label binarizer

sklearn labelbinarizer in pipeline

from sklearn.preprocessing import LabelBinarizer

class LabelBinarizerPipelineFriendly(LabelBinarizer):
    def fit(self, X, y=None):
        """this would allow us to fit the model based on the X input."""
        super(LabelBinarizerPipelineFriendly,self).fit(X)
    def transform(self, X, y=None):
        return super(LabelBinarizerPipelineFriendly, self).transform(X)
    def fit_transform(self, X, y=None):
        return super(LabelBinarizerPipelineFriendly, self).fit(X).transform(X)

Posted by: Guest on August-15-2020

Source

>>> from sklearn import preprocessing >>> lb = preprocessing.LabelBinarizer() >>> lb.fit([1, 2, 6, 4, 2]) LabelBinarizer() >>> lb.classes_ array([1, 2, 4, 6]) >>> lb.transform([1, 6]) array([[1, 0, 0, 0], [0, 0, 0, 1]])

from sklearn.preprocessing import LabelBinarizer class LabelBinarizerPipelineFriendly(LabelBinarizer): def fit(self, X, y=None): """this would allow us to fit the model based on the X input.""" super(LabelBinarizerPipelineFriendly,self).fit(X) def transform(self, X, y=None): return super(LabelBinarizerPipelineFriendly, self).transform(X) def fit_transform(self, X, y=None): return super(LabelBinarizerPipelineFriendly, self).fit(X).transform(X)

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