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import sklearn.svm as skl_svm
from Orange.classification import SklLearner
from Orange.preprocess import AdaptiveNormalize
__all__ = ["SVMLearner", "LinearSVMLearner", "NuSVMLearner"]
svm_pps = SklLearner.preprocessors + [AdaptiveNormalize()]
class SVMLearner(SklLearner):
__wraps__ = skl_svm.SVC
preprocessors = svm_pps
def __init__(self, C=1.0, kernel='rbf', degree=3, gamma="auto",
coef0=0.0, shrinking=True, probability=False,
tol=0.001, cache_size=200, max_iter=-1,
preprocessors=None):
super().__init__(preprocessors=preprocessors)
self.params = vars()
class LinearSVMLearner(SklLearner):
__wraps__ = skl_svm.LinearSVC
preprocessors = svm_pps
def __init__(self, penalty='l2', loss='squared_hinge', dual=True,
tol=0.0001, C=1.0, multi_class='ovr', fit_intercept=True,
intercept_scaling=True, random_state=None,
preprocessors=None):
super().__init__(preprocessors=preprocessors)
self.params = vars()
class NuSVMLearner(SklLearner):
__wraps__ = skl_svm.NuSVC
preprocessors = svm_pps
def __init__(self, nu=0.5, kernel='rbf', degree=3, gamma="auto", coef0=0.0,
shrinking=True, probability=False, tol=0.001, cache_size=200,
max_iter=-1, preprocessors=None):
super().__init__(preprocessors=preprocessors)
self.params = vars()
if __name__ == '__main__':
from Orange.evaluation import CrossValidation, CA
from Orange.data import Table
data_ = Table('iris')
learners = [SVMLearner(), NuSVMLearner(), LinearSVMLearner()]
res = CrossValidation()(data_, learners)
for l, ca in zip(learners, CA()(res)):
print("learner: {}\nCA: {}\n".format(l, ca))
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