| Multivariate Pattern Analysis in Python

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This content refers to the previous stable release of PyMVPA. Please visit www.pymvpa.org for the most recent version of PyMVPA and its documentation.

# measures.base

## Module: `measures.base`

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.

## Classes

### `BoostedClassifierSensitivityAnalyzer`

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

  Bases: `mvpa.measures.base.Sensitivity`

  Set sensitivity analyzers to be merged into a single output

  Note

  Available state variables:

  - base_sensitivities: Stores basic sensitivities if the sensitivity relies on combining multiple ones
  - null_prob+: State variable
  - null_t: State variable
  - raw_results: Computed results before applying any transformation algorithm

  (States enabled by default are listed with +)

  See also

  Please refer to the documentation of the base class for more information:

  `Sensitivity`

  Initialize instance of BoostedClassifierSensitivityAnalyzer

  Parameters:

  - enable_states (None or list of basestring) – Names of the state variables which should be enabled additionally to default ones
  - disable_states (None or list of basestring) – Names of the state variables which should be disabled

  `combined_analyzer`
  `untrain`()

    Untrain BoostedClassifierSensitivityAnalyzer

### `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

  Note

  Available state variables:

  - base_sensitivities: Stores basic sensitivities if the sensitivity relies on combining multiple ones
  - null_prob+: State variable
  - null_t: State variable
  - raw_results: Computed results before applying any transformation algorithm
  - sensitivities: Sensitivities produced by each analyzer

  (States enabled by default are listed with +)

  See also

  Please refer to the documentation of the base class for more information:

  `FeaturewiseDatasetMeasure`

  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
  - enable_states (None or list of basestring) – Names of the state variables which should be enabled additionally to default ones
  - disable_states (None or list of basestring) – Names of the state variables which should be disabled
  - 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.
  - 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) – The estimated distribution is used to assign a probability for a certain value of the computed measure.

  `analyzers`

    Used analyzers

  `untrain`()

    Untrain CombinedFDM

### `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_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:

  `ClassWithCollections`

  Note

  Available state variables:

  - null_prob+: State variable
  - null_t: State variable
  - raw_results: Computed results before applying any transformation algorithm

  (States enabled by default are listed with +)

  See also

  Please refer to the documentation of the base class for more information:

  `ClassWithCollections`

  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) – The estimated distribution is used to assign a probability for a certain value of the computed measure.
  - enable_states (None or list of basestring) – Names of the state variables which should be enabled additionally to default ones
  - disable_states (None or list of basestring) – Names of the state variables which should be disabled

  `null_dist`

    Return Null Distribution estimator

  `null_prob` = None

    Stores the probability of a measure under the NULL hypothesis

  `null_t` = None

    Stores the t-score corresponding to null_prob under assumption of Normal distribution

  `transformer`

    Return transformer

  `untrain`()

    ‘Untraining’ Measure

    Some derived classes might used classifiers, so we need to untrain those

### `FeatureSelectionClassifierSensitivityAnalyzer`

class `mvpa.measures.base.``FeatureSelectionClassifierSensitivityAnalyzer`(*args_, **kwargs_)

  Bases: `mvpa.measures.base.ProxyClassifierSensitivityAnalyzer`

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

  Note

  Available state variables:

  - base_sensitivities: Stores basic sensitivities if the sensitivity relies on combining multiple ones
  - clf_sensitivities: Stores sensitivities of the proxied classifier
  - null_prob+: State variable
  - null_t: State variable
  - raw_results: Computed results before applying any transformation algorithm

  (States enabled by default are listed with +)

  See also

  Please refer to the documentation of the base class for more information:

  `ProxyClassifierSensitivityAnalyzer`

  Initialize instance of ProxyClassifierSensitivityAnalyzer

  Parameters:

  - enable_states (None or list of basestring) – Names of the state variables which should be enabled additionally to default ones
  - disable_states (None or list of basestring) – Names of the state variables which should be disabled

### `FeaturewiseDatasetMeasure`

class `mvpa.measures.base.``FeaturewiseDatasetMeasure`(combiner=<function SecondAxisSumOfAbs at 0xa990dbc>, **kwargs)

  Bases: `mvpa.measures.base.DatasetMeasure`

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

  Should behave like a DatasetMeasure.

  Note

  Available state variables:

  - base_sensitivities: Stores basic sensitivities if the sensitivity relies on combining multiple ones
  - null_prob+: State variable
  - null_t: State variable
  - raw_results: Computed results before applying any transformation algorithm

  (States enabled by default are listed with +)

  See also

  Please refer to the documentation of the base class for more information:

  `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.
  - enable_states (None or list of basestring) – Names of the state variables which should be enabled additionally to default ones
  - disable_states (None or list of basestring) – Names of the state variables which should be disabled
  - 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) – The estimated distribution is used to assign a probability for a certain value of the computed measure.

  `combiner`

    Return combiner

### `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

  Note

  Available state variables:

  - base_sensitivities: Stores basic sensitivities if the sensitivity relies on combining multiple ones
  - clf_sensitivities: Stores sensitivities of the proxied classifier
  - null_prob+: State variable
  - null_t: State variable
  - raw_results: Computed results before applying any transformation algorithm

  (States enabled by default are listed with +)

  See also

  Please refer to the documentation of the base class for more information:

  `ProxyClassifierSensitivityAnalyzer`

  Initialize instance of ProxyClassifierSensitivityAnalyzer

  Parameters:

  - enable_states (None or list of basestring) – Names of the state variables which should be enabled additionally to default ones
  - disable_states (None or list of basestring) – Names of the state variables which should be disabled

### `ProxyClassifierSensitivityAnalyzer`

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

  Bases: `mvpa.measures.base.Sensitivity`

  Set sensitivity analyzer output just to pass through

  Note

  Available state variables:

  - base_sensitivities: Stores basic sensitivities if the sensitivity relies on combining multiple ones
  - clf_sensitivities: Stores sensitivities of the proxied classifier
  - null_prob+: State variable
  - null_t: State variable
  - raw_results: Computed results before applying any transformation algorithm

  (States enabled by default are listed with +)

  See also

  Please refer to the documentation of the base class for more information:

  `Sensitivity`

  Initialize instance of ProxyClassifierSensitivityAnalyzer

  Parameters:

  - enable_states (None or list of basestring) – Names of the state variables which should be enabled additionally to default ones
  - disable_states (None or list of basestring) – Names of the state variables which should be disabled

  `analyzer`
  `untrain`()

### `Sensitivity`

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

  Bases: `mvpa.measures.base.FeaturewiseDatasetMeasure`

  No documentation found. Sorry!

  Note

  Available state variables:

  - base_sensitivities: Stores basic sensitivities if the sensitivity relies on combining multiple ones
  - null_prob+: State variable
  - null_t: State variable
  - raw_results: Computed results before applying any transformation algorithm

  (States enabled by default are listed with +)

  See also

  Please refer to the documentation of the base class for more information:

  `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
  - enable_states (None or list of basestring) – Names of the state variables which should be enabled additionally to default ones
  - disable_states (None or list of basestring) – Names of the state variables which should be disabled
  - 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.
  - 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) – The estimated distribution is used to assign a probability for a certain value of the computed measure.

  `clf`
  `feature_ids`

    Return feature_ids used by the underlying classifier

  `untrain`()

    Untrain corresponding classifier for Sensitivity

### `SplitFeaturewiseDatasetMeasure`

class `mvpa.measures.base.``SplitFeaturewiseDatasetMeasure`(splitter, analyzer, insplit_index=0, combiner=None, **kwargs)

  Bases: `mvpa.measures.base.FeaturewiseDatasetMeasure`

  Compute measures across splits for a specific analyzer

  Note

  Available state variables:

  - base_sensitivities: Stores basic sensitivities if the sensitivity relies on combining multiple ones
  - null_prob+: State variable
  - null_t: State variable
  - raw_results: Computed results before applying any transformation algorithm
  - sensitivities: Sensitivities produced for each split
  - splits: Store the actual splits of the data. Can be memory expensive

  (States enabled by default are listed with +)

  See also

  Please refer to the documentation of the base class for more information:

  `FeaturewiseDatasetMeasure`

  Initialize SplitFeaturewiseDatasetMeasure

  Parameters:

  - splitter (Splitter) – Splitter to use to split the dataset
  - analyzer (DatasetMeasure) – Measure to be used. Could be analyzer as well (XXX)
  - insplit_index (int) – splitter generates tuples of dataset on each iteration (usually 0th for training, 1st for testing). On what split index in that tuple to operate.
  - enable_states (None or list of basestring) – Names of the state variables which should be enabled additionally to default ones
  - disable_states (None or list of basestring) – Names of the state variables which should be disabled
  - 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.
  - 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) – The estimated distribution is used to assign a probability for a certain value of the computed measure.

  `untrain`()

    Untrain SplitFeaturewiseDatasetMeasure

### `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

  Note

  Available state variables:

  - null_prob+: State variable
  - null_t: State variable
  - raw_results: Computed results before applying any transformation algorithm

  (States enabled by default are listed with +)

  See also

  Please refer to the documentation of the base class for more information:

  `DatasetMeasure`

  Initialize.

  Parameters:

  - measure – actual sensitivity to be returned
  - bias – optionally available bias
  - enable_states (None or list of basestring) – Names of the state variables which should be enabled additionally to default ones
  - disable_states (None or list of basestring) – Names of the state variables which should be disabled
  - 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) – The estimated distribution is used to assign a probability for a certain value of the computed measure.

  `bias`

### Navigation

- index
- next |
- previous |
- ExpPsy Project |
- NeuroDebian Project |
- PyMVPA 0.4.x Home |
- PyMVPA Home |
- Documentation »
- Module Reference »