#!/usr/bin/env python3 """Plot SAC training progress from live campaign logs. Pure-stdlib SVG output (matplotlib not available on this box). Generates: docs/graph_eval_winrates.svg - eval win rate per opponent (+ rolling-20 mean, v1 overlay) docs/graph_losses.svg - critic/actor/alpha loss curves from training_metrics.jsonl Usage: python3 tools/plot_progress.py [campaign_log] [metrics_jsonl] [v1_log] [outdir] All args optional; defaults relative to the SAC_LSTM_Bot/ root (parent of tools/). python3 tools/plot_progress.py --selftest # tiny built-in sanity check """ import json import math import re import sys import tempfile from pathlib import Path ROOT = Path(__file__).resolve().parent.parent EVAL_RE = re.compile(r">>> \[eval\] win rate: (\d+)/(\d+) \(([\d.]+)%\) vs (\S+)") ROLL_WINDOW = 20 COLORS = {"Corners": "#1f77b4", "Crazy": "#2ca02c", "Target": "#d62728"} V1_COLOR = "#888888" W, M_L, M_R, M_T, M_B = 1000, 70, 20, 40, 45 def parse_eval_series(path): """Return {opponent: [win% per eval, in file order]}.""" series = {} if not path.is_file(): print(f"[skip] eval log not found: {path}") return series for line in path.read_text(errors="replace").splitlines(): m = EVAL_RE.search(line) if not m: continue try: pct = float(m.group(3)) except ValueError: continue series.setdefault(m.group(4), []).append(pct) return series def parse_metrics(path): """Return list of metric dicts, skipping malformed lines.""" rows = [] if not path.is_file(): print(f"[skip] metrics file not found: {path}") return rows for line in path.read_text(errors="replace").splitlines(): line = line.strip() if not line: continue try: rows.append(json.loads(line)) except json.JSONDecodeError: continue return rows def rolling(vals, w=ROLL_WINDOW): out, s = [], 0.0 for i, v in enumerate(vals): s += v if i >= w: s -= vals[i - w] out.append(s / min(i + 1, w)) return out # ---------- tiny SVG helpers ---------- def esc(s): return str(s).replace("&", "&").replace("<", "<").replace(">", ">") def svg_open(w, h, title): return (f'\n' f'\n' f'' f"{esc(title)}\n") def polyline(pts, color, width=1.5, dash=None, opacity=1.0): if len(pts) < 2: return "" d = f' stroke-dasharray="{dash}"' if dash else "" p = " ".join(f"{x:.1f},{y:.1f}" for x, y in pts) return (f'\n') def axis(x0, y0, x1, y1, xt, yt, xlabel, ylabel, ylog=False): """Draw axes + ticks + labels. xt/yt are (value, px) tick lists.""" s = (f'\n' f'\n') for v, px in xt: s += (f'\n' f'' f"{esc(v)}\n") for v, py in yt: s += (f'\n' f'' f"{esc(v)}\n") s += (f'' f"{esc(xlabel)}\n" f'{esc(ylabel)}\n') return s def ticks_linear(vmin, vmax, p0, p1, n=5, fmt="{:.0f}"): return [(fmt.format(vmin + (vmax - vmin) * i / (n - 1)), p0 + (p1 - p0) * i / (n - 1)) for i in range(n)] def ticks_log(vmin, vmax, p0, p1, n=5): vals = [10 ** (vmin + (vmax - vmin) * i / (n - 1)) for i in range(n)] return [("{:.3g}".format(v), p0 + (p1 - p0) * i / (n - 1)) for i, v in enumerate(vals)] def legend(items, x, y): """items: [(color, dash, label)]""" s = "" for i, (color, dash, label) in enumerate(items): yy = y + i * 18 d = f' stroke-dasharray="{dash}"' if dash else "" s += (f'\n' f'{esc(label)}\n') return s def map_fn(p0, p1, vmin, vmax, log=False): def f(v): t = (math.log10(v) - vmin) / (vmax - vmin) if log else (v - vmin) / (vmax - vmin) return p1 - max(0.0, min(1.0, t)) * (p1 - p0) return f # ---------- graph 1: eval win rates ---------- def graph_eval(series_v2, series_v1, out): if not series_v2 and not series_v1: print("[skip] no eval data at all -> no win-rate graph") return 0 H = 560 x0, y0, x1, y1 = M_L, H - M_B, W - M_R, M_T xmax = max(max((len(v) for v in s.values()), default=1) for s in (series_v2, series_v1)) xmax = max(xmax, 2) xm = map_fn(x0, x1, 1, xmax) ym = map_fn(y0, y1, 0, 100) s = svg_open(W, H, "SAC-LSTM campaign: eval win rate vs opponents (v2 live, v1 archived)") s += axis(x0, y0, x1, y1, ticks_linear(1, xmax, x0, x1), ticks_linear(0, 100, y0, y1), f"eval number (per opponent)", "win rate (%)") counts = [] items = [] for name in ("Corners", "Crazy", "Target"): vals = series_v2.get(name, []) if not vals: continue c = COLORS[name] s += polyline([(xm(i + 1), ym(v)) for i, v in enumerate(vals)], c, 1, opacity=0.30) s += polyline([(xm(i + 1), ym(v)) for i, v in enumerate(rolling(vals))], c, 3) counts.append(f"{name}: {len(vals)} evals") items.append((c, None, f"{name} (thick = rolling-{ROLL_WINDOW} mean)")) v1 = series_v1.get("Corners", []) if v1: s += polyline([(xm(i + 1), ym(v)) for i, v in enumerate(v1)], V1_COLOR, 1, dash="6,4") s += polyline([(xm(i + 1), ym(v)) for i, v in enumerate(rolling(v1))], V1_COLOR, 2, dash="6,4") counts.append(f"v1 Corners (old run): {len(v1)} evals") items.append((V1_COLOR, "6,4", "v1 Corners (old run)")) s += legend(items, x1 - 260, y1 + 14) s += (f'' f'{esc("; ".join(counts))}\n\n') out.write_text(s) return 1 # ---------- graph 2: loss curves ---------- def graph_losses(rows, out): if not rows: print("[skip] no metric rows -> no loss graph") return 0 def col(key, log=False): vals = [] for r in rows: try: v = abs(float(r[key])) # abs: log panels plot magnitude except (KeyError, TypeError, ValueError): continue if log and v <= 0: continue vals.append(v) return vals critic = col("critic_loss", log=True) actor = col("actor_loss", log=True) alpha = col("alpha") if not (critic or actor or alpha): print("[skip] metric rows lack critic_loss/actor_loss/alpha -> no loss graph") return 0 PH, GAP = 210, 55 H = M_T + 3 * PH + 2 * GAP + M_B x0, x1 = M_L, W - M_R n = len(rows) s = svg_open(W, H, "SAC-LSTM training losses (x = metric line number)") def panel(top, vals, title, log, ylab, fixed_range=None): nonlocal s y0, y1 = top + PH, top if not vals: s += (f'' f"{esc(title)}: no valid points\n") return if log: lo = math.floor(math.log10(min(vals))) hi = math.ceil(math.log10(max(vals))) if lo == hi: hi = lo + 1 yt = ticks_log(lo, hi, y0, y1) else: lo, hi = fixed_range or (min(vals), max(vals)) if lo == hi: hi = lo + 1 yt = ticks_linear(lo, hi, y0, y1, n=5, fmt="{:.3g}") s += (f'' f"{esc(title)}\n") s += axis(x0, y0, x1, y1, ticks_linear(1, n, x0, x1, fmt="{:.0f}"), yt, "", ylab) ym = map_fn(y0, y1, lo, hi, log=log) pts = [] for i, r in enumerate(rows): try: v = abs(float(r[title_key(title)])) except (KeyError, TypeError, ValueError): continue if log and v <= 0: continue pts.append((x0 + (x1 - x0) * i / max(n - 1, 1), ym(v))) s += polyline(pts, "#1f77b4", 1.6) def title_key(title): return {"critic_loss (log scale)": "critic_loss", "actor_loss |abs| (log scale)": "actor_loss", "alpha (temperature)": "alpha"}[title] panel(M_T, critic, "critic_loss (log scale)", True, "critic_loss") panel(M_T + PH + GAP, actor, "actor_loss |abs| (log scale)", True, "|actor_loss|") panel(M_T + 2 * (PH + GAP), alpha, "alpha (temperature)", False, "alpha", fixed_range=(0, max(1.0, max(alpha)))) s += (f'metric line number ({n} rows)\n\n') out.write_text(s) return 1 # ---------- selftest ---------- def selftest(): with tempfile.TemporaryDirectory() as td: td = Path(td) (td / "log").write_text( ">>> [eval] win rate: 3/10 (30%) vs Corners\n" "garbage line\n" ">>> [eval] win rate: 7/10 (70%) vs Crazy\n" ">>> [eval] win rate: broken\n" ">>> [eval] win rate: 5/10 (50%) vs Corners\n") ser = parse_eval_series(td / "log") assert ser == {"Corners": [30.0, 50.0], "Crazy": [70.0]}, ser assert rolling([10] * 25, 20)[-1] == 10.0 assert rolling([1, 2, 3], 20) == [1.0, 1.5, 2.0] (td / "m.jsonl").write_text( '{"critic_loss": 10, "actor_loss": -2, "alpha": 0.5}\n' "not json\n" '{"critic_loss": 100, "actor_loss": -4, "alpha": 0.25}\n') rows = parse_metrics(td / "m.jsonl") assert len(rows) == 2 and rows[1]["critic_loss"] == 100 ok = graph_losses(rows, td / "g2.svg") and graph_eval(ser, {"Corners": [0, 10]}, td / "g1.svg") assert ok and (td / "g1.svg").stat().st_size > 500 assert graph_eval({}, {}, td / "none.svg") == 0 # missing data handled print("selftest OK") def main(): if "--selftest" in sys.argv: selftest() return args = [a for a in sys.argv[1:] if not a.startswith("-")] campaign = Path(args[0]) if len(args) > 0 else ROOT / "campaign_v2_stdout.log" metrics = Path(args[1]) if len(args) > 1 else ROOT / "training_metrics.jsonl" v1log = Path(args[2]) if len(args) > 2 else ROOT / "weights_v1_archive" / "campaign_stdout.log" outdir = Path(args[3]) if len(args) > 3 else ROOT / "docs" outdir.mkdir(parents=True, exist_ok=True) made = 0 series_v2 = parse_eval_series(campaign) print(f"[info] v2 evals parsed: " + ", ".join(f"{k}={len(v)}" for k, v in sorted(series_v2.items())) or "(none)") series_v1 = parse_eval_series(v1log) print(f"[info] v1 evals parsed: " + ", ".join(f"{k}={len(v)}" for k, v in sorted(series_v1.items())) or "(none)") made += graph_eval(series_v2, series_v1, outdir / "graph_eval_winrates.svg") rows = parse_metrics(metrics) print(f"[info] metric rows parsed: {len(rows)}") made += graph_losses(rows, outdir / "graph_losses.svg") if made: print(f"[done] {made} graph(s) written to {outdir}") else: print("[error] nothing plotted - check paths above") sys.exit(1) if __name__ == "__main__": main()