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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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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
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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")
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if sub.empty:
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continue
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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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