feat: write training report to file instead of stdout

pretrain.py -> checkpoints/pretrained.report.txt
train.py    -> checkpoints/finetuned.report.txt

Single-line [report] saved -> <path> printed to stdout instead.
Also fix arrow character incompatible with Windows cp1251 console.

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

scripts/pretrain.py

@@ -41,6 +41,7 @@ DATA_DIR    = Path("data/processed/mcgill")
 CHECKPOINT  = Path("checkpoints/pretrained.pt")
 LOG_CSV     = Path("checkpoints/pretrained.log.csv")
 CURVES_PNG  = Path("checkpoints/pretrained_curves.png")
+REPORT_TXT  = Path("checkpoints/pretrained.report.txt")
 
 # ---------------------------------------------------------------------------
 # Training config (mirrors the requested CLI invocation)
@@ -98,14 +99,14 @@ def plot_curves(log_csv: Path, out_png: Path) -> None:
     out_png.parent.mkdir(parents=True, exist_ok=True)
     fig.savefig(out_png, dpi=150)
     plt.close(fig)
-    print(f"[plot] saved → {out_png}")
+    print(f"[plot] saved -> {out_png}")
 
 
 # ---------------------------------------------------------------------------
 # Report
 # ---------------------------------------------------------------------------
 
-def print_report(log_csv: Path, checkpoint: Path) -> None:
+def write_report(log_csv: Path, checkpoint: Path, report_path: Path) -> None:
     rows = []
     with open(log_csv, newline="") as fh:
         rows = list(csv.DictReader(fh))
@@ -118,50 +119,54 @@ def print_report(log_csv: Path, checkpoint: Path) -> None:
     best_idx   = val_losses.index(min(val_losses))
     best_row   = rows[best_idx]
 
-    # Convergence heuristic: first epoch where val loss is within 1 % of best
     best_loss = float(best_row["val_loss"])
     conv_epoch = next(
         (int(r["epoch"]) for r in rows if float(r["val_loss"]) <= best_loss * 1.01),
         int(best_row["epoch"]),
     )
 
-    # Parameter count from checkpoint
     n_params = None
     if checkpoint.exists():
         ckpt = torch.load(checkpoint, weights_only=True)
-        mcfg = ckpt["model_config"]
-        model = ChordTransformer(**mcfg)
+        model = ChordTransformer(**ckpt["model_config"])
         tied = model.token_emb.weight.numel()
         n_params = sum(p.numel() for p in model.parameters()) - tied
 
-    print()
-    print("=" * 52)
-    print("  PRE-TRAINING REPORT")
-    print("=" * 52)
-    print(f"  Total epochs run      : {len(rows)}")
-    print(f"  Best epoch (val loss) : {best_row['epoch']}")
-    print(f"  Convergence epoch     : {conv_epoch}  (val ≤ best+1 %)")
-    print(f"  Best val loss         : {best_loss:.4f}")
-    print(f"  Best val perplexity   : {float(best_row['val_ppl']):.2f}")
-    print(f"  Final train loss      : {float(rows[-1]['train_loss']):.4f}")
+    lines = []
+    lines += [
+        "",
+        "=" * 52,
+        "  PRE-TRAINING REPORT",
+        "=" * 52,
+        f"  Total epochs run      : {len(rows)}",
+        f"  Best epoch (val loss) : {best_row['epoch']}",
+        f"  Convergence epoch     : {conv_epoch}  (val ≤ best+1 %)",
+        f"  Best val loss         : {best_loss:.4f}",
+        f"  Best val perplexity   : {float(best_row['val_ppl']):.2f}",
+        f"  Final train loss      : {float(rows[-1]['train_loss']):.4f}",
+    ]
     if n_params is not None:
-        print(f"  Unique parameters     : {n_params:,}")
-    print(f"  Checkpoint            : {checkpoint}")
-    print(f"  Log CSV               : {log_csv}")
-    print("=" * 52)
-    print()
-
-    # Full epoch table for copy-paste into the report
-    print(f"  {'epoch':>5}  {'train':>8}  {'val':>8}  {'ppl':>7}  {'lr':>10}")
-    print(f"  {'-'*5}  {'-'*8}  {'-'*8}  {'-'*7}  {'-'*10}")
+        lines.append(f"  Unique parameters     : {n_params:,}")
+    lines += [
+        f"  Checkpoint            : {checkpoint}",
+        f"  Log CSV               : {log_csv}",
+        "=" * 52,
+        "",
+        f"  {'epoch':>5}  {'train':>8}  {'val':>8}  {'ppl':>7}  {'lr':>10}",
+        f"  {'-'*5}  {'-'*8}  {'-'*8}  {'-'*7}  {'-'*10}",
+    ]
     for r in rows:
         marker = " ←" if int(r["epoch"]) == int(best_row["epoch"]) else ""
-        print(
+        lines.append(
             f"  {int(r['epoch']):>5}  {float(r['train_loss']):>8.4f}"
             f"  {float(r['val_loss']):>8.4f}  {float(r['val_ppl']):>7.2f}"
             f"  {float(r['lr']):>10.2e}{marker}"
         )
-    print()
+    lines.append("")
+
+    report_path.parent.mkdir(parents=True, exist_ok=True)
+    report_path.write_text("\n".join(lines), encoding="utf-8")
+    print(f"[report] saved -> {report_path}")
 
 
 # ---------------------------------------------------------------------------
@@ -207,7 +212,7 @@ def main() -> None:
         print(f"[skip-training] using existing log: {LOG_CSV}")
 
     plot_curves(LOG_CSV, CURVES_PNG)
-    print_report(LOG_CSV, CHECKPOINT)
+    write_report(LOG_CSV, CHECKPOINT, REPORT_TXT)
 
 
 if __name__ == "__main__":