diff --git a/routes/ml.py b/routes/ml.py index 2ec2387..5700a3c 100644 --- a/routes/ml.py +++ b/routes/ml.py @@ -1,729 +1,947 @@ -""" -routes/ml.py -============ -ML training data, policy inference, supervised training, -PPO training, and tournament endpoints. - - GET /api/ml/status - GET /api/ml/decisions/{filename} - GET /api/ml/data/status - POST /api/ml/toggle - POST /api/ml/predict - POST /api/ml/predict/batch - GET /api/ml/model - POST /api/ml/reload - POST /api/ml/train - GET /api/ml/train/status - POST /api/ml/train/ppo - GET /api/ml/train/ppo/status - POST /api/ml/tournament - GET /api/ml/tournament/status - GET /api/ml/tournament/results -""" -from __future__ import annotations - -import dataclasses -import json -import logging -import os -import threading -from datetime import datetime -from pathlib import Path - -from fastapi import APIRouter, HTTPException, Request as FastAPIRequest - -from routes.shared import CFG, _ml_logging_enabled - -log_ml = logging.getLogger("commander_ai_lab.ml") - -router = APIRouter(tags=["ml"]) - -# ── module-level singletons ────────────────────────────────────────────────── -_policy_service = None -_policy_service_init_attempted = False - -# ── Thread-safe state objects ──────────────────────────────────────────────── -@dataclasses.dataclass -class _TrainingState: - running: bool = False - progress: int = 0 - total_epochs: int = 0 - current_epoch: int = 0 - phase: str = "idle" - message: str = "" - metrics: object = None - result: object = None - error: object = None - started_at: object = None - - def snapshot(self) -> dict: - return dataclasses.asdict(self) - -@dataclasses.dataclass -class _PPOState: - running: bool = False - iteration: int = 0 - total_iterations: int = 0 - phase: str = "idle" - message: str = "" - metrics: object = None - result: object = None - error: object = None - - def snapshot(self) -> dict: - return dataclasses.asdict(self) - -@dataclasses.dataclass -class _TournamentState: - running: bool = False - phase: str = "idle" - message: str = "" - result: object = None - error: object = None - - def snapshot(self) -> dict: - return dataclasses.asdict(self) - -_training_state = _TrainingState() -_training_lock = threading.Lock() - -_ppo_state = _PPOState() -_ppo_lock = threading.Lock() - -_tournament_state = _TournamentState() -_tournament_lock = threading.Lock() - -# module-level toggle (local copy; the shared one is for batch runs) -_ml_logging_enabled_local = _ml_logging_enabled - - -def _get_policy_service(): - """Get or initialize the policy inference service (lazy loading).""" - global _policy_service, _policy_service_init_attempted - if _policy_service is None and not _policy_service_init_attempted: - _policy_service_init_attempted = True - try: - from ml.serving.policy_server import PolicyInferenceService - _policy_service = PolicyInferenceService() - if _policy_service.load(): - log_ml.info(f"Policy model loaded on {_policy_service.device}") - else: - log_ml.error(f"Policy model not available: {_policy_service._load_error}") - except Exception as e: - log_ml.error(f"Policy service init failed: {e}") - return _policy_service - - -# ============================================================== -# ML Training Data Endpoints -# ============================================================== - -@router.get("/api/ml/status") -async def ml_status(): - """Get ML decision logging status and available training data.""" - import routes.shared as _shared - lab_root = Path(__file__).resolve().parent.parent - results_dir = lab_root / CFG.results_dir - ml_files = [] - if results_dir.exists(): - for f in sorted(results_dir.glob("ml-decisions-*.jsonl")): - lines = 0 - try: - with open(f) as fh: - lines = sum(1 for _ in fh) - except Exception: - pass - ml_files.append({ - "file": f.name, - "decisions": lines, - "size_kb": round(f.stat().st_size / 1024, 1), - }) - total_decisions = sum(f["decisions"] for f in ml_files) - return { - "ml_logging_enabled": _shared._ml_logging_enabled, - "training_files": ml_files, - "total_decisions": total_decisions, - "total_files": len(ml_files), - } - - -@router.post("/api/ml/toggle") -async def ml_toggle(enable: bool = True): - """Enable or disable ML decision logging for future batch runs.""" - import routes.shared as _shared - _shared._ml_logging_enabled = enable - return { - "ml_logging_enabled": _shared._ml_logging_enabled, - "message": f"ML decision logging {'enabled' if enable else 'disabled'} for future batches", - } - - -@router.get("/api/ml/decisions/{filename}") -async def ml_get_decisions(filename: str, limit: int = 100, offset: int = 0): - """Read decision snapshots from a training data file.""" - lab_root = Path(__file__).resolve().parent.parent - filepath = lab_root / CFG.results_dir / filename - if not filepath.exists() or not filename.startswith("ml-decisions-"): - raise HTTPException(404, f"ML decisions file not found: {filename}") - decisions = [] - try: - with open(filepath) as f: - for i, line in enumerate(f): - if i < offset: - continue - if len(decisions) >= limit: - break - try: - decisions.append(json.loads(line.strip())) - except json.JSONDecodeError: - continue - except Exception as e: - raise HTTPException(500, f"Failed to read decisions: {e}") - return { - "file": filename, - "offset": offset, - "limit": limit, - "count": len(decisions), - "decisions": decisions, - } - - -# ============================================================== -# ML Policy Inference Endpoints -# ============================================================== - -@router.post("/api/ml/predict") -async def ml_predict(request: FastAPIRequest): - """Predict a macro-action from a game state snapshot.""" - svc = _get_policy_service() - if svc is None or not svc._loaded: - detail = "Policy model not loaded. " - if svc: - detail += svc._load_error or "Train a model first." - else: - detail += "PyTorch may not be installed." - raise HTTPException(status_code=503, detail=detail) - body = await request.json() - playstyle = body.pop("archetype", "midrange") - temperature = body.pop("temperature", 1.0) - greedy = body.pop("greedy", False) - result = svc.predict( - decision_snapshot=body, - playstyle=playstyle, - temperature=temperature, - greedy=greedy, - ) - if "error" in result: - raise HTTPException(status_code=500, detail=result["error"]) - return result - - -@router.post("/api/ml/predict/batch") -async def ml_predict_batch(request: FastAPIRequest): - """Predict actions for multiple snapshots at once.""" - svc = _get_policy_service() - if svc is None or not svc._loaded: - raise HTTPException(status_code=503, detail="Policy model not loaded") - body = await request.json() - snapshots = body.get("snapshots", []) - greedy = body.get("greedy", True) - if not snapshots: - return {"results": []} - results = svc.predict_batch(snapshots, greedy=greedy) - return {"results": results, "count": len(results)} - - -@router.get("/api/ml/model") -async def ml_model_info(): - """Get information about the loaded policy model.""" - svc = _get_policy_service() - if svc is None: - return { - "loaded": False, - "error": "Policy service not initialized", - "torch_available": False, - } - return svc.get_status() - - -@router.post("/api/ml/reload") -async def ml_reload_model(checkpoint: str = None): - """Hot-reload a policy model checkpoint.""" - svc = _get_policy_service() - if svc is None: - raise HTTPException(status_code=503, detail="Policy service not available") - if not svc._loaded: - ok = svc.load() - else: - ok = svc.reload(checkpoint) - return { - "success": ok, - "status": svc.get_status(), - } - - -# ============================================================== -# ML Training Management Endpoints -# ============================================================== - -def _run_training_pipeline( - results_dir: str, epochs: int, lr: float, - batch_size: int, patience: int, rebuild_dataset: bool, -): - """Run the full ML training pipeline in a background thread.""" - global _policy_service, _policy_service_init_attempted - try: - import sys - project_root = str(Path(__file__).resolve().parent.parent) - if project_root not in sys.path: - sys.path.insert(0, project_root) - with _training_lock: - _training_state.running = True - _training_state.started_at = datetime.now().isoformat() - _training_state.error = None - _training_state.result = None - data_dir = os.path.join(project_root, "ml", "models") - ckpt_dir = os.path.join(project_root, "ml", "models", "checkpoints") - os.makedirs(data_dir, exist_ok=True) - os.makedirs(ckpt_dir, exist_ok=True) - # Phase 1: Build Dataset - train_path = os.path.join(data_dir, "train.npz") - if rebuild_dataset or not os.path.exists(train_path): - with _training_lock: - _training_state.phase = "building" - _training_state.message = "Loading card embeddings & building dataset..." - log_ml.info("Building dataset (loading embeddings, may auto-download)...") - from ml.data.dataset_builder import build_dataset, split_dataset, save_dataset - dataset = build_dataset(results_dir=results_dir) - if not dataset: - raise RuntimeError("No training data produced. Check server log for details.") - train_ds, val_ds, test_ds = split_dataset(dataset) - save_dataset(train_ds, os.path.join(data_dir, "train.npz")) - save_dataset(val_ds, os.path.join(data_dir, "val.npz")) - save_dataset(test_ds, os.path.join(data_dir, "test.npz")) - total_samples = len(dataset["states"]) - with _training_lock: - _training_state.message = f"Dataset built: {total_samples} samples" - log_ml.info(f"Dataset built: {total_samples} samples") - # Phase 2: Train - with _training_lock: - _training_state.phase = "training" - _training_state.total_epochs = epochs - _training_state.current_epoch = 0 - _training_state.message = f"Training policy network ({epochs} epochs)..." - log_ml.info(f"Starting training: {epochs} epochs, lr={lr}, bs={batch_size}") - import numpy as np - import torch - from ml.training.policy_network import PolicyNetwork - from ml.training.trainer import SupervisedTrainer - def load_npz_split(path): - data = np.load(path) - return data["states"].astype(np.float32), data["labels"].astype(np.int64) - train_states, train_labels = load_npz_split(train_path) - val_path = os.path.join(data_dir, "val.npz") - val_states, val_labels = load_npz_split(val_path) - actual_dim = train_states.shape[1] - model = PolicyNetwork(input_dim=actual_dim) - device = "cpu" - if torch.cuda.is_available(): - device = "cuda" - elif hasattr(torch.backends, "mps") and torch.backends.mps.is_available(): - device = "mps" - trainer = SupervisedTrainer( - model=model, device=device, learning_rate=lr, - batch_size=batch_size, epochs=epochs, patience=patience, - checkpoint_dir=ckpt_dir, - ) - summary = trainer.train(train_states, train_labels, val_states, val_labels) - with _training_lock: - _training_state.current_epoch = summary.get("epochs_trained", epochs) - _training_state.metrics = summary - # Phase 3: Evaluate - with _training_lock: - _training_state.phase = "evaluating" - _training_state.message = "Evaluating on test set..." - test_path = os.path.join(data_dir, "test.npz") - eval_results = None - if os.path.exists(test_path): - from ml.training.trainer import load_checkpoint, evaluate_model - test_states, test_labels = load_npz_split(test_path) - best_model, _ = load_checkpoint(summary["checkpoint_path"], device) - eval_results = evaluate_model(best_model, test_states, test_labels, device) - eval_path = os.path.join(ckpt_dir, "eval_results.json") - with open(eval_path, "w") as f: - json.dump(eval_results, f, indent=2) - # Done - with _training_lock: - _training_state.phase = "done" - _training_state.running = False - _training_state.result = { - "training": summary, - "evaluation": eval_results, - "checkpoint": summary.get("checkpoint_path", ""), - "device": device, - } - _training_state.message = f"Training complete! Best val acc: {summary.get('best_val_acc', 0):.1%}" - log_ml.info(f"Complete: {summary.get('best_val_acc', 0):.1%} val accuracy") - # Auto-reload policy server - if _policy_service and _policy_service._loaded: - _policy_service.reload(summary.get("checkpoint_path")) - log_ml.info("Policy server reloaded with new checkpoint") - elif not _policy_service_init_attempted: - _policy_service_init_attempted = False - except Exception as e: - import traceback - with _training_lock: - _training_state.phase = "error" - _training_state.running = False - _training_state.error = str(e) - _training_state.message = f"Training failed: {e}" - log_ml.error(f"ERROR: {e}") - traceback.print_exc() - - -@router.post("/api/ml/train") -async def ml_start_training(request: FastAPIRequest): - """Trigger ML training pipeline from the web UI.""" - if _training_state.running: - raise HTTPException(409, "Training already in progress") - body = await request.json() if await request.body() else {} - epochs = body.get("epochs", 50) - lr = body.get("lr", 0.001) - batch_size = body.get("batchSize", 256) - patience = body.get("patience", 10) - rebuild = body.get("rebuildDataset", True) - results_dir = os.path.join(str(Path(__file__).resolve().parent.parent), "results") - with _training_lock: - _training_state.running = True - _training_state.progress = 0 - _training_state.total_epochs = epochs - _training_state.current_epoch = 0 - _training_state.phase = "starting" - _training_state.message = "Initializing training pipeline..." - _training_state.metrics = None - _training_state.result = None - _training_state.error = None - thread = threading.Thread( - target=_run_training_pipeline, - args=(results_dir, epochs, lr, batch_size, patience, rebuild), - daemon=True, - ) - thread.start() - return { - "status": "started", - "config": { - "epochs": epochs, "lr": lr, "batchSize": batch_size, - "patience": patience, "rebuildDataset": rebuild, - }, - } - - -@router.get("/api/ml/train") -async def ml_training_status_poll(): - """Return current training status (polled by Unity client).""" - with _training_lock: - return _training_state.snapshot() - - -@router.get("/api/ml/train/status") -async def ml_training_status(): - """Get current training pipeline status.""" - with _training_lock: - return _training_state.snapshot() - - -@router.get("/api/ml/data/status") -async def ml_data_status(): - """Get status of available training data and model checkpoints.""" - project_root = Path(__file__).resolve().parent.parent - results_dir = project_root / "results" - data_dir = project_root / "ml" / "models" - ckpt_dir = data_dir / "checkpoints" - decision_files = [] - if results_dir.exists(): - for f in sorted(results_dir.glob("ml-decisions-*.jsonl")): - line_count = 0 - try: - with open(f, "r") as fh: - line_count = sum(1 for _ in fh) - except Exception: - pass - decision_files.append({"name": f.name, "size": f.stat().st_size, "decisions": line_count}) - datasets = {} - for split in ["train", "val", "test"]: - path = data_dir / f"{split}.npz" - if path.exists(): - try: - import numpy as np - data = np.load(str(path)) - datasets[split] = { - "samples": int(data["states"].shape[0]), - "features": int(data["states"].shape[1]), - "size": path.stat().st_size, - } - except Exception: - datasets[split] = {"size": path.stat().st_size} - checkpoints = [] - if ckpt_dir.exists(): - for f in sorted(ckpt_dir.glob("*.pt")): - checkpoints.append({ - "name": f.name, - "size": f.stat().st_size, - "modified": datetime.fromtimestamp(f.stat().st_mtime).isoformat(), - }) - eval_path = ckpt_dir / "eval_results.json" - eval_results = None - if eval_path.exists(): - try: - with open(eval_path, "r") as f: - eval_results = json.load(f) - except Exception: - pass - return { - "decisionFiles": decision_files, - "totalDecisions": sum(d["decisions"] for d in decision_files), - "datasets": datasets, - "checkpoints": checkpoints, - "evalResults": eval_results, - "policyLoaded": _policy_service is not None and _policy_service._loaded if _policy_service else False, - } - - -# ============================================================== -# PPO Training + Tournament Endpoints -# ============================================================== - -def _run_ppo_pipeline( - iterations: int, episodes_per_iter: int, ppo_epochs: int, - batch_size: int, lr: float, clip_epsilon: float, - entropy_coeff: float, opponent: str, playstyle: str, - load_supervised: str, -): - """Run PPO training in a background thread.""" - try: - import sys - project_root = str(Path(__file__).resolve().parent.parent) - if project_root not in sys.path: - sys.path.insert(0, project_root) - with _ppo_lock: - _ppo_state.running = True - _ppo_state.error = None - _ppo_state.result = None - from ml.training.ppo_trainer import PPOTrainer, PPOConfig - ckpt_dir = os.path.join(project_root, "ml", "models", "checkpoints") - config = PPOConfig( - iterations=iterations, episodes_per_iter=episodes_per_iter, - ppo_epochs=ppo_epochs, batch_size=batch_size, - learning_rate=lr, clip_epsilon=clip_epsilon, - entropy_coeff=entropy_coeff, opponent=opponent, - playstyle=playstyle, checkpoint_dir=ckpt_dir, - load_supervised=load_supervised if load_supervised else None, - ) - trainer = PPOTrainer(config) - def progress_cb(iteration, metrics): - with _ppo_lock: - _ppo_state.iteration = iteration - _ppo_state.phase = "training" - _ppo_state.message = f"Iteration {iteration}/{iterations} | WR: {metrics.get('win_rate', 0):.0%}" - _ppo_state.metrics = metrics - summary = trainer.train(progress_callback=progress_cb) - with _ppo_lock: - _ppo_state.phase = "done" - _ppo_state.running = False - _ppo_state.result = summary - _ppo_state.message = f"PPO complete! Best win rate: {summary.get('best_win_rate', 0):.0%}" - except Exception as e: - import traceback - with _ppo_lock: - _ppo_state.phase = "error" - _ppo_state.running = False - _ppo_state.error = str(e) - _ppo_state.message = f"PPO failed: {e}" - traceback.print_exc() - - -@router.post("/api/ml/train/ppo") -async def ml_start_ppo(request: FastAPIRequest): - """Start PPO training pipeline.""" - if _ppo_state.running: - raise HTTPException(409, "PPO training already in progress") - if _training_state.running: - raise HTTPException(409, "Supervised training in progress -- wait for it to finish") - body = await request.json() if await request.body() else {} - iterations = body.get("iterations", 100) - episodes = body.get("episodesPerIter", 64) - ppo_epochs = body.get("ppoEpochs", 4) - batch_size = body.get("batchSize", 256) - lr = body.get("lr", 3e-4) - clip_eps = body.get("clipEpsilon", 0.2) - entropy = body.get("entropyCoeff", 0.01) - opponent = body.get("opponent", "heuristic") - playstyle = body.get("playstyle", "midrange") - load_sup = body.get("loadSupervised", "") - with _ppo_lock: - _ppo_state.running = True - _ppo_state.iteration = 0 - _ppo_state.total_iterations = iterations - _ppo_state.phase = "starting" - _ppo_state.message = "Initializing PPO..." - _ppo_state.metrics = None - _ppo_state.result = None - _ppo_state.error = None - thread = threading.Thread( - target=_run_ppo_pipeline, - args=(iterations, episodes, ppo_epochs, batch_size, lr, - clip_eps, entropy, opponent, playstyle, load_sup), - daemon=True, - ) - thread.start() - return {"status": "started", "iterations": iterations} - - -@router.get("/api/ml/train/ppo/status") -async def ml_ppo_status(): - """Get PPO training status.""" - with _ppo_lock: - return _ppo_state.snapshot() - - -def _run_tournament_pipeline(episodes: int, playstyle: str): - """Run tournament in a background thread.""" - try: - import sys - project_root = str(Path(__file__).resolve().parent.parent) - if project_root not in sys.path: - sys.path.insert(0, project_root) - with _tournament_lock: - _tournament_state.running = True - _tournament_state.error = None - _tournament_state.result = None - _tournament_state.phase = "running" - _tournament_state.message = "Running tournament..." - from ml.eval.tournament import ( - run_tournament, HeuristicPolicy, RandomPolicy, LearnedPolicy, - ) - ckpt_dir = os.path.join(project_root, "ml", "models", "checkpoints") - policies = { - "heuristic": HeuristicPolicy(), - "random": RandomPolicy(), - } - sup_path = os.path.join(ckpt_dir, "best_policy.pt") - if os.path.exists(sup_path): - policies["supervised"] = LearnedPolicy(sup_path) - ppo_path = os.path.join(ckpt_dir, "best_ppo.pt") - if os.path.exists(ppo_path): - policies["ppo"] = LearnedPolicy(ppo_path) - result = run_tournament( - policies=policies, - episodes_per_matchup=episodes, - playstyle=playstyle, - ) - output_path = os.path.join(ckpt_dir, "tournament_results.json") - with open(output_path, "w") as f: - json.dump(result.to_dict(), f, indent=2) - with _tournament_lock: - _tournament_state.phase = "done" - _tournament_state.running = False - _tournament_state.result = result.to_dict() - _tournament_state.message = f"Tournament complete! {result.total_matches} matches" - except Exception as e: - import traceback - with _tournament_lock: - _tournament_state.phase = "error" - _tournament_state.running = False - _tournament_state.error = str(e) - _tournament_state.message = f"Tournament failed: {e}" - traceback.print_exc() - - -@router.post("/api/ml/tournament") -async def ml_start_tournament(request: FastAPIRequest): - """Start a tournament evaluation.""" - if _tournament_state.running: - raise HTTPException(409, "Tournament already in progress") - body = await request.json() if await request.body() else {} - episodes = body.get("episodes", 50) - playstyle = body.get("playstyle", "midrange") - with _tournament_lock: - _tournament_state.running = True - _tournament_state.phase = "starting" - _tournament_state.message = "Initializing tournament..." - _tournament_state.result = None - _tournament_state.error = None - thread = threading.Thread( - target=_run_tournament_pipeline, - args=(episodes, playstyle), - daemon=True, - ) - thread.start() - return {"status": "started", "episodes": episodes} - - -@router.get("/api/ml/tournament/status") -async def ml_tournament_status(): - """Get tournament status.""" - with _tournament_lock: - return _tournament_state.snapshot() - - -@router.get("/api/ml/tournament/results") -async def ml_tournament_results(): - """Get latest tournament results.""" - project_root = Path(__file__).resolve().parent.parent - results_path = project_root / "ml" / "models" / "checkpoints" / "tournament_results.json" - if results_path.exists(): - with open(results_path, "r") as f: - return json.load(f) - return {"error": "No tournament results found. Run a tournament first."} - - -# ============================================================== -# Model listing and stats endpoints -# ============================================================== - -@router.get("/api/ml/models") -async def ml_list_models(): - """List available policy model checkpoints.""" - project_root = Path(__file__).resolve().parent.parent - ckpt_dir = project_root / "ml" / "models" / "checkpoints" - checkpoints = [] - if ckpt_dir.exists(): - for f in sorted(ckpt_dir.glob("*.pt")): - checkpoints.append({ - "name": f.name, - "path": str(f), - "size_kb": round(f.stat().st_size / 1024, 1), - "modified": datetime.fromtimestamp(f.stat().st_mtime).isoformat(), - }) - svc = _get_policy_service() - return { - "models": checkpoints, - "count": len(checkpoints), - "active_model": svc.get_status() if svc else None, - } - - -@router.get("/api/ml/stats") -async def ml_stats(): - """Return aggregate ML training statistics and model performance metrics.""" - project_root = Path(__file__).resolve().parent.parent - ckpt_dir = project_root / "ml" / "models" / "checkpoints" - eval_results = None - eval_path = ckpt_dir / "eval_results.json" - if eval_path.exists(): - try: - with open(eval_path, "r") as f: - eval_results = json.load(f) - except Exception: - pass - with _training_lock: - training_snapshot = _training_state.snapshot() - with _ppo_lock: - ppo_snapshot = _ppo_state.snapshot() - svc = _get_policy_service() - return { - "training": training_snapshot, - "ppo": ppo_snapshot, - "eval_results": eval_results, - "policy_loaded": svc._loaded if svc else False, - } +""" +routes/ml.py +============ +ML training data, policy inference, supervised training, +PPO training, and tournament endpoints. + + GET /api/ml/status + GET /api/ml/decisions/{filename} + GET /api/ml/data/status + POST /api/ml/toggle + POST /api/ml/predict + POST /api/ml/predict/batch + GET /api/ml/model + POST /api/ml/reload + POST /api/ml/train + GET /api/ml/train/status + POST /api/ml/train/ppo + GET /api/ml/train/ppo/status + POST /api/ml/tournament + GET /api/ml/tournament/status + GET /api/ml/tournament/results +""" +from __future__ import annotations + +import dataclasses +import json +import logging +import os +import threading +from datetime import datetime +from pathlib import Path + +from fastapi import APIRouter, HTTPException, Request as FastAPIRequest + +from routes.shared import CFG, _ml_logging_enabled + +log_ml = logging.getLogger("commander_ai_lab.ml") + +router = APIRouter(tags=["ml"]) + +# ── module-level singletons ────────────────────────────────────────────────── +_policy_service = None +_policy_service_init_attempted = False + +# ── Thread-safe state objects ──────────────────────────────────────────────── +@dataclasses.dataclass +class _TrainingState: + running: bool = False + progress: int = 0 + total_epochs: int = 0 + current_epoch: int = 0 + phase: str = "idle" + message: str = "" + metrics: object = None + result: object = None + error: object = None + started_at: object = None + + def snapshot(self) -> dict: + return dataclasses.asdict(self) + +@dataclasses.dataclass +class _PPOState: + running: bool = False + iteration: int = 0 + total_iterations: int = 0 + phase: str = "idle" + message: str = "" + metrics: object = None + result: object = None + error: object = None + + def snapshot(self) -> dict: + return dataclasses.asdict(self) + +@dataclasses.dataclass +class _TournamentState: + running: bool = False + phase: str = "idle" + message: str = "" + result: object = None + error: object = None + + def snapshot(self) -> dict: + return dataclasses.asdict(self) + +_training_state = _TrainingState() +_training_lock = threading.Lock() + +_ppo_state = _PPOState() +_ppo_lock = threading.Lock() + +_tournament_state = _TournamentState() +_tournament_lock = threading.Lock() + +# module-level toggle (local copy; the shared one is for batch runs) +_ml_logging_enabled_local = _ml_logging_enabled + + +def _get_policy_service(): + """Get or initialize the policy inference service (lazy loading).""" + global _policy_service, _policy_service_init_attempted + if _policy_service is None and not _policy_service_init_attempted: + _policy_service_init_attempted = True + try: + from ml.serving.policy_server import PolicyInferenceService + _policy_service = PolicyInferenceService() + if _policy_service.load(): + log_ml.info(f"Policy model loaded on {_policy_service.device}") + else: + log_ml.error(f"Policy model not available: {_policy_service._load_error}") + except Exception as e: + log_ml.error(f"Policy service init failed: {e}") + return _policy_service + + +# ============================================================== +# ML Training Data Endpoints +# ============================================================== + +@router.get("/api/ml/status") +async def ml_status(): + """Get ML decision logging status and available training data.""" + import routes.shared as _shared + lab_root = Path(__file__).resolve().parent.parent + results_dir = lab_root / CFG.results_dir + ml_files = [] + if results_dir.exists(): + for f in sorted(results_dir.glob("ml-decisions-*.jsonl")): + lines = 0 + try: + with open(f) as fh: + lines = sum(1 for _ in fh) + except Exception: + pass + ml_files.append({ + "file": f.name, + "decisions": lines, + "size_kb": round(f.stat().st_size / 1024, 1), + }) + total_decisions = sum(f["decisions"] for f in ml_files) + return { + "ml_logging_enabled": _shared._ml_logging_enabled, + "training_files": ml_files, + "total_decisions": total_decisions, + "total_files": len(ml_files), + } + + +@router.post("/api/ml/toggle") +async def ml_toggle(enable: bool = True): + """Enable or disable ML decision logging for future batch runs.""" + import routes.shared as _shared + _shared._ml_logging_enabled = enable + return { + "ml_logging_enabled": _shared._ml_logging_enabled, + "message": f"ML decision logging {'enabled' if enable else 'disabled'} for future batches", + } + + +@router.get("/api/ml/decisions/{filename}") +async def ml_get_decisions(filename: str, limit: int = 100, offset: int = 0): + """Read decision snapshots from a training data file.""" + lab_root = Path(__file__).resolve().parent.parent + filepath = lab_root / CFG.results_dir / filename + if not filepath.exists() or not filename.startswith("ml-decisions-"): + raise HTTPException(404, f"ML decisions file not found: {filename}") + decisions = [] + try: + with open(filepath) as f: + for i, line in enumerate(f): + if i < offset: + continue + if len(decisions) >= limit: + break + try: + decisions.append(json.loads(line.strip())) + except json.JSONDecodeError: + continue + except Exception as e: + raise HTTPException(500, f"Failed to read decisions: {e}") + return { + "file": filename, + "offset": offset, + "limit": limit, + "count": len(decisions), + "decisions": decisions, + } + + +# ============================================================== +# ML Policy Inference Endpoints +# ============================================================== + +@router.post("/api/ml/predict") +async def ml_predict(request: FastAPIRequest): + """Predict a macro-action from a game state snapshot.""" + svc = _get_policy_service() + if svc is None or not svc._loaded: + detail = "Policy model not loaded. " + if svc: + detail += svc._load_error or "Train a model first." + else: + detail += "PyTorch may not be installed." + raise HTTPException(status_code=503, detail=detail) + body = await request.json() + playstyle = body.pop("archetype", "midrange") + temperature = body.pop("temperature", 1.0) + greedy = body.pop("greedy", False) + result = svc.predict( + decision_snapshot=body, + playstyle=playstyle, + temperature=temperature, + greedy=greedy, + ) + if "error" in result: + raise HTTPException(status_code=500, detail=result["error"]) + return result + + +@router.post("/api/ml/predict/batch") +async def ml_predict_batch(request: FastAPIRequest): + """Predict actions for multiple snapshots at once.""" + svc = _get_policy_service() + if svc is None or not svc._loaded: + raise HTTPException(status_code=503, detail="Policy model not loaded") + body = await request.json() + snapshots = body.get("snapshots", []) + greedy = body.get("greedy", True) + if not snapshots: + return {"results": []} + results = svc.predict_batch(snapshots, greedy=greedy) + return {"results": results, "count": len(results)} + + +@router.get("/api/ml/model") +async def ml_model_info(): + """Get information about the loaded policy model.""" + svc = _get_policy_service() + if svc is None: + return { + "loaded": False, + "error": "Policy service not initialized", + "torch_available": False, + } + return svc.get_status() + + +@router.post("/api/ml/reload") +async def ml_reload_model(checkpoint: str = None): + """Hot-reload a policy model checkpoint.""" + svc = _get_policy_service() + if svc is None: + raise HTTPException(status_code=503, detail="Policy service not available") + if not svc._loaded: + ok = svc.load() + else: + ok = svc.reload(checkpoint) + return { + "success": ok, + "status": svc.get_status(), + } + + +# ============================================================== +# ML Training Management Endpoints +# ============================================================== + +def _run_training_pipeline( + results_dir: str, epochs: int, lr: float, + batch_size: int, patience: int, rebuild_dataset: bool, +): + """Run the full ML training pipeline in a background thread.""" + global _policy_service, _policy_service_init_attempted + try: + import sys + project_root = str(Path(__file__).resolve().parent.parent) + if project_root not in sys.path: + sys.path.insert(0, project_root) + with _training_lock: + _training_state.running = True + _training_state.started_at = datetime.now().isoformat() + _training_state.error = None + _training_state.result = None + data_dir = os.path.join(project_root, "ml", "models") + ckpt_dir = os.path.join(project_root, "ml", "models", "checkpoints") + os.makedirs(data_dir, exist_ok=True) + os.makedirs(ckpt_dir, exist_ok=True) + # Phase 1: Build Dataset + train_path = os.path.join(data_dir, "train.npz") + if rebuild_dataset or not os.path.exists(train_path): + with _training_lock: + _training_state.phase = "building" + _training_state.message = "Loading card embeddings & building dataset..." + log_ml.info("Building dataset (loading embeddings, may auto-download)...") + from ml.data.dataset_builder import build_dataset, split_dataset, save_dataset + dataset = build_dataset(results_dir=results_dir) + if not dataset: + raise RuntimeError("No training data produced. Check server log for details.") + train_ds, val_ds, test_ds = split_dataset(dataset) + save_dataset(train_ds, os.path.join(data_dir, "train.npz")) + save_dataset(val_ds, os.path.join(data_dir, "val.npz")) + save_dataset(test_ds, os.path.join(data_dir, "test.npz")) + total_samples = len(dataset["states"]) + with _training_lock: + _training_state.message = f"Dataset built: {total_samples} samples" + log_ml.info(f"Dataset built: {total_samples} samples") + # Phase 2: Train + with _training_lock: + _training_state.phase = "training" + _training_state.total_epochs = epochs + _training_state.current_epoch = 0 + _training_state.message = f"Training policy network ({epochs} epochs)..." + log_ml.info(f"Starting training: {epochs} epochs, lr={lr}, bs={batch_size}") + import numpy as np + import torch + from ml.training.policy_network import PolicyNetwork + from ml.training.trainer import SupervisedTrainer + def load_npz_split(path): + data = np.load(path) + return data["states"].astype(np.float32), data["labels"].astype(np.int64) + train_states, train_labels = load_npz_split(train_path) + val_path = os.path.join(data_dir, "val.npz") + val_states, val_labels = load_npz_split(val_path) + actual_dim = train_states.shape[1] + model = PolicyNetwork(input_dim=actual_dim) + device = "cpu" + if torch.cuda.is_available(): + device = "cuda" + elif hasattr(torch.backends, "mps") and torch.backends.mps.is_available(): + device = "mps" + trainer = SupervisedTrainer( + model=model, device=device, learning_rate=lr, + batch_size=batch_size, epochs=epochs, patience=patience, + checkpoint_dir=ckpt_dir, + ) + summary = trainer.train(train_states, train_labels, val_states, val_labels) + with _training_lock: + _training_state.current_epoch = summary.get("epochs_trained", epochs) + _training_state.metrics = summary + # Phase 3: Evaluate + with _training_lock: + _training_state.phase = "evaluating" + _training_state.message = "Evaluating on test set..." + test_path = os.path.join(data_dir, "test.npz") + eval_results = None + if os.path.exists(test_path): + from ml.training.trainer import load_checkpoint, evaluate_model + test_states, test_labels = load_npz_split(test_path) + best_model, _ = load_checkpoint(summary["checkpoint_path"], device) + eval_results = evaluate_model(best_model, test_states, test_labels, device) + eval_path = os.path.join(ckpt_dir, "eval_results.json") + with open(eval_path, "w") as f: + json.dump(eval_results, f, indent=2) + # Done + with _training_lock: + _training_state.phase = "done" + _training_state.running = False + _training_state.result = { + "training": summary, + "evaluation": eval_results, + "checkpoint": summary.get("checkpoint_path", ""), + "device": device, + } + _training_state.message = f"Training complete! Best val acc: {summary.get('best_val_acc', 0):.1%}" + log_ml.info(f"Complete: {summary.get('best_val_acc', 0):.1%} val accuracy") + # Auto-reload policy server + if _policy_service and _policy_service._loaded: + _policy_service.reload(summary.get("checkpoint_path")) + log_ml.info("Policy server reloaded with new checkpoint") + elif not _policy_service_init_attempted: + _policy_service_init_attempted = False + except Exception as e: + import traceback + with _training_lock: + _training_state.phase = "error" + _training_state.running = False + _training_state.error = str(e) + _training_state.message = f"Training failed: {e}" + log_ml.error(f"ERROR: {e}") + traceback.print_exc() + + +@router.post("/api/ml/train") +async def ml_start_training(request: FastAPIRequest): + """Trigger ML training pipeline from the web UI.""" + if _training_state.running: + raise HTTPException(409, "Training already in progress") + body = await request.json() if await request.body() else {} + epochs = body.get("epochs", 50) + lr = body.get("lr", 0.001) + batch_size = body.get("batchSize", 256) + patience = body.get("patience", 10) + rebuild = body.get("rebuildDataset", True) + results_dir = os.path.join(str(Path(__file__).resolve().parent.parent), "results") + with _training_lock: + _training_state.running = True + _training_state.progress = 0 + _training_state.total_epochs = epochs + _training_state.current_epoch = 0 + _training_state.phase = "starting" + _training_state.message = "Initializing training pipeline..." + _training_state.metrics = None + _training_state.result = None + _training_state.error = None + thread = threading.Thread( + target=_run_training_pipeline, + args=(results_dir, epochs, lr, batch_size, patience, rebuild), + daemon=True, + ) + thread.start() + return { + "status": "started", + "config": { + "epochs": epochs, "lr": lr, "batchSize": batch_size, + "patience": patience, "rebuildDataset": rebuild, + }, + } + + +@router.get("/api/ml/train") +async def ml_training_status_poll(): + """Return current training status (polled by Unity client).""" + with _training_lock: + return _training_state.snapshot() + + +@router.get("/api/ml/train/status") +async def ml_training_status(): + """Get current training pipeline status.""" + with _training_lock: + return _training_state.snapshot() + + +@router.get("/api/ml/data/status") +async def ml_data_status(): + """Get status of available training data and model checkpoints.""" + project_root = Path(__file__).resolve().parent.parent + results_dir = project_root / "results" + data_dir = project_root / "ml" / "models" + ckpt_dir = data_dir / "checkpoints" + decision_files = [] + if results_dir.exists(): + for f in sorted(results_dir.glob("ml-decisions-*.jsonl")): + line_count = 0 + try: + with open(f, "r") as fh: + line_count = sum(1 for _ in fh) + except Exception: + pass + decision_files.append({"name": f.name, "size": f.stat().st_size, "decisions": line_count}) + datasets = {} + for split in ["train", "val", "test"]: + path = data_dir / f"{split}.npz" + if path.exists(): + try: + import numpy as np + data = np.load(str(path)) + datasets[split] = { + "samples": int(data["states"].shape[0]), + "features": int(data["states"].shape[1]), + "size": path.stat().st_size, + } + except Exception: + datasets[split] = {"size": path.stat().st_size} + checkpoints = [] + if ckpt_dir.exists(): + for f in sorted(ckpt_dir.glob("*.pt")): + checkpoints.append({ + "name": f.name, + "size": f.stat().st_size, + "modified": datetime.fromtimestamp(f.stat().st_mtime).isoformat(), + }) + eval_path = ckpt_dir / "eval_results.json" + eval_results = None + if eval_path.exists(): + try: + with open(eval_path, "r") as f: + eval_results = json.load(f) + except Exception: + pass + return { + "decisionFiles": decision_files, + "totalDecisions": sum(d["decisions"] for d in decision_files), + "datasets": datasets, + "checkpoints": checkpoints, + "evalResults": eval_results, + "policyLoaded": _policy_service is not None and _policy_service._loaded if _policy_service else False, + } + + +# ============================================================== +# PPO Training + Tournament Endpoints +# ============================================================== + +def _run_ppo_pipeline( + iterations: int, episodes_per_iter: int, ppo_epochs: int, + batch_size: int, lr: float, clip_epsilon: float, + entropy_coeff: float, opponent: str, playstyle: str, + load_supervised: str, +): + """Run PPO training in a background thread.""" + try: + import sys + project_root = str(Path(__file__).resolve().parent.parent) + if project_root not in sys.path: + sys.path.insert(0, project_root) + with _ppo_lock: + _ppo_state.running = True + _ppo_state.error = None + _ppo_state.result = None + from ml.training.ppo_trainer import PPOTrainer, PPOConfig + ckpt_dir = os.path.join(project_root, "ml", "models", "checkpoints") + config = PPOConfig( + iterations=iterations, episodes_per_iter=episodes_per_iter, + ppo_epochs=ppo_epochs, batch_size=batch_size, + learning_rate=lr, clip_epsilon=clip_epsilon, + entropy_coeff=entropy_coeff, opponent=opponent, + playstyle=playstyle, checkpoint_dir=ckpt_dir, + load_supervised=load_supervised if load_supervised else None, + ) + trainer = PPOTrainer(config) + def progress_cb(iteration, metrics): + with _ppo_lock: + _ppo_state.iteration = iteration + _ppo_state.phase = "training" + _ppo_state.message = f"Iteration {iteration}/{iterations} | WR: {metrics.get('win_rate', 0):.0%}" + _ppo_state.metrics = metrics + summary = trainer.train(progress_callback=progress_cb) + with _ppo_lock: + _ppo_state.phase = "done" + _ppo_state.running = False + _ppo_state.result = summary + _ppo_state.message = f"PPO complete! Best win rate: {summary.get('best_win_rate', 0):.0%}" + except Exception as e: + import traceback + with _ppo_lock: + _ppo_state.phase = "error" + _ppo_state.running = False + _ppo_state.error = str(e) + _ppo_state.message = f"PPO failed: {e}" + traceback.print_exc() + + +@router.post("/api/ml/train/ppo") +async def ml_start_ppo(request: FastAPIRequest): + """Start PPO training pipeline.""" + if _ppo_state.running: + raise HTTPException(409, "PPO training already in progress") + if _training_state.running: + raise HTTPException(409, "Supervised training in progress -- wait for it to finish") + body = await request.json() if await request.body() else {} + iterations = body.get("iterations", 100) + episodes = body.get("episodesPerIter", 64) + ppo_epochs = body.get("ppoEpochs", 4) + batch_size = body.get("batchSize", 256) + lr = body.get("lr", 3e-4) + clip_eps = body.get("clipEpsilon", 0.2) + entropy = body.get("entropyCoeff", 0.01) + opponent = body.get("opponent", "heuristic") + playstyle = body.get("playstyle", "midrange") + load_sup = body.get("loadSupervised", "") + with _ppo_lock: + _ppo_state.running = True + _ppo_state.iteration = 0 + _ppo_state.total_iterations = iterations + _ppo_state.phase = "starting" + _ppo_state.message = "Initializing PPO..." + _ppo_state.metrics = None + _ppo_state.result = None + _ppo_state.error = None + thread = threading.Thread( + target=_run_ppo_pipeline, + args=(iterations, episodes, ppo_epochs, batch_size, lr, + clip_eps, entropy, opponent, playstyle, load_sup), + daemon=True, + ) + thread.start() + return {"status": "started", "iterations": iterations} + + +@router.get("/api/ml/train/ppo/status") +async def ml_ppo_status(): + """Get PPO training status.""" + with _ppo_lock: + return _ppo_state.snapshot() + + +def _run_tournament_pipeline(episodes: int, playstyle: str): + """Run tournament in a background thread.""" + try: + import sys + project_root = str(Path(__file__).resolve().parent.parent) + if project_root not in sys.path: + sys.path.insert(0, project_root) + with _tournament_lock: + _tournament_state.running = True + _tournament_state.error = None + _tournament_state.result = None + _tournament_state.phase = "running" + _tournament_state.message = "Running tournament..." + from ml.eval.tournament import ( + run_tournament, HeuristicPolicy, RandomPolicy, LearnedPolicy, + ) + ckpt_dir = os.path.join(project_root, "ml", "models", "checkpoints") + policies = { + "heuristic": HeuristicPolicy(), + "random": RandomPolicy(), + } + sup_path = os.path.join(ckpt_dir, "best_policy.pt") + if os.path.exists(sup_path): + policies["supervised"] = LearnedPolicy(sup_path) + ppo_path = os.path.join(ckpt_dir, "best_ppo.pt") + if os.path.exists(ppo_path): + policies["ppo"] = LearnedPolicy(ppo_path) + result = run_tournament( + policies=policies, + episodes_per_matchup=episodes, + playstyle=playstyle, + ) + output_path = os.path.join(ckpt_dir, "tournament_results.json") + with open(output_path, "w") as f: + json.dump(result.to_dict(), f, indent=2) + with _tournament_lock: + _tournament_state.phase = "done" + _tournament_state.running = False + _tournament_state.result = result.to_dict() + _tournament_state.message = f"Tournament complete! {result.total_matches} matches" + except Exception as e: + import traceback + with _tournament_lock: + _tournament_state.phase = "error" + _tournament_state.running = False + _tournament_state.error = str(e) + _tournament_state.message = f"Tournament failed: {e}" + traceback.print_exc() + + +@router.post("/api/ml/tournament") +async def ml_start_tournament(request: FastAPIRequest): + """Start a tournament evaluation.""" + if _tournament_state.running: + raise HTTPException(409, "Tournament already in progress") + body = await request.json() if await request.body() else {} + episodes = body.get("episodes", 50) + playstyle = body.get("playstyle", "midrange") + with _tournament_lock: + _tournament_state.running = True + _tournament_state.phase = "starting" + _tournament_state.message = "Initializing tournament..." + _tournament_state.result = None + _tournament_state.error = None + thread = threading.Thread( + target=_run_tournament_pipeline, + args=(episodes, playstyle), + daemon=True, + ) + thread.start() + return {"status": "started", "episodes": episodes} + + +@router.get("/api/ml/tournament/status") +async def ml_tournament_status(): + """Get tournament status.""" + with _tournament_lock: + return _tournament_state.snapshot() + + +@router.get("/api/ml/tournament/results") +async def ml_tournament_results(): + """Get latest tournament results.""" + project_root = Path(__file__).resolve().parent.parent + results_path = project_root / "ml" / "models" / "checkpoints" / "tournament_results.json" + if results_path.exists(): + with open(results_path, "r") as f: + return json.load(f) + return {"error": "No tournament results found. Run a tournament first."} + + +# ============================================================== +# Model listing and stats endpoints +# ============================================================== + +@router.get("/api/ml/models") +async def ml_list_models(): + """List available policy model checkpoints.""" + project_root = Path(__file__).resolve().parent.parent + ckpt_dir = project_root / "ml" / "models" / "checkpoints" + checkpoints = [] + if ckpt_dir.exists(): + for f in sorted(ckpt_dir.glob("*.pt")): + checkpoints.append({ + "name": f.name, + "path": str(f), + "size_kb": round(f.stat().st_size / 1024, 1), + "modified": datetime.fromtimestamp(f.stat().st_mtime).isoformat(), + }) + svc = _get_policy_service() + return { + "models": checkpoints, + "count": len(checkpoints), + "active_model": svc.get_status() if svc else None, + } + + +@router.get("/api/ml/stats") +async def ml_stats(): + """Return aggregate ML training statistics and model performance metrics.""" + project_root = Path(__file__).resolve().parent.parent + ckpt_dir = project_root / "ml" / "models" / "checkpoints" + eval_results = None + eval_path = ckpt_dir / "eval_results.json" + if eval_path.exists(): + try: + with open(eval_path, "r") as f: + eval_results = json.load(f) + except Exception: + pass + with _training_lock: + training_snapshot = _training_state.snapshot() + with _ppo_lock: + ppo_snapshot = _ppo_state.snapshot() + svc = _get_policy_service() + return { + "training": training_snapshot, + "ppo": ppo_snapshot, + "eval_results": eval_results, + "policy_loaded": svc._loaded if svc else False, + } + + +# ============================================================== +# Distillation Loop Endpoints +# ============================================================== + +@dataclasses.dataclass +class _DistillationState: + running: bool = False + generation: int = 0 + max_iterations: int = 0 + phase: str = "idle" + message: str = "" + current_step: str = "" + generations: list = dataclasses.field(default_factory=list) + result: object = None + error: object = None + started_at: object = None + + def snapshot(self) -> dict: + return { + "running": self.running, + "generation": self.generation, + "max_iterations": self.max_iterations, + "phase": self.phase, + "message": self.message, + "current_step": self.current_step, + "generations": self.generations, + "result": self.result, + "error": self.error, + "started_at": self.started_at, + } + +_distillation_state = _DistillationState() +_distillation_lock = threading.Lock() +_distillation_loop = None # holds the DistillationLoop instance for stop() + + +def _run_distillation_pipeline( + max_iterations: int, + convergence_window: int, + convergence_threshold: float, + supervised_epochs: int, + supervised_lr: float, + ppo_iterations: int, + ppo_episodes_per_iter: int, + ppo_lr: float, + opponent: str, + playstyle: str, + min_win_rate: float, +): + """Run the distillation loop in a background thread.""" + global _distillation_loop + try: + import sys + project_root = str(Path(__file__).resolve().parent.parent) + if project_root not in sys.path: + sys.path.insert(0, project_root) + + from ml.training.distillation_loop import DistillationLoop, DistillationConfig + + cfg = DistillationConfig( + max_iterations=max_iterations, + convergence_window=convergence_window, + convergence_threshold=convergence_threshold, + supervised_epochs=supervised_epochs, + supervised_lr=supervised_lr, + ppo_iterations=ppo_iterations, + ppo_episodes_per_iter=ppo_episodes_per_iter, + ppo_lr=ppo_lr, + opponent=opponent, + playstyle=playstyle, + min_ppo_win_rate=min_win_rate, + results_dir=os.path.join(project_root, "data", "results"), + models_dir=os.path.join(project_root, "ml", "models"), + checkpoint_dir=os.path.join(project_root, "ml", "models", "checkpoints"), + history_dir=os.path.join(project_root, "data", "results", "distillation-history"), + ) + + loop = DistillationLoop(cfg) + _distillation_loop = loop + + def progress_callback(gen_num, record): + with _distillation_lock: + _distillation_state.generation = gen_num + _distillation_state.current_step = record.status + _distillation_state.message = ( + f"Generation {gen_num}: {record.status}" + + (f" — win rate {record.ppo_best_win_rate:.1%}" if record.ppo_best_win_rate > 0 else "") + ) + _distillation_state.generations = [g.to_dict() for g in loop.generations] + + with _distillation_lock: + _distillation_state.phase = "running" + _distillation_state.message = "Initializing distillation loop..." + + summary = loop.run(progress_callback=progress_callback) + + with _distillation_lock: + _distillation_state.phase = "done" + _distillation_state.running = False + _distillation_state.result = summary + _distillation_state.generations = summary.get("generations", []) + _distillation_state.message = ( + f"Distillation complete! {summary['generations_run']} generations, " + f"best win rate: {summary['best_win_rate']:.1%}" + ) + + except Exception as e: + import traceback + with _distillation_lock: + _distillation_state.phase = "error" + _distillation_state.running = False + _distillation_state.error = str(e) + _distillation_state.message = f"Distillation failed: {e}" + traceback.print_exc() + finally: + _distillation_loop = None + + +@router.post("/api/ml/distill/start") +async def ml_start_distillation(request: FastAPIRequest): + """Start the closed-loop distillation pipeline.""" + if _distillation_state.running: + raise HTTPException(409, "Distillation loop already in progress") + if _training_state.running: + raise HTTPException(409, "Supervised training in progress — wait for it to finish") + if _ppo_state.running: + raise HTTPException(409, "PPO training in progress — wait for it to finish") + + body = await request.json() if await request.body() else {} + + max_iterations = body.get("maxIterations", 10) + convergence_window = body.get("convergenceWindow", 3) + convergence_threshold = body.get("convergenceThreshold", 0.01) + supervised_epochs = body.get("supervisedEpochs", 30) + supervised_lr = body.get("supervisedLr", 1e-3) + ppo_iterations = body.get("ppoIterations", 50) + ppo_episodes_per_iter = body.get("ppoEpisodesPerIter", 64) + ppo_lr = body.get("ppoLr", 3e-4) + opponent = body.get("opponent", "heuristic") + playstyle = body.get("playstyle", "midrange") + min_win_rate = body.get("minWinRate", 0.30) + + with _distillation_lock: + _distillation_state.running = True + _distillation_state.generation = 0 + _distillation_state.max_iterations = max_iterations + _distillation_state.phase = "starting" + _distillation_state.message = "Initializing distillation loop..." + _distillation_state.current_step = "" + _distillation_state.generations = [] + _distillation_state.result = None + _distillation_state.error = None + _distillation_state.started_at = datetime.now().isoformat() + + thread = threading.Thread( + target=_run_distillation_pipeline, + args=( + max_iterations, convergence_window, convergence_threshold, + supervised_epochs, supervised_lr, + ppo_iterations, ppo_episodes_per_iter, ppo_lr, + opponent, playstyle, min_win_rate, + ), + daemon=True, + ) + thread.start() + + return {"status": "started", "max_iterations": max_iterations} + + +@router.get("/api/ml/distill/status") +async def ml_distillation_status(): + """Get distillation loop status.""" + with _distillation_lock: + return _distillation_state.snapshot() + + +@router.post("/api/ml/distill/stop") +async def ml_stop_distillation(): + """Signal the distillation loop to stop after the current generation.""" + if not _distillation_state.running: + raise HTTPException(400, "Distillation loop is not running") + + global _distillation_loop + if _distillation_loop is not None: + _distillation_loop.stop() + + with _distillation_lock: + _distillation_state.message = "Stop signal sent — finishing current generation..." + + return {"status": "stopping", "message": "Will stop after current generation completes"} + + +@router.get("/api/ml/distill/history") +async def ml_distillation_history(): + """Get distillation generation history from disk.""" + project_root = Path(__file__).resolve().parent.parent + history_path = project_root / "data" / "results" / "distillation-history" / "generations.json" + summary_path = project_root / "data" / "results" / "distillation-history" / "summary.json" + + result = {"generations": [], "summary": None} + + if history_path.exists(): + try: + with open(history_path, "r") as f: + result["generations"] = json.load(f) + except Exception: + pass + + if summary_path.exists(): + try: + with open(summary_path, "r") as f: + result["summary"] = json.load(f) + except Exception: + pass + + return result diff --git a/ui/distillation.css b/ui/distillation.css new file mode 100644 index 0000000..f2f3ba8 --- /dev/null +++ b/ui/distillation.css @@ -0,0 +1,194 @@ +/* ══════════════════════════════════════════════════════════════ + Commander AI Lab — Distillation Loop Dashboard + Page-specific styles only; shared tokens & components in global.css + ══════════════════════════════════════════════════════════════ */ + +/* ── Legacy variable aliases (used by distillation.js inline styles) ── */ +:root { + --lab-bg: var(--bg); + --lab-surface: var(--surface-1); + --lab-border: var(--border); + --lab-accent: var(--accent); + --lab-accent-dim: var(--accent-dim); + --lab-text: var(--text); + --lab-text-dim: var(--text-secondary); + --lab-success: var(--success); + --lab-success-dim:var(--success-dim); + --lab-warning: var(--warning); + --lab-warning-dim:var(--warning-dim); + --lab-error: var(--error); + --lab-error-dim: var(--error-dim); + --lab-purple: var(--purple); + --lab-purple-dim: var(--purple-dim); +} + +/* ── Status Cards (extend global .stat-card) ───────────────── */ +.status-grid { + grid-template-columns: repeat(4, 1fr); +} + +.status-card { + text-align: center; +} + +.status-card.active { + border-color: var(--accent); +} + +.status-card.success { + border-color: var(--success); +} + +.status-card.error-state { + border-color: var(--error); +} + +.card-icon { + font-size: 24px; + margin-bottom: var(--sp-2); +} + +/* ── Panel heading with bottom border ──────────────────────── */ +.panel > .panel-header { + padding-bottom: var(--sp-3); + border-bottom: 1px solid var(--border); +} + +/* ── Training Controls Layout ──────────────────────────────── */ +.train-controls { + padding-top: var(--sp-4); +} + +.param-grid { + margin-bottom: var(--sp-3); +} + +.distill-bottom-params { + grid-template-columns: repeat(2, 1fr); +} + +.distill-actions { + display: flex; + gap: var(--sp-2); + margin-top: var(--sp-2); +} + +.btn-icon { + font-size: 14px; +} + +/* ── Progress Sections ─────────────────────────────────────── */ +.train-progress { + margin-top: var(--sp-5); + padding-top: var(--sp-4); + border-top: 1px solid var(--border); +} + +.train-progress.hidden { + display: none; +} + +/* ── Phase Badges ──────────────────────────────────────────── */ +.phase-badge { + display: inline-block; + padding: 3px 10px; + border-radius: var(--radius-md); + font-size: 11px; + font-weight: 600; + text-transform: uppercase; + letter-spacing: 0.05em; +} + +.phase-badge.running { background: var(--accent-dim); color: var(--accent); } +.phase-badge.starting { background: var(--accent-dim); color: var(--accent); } +.phase-badge.done { background: var(--success-dim); color: var(--success); } +.phase-badge.error { background: var(--error-dim); color: var(--error); } +.phase-badge.idle { background: var(--surface-3); color: var(--text-secondary); } + +#progress-message { + font-size: 13px; + color: var(--text); +} + +/* ── Progress Bar overrides ────────────────────────────────── */ +.progress-bar-container { + margin-bottom: var(--sp-2); +} + +.progress-bar { + transition: width 0.5s var(--ease); +} + +.progress-bar.done { + background: var(--success); +} + +.progress-bar.error { + background: var(--error); +} + +/* ── Chart Containers ──────────────────────────────────────── */ +.chart-container { + position: relative; + min-height: 200px; +} + +.chart-container canvas { + width: 100%; +} + +.chart-container .empty-state { + position: absolute; + inset: 0; + display: flex; + align-items: center; + justify-content: center; + padding: var(--sp-6); +} + +/* ── History Table ─────────────────────────────────────────── */ +.distill-history-table { + font-size: 13px; +} + +.distill-history-table .tag { + display: inline-flex; + align-items: center; + padding: 2px 8px; + font-size: 11px; + font-weight: 600; + border-radius: var(--radius-sm); + white-space: nowrap; + text-transform: capitalize; +} + +/* ── Responsive ────────────────────────────────────────────── */ +@media (max-width: 768px) { + .status-grid { + grid-template-columns: repeat(2, 1fr); + } + + .param-grid { + grid-template-columns: repeat(2, 1fr); + } + + .distill-bottom-params { + grid-template-columns: 1fr; + } +} + +@media (max-width: 480px) { + .status-grid { + grid-template-columns: 1fr; + } + + .param-grid { + grid-template-columns: 1fr; + } +} + +/* Refresh button spin animation */ +@keyframes spin { + from { transform: rotate(0deg); } + to { transform: rotate(360deg); } +} diff --git a/ui/distillation.html b/ui/distillation.html new file mode 100644 index 0000000..16e4618 --- /dev/null +++ b/ui/distillation.html @@ -0,0 +1,177 @@ + + + + + + Commander AI Lab — Distillation Loop + + + + + + + + +
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Closed-Loop Distillation

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Iterative supervised → PPO → quality gate → merge pipeline

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Distillation Controls

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Win Rate Trend

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No generation data yet. Start a distillation run to see win rate trends.

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+
+ + + + diff --git a/ui/distillation.js b/ui/distillation.js new file mode 100644 index 0000000..3c9949b --- /dev/null +++ b/ui/distillation.js @@ -0,0 +1,643 @@ +/** + * Commander AI Lab — Distillation Loop Dashboard + * Manages distillation pipeline controls, status polling, and chart rendering. + */ + +const API_BASE = ''; // Same origin + +// ═══════════════════════════════════════════ +// State +// ═══════════════════════════════════════════ + +let pollingInterval = null; +let isRunning = false; +let generationsData = []; + +// ═══════════════════════════════════════════ +// Init +// ═══════════════════════════════════════════ + +document.addEventListener('DOMContentLoaded', () => { + refreshAll(); +}); + +async function refreshAll() { + const btn = document.getElementById('btn-refresh'); + const icon = document.getElementById('refresh-icon'); + btn.disabled = true; + icon.style.display = 'inline-block'; + icon.style.animation = 'spin 0.8s linear infinite'; + try { + await refreshStatus(); + await loadHistory(); + } finally { + btn.disabled = false; + icon.style.animation = ''; + } +} + +// ═══════════════════════════════════════════ +// Status Polling +// ═══════════════════════════════════════════ + +async function refreshStatus() { + try { + const res = await fetch(`${API_BASE}/api/ml/distill/status`); + if (!res.ok) throw new Error(`HTTP ${res.status}`); + const data = await res.json(); + updateUI(data); + + if (data.running && !pollingInterval) { + startPolling(); + } + } catch (err) { + console.error('Failed to fetch distillation status:', err); + } +} + +function startPolling() { + if (pollingInterval) clearInterval(pollingInterval); + pollingInterval = setInterval(pollStatus, 3000); +} + +function stopPolling() { + if (pollingInterval) { + clearInterval(pollingInterval); + pollingInterval = null; + } +} + +async function pollStatus() { + try { + const res = await fetch(`${API_BASE}/api/ml/distill/status`); + const data = await res.json(); + updateUI(data); + + if (!data.running && data.phase !== 'idle') { + stopPolling(); + isRunning = false; + updateButtons(false); + // Refresh history when done + loadHistory(); + } + } catch (err) { + console.error('Poll failed:', err); + } +} + +// ═══════════════════════════════════════════ +// UI Updates +// ═══════════════════════════════════════════ + +function updateUI(data) { + // Status card + const statusVal = document.getElementById('val-status'); + const statusSub = document.getElementById('sub-status'); + const statusCard = document.getElementById('card-status'); + const phase = data.phase || 'idle'; + + statusCard.className = 'status-card stat-card'; + if (phase === 'running' || phase === 'starting') { + statusVal.textContent = 'Running'; + statusSub.textContent = data.current_step || 'in progress'; + statusCard.classList.add('active'); + } else if (phase === 'done') { + statusVal.textContent = 'Complete'; + statusSub.textContent = 'finished'; + statusCard.classList.add('success'); + } else if (phase === 'error') { + statusVal.textContent = 'Error'; + statusSub.textContent = data.error ? data.error.substring(0, 40) : 'failed'; + statusCard.classList.add('error-state'); + } else { + statusVal.textContent = 'Idle'; + statusSub.textContent = 'not running'; + } + + // Generation card + document.getElementById('val-generation').textContent = + data.generation > 0 ? data.generation : '--'; + document.getElementById('sub-generation').textContent = + data.max_iterations > 0 ? `of ${data.max_iterations}` : 'of --'; + + if (data.generation > 0) { + document.getElementById('card-generation').classList.add('active'); + } + + // Win rate and passed cards from generations data + const gens = data.generations || []; + if (gens.length > 0) { + const bestWR = Math.max(...gens.map(g => g.ppo_best_win_rate || 0)); + document.getElementById('val-winrate').textContent = + bestWR > 0 ? (bestWR * 100).toFixed(1) + '%' : '--'; + if (bestWR > 0) { + document.getElementById('card-winrate').classList.add('active'); + } + + const passed = gens.filter(g => g.status === 'passed').length; + document.getElementById('val-passed').textContent = passed; + document.getElementById('sub-passed').textContent = + `of ${gens.length} generations`; + if (passed > 0) { + document.getElementById('card-passed').classList.add('success'); + } + + generationsData = gens; + renderWinRateChart(gens); + renderCompositionChart(gens); + renderHistoryTable(gens); + } + + // Progress section + const progressEl = document.getElementById('distill-progress'); + if (phase !== 'idle') { + progressEl.classList.remove('hidden'); + updateProgress(data); + } + + // Button states + updateButtons(data.running); + isRunning = data.running; +} + +function updateProgress(data) { + const phaseBadge = document.getElementById('progress-phase'); + const message = document.getElementById('progress-message'); + const bar = document.getElementById('progress-bar'); + const detail = document.getElementById('progress-detail'); + + const phase = data.phase || 'idle'; + phaseBadge.textContent = phase.toUpperCase(); + phaseBadge.className = 'phase-badge ' + phase; + + message.textContent = data.message || ''; + + bar.className = 'progress-bar progress-fill'; + if (phase === 'done') { + bar.style.width = '100%'; + bar.classList.add('done'); + } else if (phase === 'error') { + bar.style.width = '100%'; + bar.classList.add('error'); + } else if (data.max_iterations > 0 && data.generation > 0) { + const pct = Math.min(100, (data.generation / data.max_iterations) * 100); + bar.style.width = pct + '%'; + } else if (phase === 'starting') { + bar.style.width = '3%'; + } + + if (phase === 'running' && data.generation > 0) { + detail.textContent = `Generation ${data.generation} / ${data.max_iterations}`; + } else if (phase === 'done' && data.result) { + const r = data.result; + detail.textContent = `${r.generations_run} generations | Best WR: ${(r.best_win_rate * 100).toFixed(1)}% | ${r.converged ? 'Converged' : 'Max reached'} | ${r.total_time_s}s`; + } else if (phase === 'error') { + detail.textContent = data.error || 'Unknown error'; + detail.style.color = 'var(--lab-error, var(--error))'; + } else { + detail.textContent = ''; + detail.style.color = ''; + } +} + +function updateButtons(running) { + const startBtn = document.getElementById('btn-start'); + const stopBtn = document.getElementById('btn-stop'); + + if (running) { + startBtn.disabled = true; + startBtn.innerHTML = '⌛ Running...'; + stopBtn.disabled = false; + } else { + startBtn.disabled = false; + startBtn.innerHTML = '▶ Start Distillation'; + stopBtn.disabled = true; + } +} + +// ═══════════════════════════════════════════ +// Controls +// ═══════════════════════════════════════════ + +async function startDistillation() { + const body = { + maxIterations: parseInt(document.getElementById('param-iterations').value) || 10, + convergenceWindow: parseInt(document.getElementById('param-conv-window').value) || 3, + convergenceThreshold: 0.01, + supervisedEpochs: parseInt(document.getElementById('param-sup-epochs').value) || 30, + supervisedLr: parseFloat(document.getElementById('param-sup-lr').value) || 1e-3, + ppoIterations: parseInt(document.getElementById('param-ppo-iters').value) || 50, + ppoEpisodesPerIter: parseInt(document.getElementById('param-ppo-episodes').value) || 64, + ppoLr: parseFloat(document.getElementById('param-ppo-lr').value) || 3e-4, + opponent: document.getElementById('param-opponent').value, + playstyle: document.getElementById('param-playstyle').value, + minWinRate: parseFloat(document.getElementById('param-min-wr').value) || 0.30, + }; + + const btn = document.getElementById('btn-start'); + btn.disabled = true; + btn.innerHTML = '⌛ Starting...'; + + try { + const res = await fetch(`${API_BASE}/api/ml/distill/start`, { + method: 'POST', + headers: { 'Content-Type': 'application/json' }, + body: JSON.stringify(body), + }); + + if (!res.ok) { + const err = await res.json(); + throw new Error(err.detail || `HTTP ${res.status}`); + } + + isRunning = true; + document.getElementById('distill-progress').classList.remove('hidden'); + updateButtons(true); + startPolling(); + + } catch (err) { + alert('Failed to start distillation: ' + err.message); + btn.disabled = false; + btn.innerHTML = '▶ Start Distillation'; + } +} + +async function stopDistillation() { + try { + const res = await fetch(`${API_BASE}/api/ml/distill/stop`, { method: 'POST' }); + const data = await res.json(); + if (data.status === 'stopping') { + document.getElementById('progress-message').textContent = + 'Stop signal sent — finishing current generation...'; + } + } catch (err) { + alert('Failed to stop: ' + err.message); + } +} + +// ═══════════════════════════════════════════ +// History Loading +// ═══════════════════════════════════════════ + +async function loadHistory() { + try { + const res = await fetch(`${API_BASE}/api/ml/distill/history`); + if (!res.ok) return; + const data = await res.json(); + + if (data.generations && data.generations.length > 0) { + generationsData = data.generations; + renderWinRateChart(data.generations); + renderCompositionChart(data.generations); + renderHistoryTable(data.generations); + + // Update summary cards from history + const bestWR = Math.max(...data.generations.map(g => g.ppo_best_win_rate || 0)); + if (bestWR > 0) { + document.getElementById('val-winrate').textContent = (bestWR * 100).toFixed(1) + '%'; + document.getElementById('card-winrate').classList.add('active'); + } + const passed = data.generations.filter(g => g.status === 'passed').length; + if (passed > 0 || data.generations.length > 0) { + document.getElementById('val-passed').textContent = passed; + document.getElementById('sub-passed').textContent = + `of ${data.generations.length} generations`; + } + } + } catch (err) { + console.error('Failed to load history:', err); + } +} + +// ═══════════════════════════════════════════ +// Chart Rendering (Canvas-based, no deps) +// ═══════════════════════════════════════════ + +function renderWinRateChart(generations) { + const canvas = document.getElementById('winrate-canvas'); + const empty = document.getElementById('winrate-empty'); + + if (!generations || generations.length === 0) { + canvas.style.display = 'none'; + empty.style.display = ''; + return; + } + + canvas.style.display = 'block'; + empty.style.display = 'none'; + + const ctx = canvas.getContext('2d'); + const dpr = window.devicePixelRatio || 1; + const rect = canvas.parentElement.getBoundingClientRect(); + const w = rect.width; + const h = 260; + + canvas.width = w * dpr; + canvas.height = h * dpr; + canvas.style.width = w + 'px'; + canvas.style.height = h + 'px'; + ctx.scale(dpr, dpr); + ctx.clearRect(0, 0, w, h); + + const pad = { top: 30, right: 30, bottom: 40, left: 55 }; + const cw = w - pad.left - pad.right; + const ch = h - pad.top - pad.bottom; + + const winRates = generations.map(g => (g.ppo_best_win_rate || 0) * 100); + const maxY = Math.max(100, Math.ceil(Math.max(...winRates) / 10) * 10); + const minY = 0; + + // Grid lines + ctx.strokeStyle = '#222533'; + ctx.lineWidth = 1; + const ySteps = 5; + for (let i = 0; i <= ySteps; i++) { + const y = pad.top + (ch / ySteps) * i; + ctx.beginPath(); + ctx.moveTo(pad.left, y); + ctx.lineTo(pad.left + cw, y); + ctx.stroke(); + + // Y axis labels + const val = maxY - (maxY - minY) * (i / ySteps); + ctx.fillStyle = '#8b90a0'; + ctx.font = '11px Inter, sans-serif'; + ctx.textAlign = 'right'; + ctx.fillText(val.toFixed(0) + '%', pad.left - 8, y + 4); + } + + // X axis labels + ctx.textAlign = 'center'; + ctx.fillStyle = '#8b90a0'; + for (let i = 0; i < generations.length; i++) { + const x = pad.left + (cw / Math.max(1, generations.length - 1)) * i; + ctx.fillText('Gen ' + generations[i].generation, x, h - 8); + } + + if (generations.length < 2) { + // Single point + const x = pad.left + cw / 2; + const y = pad.top + ch - (winRates[0] / maxY) * ch; + ctx.fillStyle = '#5b9ef0'; + ctx.beginPath(); + ctx.arc(x, y, 5, 0, Math.PI * 2); + ctx.fill(); + return; + } + + // Line + ctx.strokeStyle = '#5b9ef0'; + ctx.lineWidth = 2.5; + ctx.lineJoin = 'round'; + ctx.lineCap = 'round'; + ctx.beginPath(); + for (let i = 0; i < winRates.length; i++) { + const x = pad.left + (cw / (winRates.length - 1)) * i; + const y = pad.top + ch - (winRates[i] / maxY) * ch; + if (i === 0) ctx.moveTo(x, y); + else ctx.lineTo(x, y); + } + ctx.stroke(); + + // Area fill + ctx.globalAlpha = 0.1; + ctx.fillStyle = '#5b9ef0'; + ctx.beginPath(); + for (let i = 0; i < winRates.length; i++) { + const x = pad.left + (cw / (winRates.length - 1)) * i; + const y = pad.top + ch - (winRates[i] / maxY) * ch; + if (i === 0) ctx.moveTo(x, y); + else ctx.lineTo(x, y); + } + ctx.lineTo(pad.left + cw, pad.top + ch); + ctx.lineTo(pad.left, pad.top + ch); + ctx.closePath(); + ctx.fill(); + ctx.globalAlpha = 1; + + // Points + for (let i = 0; i < winRates.length; i++) { + const x = pad.left + (cw / (winRates.length - 1)) * i; + const y = pad.top + ch - (winRates[i] / maxY) * ch; + const gen = generations[i]; + + // Dot color based on status + ctx.fillStyle = gen.status === 'passed' ? '#4ade80' : + gen.status === 'failed' ? '#f87171' : '#5b9ef0'; + ctx.beginPath(); + ctx.arc(x, y, 4, 0, Math.PI * 2); + ctx.fill(); + + // White center + ctx.fillStyle = '#12141c'; + ctx.beginPath(); + ctx.arc(x, y, 2, 0, Math.PI * 2); + ctx.fill(); + } + + // Legend + ctx.font = '11px Inter, sans-serif'; + const legendX = pad.left + 10; + const legendY = pad.top + 15; + + ctx.fillStyle = '#4ade80'; + ctx.beginPath(); + ctx.arc(legendX, legendY, 4, 0, Math.PI * 2); + ctx.fill(); + ctx.fillStyle = '#8b90a0'; + ctx.textAlign = 'left'; + ctx.fillText('Passed', legendX + 10, legendY + 4); + + ctx.fillStyle = '#f87171'; + ctx.beginPath(); + ctx.arc(legendX + 70, legendY, 4, 0, Math.PI * 2); + ctx.fill(); + ctx.fillStyle = '#8b90a0'; + ctx.fillText('Failed', legendX + 80, legendY + 4); +} + +function renderCompositionChart(generations) { + const canvas = document.getElementById('composition-canvas'); + const empty = document.getElementById('composition-empty'); + + if (!generations || generations.length === 0) { + canvas.style.display = 'none'; + empty.style.display = ''; + return; + } + + canvas.style.display = 'block'; + empty.style.display = 'none'; + + const ctx = canvas.getContext('2d'); + const dpr = window.devicePixelRatio || 1; + const rect = canvas.parentElement.getBoundingClientRect(); + const w = rect.width; + const h = 220; + + canvas.width = w * dpr; + canvas.height = h * dpr; + canvas.style.width = w + 'px'; + canvas.style.height = h + 'px'; + ctx.scale(dpr, dpr); + ctx.clearRect(0, 0, w, h); + + const pad = { top: 30, right: 30, bottom: 40, left: 55 }; + const cw = w - pad.left - pad.right; + const ch = h - pad.top - pad.bottom; + + const barWidth = Math.min(40, cw / generations.length - 8); + + // Find max dataset size for scaling + const maxDataset = Math.max(...generations.map(g => (g.forge_samples || 0) + (g.ppo_samples || 0)), 1); + + // Y axis + ctx.strokeStyle = '#222533'; + ctx.lineWidth = 1; + const ySteps = 4; + for (let i = 0; i <= ySteps; i++) { + const y = pad.top + (ch / ySteps) * i; + ctx.beginPath(); + ctx.moveTo(pad.left, y); + ctx.lineTo(pad.left + cw, y); + ctx.stroke(); + + const val = maxDataset - (maxDataset * i / ySteps); + ctx.fillStyle = '#8b90a0'; + ctx.font = '11px Inter, sans-serif'; + ctx.textAlign = 'right'; + ctx.fillText(formatNumber(val), pad.left - 8, y + 4); + } + + // Bars + for (let i = 0; i < generations.length; i++) { + const g = generations[i]; + const forge = g.forge_samples || 0; + const ppo = g.ppo_samples || 0; + const total = forge + ppo; + + const x = pad.left + (cw / generations.length) * i + (cw / generations.length - barWidth) / 2; + + // Forge bar (bottom) + const forgeH = (forge / maxDataset) * ch; + ctx.fillStyle = '#5b9ef0'; + ctx.beginPath(); + roundedRect(ctx, x, pad.top + ch - forgeH, barWidth, forgeH, 3); + ctx.fill(); + + // PPO bar (stacked on top) + const ppoH = (ppo / maxDataset) * ch; + ctx.fillStyle = '#a78bfa'; + ctx.beginPath(); + roundedRect(ctx, x, pad.top + ch - forgeH - ppoH, barWidth, ppoH, 3); + ctx.fill(); + + // X label + ctx.fillStyle = '#8b90a0'; + ctx.font = '11px Inter, sans-serif'; + ctx.textAlign = 'center'; + ctx.fillText('G' + g.generation, x + barWidth / 2, h - 8); + } + + // Legend + ctx.font = '11px Inter, sans-serif'; + const legendX = pad.left + 10; + const legendY = pad.top + 12; + + ctx.fillStyle = '#5b9ef0'; + ctx.fillRect(legendX, legendY - 5, 10, 10); + ctx.fillStyle = '#8b90a0'; + ctx.textAlign = 'left'; + ctx.fillText('Forge', legendX + 16, legendY + 4); + + ctx.fillStyle = '#a78bfa'; + ctx.fillRect(legendX + 65, legendY - 5, 10, 10); + ctx.fillStyle = '#8b90a0'; + ctx.fillText('PPO', legendX + 81, legendY + 4); +} + +function roundedRect(ctx, x, y, w, h, r) { + if (h <= 0) return; + r = Math.min(r, h / 2, w / 2); + ctx.moveTo(x + r, y); + ctx.lineTo(x + w - r, y); + ctx.quadraticCurveTo(x + w, y, x + w, y + r); + ctx.lineTo(x + w, y + h - r); + ctx.quadraticCurveTo(x + w, y + h, x + w - r, y + h); + ctx.lineTo(x + r, y + h); + ctx.quadraticCurveTo(x, y + h, x, y + h - r); + ctx.lineTo(x, y + r); + ctx.quadraticCurveTo(x, y, x + r, y); +} + +function formatNumber(n) { + if (n >= 1000000) return (n / 1000000).toFixed(1) + 'M'; + if (n >= 1000) return (n / 1000).toFixed(1) + 'K'; + return Math.round(n).toString(); +} + +// ═══════════════════════════════════════════ +// History Table +// ═══════════════════════════════════════════ + +function renderHistoryTable(generations) { + const container = document.getElementById('history-table-container'); + const empty = document.getElementById('history-empty'); + + if (!generations || generations.length === 0) { + container.innerHTML = ''; + container.appendChild(empty); + empty.style.display = ''; + return; + } + + let html = ''; + html += ''; + html += ''; + html += ''; + html += ''; + html += ''; + html += ''; + html += ''; + html += ''; + html += ''; + + for (const g of generations) { + const statusClass = g.status === 'passed' ? 'tag-green' : + g.status === 'failed' ? 'tag-red' : + g.status === 'running' ? 'tag-blue' : 'tag-yellow'; + + const wrPct = g.ppo_best_win_rate > 0 ? (g.ppo_best_win_rate * 100).toFixed(1) + '%' : '--'; + const supAcc = g.supervised_val_acc > 0 ? (g.supervised_val_acc * 100).toFixed(1) + '%' : '--'; + const decisions = g.ppo_decisions_exported > 0 ? g.ppo_decisions_exported.toLocaleString() : '--'; + const gateIcon = g.gate_accepted ? '✅' : (g.status === 'running' ? '⌛' : '❌'); + + html += ` + + + + + + + + `; + } + + html += '
GenStatusWin RateSupervised AccPPO DecisionsGateRetries
${g.generation}${g.status}${wrPct}${supAcc}${decisions}${gateIcon}${g.retries || 0}
'; + container.innerHTML = html; +} + +// ═══════════════════════════════════════════ +// Window Resize Handler (redraw charts) +// ═══════════════════════════════════════════ + +let resizeTimeout = null; +window.addEventListener('resize', () => { + clearTimeout(resizeTimeout); + resizeTimeout = setTimeout(() => { + if (generationsData.length > 0) { + renderWinRateChart(generationsData); + renderCompositionChart(generationsData); + } + }, 200); +}); diff --git a/ui/nav.js b/ui/nav.js index fdfd76a..a3138aa 100644 --- a/ui/nav.js +++ b/ui/nav.js @@ -11,6 +11,7 @@ { href: 'simulator.html', label: 'Simulator', icon: 'play' }, { href: 'coach.html', label: 'Coach', icon: 'brain' }, { href: 'training.html', label: 'Training', icon: 'chart' }, + { href: 'distillation.html', label: 'Distill', icon: 'loop' }, ]; const ICONS = { @@ -20,6 +21,7 @@ sparkle: '', play: '', brain: '', + loop: '', chart: '', };