Advanced Sampling

PyTorch-BSF provides utilities for generating parameter points on a simplex beyond the default uniform grid.

The torch_bsf.sampling Module

Three functions are available:

simplex_grid(n_params, degree)

Generates a uniform grid on the simplex using the stars-and-bars method. This is the default sampling used by fit() and the CLI.

simplex_random(n_params, n_samples)

Generates random points uniformly distributed over the simplex via the Dirichlet distribution. Useful when you want stochastic coverage or a large number of samples.

from torch_bsf.sampling import simplex_random

pts = simplex_random(n_params=3, n_samples=200)

simplex_sobol(n_params, n_samples)

Generates quasi-random points using a scrambled Sobol sequence projected onto the simplex. Sobol sequences provide better coverage than purely random sampling, reducing clustering and gaps. Requires scipy (pip install scipy).

from torch_bsf.sampling import simplex_sobol

pts = simplex_sobol(n_params=3, n_samples=200)

When to Use Each Method

Method	Recommended use
simplex_grid	Small, structured datasets; default CLI behavior.
simplex_random	Large-scale or stochastic sampling experiments.
simplex_sobol	When uniform coverage is critical (e.g., active learning initialization, quasi-Monte Carlo integration).