Also, there is an effort on converting trained scikit-learn models to
other languages (e.g. C) in https://github.com/nok/sklearn-porter
but it does not support GradientBoostingRegressor (yet).
On 13/04/17 23:27, federico vaggi wrote:
If you want to use the model from C++ code, the easiest way is to
probably use Boost/Python
(http://www.boost.org/doc/libs/1_62_0/libs/python/doc/html/index.html).
Alternatively, use another gradient boosting library that has a C++ API
(like XGBoost).
Keep in mind, if you want to call Python code from C++ you will have to
bundle a Python interpreter as well as all the dependencies.
On Thu, 13 Apr 2017 at 14:23 Sebastian Raschka <[email protected]
<mailto:[email protected]>> wrote:
Hi,
not sure how this could generally work. However, you could at least
dump the model parameters for e.g., linear models and compute the
prediction via
w_1 * x1 + w_2 * x_2 + … + w_n * x_n + bias
over the n features.
To write various model attributes to text files, you could use json,
e.g., see https://cmry.github.io/notes/serialize
However, I don’t think that this approach will solve the problem of
loading the model into C++.
Best,
Sebastian
> On Apr 13, 2017, at 4:58 PM, 老陈 <[email protected]
<mailto:[email protected]>> wrote:
>
> Hi,
>
> I am working on GradientBoostingRegressor these days and I am
wondering if there is a way to dump the model into txt file, or any
other format that can be processed by c++
>
> My production system is in c++, so I want use the python-trained
tree model in c++ for production.
>
> Has anyone ever done this before?
>
> thanks
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