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PyMVPA: Python MultiVariate Pattern Analysis
Package mvpa :: Package clfs :: Module model_selector :: Class ModelSelector
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Class ModelSelector

source code

Model selection facility.

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

Instance Methods [hide private]
 
__init__(self, parametric_model, dataset)
x.__init__(...) initializes x; see help(type(x)) for signature		 source code
 
max_log_marginal_likelihood(self, hyp_initial_guess, maxiter=1, optimization_algorithm="scipy_cg", ftol=1.0e-3, fixedHypers=None, use_gradient=False, logscale=False)
Set up the optimization problem in order to maximize the log_marginal_likelihood.		 source code
 
solve(self, problem=None)
Solve the maximization problem, check outcome and collect results.		 source code

Inherited from object: __delattr__, __format__, __getattribute__, __hash__, __new__, __reduce__, __reduce_ex__, __repr__, __setattr__, __sizeof__, __str__, __subclasshook__

Properties [hide private]

Inherited from object: __class__

Method Details [hide private]

__init__(self, parametric_model, dataset)
(Constructor)

source code 
x.__init__(...) initializes x; see help(type(x)) for signature
Overrides: object.__init__
(inherited documentation)

max_log_marginal_likelihood(self, hyp_initial_guess, maxiter=1, optimization_algorithm="scipy_cg", ftol=1.0e-3, fixedHypers=None, use_gradient=False, logscale=False)

source code 

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

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

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)

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PyMVPA: Python MultiVariate Pattern Analysis
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