[SPARK-20899][PYSPARK] PySpark supports stringIndexerOrderType in RFormula

## What changes were proposed in this pull request?

PySpark supports stringIndexerOrderType in RFormula as in #17967.

## How was this patch tested?
docstring test

Author: actuaryzhang <[email protected]>

Closes #18122 from actuaryzhang/PythonRFormula.

Author: actuaryzhang

python/pyspark/ml/feature.py

@@ -3043,26 +3043,35 @@ class RFormula(JavaEstimator, HasFeaturesCol, HasLabelCol, JavaMLReadable, JavaM
                             "Force to index label whether it is numeric or string",
                             typeConverter=TypeConverters.toBoolean)
 
+    stringIndexerOrderType = Param(Params._dummy(), "stringIndexerOrderType",
+                                   "How to order categories of a string feature column used by " +
+                                   "StringIndexer. The last category after ordering is dropped " +
+                                   "when encoding strings. Supported options: frequencyDesc, " +
+                                   "frequencyAsc, alphabetDesc, alphabetAsc. The default value " +
+                                   "is frequencyDesc. When the ordering is set to alphabetDesc, " +
+                                   "RFormula drops the same category as R when encoding strings.",
+                                   typeConverter=TypeConverters.toString)
+
     @keyword_only
     def __init__(self, formula=None, featuresCol="features", labelCol="label",
-                 forceIndexLabel=False):
+                 forceIndexLabel=False, stringIndexerOrderType="frequencyDesc"):
         """
         __init__(self, formula=None, featuresCol="features", labelCol="label", \
-                 forceIndexLabel=False)
+                 forceIndexLabel=False, stringIndexerOrderType="frequencyDesc")
         """
         super(RFormula, self).__init__()
         self._java_obj = self._new_java_obj("org.apache.spark.ml.feature.RFormula", self.uid)
-        self._setDefault(forceIndexLabel=False)
+        self._setDefault(forceIndexLabel=False, stringIndexerOrderType="frequencyDesc")
         kwargs = self._input_kwargs
         self.setParams(**kwargs)
 
     @keyword_only
     @since("1.5.0")
     def setParams(self, formula=None, featuresCol="features", labelCol="label",
-                  forceIndexLabel=False):
+                  forceIndexLabel=False, stringIndexerOrderType="frequencyDesc"):
         """
         setParams(self, formula=None, featuresCol="features", labelCol="label", \
-                  forceIndexLabel=False)
+                  forceIndexLabel=False, stringIndexerOrderType="frequencyDesc")
         Sets params for RFormula.
         """
         kwargs = self._input_kwargs
@@ -3096,6 +3105,20 @@ def getForceIndexLabel(self):
         """
         return self.getOrDefault(self.forceIndexLabel)
 
+    @since("2.3.0")
+    def setStringIndexerOrderType(self, value):
+        """
+        Sets the value of :py:attr:`stringIndexerOrderType`.
+        """
+        return self._set(stringIndexerOrderType=value)
+
+    @since("2.3.0")
+    def getStringIndexerOrderType(self):
+        """
+        Gets the value of :py:attr:`stringIndexerOrderType` or its default value 'frequencyDesc'.
+        """
+        return self.getOrDefault(self.stringIndexerOrderType)
+
     def _create_model(self, java_model):
         return RFormulaModel(java_model)
 

python/pyspark/ml/tests.py

@@ -538,6 +538,19 @@ def test_rformula_force_index_label(self):
         transformedDF2 = model2.transform(df)
         self.assertEqual(transformedDF2.head().label, 0.0)
 
+    def test_rformula_string_indexer_order_type(self):
+        df = self.spark.createDataFrame([
+            (1.0, 1.0, "a"),
+            (0.0, 2.0, "b"),
+            (1.0, 0.0, "a")], ["y", "x", "s"])
+        rf = RFormula(formula="y ~ x + s", stringIndexerOrderType="alphabetDesc")
+        self.assertEqual(rf.getStringIndexerOrderType(), 'alphabetDesc')
+        transformedDF = rf.fit(df).transform(df)
+        observed = transformedDF.select("features").collect()
+        expected = [[1.0, 0.0], [2.0, 1.0], [0.0, 0.0]]
+        for i in range(0, len(expected)):
+            self.assertTrue(all(observed[i]["features"].toArray() == expected[i]))
+
 
 class HasInducedError(Params):