Hi Roberto,
One thing you might try to get an integer instead of one-hot encoded values
is a LabelEncoder:
http://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.LabelEncoder.html
It's really useful if you process the complete dataset in memory. If you
can't hold your complete dataset in memory, what I like to do is just use
pythons hash function, but that has the danger of scaling the data in a
weird way.
To answer your question on loading a csv of mixed type, you can try
numpy.array(pandas.load_csv("my_csv.csv")). This will create a numpy array
of dtype object. From there you can select columns by doing something like
nparray[number_column].astype(np.float64).
Let me know if you have any other questions.
-Danny
On Tue, Dec 16, 2014 at 4:28 AM, Pagliari, Roberto <rpagli...@appcomsci.com>
wrote:
>
> When using OneHotEncoder, is it possible to have one integer per feature
> as the output, as opposed to binary representation?
>
>
>
> Also, when using OneHotEncoder, what would be the method to load data
> (.csv) with mixed type (number and categorical)?
>
>
>
> Thanks,
>
>
>
>
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