[SPARK-15442][ML][PYSPARK] Add 'relativeError' param to PySpark QuantileDiscretizer

This PR adds the `relativeError` param to PySpark's `QuantileDiscretizer` to match Scala.

Also cleaned up a duplication of `numBuckets` where the param is both a class and instance attribute (I removed the instance attr to match the style of params throughout `ml`).

Finally, cleaned up the docs for `QuantileDiscretizer` to reflect that it now uses `approxQuantile`.

## How was this patch tested?

A little doctest and built API docs locally to check HTML doc generation.

Author: Nick Pentreath <nickp@za.ibm.com>

Closes #13228 from MLnick/SPARK-15442-py-relerror-param.

1 files changed, 8 insertions, 5 deletions

diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/QuantileDiscretizer.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/QuantileDiscretizer.scala
index 5a6daa06ef..61483590cd 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/feature/QuantileDiscretizer.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/feature/QuantileDiscretizer.scala
@@ -50,13 +50,13 @@ private[feature] trait QuantileDiscretizerBase extends Params
   /**
    * Relative error (see documentation for
    * [[org.apache.spark.sql.DataFrameStatFunctions.approxQuantile approxQuantile]] for description)
-   * Must be a number in [0, 1].
+   * Must be in the range [0, 1].
    * default: 0.001
    * @group param
    */
   val relativeError = new DoubleParam(this, "relativeError", "The relative target precision " +
-    "for approxQuantile",
-    ParamValidators.inRange(0.0, 1.0))
+    "for the approximate quantile algorithm used to generate buckets. " +
+    "Must be in the range [0, 1].", ParamValidators.inRange(0.0, 1.0))
   setDefault(relativeError -> 0.001)
 
   /** @group getParam */
@@ -66,8 +66,11 @@ private[feature] trait QuantileDiscretizerBase extends Params
 /**
  * :: Experimental ::
  * `QuantileDiscretizer` takes a column with continuous features and outputs a column with binned
- * categorical features. The bin ranges are chosen by taking a sample of the data and dividing it
- * into roughly equal parts. The lower and upper bin bounds will be -Infinity and +Infinity,
+ * categorical features. The number of bins can be set using the `numBuckets` parameter.
+ * The bin ranges are chosen using an approximate algorithm (see the documentation for
+ * [[org.apache.spark.sql.DataFrameStatFunctions.approxQuantile approxQuantile]]
+ * for a detailed description). The precision of the approximation can be controlled with the
+ * `relativeError` parameter. The lower and upper bin bounds will be `-Infinity` and `+Infinity`,
  * covering all real values.
  */
 @Experimental