Multivariate Pattern Analysis in Python

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# mvpa2.measures.base

Base classes 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

`asobjarray`(x) | Generates numpy.ndarray with dtype object from an iterable Is needed to assure object dtype, so first empty array of dtype=object needs to be constructed and then only items to be assigned.
`auto_null_dist`(dist) | Cheater for human beings – wraps `dist` if needed with some
`enhanced_doc_string`(item, *args, **kwargs) | Generate enhanced doc strings for various items.
`group_kwargs`(prefixes[, assign, passthrough]) | Decorator function to join parts of kwargs together :Parameters: prefixes : list of strs Prefixes to split based on.
`hstack`(datasets) | Stacks datasets horizontally (appending features).
`mean_mismatch_error`(predicted, target) | Computes the percentage of mismatches between some target and some predicted values.
`vstack`(datasets) | Stacks datasets vertically (appending samples).

Classes

`AttrDataset`(samples[, sa, fa, a]) | Generic storage class for datasets with multiple attributes.
`AttributeMap`([map, mapnumeric, ...]) | Map to translate literal values to numeric ones (and back).
`BinaryClassifierSensitivityAnalyzer`(*args_, ...) | Set sensitivity analyzer output to have proper labels ..
`BinaryFxNode`(fx, space, **kwargs) | Extract a dataset attribute and call a function with it and the samples.
`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 ..
`ConditionalAttribute`([enabled]) | Simple container intended to conditionally store the value ..
`CrossValidation`(learner, generator[, ...]) | Cross-validate a learner’s transfer on datasets.
`Dataset`(samples[, sa, fa, a]) | Generic storage class for datasets with multiple attributes.
`FeatureSelectionClassifierSensitivityAnalyzer`(...) | Notes
`FeaturewiseMeasure`([null_dist]) | A per-feature-measure computed from a `Dataset` (base class).
`Learner`([auto_train, force_train]) | Common trainable processing object.
`MappedClassifierSensitivityAnalyzer`(*args_, ...) | Set sensitivity analyzer output be reverse mapped using mapper of the slave classifier ..
`Measure`([null_dist]) | A measure computed from a `Dataset` All dataset measures support arbitrary transformation of the measure after it has been computed.
`Node`([space, postproc]) | Common processing object.
`ProxyClassifierSensitivityAnalyzer`(*args_, ...) | Set sensitivity analyzer output just to pass through ..
`ProxyMeasure`(measure, **kwargs) | 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.
`Splitter`(attr[, attr_values, count, ...]) | Generator node for dataset splitting.
`StaticMeasure`([measure, bias]) | A static (assigned) sensitivity measure.
`TransferMeasure`(measure, splitter, **kwargs) | Train and run a measure on two different parts of a dataset.

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