Multivariate Pattern Analysis in Python

# mvpa2.measures.base

Plumbing for measures: algorithms that quantify properties of datasets.

Besides the `Measure` base class this module also provides the (abstract) `FeaturewiseMeasure` class. The difference between a general measure and the output of the `FeaturewiseMeasure` 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 `Measure` except for that it has to be in some iterable container.

Functions

Classes

`BinaryClassifierSensitivityAnalyzer`(*args_, ...) | Set sensitivity analyzer output to have proper labels
`BoostedClassifierSensitivityAnalyzer`(*args_, ...) | Set sensitivity analyzers to be merged into a single output
`CombinedFeaturewiseMeasure`([analyzers, sa_attr]) | Set sensitivity analyzers to be merged into a single output
`CrossValidation`(learner, generator[, ...]) | Cross-validate a learner’s transfer on datasets.
`FeatureSelectionClassifierSensitivityAnalyzer`(...) | Notes
`FeaturewiseMeasure`([null_dist]) | A per-feature-measure computed from a `Dataset` (base class).
`MappedClassifierSensitivityAnalyzer`(*args_, ...) | Set sensitivity analyzer output be reverse mapped using mapper of the
`Measure`([null_dist]) | A measure computed from a `Dataset`
`ProxyClassifierSensitivityAnalyzer`(*args_, ...) | Set sensitivity analyzer output just to pass through
`ProxyMeasure`(measure[, skip_train]) | Wrapper to allow for alternative post-processing of a shared measure.
`RegressionAsClassifierSensitivityAnalyzer`(...) | Set sensitivity analyzer output to have proper labels
`RepeatedMeasure`(node, generator[, callback, ...]) | Repeatedly run a measure on generated dataset.
`Sensitivity`(clf[, force_train]) | Sensitivities of features for a given Classifier.
`StaticMeasure`([measure, bias]) | A static (assigned) sensitivity measure.
`TransferMeasure`(measure, splitter, **kwargs) | Train and run a measure on two different parts of a dataset.