Datasets:
File size: 3,113 Bytes
a498c8b | 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 | from __future__ import annotations
import json
from pathlib import Path
import pandas as pd
BASE_DIR = Path(".")
EXP_DIR = BASE_DIR / "experiments" / "ia_failure" / "full"
RESULTS_DIR = BASE_DIR / "results"
RAW_OUT = RESULTS_DIR / "ia_failure_full_all_seeds_raw.csv"
AGG_OUT = RESULTS_DIR / "ia_failure_full_all_seeds_mean_std.csv"
MD_OUT = RESULTS_DIR / "ia_failure_full_all_seeds_mean_std.md"
METRIC_COLUMNS = [
"val_auprc",
"test_auprc",
"val_auroc",
"test_auroc",
"test_f1",
"test_precision",
"test_recall",
"test_iou",
"test_brier",
"test_ece",
"test_precision_at_1",
"test_recall_at_1",
"test_precision_at_5",
"test_recall_at_5",
"test_precision_at_10",
"test_recall_at_10",
"best_epoch",
"runtime_seconds",
]
def read_metrics(path: Path) -> dict:
with path.open("r", encoding="utf-8") as file:
payload = json.load(file)
row = {
"task": payload.get("task", "ia_failure"),
"experiment_type": payload.get("experiment_type", "full"),
"representation": payload.get("representation", path.parents[2].name if len(path.parents) > 2 else None),
"model_name": payload.get("model_name", path.parent.name.split("_seed")[0]),
"seed": payload.get("seed"),
"output_dir": payload.get("output_dir", str(path.parent)),
}
# Backfill from path: full/{representation}/weather5_all/{model}_seed{seed}/metrics.json
try:
row["representation"] = path.parents[2].name
except Exception:
pass
for col in METRIC_COLUMNS:
row[col] = payload.get(col)
return row
def main() -> None:
RESULTS_DIR.mkdir(parents=True, exist_ok=True)
metrics_paths = sorted(EXP_DIR.glob("*/*/*_seed*/metrics.json"))
rows = [read_metrics(path) for path in metrics_paths]
columns = [
"task",
"experiment_type",
"representation",
"model_name",
"seed",
*METRIC_COLUMNS,
"output_dir",
]
raw = pd.DataFrame(rows, columns=columns)
raw.to_csv(RAW_OUT, index=False)
if raw.empty:
agg = pd.DataFrame(columns=["representation", "model_name", "num_seeds_completed"])
else:
numeric_metrics = [col for col in METRIC_COLUMNS if col in raw.columns]
grouped = raw.groupby(["representation", "model_name"], dropna=False)
count = grouped["seed"].nunique().rename("num_seeds_completed")
means = grouped[numeric_metrics].mean(numeric_only=True).add_suffix("_mean")
stds = grouped[numeric_metrics].std(numeric_only=True).add_suffix("_std")
agg = pd.concat([count, means, stds], axis=1).reset_index()
if "test_auprc_mean" in agg.columns:
agg = agg.sort_values("test_auprc_mean", ascending=False, na_position="last")
agg.to_csv(AGG_OUT, index=False)
MD_OUT.write_text(agg.to_markdown(index=False) + "\n", encoding="utf-8")
print(f"Read {len(raw)} metrics files from {EXP_DIR}")
print(f"Wrote {RAW_OUT}")
print(f"Wrote {AGG_OUT}")
print(f"Wrote {MD_OUT}")
if __name__ == "__main__":
main()
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