Update LabelEncodingTransformer to make it user friendly

[npatki]

Problem Description

Let's update LabelEncodingTransformer to make it more user friendly.

Expected behavior

Name: LabelEncoder
Functionality:

• Change columns (see Rename columns parameter in fit for each individual transformer #376 )
• If there are unseen categories in the forward transform: Add a warning and randomly assign labels
• Clip during reverse transform

>>> from transformers.categorical import LabelEncoder

>>> le = LabelEncoder()
>>> le.fit(data, column='categorical_column')

# if the transform contains new categories unseen during fit
>>> le.transform(data_new_categories)
Warning: The data contains new categories ('hello', 'world') that were not in the original data.
Assigning them random values. If you want to model new categories, please fit the transformer
again with the new data.

# if there is out-of-bounds data in the reverse, clip it silently
>>> le.reverse_transform(out_of_bounds_data)