fit_transform() takes 2 positional arguments but 3 were given with LabelBinarizer

The Problem:

The pipeline is assuming LabelBinarizer’s fit_transform method is defined to take three positional arguments:

def fit_transform(self, x, y)
    ...rest of the code

while it is defined to take only two:

def fit_transform(self, x):
    ...rest of the code

Possible Solution:

This can be solved by making a custom transformer that can handle 3 positional arguments:

1. Import and make a new class:

   from sklearn.base import TransformerMixin #gives fit_transform method for free
   class MyLabelBinarizer(TransformerMixin):
       def __init__(self, *args, **kwargs):
           self.encoder = LabelBinarizer(*args, **kwargs)
       def fit(self, x, y=0):
           self.encoder.fit(x)
           return self
       def transform(self, x, y=0):
           return self.encoder.transform(x)
   

2. Keep your code the same only instead of using LabelBinarizer(), use the class we created : MyLabelBinarizer().

Note: If you want access to LabelBinarizer Attributes (e.g. classes_), add the following line to the fit method:

    self.classes_, self.y_type_, self.sparse_input_ = self.encoder.classes_, self.encoder.y_type_, self.encoder.sparse_input_