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