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Maciej Szymkiewicz updated SPARK-30504: --------------------------------------- Description: Current behaviour {code:python} from pyspark.ml.classification import LogisticRegression, OneVsRest, OneVsRestModel from pyspark.ml.linalg import DenseVector df = spark.createDataFrame([(0, 1, DenseVector([1.0, 0.0])), (0, 1, DenseVector([1.0, 0.0]))], ("label", "w", "features")) ovr = OneVsRest(classifier=LogisticRegression()).setWeightCol("w") ovrm = ovr.fit(df) ovr.getWeightCol() ## 'w' ovrm.getWeightCol() ## 'w' ovr.write().overwrite().save("/tmp/ovr") ovr_ = OneVsRest.load("/tmp/ovr") ovr_.getWeightCol() ## KeyError ## ... ## KeyError: Param(parent='OneVsRest_5145d56b6bd1', name='weightCol', doc='weight column name. ...) ovrm.write().overwrite().save("/tmp/ovrm") ovrm_ = OneVsRestModel.load("/tmp/ovrm") ovrm_ .getWeightCol() ## KeyError ## ... ## KeyError: Param(parent='OneVsRestModel_598c6d900fad', name='weightCol', doc='weight column name ... {code} Expected behaviour: {{OneVsRest}} and {{OneVsRestModel}} loaded from disk should have {{weightCol}}. was: Current behaviour {code:python} from pyspark.ml.classification import LogisticRegression, OneVsRest, OneVsRestModel from pyspark.ml.linalg import DenseVector df = spark.createDataFrame([(0, 1, DenseVector([1.0, 0.0])), (0, 1, DenseVector([1.0, 0.0]))], ("label", "w", "features")) ovr = OneVsRest(classifier=LogisticRegression()).setWeightCol("w") ovrm = ovr.fit(df) ovr.getWeightCol() ## 'w' ovrm.getWeightCol() ## 'w' ovr.write().overwrite().save("/tmp/ovr") ovr_ = OneVsRest.load("/tmp/ovr") ovr_.getWeightCol() ## KeyError ## ... ## KeyError: Param(parent='OneVsRest_5145d56b6bd1', name='weightCol', doc='weight column name. ...) ovrm.write().overwrite().save("/tmp/ovrm") ovrm_ = OneVsRestModel.load("/tmp/ovrm") ovrm_ .getWeightCol() ## KeyError ## ... ## KeyError: Param(parent='OneVsRestModel_598c6d900fad', name='weightCol', doc='weight column name ... {code} Expected behaviour: {{OneVsRest}} and {{ OneVsRestModel}} loaded from disk should have {{weightCol}}. > OneVsRest and OneVsRestModel _from_java and _to_java should handle weightCol > ---------------------------------------------------------------------------- > > Key: SPARK-30504 > URL: https://issues.apache.org/jira/browse/SPARK-30504 > Project: Spark > Issue Type: Bug > Components: ML, PySpark > Affects Versions: 3.0.0 > Reporter: Maciej Szymkiewicz > Priority: Major > > Current behaviour > {code:python} > from pyspark.ml.classification import LogisticRegression, OneVsRest, > OneVsRestModel > from pyspark.ml.linalg import DenseVector > df = spark.createDataFrame([(0, 1, DenseVector([1.0, 0.0])), (0, 1, > DenseVector([1.0, 0.0]))], ("label", "w", "features")) > ovr = OneVsRest(classifier=LogisticRegression()).setWeightCol("w") > ovrm = ovr.fit(df) > ovr.getWeightCol() > ## 'w' > ovrm.getWeightCol() > ## 'w' > ovr.write().overwrite().save("/tmp/ovr") > ovr_ = OneVsRest.load("/tmp/ovr") > ovr_.getWeightCol() > ## KeyError > ## ... > ## KeyError: Param(parent='OneVsRest_5145d56b6bd1', name='weightCol', > doc='weight column name. ...) > ovrm.write().overwrite().save("/tmp/ovrm") > ovrm_ = OneVsRestModel.load("/tmp/ovrm") > ovrm_ .getWeightCol() > ## KeyError > ## ... > ## KeyError: Param(parent='OneVsRestModel_598c6d900fad', name='weightCol', > doc='weight column name ... > {code} > Expected behaviour: > {{OneVsRest}} and {{OneVsRestModel}} loaded from disk should have > {{weightCol}}. -- This message was sent by Atlassian Jira (v8.3.4#803005) --------------------------------------------------------------------- To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org