def __init__(self,
**kwargs):
# XXX Determine which parameters depend on each other and implement
# safety/simplifying logic around them
# already done for: nr_weight
# thought: weight and weight_label should be a dict
"""Interface class to LIBSVM classifiers and regressions.
Default implementation (C/nu/epsilon SVM) is chosen depending
on the given parameters (C/nu/tube_epsilon).
"""
svm_impl = kwargs.get('svm_impl', None)
# Depending on given arguments, figure out desired SVM
# implementation
if svm_impl is None:
for arg, impl in [ ('tube_epsilon', 'EPSILON_SVR'),
('C', 'C_SVC'),
('nu', 'NU_SVC') ]:
if kwargs.has_key(arg):
svm_impl = impl
if __debug__:
debug('SVM', 'No implementation was specified. Since '
'%s is given among arguments, assume %s' %
(arg, impl))
break
if svm_impl is None:
svm_impl = 'C_SVC'
if __debug__:
debug('SVM', 'Assign C_SVC "by default"')
kwargs['svm_impl'] = svm_impl
# init base class
_SVM.__init__(self, **kwargs)
self._svm_type = self._KNOWN_IMPLEMENTATIONS[svm_impl][0]
if 'nu' in self._KNOWN_PARAMS and 'epsilon' in self._KNOWN_PARAMS:
# overwrite eps param with new default value (information
# taken from libSVM docs
self.params['epsilon']._set_default(0.001)
self.__model = None
"""Holds the trained SVM."""
def __init__(self, **kwargs):
# XXX Determine which parameters depend on each other and implement
# safety/simplifying logic around them
# already done for: nr_weight
# thought: weight and weight_label should be a dict
"""Interface class to LIBSVM classifiers and regressions.
Default implementation (C/nu/epsilon SVM) is chosen depending
on the given parameters (C/nu/tube_epsilon).
"""
svm_impl = kwargs.get('svm_impl', None)
# Depending on given arguments, figure out desired SVM
# implementation
if svm_impl is None:
for arg, impl in [('tube_epsilon', 'EPSILON_SVR'), ('C', 'C_SVC'),
('nu', 'NU_SVC')]:
if arg in kwargs:
svm_impl = impl
if __debug__:
debug(
'SVM', 'No implementation was specified. Since '
'%s is given among arguments, assume %s' %
(arg, impl))
break
if svm_impl is None:
svm_impl = 'C_SVC'
if __debug__:
debug('SVM', 'Assign C_SVC "by default"')
kwargs['svm_impl'] = svm_impl
# init base class
_SVM.__init__(self, **kwargs)
self._svm_type = self._KNOWN_IMPLEMENTATIONS[svm_impl][0]
if 'nu' in self._KNOWN_PARAMS and 'epsilon' in self._KNOWN_PARAMS:
# overwrite eps param with new default value (information
# taken from libSVM docs
self.params['epsilon']._set_default(0.001)
self.params['nr_weight'] = Parameter(len(self.params['weight'].value))
self.__model = None
"""Holds the trained SVM."""
def __init__(self, **kwargs):
"""Interface class to Shogun's classifiers and regressions.
Default implementation is 'libsvm'.
"""
svm_impl = kwargs.get('svm_impl', 'libsvm').lower()
kwargs['svm_impl'] = svm_impl
# init base class
_SVM.__init__(self, **kwargs)
self.__svm = None
"""Holds the trained svm."""
self.__svm_apply = None
"""Compatibility convenience to bind to the classify/apply method
of __svm"""
# Need to store original data...
# TODO: keep 1 of them -- just __traindata or __traindataset
# For now it is needed for computing sensitivities
self.__traindataset = None
# internal SG swig proxies
self.__traindata = None
self.__kernel = None
self.__kernel_test = None
self.__testdata = None
# remove kernel-based for some
# TODO RF: provide separate handling for non-kernel machines
if svm_impl in ['svmocas']:
if not (self.__kernel is None
or self.__kernel.__kernel_name__ == 'linear'):
raise ValueError(
"%s is inherently linear, thus provided kernel %s "
"is of no effect" % (svm_impl, self.__kernel))
self.__tags__.pop(self.__tags__.index('kernel-based'))
self.__tags__.pop(self.__tags__.index('retrainable'))
