#!/usr/bin/env python3
"""
DiffDock Results Analysis Script
This script analyzes DiffDock prediction results, extracting confidence scores,
ranking predictions, and generating summary reports.
Usage:
python analyze_results.py results/output_dir/
python analyze_results.py results/ --top 50 --threshold 0.0
python analyze_results.py results/ --export summary.csv
"""
import argparse
import os
import sys
import json
from pathlib import Path
from collections import defaultdict
import re
def parse_confidence_scores(results_dir):
"""
Parse confidence scores from DiffDock output directory.
Args:
results_dir: Path to DiffDock results directory
Returns:
dict: Dictionary mapping complex names to their predictions and scores
"""
results = {}
results_path = Path(results_dir)
# Check if this is a single complex or batch results
sdf_files = list(results_path.glob("*.sdf"))
if sdf_files:
# Single complex output
results['single_complex'] = parse_single_complex(results_path)
else:
# Batch output - multiple subdirectories
for subdir in results_path.iterdir():
if subdir.is_dir():
complex_results = parse_single_complex(subdir)
if complex_results:
results[subdir.name] = complex_results
return results
def parse_single_complex(complex_dir):
"""Parse results for a single complex."""
predictions = []
# Look for SDF files with rank information
for sdf_file in complex_dir.glob("*.sdf"):
filename = sdf_file.name
# Extract rank from filename (e.g., "rank_1.sdf" or "index_0_rank_1.sdf")
rank_match = re.search(r'rank_(\d+)', filename)
if rank_match:
rank = int(rank_match.group(1))
# Try to extract confidence score from filename or separate file
confidence = extract_confidence_score(sdf_file, complex_dir)
predictions.append({
'rank': rank,
'file': sdf_file.name,
'path': str(sdf_file),
'confidence': confidence
})
# Sort by rank
predictions.sort(key=lambda x: x['rank'])
return {'predictions': predictions} if predictions else None
def extract_confidence_score(sdf_file, complex_dir):
"""
Extract confidence score for a prediction.
Tries multiple methods:
1. Read from confidence_scores.txt file
2. Parse from SDF file properties
3. Extract from filename if present
"""
# Method 1: confidence_scores.txt
confidence_file = complex_dir / "confidence_scores.txt"
if confidence_file.exists():
try:
with open(confidence_file) as f:
lines = f.readlines()
# Extract rank from filename
rank_match = re.search(r'rank_(\d+)', sdf_file.name)
if rank_match:
rank = int(rank_match.group(1))
if rank <= len(lines):
return float(lines[rank - 1].strip())
except Exception:
pass
# Method 2: Parse from SDF file
try:
with open(sdf_file) as f:
content = f.read()
# Look for confidence score in SDF properties
conf_match = re.search(r'confidence[:\s]+(-?\d+\.?\d*)', content, re.IGNORECASE)
if conf_match:
return float(conf_match.group(1))
except Exception:
pass
# Method 3: Filename (e.g., "rank_1_conf_0.95.sdf")
conf_match = re.search(r'conf_(-?\d+\.?\d*)', sdf_file.name)
if conf_match:
return float(conf_match.group(1))
return None
def classify_confidence(score):
"""Classify confidence score into categories."""
if score is None:
return "Unknown"
elif score > 0:
return "High"
elif score > -1.5:
return "Moderate"
else:
return "Low"
def print_summary(results, top_n=None, min_confidence=None):
"""Print a formatted summary of results."""
print("\n" + "="*80)
print("DiffDock Results Summary")
print("="*80)
all_predictions = []
for complex_name, data in results.items():
predictions = data.get('predictions', [])
print(f"\n{complex_name}")
print("-" * 80)
if not predictions:
print(" No predictions found")
continue
# Filter by confidence if specified
filtered_predictions = predictions
if min_confidence is not None:
filtered_predictions = [p for p in predictions if p['confidence'] is not None and p['confidence'] >= min_confidence]
# Limit to top N if specified
if top_n is not None:
filtered_predictions = filtered_predictions[:top_n]
for pred in filtered_predictions:
confidence = pred['confidence']
confidence_class = classify_confidence(confidence)
conf_str = f"{confidence:>7.3f}" if confidence is not None else " N/A"
print(f" Rank {pred['rank']:2d}: Confidence = {conf_str} ({confidence_class:8s}) | {pred['file']}")
# Add to all predictions for overall statistics
if confidence is not None:
all_predictions.append((complex_name, pred['rank'], confidence))
# Show statistics for this complex
if filtered_predictions and any(p['confidence'] is not None for p in filtered_predictions):
confidences = [p['confidence'] for p in filtered_predictions if p['confidence'] is not None]
print(f"\n Statistics: {len(filtered_predictions)} predictions")
print(f" Mean confidence: {sum(confidences)/len(confidences):.3f}")
print(f" Max confidence: {max(confidences):.3f}")
print(f" Min confidence: {min(confidences):.3f}")
# Overall statistics
if all_predictions:
print("\n" + "="*80)
print("Overall Statistics")
print("="*80)
confidences = [conf for _, _, conf in all_predictions]
print(f" Total predictions: {len(all_predictions)}")
print(f" Total complexes: {len(results)}")
print(f" Mean confidence: {sum(confidences)/len(confidences):.3f}")
print(f" Max confidence: {max(confidences):.3f}")
print(f" Min confidence: {min(confidences):.3f}")
# Confidence distribution
high = sum(1 for c in confidences if c > 0)
moderate = sum(1 for c in confidences if -1.5 < c <= 0)
low = sum(1 for c in confidences if c <= -1.5)
print(f"\n Confidence distribution:")
print(f" High (> 0): {high:4d} ({100*high/len(confidences):5.1f}%)")
print(f" Moderate (-1.5 to 0): {moderate:4d} ({100*moderate/len(confidences):5.1f}%)")
print(f" Low (< -1.5): {low:4d} ({100*low/len(confidences):5.1f}%)")
print("\n" + "="*80)
def export_to_csv(results, output_path):
"""Export results to CSV file."""
import csv
with open(output_path, 'w', newline='') as f:
writer = csv.writer(f)
writer.writerow(['complex_name', 'rank', 'confidence', 'confidence_class', 'file_path'])
for complex_name, data in results.items():
predictions = data.get('predictions', [])
for pred in predictions:
confidence = pred['confidence']
confidence_class = classify_confidence(confidence)
conf_value = confidence if confidence is not None else ''
writer.writerow([
complex_name,
pred['rank'],
conf_value,
confidence_class,
pred['path']
])
print(f"✓ Exported results to: {output_path}")
def get_top_predictions(results, n=10, sort_by='confidence'):
"""Get top N predictions across all complexes."""
all_predictions = []
for complex_name, data in results.items():
predictions = data.get('predictions', [])
for pred in predictions:
if pred['confidence'] is not None:
all_predictions.append({
'complex': complex_name,
**pred
})
# Sort by confidence (descending)
all_predictions.sort(key=lambda x: x['confidence'], reverse=True)
return all_predictions[:n]
def print_top_predictions(results, n=10):
"""Print top N predictions across all complexes."""
top_preds = get_top_predictions(results, n)
print("\n" + "="*80)
print(f"Top {n} Predictions Across All Complexes")
print("="*80)
for i, pred in enumerate(top_preds, 1):
confidence_class = classify_confidence(pred['confidence'])
print(f"{i:2d}. {pred['complex']:30s} | Rank {pred['rank']:2d} | "
f"Confidence: {pred['confidence']:7.3f} ({confidence_class})")
print("="*80)
def main():
parser = argparse.ArgumentParser(
description='Analyze DiffDock prediction results',
formatter_class=argparse.RawDescriptionHelpFormatter,
epilog="""
Examples:
# Analyze all results in directory
python analyze_results.py results/output_dir/
# Show only top 5 predictions per complex
python analyze_results.py results/ --top 5
# Filter by confidence threshold
python analyze_results.py results/ --threshold 0.0
# Export to CSV
python analyze_results.py results/ --export summary.csv
# Show top 20 predictions across all complexes
python analyze_results.py results/ --best 20
"""
)
parser.add_argument('results_dir', help='Path to DiffDock results directory')
parser.add_argument('--top', '-t', type=int,
help='Show only top N predictions per complex')
parser.add_argument('--threshold', type=float,
help='Minimum confidence threshold')
parser.add_argument('--export', '-e', metavar='FILE',
help='Export results to CSV file')
parser.add_argument('--best', '-b', type=int, metavar='N',
help='Show top N predictions across all complexes')
args = parser.parse_args()
# Validate results directory
if not os.path.exists(args.results_dir):
print(f"Error: Results directory not found: {args.results_dir}")
return 1
# Parse results
print(f"Analyzing results in: {args.results_dir}")
results = parse_confidence_scores(args.results_dir)
if not results:
print("No DiffDock results found in directory")
return 1
# Print summary
print_summary(results, top_n=args.top, min_confidence=args.threshold)
# Print top predictions across all complexes
if args.best:
print_top_predictions(results, args.best)
# Export to CSV if requested
if args.export:
export_to_csv(results, args.export)
return 0
if __name__ == '__main__':
sys.exit(main())