repleafgbm 1.0.1

Representation-enhanced Leaf Gradient Boosting Machine: raw-feature tree routing with representation-conditioned leaf models.

RepLeafGBM

Representation-enhanced Leaf Gradient Boosting Machine — gradient boosting that routes on raw features and predicts with small linear models over learned representations inside each leaf.

RepLeafGBM is not a neural network inside a tree, nor a tree over embeddings. It is a boosted ensemble of raw-feature routers with representation-conditioned local predictors.

Stability: from 1.0.0 the public API follows Semantic Versioning. What that covers (estimator parameters, the save/load format, registered encoder/objective/metric names, exported symbols) and what stays experimental (repleafgbm.external, router_extraction, native-backend internals) is spelled out in docs/adr/0003-api-stability.md.

Highlights from the experiment log (see docs/audit_v0.md and experiments/results/):

• On synthetic signals with smooth structure inside regimes, embedded-linear leaves beat constant leaves, LightGBM, and sklearn HistGradientBoosting.
• Refitting LightGBM's own routes with representation-conditioned leaves (router_extraction) improves LightGBM by 2-12% RMSE — isolating the leaf contribution from split quality.
• On standard real OpenML datasets RepLeafGBM is competitive with the major GBM libraries (mean rank across 9 datasets; constant-leaf RepLeaf even edges out LightGBM and XGBoost on classification), though there the leaf embeddings add no real-data accuracy over a constant leaf — consistent with the documented finding that their advantage is specific to smooth/periodic structure. Reproduce: python benchmarks/openml_suite.py (see experiments/results/openml_benchmark.md).

Motivation

GBDTs dominate tabular ML because axis-aligned splits on raw features handle discontinuities, interactions, and messy data extremely well. Tabular deep learning has shown that numerical feature embeddings (PLR, periodic) capture smooth nonlinear structure that constant-leaf trees approximate only with many splits.

RepLeafGBM combines both with a deliberately asymmetric design:

• Routing — every split is on a raw feature, exactly like a normal GBDT. Trees do what trees are good at: finding discontinuous boundaries and partitioning the space into local regions.
• Leaf output — each leaf holds a small ridge-regularized linear model over a learned representation z_theta(x) instead of a constant. The embedding does what it is good at: smooth interpolation within a local region.

f_t(x) = b_{t, l_t(x_raw)} + w_{t, l_t(x_raw)}^T z_theta(x)
F_T(x) = F_0(x) + sum_t eta * f_t(x)

What makes it different

• Embeddings are never used for splitting (interpretable raw-feature routing, no histogram blow-up, no curse of dimensionality in split search).
• The encoder is frozen during boosting in v0, preserving the stage-wise additive structure of gradient boosting.
• Leaf models fall back to constants when leaves are too small, so the model degrades gracefully toward a classic GBDT.

Minimal example

import numpy as np
from repleafgbm import RepLeafRegressor
from repleafgbm.data import RepLeafDataset

rng = np.random.default_rng(0)
X = rng.normal(size=(500, 4))
y = np.where(X[:, 0] > 0, 3.0, -2.0) + 2.0 * X[:, 1] + rng.normal(0, 0.1, 500)

model = RepLeafRegressor(
    n_estimators=50,
    learning_rate=0.1,
    num_leaves=8,
    leaf_model="embedded_linear",   # or "constant", "raw_linear"
    encoder="plr",                  # or "identity"
    max_leaf_emb_dim=16,
    random_state=42,
)
model.fit(X, y)
pred = model.predict(X)

model.save_model("repleaf_model")
loaded = RepLeafRegressor.load_model("repleaf_model")

pandas DataFrames with categorical columns are supported through the dataset API:

train_data = RepLeafDataset(df_train, y_train, categorical_features=["city"])
model.fit(train_data, eval_set=[RepLeafDataset(df_valid, y_valid,
                                               metadata=train_data.metadata)])

Current status (v0)

Implemented:

• Native NumPy backend: histogram-based split search with sibling-histogram subtraction, leaf-wise tree growth — plus optional Rust kernels (pip install ./native, auto-detected; ~5.8x faster constant-leaf training, parity-tested against the NumPy reference)
• leaf_model: "constant", "embedded_linear", "raw_linear"
• Encoders: "identity", "plr" (simplified piecewise-linear + linear term), "periodic" (PBLD-style frozen sinusoidal features), "cross" (residual-correlated pairwise products), and learned "torch_periodic" / "torch_plr" / "torch_mlp" (optional [torch] extra; pretrained on the initial residual then frozen — torch is needed only at fit time). identity is the evidence-backed default on real tabular data; the others are specialists for known smooth/oscillatory or interaction structure (see docs for guidance). Random projection down to max_leaf_emb_dim as an emergency cap
• Regression (squared error, plus objective="huber" / "quantile" / "poisson" — parameterized instances like Quantile(alpha=0.9) work too), binary classification (logistic), and multiclass classification (softmax, one tree per class per round — automatic at 3+ classes)
• Multi-output regression via shared-routing vector leaves (pass a 2-D y; one tree per round whose leaves emit a vector — squared-error only), and label_smoothing for classification
• Early stopping (early_stopping_rounds, best_iteration_, prediction at the best iteration) and eval metrics: rmse, mae, logloss, multi_logloss, auc, accuracy, or any user-supplied callable (repleafgbm.make_metric)
• Feature importance (feature_importances_, gain or split count)
• RepLeafDataset with pandas/categorical support (native subset splits, pandas.Categorical order fidelity, opt-in frequency encoding) and embedding caching
• Directory-based save_model / load_model with schema validation and a human-readable summary.txt (model.summary())
• repleafgbm.external: LightGBM, XGBoost, and CatBoost as external base models (scores + leaf indices, optional native early stopping), generic OOF utility, stacking feature builders, and RouterExtractionRegressor / RouterExtractionClassifier — LightGBM routing with RepLeaf leaf models refit on the frozen routes, with replay-stage early stopping (pip install "repleafgbm[external]")
• pytest suite, runnable examples, and an experiments/ research scaffold

Not implemented (see docs/roadmap.md): encoder updates during boosting, GPU/distributed training.

Installation

pip install repleafgbm                 # core (numpy, pandas, scikit-learn)
pip install "repleafgbm[external]"     # + LightGBM external_model / router_extraction
pip install "repleafgbm[bench]"        # + XGBoost / CatBoost for benchmarks
pip install "repleafgbm[torch]"        # + learned torch encoders

Development

git clone <repo-url> && cd repleafgbm
pip install -e ".[dev]"

Or without installing, run everything from the repo root with PYTHONPATH=src. Build the API reference with pip install -e ".[docs]" && bash scripts/build_docs.sh.

Running tests and examples

bash scripts/check.sh               # lint + tests + all examples
python -m pytest tests/ -q          # PYTHONPATH=src if not installed
python examples/regression_basic.py
python examples/binary_classification_basic.py
python examples/multiclass_classification_basic.py
python examples/dataset_api_basic.py

Benchmarks

Small synthetic benchmarks track progress across development (they are not performance claims):

python benchmarks/benchmark_synthetic_regression.py [--quick]
python benchmarks/benchmark_synthetic_binary.py [--quick]

LightGBM / XGBoost / CatBoost are included automatically when installed (pip install -e ".[bench]"). The latest snapshot and analysis live in docs/audit_v0.md.

Documentation

• docs/design.md — architecture and responsibilities
• docs/math.md — boosting formulation and leaf fitting
• docs/roadmap.md — implemented vs planned
• docs/backend_strategy.md — multi-backend plans
• docs/serialization.md — model format
• docs/dataset_and_memory.md — memory strategy
• docs/categorical_features.md — categorical policy
• docs/audit_v0.md — Phase 0.5 audit, fixes, benchmark snapshot

License

MIT