| |
| 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() |
|
|
