如何在PySpark管道中使用XGboost [英] How to use XGboost in PySpark Pipeline

问题描述

我想更新我的pyspark代码.在pyspark中，必须将基本模型放入管道中，办公室演示管道使用LogistictRegression作为基本模型.但是，似乎无法在管道API中使用XGboost模型.我该如何使用pyspark

I want to update my code of pyspark. In the pyspark, it must put the base model in a pipeline, the office demo of pipeline use the LogistictRegression as an base model. However, it seems not be able to use XGboost model in the pipeline api. How can I use the pyspark like this

from xgboost import XGBClassifier
...
model = XGBClassifier()
model.fit(X_train, y_train)
pipeline = Pipeline(stages=[..., model, ...])
...

使用管道api很方便，因此有人可以提出建议吗?谢谢.

It is convenient to use the pipeline api, so can anybody give some advices? Thanks.

推荐答案

此处有适用于Spark 2.4的XBoost实现:

There is an XBoost Implementation for Spark 2.4 and over here:

https://xgboost.readthedocs.io

请注意，这是一个外部库，但可以轻松地与spark配合使用.

Note that this is an external library but it should work easily with spark.

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