mvpa2.clfs.model_selector.ModelSelector

class mvpa2.clfs.model_selector.ModelSelector(parametric_model, dataset)

Model selection facility.

Select a model among multiple models (i.e., a parametric model, parametrized by a set of hyperparamenters).

Methods

max_log_marginal_likelihood(hyp_initial_guess)	Set up the optimization problem in order to maximize the log_marginal_likelihood.
solve([problem])	Solve the maximization problem, check outcome and collect results.

TODO:

Methods

max_log_marginal_likelihood(hyp_initial_guess)	Set up the optimization problem in order to maximize the log_marginal_likelihood.
solve([problem])	Solve the maximization problem, check outcome and collect results.

max_log_marginal_likelihood(hyp_initial_guess, maxiter=1, optimization_algorithm='scipy_cg', ftol=0.001, fixedHypers=None, use_gradient=False, logscale=False)

Set up the optimization problem in order to maximize the log_marginal_likelihood.

Parameters:

parametric_model : Classifier

the actual parameteric model to be optimized.

hyp_initial_guess : numpy.ndarray

set of hyperparameters’ initial values where to start optimization.

optimization_algorithm : string

actual name of the optimization algorithm. See http://scipy.org/scipy/scikits/wiki/NLP for a comprehensive/updated list of available NLP solvers. (Defaults to ‘ralg’)

ftol : float

threshold for the stopping criterion of the solver, which is mapped in OpenOpt NLP.ftol (Defaults to 1.0e-3)

fixedHypers : numpy.ndarray (boolean array)

boolean vector of the same size of hyp_initial_guess; ‘False’ means that the corresponding hyperparameter must be kept fixed (so not optimized). (Defaults to None, which during means all True)

Notes

The maximization of log_marginal_likelihood is a non-linear optimization problem (NLP). This fact is confirmed by Dmitrey, author of OpenOpt.

solve(problem=None)

Solve the maximization problem, check outcome and collect results.