6251434bd1d71c960b1ac340f4645d9e8439ca86,autosklearn/pipeline/components/data_preprocessing/one_hot_encoding/one_hot_encoding.py,OneHotEncoder,fit,#OneHotEncoder#Any#Any#,22

Before Change

        self.categorical_features = categorical_features

    def fit(self, X, y=None):
        if self.use_minimum_fraction is None or \
                self.use_minimum_fraction is False or \
                (isinstance(self.use_minimum_fraction, str) and
                 self.use_minimum_fraction.lower() == "false"):
            self.minimum_fraction = None
        else:
            self.minimum_fraction = float(self.minimum_fraction)

        if self.categorical_features is None:
            categorical_features = []
        else:
            categorical_features = self.categorical_features

        self.preprocessor = autosklearn.pipeline.implementations.OneHotEncoder\
            .OneHotEncoder(minimum_fraction=self.minimum_fraction,
                           categorical_features=categorical_features)

        self.preprocessor = self.preprocessor.fit(X)
        return self

    def transform(self, X):
        import scipy.sparse

After Change

        return self.preprocessor.fit_transform(X)

    def fit(self, X, y=None):
        self._fit(X, y)
        return self

    def fit_transform(self, X, y=None):
        return self._fit(X, y)

Instances