Well, the columns after the OneHotEncoder correspond to feature values,
not feature names, right?
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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