229d418b9e473948294e12310cb87a38f32a831c,autosklearn/pipeline/components/feature_preprocessing/liblinear_svc_preprocessor.py,LibLinear_Preprocessor,fit,#LibLinear_Preprocessor#Any#Any#,31

Before Change

        self.C = float(self.C)
        self.tol = float(self.tol)

        if check_true(self.dual):
            self.dual = True
        elif check_false(self.dual):
            self.dual = False
        else:
            self.dual = self.dual

        if check_true(self.fit_intercept):
            self.fit_intercept = True
        elif check_false(self.fit_intercept):
            self.fit_intercept = False
        else:
            self.fit_intercept = self.fit_intercept

        self.intercept_scaling = float(self.intercept_scaling)

        if check_none(self.class_weight):
            self.class_weight = None

        estimator = sklearn.svm.LinearSVC(penalty=self.penalty,
                                          loss=self.loss,
                                          dual=self.dual,
                                          tol=self.tol,
                                          C=self.C,
                                          class_weight=self.class_weight,
                                          fit_intercept=self.fit_intercept,
                                          intercept_scaling=self.intercept_scaling,
                                          multi_class=self.multi_class,
                                          random_state=self.random_state)

        estimator.fit(X, Y)
        self.preprocessor = SelectFromModel(estimator=estimator,
                                            threshold="mean",
                                            prefit=True)

After Change

        self.C = float(self.C)
        self.tol = float(self.tol)
        self.dual = check_for_bool(self.dual)
        self.fit_intercept = check_for_bool(self.fit_intercept)
        self.intercept_scaling = float(self.intercept_scaling)

        if check_none(self.class_weight):
            self.class_weight = None

        estimator = sklearn.svm.LinearSVC(penalty=self.penalty,
                                          loss=self.loss,
                                          dual=self.dual,
                                          tol=self.tol,
                                          C=self.C,
                                          class_weight=self.class_weight,
                                          fit_intercept=self.fit_intercept,
                                          intercept_scaling=self.intercept_scaling,
                                          multi_class=self.multi_class,
                                          random_state=self.random_state)

        estimator.fit(X, Y)
        self.preprocessor = SelectFromModel(estimator=estimator,
                                            threshold="mean",
                                            prefit=True)

Instances