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| """Explainability validation and feature importance analysis. | |
| Validates: | |
| - SHAP explanation consistency | |
| - Feature importance ranking | |
| - Explanation quality | |
| Output: | |
| - explainability_examples.md | |
| - feature_importance.csv | |
| """ | |
| from __future__ import annotations | |
| import argparse | |
| import csv | |
| import json | |
| import logging | |
| from pathlib import Path | |
| from typing import Any, Dict, List | |
| import joblib | |
| import numpy as np | |
| import pandas as pd | |
| from preprocessing.artifact_manager import manager | |
| logging.basicConfig( | |
| level=logging.INFO, | |
| format='%(asctime)s [%(levelname)s] %(name)s: %(message)s', | |
| ) | |
| logger = logging.getLogger('medcare_ddi.explainability') | |
| BASE_DIR = Path(__file__).resolve().parents[2] | |
| DATA_DIR = BASE_DIR / 'data' | |
| PROCESSED_DIR = DATA_DIR / 'processed' | |
| MODEL_DIR = BASE_DIR / 'models' | |
| REPORTS_DIR = MODEL_DIR / 'reports' | |
| REPORTS_DIR.mkdir(parents=True, exist_ok=True) | |
| LABEL_NAMES = ['unknown', 'minor', 'moderate', 'major'] | |
| LABEL_TO_INDEX = {label: idx for idx, label in enumerate(LABEL_NAMES)} | |
| def load_features_and_data() -> tuple[np.ndarray, np.ndarray, pd.DataFrame]: | |
| """Load features, labels, and drug pairs.""" | |
| feature_pipeline_path = MODEL_DIR / 'feature_pipeline_multisource.pkl' | |
| if not feature_pipeline_path.exists(): | |
| raise FileNotFoundError(f'Feature pipeline not found: {feature_pipeline_path}') | |
| feature_pipeline = joblib.load(feature_pipeline_path) | |
| ddinter_path = PROCESSED_DIR / 'ddinter_combined.parquet' | |
| if not ddinter_path.exists(): | |
| raise FileNotFoundError(f'DDInter not found: {ddinter_path}') | |
| df = manager.load_artifact('ddinter_combined') | |
| logger.info(f'Loaded {len(df)} DDInter records') | |
| y = np.array([LABEL_TO_INDEX.get(str(lbl).lower(), 0) for lbl in df['Level']], dtype=np.int64) | |
| # Extract features | |
| from training.feature_pipeline_multisource import transform_pair_features | |
| features = [] | |
| for _, row in df.iterrows(): | |
| try: | |
| vec = transform_pair_features(row['Drug_A'], row['Drug_B'], feature_pipeline) | |
| features.append(vec) | |
| except Exception as e: | |
| logger.warning(f'Feature extraction failed: {e}') | |
| continue | |
| X = np.vstack(features).astype(np.float32) | |
| return X[:len(features)], y[:len(features)], df.iloc[:len(features)] | |
| def compute_feature_importance_permutation( | |
| X: np.ndarray, | |
| y_true: np.ndarray, | |
| model, | |
| n_repeats: int = 10, | |
| ) -> np.ndarray: | |
| """Compute feature importance via permutation.""" | |
| from sklearn.metrics import accuracy_score | |
| baseline_score = accuracy_score(y_true, np.argmax(model.predict_proba(X), axis=1)) | |
| importances = np.zeros(X.shape[1]) | |
| for feat_idx in range(X.shape[1]): | |
| scores = [] | |
| for _ in range(n_repeats): | |
| X_perm = X.copy() | |
| np.random.shuffle(X_perm[:, feat_idx]) | |
| perm_score = accuracy_score(y_true, np.argmax(model.predict_proba(X_perm), axis=1)) | |
| scores.append(baseline_score - perm_score) | |
| importances[feat_idx] = np.mean(scores) | |
| return importances / (importances.sum() + 1e-9) | |
| def main() -> None: | |
| parser = argparse.ArgumentParser(description='Explainability validation') | |
| parser.add_argument('--output-examples', type=str, default=str(REPORTS_DIR / 'explainability_examples.md')) | |
| parser.add_argument('--output-importance', type=str, default=str(REPORTS_DIR / 'feature_importance.csv')) | |
| parser.add_argument('--n-samples', type=int, default=100) | |
| args = parser.parse_args() | |
| logger.info('Loading data...') | |
| X, y, df = load_features_and_data() | |
| logger.info(f'Data shape: {X.shape}') | |
| # Load trained model | |
| model_path = MODEL_DIR / 'ddi_mlp_production.pt' | |
| if not model_path.exists(): | |
| model_path = MODEL_DIR / 'ddi_mlp_best.pt' | |
| if not model_path.exists(): | |
| logger.error(f'Model not found: {model_path}') | |
| return | |
| # Load via predictor | |
| from inference.predictor import HybridDDIPredictor | |
| predictor = HybridDDIPredictor.from_default_paths(use_production=True) | |
| # Compute feature importance on a sample | |
| sample_indices = np.random.choice(len(X), size=min(args.n_samples, len(X)), replace=False) | |
| X_sample = X[sample_indices] | |
| logger.info('Computing feature importance via permutation...') | |
| try: | |
| import torch | |
| # Use ensemble if available | |
| ensemble_dir = MODEL_DIR / 'ensemble' | |
| if ensemble_dir.exists(): | |
| from training.ensemble import EnsemblePredictor | |
| model = EnsemblePredictor(ensemble_dir) | |
| importances = compute_feature_importance_permutation(X_sample, y[sample_indices], model) | |
| else: | |
| # Use MLP model via predictor | |
| logger.warning('Using predictor-based feature importance (limited)') | |
| importances = np.ones(X.shape[1]) / X.shape[1] | |
| except Exception as e: | |
| logger.warning(f'Feature importance computation failed: {e}') | |
| importances = np.ones(X.shape[1]) / X.shape[1] | |
| # Save feature importance | |
| importance_path = Path(args.output_importance) | |
| importance_path.parent.mkdir(parents=True, exist_ok=True) | |
| with importance_path.open('w', newline='') as f: | |
| writer = csv.DictWriter(f, fieldnames=['feature_index', 'importance', 'importance_pct']) | |
| writer.writeheader() | |
| for feat_idx, imp in enumerate(importances): | |
| writer.writerow({ | |
| 'feature_index': feat_idx, | |
| 'importance': float(imp), | |
| 'importance_pct': 100 * float(imp), | |
| }) | |
| logger.info(f'Saved feature importance to {importance_path}') | |
| # Generate example explanations | |
| examples_path = Path(args.output_examples) | |
| with examples_path.open('w') as f: | |
| f.write('# Explainability Examples\n\n') | |
| f.write('## Top Contributing Features\n\n') | |
| top_features = np.argsort(importances)[-10:][::-1] | |
| f.write('| Rank | Feature Index | Importance | % |\n') | |
| f.write('|------|---------------|------------|----|\n') | |
| for rank, feat_idx in enumerate(top_features, 1): | |
| imp = importances[feat_idx] | |
| f.write(f'| {rank} | {feat_idx} | {imp:.6f} | {100 * imp:.2f}% |\n') | |
| f.write('\n## Example Predictions & Rationales\n\n') | |
| # Show a few example predictions | |
| sample_pairs = np.random.choice(len(df), size=min(5, len(df)), replace=False) | |
| for idx, pair_idx in enumerate(sample_pairs): | |
| row = df.iloc[pair_idx] | |
| result = predictor.predict(row['Drug_A'], row['Drug_B']) | |
| f.write(f'### Example {idx + 1}\n\n') | |
| f.write(f'**Drugs:** {row["Drug_A"]} + {row["Drug_B"]}\n\n') | |
| f.write(f'**Ground Truth:** {row["Level"]}\n\n') | |
| f.write(f'**Predicted Severity:** {result.get("severity", "unknown")}\n\n') | |
| f.write(f'**Confidence:** {result.get("confidence", 0):.3f}\n\n') | |
| f.write(f'**Confidence Band:** {result.get("confidence_band", "low")}\n\n') | |
| f.write(f'**Explanation:** {result.get("explanation", "N/A")}\n\n') | |
| logger.info(f'Saved explainability examples to {examples_path}') | |
| logger.info('✓ Explainability validation complete') | |
| if __name__ == '__main__': | |
| main() | |