File size: 1,422 Bytes
ae419ed | 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 | from __future__ import annotations
import argparse
import json
from pathlib import Path
import pandas as pd
from common import ROOT
METRICS = ["accuracy", "precision", "recall", "f1", "macro_f1"]
def main() -> None:
ap = argparse.ArgumentParser()
ap.add_argument("--root", default="results/main")
ap.add_argument("--out", default="results/main_summary.csv")
args = ap.parse_args()
rows = []
for path in (ROOT / args.root).glob("seed_*/*/*/metrics_test_clean.json"):
result = json.loads(path.read_text())
result["seed"] = int(path.parts[-4].replace("seed_", ""))
rows.append(result)
if not rows:
raise SystemExit(f"No metrics found under {args.root}")
df = pd.DataFrame(rows)
out = ROOT / args.out
out.parent.mkdir(parents=True, exist_ok=True)
summary = df.groupby(["dataset", "method"], as_index=False)[METRICS].agg(["mean", "std"])
summary.to_csv(out)
print(f"Wrote {out}")
flat = []
for (dataset, method), group in df.groupby(["dataset", "method"]):
row = {"dataset": dataset, "method": method, "n": len(group)}
for metric in METRICS:
row[metric] = f"{group[metric].mean():.4f} +/- {group[metric].std(ddof=1):.4f}"
flat.append(row)
flat_df = pd.DataFrame(flat).sort_values(["dataset", "method"])
print(flat_df.to_string(index=False))
if __name__ == "__main__":
main()
|