sgtlearn 0.1.0

Shape Generalized Trees learning library

SGTLearn

sgtlearn is a Python package for learning Shape Generalized Trees (SGTs).

• 🌳 Shape Generalized Trees (SGTs): A class of decision trees where each node applies a learnable, axis-aligned shape function to a feature for non-linear and interpretable splits.
• 👁 Interpretability: Each node's shape function can be visualized directly.
• ⚡ ShapeCART Algorithm: An efficient induction method for learning SGTs from data.
• 🔀 Extensions:
  • Shape²GT (S²GT): Bivariate shape functions for richer splits.
  • SGT_K: Multi-way branching generalization.
  • Shape²CART & ShapeCART_K: Algorithms for learning S²GTs and SGT_Ks.

Installation

pip install sgtlearn

Wheels are published for CPython 3.11–3.14 on Linux, macOS, and Windows (x86_64 + arm64); no compiler is needed for a binary install. To build from source instead, see Developer Setup.

Quick Start

from sklearn.model_selection import train_test_split
from sgtlearn import SGTClassifier, plot_tree, make_plus

X, y = make_plus(n_samples=1500, grid=3, margin=0.07, random_state=42)

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

model = SGTClassifier(max_depth=4, random_state=42)
model.fit(X_train, y_train)

plot_tree(model, X=X_train)

Developer Setup

Use a project-local virtual environment (.venv) so Python, pytest, and scikit-learn stay isolated and reproducible. Pick one of the paths below (uv is recommended). All require Python ≥ 3.11.

Path 1 — uv (recommended)

uv provisions a hermetic CPython and resolves the dev extras in one step:

uv sync --all-extras
source .venv/bin/activate   # Windows: .venv\Scripts\activate

Path 2 — pip + venv (editable)

python3 -m venv .venv
source .venv/bin/activate   # Windows: .venv\Scripts\activate
pip install -U pip
pip install -e ".[dev]"

The editable install builds the C++ extensions via scikit-build-core and installs the sgtlearn package plus native modules into .venv.

Path 3 — pip non-editable (into the active environment)

pip install .
pip install ".[dev]"   # dev extras (pytest, scikit-learn) only if needed

Anaconda users: Do not bootstrap the venv from an Anaconda Python. Anaconda ships a libstdc++.so.6 that lags the symbol versions produced by recent system compilers (gcc ≥ 13), so the install succeeds but import sgtlearn fails with ImportError: GLIBCXX_3.4.NN not found. Use a non-Anaconda Python — e.g. uv venv --python 3.12 .venv (downloads a hermetic CPython), pyenv, or your distro's python3.

Build Workflow (scikit-build + CMake)

pip install . drives this build path:

1. pyproject.toml selects scikit_build_core.build as the backend.
2. CMake is configured from cpp/CMakeLists.txt.
3. Each file in cpp/bindings/*.cpp becomes one pybind11 module target.
4. After each module is built, pybind11-stubgen generates a matching .pyi.
5. The .pyi is generated and installed in the same location as the module .so.

C++ Folder Conventions

• cpp/include/sgtlearn/: public headers for the core C++ API.
• cpp/src/: internal C++ implementation for the core library.
• cpp/bindings/: pybind11 binding entrypoints; one .cpp file maps to one Python extension module.
• cpp/tests/: C++ unit tests consumed by the cpp_tests executable target.

CMake Targets

• sgtlearn_core (static library): shared C++ logic used by Python modules and tests.
• <module_name> (pybind11 module, one per file in cpp/bindings/): compiled extension modules installed into the package.
• cpp_tests (Catch2 executable): optional C++ test target, controlled by:
  • -DSGTLEARN_BUILD_TESTS=ON (build C++ tests)
  • -DSGTLEARN_BUILD_TESTS=OFF (default for pip install; the CMake option itself defaults to ON, but pyproject.toml overrides this so wheels don't ship test binaries)

Overriding CMake options from pip

Example (build C++ tests for one install):

pip install . --config-settings=cmake.args="-DSGTLEARN_BUILD_TESTS=ON"

License

MIT License - see LICENSE for details.

Contributing

Contributions are welcome. Please feel free to submit a pull request.

Citation

For the canonical code base for the paper "Empowering Decision Trees via Shape Function Branching", please refer to https://github.com/optimal-uoft/Empowering-DTs-via-Shape-Functions.

If you use this package in your research, please cite:

@article{upadhya2026empowering,
  title={Empowering Decision Trees via Shape Function Branching},
  author={Upadhya, Nakul and Cohen, Eldan},
  journal={Advances in Neural Information Processing Systems},
  volume={38},
  pages={122263--122308},
  year={2026}
}

Additionally, check out our other works on our lab website.