fez5587/commander-ai-lab
1
0 Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

chore: ignore runtime artifacts, add patch scripts & training manifest

Browse Source 
Ignore static/, logs/, data/rag_chroma/, api/, image_cache/, .claude/,
precon-decks/, and dependency-reduced-pom.xml — all runtime/build
artifacts that were polluting git status. Also stage the one-off
patch scripts (fix_paths.py, patch_eval.py, patch_phase1.py,
forge_evaluator_FIXED.py) and training_manifest.json so they're
tracked alongside the code they support.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
This commit is contained in:
rgriffithandClaude Opus 4.6 committed 2026-04-05 10:48:35 -04:00
1 parent a23fdc55a4
commit ea23f97b9c
6 files changed
+1329
Download Patch File Download Diff File Expand all files Collapse all files

No files matched your search

.gitignore
+12
View File
@@ -41,3 +41,15 @@ Thumbs.db
# Logs # Logs
*.log *.log
logs/
# Runtime artifacts / caches
static/
api/
image_cache/
data/rag_chroma/
.claude/
dependency-reduced-pom.xml
# Precon decks (generated / imported at runtime)
precon-decks/
fix_paths.py
+42
View File
@@ -0,0 +1,42 @@
with open('ml/eval/forge_evaluator.py', 'rb') as f:
data = f.read()
# Fix 1: absolute JAR path in subprocess call
data = data.replace(
b'"--forge-jar", self.cfg.forge_jar,',
b'"--forge-jar", str(Path(self.cfg.forge_jar).resolve()),'
)
# Fix 2: resolve forge_work_dir to absolute path too
data = data.replace(
b'"--forge-dir", self.cfg.forge_work_dir,',
b'"--forge-dir", str(Path(self.cfg.forge_work_dir).resolve()),'
)
# Fix 3: change default forge_work_dir
data = data.replace(
b'forge_work_dir: str = "forge"',
b'forge_work_dir: str = "D:/ForgeCommander/forge-repo/forge-gui"'
)
with open('ml/eval/forge_evaluator.py', 'wb') as f:
f.write(data)
import py_compile
py_compile.compile('ml/eval/forge_evaluator.py', doraise=True)
print('forge_evaluator.py - Syntax OK')
# Fix eval_policy.py too
with open('scripts/eval_policy.py', 'rb') as f:
data = f.read()
data = data.replace(
b'"--forge-dir", default="forge"',
b'"--forge-dir", default="D:/ForgeCommander/forge-repo/forge-gui"'
)
with open('scripts/eval_policy.py', 'wb') as f:
f.write(data)
py_compile.compile('scripts/eval_policy.py', doraise=True)
print('eval_policy.py - Syntax OK')
ml/eval/forge_evaluator_FIXED.py
+548
View File
@@ -0,0 +1,548 @@
"""
Commander AI Lab — Forge Evaluator (Issue #153)
═════════════════════════════════════════════════
Reusable eval harness that:
1. Starts the policy inference server pointing at a given checkpoint
2. Launches Forge games via MultiThreadBatchRunner with the policy
server wired in as the AI decision-maker
3. Collects per-game metrics and writes a JSON results file
Metrics collected per game:
- won : bool
- turns : int
- life_final : int (winning seat final life)
- life_delta : int (winner life - loser life at game end)
- cmd_damage : int (commander damage dealt by policy seat)
- illegal_acts : int (actions rejected by Forge — should be 0)
- entropy_mean : float (mean policy entropy across decision steps)
- inference_ms : float (mean per-step inference latency)
Usage:
from ml.eval.forge_evaluator import ForgeEvaluator, EvalConfig
cfg = EvalConfig(
checkpoint_path="ml/models/checkpoints/forge-trained/final.pt",
forge_jar="forge/forge-gui-desktop-2.0.12-SNAPSHOT-jar-with-dependencies.jar",
deck_files=["decks/cedh_test_a.dck", "decks/cedh_test_b.dck"],
num_games=200,
num_threads=4,
run_id="forge-trained",
results_dir="results",
)
evaluator = ForgeEvaluator(cfg)
summary = evaluator.run()
print(summary)
"""
import json
import logging
import os
import sys
import time
import uuid
import subprocess
from dataclasses import dataclass, field, asdict
from pathlib import Path
from typing import Dict, List, Optional
import numpy as np
project_root = str(Path(__file__).parent.parent.parent)
if project_root not in sys.path:
sys.path.insert(0, project_root)
logger = logging.getLogger("ml.eval.forge_evaluator")
# ─────────────────────────────────────────────
# Configuration
# ─────────────────────────────────────────────
@dataclass
class EvalConfig:
"""All parameters needed for a single evaluation run."""
# Model
checkpoint_path: str = "ml/models/checkpoints/best_policy.pt"
embeddings_dir: str = "embeddings"
device: str = None # None = auto-detect
# Forge
forge_jar: str = "forge/forge-gui-desktop-2.0.12-SNAPSHOT-jar-with-dependencies.jar"
forge_work_dir: str = "forge"
java_path: str = "java"
deck_files: List[str] = field(default_factory=list)
deck_names: List[str] = field(default_factory=list)
policy_server_url: str = "http://localhost:8091"
clock_seconds: int = 120
# Run
num_games: int = 200
num_threads: int = 4
seed: Optional[int] = None
run_id: str = ""
results_dir: str = "results"
# Inference
greedy: bool = True
temperature: float = 1.0
def __post_init__(self):
if not self.run_id:
self.run_id = str(uuid.uuid4())[:8]
# ─────────────────────────────────────────────
# Per-game result
# ─────────────────────────────────────────────
@dataclass
class GameRecord:
game_index: int
won: bool
turns: int
life_final_policy: int # life total of policy seat at game end
life_final_opponent: int
life_delta: int # policy life - opponent life
commander_damage_dealt: int
illegal_actions: int
entropy_mean: float # mean per-step policy entropy
inference_ms_mean: float # mean per-step inference latency
decision_steps: int # number of decision points in this game
error: str = ""
# ─────────────────────────────────────────────
# Eval summary
# ─────────────────────────────────────────────
@dataclass
class EvalSummary:
run_id: str
checkpoint_path: str
num_games: int
wins: int
losses: int
win_rate: float
win_rate_ci95_low: float
win_rate_ci95_high: float
avg_turns: float
avg_life_delta: float
avg_commander_damage: float
avg_illegal_actions: float
avg_entropy: float
avg_inference_ms: float
total_decision_steps: int
elapsed_wall_s: float
games_per_sec: float
error_count: int
result_file: str
def __str__(self) -> str:
return (
f"EvalSummary run={self.run_id}\n"
f" checkpoint : {self.checkpoint_path}\n"
f" games : {self.num_games} ({self.wins}W / {self.losses}L)\n"
f" win rate : {self.win_rate:.1%} "
f"[{self.win_rate_ci95_low:.1%}, {self.win_rate_ci95_high:.1%}] 95% CI\n"
f" avg turns : {self.avg_turns:.1f}\n"
f" avg life Δ : {self.avg_life_delta:+.1f}\n"
f" avg cmd dmg: {self.avg_commander_damage:.1f}\n"
f" illegal : {self.avg_illegal_actions:.2f}/game\n"
f" entropy : {self.avg_entropy:.4f}\n"
f" latency : {self.avg_inference_ms:.1f} ms/step\n"
f" throughput : {self.games_per_sec:.2f} games/sec\n"
f" result file: {self.result_file}\n"
)
# ─────────────────────────────────────────────
# Main evaluator
# ─────────────────────────────────────────────
class ForgeEvaluator:
"""
Orchestrates a full eval run:
1. Spin up PolicyInferenceService on cfg.policy_server_url port
2. Run cfg.num_games Forge games via MultiThreadBatchRunner
3. Parse Forge JSONL output for per-game metrics
4. Write results to {results_dir}/eval-{run_id}.json
"""
def __init__(self, cfg: EvalConfig):
self.cfg = cfg
self._records: List[GameRecord] = []
self._result_path = os.path.join(
cfg.results_dir, f"eval-{cfg.run_id}.json"
)
# ── Public entry point ───────────────────
def run(self) -> EvalSummary:
"""Run the full evaluation. Returns an EvalSummary."""
t0 = time.time()
os.makedirs(self.cfg.results_dir, exist_ok=True)
logger.info("[Eval %s] Starting %d-game eval run", self.cfg.run_id, self.cfg.num_games)
logger.info("[Eval %s] Checkpoint: %s", self.cfg.run_id, self.cfg.checkpoint_path)
# Load policy service
service = self._load_policy_service()
# Run games — either via real Forge or synthetic fallback
forge_available = (
self.cfg.forge_jar
and Path(self.cfg.forge_jar).exists()
and len(self.cfg.deck_files) >= 3
)
if forge_available:
self._records = self._run_forge_games(service)
else:
logger.warning(
"[Eval %s] Forge JAR or decks not found — running synthetic eval.",
self.cfg.run_id
)
self._records = self._run_synthetic_eval(service)
elapsed = time.time() - t0
summary = self._build_summary(elapsed)
self._write_results(summary)
logger.info("[Eval %s] Done. %s", self.cfg.run_id, summary)
return summary
# ── Policy service setup ─────────────────
def _load_policy_service(self):
try:
from ml.serving.policy_server import PolicyInferenceService
svc = PolicyInferenceService(
checkpoint_dir=str(Path(self.cfg.checkpoint_path).parent),
embeddings_dir=self.cfg.embeddings_dir,
device=self.cfg.device,
)
svc.load()
ok = svc.reload(self.cfg.checkpoint_path)
if not ok:
logger.warning("[Eval] Policy service load failed — entropy/latency metrics unavailable")
return svc
except Exception as e:
logger.error("[Eval] Could not load PolicyInferenceService: %s", e)
return None
# ── Real Forge game runner ───────────────
def _run_forge_games(self, service) -> List[GameRecord]:
"""Launch LabCli Java subprocess with --ml-log, then parse JSONL output."""
logger.info("[Eval %s] Launching Forge CLI subprocess for %d games", self.cfg.run_id, self.cfg.num_games)
deck_args = []
for i, df in enumerate(self.cfg.deck_files[:4]):
deck_args.extend([f"--deck{i+1}", Path(df).stem])
# Resolve Java 17
java_exe = self.cfg.java_path
for c in [r"C:\Program Files\Eclipse Adoptium\jdk-17.0.18.8-hotspot\bin\java.exe",
r"C:\Program Files\Java\jdk-17\bin\java.exe"]:
if Path(c).exists():
java_exe = c
break
lab_jar = os.path.join(project_root, "target", "commander-ai-lab-1.0.0-SNAPSHOT.jar")
if not Path(lab_jar).exists():
logger.error("[Eval] Lab JAR not found at %s -- run mvn package -DskipTests first", lab_jar)
return self._run_synthetic_eval(service)
cmd = [
java_exe, "-jar", lab_jar,
"--forge-jar", self.cfg.forge_jar,
"--forge-dir", self.cfg.forge_work_dir,
*deck_args,
"--games", str(self.cfg.num_games),
"--threads", str(self.cfg.num_threads),
"--clock", str(self.cfg.clock_seconds),
"--ml-log",
"--output", os.path.join(self.cfg.results_dir, f"eval-{self.cfg.run_id}-batch.json"),
]
if self.cfg.seed is not None:
cmd.extend(["--seed", str(self.cfg.seed)])
logger.info("[Eval %s] CMD: %s", self.cfg.run_id, " ".join(cmd))
try:
result = subprocess.run(cmd, capture_output=True, text=True,
timeout=self.cfg.clock_seconds * self.cfg.num_games + 300)
if result.returncode != 0:
logger.warning("[Eval %s] Forge CLI exit %d: %s", self.cfg.run_id, result.returncode, result.stderr[:500])
except (subprocess.TimeoutExpired, FileNotFoundError) as e:
logger.error("[Eval %s] Forge CLI failed: %s -- falling back to synthetic", self.cfg.run_id, e)
return self._run_synthetic_eval(service)
return self._parse_batch_json(service)
def _parse_batch_json(self, service) -> List[GameRecord]:
"""Parse the batch JSON output from LabCli Java subprocess."""
batch_path = os.path.join(
self.cfg.results_dir, f"eval-{self.cfg.run_id}-batch.json"
)
if not Path(batch_path).exists():
logger.warning("[Eval] Batch JSON not found at %s — falling back to synthetic", batch_path)
return self._run_synthetic_eval(service)
with open(batch_path) as f:
batch = json.load(f)
games_list = batch.get("games", [])
if not games_list:
logger.warning("[Eval] No games in batch JSON — falling back to synthetic")
return self._run_synthetic_eval(service)
records = []
for idx, g in enumerate(games_list):
won = g.get("winningSeat", -1) == 0
turns = g.get("totalTurns", 0)
pr = g.get("playerResults", [])
lp = pr[0].get("finalLife", 40) if len(pr) > 0 else 40
lo = pr[1].get("finalLife", 40) if len(pr) > 1 else 40
records.append(GameRecord(
game_index=idx,
won=won,
turns=turns,
life_final_policy=lp,
life_final_opponent=lo,
life_delta=lp - lo,
commander_damage_dealt=0,
illegal_actions=0,
entropy_mean=0.0,
inference_ms_mean=0.0,
decision_steps=turns,
))
if len(records) >= self.cfg.num_games:
break
logger.info("[Eval %s] Parsed %d games from batch JSON", self.cfg.run_id, len(records))
return records
def _run_forge_via_subprocess(self, service) -> List[GameRecord]:
"""Parse JSONL files produced by a separately-launched Forge batch."""
records = []
results_path = Path(self.cfg.results_dir)
pattern = f"ml-decisions-forge-eval-{self.cfg.run_id}*.jsonl"
all_files = sorted(results_path.glob(pattern))
if not all_files:
logger.warning("[Eval] No JSONL files found matching %s — falling back to synthetic", pattern)
return self._run_synthetic_eval(service)
from ml.training.forge_episode_generator import parse_forge_jsonl, snapshots_to_episode
games_seen: Dict[str, list] = {}
for f in all_files:
for snap in parse_forge_jsonl(f):
gid = snap.get("game_id", "g0")
games_seen.setdefault(gid, []).append(snap)
for idx, (gid, snaps) in enumerate(games_seen.items()):
records.append(self._snaps_to_record(idx, gid, snaps, service))
if len(records) >= self.cfg.num_games:
break
return records
def _java_results_to_records(self, java_results, service) -> List[GameRecord]:
"""Convert Java GameResult objects to GameRecord."""
records = []
for jr in java_results:
try:
won = getattr(jr, "winningSeat", 0) == 0 # seat 0 = policy
records.append(GameRecord(
game_index=getattr(jr, "gameIndex", len(records)),
won=won,
turns=getattr(jr, "turns", 0),
life_final_policy=getattr(jr, "lifeAtEnd", [40])[0] if hasattr(jr, "lifeAtEnd") else 40,
life_final_opponent=getattr(jr, "lifeAtEnd", [40, 40])[1] if hasattr(jr, "lifeAtEnd") else 40,
life_delta=0,
commander_damage_dealt=0,
illegal_actions=getattr(jr, "illegalActions", 0),
entropy_mean=0.0,
inference_ms_mean=0.0,
decision_steps=0,
))
except Exception as e:
logger.warning("[Eval] Skipping malformed GameResult: %s", e)
return records
# ── Synthetic fallback ───────────────────
def _run_synthetic_eval(self, service) -> List[GameRecord]:
"""Run synthetic games using self_play engine (no Forge required).
Used when Forge JAR is missing or deck files aren't configured.
Metrics like illegal_actions and commander_damage are 0 in this mode.
"""
import warnings
warnings.filterwarnings("ignore", category=DeprecationWarning)
records = []
try:
from ml.training.self_play import (
create_random_initial_state, apply_action, advance_turn,
encode_state_simple, HeuristicPolicy
)
from ml.config.scope import NUM_ACTIONS, IDX_TO_ACTION
import torch
import torch.nn.functional as F
except ImportError as e:
logger.error("[Eval] Cannot import self_play: %s", e)
return []
opponent = HeuristicPolicy()
agent_has_model = service is not None and service._loaded
for game_idx in range(self.cfg.num_games):
state = create_random_initial_state()
entropies, latencies = [], []
steps = 0
while not state.game_over and steps < 60:
sv = encode_state_simple(state, 0, "midrange")
t0 = time.time()
if agent_has_model:
snap = state.to_snapshot()
result = service.predict(
snap, playstyle="midrange",
greedy=self.cfg.greedy,
temperature=self.cfg.temperature,
)
action_idx = result.get("action_index", 7)
probs_map = result.get("probabilities", {})
probs = np.array(list(probs_map.values())) if probs_map else np.ones(NUM_ACTIONS) / NUM_ACTIONS
else:
action_idx = np.random.randint(NUM_ACTIONS)
probs = np.ones(NUM_ACTIONS) / NUM_ACTIONS
latencies.append((time.time() - t0) * 1000)
p = np.clip(probs, 1e-9, 1.0)
p /= p.sum()
entropies.append(float(-np.sum(p * np.log(p))))
state = apply_action(state, action_idx, 0)
if not state.game_over:
opp_sv = encode_state_simple(state, 1, "midrange")
opp_a, _ = opponent.select_action(opp_sv)
state = apply_action(state, opp_a, 1)
if not state.game_over:
state = advance_turn(state)
steps += 1
won = state.winner == 0
lp = state.players[0].life_total
lo = state.players[1].life_total
records.append(GameRecord(
game_index=game_idx,
won=won,
turns=state.turn,
life_final_policy=lp,
life_final_opponent=lo,
life_delta=lp - lo,
commander_damage_dealt=0,
illegal_actions=0,
entropy_mean=float(np.mean(entropies)) if entropies else 0.0,
inference_ms_mean=float(np.mean(latencies)) if latencies else 0.0,
decision_steps=steps,
))
return records
# ── JSONL snapshot → GameRecord ──────────
def _snaps_to_record(self, idx: int, gid: str, snaps: list, service) -> GameRecord:
entropies, latencies = [], []
won = False
turns = len(snaps)
cmd_dmg = 0
illegal = 0
lp, lo = 40, 40
for snap in snaps:
if "outcome" in snap and snap == snaps[-1]:
won = snap["outcome"].get("winner_seat", -1) == 0
lp = snap["outcome"].get("life_remaining", 40)
lo = snap["outcome"].get("opp_life_remaining", 0)
cmd_dmg = snap["outcome"].get("commander_damage_dealt", 0)
if service and service._loaded and "state_vector" in snap:
t0 = time.time()
result = service.predict(snap, playstyle="midrange", greedy=self.cfg.greedy)
latencies.append((time.time() - t0) * 1000)
probs_map = result.get("probabilities", {})
if probs_map:
p = np.clip(list(probs_map.values()), 1e-9, 1.0)
p /= p.sum()
entropies.append(float(-np.sum(p * np.log(p))))
return GameRecord(
game_index=idx,
won=won,
turns=turns,
life_final_policy=lp,
life_final_opponent=lo,
life_delta=lp - lo,
commander_damage_dealt=cmd_dmg,
illegal_actions=illegal,
entropy_mean=float(np.mean(entropies)) if entropies else 0.0,
inference_ms_mean=float(np.mean(latencies)) if latencies else 0.0,
decision_steps=len(snaps),
)
# ── Summary builder ──────────────────────
def _build_summary(self, elapsed: float) -> EvalSummary:
n = len(self._records)
if n == 0:
return EvalSummary(
run_id=self.cfg.run_id,
checkpoint_path=self.cfg.checkpoint_path,
num_games=0, wins=0, losses=0,
win_rate=0.0, win_rate_ci95_low=0.0, win_rate_ci95_high=0.0,
avg_turns=0.0, avg_life_delta=0.0, avg_commander_damage=0.0,
avg_illegal_actions=0.0, avg_entropy=0.0, avg_inference_ms=0.0,
total_decision_steps=0, elapsed_wall_s=elapsed, games_per_sec=0.0,
error_count=0, result_file=self._result_path,
)
wins = sum(1 for r in self._records if r.won)
losses = n - wins
wr = wins / n
# Wilson score 95% CI
z = 1.96
denom = 1 + z**2 / n
centre = (wr + z**2 / (2 * n)) / denom
margin = (z * (wr * (1 - wr) / n + z**2 / (4 * n**2)) ** 0.5) / denom
ci_lo = max(0.0, centre - margin)
ci_hi = min(1.0, centre + margin)
errors = sum(1 for r in self._records if r.error)
return EvalSummary(
run_id=self.cfg.run_id,
checkpoint_path=self.cfg.checkpoint_path,
num_games=n,
wins=wins,
losses=losses,
win_rate=wr,
win_rate_ci95_low=ci_lo,
win_rate_ci95_high=ci_hi,
avg_turns=float(np.mean([r.turns for r in self._records])),
avg_life_delta=float(np.mean([r.life_delta for r in self._records])),
avg_commander_damage=float(np.mean([r.commander_damage_dealt for r in self._records])),
avg_illegal_actions=float(np.mean([r.illegal_actions for r in self._records])),
avg_entropy=float(np.mean([r.entropy_mean for r in self._records])),
avg_inference_ms=float(np.mean([r.inference_ms_mean for r in self._records])),
total_decision_steps=sum(r.decision_steps for r in self._records),
elapsed_wall_s=elapsed,
games_per_sec=n / max(elapsed, 0.001),
error_count=errors,
result_file=self._result_path,
)
# ── Result writer ────────────────────────
def _write_results(self, summary: EvalSummary) -> None:
payload = {
"summary": asdict(summary),
"games": [asdict(r) for r in self._records],
}
os.makedirs(self.cfg.results_dir, exist_ok=True)
with open(self._result_path, "w") as f:
json.dump(payload, f, indent=2)
logger.info("[Eval %s] Results written to %s", self.cfg.run_id, self._result_path)
patch_eval.py
+68
View File
@@ -0,0 +1,68 @@
import re
with open('ml/eval/forge_evaluator.py', 'r', encoding='utf-8') as f:
content = f.read()
# Fix 1: >= 2 to >= 3
content = content.replace('deck_files) >= 2', 'deck_files) >= 3')
# Fix 2: replace call to _run_forge_via_subprocess with _parse_batch_json
content = content.replace(
'return self._run_forge_via_subprocess(service)',
'return self._parse_batch_json(service)'
)
# Fix 3: Insert _parse_batch_json method before _run_forge_via_subprocess
new_method = '''
def _parse_batch_json(self, service) -> List[GameRecord]:
"""Parse the batch JSON output from LabCli Java subprocess."""
batch_path = os.path.join(
self.cfg.results_dir, f"eval-{self.cfg.run_id}-batch.json"
)
if not Path(batch_path).exists():
logger.warning("[Eval] Batch JSON not found at %s -- falling back to synthetic", batch_path)
return self._run_synthetic_eval(service)
with open(batch_path) as f:
batch = json.load(f)
games_list = batch.get("games", [])
if not games_list:
logger.warning("[Eval] No games in batch JSON -- falling back to synthetic")
return self._run_synthetic_eval(service)
records = []
for idx, g in enumerate(games_list):
won = g.get("winningSeat", -1) == 0
turns = g.get("totalTurns", 0)
pr = g.get("playerResults", [])
lp = pr[0].get("finalLife", 40) if len(pr) > 0 else 40
lo = pr[1].get("finalLife", 40) if len(pr) > 1 else 40
records.append(GameRecord(
game_index=idx,
won=won,
turns=turns,
life_final_policy=lp,
life_final_opponent=lo,
life_delta=lp - lo,
commander_damage_dealt=0,
illegal_actions=0,
entropy_mean=0.0,
inference_ms_mean=0.0,
decision_steps=turns,
))
if len(records) >= self.cfg.num_games:
break
logger.info("[Eval %s] Parsed %d games from batch JSON", self.cfg.run_id, len(records))
return records
'''
content = content.replace(
' def _run_forge_via_subprocess(',
new_method + ' def _run_forge_via_subprocess('
)
with open('ml/eval/forge_evaluator.py', 'w', encoding='utf-8') as f:
f.write(content)
import py_compile
py_compile.compile('ml/eval/forge_evaluator.py', doraise=True)
print('Syntax OK - patch applied')
patch_phase1.py
+62
View File
@@ -0,0 +1,62 @@
#!/usr/bin/env python3
"""
Phase 1 patch: no-op mana pool display functions in lan-client/ui.js
Run from anywhere — edit UI_JS_PATH to point at your file.
"""
import re
UI_JS_PATH = r"D:\ForgeCommander\commander-ai-lab\lan-client\ui.js"
with open(UI_JS_PATH, "r", encoding="utf-8") as f:
content = f.read()
original_size = len(content)
changes = []
# ── Change 1: no-op renderManaPool ───────────────────────────────────────────
old1 = re.search(
r'function renderManaPool\(playerIdx\) \{[^}]*?display\.style\.display[^}]*?\}',
content, re.DOTALL
)
if old1:
content = content[:old1.start()] + \
"function renderManaPool(playerIdx) {\n // mana pool display removed (Phase 1)\n}" + \
content[old1.end():]
changes.append("renderManaPool -> stub")
else:
print("WARNING: renderManaPool pattern not matched — skipping")
# ── Change 2: no-op showManaPool ─────────────────────────────────────────────
old2 = re.search(
r'function showManaPool\(\) \{.*?\}',
content, re.DOTALL
)
if old2:
content = content[:old2.start()] + \
"function showManaPool() {\n // mana pool display removed (Phase 1)\n}" + \
content[old2.end():]
changes.append("showManaPool -> stub")
else:
print("WARNING: showManaPool pattern not matched — skipping")
# ── Change 3: remove Mana Auto-Clear row from renderSettingsPanel ─────────────
mana_row = (
" +\n '<div class=\"settings-row\"><span>Mana Auto-Clear</span>'"
" + renderToggle('manaAutoClear', gameState.manaAutoClear) + '</div>';"
)
if mana_row in content:
content = content.replace(mana_row, ";", 1)
changes.append("Mana Auto-Clear row removed from renderSettingsPanel")
else:
print("WARNING: Mana Auto-Clear row not found — skipping")
# ── Write ─────────────────────────────────────────────────────────────────────
if changes:
with open(UI_JS_PATH, "w", encoding="utf-8") as f:
f.write(content)
print(f"Done. {original_size} -> {len(content)} chars ({len(content)-original_size:+d})")
for c in changes:
print(f" ✓ {c}")
else:
print("No changes applied.")
training_manifest.json
+597
View File
@@ -0,0 +1,597 @@
[
{
"path": "D:\\ForgeCommander\\commander-ai-lab\\results\\ml-decisions-sim-01a993df.jsonl",
"weight": 1,
"source": "sim"
},
{
"path": "D:\\ForgeCommander\\commander-ai-lab\\results\\ml-decisions-sim-02ce51bc.jsonl",
"weight": 1,
"source": "sim"
},
{
"path": "D:\\ForgeCommander\\commander-ai-lab\\results\\ml-decisions-sim-050da93a.jsonl",
"weight": 1,
"source": "sim"
},
{
"path": "D:\\ForgeCommander\\commander-ai-lab\\results\\ml-decisions-sim-07325fb5.jsonl",
"weight": 1,
"source": "sim"
},
{
"path": "D:\\ForgeCommander\\commander-ai-lab\\results\\ml-decisions-sim-0791d58e.jsonl",
"weight": 1,
"source": "sim"
},
{
"path": "D:\\ForgeCommander\\commander-ai-lab\\results\\ml-decisions-sim-083a55e5.jsonl",
"weight": 1,
"source": "sim"
},
{
"path": "D:\\ForgeCommander\\commander-ai-lab\\results\\ml-decisions-sim-09f6ab6b.jsonl",
"weight": 1,
"source": "sim"
},
{
"path": "D:\\ForgeCommander\\commander-ai-lab\\results\\ml-decisions-sim-0d39aa71.jsonl",
"weight": 1,
"source": "sim"
},
{
"path": "D:\\ForgeCommander\\commander-ai-lab\\results\\ml-decisions-sim-0dadbf18.jsonl",
"weight": 1,
"source": "sim"
},
{
"path": "D:\\ForgeCommander\\commander-ai-lab\\results\\ml-decisions-sim-12802330.jsonl",
"weight": 1,
"source": "sim"
},
{
"path": "D:\\ForgeCommander\\commander-ai-lab\\results\\ml-decisions-sim-186b4c2d.jsonl",
"weight": 1,
"source": "sim"
},
{
"path": "D:\\ForgeCommander\\commander-ai-lab\\results\\ml-decisions-sim-19f0c0c3.jsonl",
"weight": 1,
"source": "sim"
},
{
"path": "D:\\ForgeCommander\\commander-ai-lab\\results\\ml-decisions-sim-1a0a0840.jsonl",
"weight": 1,
"source": "sim"
},
{
"path": "D:\\ForgeCommander\\commander-ai-lab\\results\\ml-decisions-sim-1d001e22.jsonl",
"weight": 1,
"source": "sim"
},
{
"path": "D:\\ForgeCommander\\commander-ai-lab\\results\\ml-decisions-sim-1df7d6d8.jsonl",
"weight": 1,
"source": "sim"
},
{
"path": "D:\\ForgeCommander\\commander-ai-lab\\results\\ml-decisions-sim-220e4942.jsonl",
"weight": 1,
"source": "sim"
},
{
"path": "D:\\ForgeCommander\\commander-ai-lab\\results\\ml-decisions-sim-26f83797.jsonl",
"weight": 1,
"source": "sim"
},
{
"path": "D:\\ForgeCommander\\commander-ai-lab\\results\\ml-decisions-sim-291a225a.jsonl",
"weight": 1,
"source": "sim"
},
{
"path": "D:\\ForgeCommander\\commander-ai-lab\\results\\ml-decisions-sim-309b1008.jsonl",
"weight": 1,
"source": "sim"
},
{
"path": "D:\\ForgeCommander\\commander-ai-lab\\results\\ml-decisions-sim-31557831.jsonl",
"weight": 1,
"source": "sim"
},
{
"path": "D:\\ForgeCommander\\commander-ai-lab\\results\\ml-decisions-sim-37d57d02.jsonl",
"weight": 1,
"source": "sim"
},
{
"path": "D:\\ForgeCommander\\commander-ai-lab\\results\\ml-decisions-sim-391e4632.jsonl",
"weight": 1,
"source": "sim"
},
{
"path": "D:\\ForgeCommander\\commander-ai-lab\\results\\ml-decisions-sim-39ed0be8.jsonl",
"weight": 1,
"source": "sim"
},
{
"path": "D:\\ForgeCommander\\commander-ai-lab\\results\\ml-decisions-sim-3bb88339.jsonl",
"weight": 1,
"source": "sim"
},
{
"path": "D:\\ForgeCommander\\commander-ai-lab\\results\\ml-decisions-sim-3c98f83b.jsonl",
"weight": 1,
"source": "sim"
},
{
"path": "D:\\ForgeCommander\\commander-ai-lab\\results\\ml-decisions-sim-3de43fb6.jsonl",
"weight": 1,
"source": "sim"
},
{
"path": "D:\\ForgeCommander\\commander-ai-lab\\results\\ml-decisions-sim-3e0639f7.jsonl",
"weight": 1,
"source": "sim"
},
{
"path": "D:\\ForgeCommander\\commander-ai-lab\\results\\ml-decisions-sim-3ea0bfb2.jsonl",
"weight": 1,
"source": "sim"
},
{
"path": "D:\\ForgeCommander\\commander-ai-lab\\results\\ml-decisions-sim-3f0dc5c4.jsonl",
"weight": 1,
"source": "sim"
},
{
"path": "D:\\ForgeCommander\\commander-ai-lab\\results\\ml-decisions-sim-3f2f9f04.jsonl",
"weight": 1,
"source": "sim"
},
{
"path": "D:\\ForgeCommander\\commander-ai-lab\\results\\ml-decisions-sim-425a6728.jsonl",
"weight": 1,
"source": "sim"
},
{
"path": "D:\\ForgeCommander\\commander-ai-lab\\results\\ml-decisions-sim-46517693.jsonl",
"weight": 1,
"source": "sim"
},
{
"path": "D:\\ForgeCommander\\commander-ai-lab\\results\\ml-decisions-sim-471701d5.jsonl",
"weight": 1,
"source": "sim"
},
{
"path": "D:\\ForgeCommander\\commander-ai-lab\\results\\ml-decisions-sim-4c709ff1.jsonl",
"weight": 1,
"source": "sim"
},
{
"path": "D:\\ForgeCommander\\commander-ai-lab\\results\\ml-decisions-sim-4f5be65a.jsonl",
"weight": 1,
"source": "sim"
},
{
"path": "D:\\ForgeCommander\\commander-ai-lab\\results\\ml-decisions-sim-501f6ef9.jsonl",
"weight": 1,
"source": "sim"
},
{
"path": "D:\\ForgeCommander\\commander-ai-lab\\results\\ml-decisions-sim-592d3d5a.jsonl",
"weight": 1,
"source": "sim"
},
{
"path": "D:\\ForgeCommander\\commander-ai-lab\\results\\ml-decisions-sim-644cc2f1.jsonl",
"weight": 1,
"source": "sim"
},
{
"path": "D:\\ForgeCommander\\commander-ai-lab\\results\\ml-decisions-sim-64759781.jsonl",
"weight": 1,
"source": "sim"
},
{
"path": "D:\\ForgeCommander\\commander-ai-lab\\results\\ml-decisions-sim-6575cbf7.jsonl",
"weight": 1,
"source": "sim"
},
{
"path": "D:\\ForgeCommander\\commander-ai-lab\\results\\ml-decisions-sim-682fa8d1.jsonl",
"weight": 1,
"source": "sim"
},
{
"path": "D:\\ForgeCommander\\commander-ai-lab\\results\\ml-decisions-sim-68352096.jsonl",
"weight": 1,
"source": "sim"
},
{
"path": "D:\\ForgeCommander\\commander-ai-lab\\results\\ml-decisions-sim-6c982613.jsonl",
"weight": 1,
"source": "sim"
},
{
"path": "D:\\ForgeCommander\\commander-ai-lab\\results\\ml-decisions-sim-6cdb4b9b.jsonl",
"weight": 1,
"source": "sim"
},
{
"path": "D:\\ForgeCommander\\commander-ai-lab\\results\\ml-decisions-sim-6dee8fdf.jsonl",
"weight": 1,
"source": "sim"
},
{
"path": "D:\\ForgeCommander\\commander-ai-lab\\results\\ml-decisions-sim-7018972e.jsonl",
"weight": 1,
"source": "sim"
},
{
"path": "D:\\ForgeCommander\\commander-ai-lab\\results\\ml-decisions-sim-7088479f.jsonl",
"weight": 1,
"source": "sim"
},
{
"path": "D:\\ForgeCommander\\commander-ai-lab\\results\\ml-decisions-sim-74d0f773.jsonl",
"weight": 1,
"source": "sim"
},
{
"path": "D:\\ForgeCommander\\commander-ai-lab\\results\\ml-decisions-sim-76303df4.jsonl",
"weight": 1,
"source": "sim"
},
{
"path": "D:\\ForgeCommander\\commander-ai-lab\\results\\ml-decisions-sim-76c602e0.jsonl",
"weight": 1,
"source": "sim"
},
{
"path": "D:\\ForgeCommander\\commander-ai-lab\\results\\ml-decisions-sim-76edf7a3.jsonl",
"weight": 1,
"source": "sim"
},
{
"path": "D:\\ForgeCommander\\commander-ai-lab\\results\\ml-decisions-sim-77551df3.jsonl",
"weight": 1,
"source": "sim"
},
{
"path": "D:\\ForgeCommander\\commander-ai-lab\\results\\ml-decisions-sim-77ee4077.jsonl",
"weight": 1,
"source": "sim"
},
{
"path": "D:\\ForgeCommander\\commander-ai-lab\\results\\ml-decisions-sim-786ff020.jsonl",
"weight": 1,
"source": "sim"
},
{
"path": "D:\\ForgeCommander\\commander-ai-lab\\results\\ml-decisions-sim-7d4ad84d.jsonl",
"weight": 1,
"source": "sim"
},
{
"path": "D:\\ForgeCommander\\commander-ai-lab\\results\\ml-decisions-sim-7f74c9f3.jsonl",
"weight": 1,
"source": "sim"
},
{
"path": "D:\\ForgeCommander\\commander-ai-lab\\results\\ml-decisions-sim-7fbdfb25.jsonl",
"weight": 1,
"source": "sim"
},
{
"path": "D:\\ForgeCommander\\commander-ai-lab\\results\\ml-decisions-sim-7fd1bee8.jsonl",
"weight": 1,
"source": "sim"
},
{
"path": "D:\\ForgeCommander\\commander-ai-lab\\results\\ml-decisions-sim-804d0dfe.jsonl",
"weight": 1,
"source": "sim"
},
{
"path": "D:\\ForgeCommander\\commander-ai-lab\\results\\ml-decisions-sim-805211db.jsonl",
"weight": 1,
"source": "sim"
},
{
"path": "D:\\ForgeCommander\\commander-ai-lab\\results\\ml-decisions-sim-812a5124.jsonl",
"weight": 1,
"source": "sim"
},
{
"path": "D:\\ForgeCommander\\commander-ai-lab\\results\\ml-decisions-sim-826a32fb.jsonl",
"weight": 1,
"source": "sim"
},
{
"path": "D:\\ForgeCommander\\commander-ai-lab\\results\\ml-decisions-sim-878d0278.jsonl",
"weight": 1,
"source": "sim"
},
{
"path": "D:\\ForgeCommander\\commander-ai-lab\\results\\ml-decisions-sim-8dd40e1b.jsonl",
"weight": 1,
"source": "sim"
},
{
"path": "D:\\ForgeCommander\\commander-ai-lab\\results\\ml-decisions-sim-8fa57d21.jsonl",
"weight": 1,
"source": "sim"
},
{
"path": "D:\\ForgeCommander\\commander-ai-lab\\results\\ml-decisions-sim-8fe4d31c.jsonl",
"weight": 1,
"source": "sim"
},
{
"path": "D:\\ForgeCommander\\commander-ai-lab\\results\\ml-decisions-sim-91a3ac36.jsonl",
"weight": 1,
"source": "sim"
},
{
"path": "D:\\ForgeCommander\\commander-ai-lab\\results\\ml-decisions-sim-92418b72.jsonl",
"weight": 1,
"source": "sim"
},
{
"path": "D:\\ForgeCommander\\commander-ai-lab\\results\\ml-decisions-sim-96fb5377.jsonl",
"weight": 1,
"source": "sim"
},
{
"path": "D:\\ForgeCommander\\commander-ai-lab\\results\\ml-decisions-sim-9b0adb9d.jsonl",
"weight": 1,
"source": "sim"
},
{
"path": "D:\\ForgeCommander\\commander-ai-lab\\results\\ml-decisions-sim-9e3ca48f.jsonl",
"weight": 1,
"source": "sim"
},
{
"path": "D:\\ForgeCommander\\commander-ai-lab\\results\\ml-decisions-sim-9ee053b3.jsonl",
"weight": 1,
"source": "sim"
},
{
"path": "D:\\ForgeCommander\\commander-ai-lab\\results\\ml-decisions-sim-9ef2148f.jsonl",
"weight": 1,
"source": "sim"
},
{
"path": "D:\\ForgeCommander\\commander-ai-lab\\results\\ml-decisions-sim-a153e04e.jsonl",
"weight": 1,
"source": "sim"
},
{
"path": "D:\\ForgeCommander\\commander-ai-lab\\results\\ml-decisions-sim-a2676323.jsonl",
"weight": 1,
"source": "sim"
},
{
"path": "D:\\ForgeCommander\\commander-ai-lab\\results\\ml-decisions-sim-a374549a.jsonl",
"weight": 1,
"source": "sim"
},
{
"path": "D:\\ForgeCommander\\commander-ai-lab\\results\\ml-decisions-sim-a7feffa1.jsonl",
"weight": 1,
"source": "sim"
},
{
"path": "D:\\ForgeCommander\\commander-ai-lab\\results\\ml-decisions-sim-ab868d56.jsonl",
"weight": 1,
"source": "sim"
},
{
"path": "D:\\ForgeCommander\\commander-ai-lab\\results\\ml-decisions-sim-ab8d55a5.jsonl",
"weight": 1,
"source": "sim"
},
{
"path": "D:\\ForgeCommander\\commander-ai-lab\\results\\ml-decisions-sim-aee3677b.jsonl",
"weight": 1,
"source": "sim"
},
{
"path": "D:\\ForgeCommander\\commander-ai-lab\\results\\ml-decisions-sim-af39ff37.jsonl",
"weight": 1,
"source": "sim"
},
{
"path": "D:\\ForgeCommander\\commander-ai-lab\\results\\ml-decisions-sim-b101fe7b.jsonl",
"weight": 1,
"source": "sim"
},
{
"path": "D:\\ForgeCommander\\commander-ai-lab\\results\\ml-decisions-sim-bab7b2bf.jsonl",
"weight": 1,
"source": "sim"
},
{
"path": "D:\\ForgeCommander\\commander-ai-lab\\results\\ml-decisions-sim-bb3e9d83.jsonl",
"weight": 1,
"source": "sim"
},
{
"path": "D:\\ForgeCommander\\commander-ai-lab\\results\\ml-decisions-sim-bb9772ca.jsonl",
"weight": 1,
"source": "sim"
},
{
"path": "D:\\ForgeCommander\\commander-ai-lab\\results\\ml-decisions-sim-bf0f9371.jsonl",
"weight": 1,
"source": "sim"
},
{
"path": "D:\\ForgeCommander\\commander-ai-lab\\results\\ml-decisions-sim-c21b782b.jsonl",
"weight": 1,
"source": "sim"
},
{
"path": "D:\\ForgeCommander\\commander-ai-lab\\results\\ml-decisions-sim-c3e05ffc.jsonl",
"weight": 1,
"source": "sim"
},
{
"path": "D:\\ForgeCommander\\commander-ai-lab\\results\\ml-decisions-sim-c83d2002.jsonl",
"weight": 1,
"source": "sim"
},
{
"path": "D:\\ForgeCommander\\commander-ai-lab\\results\\ml-decisions-sim-c879aeab.jsonl",
"weight": 1,
"source": "sim"
},
{
"path": "D:\\ForgeCommander\\commander-ai-lab\\results\\ml-decisions-sim-c9d696cc.jsonl",
"weight": 1,
"source": "sim"
},
{
"path": "D:\\ForgeCommander\\commander-ai-lab\\results\\ml-decisions-sim-ccc989e9.jsonl",
"weight": 1,
"source": "sim"
},
{
"path": "D:\\ForgeCommander\\commander-ai-lab\\results\\ml-decisions-sim-d09dea45.jsonl",
"weight": 1,
"source": "sim"
},
{
"path": "D:\\ForgeCommander\\commander-ai-lab\\results\\ml-decisions-sim-d262f1dc.jsonl",
"weight": 1,
"source": "sim"
},
{
"path": "D:\\ForgeCommander\\commander-ai-lab\\results\\ml-decisions-sim-d3d97788.jsonl",
"weight": 1,
"source": "sim"
},
{
"path": "D:\\ForgeCommander\\commander-ai-lab\\results\\ml-decisions-sim-d68ecb62.jsonl",
"weight": 1,
"source": "sim"
},
{
"path": "D:\\ForgeCommander\\commander-ai-lab\\results\\ml-decisions-sim-d79c87bc.jsonl",
"weight": 1,
"source": "sim"
},
{
"path": "D:\\ForgeCommander\\commander-ai-lab\\results\\ml-decisions-sim-dbf46854.jsonl",
"weight": 1,
"source": "sim"
},
{
"path": "D:\\ForgeCommander\\commander-ai-lab\\results\\ml-decisions-sim-dd5b74d0.jsonl",
"weight": 1,
"source": "sim"
},
{
"path": "D:\\ForgeCommander\\commander-ai-lab\\results\\ml-decisions-sim-e1e6c928.jsonl",
"weight": 1,
"source": "sim"
},
{
"path": "D:\\ForgeCommander\\commander-ai-lab\\results\\ml-decisions-sim-e1ff699f.jsonl",
"weight": 1,
"source": "sim"
},
{
"path": "D:\\ForgeCommander\\commander-ai-lab\\results\\ml-decisions-sim-e738a169.jsonl",
"weight": 1,
"source": "sim"
},
{
"path": "D:\\ForgeCommander\\commander-ai-lab\\results\\ml-decisions-sim-efd579c0.jsonl",
"weight": 1,
"source": "sim"
},
{
"path": "D:\\ForgeCommander\\commander-ai-lab\\results\\ml-decisions-sim-f06c0e00.jsonl",
"weight": 1,
"source": "sim"
},
{
"path": "D:\\ForgeCommander\\commander-ai-lab\\results\\ml-decisions-sim-f144217e.jsonl",
"weight": 1,
"source": "sim"
},
{
"path": "D:\\ForgeCommander\\commander-ai-lab\\results\\ml-decisions-sim-f1d0e453.jsonl",
"weight": 1,
"source": "sim"
},
{
"path": "D:\\ForgeCommander\\commander-ai-lab\\results\\ml-decisions-sim-f2bdceeb.jsonl",
"weight": 1,
"source": "sim"
},
{
"path": "D:\\ForgeCommander\\commander-ai-lab\\results\\ml-decisions-sim-f79c8493.jsonl",
"weight": 1,
"source": "sim"
},
{
"path": "D:\\ForgeCommander\\commander-ai-lab\\results\\ml-decisions-sim-fa8a574b.jsonl",
"weight": 1,
"source": "sim"
},
{
"path": "D:\\ForgeCommander\\commander-ai-lab\\results\\ml-decisions-sim-fae68ec0.jsonl",
"weight": 1,
"source": "sim"
},
{
"path": "D:\\ForgeCommander\\commander-ai-lab\\results\\ml-decisions-sim-fee36e36.jsonl",
"weight": 1,
"source": "sim"
},
{
"path": "D:\\ForgeCommander\\commander-ai-lab\\results\\ml-decisions-ds-03449551-bbc.jsonl",
"weight": 4,
"source": "ds"
},
{
"path": "D:\\ForgeCommander\\commander-ai-lab\\results\\ml-decisions-ds-04380aa6-f62.jsonl",
"weight": 4,
"source": "ds"
},
{
"path": "D:\\ForgeCommander\\commander-ai-lab\\results\\ml-decisions-ds-23db752f-de5.jsonl",
"weight": 4,
"source": "ds"
},
{
"path": "D:\\ForgeCommander\\commander-ai-lab\\results\\ml-decisions-ds-310629be-8f7.jsonl",
"weight": 4,
"source": "ds"
},
{
"path": "D:\\ForgeCommander\\commander-ai-lab\\results\\ml-decisions-ds-63b719a8-1b0.jsonl",
"weight": 4,
"source": "ds"
},
{
"path": "D:\\ForgeCommander\\commander-ai-lab\\results\\ml-decisions-ds-7f50242f-986.jsonl",
"weight": 4,
"source": "ds"
},
{
"path": "D:\\ForgeCommander\\commander-ai-lab\\results\\ml-decisions-ds-a2f9bbae-f50.jsonl",
"weight": 4,
"source": "ds"
},
{
"path": "D:\\ForgeCommander\\commander-ai-lab\\results\\ml-decisions-ds-bd331a14-a89.jsonl",
"weight": 4,
"source": "ds"
}
]