![]() |
Multivariate Pattern Analysis in Python |
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).
Bases: mvpa.measures.base.Sensitivity
Set sensitivity analyzers to be merged into a single output
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.
Bases: mvpa.measures.base.FeaturewiseDatasetMeasure
Set sensitivity analyzers to be merged into a single output
Initialize CombinedFeaturewiseDatasetMeasure
Parameters: |
|
---|
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.
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: |
|
---|
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.
Bases: mvpa.measures.base.DatasetMeasure
A per-feature-measure computed from a Dataset (base class).
Should behave like a DatasetMeasure.
Initialize
Parameters: |
|
---|
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.
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.
Bases: mvpa.measures.base.Sensitivity
Set sensitivity analyzer output just to pass through
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.
Bases: mvpa.measures.base.FeaturewiseDatasetMeasure
Initialize the analyzer with the classifier it shall use.
Parameters: |
|
---|
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.
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: |
|
---|
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.