How to use XGboost in PySpark Pipeline

Question

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, ...])
...

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

Answer 1

There is a maintained (used in production by several companies) distributed XGBoost library as mentioned above (https://github.com/dmlc/xgboost), however to use it from PySpark is a bit tricky, someone made a working pyspark wrapper for version 0.72 of the library, with 0.8 support in progress.

See here https://medium.com/@bogdan.cojocar/pyspark-and-xgboost-integration-tested-on-the-kaggle-titanic-dataset-4e75a568bdb, and https://github.com/dmlc/xgboost/issues/1698 for the full discussion.

Make sure the xgboost jars are in your pyspark jar path.