def __init__(self, generator, queryengine, errorfx=mean_mismatch_error,
indexsum=None,
reuse_neighbors=False,
splitter=None,
**kwargs):
"""Initialize the base class for "naive" searchlight classifiers
Parameters
----------
generator : `Generator`
Some `Generator` to prepare partitions for cross-validation.
It must not change "targets", thus e.g. no AttributePermutator's
errorfx : func, optional
Functor that computes a scalar error value from the vectors of
desired and predicted values (e.g. subclass of `ErrorFunction`).
indexsum : ('sparse', 'fancy'), optional
What use to compute sums over arbitrary columns. 'fancy'
corresponds to regular fancy indexing over columns, whenever
in 'sparse', product of sparse matrices is used (usually
faster, so is default if `scipy` is available).
reuse_neighbors : bool, optional
Compute neighbors information only once, thus allowing for
efficient reuse on subsequent calls where dataset's feature
attributes remain the same (e.g. during permutation testing)
splitter : Splitter, optional
Which will be used to split partitioned datasets. If None specified
then standard one operating on partitions will be used
"""
# init base class first
BaseSearchlight.__init__(self, queryengine, **kwargs)
self._errorfx = errorfx
self._generator = generator
self._splitter = splitter
# TODO: move into _call since resetting over default None
# obscures __repr__
if indexsum is None:
if externals.exists('scipy'):
indexsum = 'sparse'
else:
indexsum = 'fancy'
else:
if indexsum == 'sparse' and not externals.exists('scipy'):
warning("Scipy.sparse isn't available so taking 'fancy' as "
"'indexsum' method.")
indexsum = 'fancy'
self._indexsum = indexsum
if not self.nproc in (None, 1):
raise NotImplementedError, "For now only nproc=1 (or None for " \
"autodetection) is supported by GNBSearchlight"
self.__pb = None # statistics per each block/label
self.__reuse_neighbors = reuse_neighbors
# Storage to be used for neighborhood information
self.__roi_fids = None
def __init__(self,
generator,
queryengine,
errorfx=mean_mismatch_error,
indexsum=None,
reuse_neighbors=False,
**kwargs):
"""Initialize the base class for "naive" searchlight classifiers
Parameters
----------
generator : `Generator`
Some `Generator` to prepare partitions for cross-validation.
It must not change "targets", thus e.g. no AttributePermutator's
errorfx : func, optional
Functor that computes a scalar error value from the vectors of
desired and predicted values (e.g. subclass of `ErrorFunction`).
indexsum : ('sparse', 'fancy'), optional
What use to compute sums over arbitrary columns. 'fancy'
corresponds to regular fancy indexing over columns, whenever
in 'sparse', product of sparse matrices is used (usually
faster, so is default if `scipy` is available).
reuse_neighbors : bool, optional
Compute neighbors information only once, thus allowing for
efficient reuse on subsequent calls where dataset's feature
attributes remain the same (e.g. during permutation testing)
"""
# init base class first
BaseSearchlight.__init__(self, queryengine, **kwargs)
self._errorfx = errorfx
self._generator = generator
# TODO: move into _call since resetting over default None
# obscures __repr__
if indexsum is None:
if externals.exists('scipy'):
indexsum = 'sparse'
else:
indexsum = 'fancy'
else:
if indexsum == 'sparse' and not externals.exists('scipy'):
warning("Scipy.sparse isn't available so taking 'fancy' as "
"'indexsum' method.")
indexsum = 'fancy'
self._indexsum = indexsum
if not self.nproc in (None, 1):
raise NotImplementedError, "For now only nproc=1 (or None for " \
"autodetection) is supported by GNBSearchlight"
self.__pb = None # statistics per each block/label
self.__reuse_neighbors = reuse_neighbors
# Storage to be used for neighborhood information
self.__roi_fids = None
def __init__(self, gnb, generator, qe, errorfx=mean_mismatch_error,
indexsum=None, **kwargs):
"""Initialize a GNBSearchlight
Parameters
----------
gnb : `GNB`
`GNB` classifier as the specification of what GNB parameters
to use. Instance itself isn't used.
generator : `Generator`
Some `Generator` to prepare partitions for cross-validation.
errorfx : func, optional
Functor that computes a scalar error value from the vectors of
desired and predicted values (e.g. subclass of `ErrorFunction`).
indexsum : ('sparse', 'fancy'), optional
What use to compute sums over arbitrary columns. 'fancy'
corresponds to regular fancy indexing over columns, whenever
in 'sparse', produce of sparse matrices is used (usually
faster, so is default if `scipy` is available.
"""
# init base class first
BaseSearchlight.__init__(self, qe, **kwargs)
self._errorfx = errorfx
self._generator = generator
self._gnb = gnb
if indexsum is None:
if externals.exists('scipy'):
indexsum = 'sparse'
else:
indexsum = 'fancy'
else:
if indexsum == 'sparse' and not externals.exists('scipy'):
warning("Scipy.sparse isn't available so taking 'fancy' as "
"'indexsum' method.")
indexsum = 'fancy'
self._indexsum = indexsum
if not self.nproc in (None, 1):
raise NotImplementedError, "For now only nproc=1 (or None for " \
"autodetection) is supported by GNBSearchlight"
