将用python训练的XGBoost模型端口移植到用C/C ++编写的另一个系统 [英] Port XGBoost model trained in python to another system written in C/C++

问题描述

假设我已经成功地用python训练了XGBoost机器学习模型.

Suppose I have successfully trained a XGBoost machine learning model in python.

x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=7)
model = XGBClassifier()
model.fit(x_train, y_train)
y_pred = model.predict(x_test)

我想将此模型移植到将用C/C ++编写的另一个系统.为此，我需要了解经过XGboost训练的模型的内部逻辑，并将它们转换为一系列if-then-else语句，例如决策树(如果我没有记错的话).

I want to port this model to another system which will be writte in C/C++. To do this, I need to know the internal logic of the XGboost trained model and translate them into a series of if-then-else statements like decision trees, if I am not wrong.

这怎么办?如何找出经过XGBoost训练的模型的内部逻辑，以在另一个系统上实现它?

How can this be done? How to find out the internal logic of the XGBoost trained model to implement it on another system?

我正在使用python 3.7.

I am using python 3.7.

推荐答案

m2cgen 是一个很棒的软件包，会将Scikit-Learn兼容模型转换为原始代码.如果您使用的是XGBoosts sklearn包装器(看起来像您一样)，则可以简单地调用以下内容:

m2cgen Is an awesome package that will convert Scikit-Learn compatible models into raw code. If you are using XGBoosts sklearn wrappers (which it looks like you are), then you can simply call something like this:

model = XGBClassifier()
model.fit(x_train, y_train)
 ...
import m2cgen as m2c

with open('./model.c','w') as f:
    code = m2c.export_to_c(model)
    f.write(code)

此软件包的真正妙处在于，它支持许多不同类型的模型，例如

The really awesome thing about this package, is that it supports many different types of models, such as

• 线性
• 支持向量机
• 树
• 随机森林
• 提升

还有一件事.m2cgen还支持多种语言，例如

One more thing. m2cgen also supports multiple languages such as

• C
• C#
• 飞镖
• 开始
• Haskell
• Java
• JavaScript
• PHP
• PowerShell
• Python
• R
• Visual Basic

我希望这会有所帮助！

这篇关于将用python训练的XGBoost模型端口移植到用C/C ++编写的另一个系统的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！