Thanks Mahesh,
That particular code was committed in early 2012, which (if I remember
correctly) was before Cython supported the typed-memoryview interface. I
suspect this is why raw pointers were used... looking at the code it seems
that replacing this with typed memoryviews should be just fine (as long as
the boundscheck and wraparound are turned off).
Jake
Jake VanderPlas
Senior Data Science Fellow
Director of Research in Physical Sciences
University of Washington eScience Institute
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
>
>
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