fast-model-trees 0.0.4

PILOT and RaFFLE: Piecewise Linear Organic Trees and Random Forest Featuring Linear Extensions

fast-model-trees

Fast implementation of PILOT (PIecewise Linear Organic Trees) and RaFFLE (Random Forest Featuring Linear Extensions) algorithms.

Overview

This package provides efficient C++-based implementations of:

• PILOT: A linear model tree algorithm that builds piecewise linear models
• RaFFLE: A random forest ensemble method using PILOT trees as base learners

Papers

• PILOT: Raymaekers, J., Rousseeuw, P. J., Verdonck, T., & Yao, R. (2024). Fast linear model trees by PILOT. Machine Learning, 1-50. https://doi.org/10.1007/s10994-024-06590-3

• RaFFLE: Raymaekers, J., Rousseeuw, P. J., Servotte, T., Verdonck, T., & Yao, R. (2025). A Powerful Random Forest Featuring Linear Extensions (RaFFLE). Under Review

Installation

pip install fast-model-trees

Building from Source

If you need to build from source, you'll need:

• C++17 compatible compiler
• CMake >= 3.12
• Armadillo linear algebra library
• BLAS and LAPACK libraries
• pybind11
• carma (C++ Armadillo/NumPy bridge)

See INSTALL.md for detailed installation instructions.

Development

For development setup, running benchmarks, and reproducing paper results, see DEVELOPMENT.md.

Quick Start

Using RaFFLE (Random Forest)

from pilot import RaFFLE
import numpy as np

# Create sample data
X = np.random.randn(1000, 10)
y = X[:, 0] + 0.5 * X[:, 1] ** 2 + np.random.randn(1000) * 0.1

# Train RaFFLE model
model = RaFFLE(
    n_estimators=100,
    max_depth=5,
    random_state=42
)
model.fit(X, y)

# Make predictions
predictions = model.predict(X)

# Get feature importances
importances = model.feature_importances_

Using PILOT (Single Tree)

from pilot import PILOT
import numpy as np

# Create sample data
X = np.random.randn(1000, 10)
y = X[:, 0] + 0.5 * X[:, 1] ** 2 + np.random.randn(1000) * 0.1

# Train PILOT model — all parameters have sensible defaults
model = PILOT(max_depth=5)
model.train(X, y)  # categorical defaults to all-numerical

# Make predictions
predictions = model.predict(X)

# Inspect the tree structure
print(model.tree_summary())

Key Features

• Fast C++ implementation: Optimized for performance using Armadillo linear algebra
• Scikit-learn compatible: Follows scikit-learn API conventions
• Flexible model complexity: Control tree depth and piecewise linear behavior
• Feature importance: Built-in feature importance calculation
• Bootstrap aggregation: RaFFLE uses bootstrap sampling for robust predictions

Parameters

RaFFLE

• n_estimators: Number of trees in the forest (default: 10)
• max_depth: Maximum depth of each tree (default: 12)
• min_sample_fit: Minimum samples needed to fit any node (default: 10)
• min_sample_alpha: Minimum samples for piecewise nodes (default: 5)
• min_sample_leaf: Minimum samples in each leaf (default: 5)
• random_state: Random seed for reproducibility (default: 42)
• n_features_tree: Fraction of features to consider per tree (default: 1.0)
• n_features_node: Fraction of features to consider per node (default: 1.0)
• alpha: Controls piecewise linear complexity (default: 1)

PILOT

• df_settings: Degrees of freedom for node types [con, lin, pcon, blin, plin, pconc]. Defaults to [1, 2, 5, 5, 7, 5]. Set a value to -1 to disable that node type.
• max_depth: Maximum split depth (default: 20)
• max_model_depth: Maximum total depth including linear model nodes (default: 100)
• max_features: Features considered per split; None (default) uses all features
• min_sample_fit: Minimum samples to fit any node (default: 5)
• min_sample_alpha: Minimum samples for piecewise nodes (default: 5)
• min_sample_leaf: Minimum samples per leaf (default: 5)
• rel_tolerance: Minimum relative RSS improvement to keep growing (default: 0.01)

License

MIT License - see LICENSE file for details

Citation

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

@article{raymaekers2024pilot,
  title={Fast linear model trees by PILOT},
  author={Raymaekers, J. and Rousseeuw, P.J. and Verdonck, T. and Yao, R.},
  journal={Machine Learning},
  pages={1--50},
  year={2024},
  doi={10.1007/s10994-024-06590-3}
}

Contributing

Contributions are welcome! Please see the GitHub repository for more information.

Support

For issues and questions, please use the GitHub issue tracker.