How To Reverse Label Encoder From Sklearn For Multiple Columns?

I would like to use the inverse_transform function for LabelEncoder on multiple columns. This is the code I use for more than one columns when applying LabelEncoder on a dataframe:

Solution 1:

In order to inverse transform the data you need to remember the encoders that were used to transform every column. A possible way to do this is to save the LabelEncoders in a dict inside your object. The way it would work:

• when you call fit the encoders for every column are fit and saved
• when you call transform they get used to transform data
• when you call inverse_transform they get used to do the inverse transformation

Example code:

classMultiColumnLabelEncoder:

    def__init__(self, columns=None):
        self.columns = columns # array of column names to encodedeffit(self, X, y=None):
        self.encoders = {}
        columns = X.columns if self.columns isNoneelse self.columns
        for col in columns:
            self.encoders[col] = LabelEncoder().fit(X[col])
        return self


    deftransform(self, X):
        output = X.copy()
        columns = X.columns if self.columns isNoneelse self.columns
        for col in columns:
            output[col] = self.encoders[col].transform(X[col])
        return output


    deffit_transform(self, X, y=None):
        return self.fit(X,y).transform(X)


    definverse_transform(self, X):
        output = X.copy()
        columns = X.columns if self.columns isNoneelse self.columns
        for col in columns:
            output[col] = self.encoders[col].inverse_transform(X[col])
        return output

You can then use it like this:

multi = MultiColumnLabelEncoder(columns=['city','size'])
df = pd.DataFrame({'city':    ['London','Paris','Moscow'],
                   'size':    ['M',     'M',    'L'],
                   'quantity':[12,       1,      4]})
X = multi.fit_transform(df)
print(X)
#    city  size  quantity# 0     0     1        12# 1     2     1         1# 2     1     0         4
inv = multi.inverse_transform(X)
print(inv)
#      city size  quantity# 0  London    M        12# 1   Paris    M         1# 2  Moscow    L         4

There could be a separate implementation of fit_transform that would call the same method of LabelEncoders. Just make sure to keep the encoders around for when you need the inverse transformation.

Solution 2:

You do not need to modify it this way. It's already implemented as a method inverse_transform.

Example:

from sklearn import preprocessing

le = preprocessing.LabelEncoder()
df = ["paris", "paris", "tokyo", "amsterdam"]

le_fitted = le.fit_transform(df)

inverted = le.inverse_transform(le_fitted)

print(inverted)
# array(['paris', 'paris', 'tokyo', 'amsterdam'], dtype='|S9')

Solution 3:

LabelEncoder() should only be used to encode the target. That's why you can't use it on multiple columns at the same time as any other transformers. The alternative is the OrdinalEncoder which does the same job as LabelEncoder but can be used on all categorical columns at the same time just like OneHotEncoder:

from sklear.preprocessing importOrdinalEncoderoe= OrdinalEncoder()
X = or.fit_transform(X)