Hello,
If I can remember correctly coefficients are Numpy array. You can try
model.coef_.flatten()
to get flat Numpy Array. If you really want a python list, then you
probably should wrap it up with list(model.coef_.flatten()).
The main reason, why the vector is nested is that you can have many
output values for one feature vector.
PS.
I could also recommend my "Open Drug Discovery Toolkit" for playing around
with RDKit and scikit-learn.
https://github.com/oddt/oddt
----
Pozdrawiam, | Best regards,
Maciek Wójcikowski
[email protected]
2015-02-20 7:29 GMT+01:00 Greg Landrum <[email protected]>:
>
>
> On Thu, Feb 19, 2015 at 11:59 PM, Matthew Lardy <[email protected]> wrote:
>
>>
>> I have been able to build models via scikit-learn with the RDKit python
>> wrappers. That all works beautifully!
>>
>
> It's a nice combination, isn't it?
>
>
>> What I am struggling to get are the weights, or scalers, applied to each
>> bit position. For a SVM regression model (SVR) I think that the values I
>> seek are in the coef_ (if the model is created via the linear kernel).
>> But, all I get is something like this when I print that out:
>>
>> [[-0. -0.87146158 -0.46331996 ..., 0.31076767 -0.
>> -0.81882195]]
>>
>>
> I don't really know the SVM regression approach particularly well, but it
> looks like that's a vector of vectors. Is the length of the inner vector
> the same as the length of the fingerprint/descriptor vector you are
> providing?
>
> -greg
>
>
>
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