CODEWITHSUNDEEP
pythonpandasscikit-learn
Quickbeam2k1
Quickbeam2k1

Reputation: 5437

Dummy creation in pipeline with different levels in train and test set

I'm currently exploring the scikit learn pipelines. I also want to preprocess the data with a pipeline. However, my train and test data have different levels of the categorical variable. Example: Consider:

import pandas as pd
train = pd.Series(list('abbaa'))
test = pd.Series(list('abcd'))

I wrote a TransformerMixinClass using pandas

class CreateDummies(TransformerMixin):

def transform(self, X, **transformparams):
    return pd.get_dummies(X).copy()

def fit(self, X, y=None, **fitparams):
    return self

fit_transform yields for the train data 2 columns and for the test data 4 columns. So no surprise here, but not suitable for a pipeline

Similary, I tried to import the label encoder (and OneHotEncoder for the potential next steps):

from sklearn.preprocessing import LabelEncoder, OneHotEncoder
le = LabelEncoder()
le.fit_transform(train)
le.transform(test)

which yields, not surprisingly, an error.

So the problem here is that I need some information contained in the test set. Is there a good way to include this in a pipeline?

Upvotes: 11

Views: 10183

Answers (1)

user2285236
user2285236

Reputation:

You can use categoricals as explained in this answer:

categories = np.union1d(train, test)
train = train.astype('category', categories=categories)
test = test.astype('category', categories=categories)

pd.get_dummies(train)
Out: 
   a  b  c  d
0  1  0  0  0
1  0  1  0  0
2  0  1  0  0
3  1  0  0  0
4  1  0  0  0

pd.get_dummies(test)
Out: 
   a  b  c  d
0  1  0  0  0
1  0  1  0  0
2  0  0  1  0
3  0  0  0  1

Upvotes: 10

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