| |
| import argparse |
| import json |
| from pathlib import Path |
|
|
| import matplotlib.pyplot as plt |
| import numpy as np |
|
|
|
|
| def load_json(path): |
| with open(path, "r", encoding="utf-8") as f: |
| return json.load(f) |
|
|
|
|
| def main(): |
| parser = argparse.ArgumentParser(description="Plot predictor test metrics from test_results.json.") |
| parser.add_argument("--test_results", required=True) |
| parser.add_argument("--output_dir", required=True) |
| args = parser.parse_args() |
|
|
| metrics = load_json(args.test_results) |
| output_dir = Path(args.output_dir) |
| output_dir.mkdir(parents=True, exist_ok=True) |
|
|
| labels = ["label_0", "label_1", "overall"] |
| pearson = [ |
| metrics.get("test_pearson_label_0", np.nan), |
| metrics.get("test_pearson_label_1", np.nan), |
| metrics.get("test_pearson", np.nan), |
| ] |
| r2 = [ |
| metrics.get("test_r2_label_0", np.nan), |
| metrics.get("test_r2_label_1", np.nan), |
| metrics.get("test_r2", np.nan), |
| ] |
| mae = [ |
| metrics.get("test_mae_label_0", np.nan), |
| metrics.get("test_mae_label_1", np.nan), |
| metrics.get("test_mae", np.nan), |
| ] |
|
|
| fig, axes = plt.subplots(1, 3, figsize=(12, 3.8)) |
| for ax, values, title in zip(axes, [pearson, r2, mae], ["Pearson", "R2", "MAE"]): |
| ax.bar(labels, values, color=["#3b82f6", "#10b981", "#6366f1"]) |
| ax.set_title(title) |
| ax.tick_params(axis="x", rotation=20) |
| for i, v in enumerate(values): |
| if np.isfinite(v): |
| ax.text(i, v, f"{v:.3f}", ha="center", va="bottom", fontsize=9) |
| fig.tight_layout() |
| out = output_dir / "predictor_metrics.png" |
| fig.savefig(out, dpi=220, bbox_inches="tight") |
| print(out) |
|
|
|
|
| if __name__ == "__main__": |
| main() |
|
|
|
|
