Python sklearn - Determine the encoding order of LabelEncoder

Question

I wish to determine the labels of sklearn LabelEncoder (namely 0,1,2,3,...) to fit a specific order of the possible values of categorical variable (say ['b', 'a', 'c', 'd' ]). LabelEncoder chooses to fit the labels lexicographically I guess as can be seen in this example:

from sklearn.preprocessing import LabelEncoder
le = LabelEncoder()
le.fit(['b', 'a', 'c', 'd' ])
le.classes_
array(['a', 'b', 'c', 'd'], dtype='<U1')
le.transform(['a', 'b'])
array([0, 1])

How can I force the encoder to stick to the order of data as it is first met in the .fit method (namely to encode 'b' to 0, 'a' to 1, 'c' to 2, and 'd' to 3)?

Answer 1

You cannot do that in original one.

LabelEncoder.fit() uses numpy.unique which will always return the data as sorted, as given in source:

def fit(...):
    y = column_or_1d(y, warn=True)
    self.classes_ = np.unique(y)
    return self

So if you want to do that, you need to override the fit() function. Something like this:

import pandas as pd
from sklearn.preprocessing import LabelEncoder
from sklearn.utils import column_or_1d

class MyLabelEncoder(LabelEncoder):

    def fit(self, y):
        y = column_or_1d(y, warn=True)
        self.classes_ = pd.Series(y).unique()
        return self

Then you can do this:

le = MyLabelEncoder()
le.fit(['b', 'a', 'c', 'd' ])
le.classes_
#Output:  array(['b', 'a', 'c', 'd'], dtype=object)

Here, I am using pandas.Series.unique(), to get unique classes. If you cannot use pandas for any reason, refer to this question which does this question using numpy:

• numpy unique without sort