Datasets:
File size: 2,844 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 | #!/usr/bin/env python3
from __future__ import annotations
import json
from pathlib import Path
import pandas as pd
BASE_DIR = Path(".")
ROOT = BASE_DIR / "experiments" / "containment_time" / "full"
RESULTS_DIR = BASE_DIR / "results"
METRICS = [
"val_mae_hours",
"test_mae_hours",
"val_rmse_hours",
"test_rmse_hours",
"val_median_ae_hours",
"test_median_ae_hours",
"val_log_mae",
"test_log_mae",
"val_log_rmse",
"test_log_rmse",
"val_r2",
"test_r2",
"val_spearman",
"test_spearman",
"val_pearson",
"test_pearson",
]
def read_json(path: Path) -> dict:
with path.open("r") as f:
return json.load(f)
def main() -> None:
RESULTS_DIR.mkdir(parents=True, exist_ok=True)
rows = []
for metrics_path in sorted(ROOT.glob("*/*/*_seed*/metrics.json")):
m = read_json(metrics_path)
row = {
"task": m.get("task"),
"experiment_type": m.get("experiment_type"),
"representation": m.get("representation"),
"model_name": m.get("model_name"),
"seed": m.get("seed"),
"best_epoch": m.get("best_epoch"),
"runtime_seconds": m.get("runtime_seconds"),
"output_dir": str(metrics_path.parent),
}
for metric in METRICS:
row[metric] = m.get(metric)
rows.append(row)
raw = pd.DataFrame(rows)
raw_path = RESULTS_DIR / "containment_time_full_all_seeds_raw.csv"
raw.to_csv(raw_path, index=False)
if raw.empty:
summary = pd.DataFrame()
else:
grouped = raw.groupby(["representation", "model_name"], dropna=False)
parts = []
for (representation, model_name), g in grouped:
row = {
"representation": representation,
"model_name": model_name,
"num_seeds_completed": int(g["seed"].nunique()),
}
for metric in METRICS:
row[f"{metric}_mean"] = g[metric].mean()
row[f"{metric}_std"] = g[metric].std()
row["best_epoch_mean"] = g["best_epoch"].mean()
row["runtime_seconds_mean"] = g["runtime_seconds"].mean()
parts.append(row)
summary = pd.DataFrame(parts)
if "test_mae_hours_mean" in summary.columns:
summary = summary.sort_values("test_mae_hours_mean", ascending=True)
summary_path = RESULTS_DIR / "containment_time_full_all_seeds_mean_std.csv"
md_path = RESULTS_DIR / "containment_time_full_all_seeds_mean_std.md"
summary.to_csv(summary_path, index=False)
summary.to_markdown(md_path, index=False)
print(f"Read {len(raw)} metrics files from {ROOT}")
print(f"Wrote {raw_path}")
print(f"Wrote {summary_path}")
print(f"Wrote {md_path}")
if __name__ == "__main__":
main()
|