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mvpa.measures.base

Base class for data measures: algorithms that quantify properties of datasets.

Besides the DatasetMeasure base class this module also provides the (abstract) FeaturewiseDatasetMeasure class. The difference between a general measure and the output of the FeaturewiseDatasetMeasure is that the latter returns a 1d map (one value per feature in the dataset). In contrast there are no restrictions on the returned value of DatasetMeasure except for that it has to be in some iterable container.

The comprehensive API documentation for this module, including all technical details, is available in the Epydoc-generated API reference for mvpa.measures.base (for developers).

Classes

BoostedClassifierSensitivityAnalyzer

class mvpa.measures.base.BoostedClassifierSensitivityAnalyzer(*args_, **kwargs_)

Bases: mvpa.measures.base.Sensitivity

Set sensitivity analyzers to be merged into a single output

combined_analyzer

See also

Derived classes might provide additional methods via their base classes. Please refer to the list of base classes (if it exists) at the begining of the BoostedClassifierSensitivityAnalyzer documentation.

Full API documentation of BoostedClassifierSensitivityAnalyzer in module mvpa.measures.base.

CombinedFeaturewiseDatasetMeasure

class mvpa.measures.base.CombinedFeaturewiseDatasetMeasure(analyzers=None, combiner=None, **kwargs)

Bases: mvpa.measures.base.FeaturewiseDatasetMeasure

Set sensitivity analyzers to be merged into a single output

Initialize CombinedFeaturewiseDatasetMeasure

Parameters:
  • analyzers (list or None) – List of analyzers to be used. There is no logic to populate such a list in __call__, so it must be either provided to the constructor or assigned to .analyzers prior calling
analyzers
Used analyzers

See also

Derived classes might provide additional methods via their base classes. Please refer to the list of base classes (if it exists) at the begining of the CombinedFeaturewiseDatasetMeasure documentation.

Full API documentation of CombinedFeaturewiseDatasetMeasure in module mvpa.measures.base.

DatasetMeasure

class mvpa.measures.base.DatasetMeasure(transformer=None, null_dist=None, **kwargs)

Bases: mvpa.misc.state.ClassWithCollections

A measure computed from a Dataset

All dataset measures support arbitrary transformation of the measure after it has been computed. Transformation are done by processing the measure with a functor that is specified via the transformer keyword argument of the constructor. Upon request, the raw measure (before transformations are applied) is stored in the raw_result state variable.

Additionally all dataset measures support the estimation of the probabilit(y,ies) of a measure under some distribution. Typically this will be the NULL distribution (no signal), that can be estimated with permutation tests. If a distribution estimator instance is passed to the null_dist keyword argument of the constructor the respective probabilities are automatically computed and stored in the null_prob state variable.

Developer note:All subclasses shall get all necessary parameters via their constructor, so it is possible to get the same type of measure for multiple datasets by passing them to the __call__() method successively.

Does nothing special.

Parameters:
  • transformer (Functor) – This functor is called in __call__() to perform a final processing step on the to be returned dataset measure. If None, nothing is called
  • null_dist (instance of distribution estimator) –
null_dist

See also

Derived classes might provide additional methods via their base classes. Please refer to the list of base classes (if it exists) at the begining of the DatasetMeasure documentation.

Full API documentation of DatasetMeasure in module mvpa.measures.base.

FeaturewiseDatasetMeasure

class mvpa.measures.base.FeaturewiseDatasetMeasure(combiner=<function SecondAxisSumOfAbs at 0x8d1be9c>, **kwargs)

Bases: mvpa.measures.base.DatasetMeasure

A per-feature-measure computed from a Dataset (base class).

Should behave like a DatasetMeasure.

Initialize

Parameters:
  • combiner (Functor) – The combiner is only applied if the computed featurewise dataset measure is more than one-dimensional. This is different from a transformer, which is always applied. By default, the sum of absolute values along the second axis is computed.

See also

Derived classes might provide additional methods via their base classes. Please refer to the list of base classes (if it exists) at the begining of the FeaturewiseDatasetMeasure documentation.

Full API documentation of FeaturewiseDatasetMeasure in module mvpa.measures.base.

MappedClassifierSensitivityAnalyzer

class mvpa.measures.base.MappedClassifierSensitivityAnalyzer(*args_, **kwargs_)

Bases: mvpa.measures.base.ProxyClassifierSensitivityAnalyzer

Set sensitivity analyzer output be reverse mapped using mapper of the slave classifier

See also

Derived classes might provide additional methods via their base classes. Please refer to the list of base classes (if it exists) at the begining of the MappedClassifierSensitivityAnalyzer documentation.

Full API documentation of MappedClassifierSensitivityAnalyzer in module mvpa.measures.base.

ProxyClassifierSensitivityAnalyzer

class mvpa.measures.base.ProxyClassifierSensitivityAnalyzer(*args_, **kwargs_)

Bases: mvpa.measures.base.Sensitivity

Set sensitivity analyzer output just to pass through

analyzer

See also

Derived classes might provide additional methods via their base classes. Please refer to the list of base classes (if it exists) at the begining of the ProxyClassifierSensitivityAnalyzer documentation.

Full API documentation of ProxyClassifierSensitivityAnalyzer in module mvpa.measures.base.

Sensitivity

class mvpa.measures.base.Sensitivity(clf, force_training=True, **kwargs)

Bases: mvpa.measures.base.FeaturewiseDatasetMeasure

Initialize the analyzer with the classifier it shall use.

Parameters:
  • clf (Classifier) – classifier to use.
  • force_training (Bool) – if classifier was already trained – do not retrain
clf
feature_ids
Return feature_ids used by the underlying classifier

See also

Derived classes might provide additional methods via their base classes. Please refer to the list of base classes (if it exists) at the begining of the Sensitivity documentation.

Full API documentation of Sensitivity in module mvpa.measures.base.

StaticDatasetMeasure

class mvpa.measures.base.StaticDatasetMeasure(measure=None, bias=None, *args, **kwargs)

Bases: mvpa.measures.base.DatasetMeasure

A static (assigned) sensitivity measure.

Since implementation is generic it might be per feature or per whole dataset

Initialize.

Parameters:
  • measure – actual sensitivity to be returned
  • bias – optionally available bias
bias

See also

Derived classes might provide additional methods via their base classes. Please refer to the list of base classes (if it exists) at the begining of the StaticDatasetMeasure documentation.

Full API documentation of StaticDatasetMeasure in module mvpa.measures.base.