Shinrin
Shinrin (森林, "forest" in Japanese) is a scikit-learn-compatible library for decision tree and forest models and tabular neural networks, with Rust and Mojo bindings for performance and ONNX export support.
Since skope-rules and scikit-garden are no longer actively maintained, this project aims to bring them together with extensions for tree models — including SHAP explanations, ONNX export, and benchmarking utilities.
Shinrin also includes TabM — a parameter-efficient ensemble MLP for tabular data (ICLR 2025) that trains an ensemble of k members jointly through BatchEnsemble-style multiplicative adapters, matching ensemble accuracy at a fraction of the training cost. Training runs entirely on NumPy or Mojo kernels — PyTorch is not required.
Features
- Mondrian Trees & Forests — Full scikit-learn API compatibility
- CORELS Optimal Rule Lists — Certifiably optimal rule lists for binary data (
CorelsClassifier), vendored from pycorels with bundled mini-GMP (no system dependency) - GOSDT Optimal Sparse Decision Trees — Globally optimized sparse decision trees with reference-ensemble guesses (
GOSDTClassifier,ThresholdGuessBinarizer), vendored from gosdt-guesses; runs single-threaded with no TBB/GMP system dependencies - Tabular Neural Networks — scikit-learn compatible
MLPClassifier/MLPRegressorandTabMClassifier/TabMRegressorwith optional PLE embeddings and Mojo-accelerated training - TabM Neural Networks — Parameter-efficient ensemble MLPs for tabular data with BatchEnsemble-style multiplicative adapters (ICLR 2025)
- TabICL — Tabular in-context learning foundation model (torch/NumPy/Mojo backends)
- TreeSHAP Explanations —
TreeExplainerfor single trees and forests withexplanation()visualization helper - ONNX Export — Export trained trees, forests, and TabM models to ONNX format for deployment
- Benchmarking — Built-in utilities for training speed, prediction speed, and model size
- Rust & Mojo Bindings — Performance-critical code in Rust via PyO3 and Mojo kernels
Quick Start
Tree Models
from shinrin import MondrianTreeRegressor, MondrianForestClassifier
from shinrin import TreeExplainer, explanation
# Train a model
X, y = ...
tree = MondrianTreeRegressor(max_depth=8, random_state=0)
tree.fit(X, y)
predictions = tree.predict(X)
# Get SHAP explanations
explainer = TreeExplainer(tree)
shap_values = explainer.shap_values(X)
# Or use the convenience function:
# explanation(tree, X) # opens matplotlib visualization
TabM Neural Networks
from shinrin import TabMClassifier, TabMRegressor
# Train a TabM model
model = TabMRegressor(
hidden_layer_sizes=(256,),
k=32, # ensemble size
random_state=0,
)
model.fit(X, y)
predictions = model.predict(X)
# Classification
clf = TabMClassifier(k=32, max_iter=200)
clf.fit(X, y)
CORELS Optimal Rule Lists
from shinrin import CorelsClassifier
# Binary features, binary classification — provably optimal rule list
clf = CorelsClassifier(c=0.01, verbosity=["rulelist"])
clf.fit(X, y, features=["feature1", "feature2"])
print(clf.rl()) # human-readable optimal rule list
predictions = clf.predict(X)
GOSDT Optimal Sparse Decision Trees
from shinrin import GOSDTClassifier, ThresholdGuessBinarizer
# Binarize continuous features via gradient-boosting threshold guesses
X_bin = ThresholdGuessBinarizer().fit_transform(X, y)
# Optionally guide the search with a blackbox reference ensemble
clf = GOSDTClassifier(regularization=0.05, depth_budget=4)
clf.fit(X_bin, y) # or: clf.fit(X_bin, y, y_ref=y_ref)
print(str(clf.trees_[0])) # globally optimal tree
accuracy = clf.score(X_bin, y)
Native Backends
Both Mondrian trees and TabM support interchangeable native backends:
- Rust (default) — PyO3/maturin extension for tree models
- Mojo — Experimental Mojo port for TabM training kernels
Select the backend with environment variables:
SHINRIN_BACKEND=mojo python your_script.py # TabM Mojo backend
SHINRIN_TABM_BACKEND=mojo python your_script.py # TabM-specific backend
Benchmarks
See scripts/benchmarks/BENCHMARK.md for detailed benchmark results comparing Shinrin against LightGBM and scikit-learn SGD.
See scripts/benchmarks/TABM_BENCHMARK.md for TabM backend comparisons (NumPy vs Mojo vs PyTorch).
See scripts/benchmarks/GOSDT_BENCHMARK.md for GOSDT vs scikit-learn CART comparisons (just bench-gosdt).
To run benchmarks yourself: python scripts/benchmarks/bench_baselines.py (or just bench-backends for Rust vs Mojo backend comparisons, just bench-tabm for TabM backends).
Installation
pip install shinrin
Optional dependencies:
pip install shinrin[sklearn] # scikit-learn for benchmarks and SHAP plotting
pip install shinrin[onnx] # ONNX export
pip install shinrin[mojo] # TabM Mojo kernels (`just build-tabm-mojo`)
pip install shinrin[full] # All optional dependencies
Native backends
The tree/forest internals ship with two interchangeable native backends:
rust(default) – the original pyo3/maturin extension (shinrin._native)mojo– an experimental Mojo port (shinrin._native_mojo)
Select the backend with the SHINRIN_BACKEND environment variable:
SHINRIN_BACKEND=mojo python your_script.py
The default remains rust; the Mojo backend is opt-in while Mojo is in alpha.
Both backends produce identical trees for identical random states (verified by
tests/test_mojo_parity.py). Build the Mojo extension with just build-mojo
(requires the mojo package, e.g. pip install 'shinrin[mojo]').
API Overview
Models
Tree Models
| Model | Description |
|---|---|
MondrianTreeRegressor |
Single Mondrian tree for regression |
MondrianTreeClassifier |
Single Mondrian tree for classification |
MondrianForestRegressor |
Ensemble of Mondrian trees for regression |
MondrianForestClassifier |
Ensemble of Mondrian trees for classification |
TabMClassifier / TabMRegressor |
Ensemble MLP trainers (NumPy / Mojo backends) |
TabICLClassifier / TabICLRegressor |
Tabular in-context learning estimators (TabICLv2) |
TabM Neural Networks
| Model | Description |
|---|---|
TabMRegressor |
TabM ensemble regressor (drop-in for MLPRegressor) |
TabMClassifier |
TabM ensemble classifier (drop-in for MLPClassifier) |
TabM parameters:
| Parameter | Default | Description |
|---|---|---|
hidden_layer_sizes |
(256,) |
Backbone block widths |
k |
32 |
Number of ensemble members |
solver |
'adam' |
'adam', 'sgd', or 'lbfgs' |
arch_type |
'tabm' |
'tabm', 'tabm-mini', or 'tabm-packed' |
dropout |
0.1 |
Backbone dropout rate |
use_embeddings |
True |
Piecewise-linear + linear embeddings for numeric features |
n_bins |
64 |
Quantile bins per numeric feature |
d_embedding |
8 |
Embedding width per numeric feature |
categorical_indices |
None |
Columns to treat as categorical |
categorical_cardinality_threshold |
32 |
Max unique values for auto-detecting categoricals |
Explanations (Tree Models)
from shinrin import TreeExplainer, explanation
explainer = TreeExplainer(model)
shap_values = explainer.shap_values(X)
expected_value = explainer.expected_value
# Quick visualization (requires matplotlib)
explanation(model, X)
ONNX Export
from shinrin.onnx import to_onnx, save_onnx
# Export to ONNX protobuf
onnx_model = to_onnx(model, X)
# Save to file
save_onnx(model, "model.onnx", X)
Works for tree/forest models and TabM. TabM exports a self-contained graph (preprocessing, embeddings, ensemble backbone, averaged head) that runs on raw feature vectors with any batch size:
import onnxruntime as ort
from shinrin import TabMRegressor
from shinrin.onnx import save_onnx
model = TabMRegressor(hidden_layer_sizes=(256,), k=32, random_state=0)
model.fit(X_train, y_train)
save_onnx(model, "tabm.onnx", X_train)
session = ort.InferenceSession("tabm.onnx")
predictions = session.run(None, {"X": X_test.astype(np.float32)})[0]
Benchmarking
from shinrin.benchmark import (
benchmark_training,
benchmark_prediction,
benchmark_model_size,
full_benchmark,
print_benchmark_report,
)
models = {
"shinrin_tree": MondrianTreeRegressor(max_depth=8),
"shinrin_forest": MondrianForestRegressor(n_estimators=10, max_depth=8),
"shinrin_tabm": TabMRegressor(hidden_layer_sizes=(256,), k=32),
}
results = full_benchmark(models, X_train, y_train, X_test)
print_benchmark_report(results)
Test Coverage
All vendored tests are included and passing — these are ported from scikit-garden and skope-rules to verify compatibility. Run pytest --cov=src/shinrin tests/ for a full coverage report.
TabM parity tests (tests/test_tabm_parity.py) verify that the Mojo kernels produce identical results to the NumPy reference implementation. TabM functional tests (tests/test_tabm.py) cover training, prediction, and determinism. TabM ONNX export tests (tests/test_tabm_onnx.py) verify onnxruntime inference parity for all architectures, tasks, and preprocessing configurations.
License
MIT
1 maintainer
Metadata
1 maintainer
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