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Multivariate Pattern Analysis in Python |
Inheritance diagram for 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.
Bases: mvpa.measures.base.Sensitivity
Set sensitivity analyzers to be merged into a single output
Note
Available state variables:
(States enabled by default are listed with +)
Initialize instance of BoostedClassifierSensitivityAnalyzer
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Untrain BoostedClassifierSensitivityAnalyzer
Bases: mvpa.measures.base.FeaturewiseDatasetMeasure
Set sensitivity analyzers to be merged into a single output
Note
Available state variables:
(States enabled by default are listed with +)
See also
Please refer to the documentation of the base class for more information:
Initialize CombinedFeaturewiseDatasetMeasure
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Used analyzers
Untrain CombinedFDM
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_results 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.
Note
For developers: 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.
See also
Please refer to the documentation of the base class for more information:
Note
Available state variables:
(States enabled by default are listed with +)
See also
Please refer to the documentation of the base class for more information:
Does nothing special.
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Return Null Distribution estimator
Stores the probability of a measure under the NULL hypothesis
Stores the t-score corresponding to null_prob under assumption of Normal distribution
Return transformer
‘Untraining’ Measure
Some derived classes might used classifiers, so we need to untrain those
Bases: mvpa.measures.base.ProxyClassifierSensitivityAnalyzer
Set sensitivity analyzer output be reverse mapped using mapper of the slave classifier
Note
Available state variables:
(States enabled by default are listed with +)
See also
Please refer to the documentation of the base class for more information:
Initialize instance of ProxyClassifierSensitivityAnalyzer
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Bases: mvpa.measures.base.DatasetMeasure
A per-feature-measure computed from a Dataset (base class).
Should behave like a DatasetMeasure.
Note
Available state variables:
(States enabled by default are listed with +)
Initialize
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Return combiner
Bases: mvpa.measures.base.ProxyClassifierSensitivityAnalyzer
Set sensitivity analyzer output be reverse mapped using mapper of the slave classifier
Note
Available state variables:
(States enabled by default are listed with +)
See also
Please refer to the documentation of the base class for more information:
Initialize instance of ProxyClassifierSensitivityAnalyzer
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Bases: mvpa.measures.base.Sensitivity
Set sensitivity analyzer output just to pass through
Note
Available state variables:
(States enabled by default are listed with +)
Initialize instance of ProxyClassifierSensitivityAnalyzer
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Bases: mvpa.measures.base.FeaturewiseDatasetMeasure
No documentation found. Sorry!
Note
Available state variables:
(States enabled by default are listed with +)
See also
Please refer to the documentation of the base class for more information:
Initialize the analyzer with the classifier it shall use.
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Return feature_ids used by the underlying classifier
Untrain corresponding classifier for Sensitivity
Bases: mvpa.measures.base.FeaturewiseDatasetMeasure
Compute measures across splits for a specific analyzer
Note
Available state variables:
(States enabled by default are listed with +)
See also
Please refer to the documentation of the base class for more information:
Initialize SplitFeaturewiseDatasetMeasure
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Untrain SplitFeaturewiseDatasetMeasure
Bases: mvpa.measures.base.DatasetMeasure
A static (assigned) sensitivity measure.
Since implementation is generic it might be per feature or per whole dataset
Note
Available state variables:
(States enabled by default are listed with +)
Initialize.
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