LabelEncoder throws an error when it's used in a Pipeline or in a ColumnTransform

Description

fit and fit_transform methods in LabelEncoder don’t follow the standard scikit-lean convention for these methods: fit(X[, y]) and fit_transform(X[, y]). The fit and fit_transform method in the LabelEncoder only accepts one argument: fit(y) and fit_transform(y).

Therefore, LabelEncoder couldn’t be used inside a Pipeline or a ColumnTransform. I suspect that there are a bunch of other classes in which it doesn’t work (GridSearchCV, …) but I haven’t tested it.

In contrast, fit and fit_transform methods in OneHotEncoder and OrdinalEncoder follows the standard scikit-learn signature.

See reference: LabelEncoder: https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.LabelEncoder.html OneHotEnconder: https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.OneHotEncoder.html#sklearn.preprocessing.OneHotEncoder OrdinalEncoder:https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.OrdinalEncoder.html#sklearn.preprocessing.OrdinalEncoder

Steps/Code to Reproduce

Example:

import pandas as pd

from sklearn.preprocessing import LabelEncoder
from sklearn.pipeline import make_pipeline
from sklearn.compose import ColumnTransformer
import sklearn.tree as tree

X = pd.DataFrame(
    {'city': ['London', 'London', 'Paris', 'Sallisaw'],
     'title': ["His Last Bow", "How Watson Learned the Trick",
               "A Moveable Feast", "The Grapes of Wrath"],
     'expert_rating': [5, 3, 4, 5],
     'user_rating': [4, 5, 4, 3]})

column_trans = ColumnTransformer(
    [('title_bow', LabelEncoder(), 'title')],
    remainder='drop').fit(X)

pipe = make_pipeline(LabelEncoder(), tree.DecisionTreeClassifier()).fit(X)

Expected Results

No error is thrown.

Actual Results

The same error in both cases: TypeError: fit_transform() takes 2 positional arguments but 3 were given.

Versions

System: python: 3.6.6 |Anaconda, Inc.| (default, Oct 9 2018, 12:34:16) [GCC 7.3.0] executable: /home/twins/anaconda3/envs/pytorch/bin/python machine: Linux-4.8.0-56-generic-x86_64-with-debian-stretch-sid

BLAS: macros: SCIPY_MKL_H=None, HAVE_CBLAS=None lib_dirs: /home/twins/anaconda3/envs/pytorch/lib cblas_libs: mkl_rt, pthread

Python deps: pip: 18.1 setuptools: 40.6.2 sklearn: 0.20.1 numpy: 1.15.4 scipy: 1.1.0 Cython: None pandas: 0.23.4

Thanks for the amazing job you do !

[TomDLT]

Yes indeed, from the user guide:

These are transformers that are not intended to be used on features, only on supervised learning targets.

To encode features, you need to use OneHotEncoder or OrdinalEncoder.

[kuabhish]

Yes indeed, from the user guide:

These are transformers that are not intended to be used on features, only on supervised learning targets.

To encode features, you need to use OneHotEncoder or OrdinalEncoder.

What if I want to Labelencode the input feature? With Labelencode there is also problem with unseen category (for e.x in the test set) What should I do then?