1 files changed, 95 insertions, 0 deletions
diff --git a/ep_run/analyze.py b/ep_run/analyze.py
new file mode 100644
index 0000000..9514773
--- /dev/null
+++ b/ep_run/analyze.py
@@ -0,0 +1,95 @@
+"""Load runs/*/log.jsonl, print comparison table, and save loss curves as ASCII/PNG."""
+import json
+import sys
+from pathlib import Path
+
+import numpy as np
+
+
+def load_run(run_dir: Path):
+    log_path = run_dir / "log.jsonl"
+    if not log_path.exists():
+        return None
+    steps, step_losses, evals = [], [], []
+    for line in log_path.read_text().splitlines():
+        rec = json.loads(line)
+        if rec.get("event") == "step":
+            steps.append(rec["iter"])
+            step_losses.append(rec["train_loss"])
+        elif rec.get("event") == "eval":
+            evals.append((rec["iter"], rec["train_loss"], rec["val_loss"]))
+    return {
+        "name": run_dir.name,
+        "steps": np.array(steps),
+        "step_losses": np.array(step_losses),
+        "evals": np.array(evals) if evals else np.zeros((0, 3)),
+    }
+
+
+def ascii_plot(runs, key_idx=2, width=60, height=15, title="val loss"):
+    """key_idx: 1 = train loss (from eval), 2 = val loss (from eval)."""
+    lines = [title]
+    all_y = np.concatenate([r["evals"][:, key_idx] for r in runs if len(r["evals"]) > 0])
+    all_x = np.concatenate([r["evals"][:, 0] for r in runs if len(r["evals"]) > 0])
+    if len(all_y) == 0:
+        return "(no eval data)"
+    ymin, ymax = float(all_y.min()), float(all_y.max())
+    xmin, xmax = float(all_x.min()), float(all_x.max())
+    ymin -= 0.02 * (ymax - ymin + 1e-9)
+    ymax += 0.02 * (ymax - ymin + 1e-9)
+    grid = [[" "] * width for _ in range(height)]
+    markers = {0: "o", 1: "x", 2: "+", 3: "*"}
+    for i, r in enumerate(runs):
+        if len(r["evals"]) == 0:
+            continue
+        mk = markers.get(i, "#")
+        for x, _tl, vl in r["evals"]:
+            col = int((x - xmin) / (xmax - xmin + 1e-9) * (width - 1))
+            row = height - 1 - int((vl - ymin) / (ymax - ymin + 1e-9) * (height - 1))
+            row = max(0, min(height - 1, row))
+            col = max(0, min(width - 1, col))
+            grid[row][col] = mk
+    lines.append(f"  y: [{ymin:.3f} .. {ymax:.3f}]   x: [{int(xmin)} .. {int(xmax)}]")
+    for i, row in enumerate(grid):
+        lines.append("  |" + "".join(row) + "|")
+    lines.append("  +" + "-" * width + "+")
+    legend = "  legend: " + "  ".join(
+        f"{markers.get(i, '#')}={r['name']}" for i, r in enumerate(runs)
+    )
+    lines.append(legend)
+    return "\n".join(lines)
+
+
+def main():
+    runs_dir = Path("runs")
+    run_names = sys.argv[1:] if len(sys.argv) > 1 else [
+        "softmax_baseline", "sigmoid_b0", "sigmoid_blogn",
+    ]
+    runs = []
+    for name in run_names:
+        r = load_run(runs_dir / name)
+        if r is None:
+            print(f"WARNING: {name}/log.jsonl missing")
+            continue
+        runs.append(r)
+
+    print("\n=== final losses ===")
+    print(f"{'run':<20s}  {'final train':>12s}  {'final val':>10s}  {'best val':>10s}  {'iter':>6s}")
+    for r in runs:
+        if len(r["evals"]) == 0:
+            print(f"{r['name']:<20s}  (no evals)")
+            continue
+        last = r["evals"][-1]
+        best_idx = int(np.argmin(r["evals"][:, 2]))
+        best = r["evals"][best_idx]
+        print(
+            f"{r['name']:<20s}  {last[1]:>12.4f}  {last[2]:>10.4f}  "
+            f"{best[2]:>10.4f}  {int(best[0]):>6d}"
+        )
+
+    print("\n" + ascii_plot(runs, key_idx=2, title="val loss vs iter"))
+    print("\n" + ascii_plot(runs, key_idx=1, title="eval train loss vs iter"))
+
+
+if __name__ == "__main__":
+    main()