整理 Q1-Q3 实验代码与结果

deep_learning/Q2/q2/finalize_summary.py

@@ -0,0 +1,29 @@
+from __future__ import annotations
+
+import argparse
+import csv
+from pathlib import Path
+
+from .train_compare import _plot, _summary, _write_csv
+
+
+def main() -> None:
+    parser = argparse.ArgumentParser(description="Rebuild Q2 summary tables from saved validation predictions")
+    parser.add_argument("--output-dir", default=str(Path(__file__).resolve().parents[1] / "outputs" / "algorithm_selection"))
+    args = parser.parse_args()
+    output = Path(args.output_dir)
+    with (output / "validation_metrics_by_condition.csv").open(encoding="utf-8-sig", newline="") as stream:
+        rows = list(csv.DictReader(stream))
+    for row in rows:
+        for key in ("missing_rate", "accuracy", "macro_f1", "mae", "pearson", "n_valid"):
+            row[key] = float(row[key])
+        row["seed"] = int(row["seed"])
+    summary = _summary(rows)
+    _write_csv(output / "summary.csv", summary)
+    aligned = [row for row in summary if row["representation"] == "provided_word_aligned_50"]
+    _plot(aligned, rows, output / "missing_rate_comparison.png")
+    print(f"rebuilt summary table and plot from {len(rows)} saved validation rows")
+
+
+if __name__ == "__main__":
+    main()