mvpa2.mappers.glm.NiPyGLMMapper

class mvpa2.mappers.glm.NiPyGLMMapper(regs, glmfit_kwargs=None, **kwargs)

NiPy-based GLMMapper implementation

This is basically a front-end for GeneralLinearModel. In particular, it supports all keyword arguments of its fit() method.

Notes

Available conditional attributes:

• calling_time+: None
• raw_results: None
• trained_dataset: None
• trained_nsamples+: None
• trained_targets+: None
• training_time+: None

(Conditional attributes enabled by default suffixed with +)

Methods

Parameters:

regs : list

Names of sample attributes to be extracted from an input dataset and used as design matrix columns.

glmfit_kwargs : dict, optional

Keyword arguments to be passed to GeneralLinearModel.fit(). By default an AR1 model is used.

add_constant : bool, optional

If True, a constant will be added as last column in the design matrix. Constraints: value must be convertible to type bool. [Default: False]

return_design : bool, optional

If True, the mapped dataset will contain a sample attribute regressors with the design matrix columns. Constraints: value must be convertible to type bool. [Default: False]

return_model : bool, optional

If True, the mapped dataset will contain am attribute model for an instance of the fitted GLM. The type of this instance dependent on the actual implementation used. Constraints: value must be convertible to type bool. [Default: False]

enable_ca : None or list of str

Names of the conditional attributes which should be enabled in addition to the default ones

disable_ca : None or list of str

Names of the conditional attributes which should be disabled

add_regs : tuple, optional

Additional regressors to be used in the design matrix. Each tuple element is a 2-tuple: the first element is a literal label for the regressor, and the second element is a 1D array with the regressor values. The length of the array needs to match the length of any input dataset.

auto_train : bool

Flag whether the learner will automatically train itself on the input dataset when called untrained.

force_train : bool

Flag whether the learner will enforce training on the input dataset upon every call.

space : str, optional

Name of the ‘processing space’. The actual meaning of this argument heavily depends on the sub-class implementation. In general, this is a trigger that tells the node to compute and store information about the input data that is “interesting” in the context of the corresponding processing in the output dataset.

pass_attr : str, list of str|tuple, optional

Additional attributes to pass on to an output dataset. Attributes can be taken from all three attribute collections of an input dataset (sa, fa, a – see Dataset.get_attr()), or from the collection of conditional attributes (ca) of a node instance. Corresponding collection name prefixes should be used to identify attributes, e.g. ‘ca.null_prob’ for the conditional attribute ‘null_prob’, or ‘fa.stats’ for the feature attribute stats. In addition to a plain attribute identifier it is possible to use a tuple to trigger more complex operations. The first tuple element is the attribute identifier, as described before. The second element is the name of the target attribute collection (sa, fa, or a). The third element is the axis number of a multidimensional array that shall be swapped with the current first axis. The fourth element is a new name that shall be used for an attribute in the output dataset. Example: (‘ca.null_prob’, ‘fa’, 1, ‘pvalues’) will take the conditional attribute ‘null_prob’ and store it as a feature attribute ‘pvalues’, while swapping the first and second axes. Simplified instructions can be given by leaving out consecutive tuple elements starting from the end.

postproc : Node instance, optional

Node to perform post-processing of results. This node is applied in __call__() to perform a final processing step on the to be result dataset. If None, nothing is done.

descr : str

Description of the instance

Methods