[SPARK-14373][PYSPARK] PySpark RandomForestClassifier, Regressor support export/import

## What changes were proposed in this pull request?
supporting `RandomForest{Classifier, Regressor}` save/load for Python API.
[JIRA](https://issues.apache.org/jira/browse/SPARK-14373)
## How was this patch tested?
doctest

Author: Kai Jiang <jiangkai@gmail.com>

Closes #12238 from vectorijk/spark-14373.

python/pyspark/ml/classification.py

@@ -621,7 +621,8 @@ class DecisionTreeClassificationModel(DecisionTreeModel, JavaMLWritable, JavaMLR
 @inherit_doc
 class RandomForestClassifier(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredictionCol, HasSeed,
                              HasRawPredictionCol, HasProbabilityCol,
-                             RandomForestParams, TreeClassifierParams, HasCheckpointInterval):
+                             RandomForestParams, TreeClassifierParams, HasCheckpointInterval,
+                             JavaMLWritable, JavaMLReadable):
     """
     `http://en.wikipedia.org/wiki/Random_forest  Random Forest`
     learning algorithm for classification.
@@ -655,6 +656,16 @@ class RandomForestClassifier(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPred
     >>> test1 = sqlContext.createDataFrame([(Vectors.sparse(1, [0], [1.0]),)], ["features"])
     >>> model.transform(test1).head().prediction
     1.0
+    >>> rfc_path = temp_path + "/rfc"
+    >>> rf.save(rfc_path)
+    >>> rf2 = RandomForestClassifier.load(rfc_path)
+    >>> rf2.getNumTrees()
+    3
+    >>> model_path = temp_path + "/rfc_model"
+    >>> model.save(model_path)
+    >>> model2 = RandomForestClassificationModel.load(model_path)
+    >>> model.featureImportances == model2.featureImportances
+    True
 
     .. versionadded:: 1.4.0
     """
@@ -703,7 +714,7 @@ class RandomForestClassifier(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPred
         return RandomForestClassificationModel(java_model)
 
 
-class RandomForestClassificationModel(TreeEnsembleModels):
+class RandomForestClassificationModel(TreeEnsembleModels, JavaMLWritable, JavaMLReadable):
     """
     Model fitted by RandomForestClassifier.
 

python/pyspark/ml/regression.py

@@ -782,7 +782,8 @@ class DecisionTreeRegressionModel(DecisionTreeModel, JavaMLWritable, JavaMLReada
 
 @inherit_doc
 class RandomForestRegressor(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredictionCol, HasSeed,
-                            RandomForestParams, TreeRegressorParams, HasCheckpointInterval):
+                            RandomForestParams, TreeRegressorParams, HasCheckpointInterval,
+                            JavaMLWritable, JavaMLReadable):
     """
     `http://en.wikipedia.org/wiki/Random_forest  Random Forest`
     learning algorithm for regression.
@@ -805,6 +806,16 @@ class RandomForestRegressor(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredi
     >>> test1 = sqlContext.createDataFrame([(Vectors.sparse(1, [0], [1.0]),)], ["features"])
     >>> model.transform(test1).head().prediction
     0.5
+    >>> rfr_path = temp_path + "/rfr"
+    >>> rf.save(rfr_path)
+    >>> rf2 = RandomForestRegressor.load(rfr_path)
+    >>> rf2.getNumTrees()
+    2
+    >>> model_path = temp_path + "/rfr_model"
+    >>> model.save(model_path)
+    >>> model2 = RandomForestRegressionModel.load(model_path)
+    >>> model.featureImportances == model2.featureImportances
+    True
 
     .. versionadded:: 1.4.0
     """
@@ -854,7 +865,7 @@ class RandomForestRegressor(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredi
         return RandomForestRegressionModel(java_model)
 
 
-class RandomForestRegressionModel(TreeEnsembleModels):
+class RandomForestRegressionModel(TreeEnsembleModels, JavaMLWritable, JavaMLReadable):
     """
     Model fitted by RandomForestRegressor.