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5 Commits

Author SHA1 Message Date
jgrusewski
b95ee9d336 refactor(ml): remove dead code across 8 sub-crates — delete 742 lines 
Remove unused struct fields, dead methods, unreachable code across
ml-dqn, ml-supervised, ml-features, ml-ppo, ml-checkpoint, ml-labeling,
ml-observability, and ml-universe. Gate test-only infra behind #[cfg(test)].

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
 2026-03-08 15:17:22 +01:00
jgrusewski
aa19b42255 refactor(ml): move UnifiedTrainable to ml-core + delete 6K dead deployment code 
- Move UnifiedTrainable trait, TrainingMetrics, CheckpointMetadata, and
  checkpoint helpers from ml to ml-core (zero new dependencies — ml-core
  already had candle-core + serde_json)
- Wrap Mamba2SSM in Mamba2TrainableAdapter to satisfy orphan rule (trait
  in ml-core, type in ml-supervised — all other 9 models already used
  wrapper pattern)
- Make Mamba2SSM::validate() pub for cross-crate adapter access
- Delete 5 permanently disabled deployment modules (cfg(any()) — never
  compiled): registry, hot_swap, validation, monitoring, endpoints
  (-5,842 lines)
- Delete 2 undeclared dead files in training/: dqn_trainer.rs,
  transformer_trainer.rs (-138 lines)
- Fix pre-existing compute_loss test shape mismatch in mamba adapter

UnifiedTrainable in ml-core unblocks future trainers/ extraction (17.8K
lines) since model-specific trainers can now depend on ml-core for the
trait without pulling in the full ml monolith.

14 files changed, +128 -6,497 (net -6,369 lines)
Tests: 274 ml-core + 948 ml = 1,222 passed, 0 failed

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
 2026-03-08 15:17:22 +01:00
jgrusewski
d313486dc2 refactor(ml): split monolith into 9 sub-crates + delete dead code 
Extract 9 new sub-crates from the ml monolith to enable parallel
compilation across the workspace:

New crates (this commit):
- ml-features (282 tests): feature engineering, 21 modules
- ml-labeling (45 tests): triple barrier, meta-labeling, fractional diff
- ml-ensemble (116 tests): ensemble coordination, voting, confidence
- ml-hyperopt (47 tests): core PSO/TPE optimizer, parameter space
- ml-checkpoint (41 tests): checkpoint persistence, compression, signing
- ml-regime (68 tests): CUSUM, Bayesian changepoint, regime classification
- ml-data-validation (67 tests): FDR correction, CPCV, data quality
- ml-risk (33 tests): neural VaR, Kelly criterion, circuit breakers
- ml-validation (43 tests): statistical validation, walk-forward, DSR

Extended existing crates:
- ml-dqn: added evaluation/ (backtesting engine, metrics, reports)
  and checkpoint implementation
- ml-supervised: added checkpoint implementations
- ml-core: added shared types needed by new sub-crates

Pattern: each module in ml/ becomes a thin facade (pub use subcrate::*)
with bridge modules staying in ml for cross-model adapter code.

Dead code deleted (~7K lines):
- 13 undeclared files in microstructure/ (never compiled)
- 7 undeclared files + tests/ in risk/ (never compiled)
- parquet_io, cache_service, cache_storage, minio_integration (unused)
- extraction_wave_d_impl.rs (bare fn outside impl block)

All 2,746 sub-crate tests + 951 ml tests pass.
Full workspace builds clean.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
 2026-03-08 15:17:22 +01:00
jgrusewski
3db7f4828b refactor(ml): extract 8 supervised models into ml-supervised crate (task 8) 
Move TFT, Mamba-2, Liquid, TGGN, TLOB, KAN, xLSTM, and Diffusion model
implementations to ml-supervised. Bridge files (UnifiedTrainable adapters,
Checkpointable impls) stay in ml. Delete AsyncDataLoader (replaced by
StreamingDbnLoader + simple .chunks() batching). Remove empty ml-infra
scaffold — the remaining ml modules are too tightly coupled for clean
extraction, so ml stays as the orchestration facade.

- ml-supervised: 234 tests, 0 failures
- ml: 1687 tests, 0 failures
- Workspace: 0 compilation errors

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
 2026-03-08 15:16:08 +01:00
jgrusewski
e440de4c6b feat(ml): scaffold sub-crate directory structure for ml split 
Add 5 empty sub-crates (ml-core, ml-dqn, ml-ppo, ml-supervised, ml-infra)
to workspace. Modules will be moved from monolithic ml crate in subsequent
tasks.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
 2026-03-08 15:13:07 +01:00