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"<Figure size 1000x600 with 1 Axes>","image/png":"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"},"metadata":{}}],"execution_count":21},{"cell_type":"code","source":"import time\nimport numpy as np\n\n# Start time for training predictions\nstart_train_time = time.time()\ny_pred_train_prob = model(X_train_tensor).numpy()\nend_train_time = time.time()\n\n# Convert probabilities to class indices for training predictions\ny_pred_train_prob = np.argmax(y_pred_train_prob, axis=1)\n\n# Calculate time taken for training predictions\ntrain_time = end_train_time - start_train_time\nprint(f\"Time taken for training predictions: {train_time:.4f} seconds\")\n\n# Start time for testing predictions\nstart_test_time = time.time()\ny_pred_test_prob = model(X_test_tensor).numpy()\nend_test_time = time.time()\n\n# Convert probabilities to class indices for testing predictions\ny_pred_test_prob = np.argmax(y_pred_test_prob, axis=1)\n\n# Calculate time taken for testing predictions\ntest_time = end_test_time - start_test_time\nprint(f\"Time taken for testing predictions: {test_time:.4f} seconds\")\n","metadata":{"id":"OUxW0ueGr2YD","trusted":true,"execution":{"iopub.status.busy":"2025-01-17T21:05:29.701616Z","iopub.execute_input":"2025-01-17T21:05:29.701820Z","iopub.status.idle":"2025-01-17T21:05:30.061178Z","shell.execute_reply.started":"2025-01-17T21:05:29.701799Z","shell.execute_reply":"2025-01-17T21:05:30.060441Z"}},"outputs":[{"name":"stdout","text":"Time taken for training predictions: 0.2366 seconds\nTime taken for testing predictions: 0.1172 seconds\n","output_type":"stream"}],"execution_count":22},{"cell_type":"code","source":"# Parameters for Early Stopping\npatience = 5\nbest_loss = float('inf')\nwait = 0\nvalidation_split = 0.1  # Use a small validation set for faster evaluation\n\n# Split a portion of training data for validation\nval_size = int(len(X_train_tensor) * validation_split)\nX_val_tensor = X_train_tensor[:val_size]\ny_val_tensor = y_train_tensor[:val_size]\nX_train_tensor = X_train_tensor[val_size:]\ny_train_tensor = y_train_tensor[val_size:]\n\nfor 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","metadata":{"id":"tUnEjkGEZNSg","trusted":true,"execution":{"iopub.status.busy":"2025-01-17T21:05:30.062141Z","iopub.execute_input":"2025-01-17T21:05:30.062366Z","iopub.status.idle":"2025-01-17T21:24:13.945555Z","shell.execute_reply.started":"2025-01-17T21:05:30.062343Z","shell.execute_reply":"2025-01-17T21:24:13.944320Z"}},"outputs":[{"name":"stdout","text":"Epoch 1/50, Val Loss: 0.1040, Val Accuracy: 0.9652\nEpoch 2/50, Val Loss: 0.0947, Val Accuracy: 0.9697\nEpoch 3/50, Val Loss: 0.0953, Val Accuracy: 0.9689\nNo improvement in validation loss for 1 epoch(s).\nEpoch 4/50, Val Loss: 0.0934, Val Accuracy: 0.9704\nEpoch 5/50, Val Loss: 0.0987, Val Accuracy: 0.9682\nNo improvement in validation loss for 1 epoch(s).\nEpoch 6/50, Val Loss: 0.0957, Val Accuracy: 0.9697\nNo improvement in validation loss for 2 epoch(s).\nEpoch 7/50, Val Loss: 0.0832, Val Accuracy: 0.9756\nEpoch 8/50, Val Loss: 0.0909, Val Accuracy: 0.9715\nNo improvement in validation loss for 1 epoch(s).\nEpoch 9/50, Val Loss: 0.0759, Val Accuracy: 0.9774\nEpoch 10/50, Val Loss: 0.0783, Val Accuracy: 0.9767\nNo improvement in validation loss for 1 epoch(s).\nEpoch 11/50, Val Loss: 0.0826, Val Accuracy: 0.9726\nNo improvement in validation loss for 2 epoch(s).\nEpoch 12/50, Val Loss: 0.0866, Val Accuracy: 0.9730\nNo improvement in validation loss for 3 epoch(s).\nEpoch 13/50, Val Loss: 0.0944, Val Accuracy: 0.9697\nNo improvement in validation loss for 4 epoch(s).\nEpoch 14/50, Val Loss: 0.0848, Val Accuracy: 0.9723\nNo improvement in validation loss for 5 epoch(s).\nEarly stopping triggered. Stopping training.\n","output_type":"stream"}],"execution_count":23},{"cell_type":"code","source":"import time\nimport numpy as np\n\n# Start time for training predictions\nstart_train_time = time.time()\ny_pred_train_prob = model(X_train_tensor).numpy()\nend_train_time = time.time()\n\n# Convert probabilities to class indices for training predictions\ny_pred_train_prob = np.argmax(y_pred_train_prob, axis=1)\n\n# Calculate time taken for training predictions\ntrain_time = end_train_time - start_train_time\nprint(f\"Time taken for training predictions: {train_time:.4f} seconds\")\n\n# Start time for testing predictions\nstart_test_time = time.time()\ny_pred_test_prob = model(X_test_tensor).numpy()\nend_test_time = time.time()\n\n# Convert probabilities to class indices for testing predictions\ny_pred_test_prob = np.argmax(y_pred_test_prob, axis=1)\n\n# Calculate time taken for testing predictions\ntest_time = end_test_time - start_test_time\nprint(f\"Time taken for testing predictions: {test_time:.4f} seconds\")\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-01-17T21:24:17.645697Z","iopub.execute_input":"2025-01-17T21:24:17.646022Z","iopub.status.idle":"2025-01-17T21:24:17.952611Z","shell.execute_reply.started":"2025-01-17T21:24:17.645994Z","shell.execute_reply":"2025-01-17T21:24:17.951295Z"}},"outputs":[{"name":"stdout","text":"Time taken for training predictions: 0.1731 seconds\nTime taken for testing predictions: 0.1266 seconds\n","output_type":"stream"}],"execution_count":24}]}