Package mvpa :: Package measures :: Module splitmeasure :: Class TScoredFeaturewiseMeasure

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# Class TScoredFeaturewiseMeasure

source code

SplitFeaturewiseMeasure computing featurewise t-score of sensitivities across splits.

Nested Classes | [hide private]

Inherited from `misc.state.ClassWithCollections`: `__metaclass__`

Instance Methods | [hide private]

__init__(self, sensana, splitter, noise_level=0.0, **kwargs) Cheap initialization. | source code

_call(self, dataset, callables=[]) Compute sensitivity maps for all dataset splits and return the featurewise t-score of them. | source code

Inherited from `base.FeaturewiseDatasetMeasure`: `__repr__`, `combiner`

Inherited from `base.FeaturewiseDatasetMeasure` (private): `_postcall`

Inherited from `base.DatasetMeasure`: `__call__`, `null_dist`, `transformer`, `untrain`

Inherited from `misc.state.ClassWithCollections`: `__getattribute__`, `__new__`, `__setattr__`, `__str__`, `reset`

Inherited from `object`: `__delattr__`, `__format__`, `__hash__`, `__reduce__`, `__reduce_ex__`, `__sizeof__`, `__subclasshook__`

Class Variables | [hide private]

Inherited from `base.FeaturewiseDatasetMeasure`: `base_sensitivities` Inherited from `base.DatasetMeasure`: `__doc__`, `null_prob`, `null_t`, `raw_results` Inherited from `misc.state.ClassWithCollections`: `_DEV__doc__`, `descr`

Instance Variables | [hide private]

| __noise_level Output of the sensitivity analyzer when there is no signal.
Inherited from `SplitFeaturewiseMeasure`: `maps`

Properties | [hide private]

Inherited from `object`: `__class__`

Method Details | [hide private]

### __init__(self, sensana, splitter, noise_level=0.0, **kwargs)
(Constructor)

source code

Cheap initialization.

Parameters:
  - `sensana` (SensitivityAnalyzer) - that shall be run on the Dataset splits.
  - `splitter` (Splitter) - used to split the Dataset. By convention the first dataset in the tuple returned by the splitter on each iteration is used to compute the sensitivity map.
  - `noise_level`, `float` - Theoretical output of the respective `SensitivityAnalyzer` for a pure noise pattern. For most algorithms this is probably zero, hence the default.
Overrides: object.__init__

### _call(self, dataset, callables=[])

source code

Compute sensitivity maps for all dataset splits and return the featurewise t-score of them.

Overrides: base.DatasetMeasure._call