[SPARK-14714][ML][PYTHON] Fixed issues with non-kwarg typeConverter arg for Param constructor

## What changes were proposed in this pull request?

PySpark Param constructors need to pass the TypeConverter argument by name, partly to make sure it is not mistaken for the expectedType arg and partly because we will remove the expectedType arg in 2.1. In several places, this is not being done correctly.

This PR changes all usages in pyspark/ml/ to keyword args.

## How was this patch tested?

Existing unit tests.  I will not test type conversion for every Param unless we really think it necessary.

Also, if you start the PySpark shell and import classes (e.g., pyspark.ml.feature.StandardScaler), then you no longer get this warning:
```
/Users/josephkb/spark/python/pyspark/ml/param/__init__.py:58: UserWarning: expectedType is deprecated and will be removed in 2.1. Use typeConverter instead, as a keyword argument.
  "Use typeConverter instead, as a keyword argument.")
```
That warning came from the typeConverter argument being passes as the expectedType arg by mistake.

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

Closes #12480 from jkbradley/typeconverter-fix.

1 files changed, 7 insertions, 5 deletions

diff --git a/python/pyspark/ml/recommendation.py b/python/pyspark/ml/recommendation.py
index 9c38f2431b..9d7f22a66f 100644
--- a/python/pyspark/ml/recommendation.py
+++ b/python/pyspark/ml/recommendation.py
@@ -107,16 +107,18 @@ class ALS(JavaEstimator, HasCheckpointInterval, HasMaxIter, HasPredictionCol, Ha
     numItemBlocks = Param(Params._dummy(), "numItemBlocks", "number of item blocks",
                           typeConverter=TypeConverters.toInt)
     implicitPrefs = Param(Params._dummy(), "implicitPrefs", "whether to use implicit preference",
-                          TypeConverters.toBoolean)
+                          typeConverter=TypeConverters.toBoolean)
     alpha = Param(Params._dummy(), "alpha", "alpha for implicit preference",
                   typeConverter=TypeConverters.toFloat)
-    userCol = Param(Params._dummy(), "userCol", "column name for user ids", TypeConverters.toString)
-    itemCol = Param(Params._dummy(), "itemCol", "column name for item ids", TypeConverters.toString)
+    userCol = Param(Params._dummy(), "userCol", "column name for user ids",
+                    typeConverter=TypeConverters.toString)
+    itemCol = Param(Params._dummy(), "itemCol", "column name for item ids",
+                    typeConverter=TypeConverters.toString)
     ratingCol = Param(Params._dummy(), "ratingCol", "column name for ratings",
-                      TypeConverters.toString)
+                      typeConverter=TypeConverters.toString)
     nonnegative = Param(Params._dummy(), "nonnegative",
                         "whether to use nonnegative constraint for least squares",
-                        TypeConverters.toBoolean)
+                        typeConverter=TypeConverters.toBoolean)
 
     @keyword_only
     def __init__(self, rank=10, maxIter=10, regParam=0.1, numUserBlocks=10, numItemBlocks=10,