Oops, sorry, got into a wrong branch! I am just repeating Maciek's answer
it looks like!
Igor
On Fri, Feb 20, 2015 at 7:50 AM, Igor Filippov <[email protected]>
wrote:
> Maciek,
>
> I think scikit-learn is using numpy arrays and not plain Python lists.
> They look very similar, but are not quite the same thing.
> Maybe post a bit more complete code sample for people to play with?
>
> Igor
>
> On Fri, Feb 20, 2015 at 4:06 AM, Maciek Wójcikowski <[email protected]
> > wrote:
>
>> 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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>>
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