Add user-friendly training and prediction scripts

scripts/train_from_labeled_data.py

@@ -0,0 +1,72 @@
+#!/usr/bin/env python3
+import sys
+import os
+sys.path.append(os.path.join(os.path.dirname(__file__), '..', 'src'))
+
+import pandas as pd
+from sklearn.model_selection import train_test_split
+from ohca_training_pipeline import prepare_training_data, train_ohca_model, find_optimal_threshold, save_model_with_metadata
+
+def train_from_labeled_data(data_path, model_save_path="./trained_ohca_model", test_size=0.2, num_epochs=3):
+    print("OHCA Classifier Training from Pre-labeled Data")
+    print("="*50)
+    
+    # Load data
+    print(f"Loading labeled data from: {data_path}")
+    df = pd.read_csv(data_path)
+    
+    # Add subject_id if missing
+    if 'subject_id' not in df.columns:
+        print("Adding subject_id column (using hadm_id as patient ID)")
+        df['subject_id'] = df['hadm_id']
+    
+    print(f"Data loaded: {len(df)} cases ({(df['ohca_label']==1).sum()} OHCA, {(df['ohca_label']==0).sum()} non-OHCA)")
+    
+    # Split data
+    train_df, val_df = train_test_split(df, test_size=test_size, stratify=df['ohca_label'], random_state=42)
+    print(f"Training: {len(train_df)}, Validation: {len(val_df)}")
+    
+    # Save temporary files
+    train_df.to_excel('temp_train.xlsx', index=False)
+    val_df.to_excel('temp_val.xlsx', index=False)
+    
+    try:
+        # Train
+        print("Preparing training data...")
+        train_dataset, val_dataset, train_df_balanced, val_df_clean, tokenizer = prepare_training_data('temp_train.xlsx', 'temp_val.xlsx')
+        
+        print(f"Training model for {num_epochs} epochs...")
+        model, trained_tokenizer = train_ohca_model(
+            train_dataset, val_dataset, train_df_balanced, tokenizer,
+            num_epochs=num_epochs, save_path=model_save_path
+        )
+        
+        print("Finding optimal threshold...")
+        optimal_threshold, val_metrics = find_optimal_threshold(model, trained_tokenizer, val_df_clean)
+        
+        print("Saving model with metadata...")
+        test_metrics = {'message': 'Trained on user data', 'test_set_size': 0}
+        save_model_with_metadata(model, trained_tokenizer, optimal_threshold, val_metrics, test_metrics, model_save_path)
+        
+        print(f"Training completed!")
+        print(f"Model saved to: {model_save_path}")
+        print(f"Optimal threshold: {optimal_threshold:.3f}")
+        print(f"F1-score: {val_metrics['f1_score']:.3f}")
+        
+    finally:
+        # Clean up
+        if os.path.exists('temp_train.xlsx'):
+            os.remove('temp_train.xlsx')
+        if os.path.exists('temp_val.xlsx'):
+            os.remove('temp_val.xlsx')
+
+if __name__ == "__main__":
+    import argparse
+    parser = argparse.ArgumentParser()
+    parser.add_argument('data_path', help='Path to labeled CSV file')
+    parser.add_argument('--model_path', default='./trained_ohca_model', help='Model save path')
+    parser.add_argument('--epochs', type=int, default=3, help='Training epochs')
+    parser.add_argument('--test_size', type=float, default=0.2, help='Validation split')
+    
+    args = parser.parse_args()
+    train_from_labeled_data(args.data_path, args.model_path, args.test_size, args.epochs)