That's true. it's been a while since last release of OB and I'm currently
working on git master branch, since it has less bugs and more features.
Have fun and feel free to mail me if you'd have trouble with ODDT.
----
Pozdrawiam, | Best regards,
Maciek Wójcikowski
[email protected]
2015-02-20 18:21 GMT+01:00 Matthew Lardy <[email protected]>:
> Hi Maciek,
>
> Thanks! My brain was stuck on this for a while, as it has been ages since
> I have written any Python.
>
> BTW- I also took a look at your ODDT, and it reminded me that I need to
> get the OB python wrappers re-compiled. :)
>
> Thanks,
> Matthew
>
> On Fri, Feb 20, 2015 at 1:06 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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