More information about how using buffer objects works in Cython :
http://docs.cython.org/src/userguide/memoryviews.html#memoryview-objects-and-cython-arrays
On Thu, Feb 11, 2016 at 11:05 AM, mahesh ravishankar <
mahesh.ravishan...@gmail.com> wrote:
> Hi Jacob,
>
> For example, in _gradient_boosting.pyx (in //sklearn/ensemble/) the
> function _predict_regression_tree_inplace_fast has the first parameter of
> type np.float32_t* . When this function is called from predict_stages, the
> first argument is X.data , where X is a numpy.ndarray. The reason this
> works is that cython knows that the underlying C object that is used for
> numpy.ndarray's has a field "data" of type char* that points to the raw
> data buffer of the numpy array.
>
> What I am planning to do is as follows. The current signature of the
> function is
>
> _predict_regression_tree_inplace_fast(np.float32_t* X , ...)
>
> If this can be changed to
>
> _predict_regression_tree_inplace_fast(np.float32 [:,:] X, ... )
>
> then this generalizes to use any object X that exposes the buffer protocol
> (described by PEP 3118 of python). Thoughts on whether this is something
> useful for the scikit community? I am probably going to make this change in
> my local branch, anyway. I can push these changes back to scikit if there
> is interest.
>
> Thanks,
> Mahesh
>
>
>
>
>
>
>
>
>
> On Wed, Feb 10, 2016 at 8:15 PM, Jacob Vanderplas <
> jake...@cs.washington.edu> wrote:
>
>> Hi Mahesh,
>> Regarding the raw data access, what specific parts of the code are you
>> looking at?
>> Thanks,
>> Jake
>>
>> Jake VanderPlas
>> Senior Data Science Fellow
>> Director of Research in Physical Sciences
>> University of Washington eScience Institute
>>
>> On Wed, Feb 10, 2016 at 6:09 PM, mahesh ravishankar <
>> mahesh.ravishan...@gmail.com> wrote:
>>
>>> Hi,
>>>
>>> I am looking at scikit as an app for prototyping a Python module that
>>> exposes an array-like object I am developing. I was going through the
>>> Cython files and see that a lot of places, the raw data buffer of numpy are
>>> accessed by accessing the c-field (i.e. "data" field) exposed through the
>>> cython/numpy interface. I am a relative newbie to cython, but from my
>>> understanding using typed memoryview (
>>> http://docs.cython.org/src/userguide/memoryviews.html#memoryview-objects-and-cython-arrays)
>>> is the recommended way of accessing data in an array object. I was
>>> wondering if this was done due to legacy reasons, or performance reasons?
>>>
>>> For me to evaluate my array object interface, I am thinking of changing
>>> scikit to use the typed memoryview. If there is interest in this, I can
>>> push this change to scikit. Any comments about why this would not be a good
>>> idea are deeply appreciated.
>>>
>>> Thanks,
>>> --
>>> Mahesh
>>>
>>>
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>>
>>
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>
>
> --
> Mahesh
>
--
Mahesh
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