File size: 1,977 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 49 50 51 52 53 54 55 56 57 58 59 60 | 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"]
VARIANT_NAMES = {
"dynafall_joint": "A1 joint",
"dynafall_joint_bone": "A2 joint+bone",
"dynafall_full_no_dropout": "A3 +dynamics",
"dynafall_random_dropout": "A4 +random dropout",
"dynafall": "A5 +confidence dropout",
}
def main() -> None:
ap = argparse.ArgumentParser()
ap.add_argument("--root", default="results/ablation")
ap.add_argument("--out", default="results/ablation_summary.csv")
args = ap.parse_args()
rows = []
for path in (ROOT / args.root).glob("seed_*/*/*/metrics_test_clean.json"):
row = json.loads(path.read_text())
row["seed"] = int(path.parts[-4].replace("seed_", ""))
row["variant"] = VARIANT_NAMES.get(row["method"], row["method"])
rows.append(row)
if not rows:
raise SystemExit(f"No ablation metrics found under {args.root}")
df = pd.DataFrame(rows)
out = ROOT / args.out
out.parent.mkdir(parents=True, exist_ok=True)
df.to_csv(out.with_name(out.stem + "_raw.csv"), index=False)
summary = df.groupby(["dataset", "variant"], as_index=False)[METRICS].agg(["mean", "std"])
summary.to_csv(out)
print(f"Wrote {out}")
flat = []
order = {v: i for i, v in enumerate(VARIANT_NAMES.values())}
for (dataset, variant), group in df.groupby(["dataset", "variant"]):
row = {"dataset": dataset, "variant": variant, "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)
flat_df["order"] = flat_df["variant"].map(order)
flat_df = flat_df.sort_values(["dataset", "order"]).drop(columns=["order"])
print(flat_df.to_string(index=False))
if __name__ == "__main__":
main()
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