def __init__(self, dsmatrix, dset_metric, output_metric='spearman'):
DatasetMeasure.__init__(self)
self.dsmatrix = dsmatrix
self.dset_metric = dset_metric
self.output_metric = output_metric
self.dset_dsm = []
def __init__(self, queryengine, roi_ids=None, nproc=None, **kwargs):
"""
Parameters
----------
queryengine : QueryEngine
Engine to use to discover the "neighborhood" of each feature.
See :class:`~mvpa.misc.neighborhood.QueryEngine`.
roi_ids : None or list of int
List of feature ids (not coordinates) the shall serve as sphere
centers. By default all features will be used.
nproc : None or int
How many processes to use for computation. Requires `pprocess`
external module. If None -- all available cores will be used.
**kwargs
In addition this class supports all keyword arguments of its
base-class :class:`~mvpa.measures.base.DatasetMeasure`.
"""
DatasetMeasure.__init__(self, **(kwargs))
if nproc > 1 and not externals.exists('pprocess'):
raise RuntimeError("The 'pprocess' module is required for "
"multiprocess searchlights. Please either "
"install python-pprocess, or reduce `nproc` "
"to 1 (got nproc=%i)" % nproc)
self._qe = queryengine
if roi_ids is not None and not len(roi_ids):
raise ValueError, \
"Cannot run searchlight on an empty list of roi_ids"
self.__roi_ids = roi_ids
self._nproc = nproc
def __init__(self, datameasure, radius=1.0, center_ids=None, **kwargs):
"""
:Parameters:
datameasure: callable
Any object that takes a :class:`~mvpa.datasets.base.Dataset`
and returns some measure when called.
radius: float
All features within the radius around the center will be part
of a sphere. Provided dataset should have a metric assigned
(for NiftiDataset, voxel size is used to provide such a metric,
hence radius should be specified in mm).
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 :class:`~mvpa.measures.base.DatasetMeasure`.
.. note::
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`.
"""
DatasetMeasure.__init__(self, **(kwargs))
self.__datameasure = datameasure
self.__radius = radius
self.__center_ids = center_ids
def __init__(self,
transerror,
splitter=None,
combiner='mean',
expose_testdataset=False,
harvest_attribs=None,
copy_attribs='copy',
**kwargs):
"""
:Parameters:
transerror: TransferError instance
Provides the classifier used for cross-validation.
splitter: Splitter | None
Used to split the dataset for cross-validation folds. By
convention the first dataset in the tuple returned by the
splitter is used to train the provided classifier. If the
first element is 'None' no training is performed. The second
dataset is used to generate predictions with the (trained)
classifier. If `None` (default) an instance of
:class:`~mvpa.datasets.splitters.NoneSplitter` is used.
combiner: Functor | 'mean'
Used to aggregate the error values of all cross-validation
folds. If 'mean' (default) the grand mean of the transfer
errors is computed.
expose_testdataset: bool
In the proper pipeline, classifier must not know anything
about testing data, but in some cases it might lead only
to marginal harm, thus migth wanted to be enabled (provide
testdataset for RFE to determine stopping point).
harvest_attribs: list of basestr
What attributes of call to store and return within
harvested state variable
copy_attribs: None | basestr
Force copying values of attributes on harvesting
**kwargs:
All additional arguments are passed to the
:class:`~mvpa.measures.base.DatasetMeasure` base class.
"""
DatasetMeasure.__init__(self, **kwargs)
Harvestable.__init__(self, harvest_attribs, copy_attribs)
if splitter is None:
self.__splitter = NoneSplitter()
else:
self.__splitter = splitter
if combiner == 'mean':
self.__combiner = GrandMean
else:
self.__combiner = combiner
self.__transerror = transerror
self.__expose_testdataset = expose_testdataset
def __init__(self,
transerror,
splitter=None,
expose_testdataset=False,
harvest_attribs=None,
copy_attribs='copy',
samples_idattr='origids',
**kwargs):
"""
Parameters
----------
transerror : TransferError instance
Provides the classifier used for cross-validation.
splitter : Splitter or None
Used to split the dataset for cross-validation folds. By
convention the first dataset in the tuple returned by the
splitter is used to train the provided classifier. If the
first element is 'None' no training is performed. The second
dataset is used to generate predictions with the (trained)
classifier. If `None` (default) an instance of
:class:`~mvpa.datasets.splitters.NoneSplitter` is used.
expose_testdataset : bool, optional
In the proper pipeline, classifier must not know anything
about testing data, but in some cases it might lead only
to marginal harm, thus migth wanted to be enabled (provide
testdataset for RFE to determine stopping point).
harvest_attribs : list of str
What attributes of call to store and return within
harvested conditional attribute
copy_attribs : None or str, optional
Force copying values of attributes on harvesting
samples_idattr : str, optional
What samples attribute to use to identify and store samples_errors
conditional attribute
**kwargs
All additional arguments are passed to the
:class:`~mvpa.measures.base.DatasetMeasure` base class.
"""
DatasetMeasure.__init__(self, **kwargs)
Harvestable.__init__(self, harvest_attribs, copy_attribs)
if splitter is None:
self.__splitter = NoneSplitter()
else:
self.__splitter = splitter
self.__transerror = transerror
self.__expose_testdataset = expose_testdataset
self.__samples_idattr = samples_idattr
def __init__(self,
transerror,
splitter=None,
expose_testdataset=False,
harvest_attribs=None,
copy_attribs='copy',
samples_idattr='origids',
**kwargs):
"""
Parameters
----------
transerror : TransferError instance
Provides the classifier used for cross-validation.
splitter : Splitter or None
Used to split the dataset for cross-validation folds. By
convention the first dataset in the tuple returned by the
splitter is used to train the provided classifier. If the
first element is 'None' no training is performed. The second
dataset is used to generate predictions with the (trained)
classifier. If `None` (default) an instance of
:class:`~mvpa.datasets.splitters.NoneSplitter` is used.
expose_testdataset : bool, optional
In the proper pipeline, classifier must not know anything
about testing data, but in some cases it might lead only
to marginal harm, thus migth wanted to be enabled (provide
testdataset for RFE to determine stopping point).
harvest_attribs : list of str
What attributes of call to store and return within
harvested conditional attribute
copy_attribs : None or str, optional
Force copying values of attributes on harvesting
samples_idattr : str, optional
What samples attribute to use to identify and store samples_errors
conditional attribute
**kwargs
All additional arguments are passed to the
:class:`~mvpa.measures.base.DatasetMeasure` base class.
"""
DatasetMeasure.__init__(self, **kwargs)
Harvestable.__init__(self, harvest_attribs, copy_attribs)
if splitter is None:
self.__splitter = NoneSplitter()
else:
self.__splitter = splitter
self.__transerror = transerror
self.__expose_testdataset = expose_testdataset
self.__samples_idattr = samples_idattr