Hi Joel -
Conceptually, that makes sense. But when I assign n_values, I can't make
it match the result when you don't specify them. See below. I used the
number of unique levels per column.
>>> enc = OneHotEncoder(sparse=False)
>>> test = enc.fit_transform([[7, 0, 3], [1, 2, 0], [0, 2, 1], [1, 0, 2]])
>>> test
array([[0., 0., 1., 1., 0., 0., 0., 0., 1.],
[0., 1., 0., 0., 1., 1., 0., 0., 0.],
[1., 0., 0., 0., 1., 0., 1., 0., 0.],
[0., 1., 0., 1., 0., 0., 0., 1., 0.]])
>>> enc = OneHotEncoder(sparse=False,n_values=[3,2,4])
>>> test = enc.fit_transform([[7, 0, 3], [1, 2, 0], [0, 2, 1], [1, 0, 2]])
>>> test
array([[0., 0., 0., 1., 0., 0., 0., 1., 1.],
[0., 1., 0., 0., 0., 2., 0., 0., 0.],
[1., 0., 0., 0., 0., 1., 1., 0., 0.],
[0., 1., 0., 1., 0., 0., 0., 1., 0.]])
Cheers,
Sarah
Cheers,
Sarah
On Mon, Feb 5, 2018 at 12:02 AM, Joel Nothman <joel.noth...@gmail.com>
wrote:
> If each input column is encoded as a value from 0 to the (number of
> possible values for that column - 1) then n_values for that column should
> be the highest value + 1, which is also the number of levels per column.
> Does that make sense?
>
> Actually, I've realised there's a somewhat slow and unnecessary bit of
> code in the one-hot encoder: where the COO matrix is converted to CSR. I
> suspect this was done because most of our ML algorithms perform better on
> CSR, or else to maintain backwards compatibility with an earlier
> implementation.
>
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