mvpa2.mappers.base.CombinedMapper¶

class mvpa2.mappers.base.CombinedMapper(mappers, combine_axis, a=None, **kwargs)¶

Mapper to pass a dataset on to a set of mappers and combine there output.

Output combination or aggregation is currently done by hstacking or vstacking the resulting datasets.

Notes

Available conditional attributes:

calling_time+: Time (in seconds) it took to call the node
raw_results: Computed results before invoking postproc. Stored only if postproc is not None.
trained_dataset: The dataset it has been trained on
trained_nsamples+: Number of samples it has been trained on
trained_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.
`mappers`
`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:

mappers : list

combine_axis : [‘h’, ‘v’]

a: {‘unique’,’drop_nonunique’,’uniques’,’all’} or True or False or None (default: None)

Indicates which dataset attributes from datasets are stored in merged_dataset. If an int k, then the dataset attributes from datasets[k] are taken. If ‘unique’ then it is assumed that any attribute common to more than one dataset in datasets is unique; if not an exception is raised. If ‘drop_nonunique’ then as ‘unique’, except that exceptions are not raised. If ‘uniques’ then, for each attribute, any unique value across the datasets is stored in a tuple in merged_datasets. If ‘all’ then each attribute present in any dataset across datasets is stored as a tuple in merged_datasets; missing values are replaced by None. If None (the default) then no attributes are stored in merged_dataset. True is equivalent to ‘drop_nonunique’. False is equivalent to None.

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.

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

Examples

>>> import numpy as np
>>> from mvpa2.mappers.base import CombinedMapper
>>> from mvpa2.featsel.base import StaticFeatureSelection
>>> from mvpa2.datasets import Dataset
>>> mp = CombinedMapper([StaticFeatureSelection([1,2]),
...                      StaticFeatureSelection([2,3])],
...                     combine_axis='h')
>>> mp.is_trained = True
>>> ds = Dataset(np.arange(12).reshape(3,4))
>>> out = mp(ds)
>>> out.samples
array([[ 1,  2,  2,  3],
       [ 5,  6,  6,  7],
       [ 9, 10, 10, 11]])

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.
`mappers`
`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

mappers¶