def __init__(self, arg, **args) :
"""
:Parameters:
- `arg` - another ModelSelector or a Param object
:Keywords:
- `measure` - which measure of accuracy to use for selecting the
best classifier (default = 'balancedSuccessRate')
supported measures are: 'balancedSuccessRate', 'successRate',
'roc', 'roc50' (you can substitute any number instead of 50)
- `numFolds` - number of CV folds to use when performing model selection
- `foldsToPerform` - the number of folds to actually perform
"""
Classifier.__init__(self, **args)
if arg.__class__ == self.__class__ :
self.param = arg.param.__class__(arg.param)
self.measure = arg.measure
self.numFolds = arg.numFolds
elif arg.__class__.__name__.find('Param') >= 0 :
self.param = arg.__class__(arg)
else :
raise ValueError, 'wrong type of input for ModelSelector'
self.classifier = None
def __init__(self, arg, **args):
"""
:Parameters:
- `arg` - another ModelSelector or a Param object
:Keywords:
- `measure` - which measure of accuracy to use for selecting the
best classifier (default = 'balancedSuccessRate')
supported measures are: 'balancedSuccessRate', 'successRate',
'roc', 'roc50' (you can substitute any number instead of 50)
- `numFolds` - number of CV folds to use when performing model selection
- `foldsToPerform` - the number of folds to actually perform
"""
Classifier.__init__(self, **args)
if arg.__class__ == self.__class__:
self.param = arg.param.__class__(arg.param)
self.measure = arg.measure
self.numFolds = arg.numFolds
elif arg.__class__.__name__.find('Param') >= 0:
self.param = arg.__class__(arg)
else:
raise ValueError, 'wrong type of input for ModelSelector'
self.classifier = None
def __init__ (self, arg) :
Classifier.__init__(self)
if arg.__class__ == self.__class__ :
self.classifiers = [classifier.__class__(classifier)
for classifier in arg.classifiers]
elif type(arg) == type([]) :
self.classifiers = [classifier.__class__(classifier)
for classifier in arg]
def __init__(self, classifier, **args) :
Classifier.__init__(self, classifier, **args)
if type(classifier) == type('') : return
if (not hasattr(classifier, 'type')) or classifier.type != 'classifier' :
raise ValueError, 'argument should be a classifier'
if classifier.__class__ == self.__class__ :
self.classifier = classifier.classifier.__class__(
classifier.classifier)
else :
self.classifier = classifier.__class__(classifier)
def __init__(self, arg):
Classifier.__init__(self)
if arg.__class__ == self.__class__:
self.classifiers = [
classifier.__class__(classifier)
for classifier in arg.classifiers
]
elif type(arg) == type([]):
self.classifiers = [
classifier.__class__(classifier) for classifier in arg
]
def __init__(self, classifier, **args):
Classifier.__init__(self, classifier, **args)
if type(classifier) == type(''): return
if (not hasattr(classifier,
'type')) or classifier.type != 'classifier':
raise ValueError, 'argument should be a classifier'
if classifier.__class__ == self.__class__:
self.classifier = classifier.classifier.__class__(
classifier.classifier)
else:
self.classifier = classifier.__class__(classifier)
def __init__(self, arg1, arg2 = None) :
Classifier.__init__(self)
if arg1.__class__ == self.__class__ :
other = arg1
self.classifier = other.classifier.__class__(other.classifier)
self.featureSelector = other.featureSelector.__class__(
other.featureSelector)
else :
for arg in (arg1, arg2) :
if arg.type == 'classifier' :
self.classifier = arg.__class__(arg)
elif arg.type == 'featureSelector' :
self.featureSelector = arg.__class__(arg)
else :
raise ValueError, \
'argument should be either classifier or featureSelector'
def __init__(self, arg=None, **args):
"""
:Parameters:
- `arg` - another ModelSelector object
:Keywords:
- `C` - a list of values to try for C
- `gamma` - a list of value to try for gamma
- `measure` - which measure of accuracy to use for selecting the
best classifier (default = 'balancedSuccessRate')
supported measures are: 'balancedSuccessRate', 'successRate',
'roc', 'roc50' (you can substitute another number instead of 50)
- `numFolds` - number of CV folds to use when performing model selection
"""
Classifier.__init__(self, arg, **args)
self.classifier = None
def __init__(self, arg1, arg2=None):
Classifier.__init__(self)
if arg1.__class__ == self.__class__:
other = arg1
self.classifier = other.classifier.__class__(other.classifier)
self.featureSelector = other.featureSelector.__class__(
other.featureSelector)
else:
for arg in (arg1, arg2):
if arg.type == 'classifier':
self.classifier = arg.__class__(arg)
elif arg.type == 'featureSelector':
self.featureSelector = arg.__class__(arg)
else:
raise ValueError, \
'argument should be either classifier or featureSelector'
def __init__(self, arg = None, **args) :
"""
:Parameters:
- `arg` - another ModelSelector object
:Keywords:
- `C` - a list of values to try for C
- `gamma` - a list of value to try for gamma
- `measure` - which measure of accuracy to use for selecting the
best classifier (default = 'balancedSuccessRate')
supported measures are: 'balancedSuccessRate', 'successRate',
'roc', 'roc50' (you can substitute another number instead of 50)
- `numFolds` - number of CV folds to use when performing model selection
"""
Classifier.__init__(self, arg, **args)
self.classifier = None
def __init__(self, arg) :
"""
:Parameters:
- `arg` - a Chain object of a list of objects, each of which implements
a 'train', 'test' and has a copy constructor
"""
Classifier.__init__(self)
if arg.__class__ == self.__class__ :
other = arg
self.classifier = other.classifier.__class__(other.classifier)
self.chain = [component.__class__(component)
for component in other.chain]
elif type(arg) == type([]) :
self.classifier = arg[-1].__class__(arg[-1])
self.chain = [arg[i].__class__(arg[i])
for i in range(len(arg) - 1)]
def __init__(self, arg):
"""
:Parameters:
- `arg` - a Chain object of a list of objects, each of which implements
a 'train', 'test' and has a copy constructor
"""
Classifier.__init__(self)
if arg.__class__ == self.__class__:
other = arg
self.classifier = other.classifier.__class__(other.classifier)
self.chain = [
component.__class__(component) for component in other.chain
]
elif type(arg) == type([]):
self.classifier = arg[-1].__class__(arg[-1])
self.chain = [
arg[i].__class__(arg[i]) for i in range(len(arg) - 1)
]
def __init__(self, arg = None, **args) :
Classifier.__init__(self, **args)
