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https://issues.apache.org/jira/browse/SPARK-32048?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17268560#comment-17268560
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Hyukjin Kwon commented on SPARK-32048:
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Thanks [~nmarcott] for assessing tickets.
> PySpark: error in serializing ML pipelines with training strategy and
> pipeline as estimator
> -------------------------------------------------------------------------------------------
>
> Key: SPARK-32048
> URL: https://issues.apache.org/jira/browse/SPARK-32048
> Project: Spark
> Issue Type: Bug
> Components: ML, PySpark
> Affects Versions: 2.4.5
> Reporter: Marcello Leida
> Priority: Major
>
> Hi all,
> I get the following error when serializing a pipeline with a CrossValidation
> and/or TrainValidationSplit training strategy and an estimator of type
> Pipeline through pyspark:
> {code:java}
> AttributeError: 'Pipeline' object has no attribute
> '_transfer_param_map_to_java
> {code}
> In scala the serialization works without problems, so i assume the issue
> should be in pyspark
> In case of using the LinearRegression as estimator the serialization is
> working properly.
> I see that in the tests of CrossValidation and TrainValidatioSplit, there is
> not a test with Pipeline as an estimator.
> I do not know if there is a workaround for this or another way to serialize
> the pipeline, or if this is a known issue
> Code for replicating the issue:
> {code:java}
> from pyspark.ml import Pipeline
> from pyspark.ml.classification import LogisticRegression,
> DecisionTreeClassifier
> from pyspark.ml.evaluation import BinaryClassificationEvaluator
> from pyspark.ml.feature import HashingTF, Tokenizer
> from pyspark.ml.tuning import CrossValidator, ParamGridBuilder,
> TrainValidationSplit
> # Prepare training documents from a list of (id, text, label) tuples.
> df = spark.createDataFrame([
> (0, "a b c d e spark", 1.0),
> (1, "b d", 0.0),
> (2, "spark f g h", 1.0),
> (3, "hadoop mapreduce", 3.0)
> ], ["id", "text", "label"])
> # Configure an ML pipeline, which consists of three stages: tokenizer,
> hashingTF, and lr.
> lr = LogisticRegression(maxIter=10, regParam=0.001)
> tokenizer = Tokenizer(inputCol="text", outputCol="words")
> hashingTF = HashingTF(inputCol=tokenizer.getOutputCol(),
> outputCol="features", numFeatures=1000)
> #treeClassifier = DecisionTreeClassifier()
> sub_pipeline = Pipeline(stages=[tokenizer, hashingTF, lr])
> sub_pipeline2 = Pipeline(stages=[tokenizer, hashingTF])
> paramGrid = ParamGridBuilder() \
> .addGrid(lr.regParam, [0.1, 0.01]) \
> .build()
> pipeline_cv = CrossValidator(estimator=lr,
> estimatorParamMaps=paramGrid,
> evaluator=BinaryClassificationEvaluator(),
> numFolds=2)
> cvPath = "/tmp/cv"
> pipeline_cv.write().overwrite().save(cvPath)
> model = pipeline_cv.fit(sub_pipeline2.fit(df).transform(df))
> model.write().overwrite().save(cvPath)
> pipeline_cv2 = CrossValidator(estimator=sub_pipeline,
> estimatorParamMaps=paramGrid,
> evaluator=BinaryClassificationEvaluator(),
> numFolds=2)
> cvPath = "/tmp/cv2"
> model2 = pipeline_cv2.fit(df).bestModel
> model2.write().overwrite().save(cvPath)
> pipeline_cv2.write().overwrite().save(cvPath)
> {code}
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