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pythonnumpyscikit-learntransformationone-hot-encoding
PascalVKooten
PascalVKooten

Reputation: 21461

sklearn mask for onehotencoder does not work

Considering data like:

from sklearn.preprocessing import OneHotEncoder
import numpy as np
dt = 'object, i4, i4'
d = np.array([('aaa', 1, 1), ('bbb', 2, 2)], dtype=dt)

I want to exclude the text column using the OHE functionality.

Why does the following not work?

ohe = OneHotEncoder(categorical_features=np.array([False,True,True], dtype=bool))       
ohe.fit(d)
ValueError: could not convert string to float: 'bbb'

It says in the documentation:

categorical_features: “all” or array of indices or mask :
  Specify what features are treated as categorical.
   ‘all’ (default): All features are treated as categorical.
   array of indices: Array of categorical feature indices.
   mask: Array of length n_features and with dtype=bool.

I'm using a mask, yet it still tries to convert to float.

Even using 

ohe = OneHotEncoder(categorical_features=np.array([False,True,True], dtype=bool), 
                    dtype=dt)        
ohe.fit(d)

Same error.

And also in the case of "array of indices":

ohe = OneHotEncoder(categorical_features=np.array([1, 2]), dtype=dt)        
ohe.fit(d)

Upvotes: 5

Views: 12415

Answers (3)

Bahman Engheta
Bahman Engheta

Reputation: 116

I encountered the same behavior and found it frustrating. As others have pointed out, Scikit-Learn requires all data to be numerical before it even considers selecting the columns provided in the categorical_features parameter.

Specifically, the column selection is handled by the _transform_selected() method in /sklearn/preprocessing/data.py and the very first line of that method is

X = check_array(X, accept_sparse='csc', copy=copy, dtype=FLOAT_DTYPES).

This check fails if any of the data in the provided dataframe X cannot be successfully converted to a float.

I agree that the documentation of sklearn.preprocessing.OneHotEncoder is rather misleading in that regard.

Upvotes: 1

Ibraim Ganiev
Ibraim Ganiev

Reputation: 9410

You should understand that all estimators in Scikit-Learn were designed only for numerical inputs. Thus from this point of view there is no sense to leave text column in this form. You have to transform that text column in something numerical, or get rid of it.

If you obtained your dataset from Pandas DataFrame - you can take a look at this small wrapper: https://github.com/paulgb/sklearn-pandas. It will help you to transform all needed columns simultaneously (or leave some of rows in numerical form)

import pandas as pd
import numpy as np
from sklearn_pandas import DataFrameMapper
from sklearn.preprocessing import OneHotEncoder

data = pd.DataFrame({'text':['aaa', 'bbb'], 'number_1':[1, 1], 'number_2':[2, 2]})

#    number_1  number_2 text
# 0         1         2  aaa
# 1         1         2  bbb

# SomeEncoder here must be any encoder which will help you to get
# numerical representation from text column
mapper = DataFrameMapper([
    ('text', SomeEncoder),
    (['number_1', 'number_2'], OneHotEncoder())
])
mapper.fit_transform(data)

Upvotes: 3

Leb
Leb

Reputation: 15953

I think there's some confusion here. You still need to enter the numerical values, but within the encoder you can specify which values are categorical which are not.

The input to this transformer should be a matrix of integers, denoting the values taken on by categorical (discrete) features.

So in the example below I change aaa to 5 and bbb to 6. This way it will distinguish from the 1 and 2 numerical values:

d = np.array([[5, 1, 1], [6, 2, 2]])
ohe = OneHotEncoder(categorical_features=np.array([True,False,False], dtype=bool))
ohe.fit(d)

Now you can check your feature categories:

ohe.active_features_
Out[22]: array([5, 6], dtype=int64)

Upvotes: 2

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