def __init__(self, **kwargs):
"""Cheap initialization.
"""
ClassWithCollections.__init__(self, **kwargs)
self.__trainednfeatures = None
"""Stores number of features for which classifier was trained.
If None -- it wasn't trained at all"""
self._setRetrainable(self.params.retrainable, force=True)
if self.params.regression:
for statevar in [ "trained_labels"]: #, "training_confusion" ]:
if self.states.isEnabled(statevar):
if __debug__:
debug("CLF",
"Disabling state %s since doing regression, " %
statevar + "not classification")
self.states.disable(statevar)
self._summaryClass = RegressionStatistics
else:
self._summaryClass = ConfusionMatrix
clf_internals = self._clf_internals
if 'regression' in clf_internals and not ('binary' in clf_internals):
# regressions are used as binary classifiers if not
# asked to perform regression explicitly
# We need a copy of the list, so we don't override class-wide
self._clf_internals = clf_internals + ['binary']
def __init__(self, sd=0, distribution='rdist', fpp=None, nbins=400, **kwargs):
"""L2-Norm the values, convert them to p-values of a given distribution.
Parameters
----------
sd : int
Samples dimension (if len(x.shape)>1) on which to operate
distribution : string
Which distribution to use. Known are: 'rdist' (later normal should
be there as well)
fpp : float
At what p-value (both tails) if not None, to control for false
positives. It would iteratively prune the tails (tentative real positives)
until empirical p-value becomes less or equal to numerical.
nbins : int
Number of bins for the iterative pruning of positives
WARNING: Highly experimental/slow/etc: no theoretical grounds have been
presented in any paper, nor proven
"""
externals.exists('scipy', raise_=True)
ClassWithCollections.__init__(self, **kwargs)
self.sd = sd
if not (distribution in ['rdist']):
raise ValueError, "Actually only rdist supported at the moment" \
" got %s" % distribution
self.distribution = distribution
self.fpp = fpp
self.nbins = nbins
def __init__(self,
sd=0,
distribution='rdist',
fpp=None,
nbins=400,
**kwargs):
"""L2-Norm the values, convert them to p-values of a given distribution.
Parameters
----------
sd : int
Samples dimension (if len(x.shape)>1) on which to operate
distribution : string
Which distribution to use. Known are: 'rdist' (later normal should
be there as well)
fpp : float
At what p-value (both tails) if not None, to control for false
positives. It would iteratively prune the tails (tentative real positives)
until empirical p-value becomes less or equal to numerical.
nbins : int
Number of bins for the iterative pruning of positives
WARNING: Highly experimental/slow/etc: no theoretical grounds have been
presented in any paper, nor proven
"""
externals.exists('scipy', raise_=True)
ClassWithCollections.__init__(self, **kwargs)
self.sd = sd
if not (distribution in ['rdist']):
raise ValueError, "Actually only rdist supported at the moment" \
" got %s" % distribution
self.distribution = distribution
self.fpp = fpp
self.nbins = nbins
def __init__(self, mode='discard', **kwargs):
"""Cheap initialization.
:Parameters:
mode : ['discard', 'select']
Decides whether to `select` or to `discard` features.
"""
ClassWithCollections.__init__(self, **kwargs)
self._setMode(mode)
"""Flag whether to select or to discard elements."""
def __init__(self, mode='discard', **kwargs):
"""
Parameters
----------
mode : {'discard', 'select'}
Decides whether to `select` or to `discard` features.
"""
ClassWithCollections.__init__(self, **kwargs)
self._set_mode(mode)
"""Flag whether to select or to discard elements."""
def __init__(self, tail='both', **kwargs):
"""
Parameters
----------
tail : {'left', 'right', 'any', 'both'}
Which tail of the distribution to report. For 'any' and 'both'
it chooses the tail it belongs to based on the comparison to
p=0.5. In the case of 'any' significance is taken like in a
one-tailed test.
"""
ClassWithCollections.__init__(self, **kwargs)
self._set_tail(tail)
def __init__(self, tail='both', **kwargs):
"""
Parameters
----------
tail : {'left', 'right', 'any', 'both'}
Which tail of the distribution to report. For 'any' and 'both'
it chooses the tail it belongs to based on the comparison to
p=0.5. In the case of 'any' significance is taken like in a
one-tailed test.
"""
ClassWithCollections.__init__(self, **kwargs)
self._set_tail(tail)
def __init__(self, **kwargs):
ClassWithCollections.__init__(self, **kwargs)
# XXX
# the place to map literal to numerical labels (and back)
# this needs to be in the base class, since some classifiers also
# have this nasty 'regression' mode, and the code in this class
# needs to deal with converting the regression output into discrete
# labels
# however, preferably the mapping should be kept in the respective
# low-level implementations that need it
self._attrmap = AttributeMap()
self.__trainednfeatures = None
"""Stores number of features for which classifier was trained.
If None -- it wasn't trained at all"""
self._set_retrainable(self.params.retrainable, force=True)
def __init__(self, **kwargs):
ClassWithCollections.__init__(self, **kwargs)
# XXX
# the place to map literal to numerical labels (and back)
# this needs to be in the base class, since some classifiers also
# have this nasty 'regression' mode, and the code in this class
# needs to deal with converting the regression output into discrete
# labels
# however, preferably the mapping should be kept in the respective
# low-level implementations that need it
self._attrmap = AttributeMap()
self.__trainednfeatures = None
"""Stores number of features for which classifier was trained.
If None -- it wasn't trained at all"""
self._set_retrainable(self.params.retrainable, force=True)
def __init__(self, transformer=None, null_dist=None, **kwargs):
"""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.
"""
ClassWithCollections.__init__(self, **kwargs)
self.__transformer = transformer
"""Functor to be called in return statement of all subclass __call__()
methods."""
null_dist_ = autoNullDist(null_dist)
if __debug__:
debug('SA', 'Assigning null_dist %s whenever original given was %s'
% (null_dist_, null_dist))
self.__null_dist = null_dist_
def __init__(self, postproc=None, null_dist=None, **kwargs):
"""Does nothing special.
Parameters
----------
postproc : Mapper instance
Mapper to perform post-processing of results. This mapper is applied
in `__call__()` to perform a final processing step on the to be
returned dataset measure. If None, nothing is done.
null_dist : instance of distribution estimator
The estimated distribution is used to assign a probability for a
certain value of the computed measure.
"""
ClassWithCollections.__init__(self, **kwargs)
self.__postproc = postproc
"""Functor to be called in return statement of all subclass __call__()
methods."""
null_dist_ = auto_null_dist(null_dist)
if __debug__:
debug('SA', 'Assigning null_dist %s whenever original given was %s'
% (null_dist_, null_dist))
self.__null_dist = null_dist_
def __init__(self, postproc=None, null_dist=None, **kwargs):
"""Does nothing special.
Parameters
----------
postproc : Mapper instance
Mapper to perform post-processing of results. This mapper is applied
in `__call__()` to perform a final processing step on the to be
returned dataset measure. If None, nothing is done.
null_dist : instance of distribution estimator
The estimated distribution is used to assign a probability for a
certain value of the computed measure.
"""
ClassWithCollections.__init__(self, **kwargs)
self.__postproc = postproc
"""Functor to be called in return statement of all subclass __call__()
methods."""
null_dist_ = auto_null_dist(null_dist)
if __debug__:
debug('SA', 'Assigning null_dist %s whenever original given was %s'
% (null_dist_, null_dist))
self.__null_dist = null_dist_
def __init__(self, **kwargs):
# base init first
ClassWithCollections.__init__(self, **kwargs)
def __init__(self, **kwargs):
# base init first
ClassWithCollections.__init__(self, **kwargs)
def __init__(self, *args, **kwargs):
ClassWithCollections.__init__(self, *args, **kwargs)
self._k = None
"""Implementation specific version of the kernel"""
def __init__(self, *args, **kwargs):
"""Base Kernel class has no parameters
"""
ClassWithCollections.__init__(self, *args, **kwargs)
self._k = None
"""Implementation specific version of the kernel"""
def __init__(self, **kwargs):
ClassWithCollections.__init__(self, **kwargs)
def __init__(self, **kwargs):
# XXX make such example when we actually need to invoke
# constructor
# TestClassProper.__init__(self, **kwargs)
ClassWithCollections.__init__(self, **kwargs)
def __init__(self, **kwargs):
# XXX make such example when we actually need to invoke
# constructor
# TestClassProper.__init__(self, **kwargs)
ClassWithCollections.__init__(self, **kwargs)
