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SciPy Samplers

ropt.plugins.sampler.scipy.SciPySampler

Bases: Sampler

A sampler implementation utilizing SciPy's statistical functions.

This sampler leverages functions from the scipy.stats and scipy.stats.qmc modules to generate perturbation samples for optimization.

Supported Sampling Methods:

  • From Probability Distributions (scipy.stats):

    • norm: Samples from a standard normal distribution (mean 0, standard deviation 1). This is the default method if none is specified or if "default" is requested.
    • truncnorm: Samples from a truncated normal distribution (mean 0, std dev 1), truncated to the range [-1, 1] by default.
    • uniform: Samples from a uniform distribution. Defaults to the range [-1, 1].

  • From Quasi-Monte Carlo Sequences (scipy.stats.qmc):

    • sobol: Uses Sobol' sequences.
    • halton: Uses Halton sequences.
    • lhs: Uses Latin Hypercube Sampling. (Note: QMC samples are generated in the unit hypercube [0, 1]^d and then scaled to the hypercube [-1, 1]^d)

Configuration:

The specific sampling method is chosen via the method field in the SamplerConfig. Additional method-specific parameters (e.g., distribution parameters like loc, scale, a, b for stats methods, or engine parameters for qmc methods) can be passed through the options dictionary within the SamplerConfig. Refer to the scipy.stats and documentation for available options.