[SPARK-14931][ML][PYTHON] Mismatched default values between pipelines in Spark and PySpark - update

## What changes were proposed in this pull request?

This PR is an update for [https://github.com/apache/spark/pull/12738] which:
* Adds a generic unit test for JavaParams wrappers in pyspark.ml for checking default Param values vs. the defaults in the Scala side
* Various fixes for bugs found
  * This includes changing classes taking weightCol to treat unset and empty String Param values the same way.

Defaults changed:
* Scala
 * LogisticRegression: weightCol defaults to not set (instead of empty string)
 * StringIndexer: labels default to not set (instead of empty array)
 * GeneralizedLinearRegression:
   * maxIter always defaults to 25 (simpler than defaulting to 25 for a particular solver)
   * weightCol defaults to not set (instead of empty string)
 * LinearRegression: weightCol defaults to not set (instead of empty string)
* Python
 * MultilayerPerceptron: layers default to not set (instead of [1,1])
 * ChiSqSelector: numTopFeatures defaults to 50 (instead of not set)

## How was this patch tested?

Generic unit test.  Manually tested that unit test by changing defaults and verifying that broke the test.

Author: Joseph K. Bradley <joseph@databricks.com>
Author: yinxusen <yinxusen@gmail.com>

Closes #12816 from jkbradley/yinxusen-SPARK-14931.

1 files changed, 1 insertions, 0 deletions

diff --git a/python/pyspark/ml/feature.py b/python/pyspark/ml/feature.py
index 1b059a7199..b95d288198 100644
--- a/python/pyspark/ml/feature.py
+++ b/python/pyspark/ml/feature.py
@@ -2617,6 +2617,7 @@ class ChiSqSelector(JavaEstimator, HasFeaturesCol, HasOutputCol, HasLabelCol, Ja
         """
         super(ChiSqSelector, self).__init__()
         self._java_obj = self._new_java_obj("org.apache.spark.ml.feature.ChiSqSelector", self.uid)
+        self._setDefault(numTopFeatures=50)
         kwargs = self.__init__._input_kwargs
         self.setParams(**kwargs)