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 serialfit()
andtransform()
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+
: Time (in seconds) it took to call the noderaw_results
: Computed results before invoking postproc. Stored only if postproc is not None.trained_dataset
: The dataset it has been trained ontrained_nsamples+
: Number of samples it has been trained ontrained_targets+
: Set of unique targets (or any other space) it has been trained on (if present in the dataset trained on)training_time+
: Time (in seconds) it took to train the learner
(Conditional attributes enabled by default suffixed with
+
)Attributes
auto_train
Whether the Learner performs automatic trainingwhen called untrained. descr
Description of the object if any force_train
Whether the Learner enforces training upon every call. is_trained
Whether the Learner is currently trained. pass_attr
Which attributes of the dataset or self.ca to pass into result dataset upon call postproc
Node to perform post-processing of results space
Processing space name of this node Methods
__call__
(ds)forward
(data)Map data from input to output space. forward1
(data)Wrapper method to map single samples. generate
(ds)Yield processing results. get_postproc
()Returns the post-processing node or None. get_space
()Query the processing space name of this node. reset
()reverse
(data)Reverse-map data from output back into input space. reverse1
(data)Wrapper method to map single samples. set_postproc
(node)Assigns a post-processing node set_space
(name)Set the processing space name of this node. train
(ds)The default implementation calls _pretrain()
,_train()
, and finally_posttrain()
.untrain
()Reverts changes in the state of this node caused by previous training 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 asy
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
Attributes
auto_train
Whether the Learner performs automatic trainingwhen called untrained. descr
Description of the object if any force_train
Whether the Learner enforces training upon every call. is_trained
Whether the Learner is currently trained. pass_attr
Which attributes of the dataset or self.ca to pass into result dataset upon call postproc
Node to perform post-processing of results space
Processing space name of this node Methods
__call__
(ds)forward
(data)Map data from input to output space. forward1
(data)Wrapper method to map single samples. generate
(ds)Yield processing results. get_postproc
()Returns the post-processing node or None. get_space
()Query the processing space name of this node. reset
()reverse
(data)Reverse-map data from output back into input space. reverse1
(data)Wrapper method to map single samples. set_postproc
(node)Assigns a post-processing node set_space
(name)Set the processing space name of this node. train
(ds)The default implementation calls _pretrain()
,_train()
, and finally_posttrain()
.untrain
()Reverts changes in the state of this node caused by previous training