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[SPARK-19714][ML] Bucketizer.handleInvalid docs improved #17169

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[SPARK-19714][ML] Bucketizer.handleInvalid docs improved #17169

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13e0245
Bucketizer.handleInvalid docs improved
wojtek-szymanski Mar 5, 2017
ca6e957
Bucketizer docs update - nulls not supported in handleInvalid
wojtek-szymanski Mar 6, 2017
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13 changes: 8 additions & 5 deletions mllib/src/main/scala/org/apache/spark/ml/feature/Bucketizer.scala
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Original file line number Diff line number Diff line change
Expand Up @@ -78,16 +78,19 @@ final class Bucketizer @Since("1.4.0") (@Since("1.4.0") override val uid: String
def setOutputCol(value: String): this.type = set(outputCol, value)

/**
* Param for how to handle invalid entries. Options are 'skip' (filter out rows with
* invalid values), 'error' (throw an error), or 'keep' (keep invalid values in a special
* additional bucket).
* Param for how to handle invalid entries containing NaN values.
* Values outside the splits will always be treated as errors.
* Options are 'skip' (filter out rows with invalid values), 'error' (throw an error),
* or 'keep' (keep invalid values in a special additional bucket).
* Default: "error"
* @group param
*/
// TODO: SPARK-18619 Make Bucketizer inherit from HasHandleInvalid.
@Since("2.1.0")
val handleInvalid: Param[String] = new Param[String](this, "handleInvalid", "how to handle " +
"invalid entries. Options are skip (filter out rows with invalid values), " +
val handleInvalid: Param[String] = new Param[String](this, "handleInvalid",
"how to handle invalid entries containing NaN values. " +
"Values outside the splits will always be treated as errors. " +
"Options are skip (filter out rows with invalid values), " +
"error (throw an error), or keep (keep invalid values in a special additional bucket).",
ParamValidators.inArray(Bucketizer.supportedHandleInvalids))

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4 changes: 3 additions & 1 deletion python/pyspark/ml/feature.py
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Expand Up @@ -356,7 +356,9 @@ class Bucketizer(JavaTransformer, HasInputCol, HasOutputCol, JavaMLReadable, Jav
"splits specified will be treated as errors.",
typeConverter=TypeConverters.toListFloat)

handleInvalid = Param(Params._dummy(), "handleInvalid", "how to handle invalid entries. " +
handleInvalid = Param(Params._dummy(), "handleInvalid",
"how to handle invalid entries containing NaN values. " +
"Values outside the splits will always be treated as errors. " +
"Options are 'skip' (filter out rows with invalid values), " +
"'error' (throw an error), or 'keep' (keep invalid values in a special " +
"additional bucket).",
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