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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()
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