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From "Apache Spark (JIRA)" <j...@apache.org>
Subject [jira] [Assigned] (SPARK-25124) VectorSizeHint.size is buggy, breaking streaming pipeline
Date Fri, 17 Aug 2018 18:25:00 GMT

     [ https://issues.apache.org/jira/browse/SPARK-25124?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]

Apache Spark reassigned SPARK-25124:
------------------------------------

    Assignee: Apache Spark

> VectorSizeHint.size is buggy, breaking streaming pipeline
> ---------------------------------------------------------
>
>                 Key: SPARK-25124
>                 URL: https://issues.apache.org/jira/browse/SPARK-25124
>             Project: Spark
>          Issue Type: Bug
>          Components: ML
>    Affects Versions: 2.3.1
>            Reporter: Timothy Hunter
>            Assignee: Apache Spark
>            Priority: Major
>              Labels: beginner, starter
>
> Currently, when using {{VectorSizeHint().setSize(3)}} in an ML pipeline, transforming
a stream will return a nondescript exception about the stream not started. At core are the
following bugs that {{setSize}} and {{getSize}} do not {{return}} values but {{None}}:
> https://github.com/apache/spark/blob/master/python/pyspark/ml/feature.py#L3846
> How to reproduce, using the example in the doc:
> {code}
> from pyspark.ml.linalg import Vectors
> from pyspark.ml import Pipeline, PipelineModel
> from pyspark.ml.feature import VectorAssembler, VectorSizeHint
> data = [(Vectors.dense([1., 2., 3.]), 4.)]
> df = spark.createDataFrame(data, ["vector", "float"])
> sizeHint = VectorSizeHint(inputCol="vector", handleInvalid="skip").setSize(3) # Will
fail
> vecAssembler = VectorAssembler(inputCols=["vector", "float"], outputCol="assembled")
> pipeline = Pipeline(stages=[sizeHint, vecAssembler])
> pipelineModel = pipeline.fit(df)
> pipelineModel.transform(df).head().assembled
> {code}



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