def __init__(self, **kwargs):
# force disable auto-train: would make no sense
Learner.__init__(self, auto_train=False, **kwargs)
if 1. / (self.params.n_bootstrap + 1) > self.params.feature_thresh_prob:
raise ValueError('number of bootstrap samples is insufficient for'
' the desired threshold probability')
self.untrain()
def __init__(self, **kwargs):
"""
Parameters
----------
**kwargs
All additional arguments are passed to the baseclass.
"""
Learner.__init__(self, **kwargs)
# internal settings that influence what should be done to the dataset
# attributes in the default forward() and reverse() implementations.
# they are passed to the Dataset.copy() method
self._sa_filter = None
self._fa_filter = None
self._a_filter = None
def __init__(self, **kwargs):
"""
Parameters
----------
**kwargs
All additional arguments are passed to the baseclass.
"""
Learner.__init__(self, **kwargs)
# internal settings that influence what should be done to the dataset
# attributes in the default forward() and reverse() implementations.
# they are passed to the Dataset.copy() method
self._sa_filter = None
self._fa_filter = None
self._a_filter = None
def __init__(self, null_dist=None, **kwargs):
"""
Parameters
----------
null_dist : instance of distribution estimator
The estimated distribution is used to assign a probability for a
certain value of the computed measure.
"""
Learner.__init__(self, **kwargs)
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, null_dist=None, **kwargs):
"""
Parameters
----------
null_dist : instance of distribution estimator
The estimated distribution is used to assign a probability for a
certain value of the computed measure.
"""
Learner.__init__(self, **kwargs)
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, space=None, **kwargs):
# by default we want classifiers to use the 'targets' sample attribute
# for training/testing
if space is None:
space = 'targets'
Learner.__init__(self, space=space, **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 = 0
"""Stores number of features for which classifier was trained.
If 0 -- it wasn't trained at all"""
self._set_retrainable(self.params.retrainable, force=True)
def __init__(self, space=None, **kwargs):
# by default we want classifiers to use the 'targets' sample attribute
# for training/testing
if space is None:
space = 'targets'
Learner.__init__(self, space=space, **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 = 0
"""Stores number of features for which classifier was trained.
If 0 -- it wasn't trained at all"""
self._set_retrainable(self.params.retrainable, force=True)