Class Searchlight
source code
Runs a scalar `DatasetMeasure` on all possible spheres of a certain size
within a dataset.
The idea for a searchlight algorithm stems from this paper:
Kriegeskorte, N., Goebel, R. & Bandettini, P. (2006).
'Information-based functional brain mapping.' Proceedings of the
National Academy of Sciences of the United States of America 103,
3863-3868.
Constructor information for `Searchlight` class
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Initialize Searchlight to compute `datameasure` for each sphere with
a certain `radius` in a given dataset.
:Parameters:
datameasure: callable
Any object that takes a `Dataset` and returns some measure when
called.
radius: float
All features within the radius around the center will be part
of a sphere.
center_ids: list(int)
List of feature ids (not coordinates) the shall serve as sphere
centers. By default all features will be used.
**kwargs:
In additions this class supports all keyword arguments of its
base-class `DatasetMeasure`.
ATTENTION: If `Searchlight` is used as `SensitivityAnalyzer` one has to
make sure that the specified scalar `DatasetMeasure` returns large
(absolute) values for high sensitivities and small (absolute) values
for low sensitivities. Especially when using error functions usually
low values imply high performance and therefore high sensitivity. This
would in turn result in sensitivity maps that have low (absolute)
values indicating high sensitivites and this conflicts with the
intended behavior of a `SensitivityAnalyzer`.
Documentation for base classes of `Searchlight`
================================================
Documentation for 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_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.
:Developer note:
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.
Constructor information for `DatasetMeasure` class
__________________________________________________
Does nothing special.
:Parameter:
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
Documentation for base classes of `DatasetMeasure`
===================================================
Documentation for class `Stateful`
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Base class for stateful objects.
Classes inherited from this class gain ability to provide state
variables, accessed as simple properties. Access to state variables
"internals" is done via states property and interface of
`StateCollection`.
NB This one is to replace old State base class
TODO: rename 'descr'? -- it should simply
be 'doc' -- no need to drag classes docstring imho.
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__init__(self,
datameasure,
radius=1.0,
center_ids=None,
**kwargs)
Initialize Searchlight to compute datameasure for each sphere with
a certain radius in a given dataset. |
source code
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Inherited from base.DatasetMeasure :
__call__ ,
__str__
Inherited from misc.state.Stateful :
__getattribute__ ,
__repr__ ,
__setattr__ ,
reset
Inherited from object :
__delattr__ ,
__hash__ ,
__new__ ,
__reduce__ ,
__reduce_ex__
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__init__(self,
datameasure,
radius=1.0,
center_ids=None,
**kwargs)
(Constructor)
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Initialize Searchlight to compute datameasure for each sphere with
a certain radius in a given dataset.
ATTENTION: If Searchlight is used as SensitivityAnalyzer one has to
make sure that the specified scalar DatasetMeasure returns large
(absolute) values for high sensitivities and small (absolute) values
for low sensitivities. Especially when using error functions usually
low values imply high performance and therefore high sensitivity. This
would in turn result in sensitivity maps that have low (absolute)
values indicating high sensitivites and this conflicts with the
intended behavior of a SensitivityAnalyzer .
- Parameters:
enable_states - Names of the state variables which should be enabled additionally
to default ones
disable_states - Names of the state variables which should be disabled
descr - Description of the instance
- Overrides:
object.__init__
Parameters:
- datameasure: callable
- Any object that takes a Dataset and returns some measure when
called.
- radius: float
- All features within the radius around the center will be part
of a sphere.
- center_ids: list(int)
- List of feature ids (not coordinates) the shall serve as sphere
centers. By default all features will be used.
- **kwargs:
- In additions this class supports all keyword arguments of its
base-class DatasetMeasure.
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spheresizes
- Value:
StateVariable(enabled= False, doc= "Number of features in each sphere.
")
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__doc__
- Value:
enhancedDocString('Searchlight', locals(), DatasetMeasure)
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_collections_template
- Value:
{ ' states ' : <mvpa.misc.state.StateCollection object at 0x8ca3a8c>}
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