Package mvpa :: Package measures :: Module base :: Class DatasetMeasure

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# Class DatasetMeasure

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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.

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.

Nested Classes | [hide private]

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

Instance Methods | [hide private]

__init__(self, transformer=None, null_dist=None, **kwargs) Does nothing special. | source code

__call__(self, dataset) Compute measure on a given Dataset. | source code

_call(self, dataset) Actually compute measure on a given Dataset. | source code

_postcall(self, dataset, result) Some postprocessing on the result | source code

__repr__(self, prefixes=[]) String representation of DatasetMeasure | source code

untrain(self) 'Untraining' Measure | source code

null_dist(self) Return Null Distribution estimator | source code

transformer(self) Return transformer | source code

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]

| raw_results = `StateVariable(enabled= False, doc= "Computed res`...``
| null_prob = `StateVariable(enabled= True)` Stores the probability of a measure under the NULL hypothesis
| null_t = `StateVariable(enabled= False)` Stores the t-score corresponding to null_prob under assumption of Normal distribution
| __doc__ = `enhancedDocString('DatasetMeasure', locals(), ClassW`...``
Inherited from `misc.state.ClassWithCollections`: `_DEV__doc__`, `descr`

Instance Variables | [hide private]

| __transformer Functor to be called in return statement of all subclass __call__() methods.

Properties | [hide private]

Inherited from `object`: `__class__`

Method Details | [hide private]

### __init__(self, transformer=None, null_dist=None, **kwargs)
(Constructor)

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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.
Overrides: object.__init__

### __call__(self, dataset)
(Call operator)

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Compute measure on a given Dataset.

Each implementation has to handle a single arguments: the source dataset.

Returns the computed measure in some iterable (list-like) container applying transformer if such is defined

### _call(self, dataset)

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Actually compute measure on a given Dataset.

Each implementation has to handle a single arguments: the source dataset.

Returns the computed measure in some iterable (list-like) container.

### __repr__(self, prefixes=[])
(Representation operator)

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String representation of DatasetMeasure

Includes only arguments which differ from default ones

Parameters:
  - `fullname` - Either to include full name of the module
  - `prefixes` - What other prefixes to prepend to list of arguments
Overrides: object.__repr__

### untrain(self)

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'Untraining' Measure

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

### null_dist(self)

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Return Null Distribution estimator

Decorators:
  - `@property`

### transformer(self)

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Return transformer

Decorators:
  - `@property`

Class Variable Details | [hide private]

### raw_results

Value:

      StateVariable(enabled= False, doc= "Computed results before applying a
      ny "+ "transformation algorithm")

### __doc__

Value:

      enhancedDocString('DatasetMeasure', locals(), ClassWithCollections)