mvpa2.mappers.skl_adaptor.SKLTransformer

class mvpa2.mappers.skl_adaptor.SKLTransformer(transformer, **kwargs)

Adaptor to use arbitrary sklearn transformer as a mapper.

This basic adaptor support forward mapping only. It is clever enough to call fit_transform() instead of a serial fit() and transform() combo when an untrained instance is called with a dataset.

>>> from sklearn.manifold import MDS
>>> from mvpa2.misc.data_generators import normal_feature_dataset
>>> ds = normal_feature_dataset(perlabel=10, nlabels=2)
>>> print ds.shape
(20, 4)
>>> mds = SKLTransformer(MDS())
>>> mapped = mds(ds)
>>> print mapped.shape
(20, 2)

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:

transformer : sklearn.transformer instance

space : str or None, optional

If not None, a sample attribute of the given name will be extracted from the training dataset and passed to the sklearn transformer’s fit() method as y argument.

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

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.

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