[SPARK-29464][PYTHON][ML] PySpark ML should expose Params.clear() to unset a user supplied Param #26130
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…unset a user supplied Param
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@BryanCutler @srowen |
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Test build #112120 has finished for PR 26130 at commit
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@huaxingao I think there is a problem when the Java object has the param set, then it is cleared in Python but not Java. For example: In [4]: >>> from pyspark.ml.linalg import Vectors
...: >>> df = spark.createDataFrame([(Vectors.dense([1.0]),), (Vectors.dense([2.0]),)], ["a"])
...: >>> maScaler = MaxAbsScaler(inputCol="a", outputCol="scaled")
...: >>> model = maScaler.fit(df)
...: >>> model.setOutputCol("scaledOutput")
Out[4]: MaxAbsScaler_f91118f1dd81
In [7]: model.clear(model.outputCol)
In [9]: model.getOutputCol()
Out[9]: 'MaxAbsScaler_f91118f1dd81__output'
In [11]: model.transform(df).show()
+-----+------+
| a|scaled|
+-----+------+
|[1.0]| [0.5]|
|[2.0]| [1.0]|
+-----+------+ |
BryanCutler
requested changes
Oct 15, 2019
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Test build #112128 has finished for PR 26130 at commit
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srowen
approved these changes
Oct 16, 2019
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retest this please |
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Test build #4900 has finished for PR 26130 at commit
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BryanCutler
reviewed
Oct 17, 2019
| self.assertEqual(model.getOutputCol(), "scaled") | ||
| model.clear(model.outputCol) | ||
| self.assertFalse(model.isSet(model.outputCol)) | ||
| self.assertEqual(model.getOutputCol()[:12], 'MaxAbsScaler') |
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Instead, could you check that outputCol is equal to the default value?
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Nvm, I guess there isn't a way to get the default value directly. This is fine then.
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merged to master, thanks @huaxingao ! |
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Thank you all for the help! |
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What changes were proposed in this pull request?
change PySpark ml
Params._cleartoParams.clearWhy are the changes needed?
PySpark ML currently has a private _clear() method that will unset a param. This should be made public to match the Scala API and give users a way to unset a user supplied param.
Does this PR introduce any user-facing change?
Yes. PySpark ml
Params._clear--->Params.clearHow was this patch tested?
Add test.