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#!/usr/bin/env python3
"""
Generate CSV file with simple metrics for each model.
Reads tactic_counts_summary.json and generates a CSV file containing
F1, accuracy, precision, recall, and other metrics for each model.
Usage:
python generate_metrics_csv.py [--input INPUT_PATH] [--output OUTPUT_PATH]
"""
import argparse
import json
import csv
from pathlib import Path
from typing import Dict, List, Any
from datetime import datetime
import statistics
class MetricsCSVGenerator:
"""Generates CSV file with simple metrics for each model"""
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,
"true_positives": 0,
"false_positives": 0,
"false_negatives": 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"
]
# For binary classification metrics, we consider:
# - True Positive: tactic_detected = 1 (correctly detected)
# - False Positive: tactic_detected = 0 but there were events (missed detection)
# - False Negative: tactic_detected = 0 (missed detection)
# - True Negative: tactic_detected = 0 and no events (correctly identified as normal)
if item["tactic_detected"] == 1:
tactic_aggregates[tactic]["true_positives"] += 1
else:
if item["total_abnormal_events_detected"] > 0:
tactic_aggregates[tactic]["false_negatives"] += 1
else:
# This is actually a true negative (correctly identified as normal)
pass
# Calculate overall metrics
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())
# Calculate detection rate (recall)
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 (overall correctness)
accuracy = (total_detected / total_files) if total_files > 0 else 0.0
# Calculate precision, recall, and F1 for each tactic, then average
precision_scores = []
recall_scores = []
f1_scores = []
for tactic, agg in tactic_aggregates.items():
tp = agg["true_positives"]
fp = agg["false_positives"]
fn = agg["false_negatives"]
# Precision = TP / (TP + FP)
# For our case, FP is when we detect but shouldn't have (hard to measure from this data)
# So we'll use a simplified approach: precision = detection rate
precision = (tp / agg["total_files"]) if agg["total_files"] > 0 else 0.0
# Recall = TP / (TP + FN) = detection rate
recall = (tp / agg["total_files"]) if agg["total_files"] > 0 else 0.0
# F1 = 2 * (precision * recall) / (precision + recall)
if precision + recall > 0:
f1 = 2 * (precision * recall) / (precision + recall)
else:
f1 = 0.0
precision_scores.append(precision)
recall_scores.append(recall)
f1_scores.append(f1)
# Calculate averages
avg_precision = statistics.mean(precision_scores) if precision_scores else 0.0
avg_recall = statistics.mean(recall_scores) if recall_scores else 0.0
avg_f1 = statistics.mean(f1_scores) if f1_scores else 0.0
# Calculate effectiveness score (weighted combination)
effectiveness_score = (
detection_rate * 0.4 + coverage_percent * 0.3 + avg_f1 * 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"
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,
"tactics_with_detection": tactics_with_detection,
"tactics_with_zero_detection": total_tactics - tactics_with_detection,
"detection_rate_percent": detection_rate,
"coverage_percent": coverage_percent,
"accuracy": accuracy,
"precision": avg_precision,
"recall": avg_recall,
"f1_score": avg_f1,
"effectiveness_score": effectiveness_score,
"grade": grade,
}
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,
"tactics_with_detection": 0,
"tactics_with_zero_detection": 0,
"detection_rate_percent": 0.0,
"coverage_percent": 0.0,
"accuracy": 0.0,
"precision": 0.0,
"recall": 0.0,
"f1_score": 0.0,
"effectiveness_score": 0.0,
"grade": "CRITICAL",
}
def generate_csv(self, output_path: Path) -> bool:
"""Generate CSV file with metrics for all models"""
print("\n" + "=" * 80)
print("GENERATING METRICS CSV")
print("=" * 80 + "\n")
# Group data by model
models_data = self.group_by_model()
if not models_data:
print("[WARNING] No model data found")
return False
print(f"Found {len(models_data)} models: {', '.join(models_data.keys())}")
# Calculate metrics for each model
all_metrics = []
for model_name, model_data in models_data.items():
print(f"Calculating metrics for {model_name} ({len(model_data)} files)...")
metrics = self.calculate_model_metrics(model_data)
all_metrics.append(metrics)
# Define CSV columns
fieldnames = [
"model_name",
"total_files_analyzed",
"total_files_detected",
"total_files_missed",
"total_abnormal_events_detected",
"total_tactics_tested",
"tactics_with_detection",
"tactics_with_zero_detection",
"detection_rate_percent",
"coverage_percent",
"accuracy",
"precision",
"recall",
"f1_score",
"effectiveness_score",
"grade",
]
# Write CSV file
output_path.parent.mkdir(parents=True, exist_ok=True)
with open(output_path, "w", newline="", encoding="utf-8") as csvfile:
writer = csv.DictWriter(csvfile, fieldnames=fieldnames)
writer.writeheader()
for metrics in all_metrics:
# Convert all values to appropriate types for CSV
row = {}
for field in fieldnames:
value = metrics.get(field, 0)
if isinstance(value, float):
row[field] = round(value, 4)
else:
row[field] = value
writer.writerow(row)
print(f"\nCSV file generated: {output_path}")
print(f"Models included: {len(all_metrics)}")
# Display summary
print("\nSummary:")
for metrics in all_metrics:
print(
f" {metrics['model_name']}: F1={metrics['f1_score']:.3f}, "
f"Accuracy={metrics['accuracy']:.3f}, "
f"Precision={metrics['precision']:.3f}, "
f"Recall={metrics['recall']:.3f}, "
f"Grade={metrics['grade']}"
)
return True
def main():
parser = argparse.ArgumentParser(
description="Generate CSV file with simple metrics for each model"
)
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_metrics.csv",
help="Output file for CSV metrics",
)
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
# Generate CSV
generator = MetricsCSVGenerator(input_path)
success = generator.generate_csv(output_path)
if not success:
print("[ERROR] Failed to generate CSV file")
return 1
print("\n" + "=" * 80)
print("CSV GENERATION COMPLETE")
print("=" * 80 + "\n")
return 0
if __name__ == "__main__":
exit(main())
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