| | import os
|
| | import json
|
| | import argparse
|
| | from pathlib import Path
|
| | import pandas as pd
|
| | import matplotlib.pyplot as plt
|
| |
|
| |
|
| | def parse_args():
|
| | p = argparse.ArgumentParser()
|
| | p.add_argument("root", type=str,
|
| | help="Directory containing 12 month subfolders (e.g., 2407 .. 2506)")
|
| | p.add_argument("--out-dir", type=str, default=None,
|
| | help="Output directory (default: ROOT)")
|
| | return p.parse_args()
|
| |
|
| |
|
| | def load_month_values_json(root: Path, month: str):
|
| |
|
| | candidates = []
|
| | candidates.append(root / month / "values.json")
|
| | candidates.append(root / f"{month}_full" / "values.json")
|
| | candidates.append(root / f"{month}_lr4e-5" / "values.json")
|
| |
|
| |
|
| | for path in sorted(root.glob(f"{month}*/values.json")):
|
| | if path not in candidates:
|
| | candidates.append(path)
|
| | for path in sorted(root.glob(f"{month}*/metrics.json")):
|
| | if path not in candidates:
|
| | candidates.append(path)
|
| |
|
| | for c in candidates:
|
| | if c.exists():
|
| | return c
|
| | return None
|
| |
|
| |
|
| | def main():
|
| | args = parse_args()
|
| | root = Path(args.root)
|
| | out_dir = Path(args.out_dir) if args.out_dir else root
|
| |
|
| | months = ["origin", "2407","2408","2409","2410","2411","2412",
|
| | "2501","2502","2503","2504","2505","2506"]
|
| |
|
| | main_tasks = [
|
| | "arc_easy",
|
| | "arc_challenge",
|
| | "hellaswag",
|
| | "sciq",
|
| | "truthfulqa_mc1",
|
| | "truthfulqa_mc2",
|
| | ]
|
| |
|
| | records = []
|
| |
|
| | for tag in months:
|
| | path = load_month_values_json(root, tag)
|
| | if path is None:
|
| | continue
|
| |
|
| | with open(path, "r", encoding="utf-8") as f:
|
| | data = json.load(f)
|
| |
|
| | for rec in data.get("tasks", []):
|
| | task = rec.get("task", "")
|
| | metric = rec.get("metric", "")
|
| | value = rec.get("value", None)
|
| | if task in main_tasks and metric in ("acc", "acc_norm"):
|
| | records.append({
|
| | "month": tag,
|
| | "task": task,
|
| | "metric": metric,
|
| | "value": value
|
| | })
|
| |
|
| | for rec in data.get("groups", []):
|
| | group = rec.get("group", "")
|
| | metric = rec.get("metric", "")
|
| | value = rec.get("value", None)
|
| | if group == "mmlu" and metric == "acc":
|
| | records.append({
|
| | "month": tag,
|
| | "task": "mmlu",
|
| | "metric": "acc",
|
| | "value": value
|
| | })
|
| |
|
| | df = pd.DataFrame.from_records(records)
|
| | if df.empty:
|
| | df = pd.DataFrame(columns=["month","task","metric","value"])
|
| |
|
| |
|
| | def month_sort_key(x):
|
| | if x == "origin":
|
| | return (0, 0)
|
| | try:
|
| | return (1, int(x))
|
| | except Exception:
|
| | return (2, x)
|
| |
|
| | df["month"] = pd.Categorical(
|
| | df["month"],
|
| | categories=sorted(df["month"].unique(), key=month_sort_key),
|
| | ordered=True
|
| | )
|
| | df = df.sort_values(["task","metric","month"])
|
| |
|
| |
|
| | csv_path = out_dir / "monthly_metrics.csv"
|
| | df.to_csv(csv_path, index=False)
|
| |
|
| |
|
| | plt.figure(figsize=(12, 6))
|
| | series_keys = sorted(df[["task","metric"]].drop_duplicates().apply(tuple, axis=1))
|
| | n = len(series_keys)
|
| | cmap = plt.colormaps['tab20'].resampled(n)
|
| |
|
| |
|
| | for i, (task, metric) in enumerate(series_keys):
|
| | sub = df[(df["task"] == task) & (df["metric"] == metric)].sort_values("month")
|
| | if sub.empty:
|
| | continue
|
| | color = cmap(i % n) if n <= 20 else cmap(i / n)
|
| | plt.plot(sub["month"].astype(str), sub["value"],
|
| | marker="o",
|
| | color=color,
|
| | label=f"{task}—{metric}")
|
| |
|
| | plt.xlabel("Month")
|
| | plt.ylabel("Score")
|
| | plt.title("Monthly Evaluation Trends (Main Tasks)")
|
| | plt.legend(loc='best', bbox_to_anchor=(1, 0.5))
|
| | plt.tight_layout()
|
| |
|
| | png_path = out_dir / "monthly_metrics.png"
|
| | plt.savefig(png_path, dpi=150)
|
| |
|
| | if __name__ == "__main__":
|
| | main()
|
| |
|
