Installation

pip install neural-trees

Requirements

Python 3.9 to 3.13. The package depends on NumPy, scikit-learn and PyTorch.

PyTorch is imported lazily: import neural_trees does not pull it in, and the classical estimators (WeightedKNN, NaiveBayesClassifier) run without it. The differentiable models raise a clear error if torch is missing rather than failing at fit time.

CPU-only installation

The default PyTorch wheel is large because it carries CUDA. For a CPU-only environment:

pip install neural-trees --extra-index-url https://download.pytorch.org/whl/cpu

Device selection

Every differentiable estimator takes a device argument, defaulting to "cpu". Pass device="auto" to pick CUDA when it is available, then Apple silicon’s MPS, then CPU. Anything else is handed to torch as given, so "cuda:1" works. The choice is resolved once in fit and recorded as device_, so prediction always runs where training did.

HierarchicalMixtureOfExperts and GALNetwork additionally keep a float64 copy of the fitted model on the CPU and predict from it, so that the same input gives the same output across machines. This costs a little speed and buys reproducibility: float32 matrix products are sensitive to row order and to the BLAS implementation, which is enough to flip a prediction near a decision boundary.

From source

git clone https://github.com/cgrtml/neural-trees
cd neural-trees
pip install -e ".[dev]"
pytest