c5045c009e
Two ghost-feature fixes from the pre-L40S cleanup audit (tasks #66 and #68 tracked internally). ### #66 — delete apply_accumulated_gradients (dead code from removed path) The agent-audit confirmed this function is residue from a prior Candle-gradient-tracking training path that was REMOVED (see the now-deleted test crates/ml/tests/test_var_source_gradients.rs which was `#[ignore]`'d with the comment "Candle gradient tracking removed -- DQN/PPO use custom CUDA backward passes"). Evidence: - `DQN::optimizer: Option<GpuAdamW>` is always `None` — never initialised anywhere in the codebase. - `DQN` uses `OwnedGpuLinear` + `NoisyLinear` with standalone `CudaSlice<f32>` BY DESIGN (see comment at branching.rs:988-989 "this layout exists to avoid a GpuVarStore intermediate"). There is no GpuVarStore to feed GpuAdamW. - Zero external callers for `DqnTrainer::apply_accumulated_gradients`, `DQN::apply_accumulated_gradients`, `RegimeConditionalDQN:: apply_accumulated_gradients`, or the various `optimizer_vars()` wrappers. - The only test exercising this path was `#[ignore]`'d with the "Candle gradient tracking removed" rationale. - Production training goes through the fused CUDA trainer (`trainers/dqn/fused_training.rs`) which applies gradients into a flat `params_buf` — a separate, live path. Deleted: 6 functions across 3 files + the stale test. Preserving a ghost that has zero callers, zero initialisation path, and a design direction explicitly chosen AWAY from its premise is not "keep and wire" — it's accumulating fiction. Per feedback_no_functionality_ removal.md the rule preserves FUNCTIONAL features; this wasn't one. ### #68 — TFT honest error message Audit found TFT is architecturally incompatible with GpuAdamW in its current form (not a wiring gap, an architectural absence): - `TemporalFusionTransformer` has no GpuVarStore, no `parameters()`, no `named_parameters()` accessor - Internal layers use `StreamLinear` which has NO `backward()` - `TFTModel` trait only exposes `forward()`, `get_config()`, `clear_cache()` — no gradient accessor - `TemporalFusionTransformer::train()` runs forward + accumulates loss but never calls backward or optimizer step - `TrainableTFT` adapter's `backward()` explicitly returns "not supported — use TFTTrainer::train() instead" The previous error string "TFT optimizer not yet migrated to GpuAdamW" implied 99% done and a simple constructor wiring would finish it. Actual gap: GpuVarStore threading through 5+ layers + StreamLinear→GpuLinear migration + backward ops for softmax attention / layer norm 3D / quantile monotonicity chain / stack + trait extension for var_store accessor + train-loop gradient assembly. ~3-7 day dedicated architectural project. New error message states this explicitly so no one wastes time chasing an "almost migrated" fiction. Full-lift tracked separately. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
ml
10-model ML ensemble for the Foxhunt HFT system, built on Candle v0.9.1.
Models
- DQN (Rainbow) — deep Q-network with prioritized replay, dueling heads, noisy nets
- PPO — proximal policy optimization with GAE, LSTM policies, clip-higher
- TFT — temporal fusion transformer for multi-horizon forecasting
- Mamba2 — state space model for sequence prediction
- Liquid Networks — biologically inspired networks for non-stationary data
- TLOB — transformer-based limit order book analysis
- KAN — Kolmogorov-Arnold networks
- xLSTM — extended LSTM architecture
- TGGN — temporal graph neural network
- Diffusion — diffusion-based generative model
Key Modules
ensemble— model ensemble coordination and confidence aggregationhyperopt— PSO-based hyperparameter optimization with per-model adapterstrainers— unified training loops (DQN, PPO, supervised)inference—InferenceAdaptertrait for predictioncheckpoint— model checkpointing and restorationevaluation— walk-forward evaluation pipeline
Usage
use ml::dqn::DQN;
use ml::ppo::PpoTrainer;