mvpa2.mappers.fx.FxMapper

class mvpa2.mappers.fx.FxMapper(axis, fx, fxargs=None, uattrs=None, attrfx='merge', order='uattrs')

Apply a custom transformation to (groups of) samples or features.

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:

axis : {‘samples’, ‘features’}

fx : callable

fxargs : tuple

Passed as *args to fx

uattrs : list

List of attribute names to consider. All possible combinations of unique elements of these attributes are used to determine the sample groups to operate on.

attrfx : callable

Functor that is called with each sample attribute elements matching the respective samples group. By default the unique value is determined. If the content of the attribute is not uniform for a samples group a unique string representation is created. If None, attributes are not altered.

order : {‘uattrs’, ‘occurrence’, None}

If which order groups should be merged together. If None (default before 2.3.1), the order is imposed only by the order of uattrs as keys in the dictionary, thus can vary from run to run. If 'occurrence', groups will be ordered by the first occurrence of group samples in original dataset. If 'uattrs', groups will be sorted by the values of uattrs with follow-up attr having higher importance for ordering (e .g. uattrs=['targets', 'chunks'] would order groups first by chunks and then by targets within each chunk).

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

Methods

attrfx

axis

fx

fxargs

is_trained = True

Indicate that this mapper is always trained.

order

uattrs