File size: 16,213 Bytes
9e3d618 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 |
#!/usr/bin/env python3
"""
Compare performance metrics across different models.
Reads tactic_counts_summary.json and generates a comparison report
showing detection rates, coverage, accuracy, and effectiveness for each model.
Usage:
python compare_models.py [--input INPUT_PATH] [--output OUTPUT_PATH]
"""
import argparse
import json
from pathlib import Path
from typing import Dict, List, Any
from datetime import datetime
import statistics
class ModelComparator:
"""Compares performance metrics across different models"""
def __init__(self, tactic_counts_file: Path):
self.tactic_counts_file = tactic_counts_file
self.tactic_data = []
self.load_tactic_counts()
def load_tactic_counts(self):
"""Load tactic counts summary data"""
if not self.tactic_counts_file.exists():
raise FileNotFoundError(
f"Tactic counts file not found: {self.tactic_counts_file}"
)
data = json.loads(self.tactic_counts_file.read_text(encoding="utf-8"))
self.tactic_data = data.get("results", [])
print(f"[INFO] Loaded {len(self.tactic_data)} tactic analysis results")
def group_by_model(self) -> Dict[str, List[Dict]]:
"""Group tactic data by model"""
models = {}
for item in self.tactic_data:
model = item["model"]
if model not in models:
models[model] = []
models[model].append(item)
return models
def calculate_model_metrics(self, model_data: List[Dict]) -> Dict[str, Any]:
"""Calculate comprehensive metrics for a single model"""
if not model_data:
return self._empty_metrics()
# Aggregate by tactic for this model
tactic_aggregates = {}
for item in model_data:
tactic = item["tactic"]
if tactic not in tactic_aggregates:
tactic_aggregates[tactic] = {
"total_files": 0,
"files_detected": 0,
"total_events": 0,
}
tactic_aggregates[tactic]["total_files"] += 1
tactic_aggregates[tactic]["files_detected"] += item["tactic_detected"]
tactic_aggregates[tactic]["total_events"] += item[
"total_abnormal_events_detected"
]
# Calculate detection rate
total_files = sum(agg["total_files"] for agg in tactic_aggregates.values())
total_detected = sum(
agg["files_detected"] for agg in tactic_aggregates.values()
)
total_events = sum(agg["total_events"] for agg in tactic_aggregates.values())
detection_rate = (
(total_detected / total_files * 100) if total_files > 0 else 0.0
)
# Calculate coverage
total_tactics = len(tactic_aggregates)
tactics_with_detection = sum(
1 for agg in tactic_aggregates.values() if agg["files_detected"] > 0
)
coverage_percent = (
(tactics_with_detection / total_tactics * 100) if total_tactics > 0 else 0.0
)
# Calculate accuracy
accuracy_scores = []
for tactic, agg in tactic_aggregates.items():
if agg["total_files"] > 0:
accuracy = agg["files_detected"] / agg["total_files"]
accuracy_scores.append(accuracy)
avg_accuracy = statistics.mean(accuracy_scores) if accuracy_scores else 0.0
# Calculate effectiveness
effectiveness_score = (
detection_rate * 0.4 + coverage_percent * 0.3 + avg_accuracy * 100 * 0.3
)
# Grade the model
if effectiveness_score >= 80:
grade = "EXCELLENT"
elif effectiveness_score >= 60:
grade = "GOOD"
elif effectiveness_score >= 40:
grade = "FAIR"
elif effectiveness_score >= 20:
grade = "POOR"
else:
grade = "CRITICAL"
# Per-tactic breakdown
per_tactic_detection = []
for tactic, agg in sorted(tactic_aggregates.items()):
files = agg["total_files"]
detected = agg["files_detected"]
events = agg["total_events"]
tactic_detection_rate = (detected / files * 100) if files > 0 else 0.0
per_tactic_detection.append(
{
"tactic": tactic,
"total_files": files,
"files_detected": detected,
"files_missed": files - detected,
"total_abnormal_events_detected": events,
"detection_rate_percent": tactic_detection_rate,
"status": (
"GOOD"
if tactic_detection_rate >= 50
else ("POOR" if tactic_detection_rate > 0 else "NONE")
),
}
)
return {
"model_name": model_data[0]["model"] if model_data else "unknown",
"total_files_analyzed": total_files,
"total_files_detected": total_detected,
"total_files_missed": total_files - total_detected,
"total_abnormal_events_detected": total_events,
"total_tactics_tested": total_tactics,
"detection_rate_percent": detection_rate,
"coverage_percent": coverage_percent,
"average_accuracy_score": avg_accuracy,
"effectiveness_score": effectiveness_score,
"grade": grade,
"per_tactic_detection": per_tactic_detection,
"tactics_with_detection": tactics_with_detection,
"tactics_with_zero_detection": total_tactics - tactics_with_detection,
}
def _empty_metrics(self) -> Dict[str, Any]:
"""Return empty metrics structure"""
return {
"model_name": "unknown",
"total_files_analyzed": 0,
"total_files_detected": 0,
"total_files_missed": 0,
"total_abnormal_events_detected": 0,
"total_tactics_tested": 0,
"detection_rate_percent": 0.0,
"coverage_percent": 0.0,
"average_accuracy_score": 0.0,
"effectiveness_score": 0.0,
"grade": "CRITICAL",
"per_tactic_detection": [],
"tactics_with_detection": 0,
"tactics_with_zero_detection": 0,
}
def generate_comparison(self) -> Dict[str, Any]:
"""Generate comprehensive model comparison report"""
print("\n" + "=" * 80)
print("GENERATING MODEL COMPARISON")
print("=" * 80 + "\n")
# Group data by model
models_data = self.group_by_model()
if not models_data:
print("[WARNING] No model data found")
return {"error": "No model data found"}
print(f"Found {len(models_data)} models: {', '.join(models_data.keys())}")
# Calculate metrics for each model
model_metrics = {}
for model_name, model_data in models_data.items():
print(
f"\nCalculating metrics for {model_name} ({len(model_data)} files)..."
)
model_metrics[model_name] = self.calculate_model_metrics(model_data)
# Generate comparison summary
comparison_summary = self._generate_comparison_summary(model_metrics)
# Generate ranking
ranking = self._generate_ranking(model_metrics)
# Generate detailed comparison
detailed_comparison = self._generate_detailed_comparison(model_metrics)
report = {
"timestamp": datetime.now().isoformat(),
"total_models_compared": len(model_metrics),
"models_analyzed": list(model_metrics.keys()),
"comparison_summary": comparison_summary,
"model_ranking": ranking,
"detailed_model_metrics": model_metrics,
"detailed_comparison": detailed_comparison,
}
return report
def _generate_comparison_summary(
self, model_metrics: Dict[str, Dict]
) -> Dict[str, Any]:
"""Generate high-level comparison summary"""
if not model_metrics:
return {}
# Find best and worst performers
best_detection = max(
model_metrics.items(), key=lambda x: x[1]["detection_rate_percent"]
)
worst_detection = min(
model_metrics.items(), key=lambda x: x[1]["detection_rate_percent"]
)
best_coverage = max(
model_metrics.items(), key=lambda x: x[1]["coverage_percent"]
)
worst_coverage = min(
model_metrics.items(), key=lambda x: x[1]["coverage_percent"]
)
best_effectiveness = max(
model_metrics.items(), key=lambda x: x[1]["effectiveness_score"]
)
worst_effectiveness = min(
model_metrics.items(), key=lambda x: x[1]["effectiveness_score"]
)
# Calculate averages
avg_detection = statistics.mean(
[m["detection_rate_percent"] for m in model_metrics.values()]
)
avg_coverage = statistics.mean(
[m["coverage_percent"] for m in model_metrics.values()]
)
avg_effectiveness = statistics.mean(
[m["effectiveness_score"] for m in model_metrics.values()]
)
return {
"average_detection_rate_percent": avg_detection,
"average_coverage_percent": avg_coverage,
"average_effectiveness_score": avg_effectiveness,
"best_detection": {
"model": best_detection[0],
"score": best_detection[1]["detection_rate_percent"],
},
"worst_detection": {
"model": worst_detection[0],
"score": worst_detection[1]["detection_rate_percent"],
},
"best_coverage": {
"model": best_coverage[0],
"score": best_coverage[1]["coverage_percent"],
},
"worst_coverage": {
"model": worst_coverage[0],
"score": worst_coverage[1]["coverage_percent"],
},
"best_overall": {
"model": best_effectiveness[0],
"score": best_effectiveness[1]["effectiveness_score"],
"grade": best_effectiveness[1]["grade"],
},
"worst_overall": {
"model": worst_effectiveness[0],
"score": worst_effectiveness[1]["effectiveness_score"],
"grade": worst_effectiveness[1]["grade"],
},
}
def _generate_ranking(self, model_metrics: Dict[str, Dict]) -> List[Dict[str, Any]]:
"""Generate ranked list of models by effectiveness"""
ranked_models = sorted(
model_metrics.items(),
key=lambda x: x[1]["effectiveness_score"],
reverse=True,
)
ranking = []
for rank, (model_name, metrics) in enumerate(ranked_models, 1):
ranking.append(
{
"rank": rank,
"model_name": model_name,
"effectiveness_score": metrics["effectiveness_score"],
"grade": metrics["grade"],
"detection_rate_percent": metrics["detection_rate_percent"],
"coverage_percent": metrics["coverage_percent"],
"average_accuracy_score": metrics["average_accuracy_score"],
"total_files_analyzed": metrics["total_files_analyzed"],
}
)
return ranking
def _generate_detailed_comparison(
self, model_metrics: Dict[str, Dict]
) -> Dict[str, Any]:
"""Generate detailed side-by-side comparison"""
if not model_metrics:
return {}
# Get all tactics across all models
all_tactics = set()
for metrics in model_metrics.values():
for tactic_data in metrics["per_tactic_detection"]:
all_tactics.add(tactic_data["tactic"])
all_tactics = sorted(list(all_tactics))
# Create tactic-by-tactic comparison
tactic_comparison = {}
for tactic in all_tactics:
tactic_comparison[tactic] = {}
for model_name, metrics in model_metrics.items():
# Find this tactic in the model's data
tactic_data = next(
(
t
for t in metrics["per_tactic_detection"]
if t["tactic"] == tactic
),
None,
)
if tactic_data:
tactic_comparison[tactic][model_name] = {
"detection_rate_percent": tactic_data["detection_rate_percent"],
"files_detected": tactic_data["files_detected"],
"total_files": tactic_data["total_files"],
"status": tactic_data["status"],
}
else:
tactic_comparison[tactic][model_name] = {
"detection_rate_percent": 0.0,
"files_detected": 0,
"total_files": 0,
"status": "NOT_TESTED",
}
return {
"tactic_by_tactic_comparison": tactic_comparison,
"all_tactics_tested": all_tactics,
}
def main():
parser = argparse.ArgumentParser(
description="Compare performance metrics across different models"
)
parser.add_argument(
"--input",
default="evaluation/full_pipeline/results/tactic_counts_summary.json",
help="Path to tactic_counts_summary.json",
)
parser.add_argument(
"--output",
default="evaluation/full_pipeline/results/model_comparison.json",
help="Output file for model comparison report",
)
args = parser.parse_args()
input_path = Path(args.input)
output_path = Path(args.output)
if not input_path.exists():
print(f"[ERROR] Input file not found: {input_path}")
print("Run count_tactics.py first to generate tactic counts")
return 1
# Run comparison
comparator = ModelComparator(input_path)
report = comparator.generate_comparison()
# Save report
output_path.parent.mkdir(parents=True, exist_ok=True)
output_path.write_text(json.dumps(report, indent=2), encoding="utf-8")
# Display summary
print("\n" + "=" * 80)
print("MODEL COMPARISON COMPLETE")
print("=" * 80)
if "error" in report:
print(f"Error: {report['error']}")
return 1
print(f"Models compared: {report['total_models_compared']}")
print(f"Models: {', '.join(report['models_analyzed'])}")
if report["model_ranking"]:
print(
f"\nTop performer: {report['model_ranking'][0]['model_name']} "
f"(Score: {report['model_ranking'][0]['effectiveness_score']:.1f}, "
f"Grade: {report['model_ranking'][0]['grade']})"
)
summary = report["comparison_summary"]
if summary:
print(f"\nAverage effectiveness: {summary['average_effectiveness_score']:.1f}")
print(
f"Best detection: {summary['best_detection']['model']} ({summary['best_detection']['score']:.1f}%)"
)
print(
f"Best coverage: {summary['best_coverage']['model']} ({summary['best_coverage']['score']:.1f}%)"
)
print(f"\nReport saved to: {output_path}")
print("=" * 80 + "\n")
return 0
if __name__ == "__main__":
exit(main())
|