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ec4da21 | 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 | import csv
import json
from pathlib import Path
from statistics import mean
from typing import Dict, List, Any
RESULTS_DIR = Path(r"E:\Practise\data\results")
RESULTS_CSV = RESULTS_DIR / "scores.csv"
SUMMARY_JSON = RESULTS_DIR / "metrics_summary.json"
CRITERIA = [
"fonts",
"colors",
"composition",
"ai_generated_score",
"quality",
]
def read_csv(path: Path) -> List[Dict[str, Any]]:
if not path.exists():
return []
with open(path, "r", encoding="utf-8-sig", newline="") as f:
return list(csv.DictReader(f))
def to_int(value: Any, default: int = 0) -> int:
try:
return int(value)
except Exception:
return default
def to_float(value: Any, default: float = 0.0) -> float:
try:
return float(value)
except Exception:
return default
def to_bool(value: Any) -> bool:
return str(value).strip().lower() == "true"
def build_metrics(rows: List[Dict[str, Any]]) -> Dict[str, Any]:
total = len(rows)
valid_rows = [r for r in rows if to_bool(r.get("valid"))]
invalid_rows = [r for r in rows if not to_bool(r.get("valid"))]
content_rows = [r for r in valid_rows if to_bool(r.get("content_present"))]
blank_rows = [r for r in valid_rows if not to_bool(r.get("content_present"))]
out = {
"total_rows": total,
"valid_rows": len(valid_rows),
"invalid_rows": len(invalid_rows),
"invalid_ratio": round(len(invalid_rows) / total, 4) if total else 0.0,
"content_present_rows": len(content_rows),
"blank_rows": len(blank_rows),
"confidence_mean": round(mean(to_float(r.get("confidence")) for r in valid_rows), 4) if valid_rows else 0.0,
"labels": {
"good": sum(1 for r in valid_rows if r.get("label") == "good"),
"medium": sum(1 for r in valid_rows if r.get("label") == "medium"),
"bad": sum(1 for r in valid_rows if r.get("label") == "bad"),
"uncertain": sum(1 for r in valid_rows if r.get("label") == "uncertain"),
},
"criteria": {},
}
if content_rows:
for criterion in CRITERIA:
values = [to_int(r.get(criterion)) for r in content_rows]
out["criteria"][criterion] = {
"mean": round(mean(values), 4),
"min": min(values),
"max": max(values),
}
return out
def main() -> None:
rows = read_csv(RESULTS_CSV)
metrics = build_metrics(rows)
with open(SUMMARY_JSON, "w", encoding="utf-8") as f:
json.dump(metrics, f, ensure_ascii=False, indent=2)
if __name__ == "__main__":
main() |