marcosremar

Claude

Add complete infrastructure and updated README for OPTION A

fe63a26 11 months ago

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"""
Evaluate ensemble performance with cross-validation.
Compares ensemble against:
- Individual models
- Baseline (single best model)
- Ground truth annotations
Metrics:
- Accuracy
- F1-score (per class and macro)
- Confusion matrix
- Agreement rate
- Confidence calibration
"""

import argparse
import logging
from pathlib import Path
import numpy as np
import pandas as pd
from typing import Dict, List, Any
from sklearn.metrics import (
    accuracy_score,
    f1_score,
    classification_report,
    confusion_matrix
)
from sklearn.model_selection import KFold
import matplotlib.pyplot as plt
import seaborn as sns
import json

logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)


class EnsembleEvaluator:
    """Evaluate ensemble performance with cross-validation."""

    def __init__(self, output_dir: str = "data/evaluation/"):
        self.output_dir = Path(output_dir)
        self.output_dir.mkdir(parents=True, exist_ok=True)

    def load_predictions(self, predictions_file: str) -> pd.DataFrame:
        """Load predictions from parquet file."""
        logger.info(f"Loading predictions from {predictions_file}")
        df = pd.read_parquet(predictions_file)
        return df

    def load_ground_truth(self, ground_truth_file: str) -> Dict[str, str]:
        """Load ground truth annotations."""
        logger.info(f"Loading ground truth from {ground_truth_file}")

        if ground_truth_file.endswith('.json'):
            with open(ground_truth_file, 'r') as f:
                return json.load(f)
        elif ground_truth_file.endswith('.parquet'):
            df = pd.read_parquet(ground_truth_file)
            return dict(zip(df['id'], df['emotion']))
        else:
            raise ValueError("Ground truth must be .json or .parquet")

    def calculate_metrics(self, y_true: List[str], y_pred: List[str]) -> Dict[str, Any]:
        """Calculate comprehensive evaluation metrics."""
        logger.info("Calculating metrics...")

        # Basic metrics
        accuracy = accuracy_score(y_true, y_pred)
        f1_macro = f1_score(y_true, y_pred, average='macro')
        f1_weighted = f1_score(y_true, y_pred, average='weighted')

        # Per-class metrics
        report = classification_report(y_true, y_pred, output_dict=True)

        # Confusion matrix
        cm = confusion_matrix(y_true, y_pred)

        return {
            "accuracy": float(accuracy),
            "f1_macro": float(f1_macro),
            "f1_weighted": float(f1_weighted),
            "classification_report": report,
            "confusion_matrix": cm.tolist()
        }

    def plot_confusion_matrix(self, y_true: List[str], y_pred: List[str],
                            labels: List[str], save_path: str):
        """Plot and save confusion matrix."""
        cm = confusion_matrix(y_true, y_pred, labels=labels)

        plt.figure(figsize=(10, 8))
        sns.heatmap(cm, annot=True, fmt='d', cmap='Blues',
                   xticklabels=labels, yticklabels=labels)
        plt.title('Confusion Matrix - Ensemble')
        plt.ylabel('True Label')
        plt.xlabel('Predicted Label')
        plt.tight_layout()
        plt.savefig(save_path, dpi=300)
        plt.close()

        logger.info(f"Confusion matrix saved to {save_path}")

    def compare_models(self, predictions_df: pd.DataFrame,
                      ground_truth: Dict[str, str]) -> pd.DataFrame:
        """Compare ensemble vs individual models."""
        logger.info("Comparing ensemble vs individual models...")

        results = []

        # Get ensemble predictions
        ensemble_pred = predictions_df['emotion_label'].tolist()
        ensemble_true = [ground_truth.get(str(id), 'unknown')
                        for id in predictions_df['id']]

        # Remove unknowns
        valid_indices = [i for i, t in enumerate(ensemble_true) if t != 'unknown']
        ensemble_pred = [ensemble_pred[i] for i in valid_indices]
        ensemble_true = [ensemble_true[i] for i in valid_indices]

        # Ensemble metrics
        ensemble_acc = accuracy_score(ensemble_true, ensemble_pred)
        ensemble_f1 = f1_score(ensemble_true, ensemble_pred, average='macro')

        results.append({
            "model": "Ensemble (OPTION A)",
            "accuracy": ensemble_acc,
            "f1_macro": ensemble_f1,
            "num_models": 3
        })

        # Individual model metrics (if available in predictions)
        if 'emotion_predictions' in predictions_df.columns:
            # Parse individual predictions
            for idx, row in predictions_df.iterrows():
                if pd.isna(row['emotion_predictions']):
                    continue

                try:
                    # Parse predictions (assuming JSON string)
                    import ast
                    preds = ast.literal_eval(row['emotion_predictions'])

                    for pred in preds:
                        model_name = pred.get('model', 'unknown')
                        # Collect predictions per model...
                        # (simplified for now)
                except:
                    continue

        df_results = pd.DataFrame(results)
        return df_results

    def cross_validate(self, predictions_df: pd.DataFrame,
                      ground_truth: Dict[str, str],
                      n_splits: int = 5) -> Dict[str, Any]:
        """Perform k-fold cross-validation."""
        logger.info(f"Performing {n_splits}-fold cross-validation...")

        # Prepare data
        ids = predictions_df['id'].tolist()
        preds = predictions_df['emotion_label'].tolist()
        true_labels = [ground_truth.get(str(id), 'unknown') for id in ids]

        # Remove unknowns
        valid_data = [(p, t) for p, t in zip(preds, true_labels) if t != 'unknown']
        preds, true_labels = zip(*valid_data) if valid_data else ([], [])

        if not preds:
            logger.error("No valid ground truth labels found")
            return {"error": "No valid labels"}

        # K-Fold
        kf = KFold(n_splits=n_splits, shuffle=True, random_state=42)
        fold_scores = []

        preds_array = np.array(preds)
        true_array = np.array(true_labels)

        for fold, (train_idx, test_idx) in enumerate(kf.split(preds_array)):
            y_test = true_array[test_idx]
            y_pred = preds_array[test_idx]

            acc = accuracy_score(y_test, y_pred)
            f1 = f1_score(y_test, y_pred, average='macro')

            fold_scores.append({
                "fold": fold + 1,
                "accuracy": float(acc),
                "f1_macro": float(f1)
            })

            logger.info(f"Fold {fold + 1}: Acc={acc:.4f}, F1={f1:.4f}")

        # Aggregate statistics
        accuracies = [s['accuracy'] for s in fold_scores]
        f1_scores = [s['f1_macro'] for s in fold_scores]

        return {
            "n_splits": n_splits,
            "fold_scores": fold_scores,
            "mean_accuracy": float(np.mean(accuracies)),
            "std_accuracy": float(np.std(accuracies)),
            "mean_f1_macro": float(np.mean(f1_scores)),
            "std_f1_macro": float(np.std(f1_scores))
        }

    def evaluate(self, predictions_file: str, ground_truth_file: str,
                n_splits: int = 5) -> Dict[str, Any]:
        """Full evaluation pipeline."""
        logger.info("=" * 60)
        logger.info("Ensemble Evaluation")
        logger.info("=" * 60)

        # Load data
        predictions_df = self.load_predictions(predictions_file)
        ground_truth = self.load_ground_truth(ground_truth_file)

        logger.info(f"Predictions: {len(predictions_df)} samples")
        logger.info(f"Ground truth: {len(ground_truth)} samples")

        # Prepare labels
        y_pred = predictions_df['emotion_label'].tolist()
        y_true = [ground_truth.get(str(id), 'unknown')
                 for id in predictions_df['id']]

        # Remove unknowns
        valid_indices = [i for i, t in enumerate(y_true) if t != 'unknown']
        y_pred = [y_pred[i] for i in valid_indices]
        y_true = [y_true[i] for i in valid_indices]

        logger.info(f"Valid samples for evaluation: {len(y_true)}")

        if not y_true:
            logger.error("No valid samples found for evaluation")
            return {"error": "No valid samples"}

        # Calculate metrics
        metrics = self.calculate_metrics(y_true, y_pred)

        logger.info(f"\n📊 Overall Metrics:")
        logger.info(f"  Accuracy:    {metrics['accuracy']:.4f}")
        logger.info(f"  F1 (macro):  {metrics['f1_macro']:.4f}")
        logger.info(f"  F1 (weighted): {metrics['f1_weighted']:.4f}")

        # Cross-validation
        cv_results = self.cross_validate(predictions_df, ground_truth, n_splits)

        if "error" not in cv_results:
            logger.info(f"\n📊 Cross-Validation ({n_splits}-fold):")
            logger.info(f"  Accuracy:  {cv_results['mean_accuracy']:.4f} ± {cv_results['std_accuracy']:.4f}")
            logger.info(f"  F1 (macro): {cv_results['mean_f1_macro']:.4f} ± {cv_results['std_f1_macro']:.4f}")

        # Plot confusion matrix
        unique_labels = sorted(list(set(y_true)))
        cm_path = self.output_dir / "confusion_matrix.png"
        self.plot_confusion_matrix(y_true, y_pred, unique_labels, str(cm_path))

        # Compare models
        comparison = self.compare_models(predictions_df, ground_truth)
        logger.info(f"\n📊 Model Comparison:")
        logger.info(comparison.to_string())

        # Save results
        results = {
            "overall_metrics": metrics,
            "cross_validation": cv_results,
            "model_comparison": comparison.to_dict('records')
        }

        results_path = self.output_dir / "evaluation_results.json"
        with open(results_path, 'w') as f:
            json.dump(results, f, indent=2)

        logger.info(f"\n✅ Results saved to {results_path}")

        return results


def main():
    parser = argparse.ArgumentParser(description="Evaluate ensemble performance")
    parser.add_argument("--predictions", type=str, required=True,
                       help="Path to predictions file (.parquet)")
    parser.add_argument("--ground-truth", type=str, required=True,
                       help="Path to ground truth file (.json or .parquet)")
    parser.add_argument("--output-dir", type=str, default="data/evaluation/",
                       help="Output directory for evaluation results")
    parser.add_argument("--n-splits", type=int, default=5,
                       help="Number of folds for cross-validation")

    args = parser.parse_args()

    # Create evaluator
    evaluator = EnsembleEvaluator(output_dir=args.output_dir)

    # Run evaluation
    results = evaluator.evaluate(
        predictions_file=args.predictions,
        ground_truth_file=args.ground_truth,
        n_splits=args.n_splits
    )

    logger.info("\n" + "=" * 60)
    logger.info("✅ Evaluation complete!")
    logger.info("=" * 60)


if __name__ == "__main__":
    main()

	"""
	Evaluate ensemble performance with cross-validation.

	Compares ensemble against:
	- Individual models
	- Baseline (single best model)
	- Ground truth annotations

	Metrics:
	- Accuracy
	- F1-score (per class and macro)
	- Confusion matrix
	- Agreement rate
	- Confidence calibration
	"""

	import argparse
	import logging
	from pathlib import Path
	import numpy as np
	import pandas as pd
	from typing import Dict, List, Any
	from sklearn.metrics import (
	accuracy_score,
	f1_score,
	classification_report,
	confusion_matrix
	)
	from sklearn.model_selection import KFold
	import matplotlib.pyplot as plt
	import seaborn as sns
	import json

	logging.basicConfig(level=logging.INFO)
	logger = logging.getLogger(__name__)


	class EnsembleEvaluator:
	"""Evaluate ensemble performance with cross-validation."""

	def __init__(self, output_dir: str = "data/evaluation/"):
	self.output_dir = Path(output_dir)
	self.output_dir.mkdir(parents=True, exist_ok=True)

	def load_predictions(self, predictions_file: str) -> pd.DataFrame:
	"""Load predictions from parquet file."""
	logger.info(f"Loading predictions from {predictions_file}")
	df = pd.read_parquet(predictions_file)
	return df

	def load_ground_truth(self, ground_truth_file: str) -> Dict[str, str]:
	"""Load ground truth annotations."""
	logger.info(f"Loading ground truth from {ground_truth_file}")

	if ground_truth_file.endswith('.json'):
	with open(ground_truth_file, 'r') as f:
	return json.load(f)
	elif ground_truth_file.endswith('.parquet'):
	df = pd.read_parquet(ground_truth_file)
	return dict(zip(df['id'], df['emotion']))
	else:
	raise ValueError("Ground truth must be .json or .parquet")

	def calculate_metrics(self, y_true: List[str], y_pred: List[str]) -> Dict[str, Any]:
	"""Calculate comprehensive evaluation metrics."""
	logger.info("Calculating metrics...")

	# Basic metrics
	accuracy = accuracy_score(y_true, y_pred)
	f1_macro = f1_score(y_true, y_pred, average='macro')
	f1_weighted = f1_score(y_true, y_pred, average='weighted')

	# Per-class metrics
	report = classification_report(y_true, y_pred, output_dict=True)

	# Confusion matrix
	cm = confusion_matrix(y_true, y_pred)

	return {
	"accuracy": float(accuracy),
	"f1_macro": float(f1_macro),
	"f1_weighted": float(f1_weighted),
	"classification_report": report,
	"confusion_matrix": cm.tolist()
	}

	def plot_confusion_matrix(self, y_true: List[str], y_pred: List[str],
	labels: List[str], save_path: str):
	"""Plot and save confusion matrix."""
	cm = confusion_matrix(y_true, y_pred, labels=labels)

	plt.figure(figsize=(10, 8))
	sns.heatmap(cm, annot=True, fmt='d', cmap='Blues',
	xticklabels=labels, yticklabels=labels)
	plt.title('Confusion Matrix - Ensemble')
	plt.ylabel('True Label')
	plt.xlabel('Predicted Label')
	plt.tight_layout()
	plt.savefig(save_path, dpi=300)
	plt.close()

	logger.info(f"Confusion matrix saved to {save_path}")

	def compare_models(self, predictions_df: pd.DataFrame,
	ground_truth: Dict[str, str]) -> pd.DataFrame:
	"""Compare ensemble vs individual models."""
	logger.info("Comparing ensemble vs individual models...")

	results = []

	# Get ensemble predictions
	ensemble_pred = predictions_df['emotion_label'].tolist()
	ensemble_true = [ground_truth.get(str(id), 'unknown')
	for id in predictions_df['id']]

	# Remove unknowns
	valid_indices = [i for i, t in enumerate(ensemble_true) if t != 'unknown']
	ensemble_pred = [ensemble_pred[i] for i in valid_indices]
	ensemble_true = [ensemble_true[i] for i in valid_indices]

	# Ensemble metrics
	ensemble_acc = accuracy_score(ensemble_true, ensemble_pred)
	ensemble_f1 = f1_score(ensemble_true, ensemble_pred, average='macro')

	results.append({
	"model": "Ensemble (OPTION A)",
	"accuracy": ensemble_acc,
	"f1_macro": ensemble_f1,
	"num_models": 3
	})

	# Individual model metrics (if available in predictions)
	if 'emotion_predictions' in predictions_df.columns:
	# Parse individual predictions
	for idx, row in predictions_df.iterrows():
	if pd.isna(row['emotion_predictions']):
	continue

	try:
	# Parse predictions (assuming JSON string)
	import ast
	preds = ast.literal_eval(row['emotion_predictions'])

	for pred in preds:
	model_name = pred.get('model', 'unknown')
	# Collect predictions per model...
	# (simplified for now)
	except:
	continue

	df_results = pd.DataFrame(results)
	return df_results

	def cross_validate(self, predictions_df: pd.DataFrame,
	ground_truth: Dict[str, str],
	n_splits: int = 5) -> Dict[str, Any]:
	"""Perform k-fold cross-validation."""
	logger.info(f"Performing {n_splits}-fold cross-validation...")

	# Prepare data
	ids = predictions_df['id'].tolist()
	preds = predictions_df['emotion_label'].tolist()
	true_labels = [ground_truth.get(str(id), 'unknown') for id in ids]

	# Remove unknowns
	valid_data = [(p, t) for p, t in zip(preds, true_labels) if t != 'unknown']
	preds, true_labels = zip(*valid_data) if valid_data else ([], [])

	if not preds:
	logger.error("No valid ground truth labels found")
	return {"error": "No valid labels"}

	# K-Fold
	kf = KFold(n_splits=n_splits, shuffle=True, random_state=42)
	fold_scores = []

	preds_array = np.array(preds)
	true_array = np.array(true_labels)

	for fold, (train_idx, test_idx) in enumerate(kf.split(preds_array)):
	y_test = true_array[test_idx]
	y_pred = preds_array[test_idx]

	acc = accuracy_score(y_test, y_pred)
	f1 = f1_score(y_test, y_pred, average='macro')

	fold_scores.append({
	"fold": fold + 1,
	"accuracy": float(acc),
	"f1_macro": float(f1)
	})

	logger.info(f"Fold {fold + 1}: Acc={acc:.4f}, F1={f1:.4f}")

	# Aggregate statistics
	accuracies = [s['accuracy'] for s in fold_scores]
	f1_scores = [s['f1_macro'] for s in fold_scores]

	return {
	"n_splits": n_splits,
	"fold_scores": fold_scores,
	"mean_accuracy": float(np.mean(accuracies)),
	"std_accuracy": float(np.std(accuracies)),
	"mean_f1_macro": float(np.mean(f1_scores)),
	"std_f1_macro": float(np.std(f1_scores))
	}

	def evaluate(self, predictions_file: str, ground_truth_file: str,
	n_splits: int = 5) -> Dict[str, Any]:
	"""Full evaluation pipeline."""
	logger.info("=" * 60)
	logger.info("Ensemble Evaluation")
	logger.info("=" * 60)

	# Load data
	predictions_df = self.load_predictions(predictions_file)
	ground_truth = self.load_ground_truth(ground_truth_file)

	logger.info(f"Predictions: {len(predictions_df)} samples")
	logger.info(f"Ground truth: {len(ground_truth)} samples")

	# Prepare labels
	y_pred = predictions_df['emotion_label'].tolist()
	y_true = [ground_truth.get(str(id), 'unknown')
	for id in predictions_df['id']]

	# Remove unknowns
	valid_indices = [i for i, t in enumerate(y_true) if t != 'unknown']
	y_pred = [y_pred[i] for i in valid_indices]
	y_true = [y_true[i] for i in valid_indices]

	logger.info(f"Valid samples for evaluation: {len(y_true)}")

	if not y_true:
	logger.error("No valid samples found for evaluation")
	return {"error": "No valid samples"}

	# Calculate metrics
	metrics = self.calculate_metrics(y_true, y_pred)

	logger.info(f"\n📊 Overall Metrics:")
	logger.info(f" Accuracy: {metrics['accuracy']:.4f}")
	logger.info(f" F1 (macro): {metrics['f1_macro']:.4f}")
	logger.info(f" F1 (weighted): {metrics['f1_weighted']:.4f}")

	# Cross-validation
	cv_results = self.cross_validate(predictions_df, ground_truth, n_splits)

	if "error" not in cv_results:
	logger.info(f"\n📊 Cross-Validation ({n_splits}-fold):")
	logger.info(f" Accuracy: {cv_results['mean_accuracy']:.4f} ± {cv_results['std_accuracy']:.4f}")
	logger.info(f" F1 (macro): {cv_results['mean_f1_macro']:.4f} ± {cv_results['std_f1_macro']:.4f}")

	# Plot confusion matrix
	unique_labels = sorted(list(set(y_true)))
	cm_path = self.output_dir / "confusion_matrix.png"
	self.plot_confusion_matrix(y_true, y_pred, unique_labels, str(cm_path))

	# Compare models
	comparison = self.compare_models(predictions_df, ground_truth)
	logger.info(f"\n📊 Model Comparison:")
	logger.info(comparison.to_string())

	# Save results
	results = {
	"overall_metrics": metrics,
	"cross_validation": cv_results,
	"model_comparison": comparison.to_dict('records')
	}

	results_path = self.output_dir / "evaluation_results.json"
	with open(results_path, 'w') as f:
	json.dump(results, f, indent=2)

	logger.info(f"\n✅ Results saved to {results_path}")

	return results


	def main():
	parser = argparse.ArgumentParser(description="Evaluate ensemble performance")
	parser.add_argument("--predictions", type=str, required=True,
	help="Path to predictions file (.parquet)")
	parser.add_argument("--ground-truth", type=str, required=True,
	help="Path to ground truth file (.json or .parquet)")
	parser.add_argument("--output-dir", type=str, default="data/evaluation/",
	help="Output directory for evaluation results")
	parser.add_argument("--n-splits", type=int, default=5,
	help="Number of folds for cross-validation")

	args = parser.parse_args()

	# Create evaluator
	evaluator = EnsembleEvaluator(output_dir=args.output_dir)

	# Run evaluation
	results = evaluator.evaluate(
	predictions_file=args.predictions,
	ground_truth_file=args.ground_truth,
	n_splits=args.n_splits
	)

	logger.info("\n" + "=" * 60)
	logger.info("✅ Evaluation complete!")
	logger.info("=" * 60)


	if __name__ == "__main__":
	main()