KoalaTrapLord
a5fe8cb3fa
Complete frontend rewrite from vanilla HTML/CSS/JS to React 19 + TypeScript + Tailwind CSS v4. Frontend (frontend/commander-ai-lab-ui/): - Vite + React 19 + TypeScript + Tailwind CSS v4 + React Router - 7 pages: BatchSim, Collection, Decks, AutoGen, Simulator, Coach, Training - Reusable components: Spinner, StatusBadge, CardImage, ManaSymbols, EmptyState - Full typed API client layer (8 modules matching all backend endpoints) - Recharts for mana curves, win rate charts, progress bars - Lucide icons throughout - Custom dark theme with MTG mana color tokens - Code splitting (recharts + react in separate chunks) Backend (lab_api.py): - SPA-aware static file serving with catch-all for client-side routing - Fallback to legacy ui/ folder if React dist not built - Updated startup log with SPA route list Build system: - .gitignore added (was missing - removes pyc, db, node_modules, etc.) - build-full-installer-v2.py: v23 with Node.js check + React build step - Vite build config with manual chunks for optimal loading Also includes: V3 deck generator fixes, coach improvements, DeepSeek sim modules
362 lines
13 KiB
Python
362 lines
13 KiB
Python
"""
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Commander AI Lab — State Encoder
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═════════════════════════════════
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Converts raw decision snapshots (JSONL) into fixed-size tensor vectors
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suitable for neural network input.
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Pipeline:
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1. Load card embeddings from mtg-embeddings (768-dim, ~32K cards)
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2. For each decision snapshot:
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a. Encode global features (life, mana, turn, phase, etc.)
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b. For each zone per player: mean-pool card embeddings
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c. Encode playstyle as one-hot vector
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d. Concatenate into single fixed-length vector
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Output dimensions:
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global_features (29) + zone_embeddings (6144) + playstyle (4) = 6177
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"""
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import json
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import logging
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import os
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from pathlib import Path
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from typing import Dict, List, Optional, Tuple
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import numpy as np
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logger = logging.getLogger("ml.encoder")
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# Import scope config
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import sys
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sys.path.insert(0, str(Path(__file__).parent.parent.parent))
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from ml.config.scope import (
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STATE_DIMS, PLAYSTYLE_TO_IDX, PHASE_TO_IDX,
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Playstyle, GamePhase, GAME_SCOPE,
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)
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class CardEmbeddingIndex:
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"""
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Loads and caches card embeddings from the minimaxir/mtg-embeddings dataset.
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Provides name → 768-dim vector lookup.
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Uses the same NPZ cache as coach/embeddings.py for consistency.
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"""
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def __init__(self, embeddings_dir: str = None):
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self.embeddings_dir = embeddings_dir or str(
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Path(__file__).parent.parent.parent / "embeddings"
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)
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self._name_to_idx: Dict[str, int] = {}
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self._embeddings: Optional[np.ndarray] = None
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self._names: List[str] = []
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self._loaded = False
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self._zero_vec = np.zeros(STATE_DIMS.card_embedding_dim, dtype=np.float32)
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def load(self) -> bool:
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"""Load embeddings from NPZ cache.
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Searches multiple known locations and handles two NPZ formats:
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- Coach format: keys 'vectors', 'names' (saved by coach/embeddings.py)
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- ML format: keys 'embeddings', 'names' (legacy)
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If no NPZ is found anywhere, auto-downloads from HuggingFace via
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the coach embedding index and converts to NPZ.
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"""
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project_root = Path(__file__).parent.parent.parent
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candidates = [
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str(project_root / "data" / "mtg-embeddings.npz"),
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str(project_root / "data" / "mtg_embeddings.npz"),
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os.path.join(self.embeddings_dir, "mtg_embeddings.npz"),
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os.path.join(self.embeddings_dir, "mtg-embeddings.npz"),
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str(project_root / "embeddings" / "mtg_embeddings.npz"),
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str(project_root / "embeddings" / "mtg-embeddings.npz"),
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]
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npz_path = None
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for candidate in candidates:
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if os.path.exists(candidate):
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npz_path = candidate
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break
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# Auto-download if nothing found
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if not npz_path:
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logger.info("Embeddings NPZ not found — attempting auto-download...")
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npz_path = self._auto_download(project_root)
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if not npz_path:
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return False
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try:
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data = np.load(npz_path, allow_pickle=True)
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# Handle both NPZ formats:
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# Coach format has 'vectors', ML legacy has 'embeddings'
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if "vectors" in data:
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self._embeddings = data["vectors"].astype(np.float32)
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elif "embeddings" in data:
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self._embeddings = data["embeddings"].astype(np.float32)
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else:
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logger.error("NPZ at %s has no 'vectors' or 'embeddings' key. Keys: %s",
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npz_path, list(data.keys()))
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return False
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self._names = list(data["names"])
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self._name_to_idx = {
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name.lower(): i for i, name in enumerate(self._names)
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}
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self._loaded = True
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logger.info(
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"Loaded %d card embeddings (%d-dim) from %s",
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len(self._names), self._embeddings.shape[1], npz_path,
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)
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return True
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except Exception as e:
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logger.error("Failed to load embeddings: %s", e)
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return False
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@staticmethod
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def _auto_download(project_root: Path) -> Optional[str]:
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"""Download embeddings from HuggingFace via the coach module.
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Uses coach/embeddings.py MTGEmbeddingIndex which handles downloading
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the parquet from HuggingFace and converting to NPZ. Returns the NPZ
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path on success, or None on failure.
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"""
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try:
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import sys
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pr = str(project_root)
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if pr not in sys.path:
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sys.path.insert(0, pr)
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from coach.embeddings import MTGEmbeddingIndex
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from coach.config import EMBEDDINGS_NPZ
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idx = MTGEmbeddingIndex()
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idx.download_parquet()
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idx.convert_parquet_to_npz()
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npz_str = str(EMBEDDINGS_NPZ)
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if os.path.exists(npz_str):
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logger.info("Auto-downloaded embeddings to %s", npz_str)
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return npz_str
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logger.error("Auto-download completed but NPZ not found at %s", npz_str)
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return None
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except Exception as e:
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logger.error("Auto-download failed: %s", e)
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return None
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def get_embedding(self, card_name: str) -> np.ndarray:
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"""Get embedding vector for a card name. Returns zero vector if not found."""
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if not self._loaded:
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return self._zero_vec
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idx = self._name_to_idx.get(card_name.lower())
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if idx is not None:
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return self._embeddings[idx]
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# Try fuzzy match: strip trailing set codes, articles
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clean = card_name.lower().split("|")[0].strip()
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idx = self._name_to_idx.get(clean)
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if idx is not None:
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return self._embeddings[idx]
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return self._zero_vec
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def mean_pool_zone(self, card_names: List[str]) -> np.ndarray:
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"""Mean-pool embeddings for a list of card names (a game zone)."""
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if not card_names:
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return self._zero_vec
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vectors = [self.get_embedding(name) for name in card_names]
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return np.mean(vectors, axis=0).astype(np.float32)
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@property
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def is_loaded(self) -> bool:
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return self._loaded
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class StateEncoder:
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"""
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Encodes a decision snapshot JSON dict into a fixed-size numpy vector.
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Input: a dict from the JSONL file (one decision snapshot)
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Output: np.ndarray of shape (6177,)
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Components:
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[0:29] - Global features (life, mana, turn, phase, etc.)
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[29:6173] - Zone embeddings (4 zones × 2 players × 768)
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[6173:6177] - Playstyle one-hot (4 dims)
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"""
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def __init__(self, card_index: CardEmbeddingIndex):
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self.card_index = card_index
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self.dim = STATE_DIMS
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def encode(self, decision: dict, playstyle: str = "midrange") -> np.ndarray:
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"""
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Encode a single decision snapshot into a state vector.
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Args:
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decision: Dict loaded from JSONL line
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playstyle: Deck archetype string (aggro/control/midrange/combo)
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Returns:
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np.ndarray of shape (total_state_dim,)
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"""
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players = decision.get("players", [])
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if len(players) < 2:
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# Pad to 2 players for 1v1
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while len(players) < 2:
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players.append(self._empty_player(len(players)))
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# 1. Global features
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global_vec = self._encode_global(decision, players)
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# 2. Zone embeddings
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zone_vec = self._encode_zones(players)
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# 3. Playstyle one-hot
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style_vec = self._encode_playstyle(playstyle)
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# Concatenate
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state = np.concatenate([global_vec, zone_vec, style_vec])
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assert state.shape[0] == self.dim.total_state_dim, (
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f"State dim mismatch: {state.shape[0]} != {self.dim.total_state_dim}"
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)
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return state.astype(np.float32)
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def _encode_global(self, decision: dict, players: list) -> np.ndarray:
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"""Encode global scalar features. Shape: (29,)"""
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features = []
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for p in players[:2]: # Exactly 2 players for 1v1
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# Per-player scalars (10 each)
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life = p.get("life", 40)
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features.append(life / 40.0) # Normalized life
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cmdr_dmg = p.get("cmdr_dmg", 0)
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features.append(cmdr_dmg / 21.0) # Normalized commander damage
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mana = p.get("mana", 0)
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features.append(min(mana / 20.0, 1.0)) # Normalized mana
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cmdr_tax = p.get("cmdr_tax", 0)
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features.append(min(cmdr_tax / 10.0, 1.0)) # Normalized tax
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hand_size = len(p.get("hand", []))
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features.append(min(hand_size / 15.0, 1.0))
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grave_size = len(p.get("graveyard", []))
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features.append(min(grave_size / 100.0, 1.0))
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creatures = p.get("creatures", 0)
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features.append(min(creatures / 30.0, 1.0))
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# Total power on board (not yet tracked accurately; use creatures as proxy)
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total_power = creatures * 3 # rough heuristic: avg 3 power
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features.append(min(total_power / 100.0, 1.0))
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# Is active player
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is_active = 1.0 if p.get("seat") == decision.get("active_seat") else 0.0
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features.append(is_active)
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# Land count (extra feature — helps distinguish ramp strategies)
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lands = p.get("lands", 0)
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features.append(min(lands / 15.0, 1.0))
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# Phase one-hot (4 dims per player)
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phase_str = decision.get("phase", "main_1")
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phase_oh = [0.0] * 4
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try:
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phase_enum = GamePhase(phase_str)
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phase_oh[PHASE_TO_IDX[phase_enum]] = 1.0
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except (ValueError, KeyError):
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phase_oh[0] = 1.0 # Default to main_1
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features.extend(phase_oh)
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# Turn number (1 feature, shared)
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turn = decision.get("turn", 1)
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features.append(min(turn / GAME_SCOPE.max_turns, 1.0))
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return np.array(features, dtype=np.float32)
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def _encode_zones(self, players: list) -> np.ndarray:
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"""
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Encode zone contents as mean-pooled card embeddings.
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Shape: (4 zones × 2 players × 768) = (6144,)
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"""
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zone_keys = ["hand", "battlefield", "graveyard", "command_zone"]
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vectors = []
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for p in players[:2]:
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for zone_key in zone_keys:
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cards = p.get(zone_key, [])
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pooled = self.card_index.mean_pool_zone(cards)
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vectors.append(pooled)
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return np.concatenate(vectors)
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def _encode_playstyle(self, playstyle_str: str) -> np.ndarray:
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"""One-hot encode the playstyle. Shape: (4,)"""
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vec = np.zeros(self.dim.playstyle_dim, dtype=np.float32)
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try:
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style = Playstyle(playstyle_str.lower())
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vec[PLAYSTYLE_TO_IDX[style]] = 1.0
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except (ValueError, KeyError):
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# Default: midrange
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vec[PLAYSTYLE_TO_IDX[Playstyle.MIDRANGE]] = 1.0
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return vec
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def _empty_player(self, seat: int) -> dict:
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"""Create an empty player dict for padding."""
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return {
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"seat": seat,
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"life": 40,
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"cmdr_dmg": 0,
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"mana": 0,
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"cmdr_tax": 0,
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"creatures": 0,
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"lands": 0,
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"hand": [],
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"battlefield": [],
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"graveyard": [],
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"command_zone": [],
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}
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def encode_decisions_file(
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jsonl_path: str,
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card_index: CardEmbeddingIndex,
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max_samples: int = None,
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) -> Tuple[np.ndarray, List[dict]]:
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"""
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Encode all decisions from a JSONL file into a state matrix.
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Args:
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jsonl_path: Path to ml-decisions-*.jsonl file
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card_index: Loaded card embedding index
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max_samples: Maximum number of samples to encode (None = all)
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Returns:
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(states, raw_decisions) where:
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states: np.ndarray of shape (N, 6177)
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raw_decisions: list of original JSON dicts (for action labels)
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"""
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encoder = StateEncoder(card_index)
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states = []
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raw = []
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with open(jsonl_path, "r") as f:
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for i, line in enumerate(f):
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if max_samples and i >= max_samples:
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break
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line = line.strip()
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if not line:
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continue
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try:
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decision = json.loads(line)
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playstyle = decision.get("archetype", "midrange")
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state_vec = encoder.encode(decision, playstyle)
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states.append(state_vec)
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raw.append(decision)
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except (json.JSONDecodeError, Exception) as e:
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logger.warning("Skipping line %d: %s", i, e)
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continue
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if not states:
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return np.empty((0, STATE_DIMS.total_state_dim), dtype=np.float32), []
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return np.stack(states), raw
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