| Multivariate Pattern Analysis in Python

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This content refers to the previous stable release of PyMVPA. Please visit www.pymvpa.org for the most recent version of PyMVPA and its documentation.

# clfs.model_selector

## Module: `clfs.model_selector`

Inheritance diagram for `mvpa.clfs.model_selector`:

Model selction.

## `ModelSelector`

class `mvpa.clfs.model_selector.``ModelSelector`(parametric_model, dataset)

  Bases: `object`

  Model selection facility.

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

  `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)

    NOTE: 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.

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