Update model/train.py

Restoring previous working version

model/train.py

@@ -1,4 +1,4 @@
-# Enhanced model/train.py with LightGBM ensemble integration
+# Enhanced version with comprehensive cross-validation and advanced feature engineering
 
 import seaborn as sns
 import matplotlib.pyplot as plt
@@ -14,7 +14,7 @@ from sklearn.model_selection import (
     train_test_split, cross_val_score, GridSearchCV,
     StratifiedKFold, validation_curve, cross_validate
 )
-from sklearn.ensemble import RandomForestClassifier, VotingClassifier
+from sklearn.ensemble import RandomForestClassifier
 from sklearn.linear_model import LogisticRegression
 from sklearn.feature_extraction.text import TfidfVectorizer
 import pandas as pd
@@ -31,16 +31,6 @@ from datetime import datetime, timedelta
 from typing import Dict, Tuple, Optional, Any, List
 import warnings
 import re
-
-# LightGBM import
-try:
-    import lightgbm as lgb
-    LIGHTGBM_AVAILABLE = True
-    logging.info("LightGBM available for ensemble training")
-except ImportError:
-    LIGHTGBM_AVAILABLE = False
-    logging.warning("LightGBM not available - ensemble training will use alternative algorithms")
-
 warnings.filterwarnings('ignore')
 
 # Import enhanced feature engineering components
@@ -70,727 +60,454 @@ logging.basicConfig(
 logger = logging.getLogger(__name__)
 
 
-class EnsembleModelTrainer:
-    """Production-ready ensemble model trainer with LightGBM integration"""
-
-    def __init__(self, use_enhanced_features: bool = None, use_ensemble: bool = True):
-        # Auto-detect enhanced features if not specified
-        if use_enhanced_features is None:
-            self.use_enhanced_features = ENHANCED_FEATURES_AVAILABLE
-        else:
-            self.use_enhanced_features = use_enhanced_features and ENHANCED_FEATURES_AVAILABLE
+def preprocess_text_function(texts):
+    """
+    Standalone function for text preprocessing - pickle-safe
+    """
+    def clean_single_text(text):
+        # Convert to string
+        text = str(text)
         
-        self.use_ensemble = use_ensemble and LIGHTGBM_AVAILABLE
+        # Remove URLs
+        text = re.sub(r'http\S+|www\S+|https\S+', '', text)
         
-        self.setup_paths()
-        self.setup_training_config()
-        self.setup_models()
-        self.progress_tracker = None
-        self.cv_manager = CrossValidationManager()
+        # Remove email addresses
+        text = re.sub(r'\S+@\S+', '', text)
         
-        # Enhanced feature tracking
-        self.feature_engineer = None
-        self.feature_importance_results = {}
+        # Remove excessive punctuation
+        text = re.sub(r'[!]{2,}', '!', text)
+        text = re.sub(r'[?]{2,}', '?', text)
+        text = re.sub(r'[.]{3,}', '...', text)
         
-        logger.info(f"Ensemble trainer initialized - Enhanced features: {self.use_enhanced_features}, "
-                   f"LightGBM ensemble: {self.use_ensemble}")
-
-    def setup_paths(self):
-        """Setup all necessary paths with proper permissions"""
-        self.base_dir = Path("/tmp")
-        self.data_dir = self.base_dir / "data"
-        self.model_dir = self.base_dir / "model"
-        self.results_dir = self.base_dir / "results"
-        self.features_dir = self.base_dir / "features"
+        # Remove non-alphabetic characters except spaces and basic punctuation
+        text = re.sub(r'[^a-zA-Z\s.!?]', '', text)
+        
+        # Remove excessive whitespace
+        text = re.sub(r'\s+', ' ', text)
+        
+        return text.strip().lower()
+    
+    # Process all texts
+    processed = []
+    for text in texts:
+        processed.append(clean_single_text(text))
+    
+    return processed
 
-        # Create directories with proper permissions
-        for dir_path in [self.data_dir, self.model_dir, self.results_dir, self.features_dir]:
-            dir_path.mkdir(parents=True, exist_ok=True)
-            try:
-                dir_path.chmod(0o755)
-            except:
-                pass
 
-        # File paths
-        self.data_path = self.data_dir / "combined_dataset.csv"
-        self.model_path = Path("/tmp/model.pkl")
-        self.vectorizer_path = Path("/tmp/vectorizer.pkl")
-        self.pipeline_path = Path("/tmp/pipeline.pkl")
-        self.metadata_path = Path("/tmp/metadata.json")
-        self.evaluation_path = self.results_dir / "evaluation_results.json"
+class ProgressTracker:
+    """Progress tracking with time estimation"""
+    
+    def __init__(self, total_steps: int, description: str = "Training"):
+        self.total_steps = total_steps
+        self.current_step = 0
+        self.start_time = time.time()
+        self.description = description
+        self.step_times = []
         
-        # Enhanced feature paths
-        self.feature_engineer_path = Path("/tmp/feature_engineer.pkl")
-        self.feature_importance_path = self.results_dir / "feature_importance.json"
+    def update(self, step_name: str = ""):
+        """Update progress and print status"""
+        self.current_step += 1
+        current_time = time.time()
+        elapsed = current_time - self.start_time
         
-        # Ensemble-specific paths
-        self.ensemble_path = Path("/tmp/ensemble.pkl")
-        self.ensemble_metadata_path = Path("/tmp/ensemble_metadata.json")
-
-    def setup_training_config(self):
-        """Setup training configuration with ensemble parameters"""
-        self.test_size = 0.2
-        self.validation_size = 0.1
-        self.random_state = 42
-        self.cv_folds = 5
+        # Calculate progress percentage
+        progress_pct = (self.current_step / self.total_steps) * 100
         
-        # Enhanced feature configuration
-        if self.use_enhanced_features:
-            self.max_features = 7500
-            self.feature_selection_k = 3000
-            logger.info("Using enhanced feature engineering pipeline")
+        # Estimate remaining time
+        if self.current_step > 0:
+            avg_time_per_step = elapsed / self.current_step
+            remaining_steps = self.total_steps - self.current_step
+            eta_seconds = avg_time_per_step * remaining_steps
+            eta = timedelta(seconds=int(eta_seconds))
         else:
-            self.max_features = 5000
-            self.feature_selection_k = 2000
-            logger.info("Using standard TF-IDF feature pipeline")
+            eta = "calculating..."
+            
+        # Create progress bar
+        bar_length = 30
+        filled_length = int(bar_length * self.current_step // self.total_steps)
+        bar = '█' * filled_length + '▒' * (bar_length - filled_length)
         
-        # Common parameters
-        self.min_df = 1
-        self.max_df = 0.95
-        self.ngram_range = (1, 2)
-        self.max_iter = 500
-        self.class_weight = 'balanced'
+        # Print progress (this will be visible in Streamlit logs)
+        status_msg = f"\r{self.description}: [{bar}] {progress_pct:.1f}% | Step {self.current_step}/{self.total_steps}"
+        if step_name:
+            status_msg += f" | {step_name}"
+        if eta != "calculating...":
+            status_msg += f" | ETA: {eta}"
+            
+        print(status_msg, end='', flush=True)
         
-        # LightGBM specific parameters
-        self.lgb_params = {
-            'objective': 'binary',
-            'metric': 'binary_logloss',
-            'boosting_type': 'gbdt',
-            'num_leaves': 31,
-            'learning_rate': 0.1,
-            'feature_fraction': 0.8,
-            'bagging_fraction': 0.8,
-            'bagging_freq': 5,
-            'verbose': -1,
-            'random_state': self.random_state,
-            'class_weight': 'balanced'
-        }
-
-    def setup_models(self):
-        """Setup model configurations including LightGBM ensemble"""
-        # Base models
-        self.models = {
-            'logistic_regression': {
-                'model': LogisticRegression(
-                    max_iter=self.max_iter,
-                    class_weight=self.class_weight,
-                    random_state=self.random_state,
-                    n_jobs=-1
-                ),
-                'param_grid': {
-                    'model__C': [0.1, 1, 10],
-                    'model__penalty': ['l2']
-                }
-            },
-            'random_forest': {
-                'model': RandomForestClassifier(
-                    n_estimators=50,
-                    class_weight=self.class_weight,
-                    random_state=self.random_state,
-                    n_jobs=-1
-                ),
-                'param_grid': {
-                    'model__n_estimators': [50, 100],
-                    'model__max_depth': [10, None]
-                }
-            }
+        # Also output JSON for Streamlit parsing (if needed)
+        progress_json = {
+            "type": "progress",
+            "step": self.current_step,
+            "total": self.total_steps,
+            "percentage": progress_pct,
+            "eta": str(eta) if eta != "calculating..." else None,
+            "step_name": step_name,
+            "elapsed": elapsed
         }
+        print(f"\nPROGRESS_JSON: {json.dumps(progress_json)}")
         
-        # Add LightGBM if available
-        if LIGHTGBM_AVAILABLE and self.use_ensemble:
-            self.models['lightgbm'] = {
-                'model': lgb.LGBMClassifier(
-                    **self.lgb_params,
-                    n_estimators=100
-                ),
-                'param_grid': {
-                    'model__n_estimators': [50, 100],
-                    'model__learning_rate': [0.05, 0.1],
-                    'model__num_leaves': [31, 63]
-                }
-            }
-
-    def create_lightgbm_ensemble(self, models_dict: Dict, X_train, y_train) -> VotingClassifier:
-        """Create ensemble with LightGBM and traditional models"""
-        if not LIGHTGBM_AVAILABLE:
-            logger.warning("LightGBM not available for ensemble creation")
-            return None
-        
-        logger.info("Creating LightGBM ensemble model...")
+        # Store step time for better estimation
+        if len(self.step_times) >= 3:  # Keep last 3 step times for moving average
+            self.step_times.pop(0)
+        self.step_times.append(current_time - (self.start_time + sum(self.step_times)))
         
-        # Prepare estimators for voting classifier
-        estimators = []
+    def finish(self):
+        """Complete progress tracking"""
+        total_time = time.time() - self.start_time
+        print(f"\n{self.description} completed in {timedelta(seconds=int(total_time))}")
+
+
+def estimate_training_time(dataset_size: int, enable_tuning: bool = True, cv_folds: int = 5, 
+                          use_enhanced_features: bool = False) -> Dict:
+    """Estimate training time based on dataset characteristics and feature complexity"""
+    
+    # Base time estimates (in seconds) based on empirical testing
+    base_times = {
+        'preprocessing': max(0.1, dataset_size * 0.001),  # ~1ms per sample
+        'vectorization': max(0.5, dataset_size * 0.01),   # ~10ms per sample
+        'feature_selection': max(0.2, dataset_size * 0.005), # ~5ms per sample
+        'simple_training': max(1.0, dataset_size * 0.02),  # ~20ms per sample
+        'evaluation': max(0.5, dataset_size * 0.01),       # ~10ms per sample
+    }
+    
+    # Enhanced feature engineering time multipliers
+    if use_enhanced_features:
+        base_times['preprocessing'] *= 2.5  # More complex preprocessing
+        base_times['vectorization'] *= 1.5  # Additional feature extraction
+        base_times['feature_selection'] *= 2.0  # More features to select from
+        base_times['enhanced_feature_extraction'] = max(2.0, dataset_size * 0.05)  # New step
+    
+    # Hyperparameter tuning multipliers
+    tuning_multipliers = {
+        'logistic_regression': 8 if enable_tuning else 1,  # 8 param combinations
+        'random_forest': 12 if enable_tuning else 1,       # 12 param combinations
+    }
+    
+    # Cross-validation multiplier
+    cv_multiplier = cv_folds if dataset_size > 100 else 1
+    
+    # Calculate estimates
+    estimates = {}
+    
+    # Preprocessing steps
+    estimates['data_loading'] = 0.5
+    estimates['preprocessing'] = base_times['preprocessing']
+    estimates['vectorization'] = base_times['vectorization']
+    
+    if use_enhanced_features:
+        estimates['enhanced_feature_extraction'] = base_times['enhanced_feature_extraction']
+    
+    estimates['feature_selection'] = base_times['feature_selection']
+    
+    # Model training (now includes CV)
+    for model_name, multiplier in tuning_multipliers.items():
+        model_time = base_times['simple_training'] * multiplier * cv_multiplier
+        estimates[f'{model_name}_training'] = model_time
+        estimates[f'{model_name}_evaluation'] = base_times['evaluation']
+    
+    # Cross-validation overhead
+    estimates['cross_validation'] = base_times['simple_training'] * cv_folds * 0.5
+    
+    # Model saving
+    estimates['model_saving'] = 1.0
+    
+    # Total estimate
+    total_estimate = sum(estimates.values())
+    
+    # Add buffer for overhead (more for enhanced features)
+    buffer_multiplier = 1.4 if use_enhanced_features else 1.2
+    total_estimate *= buffer_multiplier
+    
+    return {
+        'detailed_estimates': estimates,
+        'total_seconds': total_estimate,
+        'total_formatted': str(timedelta(seconds=int(total_estimate))),
+        'dataset_size': dataset_size,
+        'enable_tuning': enable_tuning,
+        'cv_folds': cv_folds,
+        'use_enhanced_features': use_enhanced_features
+    }
+
+
+class CrossValidationManager:
+    """Advanced cross-validation management with comprehensive metrics"""
+    
+    def __init__(self, cv_folds: int = 5, random_state: int = 42):
+        self.cv_folds = cv_folds
+        self.random_state = random_state
+        self.cv_results = {}
         
-        for model_name, model_info in models_dict.items():
-            if 'best_estimator' in model_info:
-                model = model_info['best_estimator']
-                # Extract the actual model from pipeline
-                if hasattr(model, 'named_steps') and 'model' in model.named_steps:
-                    actual_model = model.named_steps['model']
-                else:
-                    actual_model = model
-                
-                estimators.append((model_name, actual_model))
+    def create_cv_strategy(self, X, y) -> StratifiedKFold:
+        """Create appropriate CV strategy based on data characteristics"""
+        # Calculate appropriate CV folds for small datasets
+        n_samples = len(X)
+        min_samples_per_fold = 3  # Minimum samples per fold
+        max_folds = n_samples // min_samples_per_fold
         
-        if len(estimators) < 2:
-            logger.warning("Not enough models for ensemble creation")
-            return None
+        # Adjust folds based on data size and class distribution
+        unique_classes = np.unique(y)
+        min_class_count = min([np.sum(y == cls) for cls in unique_classes])
         
-        # Create ensemble with soft voting for probability-based predictions
-        ensemble = VotingClassifier(
-            estimators=estimators,
-            voting='soft'
-        )
+        # Ensure each fold has at least one sample from each class
+        max_folds_by_class = min_class_count
         
-        logger.info(f"Ensemble created with {len(estimators)} models: {[name for name, _ in estimators]}")
-        return ensemble
-
-    def train_ensemble_model(self, X_train, X_test, y_train, y_test, individual_results: Dict) -> Dict:
-        """Train and evaluate ensemble model"""
-        if not self.use_ensemble or not LIGHTGBM_AVAILABLE:
-            logger.info("Ensemble training skipped - using best individual model")
-            return {}
+        actual_folds = max(2, min(self.cv_folds, max_folds, max_folds_by_class))
         
-        logger.info("Training ensemble model with LightGBM integration...")
+        logger.info(f"Using {actual_folds} CV folds (requested: {self.cv_folds})")
         
-        try:
-            # Create ensemble from individual models
-            ensemble = self.create_lightgbm_ensemble(individual_results, X_train, y_train)
-            
-            if ensemble is None:
-                return {'error': 'Failed to create ensemble'}
-            
-            # Train ensemble (models are already trained, just fitting the voting mechanism)
-            logger.info("Training ensemble voting mechanism...")
-            
-            # For voting classifier with already-fitted models, we need to fit on features
-            # First, we need to prepare features the same way
-            pipeline = self.create_preprocessing_pipeline()
-            X_train_processed = pipeline.fit_transform(X_train, y_train)
-            X_test_processed = pipeline.transform(X_test)
-            
-            # Fit the ensemble
-            ensemble.fit(X_train_processed, y_train)
-            
-            # Evaluate ensemble
-            ensemble_metrics = self.comprehensive_evaluation_ensemble(
-                ensemble, X_test_processed, y_test, X_train_processed, y_train
-            )
-            
-            # Create ensemble pipeline for consistency
-            ensemble_pipeline = Pipeline([
-                ('preprocessing', pipeline.steps[0][1]),  # Use same preprocessing
-                ('ensemble', ensemble)
-            ])
-            
-            ensemble_results = {
-                'ensemble': ensemble_pipeline,
-                'evaluation_metrics': ensemble_metrics,
-                'component_models': list(individual_results.keys()),
-                'ensemble_type': 'voting_classifier_with_lightgbm' if 'lightgbm' in individual_results else 'voting_classifier',
-                'training_time': datetime.now().isoformat(),
-                'feature_type': 'enhanced' if self.use_enhanced_features else 'standard'
-            }
-            
-            logger.info(f"Ensemble training completed - F1: {ensemble_metrics.get('f1', 'N/A'):.4f}")
-            return ensemble_results
-            
-        except Exception as e:
-            logger.error(f"Ensemble training failed: {str(e)}")
-            return {'error': str(e)}
-
-    def comprehensive_evaluation_ensemble(self, model, X_test, y_test, X_train=None, y_train=None) -> Dict:
-        """Comprehensive evaluation specifically for ensemble models"""
-        
-        logger.info("Evaluating ensemble model...")
-        
-        # Predictions
-        y_pred = model.predict(X_test)
-        y_pred_proba = model.predict_proba(X_test)[:, 1]
-
-        # Basic metrics
-        metrics = {
-            'accuracy': float(accuracy_score(y_test, y_pred)),
-            'precision': float(precision_score(y_test, y_pred, average='weighted')),
-            'recall': float(recall_score(y_test, y_pred, average='weighted')),
-            'f1': float(f1_score(y_test, y_pred, average='weighted')),
-            'roc_auc': float(roc_auc_score(y_test, y_pred_proba))
-        }
-
-        # Confusion matrix
-        cm = confusion_matrix(y_test, y_pred)
-        metrics['confusion_matrix'] = cm.tolist()
-
-        # Cross-validation on full dataset
-        if X_train is not None and y_train is not None:
-            X_full = np.concatenate([X_train, X_test])
-            y_full = np.concatenate([y_train, y_test])
-            
-            logger.info("Performing cross-validation on ensemble...")
-            cv_results = self.cv_manager.perform_cross_validation(model, X_full, y_full)
-            metrics['cross_validation'] = cv_results
-            
-            if 'test_scores' in cv_results and 'f1' in cv_results['test_scores']:
-                cv_f1_mean = cv_results['test_scores']['f1']['mean']
-                cv_f1_std = cv_results['test_scores']['f1']['std']
-                logger.info(f"Ensemble CV F1 Score: {cv_f1_mean:.4f} (±{cv_f1_std:.4f})")
-
-        # Ensemble-specific metrics
-        metrics['ensemble_info'] = {
-            'model_type': 'ensemble',
-            'voting_type': getattr(model, 'voting', 'unknown'),
-            'n_estimators': len(getattr(model, 'estimators_', [])),
-            'estimator_names': [name for name, _ in getattr(model, 'estimators', [])]
-        }
+        return StratifiedKFold(
+            n_splits=actual_folds,
+            shuffle=True,
+            random_state=self.random_state
+        )
+    
+    def perform_cross_validation(self, pipeline, X, y, cv_strategy=None) -> Dict:
+        """Perform comprehensive cross-validation with multiple metrics"""
         
-        return metrics
-
-    def select_best_model(self, results: Dict, ensemble_results: Dict = None) -> Tuple[str, Any, Dict]:
-        """Select the best performing model including ensemble option"""
+        if cv_strategy is None:
+            cv_strategy = self.create_cv_strategy(X, y)
         
-        logger.info("Selecting best model from individual models and ensemble...")
+        logger.info(f"Starting cross-validation with {cv_strategy.n_splits} folds...")
         
-        best_model_name = None
-        best_model = None
-        best_score = -1
-        best_metrics = None
+        # Define scoring metrics
+        scoring_metrics = {
+            'accuracy': 'accuracy',
+            'precision': 'precision_weighted',
+            'recall': 'recall_weighted',
+            'f1': 'f1_weighted',
+            'roc_auc': 'roc_auc'
+        }
         
-        # Evaluate individual models
-        for model_name, result in results.items():
-            if 'error' in result:
-                continue
-
-            cv_results = result['evaluation_metrics'].get('cross_validation', {})
-            if 'test_scores' in cv_results and 'f1' in cv_results['test_scores']:
-                f1_score = cv_results['test_scores']['f1']['mean']
-                score_type = "CV F1"
-            else:
-                f1_score = result['evaluation_metrics']['f1']
-                score_type = "Test F1"
-
-            logger.info(f"Model {model_name}: {score_type} = {f1_score:.4f}")
-
-            if f1_score > best_score:
-                best_score = f1_score
-                best_model_name = model_name
-                best_model = result['model']
-                best_metrics = result['evaluation_metrics']
-
-        # Evaluate ensemble if available
-        if ensemble_results and 'evaluation_metrics' in ensemble_results:
-            ensemble_metrics = ensemble_results['evaluation_metrics']
-            
-            cv_results = ensemble_metrics.get('cross_validation', {})
-            if 'test_scores' in cv_results and 'f1' in cv_results['test_scores']:
-                ensemble_f1 = cv_results['test_scores']['f1']['mean']
-                score_type = "CV F1"
-            else:
-                ensemble_f1 = ensemble_metrics['f1']
-                score_type = "Test F1"
+        try:
+            # Perform cross-validation
+            cv_scores = cross_validate(
+                pipeline, X, y,
+                cv=cv_strategy,
+                scoring=scoring_metrics,
+                return_train_score=True,
+                n_jobs=1,  # Use single job for stability
+                verbose=0
+            )
             
-            logger.info(f"Ensemble model: {score_type} = {ensemble_f1:.4f}")
+            # Process results
+            cv_results = {
+                'n_splits': cv_strategy.n_splits,
+                'test_scores': {},
+                'train_scores': {},
+                'fold_results': []
+            }
             
-            if ensemble_f1 > best_score:
-                best_score = ensemble_f1
-                best_model_name = "ensemble"
-                best_model = ensemble_results['ensemble']
-                best_metrics = ensemble_metrics
-
-        if best_model_name is None:
-            raise ValueError("No models trained successfully")
-
-        logger.info(f"Best model selected: {best_model_name} with F1 score: {best_score:.4f}")
-        return best_model_name, best_model, best_metrics
-
-    def save_model_artifacts(self, model, model_name: str, metrics: Dict, results: Dict, 
-                           ensemble_results: Dict = None) -> bool:
-        """Enhanced model artifacts saving with ensemble support"""
-        try:
-            logger.info(f"Saving model artifacts for {model_name}...")
-
-            # Save the main pipeline/model
-            if model_name == "ensemble":
-                # Save ensemble model
-                joblib.dump(model, self.ensemble_path)
-                logger.info(f"Saved ensemble model to {self.ensemble_path}")
+            # Calculate statistics for each metric
+            for metric_name in scoring_metrics.keys():
+                test_key = f'test_{metric_name}'
+                train_key = f'train_{metric_name}'
                 
-                # Also save as main pipeline for API compatibility
-                joblib.dump(model, self.pipeline_path)
-                logger.info(f"Saved ensemble as main pipeline to {self.pipeline_path}")
+                if test_key in cv_scores:
+                    test_scores = cv_scores[test_key]
+                    cv_results['test_scores'][metric_name] = {
+                        'mean': float(np.mean(test_scores)),
+                        'std': float(np.std(test_scores)),
+                        'min': float(np.min(test_scores)),
+                        'max': float(np.max(test_scores)),
+                        'scores': test_scores.tolist()
+                    }
                 
-                # Save ensemble metadata
-                ensemble_metadata = {
-                    'model_type': 'ensemble',
-                    'ensemble_type': ensemble_results.get('ensemble_type', 'voting_classifier'),
-                    'component_models': ensemble_results.get('component_models', []),
-                    'ensemble_info': metrics.get('ensemble_info', {}),
-                    'timestamp': datetime.now().isoformat()
+                if train_key in cv_scores:
+                    train_scores = cv_scores[train_key]
+                    cv_results['train_scores'][metric_name] = {
+                        'mean': float(np.mean(train_scores)),
+                        'std': float(np.std(train_scores)),
+                        'min': float(np.min(train_scores)),
+                        'max': float(np.max(train_scores)),
+                        'scores': train_scores.tolist()
+                    }
+            
+            # Store individual fold results
+            for fold_idx in range(cv_strategy.n_splits):
+                fold_result = {
+                    'fold': fold_idx + 1,
+                    'test_scores': {},
+                    'train_scores': {}
                 }
                 
-                with open(self.ensemble_metadata_path, 'w') as f:
-                    json.dump(ensemble_metadata, f, indent=2)
-                logger.info(f"Saved ensemble metadata to {self.ensemble_metadata_path}")
-                
-            else:
-                # Save individual model pipeline
-                joblib.dump(model, self.pipeline_path)
-                logger.info(f"Saved {model_name} pipeline to {self.pipeline_path}")
-
-            # Save individual components for backward compatibility
-            try:
-                if hasattr(model, 'named_steps'):
-                    if 'model' in model.named_steps:
-                        joblib.dump(model.named_steps['model'], self.model_path)
-                    elif 'ensemble' in model.named_steps:
-                        joblib.dump(model.named_steps['ensemble'], self.model_path)
+                for metric_name in scoring_metrics.keys():
+                    test_key = f'test_{metric_name}'
+                    train_key = f'train_{metric_name}'
                     
-                    # Save vectorizer or enhanced features reference
-                    if 'vectorize' in model.named_steps:
-                        joblib.dump(model.named_steps['vectorize'], self.vectorizer_path)
-                    elif 'enhanced_features' in model.named_steps:
-                        enhanced_ref = {
-                            'type': 'enhanced_features',
-                            'feature_engineer_path': str(self.feature_engineer_path),
-                            'metadata': self.feature_engineer.get_feature_metadata() if self.feature_engineer else {}
-                        }
-                        joblib.dump(enhanced_ref, self.vectorizer_path)
-                        
-            except Exception as e:
-                logger.warning(f"Could not save individual components: {e}")
-
-            # Generate enhanced metadata
-            metadata = self._create_enhanced_metadata(model_name, metrics, results, ensemble_results)
+                    if test_key in cv_scores:
+                        fold_result['test_scores'][metric_name] = float(cv_scores[test_key][fold_idx])
+                    if train_key in cv_scores:
+                        fold_result['train_scores'][metric_name] = float(cv_scores[train_key][fold_idx])
+                
+                cv_results['fold_results'].append(fold_result)
             
-            # Save metadata
-            with open(self.metadata_path, 'w') as f:
-                json.dump(metadata, f, indent=2)
-            logger.info(f"Saved metadata to {self.metadata_path}")
-
-            return True
-
-        except Exception as e:
-            logger.error(f"Failed to save model artifacts: {str(e)}")
-            return False
-
-    def _create_enhanced_metadata(self, model_name: str, metrics: Dict, results: Dict, 
-                                ensemble_results: Dict = None) -> Dict:
-        """Create comprehensive metadata including ensemble information"""
-        
-        # Generate data hash and version
-        data_hash = hashlib.md5(str(datetime.now()).encode()).hexdigest()
-        version_suffix = "ensemble" if model_name == "ensemble" else model_name
-        
-        metadata = {
-            'model_version': f"v2.0_{datetime.now().strftime('%Y%m%d_%H%M%S')}_{version_suffix}",
-            'model_type': model_name,
-            'is_ensemble': model_name == "ensemble",
-            'data_version': data_hash,
-            'test_accuracy': metrics['accuracy'],
-            'test_f1': metrics['f1'],
-            'test_precision': metrics['precision'],
-            'test_recall': metrics['recall'],
-            'test_roc_auc': metrics['roc_auc'],
-            'timestamp': datetime.now().isoformat(),
-            'training_method': 'enhanced_ensemble_training' if self.use_ensemble else 'enhanced_individual_training',
-            'lightgbm_available': LIGHTGBM_AVAILABLE,
-            'lightgbm_used': self.use_ensemble and LIGHTGBM_AVAILABLE
-        }
-        
-        # Add feature engineering information
-        metadata['feature_engineering'] = {
-            'type': 'enhanced' if self.use_enhanced_features else 'standard',
-            'enhanced_features_available': ENHANCED_FEATURES_AVAILABLE,
-            'enhanced_features_used': self.use_enhanced_features
-        }
-        
-        # Add ensemble-specific metadata
-        if model_name == "ensemble" and ensemble_results:
-            metadata['ensemble_details'] = {
-                'ensemble_type': ensemble_results.get('ensemble_type', 'voting_classifier'),
-                'component_models': ensemble_results.get('component_models', []),
-                'ensemble_info': metrics.get('ensemble_info', {}),
-                'voting_type': metrics.get('ensemble_info', {}).get('voting_type', 'soft')
-            }
+            # Calculate overfitting indicators
+            if 'accuracy' in cv_results['test_scores'] and 'accuracy' in cv_results['train_scores']:
+                train_mean = cv_results['train_scores']['accuracy']['mean']
+                test_mean = cv_results['test_scores']['accuracy']['mean']
+                cv_results['overfitting_score'] = float(train_mean - test_mean)
             
-            # Add individual model performances for comparison
-            metadata['component_performance'] = {}
-            for comp_model_name in ensemble_results.get('component_models', []):
-                if comp_model_name in results and 'evaluation_metrics' in results[comp_model_name]:
-                    comp_metrics = results[comp_model_name]['evaluation_metrics']
-                    metadata['component_performance'][comp_model_name] = {
-                        'f1': comp_metrics.get('f1', 0),
-                        'accuracy': comp_metrics.get('accuracy', 0)
-                    }
-        
-        # Add CV results
-        cv_results = metrics.get('cross_validation', {})
-        if cv_results and 'test_scores' in cv_results:
-            metadata['cross_validation'] = {
-                'n_splits': cv_results.get('n_splits', self.cv_folds),
-                'test_scores': cv_results['test_scores'],
-                'train_scores': cv_results.get('train_scores', {}),
-                'overfitting_score': cv_results.get('overfitting_score', 'Unknown'),
-                'stability_score': cv_results.get('stability_score', 'Unknown')
-            }
+            # Calculate stability metrics
+            if 'accuracy' in cv_results['test_scores']:
+                test_std = cv_results['test_scores']['accuracy']['std']
+                test_mean = cv_results['test_scores']['accuracy']['mean']
+                cv_results['stability_score'] = float(1 - (test_std / test_mean)) if test_mean > 0 else 0
             
-            if 'f1' in cv_results['test_scores']:
-                metadata.update({
-                    'cv_f1_mean': cv_results['test_scores']['f1']['mean'],
-                    'cv_f1_std': cv_results['test_scores']['f1']['std']
-                })
-        
-        # Add training configuration
-        metadata['training_config'] = {
-            'test_size': self.test_size,
-            'cv_folds': self.cv_folds,
-            'max_features': self.max_features,
-            'use_ensemble': self.use_ensemble,
-            'use_enhanced_features': self.use_enhanced_features
-        }
+            logger.info(f"Cross-validation completed successfully")
+            logger.info(f"Mean test accuracy: {cv_results['test_scores'].get('accuracy', {}).get('mean', 'N/A'):.4f}")
+            logger.info(f"Mean test F1: {cv_results['test_scores'].get('f1', {}).get('mean', 'N/A'):.4f}")
+            
+            return cv_results
+            
+        except Exception as e:
+            logger.error(f"Cross-validation failed: {e}")
+            return {
+                'error': str(e),
+                'n_splits': cv_strategy.n_splits if cv_strategy else self.cv_folds,
+                'fallback': True
+            }
+    
+    def compare_cv_results(self, results1: Dict, results2: Dict, metric: str = 'f1') -> Dict:
+        """Compare cross-validation results between two models"""
         
-        return metadata
-
-    def train_model(self, data_path: str = None, force_enhanced: bool = None, 
-                   use_ensemble: bool = None) -> Tuple[bool, str]:
-        """Main training function with ensemble support"""
         try:
-            # Override settings if specified
-            if force_enhanced is not None:
-                original_enhanced = self.use_enhanced_features
-                self.use_enhanced_features = force_enhanced and ENHANCED_FEATURES_AVAILABLE
-                
-            if use_ensemble is not None:
-                self.use_ensemble = use_ensemble and LIGHTGBM_AVAILABLE
-
-            feature_type = "enhanced" if self.use_enhanced_features else "standard"
-            training_type = "ensemble" if self.use_ensemble else "individual"
+            if 'error' in results1 or 'error' in results2:
+                return {'error': 'Cannot compare results with errors'}
             
-            logger.info(f"Starting {feature_type} {training_type} model training...")
-
-            # Override data path if provided
-            if data_path:
-                self.data_path = Path(data_path)
-
-            # Load and validate data
-            success, df, message = self.load_and_validate_data()
-            if not success:
-                return False, message
-
-            # Estimate training time
-            time_estimate = estimate_training_time(
-                len(df), 
-                enable_tuning=True, 
-                cv_folds=self.cv_folds,
-                use_enhanced_features=self.use_enhanced_features,
-                use_ensemble=self.use_ensemble
-            )
+            scores1 = results1['test_scores'][metric]['scores']
+            scores2 = results2['test_scores'][metric]['scores']
             
-            model_count = len(self.models)
-            logger.info(f"Training Configuration:")
-            logger.info(f"  Dataset size: {len(df)} samples")
-            logger.info(f"  Feature engineering: {feature_type.title()}")
-            logger.info(f"  Training approach: {training_type.title()}")
-            logger.info(f"  Models to train: {model_count}")
-            logger.info(f"  LightGBM available: {LIGHTGBM_AVAILABLE}")
-            logger.info(f"  Estimated time: {time_estimate['total_formatted']}")
-
-            # Setup progress tracker
-            base_steps = 4 + (model_count * 3) + 2  # Base + model training + ensemble
-            enhanced_steps = 2 if self.use_enhanced_features else 0
-            ensemble_steps = 3 if self.use_ensemble else 0
-            total_steps = base_steps + enhanced_steps + ensemble_steps
+            # Paired t-test
+            from scipy import stats
+            t_stat, p_value = stats.ttest_rel(scores1, scores2)
             
-            self.progress_tracker = ProgressTracker(
-                total_steps, 
-                f"{feature_type.title()} {training_type.title()} Training"
-            )
-
-            # Prepare data
-            X = df['text'].values
-            y = df['label'].values
-
-            # Train-test split
-            self.progress_tracker.update("Splitting data")
+            comparison = {
+                'metric': metric,
+                'model1_mean': results1['test_scores'][metric]['mean'],
+                'model2_mean': results2['test_scores'][metric]['mean'],
+                'model1_std': results1['test_scores'][metric]['std'],
+                'model2_std': results2['test_scores'][metric]['std'],
+                'difference': results2['test_scores'][metric]['mean'] - results1['test_scores'][metric]['mean'],
+                'paired_ttest': {
+                    't_statistic': float(t_stat),
+                    'p_value': float(p_value),
+                    'significant': p_value < 0.05
+                },
+                'effect_size': float(abs(t_stat) / np.sqrt(len(scores1))) if len(scores1) > 0 else 0
+            }
             
-            if len(X) < 10:
-                test_size = max(0.1, 1/len(X))
-            else:
-                test_size = self.test_size
-                
-            label_counts = pd.Series(y).value_counts()
-            min_class_count = label_counts.min()
-            can_stratify = min_class_count >= 2 and len(y) >= 4
+            return comparison
             
-            X_train, X_test, y_train, y_test = train_test_split(
-                X, y,
-                test_size=test_size,
-                stratify=y if can_stratify else None,
-                random_state=self.random_state
-            )
-
-            logger.info(f"Data split: {len(X_train)} train, {len(X_test)} test")
-
-            # Train individual models
-            results = self.train_and_evaluate_models(X_train, X_test, y_train, y_test)
-            
-            # Train ensemble if enabled
-            ensemble_results = {}
-            if self.use_ensemble and len([r for r in results.values() if 'error' not in r]) >= 2:
-                self.progress_tracker.update("Creating ensemble model")
-                ensemble_results = self.train_ensemble_model(X_train, X_test, y_train, y_test, results)
-                
-                if ensemble_results and 'error' not in ensemble_results:
-                    logger.info("Ensemble model trained successfully")
-                else:
-                    logger.warning("Ensemble training failed, using best individual model")
-
-            # Select best model (individual or ensemble)
-            best_model_name, best_model, best_metrics = self.select_best_model(results, ensemble_results)
-
-            # Save model artifacts
-            if not self.save_model_artifacts(best_model, best_model_name, best_metrics, results, ensemble_results):
-                return False, "Failed to save model artifacts"
-
-            # Finish progress tracking
-            self.progress_tracker.finish()
-
-            # Create success message
-            cv_results = best_metrics.get('cross_validation', {})
-            cv_info = ""
-            if 'test_scores' in cv_results and 'f1' in cv_results['test_scores']:
-                cv_f1_mean = cv_results['test_scores']['f1']['mean']
-                cv_f1_std = cv_results['test_scores']['f1']['std']
-                cv_info = f", CV F1: {cv_f1_mean:.4f} (±{cv_f1_std:.4f})"
-
-            # Enhanced features info
-            feature_info = f", {feature_type.title()} Features"
-            if self.use_enhanced_features:
-                feature_metadata = best_metrics.get('feature_metadata', {})
-                if feature_metadata:
-                    total_features = feature_metadata.get('total_features', 0)
-                    feature_info = f", Enhanced Features: {total_features}"
+        except Exception as e:
+            logger.error(f"CV comparison failed: {e}")
+            return {'error': str(e)}
 
-            # Ensemble info
-            ensemble_info = ""
-            if best_model_name == "ensemble":
-                ensemble_details = best_metrics.get('ensemble_info', {})
-                n_models = ensemble_details.get('n_estimators', 0)
-                ensemble_info = f", Ensemble: {n_models} models"
 
-            success_message = (
-                f"{training_type.title()} model training completed successfully. "
-                f"Best model: {best_model_name} "
-                f"(Test F1: {best_metrics['f1']:.4f}, Test Accuracy: {best_metrics['accuracy']:.4f}{cv_info}{feature_info}{ensemble_info})"
-            )
+class EnhancedModelTrainer:
+    """Production-ready model trainer with enhanced feature engineering and comprehensive CV"""
 
-            logger.info(success_message)
-            return True, success_message
+    def __init__(self, use_enhanced_features: bool = None):
+        # Auto-detect enhanced features if not specified
+        if use_enhanced_features is None:
+            self.use_enhanced_features = ENHANCED_FEATURES_AVAILABLE
+        else:
+            self.use_enhanced_features = use_enhanced_features and ENHANCED_FEATURES_AVAILABLE
+        
+        self.setup_paths()
+        self.setup_training_config()
+        self.setup_models()
+        self.progress_tracker = None
+        self.cv_manager = CrossValidationManager()
+        
+        # Enhanced feature tracking
+        self.feature_engineer = None
+        self.feature_importance_results = {}
 
-        except Exception as e:
-            if self.progress_tracker:
-                print()
-            error_message = f"Enhanced ensemble model training failed: {str(e)}"
-            logger.error(error_message)
-            return False, error_message
+    def setup_paths(self):
+        """Setup all necessary paths with proper permissions"""
+        self.base_dir = Path("/tmp")
+        self.data_dir = self.base_dir / "data"
+        self.model_dir = self.base_dir / "model"
+        self.results_dir = self.base_dir / "results"
+        self.features_dir = self.base_dir / "features"  # New for enhanced features
 
-    # Include all other methods from the original trainer (load_and_validate_data, 
-    # create_preprocessing_pipeline, comprehensive_evaluation, train_and_evaluate_models, etc.)
-    # These remain largely the same but with minor modifications for ensemble support
+        # Create directories with proper permissions
+        for dir_path in [self.data_dir, self.model_dir, self.results_dir, self.features_dir]:
+            dir_path.mkdir(parents=True, exist_ok=True)
+            # Ensure write permissions
+            try:
+                dir_path.chmod(0o755)
+            except:
+                pass
 
+        # File paths
+        self.data_path = self.data_dir / "combined_dataset.csv"
+        self.model_path = Path("/tmp/model.pkl")
+        self.vectorizer_path = Path("/tmp/vectorizer.pkl")
+        self.pipeline_path = Path("/tmp/pipeline.pkl")
+        self.metadata_path = Path("/tmp/metadata.json")
+        self.evaluation_path = self.results_dir / "evaluation_results.json"
+        
+        # Enhanced feature paths
+        self.feature_engineer_path = Path("/tmp/feature_engineer.pkl")
+        self.feature_importance_path = self.results_dir / "feature_importance.json"
 
-def estimate_training_time(dataset_size: int, enable_tuning: bool = True, cv_folds: int = 5, 
-                          use_enhanced_features: bool = False, use_ensemble: bool = False) -> Dict:
-    """Enhanced time estimation including ensemble training"""
-    
-    # Base time estimates (in seconds)
-    base_times = {
-        'preprocessing': max(0.1, dataset_size * 0.001),
-        'vectorization': max(0.5, dataset_size * 0.01),
-        'feature_selection': max(0.2, dataset_size * 0.005),
-        'simple_training': max(1.0, dataset_size * 0.02),
-        'evaluation': max(0.5, dataset_size * 0.01),
-    }
-    
-    # Enhanced feature engineering time multipliers
-    if use_enhanced_features:
-        base_times['preprocessing'] *= 2.5
-        base_times['vectorization'] *= 1.5
-        base_times['feature_selection'] *= 2.0
-        base_times['enhanced_feature_extraction'] = max(2.0, dataset_size * 0.05)
-    
-    # LightGBM training time (typically faster than RF but slower than LogReg)
-    if use_ensemble and LIGHTGBM_AVAILABLE:
-        base_times['lightgbm_training'] = max(2.0, dataset_size * 0.03)
-        base_times['ensemble_creation'] = max(1.0, dataset_size * 0.005)
-        base_times['ensemble_evaluation'] = max(1.0, dataset_size * 0.015)
-    
-    # Hyperparameter tuning multipliers
-    tuning_multipliers = {
-        'logistic_regression': 8 if enable_tuning else 1,
-        'random_forest': 12 if enable_tuning else 1,
-    }
-    
-    if use_ensemble and LIGHTGBM_AVAILABLE:
-        tuning_multipliers['lightgbm'] = 10 if enable_tuning else 1
-    
-    # Cross-validation multiplier
-    cv_multiplier = cv_folds if dataset_size > 100 else 1
-    
-    # Calculate estimates
-    estimates = {}
-    
-    # Preprocessing steps
-    estimates['data_loading'] = 0.5
-    estimates['preprocessing'] = base_times['preprocessing']
-    estimates['vectorization'] = base_times['vectorization']
-    
-    if use_enhanced_features:
-        estimates['enhanced_feature_extraction'] = base_times['enhanced_feature_extraction']
-    
-    estimates['feature_selection'] = base_times['feature_selection']
-    
-    # Model training (includes CV)
-    for model_name, multiplier in tuning_multipliers.items():
-        model_time = base_times['simple_training'] * multiplier * cv_multiplier
-        estimates[f'{model_name}_training'] = model_time
-        estimates[f'{model_name}_evaluation'] = base_times['evaluation']
-    
-    # Ensemble-specific steps
-    if use_ensemble and LIGHTGBM_AVAILABLE:
-        estimates['ensemble_creation'] = base_times['ensemble_creation']
-        estimates['ensemble_evaluation'] = base_times['ensemble_evaluation']
-        estimates['ensemble_cross_validation'] = base_times['simple_training'] * cv_folds * 0.3
-    
-    # Cross-validation overhead
-    estimates['cross_validation'] = base_times['simple_training'] * cv_folds * 0.5
-    
-    # Model saving
-    estimates['model_saving'] = 1.0
-    
-    # Total estimate
-    total_estimate = sum(estimates.values())
-    
-    # Add buffer for overhead
-    buffer_multiplier = 1.6 if use_ensemble else (1.4 if use_enhanced_features else 1.2)
-    total_estimate *= buffer_multiplier
-    
-    return {
-        'detailed_estimates': estimates,
-        'total_seconds': total_estimate,
-        'total_formatted': str(timedelta(seconds=int(total_estimate))),
-        'dataset_size': dataset_size,
-        'enable_tuning': enable_tuning,
-        'cv_folds': cv_folds,
-        'use_enhanced_features': use_enhanced_features,
-        'use_ensemble': use_ensemble,
-        'lightgbm_available': LIGHTGBM_AVAILABLE
-    }
+    def setup_training_config(self):
+        """Setup training configuration with enhanced feature parameters"""
+        self.test_size = 0.2
+        self.validation_size = 0.1
+        self.random_state = 42
+        self.cv_folds = 5
+        
+        # Enhanced feature configuration
+        if self.use_enhanced_features:
+            self.max_features = 7500  # Increased for enhanced features
+            self.feature_selection_k = 3000  # More features to select from
+            logger.info("Using enhanced feature engineering pipeline")
+        else:
+            self.max_features = 5000  # Standard TF-IDF
+            self.feature_selection_k = 2000
+            logger.info("Using standard TF-IDF feature pipeline")
+        
+        # Common parameters
+        self.min_df = 1
+        self.max_df = 0.95
+        self.ngram_range = (1, 2)
+        self.max_iter = 500
+        self.class_weight = 'balanced'
 
+    def setup_models(self):
+        """Setup model configurations for comparison"""
+        self.models = {
+            'logistic_regression': {
+                'model': LogisticRegression(
+                    max_iter=self.max_iter,
+                    class_weight=self.class_weight,
+                    random_state=self.random_state,
+                    n_jobs=-1
+                ),
+                'param_grid': {
+                    'model__C': [0.1, 1, 10],
+                    'model__penalty': ['l2']
+                }
+            },
+            'random_forest': {
+                'model': RandomForestClassifier(
+                    n_estimators=50,
+                    class_weight=self.class_weight,
+                    random_state=self.random_state,
+                    n_jobs=-1
+                ),
+                'param_grid': {
+                    'model__n_estimators': [50, 100],
+                    'model__max_depth': [10, None]
+                }
+            }
+        }
 
-# Import all remaining methods from original trainer class
-class EnhancedModelTrainer(EnsembleModelTrainer):
-    """Complete enhanced model trainer inheriting from ensemble trainer"""
-    
     def load_and_validate_data(self) -> Tuple[bool, Optional[pd.DataFrame], str]:
         """Load and validate training data"""
         try:
@@ -1142,260 +859,356 @@ class EnhancedModelTrainer(EnsembleModelTrainer):
 
         return results
 
-
-# Continue with ProgressTracker and CrossValidationManager classes from original...
-class ProgressTracker:
-    """Progress tracking with time estimation"""
-    
-    def __init__(self, total_steps: int, description: str = "Training"):
-        self.total_steps = total_steps
-        self.current_step = 0
-        self.start_time = time.time()
-        self.description = description
-        self.step_times = []
-        
-    def update(self, step_name: str = ""):
-        """Update progress and print status"""
-        self.current_step += 1
-        current_time = time.time()
-        elapsed = current_time - self.start_time
-        
-        # Calculate progress percentage
-        progress_pct = (self.current_step / self.total_steps) * 100
-        
-        # Estimate remaining time
-        if self.current_step > 0:
-            avg_time_per_step = elapsed / self.current_step
-            remaining_steps = self.total_steps - self.current_step
-            eta_seconds = avg_time_per_step * remaining_steps
-            eta = timedelta(seconds=int(eta_seconds))
-        else:
-            eta = "calculating..."
-            
-        # Create progress bar
-        bar_length = 30
-        filled_length = int(bar_length * self.current_step // self.total_steps)
-        bar = '█' * filled_length + '░' * (bar_length - filled_length)
-        
-        # Print progress (this will be visible in Streamlit logs)
-        status_msg = f"\r{self.description}: [{bar}] {progress_pct:.1f}% | Step {self.current_step}/{self.total_steps}"
-        if step_name:
-            status_msg += f" | {step_name}"
-        if eta != "calculating...":
-            status_msg += f" | ETA: {eta}"
-            
-        print(status_msg, end='', flush=True)
-        
-        # Also output JSON for Streamlit parsing (if needed)
-        progress_json = {
-            "type": "progress",
-            "step": self.current_step,
-            "total": self.total_steps,
-            "percentage": progress_pct,
-            "eta": str(eta) if eta != "calculating..." else None,
-            "step_name": step_name,
-            "elapsed": elapsed
-        }
-        print(f"\nPROGRESS_JSON: {json.dumps(progress_json)}")
+    def select_best_model(self, results: Dict) -> Tuple[str, Any, Dict]:
+        """Select the best performing model based on CV results"""
         
-        # Store step time for better estimation
-        if len(self.step_times) >= 3:
-            self.step_times.pop(0)
-        self.step_times.append(current_time - (self.start_time + sum(self.step_times)))
-        
-    def finish(self):
-        """Complete progress tracking"""
-        total_time = time.time() - self.start_time
-        print(f"\n{self.description} completed in {timedelta(seconds=int(total_time))}")
+        if self.progress_tracker:
+            self.progress_tracker.update("Selecting best model")
 
+        best_model_name = None
+        best_model = None
+        best_score = -1
+        best_metrics = None
 
-class CrossValidationManager:
-    """Advanced cross-validation management with comprehensive metrics"""
-    
-    def __init__(self, cv_folds: int = 5, random_state: int = 42):
-        self.cv_folds = cv_folds
-        self.random_state = random_state
-        self.cv_results = {}
-        
-    def create_cv_strategy(self, X, y) -> StratifiedKFold:
-        """Create appropriate CV strategy based on data characteristics"""
-        # Calculate appropriate CV folds for small datasets
-        n_samples = len(X)
-        min_samples_per_fold = 3  # Minimum samples per fold
-        max_folds = n_samples // min_samples_per_fold
-        
-        # Adjust folds based on data size and class distribution
-        unique_classes = np.unique(y)
-        min_class_count = min([np.sum(y == cls) for cls in unique_classes])
-        
-        # Ensure each fold has at least one sample from each class
-        max_folds_by_class = min_class_count
-        
-        actual_folds = max(2, min(self.cv_folds, max_folds, max_folds_by_class))
-        
-        logger.info(f"Using {actual_folds} CV folds (requested: {self.cv_folds})")
-        
-        return StratifiedKFold(
-            n_splits=actual_folds,
-            shuffle=True,
-            random_state=self.random_state
-        )
-    
-    def perform_cross_validation(self, pipeline, X, y, cv_strategy=None) -> Dict:
-        """Perform comprehensive cross-validation with multiple metrics"""
-        
-        if cv_strategy is None:
-            cv_strategy = self.create_cv_strategy(X, y)
-        
-        logger.info(f"Starting cross-validation with {cv_strategy.n_splits} folds...")
-        
-        # Define scoring metrics
-        scoring_metrics = {
-            'accuracy': 'accuracy',
-            'precision': 'precision_weighted',
-            'recall': 'recall_weighted',
-            'f1': 'f1_weighted',
-            'roc_auc': 'roc_auc'
-        }
-        
+        for model_name, result in results.items():
+            if 'error' in result:
+                continue
+
+            # Prioritize CV F1 score if available, fallback to test F1
+            cv_results = result['evaluation_metrics'].get('cross_validation', {})
+            if 'test_scores' in cv_results and 'f1' in cv_results['test_scores']:
+                f1_score = cv_results['test_scores']['f1']['mean']
+                score_type = "CV F1"
+            else:
+                f1_score = result['evaluation_metrics']['f1']
+                score_type = "Test F1"
+
+            if f1_score > best_score:
+                best_score = f1_score
+                best_model_name = model_name
+                best_model = result['model']
+                best_metrics = result['evaluation_metrics']
+
+        if best_model_name is None:
+            raise ValueError("No models trained successfully")
+
+        logger.info(f"Best model: {best_model_name} with {score_type} score: {best_score:.4f}")
+        return best_model_name, best_model, best_metrics
+
+    def save_model_artifacts(self, model, model_name: str, metrics: Dict, results: Dict) -> bool:
+        """Save model artifacts and enhanced metadata with feature engineering results"""
         try:
-            # Perform cross-validation
-            cv_scores = cross_validate(
-                pipeline, X, y,
-                cv=cv_strategy,
-                scoring=scoring_metrics,
-                return_train_score=True,
-                n_jobs=1,  # Use single job for stability
-                verbose=0
-            )
+            if self.progress_tracker:
+                self.progress_tracker.update("Saving model")
+
+            # Save the full pipeline with error handling
+            try:
+                joblib.dump(model, self.pipeline_path)
+                logger.info(f"✅ Saved pipeline to {self.pipeline_path}")
+            except Exception as e:
+                logger.error(f"Failed to save pipeline: {e}")
+                # Try alternative path
+                alt_pipeline_path = Path("/tmp") / "pipeline.pkl"
+                joblib.dump(model, alt_pipeline_path)
+                logger.info(f"✅ Saved pipeline to {alt_pipeline_path}")
+
+            # Save enhanced feature engineer if available
+            if self.use_enhanced_features and self.feature_engineer is not None:
+                try:
+                    self.feature_engineer.save_pipeline(self.feature_engineer_path)
+                    logger.info(f"✅ Saved feature engineer to {self.feature_engineer_path}")
+                except Exception as e:
+                    logger.warning(f"Could not save feature engineer: {e}")
+
+            # Save individual components for backward compatibility
+            try:
+                if hasattr(model, 'named_steps'):
+                    if 'model' in model.named_steps:
+                        joblib.dump(model.named_steps['model'], self.model_path)
+                        logger.info(f"✅ Saved model component to {self.model_path}")
+                    
+                    # Save vectorizer (standard pipeline) or enhanced features reference
+                    if 'vectorize' in model.named_steps:
+                        joblib.dump(model.named_steps['vectorize'], self.vectorizer_path)
+                        logger.info(f"✅ Saved vectorizer to {self.vectorizer_path}")
+                    elif 'enhanced_features' in model.named_steps:
+                        # Save reference to enhanced features
+                        enhanced_ref = {
+                            'type': 'enhanced_features',
+                            'feature_engineer_path': str(self.feature_engineer_path),
+                            'metadata': self.feature_engineer.get_feature_metadata() if self.feature_engineer else {}
+                        }
+                        joblib.dump(enhanced_ref, self.vectorizer_path)
+                        logger.info(f"✅ Saved enhanced features reference to {self.vectorizer_path}")
+                        
+            except Exception as e:
+                logger.warning(f"Could not save individual components: {e}")
+
+            # Generate data hash
+            data_hash = hashlib.md5(str(datetime.now()).encode()).hexdigest()
+
+            # Extract CV results
+            cv_results = metrics.get('cross_validation', {})
             
-            # Process results
-            cv_results = {
-                'n_splits': cv_strategy.n_splits,
-                'test_scores': {},
-                'train_scores': {},
-                'fold_results': []
+            # Create enhanced metadata with feature engineering information
+            metadata = {
+                'model_version': f"v1.0_{datetime.now().strftime('%Y%m%d_%H%M%S')}",
+                'model_type': model_name,
+                'feature_engineering': {
+                    'type': 'enhanced' if self.use_enhanced_features else 'standard',
+                    'enhanced_features_available': ENHANCED_FEATURES_AVAILABLE,
+                    'enhanced_features_used': self.use_enhanced_features
+                },
+                'data_version': data_hash,
+                'test_accuracy': metrics['accuracy'],
+                'test_f1': metrics['f1'],
+                'test_precision': metrics['precision'],
+                'test_recall': metrics['recall'],
+                'test_roc_auc': metrics['roc_auc'],
+                'overfitting_score': metrics.get('overfitting_score', 'Unknown'),
+                'timestamp': datetime.now().isoformat(),
+                'training_config': {
+                    'test_size': self.test_size,
+                    'cv_folds': self.cv_folds,
+                    'max_features': self.max_features,
+                    'ngram_range': self.ngram_range,
+                    'feature_selection_k': self.feature_selection_k,
+                    'use_enhanced_features': self.use_enhanced_features
+                }
             }
             
-            # Calculate statistics for each metric
-            for metric_name in scoring_metrics.keys():
-                test_key = f'test_{metric_name}'
-                train_key = f'train_{metric_name}'
-                
-                if test_key in cv_scores:
-                    test_scores = cv_scores[test_key]
-                    cv_results['test_scores'][metric_name] = {
-                        'mean': float(np.mean(test_scores)),
-                        'std': float(np.std(test_scores)),
-                        'min': float(np.min(test_scores)),
-                        'max': float(np.max(test_scores)),
-                        'scores': test_scores.tolist()
+            # Add enhanced feature metadata
+            if self.use_enhanced_features:
+                feature_metadata = metrics.get('feature_metadata', {})
+                if feature_metadata:
+                    metadata['enhanced_features'] = {
+                        'total_features': feature_metadata.get('total_features', 0),
+                        'feature_types': feature_metadata.get('feature_types', {}),
+                        'configuration': feature_metadata.get('configuration', {})
                     }
                 
-                if train_key in cv_scores:
-                    train_scores = cv_scores[train_key]
-                    cv_results['train_scores'][metric_name] = {
-                        'mean': float(np.mean(train_scores)),
-                        'std': float(np.std(train_scores)),
-                        'min': float(np.min(train_scores)),
-                        'max': float(np.max(train_scores)),
-                        'scores': train_scores.tolist()
-                    }
+                # Add top features if available
+                top_features = metrics.get('top_features', {})
+                if top_features:
+                    metadata['top_features'] = dict(list(top_features.items())[:10])  # Top 10 features
+                    
+                    # Save detailed feature importance
+                    try:
+                        feature_analysis = {
+                            'top_features': top_features,
+                            'feature_metadata': feature_metadata,
+                            'timestamp': datetime.now().isoformat(),
+                            'model_version': metadata['model_version']
+                        }
+                        
+                        with open(self.feature_importance_path, 'w') as f:
+                            json.dump(feature_analysis, f, indent=2)
+                        logger.info(f"✅ Saved feature importance analysis to {self.feature_importance_path}")
+                        
+                    except Exception as e:
+                        logger.warning(f"Could not save feature importance: {e}")
             
-            # Store individual fold results
-            for fold_idx in range(cv_strategy.n_splits):
-                fold_result = {
-                    'fold': fold_idx + 1,
-                    'test_scores': {},
-                    'train_scores': {}
+            # Add comprehensive CV results to metadata
+            if cv_results and 'test_scores' in cv_results:
+                metadata['cross_validation'] = {
+                    'n_splits': cv_results.get('n_splits', self.cv_folds),
+                    'test_scores': cv_results['test_scores'],
+                    'train_scores': cv_results.get('train_scores', {}),
+                    'overfitting_score': cv_results.get('overfitting_score', 'Unknown'),
+                    'stability_score': cv_results.get('stability_score', 'Unknown'),
+                    'individual_fold_results': cv_results.get('fold_results', [])
                 }
                 
-                for metric_name in scoring_metrics.keys():
-                    test_key = f'test_{metric_name}'
-                    train_key = f'train_{metric_name}'
-                    
-                    if test_key in cv_scores:
-                        fold_result['test_scores'][metric_name] = float(cv_scores[test_key][fold_idx])
-                    if train_key in cv_scores:
-                        fold_result['train_scores'][metric_name] = float(cv_scores[train_key][fold_idx])
+                # Add summary statistics
+                if 'f1' in cv_results['test_scores']:
+                    metadata['cv_f1_mean'] = cv_results['test_scores']['f1']['mean']
+                    metadata['cv_f1_std'] = cv_results['test_scores']['f1']['std']
+                    metadata['cv_f1_min'] = cv_results['test_scores']['f1']['min']
+                    metadata['cv_f1_max'] = cv_results['test_scores']['f1']['max']
                 
-                cv_results['fold_results'].append(fold_result)
+                if 'accuracy' in cv_results['test_scores']:
+                    metadata['cv_accuracy_mean'] = cv_results['test_scores']['accuracy']['mean']
+                    metadata['cv_accuracy_std'] = cv_results['test_scores']['accuracy']['std']
             
-            # Calculate overfitting indicators
-            if 'accuracy' in cv_results['test_scores'] and 'accuracy' in cv_results['train_scores']:
-                train_mean = cv_results['train_scores']['accuracy']['mean']
-                test_mean = cv_results['test_scores']['accuracy']['mean']
-                cv_results['overfitting_score'] = float(train_mean - test_mean)
+            # Add model comparison results if available
+            if len(results) > 1:
+                model_comparison = {}
+                for other_model_name, other_result in results.items():
+                    if other_model_name != model_name and 'error' not in other_result:
+                        other_cv = other_result['evaluation_metrics'].get('cross_validation', {})
+                        if cv_results and other_cv:
+                            comparison = self.cv_manager.compare_cv_results(cv_results, other_cv)
+                            model_comparison[other_model_name] = comparison
+                
+                if model_comparison:
+                    metadata['model_comparison'] = model_comparison
+
+            # Save metadata with error handling
+            try:
+                with open(self.metadata_path, 'w') as f:
+                    json.dump(metadata, f, indent=2)
+                logger.info(f"✅ Saved enhanced metadata to {self.metadata_path}")
+            except Exception as e:
+                logger.warning(f"Could not save metadata: {e}")
+
+            # Log feature engineering summary
+            if self.use_enhanced_features and feature_metadata:
+                logger.info(f"✅ Enhanced features summary:")
+                logger.info(f"   Total features: {feature_metadata.get('total_features', 0)}")
+                for feature_type, count in feature_metadata.get('feature_types', {}).items():
+                    logger.info(f"   {feature_type}: {count}")
+
+            logger.info(f"✅ Model artifacts saved successfully with {'enhanced' if self.use_enhanced_features else 'standard'} features")
+            return True
+
+        except Exception as e:
+            logger.error(f"Failed to save model artifacts: {str(e)}")
+            # Try to save at least the core pipeline
+            try:
+                joblib.dump(model, Path("/tmp/pipeline_backup.pkl"))
+                logger.info("✅ Saved backup pipeline")
+                return True
+            except Exception as e2:
+                logger.error(f"Failed to save backup pipeline: {str(e2)}")
+                return False
+
+    def train_model(self, data_path: str = None, force_enhanced: bool = None) -> Tuple[bool, str]:
+        """Main training function with enhanced feature engineering pipeline"""
+        try:
+            # Override enhanced features setting if specified
+            if force_enhanced is not None:
+                original_setting = self.use_enhanced_features
+                self.use_enhanced_features = force_enhanced and ENHANCED_FEATURES_AVAILABLE
+                if force_enhanced and not ENHANCED_FEATURES_AVAILABLE:
+                    logger.warning("Enhanced features requested but not available, using standard features")
             
-            # Calculate stability metrics
-            if 'accuracy' in cv_results['test_scores']:
-                test_std = cv_results['test_scores']['accuracy']['std']
-                test_mean = cv_results['test_scores']['accuracy']['mean']
-                cv_results['stability_score'] = float(1 - (test_std / test_mean)) if test_mean > 0 else 0
+            feature_type = "enhanced" if self.use_enhanced_features else "standard"
+            logger.info(f"Starting {feature_type} model training with cross-validation...")
+
+            # Override data path if provided
+            if data_path:
+                self.data_path = Path(data_path)
+
+            # Load and validate data
+            success, df, message = self.load_and_validate_data()
+            if not success:
+                return False, message
+
+            # Estimate training time and setup progress tracker
+            time_estimate = estimate_training_time(
+                len(df), 
+                enable_tuning=True, 
+                cv_folds=self.cv_folds,
+                use_enhanced_features=self.use_enhanced_features
+            )
             
-            logger.info(f"Cross-validation completed successfully")
-            logger.info(f"Mean test accuracy: {cv_results['test_scores'].get('accuracy', {}).get('mean', 'N/A'):.4f}")
-            logger.info(f"Mean test F1: {cv_results['test_scores'].get('f1', {}).get('mean', 'N/A'):.4f}")
+            print(f"\n📊 Enhanced Training Configuration:")
+            print(f"Dataset size: {len(df)} samples")
+            print(f"Feature engineering: {feature_type.title()}")
+            print(f"Cross-validation folds: {self.cv_folds}")
+            print(f"Estimated time: {time_estimate['total_formatted']}")
+            print(f"Models to train: {len(self.models)}")
+            print(f"Hyperparameter tuning: Enabled")
+            if self.use_enhanced_features:
+                print(f"Enhanced features: Sentiment, Readability, Entities, Linguistic")
+            print()
+
+            # Setup progress tracker (adjusted for enhanced features)
+            base_steps = 4 + (len(self.models) * 3) + 1  # Basic steps
+            enhanced_steps = 2 if self.use_enhanced_features else 0  # Feature engineering steps
+            total_steps = base_steps + enhanced_steps
+            self.progress_tracker = ProgressTracker(total_steps, f"{feature_type.title()} Training Progress")
+
+            # Prepare data
+            X = df['text'].values
+            y = df['label'].values
+
+            # Train-test split with smart handling for small datasets
+            self.progress_tracker.update("Splitting data")
             
-            return cv_results
+            # Ensure minimum test size for very small datasets
+            if len(X) < 10:
+                test_size = max(0.1, 1/len(X))  # At least 1 sample for test
+            else:
+                test_size = self.test_size
+                
+            # Check if stratification is possible
+            label_counts = pd.Series(y).value_counts()
+            min_class_count = label_counts.min()
+            can_stratify = min_class_count >= 2 and len(y) >= 4
             
-        except Exception as e:
-            logger.error(f"Cross-validation failed: {e}")
-            return {
-                'error': str(e),
-                'n_splits': cv_strategy.n_splits if cv_strategy else self.cv_folds,
-                'fallback': True
-            }
+            X_train, X_test, y_train, y_test = train_test_split(
+                X, y,
+                test_size=test_size,
+                stratify=y if can_stratify else None,
+                random_state=self.random_state
+            )
 
+            logger.info(f"Data split: {len(X_train)} train, {len(X_test)} test")
+            
+            # Additional validation for very small datasets
+            if len(X_train) < 3:
+                logger.warning(f"Very small training set: {len(X_train)} samples. CV results may be unreliable.")
+            if len(X_test) < 1:
+                return False, "Cannot create test set. Dataset too small."
 
-def preprocess_text_function(texts):
-    """
-    Standalone function for text preprocessing - pickle-safe
-    """
-    def clean_single_text(text):
-        # Convert to string
-        text = str(text)
-        
-        # Remove URLs
-        text = re.sub(r'http\S+|www\S+|https\S+', '', text)
-        
-        # Remove email addresses
-        text = re.sub(r'\S+@\S+', '', text)
-        
-        # Remove excessive punctuation
-        text = re.sub(r'[!]{2,}', '!', text)
-        text = re.sub(r'[?]{2,}', '?', text)
-        text = re.sub(r'[.]{3,}', '...', text)
-        
-        # Remove non-alphabetic characters except spaces and basic punctuation
-        text = re.sub(r'[^a-zA-Z\s.!?]', '', text)
-        
-        # Remove excessive whitespace
-        text = re.sub(r'\s+', ' ', text)
-        
-        return text.strip().lower()
-    
-    # Process all texts
-    processed = []
-    for text in texts:
-        processed.append(clean_single_text(text))
-    
-    return processed
+            # Train and evaluate models with enhanced features
+            results = self.train_and_evaluate_models(X_train, X_test, y_train, y_test)
+
+            # Select best model
+            best_model_name, best_model, best_metrics = self.select_best_model(results)
+
+            # Save model artifacts with enhanced feature information
+            if not self.save_model_artifacts(best_model, best_model_name, best_metrics, results):
+                return False, "Failed to save model artifacts"
+
+            # Finish progress tracking
+            self.progress_tracker.finish()
+
+            # Create success message with enhanced feature information
+            cv_results = best_metrics.get('cross_validation', {})
+            cv_info = ""
+            if 'test_scores' in cv_results and 'f1' in cv_results['test_scores']:
+                cv_f1_mean = cv_results['test_scores']['f1']['mean']
+                cv_f1_std = cv_results['test_scores']['f1']['std']
+                cv_info = f", CV F1: {cv_f1_mean:.4f} (±{cv_f1_std:.4f})"
+
+            # Enhanced features summary
+            feature_info = ""
+            if self.use_enhanced_features:
+                feature_metadata = best_metrics.get('feature_metadata', {})
+                if feature_metadata:
+                    total_features = feature_metadata.get('total_features', 0)
+                    feature_info = f", Enhanced Features: {total_features}"
+
+            success_message = (
+                f"{feature_type.title()} model training completed successfully. "
+                f"Best model: {best_model_name} "
+                f"(Test F1: {best_metrics['f1']:.4f}, Test Accuracy: {best_metrics['accuracy']:.4f}{cv_info}{feature_info})"
+            )
+
+            logger.info(success_message)
+            return True, success_message
+
+        except Exception as e:
+            if self.progress_tracker:
+                print()  # New line after progress bar
+            error_message = f"Enhanced model training failed: {str(e)}"
+            logger.error(error_message)
+            return False, error_message
 
 
 def main():
-    """Main execution function with enhanced ensemble support"""
+    """Main execution function with enhanced feature engineering support"""
     import argparse
     
     # Parse command line arguments
-    parser = argparse.ArgumentParser(description='Train fake news detection model with LightGBM ensemble')
+    parser = argparse.ArgumentParser(description='Train fake news detection model with enhanced features')
     parser.add_argument('--data_path', type=str, help='Path to training data CSV file')
     parser.add_argument('--config_path', type=str, help='Path to training configuration JSON file')
     parser.add_argument('--cv_folds', type=int, default=5, help='Number of cross-validation folds')
     parser.add_argument('--enhanced_features', action='store_true', help='Force use of enhanced features')
     parser.add_argument('--standard_features', action='store_true', help='Force use of standard TF-IDF features only')
-    parser.add_argument('--ensemble', action='store_true', help='Force use of LightGBM ensemble')
-    parser.add_argument('--no_ensemble', action='store_true', help='Disable ensemble training')
     args = parser.parse_args()
     
     # Determine feature engineering mode
@@ -1409,18 +1222,7 @@ def main():
         use_enhanced = False
         logger.info("Standard features explicitly requested")
     
-    # Determine ensemble mode
-    use_ensemble = None
-    if args.ensemble and args.no_ensemble:
-        logger.warning("Both --ensemble and --no_ensemble specified. Using auto-detection.")
-    elif args.ensemble:
-        use_ensemble = True
-        logger.info("LightGBM ensemble explicitly requested")
-    elif args.no_ensemble:
-        use_ensemble = False
-        logger.info("Ensemble training explicitly disabled")
-    
-    trainer = EnhancedModelTrainer(use_enhanced_features=use_enhanced, use_ensemble=use_ensemble)
+    trainer = EnhancedModelTrainer(use_enhanced_features=use_enhanced)
     
     # Apply CV folds from command line
     if args.cv_folds:
@@ -1444,14 +1246,6 @@ def main():
             if 'enhanced_features' in config and use_enhanced is None:
                 trainer.use_enhanced_features = config['enhanced_features'] and ENHANCED_FEATURES_AVAILABLE
             
-            # Ensemble configuration
-            if 'use_ensemble' in config and use_ensemble is None:
-                trainer.use_ensemble = config['use_ensemble'] and LIGHTGBM_AVAILABLE
-            
-            # LightGBM specific parameters
-            if 'lightgbm_params' in config:
-                trainer.lgb_params.update(config['lightgbm_params'])
-            
             # Filter models if specified
             selected_models = config.get('selected_models')
             if selected_models and len(selected_models) < len(trainer.models):
@@ -1464,19 +1258,10 @@ def main():
             logger.info(f"Applied custom configuration with {trainer.cv_folds} CV folds")
             if trainer.use_enhanced_features:
                 logger.info("Enhanced features enabled via configuration")
-            if trainer.use_ensemble:
-                logger.info("LightGBM ensemble enabled via configuration")
             
         except Exception as e:
             logger.warning(f"Failed to load configuration: {e}, using defaults")
     
-    # Log final configuration
-    logger.info("Final Training Configuration:")
-    logger.info(f"  Enhanced Features: {trainer.use_enhanced_features} (Available: {ENHANCED_FEATURES_AVAILABLE})")
-    logger.info(f"  LightGBM Ensemble: {trainer.use_ensemble} (Available: {LIGHTGBM_AVAILABLE})")
-    logger.info(f"  Models to train: {list(trainer.models.keys())}")
-    logger.info(f"  Cross-validation folds: {trainer.cv_folds}")
-    
     success, message = trainer.train_model(data_path=args.data_path)
 
     if success:
@@ -1492,23 +1277,6 @@ def main():
                     print(f"  {feature_type}: {count}")
             except Exception as e:
                 logger.warning(f"Could not display feature summary: {e}")
-        
-        # Print ensemble summary
-        if trainer.use_ensemble and LIGHTGBM_AVAILABLE:
-            try:
-                ensemble_metadata_path = Path("/tmp/ensemble_metadata.json")
-                if ensemble_metadata_path.exists():
-                    with open(ensemble_metadata_path, 'r') as f:
-                        ensemble_metadata = json.load(f)
-                    
-                    print(f"\n🎯 Ensemble Model Summary:")
-                    print(f"Ensemble type: {ensemble_metadata.get('ensemble_type', 'unknown')}")
-                    print(f"Component models: {', '.join(ensemble_metadata.get('component_models', []))}")
-                else:
-                    print(f"\n🎯 Individual Model Selected (Ensemble not used)")
-            except Exception as e:
-                logger.warning(f"Could not display ensemble summary: {e}")
-                
     else:
         print(f"❌ {message}")
         exit(1)