hbw 0.1.0

Fast kernel bandwidth selection via analytic Hessian Newton optimization

hbw

Fast kernel bandwidth selection via analytic Hessian Newton optimization.

Installation

pip install hbw

Quick Start

import numpy as np
from hbw import kde_bandwidth, nw_bandwidth

# KDE bandwidth selection
x = np.random.randn(1000)
h = kde_bandwidth(x)
print(f"Optimal KDE bandwidth: {h:.4f}")

# Nadaraya-Watson regression bandwidth
x = np.linspace(-2, 2, 500)
y = np.sin(2 * x) + 0.3 * np.random.randn(len(x))
h = nw_bandwidth(x, y)
print(f"Optimal NW bandwidth: {h:.4f}")

# Large datasets: automatic subsampling
x_large = np.random.randn(100_000)
h = kde_bandwidth(x_large, max_n=5000, seed=42)  # Uses 5000 random points

API Reference

kde_bandwidth(x, kernel="gauss", h0=None, max_n=5000, seed=None)

Select optimal KDE bandwidth via LSCV minimization.

Parameter	Type	Description
x	array-like	Sample data
kernel	str	"gauss" or "epan" (Epanechnikov)
h0	float	Initial bandwidth (default: Silverman's rule)
max_n	int	Subsample size for large data (None to disable)
seed	int	Random seed for reproducible subsampling

Returns: float - optimal bandwidth

nw_bandwidth(x, y, kernel="gauss", h0=None, max_n=5000, seed=None)

Select optimal Nadaraya-Watson bandwidth via LOOCV-MSE minimization.

Parameter	Type	Description
x	array-like	Predictor values
y	array-like	Response values
kernel	str	"gauss" or "epan"
h0	float	Initial bandwidth (default: Silverman's rule)
max_n	int	Subsample size for large data
seed	int	Random seed

Returns: float - optimal bandwidth

lscv(x, h, kernel="gauss")

Compute LSCV score, gradient, and Hessian for KDE.

Returns: tuple[float, float, float] - (score, gradient, hessian)

loocv_mse(x, y, h, kernel="gauss")

Compute LOOCV-MSE, gradient, and Hessian for NW regression.

Returns: tuple[float, float, float] - (loss, gradient, hessian)

How It Works

Problem: Cross-validation bandwidth selection requires O(n²) per evaluation. Grid search needs 50-100 evaluations.

Solution: We derive closed-form gradients and Hessians for the LSCV (KDE) and LOOCV-MSE (NW) objectives. This enables Newton optimization that converges in 6-12 evaluations—same optimum, 4-10x fewer evaluations.

Supported kernels:

• Gaussian: K(u) = exp(-u²/2) / √(2π)
• Epanechnikov: K(u) = 0.75(1-u²) for |u| ≤ 1

For full mathematical details, see the paper.

Results

Newton-Armijo with analytic Hessian achieves identical accuracy to grid search with 2-2.5× wall-clock speedup:

Method	Evaluations	Wall-clock (n=500)	Optimum
Grid search	50	71 ms	✓
Brent	10-12	46 ms	✓
Analytic Newton	6-12	38 ms	✓
Silverman's rule	1	0.08 ms	approximate

Bootstrap use case: For 200 bootstrap resamples at n=500, Newton saves 75 seconds (125s → 50s).

Tested across sample sizes (100-500), noise levels, four DGPs (bimodal, unimodal, skewed, heavy-tailed), and both Gaussian/Epanechnikov kernels. See ms/ for full details.

Citation

@misc{hbw2024,
  author = {Sood, Gaurav},
  title = {Analytic-Hessian Bandwidth Selection for Kernel Density Estimation and Nadaraya-Watson Regression},
  year = {2024},
  url = {https://github.com/finite-sample/hbw}
}

License

MIT