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 <se.rasc...@gmail.com> 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 <26743...@qq.com> 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 > > _______________________________________________ > > scikit-learn mailing list > > scikit-learn@python.org > > https://mail.python.org/mailman/listinfo/scikit-learn > > _______________________________________________ > scikit-learn mailing list > scikit-learn@python.org > https://mail.python.org/mailman/listinfo/scikit-learn >
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