Class DatasetMeasure
source code
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_result 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.
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raw_result = StateVariable(enabled= False, doc= "Computed resu...
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null_prob = StateVariable(enabled= True)
Stores the probability of a measure under the NULL hypothesis
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null_t = StateVariable(enabled= False)
Stores the t-score corresponding to null_prob under assumption
of Normal distribution
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__doc__ = enhancedDocString('DatasetMeasure', locals(), ClassW...
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Inherited from misc.state.ClassWithCollections :
_DEV__doc__ ,
descr
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__transformer
Functor to be called in return statement of all subclass __call__()
methods.
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Inherited from object :
__class__
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__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:
misc.state.ClassWithCollections.__init__
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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
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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.
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'Untraining' Measure
Some derived classes might used classifiers, so we need to
untrain those
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Return Null Distribution estimator
- Decorators:
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Return transformer
- Decorators:
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raw_result
- Value:
StateVariable(enabled= False, doc= "Computed results before applying a
ny "+ "transformation algorithm")
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__doc__
- Value:
enhancedDocString('DatasetMeasure', locals(), ClassWithCollections)
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