无法使用 PySpark xgboost4j 保存模型 [英] Cannot save model using PySpark xgboost4j
问题描述
我有一个小的 PySpark
程序,它使用 xgboost4j
和 xgboost4j-spark
来以 spark 数据帧形式训练给定的数据集.
I have a small PySpark
program that uses xgboost4j
and xgboost4j-spark
in order to train a given dataset in a spark dataframe form.
训练已经完成,但我似乎无法保存模型.
The training is done, but It seems I cannot save the model.
当前库版本:
- Pyspark 2.4.0
- xgboost4j 0.90
- xgboost4j-spark 0.90
Spark 提交参数:
Spark submit args:
os.environ['PYSPARK_SUBMIT_ARGS'] = "--py-files dist/DNA-0.0.2-py3.6.egg " \
"--jars dna/resources/xgboost4j-spark-0.90.jar," \
"dna/resources/xgboost4j-0.90.jar pyspark-shell"
训练过程如下:
def spark_xgboost_train(spark=None, models_path='', train_df=None):
spark.sparkContext.addPyFile("dna/resources/xgboost4j-spark-0.90.jar")
spark.sparkContext.addPyFile("dna/resources/xgboost4j-0.90.jar")
spark.sparkContext.addPyFile('dna/resources/pyspark-xgboost_0.90_261ab52e07bec461c711d209b70428ab481db470.zip')
import sparkxgb as sxgb
from sparkxgb import XGBoostClassifier, XGBoostClassificationModel
# pre-process
train_df = train_df.drop('url')
train_df = train_df.na.fill(0)
x = train_df.columns
x.remove('label')
vectorAssembler = VectorAssembler() \
.setInputCols(x) \
.setOutputCol("features")
xgboost = XGBoostClassifier(
featuresCol="features",
labelCol="label",
predictionCol="prediction",
)
pipeline = Pipeline().setStages([vectorAssembler])
df = pipeline.fit(train_df).transform(train_df)
model = xgboost.fit(df)
# save
model.write().overwrite().save(models_path + "model.dat")
我得到的错误:
Traceback (most recent call last):
File "/storage/env/DNAtestenv/lib/python3.7/runpy.py", line 193, in _run_module_as_main
"__main__", mod_spec)
File "/storage/env/DNAtestenv/lib/python3.7/runpy.py", line 85, in _run_code
exec(code, run_globals)
File "/home/elad/DNA/dna/__main__.py", line 360, in <module>
main()
File "/home/elad/DNA/dna/__main__.py", line 325, in main
run_pipelines(config)
File "/home/elad/DNA/dna/__main__.py", line 311, in run_pipelines
objective=config['objective'], nthread=config['nthread'])
File "/home/elad/DNA/dna/__main__.py", line 234, in train_model
max_depth=max_depth, eta=eta, silent=silent, objective=objective, nthread=1)
File "/home/elad/DNA/dna/model/xgboost_train.py", line 82, in spark_xgboost_train
model.write().save(models_path + '/model.dat')
File "/storage/env/DNAtestenv/lib/python3.7/site-packages/pyspark/ml/util.py", line 183, in save
self._jwrite.save(path)
File "/storage/env/DNAtestenv/lib/python3.7/site-packages/py4j/java_gateway.py", line 1257, in __call__
answer, self.gateway_client, self.target_id, self.name)
File "/storage/env/DNAtestenv/lib/python3.7/site-packages/pyspark/sql/utils.py", line 63, in deco
return f(*a, **kw)
File "/storage/env/DNAtestenv/lib/python3.7/site-packages/py4j/protocol.py", line 328, in get_return_value
format(target_id, ".", name), value)
py4j.protocol.Py4JJavaError: An error occurred while calling o484.save.
: java.lang.NoSuchMethodError: org.json4s.jackson.JsonMethods$.parse(Lorg/json4s/JsonInput;Z)Lorg/json4s/JsonAST$JValue;
at ml.dmlc.xgboost4j.scala.spark.params.DefaultXGBoostParamsWriter$$anonfun$1$$anonfun$3.apply(DefaultXGBoostParamsWriter.scala:73)
at ml.dmlc.xgboost4j.scala.spark.params.DefaultXGBoostParamsWriter$$anonfun$1$$anonfun$3.apply(DefaultXGBoostParamsWriter.scala:71)
at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234)
at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234)
at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48)
at scala.collection.TraversableLike$class.map(TraversableLike.scala:234)
at scala.collection.AbstractTraversable.map(Traversable.scala:104)
at ml.dmlc.xgboost4j.scala.spark.params.DefaultXGBoostParamsWriter$$anonfun$1.apply(DefaultXGBoostParamsWriter.scala:71)
at ml.dmlc.xgboost4j.scala.spark.params.DefaultXGBoostParamsWriter$$anonfun$1.apply(DefaultXGBoostParamsWriter.scala:69)
at scala.Option.getOrElse(Option.scala:121)
at ml.dmlc.xgboost4j.scala.spark.params.DefaultXGBoostParamsWriter$.getMetadataToSave(DefaultXGBoostParamsWriter.scala:69)
at ml.dmlc.xgboost4j.scala.spark.params.DefaultXGBoostParamsWriter$.saveMetadata(DefaultXGBoostParamsWriter.scala:51)
at ml.dmlc.xgboost4j.scala.spark.XGBoostModel$XGBoostModelModelWriter.saveImpl(XGBoostModel.scala:371)
at org.apache.spark.ml.util.MLWriter.save(ReadWrite.scala:180)
at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.lang.reflect.Method.invoke(Method.java:498)
at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:244)
at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357)
at py4j.Gateway.invoke(Gateway.java:282)
at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132)
at py4j.commands.CallCommand.execute(CallCommand.java:79)
at py4j.GatewayConnection.run(GatewayConnection.java:238)
at java.lang.Thread.run(Thread.java:745)
我想做的是保存和加载模型,如下所示:
What I would like to do is to save and load the model, like this:
# save
model.write().save(models_path + '/model.dat')
# load
model2 = sxgb.xgboost.XGBoostClassificationModel().load(models_path + '/model.dat')
我也尝试使用其他 xgboost4j 版本(0.80
、0.72
)我似乎找不到原因,我什至试图阅读包装器源代码和 jars 源代码,但找不到任何东西.
I tried using other xgboost4j versions as well (0.80
, 0.72
)
I cant seem to find the cause for this, I was even trying to read the wrapper source code and the jars source code, I could not find anything.
提前致谢.
推荐答案
经过数小时的研究,我通过将 xgboost
添加到管道中,然后生成一个 PipelineModel
而不是 xgboost 模型.
After hours of researching, I got it to work by adding xgboost
to the pipeline, which then produces a PipelineModel
rather than an xgboost model.
我能够保存 PipelineModel
然后加载它就好了.
I was able to save the PipelineModel
and then load it just fine.
这是我更改的内容:
xgboost = XGBoostClassifier(
featuresCol="features",
labelCol="label",
predictionCol="prediction",
)
pipeline = Pipeline().setStages([vectorAssembler, xgboost])
model = pipeline.fit(train_df)
# save
model.write().overwrite().save(models_path + "/xgb_model.model")
# load
model2 = PipelineModel.load(models_path + "/xgb_model.model"
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