Thanks Jacob V. and Jacob S.
I have forked scikit-learn into my github and will start making my changes
to my branch. I will send a code-review once I am done.
Mahesh
On Thu, Feb 11, 2016 at 11:18 AM, Jacob Vanderplas <
jake...@cs.washington.edu> wrote:
> 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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>>
>>
>> --
>> Mahesh
>>
>>
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--
Mahesh
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