Orange.regression.NuSVRLearner

Complexity

Total Complexity

1

Size/Duplication

Total Lines	11
Duplicated Lines	0 %

1 Method

Rating	Name	Duplication	Size	Complexity
A	__init__()	0	6	1

import sklearn.svm as skl_svm

The import sklearn.svm could not be resolved.

from Orange.regression import SklLearner
from Orange.preprocess import Normalize

__all__ = ["SVRLearner", "NuSVRLearner"]

svm_pps = SklLearner.preprocessors + [Normalize()]


class SVRLearner(SklLearner):
    __wraps__ = skl_svm.SVR
    name = 'svr'
    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, C=1.0, epsilon=0.1, shrinking=True,

The argument C seems to be unused.

The argument shrinking seems to be unused.

The argument epsilon seems to be unused.

The argument tol seems to be unused.

                 cache_size=200, max_iter=-1, preprocessors=None):

The argument cache_size seems to be unused.

The argument max_iter seems to be unused.

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


class NuSVRLearner(SklLearner):
    __wraps__ = skl_svm.NuSVR
    name = 'nu svr'
    preprocessors = svm_pps

    def __init__(self, nu=0.5, 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 nu seems to be unused.

The argument degree seems to be unused.

The argument kernel seems to be unused.

                 coef0=0.0, shrinking=True, tol=0.001,

The argument shrinking seems to be unused.

The argument tol seems to be unused.

The argument coef0 seems to be unused.

                 cache_size=200, max_iter=-1, preprocessors=None):

The argument cache_size seems to be unused.

The argument max_iter seems to be unused.

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


if __name__ == '__main__':
    import Orange

    data = Orange.data.Table('housing')
    learners = [SVRLearner(), NuSVRLearner()]
    res = Orange.evaluation.CrossValidation(data, learners)
    for l, ca in zip(learners, Orange.evaluation.RMSE(res)):
        print("learner: {}\nRMSE: {}\n".format(l, ca))