Hi Andy,

the last time I did this I used DictVectorizer too. Would be great
though to have a something like OneHotTransformer.

BTW: Has anybody of your looked into patsy [1]? They have plenty of
functionality for this kind of encodings (they call it treatment
coding [2]).

best,
 Peter

[1] https://github.com/pydata/patsy
[2] http://patsy.readthedocs.org/en/latest/API-reference.html#patsy.Treatment

2012/10/19 Lars Buitinck <[email protected]>:
> 2012/10/19 Andreas Mueller <[email protected]>:
>> I'd like to convert an array of integer categorial features to a sparse
>> indicator matrix.
>> So my data points look like
>> x =[ 100, 1, 5, 10]
>> These are indices for feature-bins which don't really have an ordering.
>> Therefore I want to convert them to a one-hot encoding per feature.
>>
>> What is the best way in sklearn to achieve this? This looks a bit
>> like the DictVectorizer, I think.
>
> DictVectorizer is the only class we offer that does this, I think.
> (Unless you care to make strings out of your matrices and use
> CountVectorizer...).
>
>> If not, do you think this kind of encoding is common enough to
>> be included in sklearn?
>
> It's been requested on the ML over and over. At one point, I had a PR
> for a OneHotTransformer
> (https://github.com/scikit-learn/scikit-learn/pull/242) but that
> hasn't been updated in quite a while and I'm using DictVectorizer
> myself now. Feel free to pick it up if you need it, or start afresh.
>
> --
> Lars Buitinck
> Scientific programmer, ILPS
> University of Amsterdam
>
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-- 
Peter Prettenhofer

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