Saif Addin created SPARK-28902:
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Summary: Spark ML Pipeline with nested Pipelines fails to load
when saved from Python
Key: SPARK-28902
URL: https://issues.apache.org/jira/browse/SPARK-28902
Project: Spark
Issue Type: Bug
Components: ML
Affects Versions: 2.4.3
Reporter: Saif Addin
Hi, this error is affecting a bunch of our nested use cases.
Saving a *PipelineModel* with one of its stages being another *PipelineModel*,
fails when loading it from Scala if it is saved in Python.
*Python side:*
{code:java}
from pyspark.ml import Pipeline
from pyspark.ml.feature import Tokenizer
t = Tokenizer()
p = Pipeline().setStages([t])
d = spark.createDataFrame([["Hello Peter Parker"]])
pm = p.fit(d)
np = Pipeline().setStages([pm])
npm = np.fit(d)
npm.write().save('./npm_test')
{code}
*Scala side:*
{code:java}
scala> import org.apache.spark.ml.PipelineModel
scala> val pp = PipelineModel.load("./npm_test")
java.lang.IllegalArgumentException: requirement failed: Error loading metadata:
Expected class name org.apache.spark.ml.PipelineModel but found class name
pyspark.ml.pipeline.PipelineModel
at scala.Predef$.require(Predef.scala:224)
at
org.apache.spark.ml.util.DefaultParamsReader$.parseMetadata(ReadWrite.scala:638)
at
org.apache.spark.ml.util.DefaultParamsReader$.loadMetadata(ReadWrite.scala:616)
at org.apache.spark.ml.Pipeline$SharedReadWrite$.load(Pipeline.scala:267)
at
org.apache.spark.ml.PipelineModel$PipelineModelReader.load(Pipeline.scala:348)
at
org.apache.spark.ml.PipelineModel$PipelineModelReader.load(Pipeline.scala:342)
at org.apache.spark.ml.util.MLReadable$class.load(ReadWrite.scala:380)
at org.apache.spark.ml.PipelineModel$.load(Pipeline.scala:332)
... 50 elided
{code}
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