2c584dc7a45e40ad89cc80c90fddcc36d98e2ffc,oddt/scoring/models/regressors.py,svm,__init__,#svm#,46

Before Change

    def __init__(self, *args, **kwargs):
         Assemble a proper SVM using sklearn tools regressor 
        // Cherrypick arguments for model. Exclude "steps", which is pipeline argument
        local_kwargs = {key: kwargs.pop(key) for key in list(kwargs.keys())
                        if key != "steps" and len(key.split("__", 1)) == 1}
        self.pipeline = Pipeline([("empty_dims_remover", VarianceThreshold()),
                                  ("scaler", StandardScaler()),
                                  ("svm", SVR(*args, **local_kwargs))
                                  ]).set_params(**kwargs)

    def get_params(self, deep=True):
        return self.pipeline.get_params(deep=deep)

After Change

    _model = SVR

    def __init__(self, *args, **kwargs):
        super(svm, self).__init__(*args, **kwargs)

Instances