将PMML模型导入Python(Scikit-learn) [英] Importing PMML models into Python (Scikit-learn)

问题描述

从scikit-learn导出PMML模型似乎有一些选择，例如sklearn2pmml，但是在相反方向上的信息要少得多.我的案例是一个以前在R中内置的XGboost模型，并使用r2pmml保存到了PMML，我想在Python中使用它. Scikit通常使用pickle来保存/加载模型，但是也可以使用PMML将模型导入scikit-learn中吗?

There seem to be a few options for exporting PMML models out of scikit-learn, such as sklearn2pmml, but a lot less information going in the other direction. My case is an XGboost model previously built in R, and saved to PMML using r2pmml, that I would like to use in Python. Scikit normally uses pickle to save/load models, but is it also possible to import models into scikit-learn using PMML?

推荐答案

您不能在通用表示形式(例如PMML)上连接不同的专用表示形式(例如R和Scikit-Learn本机数据结构).尝试将R数据结构直接转换为Scikit-Learn数据结构可能会更好.

You can't connect different specialized representations (such as R and Scikit-Learn native data structures) over a generalized representation (such as PMML). You may have better luck trying to translate R data structures to Scikit-Learn data structures directly.

XGBoost实际上是上述规则的一个例外，因为它的R和Scikit-Learn实现只是本地XGBoost库的薄包装.在经过训练的R XGBoost对象中，有一个斑点raw，它是其本机XGBoost表示形式的模型.将其保存到文件中，然后使用xgb.Booster.load_model(fname)方法加载到Python中.

XGBoost is really an exception to the above rule, because its R and Scikit-Learn implementations are just thin wrappers around the native XGBoost library. Inside a trained R XGBoost object there's a blob raw, which is the model in its native XGBoost representation. Save it to a file, and load in Python using the xgb.Booster.load_model(fname) method.

如果您知道需要在Scikit-Learn中部署XGBoost模型，那么为什么要在R中对其进行训练?

If you know that you need to the deploy XGBoost model in Scikit-Learn, then why do you train it in R?

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