Beyond One-Hot: Sklearn transformers and pip release

I've just released version 1.0.0 of category_encoders on pypi, you can check out the source here:

https://github.com/wdm0006/categorical_encoding

In two previous posts (here and here), we discussed and examined the differences between encoding methods for categorical variables.  It turns out they all are a bit different and make different assumptions, and so you end up with different results from each.  For a practitioner, all of them have a time and place when they are useful, and as such I've packaged them all into scikit-learn compatible transformers so that you can use them in your machine learning pipelines easily.

To install, just:

pip install category_encoders

Then to use:

from sklearn import linear_model, pipeline
from category_encoders import HashingEncoder

ppl = pipeline.Pipeline([
    ('encoder', HashingEncoder(cols=[...])),
    ('clf', linear_model.LogisticRegression())
])

Included in the library (see previous posts for more detail on them) are:

1. Ordinal
2. One-Hot
3. Binary
4. Helmert Contrast
5. Sum Contrast
6. Polynomial Contrast
7. Backward Difference Contrast
8. Simple Hashing Trick

So try it out, send me an issue on github if you run into any trouble, and if you'd like to contribute let me know.

https://github.com/wdm0006/categorical_encoding

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