Well after a bit of tinkering it seems that OneHotEncoder has simple rules
to affect columns to the output:
1) first do the categorical, in the order given by the argument, creating
columns as needed by the values
2) then the numerical
So a piece of code like that seems to work:
>>> fn = []
>>> fc = []
>>> for c in df.columns.values:
>>> if is_categorical(c):
>>> fn += sorted(['%s=%s' % (c, v) for v in df[c].unique()])
>>> else:
>>> fc += [c]
>>> fn += fc
assuming the original data is in a pandas DataFrame (df) and you have a
list of categorical feature names (is_categorical).
Of course it's pretty retro-engineering the OHE and may break in the future
though.
E/
2015-03-06 9:01 GMT+01:00 Eustache DIEMERT <eusta...@diemert.fr>:
>
> 2015-03-05 16:57 GMT+01:00 Andy <t3k...@gmail.com>:
>
>> Well, the columns after the OneHotEncoder correspond to feature values,
>> not feature names, right?
>>
>
> Well, for the categorical ones this is right, except that not all my
> features are categorical (hence the categorical_features=...) and they
> are intertwined.
>
> So my problem is more to keep track of which categorical features got
> projected into which columns (1->N) and which numerical ones have been just
> copied and where (1->1).
>
> Re-reading your answer I'm wondering if you suggest to just separate the
> input columns by feature types and apply the encoder to the categorical
> ones only ?
>
>
>
>> There is ``feature_indices_`` which maps each feature to a range of
>> features in the encoded matrix.
>> The features in the input matrix don't really have names in scikit-learn,
>> as they are represented only as numpy matrices.
>> So you need to keep track of the indices of each feature. That shouldn't
>> be too hard, though.
>>
>> Why don't you select the features before the encoding? Or do you want to
>> exclude some values?
>>
>>
>>
>> On 03/05/2015 05:55 AM, Eustache DIEMERT wrote:
>>
>> Hi list,
>>
>> I have a X (np.array) with some columns containing ids. I also have a
>> list of column names. Then I want to transform the relevant columns to be
>> used by a logistic regression model using OneHotEncoder:
>>
>> >>> X = np.loadtxt(...) # from a CSV
>> >>> col_names = ... # from CSV header
>> >>> e = OneHotEncoder(categorical_features=id_columns)
>> >>> Xprime = e.fit_transform(X)
>>
>> But then I don't know how to deduce the names of the columns in the new
>> matrix :(
>>
>> Ideally I would want the same as DictVectorizer which has a
>> feature_names_ member.
>>
>> Anyone already had this problem ?
>>
>> Eustache
>>
>>
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