mvpa2.featsel.base.SplitSamplesProbabilityMapper

class mvpa2.featsel.base.SplitSamplesProbabilityMapper(sensitivity_analyzer, split_by_labels, select_common_features=True, probability_label=None, probability_combiner=None, selector=FractionTailSelector() fraction=0.050000, **kwargs)

Mapper to select features & samples based on some sensitivity value.

A use case is feature selection across participants, where either the same features are selected in all participants or not (see select_common_features parameter).

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 +)

Examples

>>> nf = 10
>>> ns = 100
>>> nsubj = 5
>>> nchunks = 5
>>> data = np.random.normal(size=(ns, nf))
>>> from mvpa2.base.dataset import AttrDataset
>>> from mvpa2.measures.anova import OneWayAnova
>>> ds = AttrDataset(data,
...                sa=dict(sidx=np.arange(ns),
...                        targets=np.arange(ns) % nchunks,
...                        chunks=np.floor(np.arange(ns) * nchunks / ns),
...                        subjects=np.arange(ns) / (ns / nsubj / nchunks) % nsubj),
...                fa=dict(fidx=np.arange(nf)))
>>> analyzer=OneWayAnova()
>>> element_selector=FractionTailSelector(.4, mode='select', tail='upper')
>>> common=True
>>> m=SplitSamplesProbabilityMapper(analyzer, 'subjects',
...                                 probability_label='fprob',
...                                 select_common_features=common,
...                                 selector=element_selector)
>>> m.train(ds)
>>> y=m(ds)
>>> z=m(ds.samples)
>>> np.all(np.equal(z, y.samples))
True
>>> y.shape
(100, 4)

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
selector	Function used to do selection
sensitivity_analyzer	Measure which was used to do selection
slicearg
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:

sensitivity_analyzer: FeaturewiseMeasure

Sensitivity analyzer to come up with sensitivity.

split_by_labels: str or list of str

Sample labels on which input datasets are split before data is selected.

select_common_features: bool

True means that the same features are selected after the split.

probablity_label: None or str

If None, then the output dataset ds from the sensitivity_analyzer is taken to select the samples. If not None it takes ds.sa[‘probablity_label’]. For example if sensitivity_analyzer=OneWayAnova then probablity_label=’fprob’ is a sensible value.

probability_combiner: function

If select_common_features is True, then this function is applied to the feature scores across splits. If None, it uses lambda x:np.sum(-np.log(x)) which is sensible if the scores are probability values

selector: Selector

function that returns the indices to keep.

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

slicearg

Argument for slicing

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

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
selector	Function used to do selection
sensitivity_analyzer	Measure which was used to do selection
slicearg
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

selector

Function used to do selection

sensitivity_analyzer

Measure which was used to do selection