feat(data): add DeepSeek ds- source with 4x default upweight
_discover_jsonl_files now recognises ml-decisions-ds-*.jsonl as its own "ds" source key instead of falling through to legacy/forge. DatasetConfig.source_weights gains ds=4.0 as a default so the 8 ds files (1775 records, ~5.8% raw) are oversampled to ~20% of effective training tokens, matching their higher signal quality. CLI gains --ds-weight argument (default 4.0) to override without code changes. The training_manifest.json produced by PowerShell already encodes weight=4.0 per ds file; this change makes dataset_builder.py honour the same ratio natively so the manifest and the builder stay in sync.
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@@ -6,8 +6,9 @@ Reads ML decision JSONL files, encodes states, labels actions,
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and produces a training-ready dataset saved as NPZ.
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Phase 2: Source Awareness (Issue #66)
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- Scans both Forge sim files (ml-decisions-sim-*.jsonl) and
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PPO self-play files (ml-decisions-ppo-*.jsonl)
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- Scans Forge sim files (ml-decisions-sim-*.jsonl) → source "forge"
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- Scans PPO self-play (ml-decisions-ppo-*.jsonl) → source "ppo"
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- Scans DeepSeek files (ml-decisions-ds-*.jsonl) → source "ds"
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- Configurable source_weights for sampling ratios
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- min_reward_threshold filter for PPO data quality
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- Each sample tagged with source for downstream analysis
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@@ -21,7 +22,7 @@ Output NPZ structure:
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game_ids: object (N,) — game ID for each sample
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outcomes: object (N,) — game outcome per sample
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playstyles: object (N,) — deck archetype per sample
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sources: object (N,) — data source ("forge" or "ppo")
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sources: object (N,) — data source ("forge", "ppo", or "ds")
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"""
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import argparse
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@@ -62,11 +63,11 @@ class DatasetConfig:
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embeddings_dir: Optional[str] = None
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max_samples: Optional[int] = None
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# Source weights control sampling ratios between Forge and PPO data.
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# Default: Forge-only (ppo=0.0). Use MIXED_MODE_PRESETS from
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# ml.config.scope for blended configurations (e.g. 90/10, 80/20).
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# Source weights control sampling ratios between Forge, PPO, and DS data.
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# ds=4.0 oversamples the 8 DeepSeek files from ~5.8% → ~20% of effective
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# training tokens, matching their higher signal quality relative to sim data.
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source_weights: Dict[str, float] = field(
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default_factory=lambda: {"forge": 1.0, "ppo": 0.0}
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default_factory=lambda: {"forge": 1.0, "ppo": 0.0, "ds": 4.0}
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)
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# Minimum reward threshold for PPO data — only include decisions
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@@ -81,12 +82,13 @@ class DatasetConfig:
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def _discover_jsonl_files(results_dir: str) -> Dict[str, List[Path]]:
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"""
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Scan results directory for both Forge sim and PPO decision files.
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Scan results directory for Forge sim, PPO, and DeepSeek decision files.
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Returns:
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Dict mapping source name to list of JSONL file paths.
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Keys: "forge" for ml-decisions-sim-*.jsonl
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"ppo" for ml-decisions-ppo-*.jsonl
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"ds" for ml-decisions-ds-*.jsonl
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"""
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results_path = Path(results_dir)
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sources = {}
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@@ -101,10 +103,16 @@ def _discover_jsonl_files(results_dir: str) -> Dict[str, List[Path]]:
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if ppo_files:
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sources["ppo"] = ppo_files
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# Also pick up legacy files that don't have sim/ppo prefix
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# DeepSeek high-quality decisions (upweighted by default)
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ds_files = sorted(results_path.glob("ml-decisions-ds-*.jsonl"))
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if ds_files:
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sources["ds"] = ds_files
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# Legacy files that don't match any known prefix — treat as forge
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known = set(forge_files + ppo_files + ds_files)
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legacy_files = sorted(
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f for f in results_path.glob("ml-decisions-*.jsonl")
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if f not in set(forge_files + ppo_files)
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if f not in known
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)
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if legacy_files:
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sources.setdefault("forge", []).extend(legacy_files)
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@@ -123,7 +131,7 @@ def _passes_reward_filter(
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) -> bool:
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"""
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Filter PPO decisions by reward threshold.
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Forge data always passes. PPO data must meet the threshold.
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Forge and DS data always passes. PPO data must meet the threshold.
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"""
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if source != "ppo":
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return True
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@@ -155,7 +163,7 @@ def _apply_source_weights(
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if weight <= 0:
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logger.info(" Skipping %d %s samples (weight=0)", len(samples), source)
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continue
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if weight >= 1.0 and weight == 1.0:
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if weight == 1.0:
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combined.extend(samples)
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elif weight < 1.0:
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# Subsample
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@@ -193,8 +201,9 @@ def build_dataset(
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Build complete training dataset from all ML decision files.
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Phase 2 additions:
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- Scans both Forge and PPO JSONL files
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- Scans Forge, PPO, and DeepSeek (ds) JSONL files
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- Applies source_weights for configurable mixing
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(default: ds=4.0 to oversample high-quality DeepSeek decisions)
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- Filters PPO data by min_reward_threshold
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- Tags each sample with its source
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@@ -202,14 +211,15 @@ def build_dataset(
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results_dir: Path to results/ directory containing JSONL files
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embeddings_dir: Path to embeddings/ directory (default: auto-detect)
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max_samples: Cap total samples (None = unlimited)
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source_weights: Dict of source → sampling weight (default: forge=1.0, ppo=0.5)
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source_weights: Dict of source → sampling weight
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(default: forge=1.0, ppo=0.0, ds=4.0)
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min_reward_threshold: Min episode_return for PPO data (default: 0.0)
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Returns:
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Dict with keys: states, labels, game_ids, outcomes, playstyles, sources
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"""
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if source_weights is None:
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source_weights = {"forge": 1.0, "ppo": 0.0}
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source_weights = {"forge": 1.0, "ppo": 0.0, "ds": 4.0}
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# Load card embeddings
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card_index = CardEmbeddingIndex(embeddings_dir)
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@@ -225,7 +235,7 @@ def build_dataset(
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encoder = StateEncoder(card_index)
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# --- Phase 2 Task 1: Discover files from both sources ---
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# --- Phase 2 Task 1: Discover files from all sources ---
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source_files = _discover_jsonl_files(results_dir)
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if not source_files:
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logger.error("No ML decision files found in %s", results_dir)
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@@ -503,7 +513,11 @@ def main():
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)
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parser.add_argument(
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"--ppo-weight", type=float, default=0.0,
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help="Sampling weight for PPO self-play data (default: 0.0, Forge-only)",
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help="Sampling weight for PPO self-play data (default: 0.0, disabled)",
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)
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parser.add_argument(
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"--ds-weight", type=float, default=4.0,
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help="Sampling weight for DeepSeek decision data (default: 4.0, ~20%% effective share)",
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)
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parser.add_argument(
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"--min-reward", type=float, default=0.0,
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@@ -515,6 +529,7 @@ def main():
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source_weights = {
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"forge": args.forge_weight,
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"ppo": args.ppo_weight,
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"ds": args.ds_weight,
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}
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# Build dataset
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