smart-knn 0.2.1

A modern, weighted k-nearest neighbors algorithm with learned feature importance and fast CPU inference.

SmartKNN

A modern, weighted nearest-neighbor learning algorithm with learned feature importance and adaptive neighbor search.

Overview

SmartKNN is a nearest-neighbor–based learning method that belongs to the broader KNN family of algorithms.

It is designed to address common limitations observed in classical KNN approaches, including:

• uniform treatment of all features
• sensitivity to noisy or weakly informative dimensions
• limited scalability as dataset size grows

SmartKNN incorporates data-driven feature importance estimation, dimension suppression, and adaptive neighbor search strategies. Depending on dataset characteristics, it can operate using either a brute-force search or an approximate nearest-neighbor (ANN) backend, while exposing a consistent, scikit-learn–compatible API.

The method supports both regression and classification tasks and prioritizes robustness, predictive accuracy, and practical inference latency across a range of dataset sizes.

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Key Capabilities

• Learned feature weighting
  • MSE relevance
  • Mutual Information
  • Random Forest importance
    (method configurable depending on task and dataset)
• Automatic preprocessing
  • normalization
  • NaN / Inf handling
  • feature masking
• Distance-weighted neighbor voting
• Brute-force and ANN backends
  • designed to scale to large datasets (hardware and tuning dependent)
  • optional GPU-accelerated neighbor search
• Vectorized NumPy with Numba acceleration
• Scikit-learn–compatible API

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Installation

pip install smart-knn

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Documentation

• https://thatipamula-jashwanth.github.io/SmartEco/

Detailed documentation and design notes are maintained externally. This repository README is intentionally kept concise.

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Examples

Runnable examples are available in the examples/ directory:

python examples/regression_example.py
python examples/classification_example.py

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Benchmarks & CI

• Comprehensive benchmark suites for regression and classification
• GitHub Actions CI for tests and benchmarks
• Reproducible, engineering-focused evaluation

Benchmark details are documented in benchmarks/README.md.

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Project Status

• SmartKNN v2 is stable
• API is frozen for the v2.x series (backward-compatible improvements only)
• Actively maintained
• Open to research and engineering collaboration

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License

SmartKNN is released under the MIT License. See LICENSE for details.