hugobuddel / orange3

Orange/classification/svm.py

import sklearn.svm as skl_svm

The import sklearn.svm could not be resolved.

from Orange.classification import SklLearner, SklModel
from Orange.base import SklLearner as SklLearnerBase
from Orange.preprocess import Normalize

__all__ = ["SVMLearner", "LinearSVMLearner", "NuSVMLearner",
           "OneClassSVMLearner"]


svm_pps = SklLearner.preprocessors + [Normalize()]


class SVMClassifier(SklModel):

    def predict(self, X):
        value = self.skl_model.predict(X)
        if self.skl_model.probability:
            prob = self.skl_model.predict_proba(X)
            return value, prob
        return value


class SVMLearner(SklLearner):
    __wraps__ = skl_svm.SVC
    __returns__ = SVMClassifier
    name = 'svm'
    preprocessors = svm_pps

    def __init__(self, C=1.0, kernel='rbf', degree=3, gamma=0.0,

The argument C seems to be unused.

The argument gamma seems to be unused.

The argument degree seems to be unused.

The argument kernel seems to be unused.

                 coef0=0.0, shrinking=True, probability=False,

The argument shrinking seems to be unused.

The argument probability seems to be unused.

The argument coef0 seems to be unused.

                 tol=0.001, cache_size=200, max_iter=-1,

The argument cache_size seems to be unused.

The argument tol seems to be unused.

The argument max_iter seems to be unused.

                 preprocessors=None):
        super().__init__(preprocessors=preprocessors)
        self.params = vars()
        self.supports_multiclass = True
        self.supports_weights = True


class LinearSVMLearner(SklLearner):
    __wraps__ = skl_svm.LinearSVC
    name = 'linear svm'
    preprocessors = svm_pps

    def __init__(self, penalty='l2', loss='squared_hinge', dual=True,

The argument penalty seems to be unused.

The argument loss seems to be unused.

The argument dual seems to be unused.

                 tol=0.0001, C=1.0, multi_class='ovr', fit_intercept=True,

The argument C seems to be unused.

The argument multi_class seems to be unused.

The argument tol seems to be unused.

The argument fit_intercept seems to be unused.

                 intercept_scaling=True, random_state=None,

The argument random_state seems to be unused.

The argument intercept_scaling seems to be unused.

                 preprocessors=None):
        super().__init__(preprocessors=preprocessors)
        self.params = vars()
        self.supports_multiclass = True


class NuSVMClassifier(SklModel):

    def predict(self, X):
        value = self.skl_model.predict(X)
        if self.skl_model.probability:
            prob = self.skl_model.predict_proba(X)
            return value, prob
        return value


class NuSVMLearner(SklLearner):
    __wraps__ = skl_svm.NuSVC
    __returns__ = NuSVMClassifier
    name = 'nu svm'
    preprocessors = svm_pps

    def __init__(self, nu=0.5, kernel='rbf', degree=3, gamma=0.0, coef0=0.0,

The argument gamma seems to be unused.

The argument coef0 seems to be unused.

The argument kernel seems to be unused.

The argument nu seems to be unused.

The argument degree seems to be unused.

                 shrinking=True, probability=False, tol=0.001, cache_size=200,

The argument cache_size seems to be unused.

The argument shrinking seems to be unused.

The argument tol seems to be unused.

The argument probability seems to be unused.

                 max_iter=-1, preprocessors=None):

The argument max_iter seems to be unused.

        super().__init__(preprocessors=preprocessors)
        self.params = vars()
        self.supports_multiclass = True
        self.supports_weights = True


class OneClassSVMLearner(SklLearnerBase):
    __wraps__ = skl_svm.OneClassSVM
    name = 'one class svm'
    preprocessors = svm_pps

    def __init__(self, kernel='rbf', degree=3, gamma=0.0, coef0=0.0,

The argument gamma seems to be unused.

The argument coef0 seems to be unused.

The argument degree seems to be unused.

The argument kernel seems to be unused.

                 tol=0.001, nu=0.5, shrinking=True, cache_size=200,

The argument cache_size seems to be unused.

The argument shrinking seems to be unused.

The argument tol seems to be unused.

The argument nu seems to be unused.

                 max_iter=-1, preprocessors=None):

The argument max_iter seems to be unused.

        super().__init__(preprocessors=preprocessors)
        self.params = vars()
        self.supports_weights = True

    def fit(self, X, Y=None, W=None):
        clf = self.__wraps__(**self.params)
        if W is not None:
            return self.__returns__(clf.fit(X, W.reshape(-1)))
        return self.__returns__(clf.fit(X))


if __name__ == '__main__':
    import Orange

    data = Orange.data.Table('iris')
    learners = [SVMLearner(), NuSVMLearner(), LinearSVMLearner()]
    res = Orange.evaluation.CrossValidation(data, learners)
    for l, ca in zip(learners, Orange.evaluation.CA(res)):
        print("learner: {}\nCA: {}\n".format(l, ca))