Fast-Select

A high-performance Python library powered by Numba and CUDA, offering accelerated algorithms for feature selection. Initially built to optimize the complete Relief family of algorithms, fast-select aims to expand and accelerate a wide range of feature selection methods to empower machine learning on large-scale datasets.

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Key Features

• Fast Performance: Leverages Numba for JIT compilation, Joblib for multi-core parallelism, and Numba CUDA for GPU acceleration, providing unmatched performance while scaling with modern hardware.

• ML Pipeline Integration: Fully compatible with Scikit-Learn, making it easy to fit into any machine learning pipeline with a familiar .fit(), .transform(), .fit_transform() interface.

• Flexible Backends: Offers dual execution modes for both CPU (Joblib) and GPU (CUDA). Automatically detects hardware with an easy-to-use backend parameter.

• Feature-Rich Implementation: Provides highly optimized implementations of ReliefF, SURF, SURF*, MultiSURF, MultiSURF*, and TuRF, with plans to support additional feature selection algorithms in future releases.

• Lightweight & Simple: Avoids heavy dependencies like TensorFlow or PyTorch while delivering significant speedups for feature selection workflows.

Contents:

• Installation
• Quickstart
• API Reference
  • Feature Selection Algorithms
• User Guide
  • 1. Relief-Based Algorithms
  • 2. Minimum Redundancy Maximum Relevance (mRMR)
  • 3. Correlation-based Feature Selection (CFS)
  • 4. Univariate Methods (e.g., Chi-Squared)
  • 5. Multifactor Dimensionality Reduction (MDR)