Source code for Orange.classification.svm

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()]


[docs]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()
[docs]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()
[docs]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))