[SPARK-14374][ML][PYSPARK] PySpark ml GBTClassifier, Regressor support export/import

## What changes were proposed in this pull request?
PySpark ml GBTClassifier, Regressor support export/import.

## How was this patch tested?
Doc test.

cc jkbradley

Author: Yanbo Liang <ybliang8@gmail.com>

Closes #12383 from yanboliang/spark-14374.

2 files changed, 30 insertions, 4 deletions

diff --git a/python/pyspark/ml/classification.py b/python/pyspark/ml/classification.py
index 922f8069fa..6ef119a426 100644
--- a/python/pyspark/ml/classification.py
+++ b/python/pyspark/ml/classification.py
@@ -739,7 +739,8 @@ class RandomForestClassificationModel(TreeEnsembleModels, JavaMLWritable, JavaML
 
 @inherit_doc
 class GBTClassifier(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredictionCol, HasMaxIter,
-                    GBTParams, HasCheckpointInterval, HasStepSize, HasSeed):
+                    GBTParams, HasCheckpointInterval, HasStepSize, HasSeed, JavaMLWritable,
+                    JavaMLReadable):
     """
     `http://en.wikipedia.org/wiki/Gradient_boosting Gradient-Boosted Trees (GBTs)`
     learning algorithm for classification.
@@ -767,6 +768,18 @@ class GBTClassifier(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredictionCol
     >>> test1 = sqlContext.createDataFrame([(Vectors.sparse(1, [0], [1.0]),)], ["features"])
     >>> model.transform(test1).head().prediction
     1.0
+    >>> gbtc_path = temp_path + "gbtc"
+    >>> gbt.save(gbtc_path)
+    >>> gbt2 = GBTClassifier.load(gbtc_path)
+    >>> gbt2.getMaxDepth()
+    2
+    >>> model_path = temp_path + "gbtc_model"
+    >>> model.save(model_path)
+    >>> model2 = GBTClassificationModel.load(model_path)
+    >>> model.featureImportances == model2.featureImportances
+    True
+    >>> model.treeWeights == model2.treeWeights
+    True
 
     .. versionadded:: 1.4.0
     """
@@ -831,7 +844,7 @@ class GBTClassifier(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredictionCol
         return self.getOrDefault(self.lossType)
 
 
-class GBTClassificationModel(TreeEnsembleModels):
+class GBTClassificationModel(TreeEnsembleModels, JavaMLWritable, JavaMLReadable):
     """
     Model fitted by GBTClassifier.
 
diff --git a/python/pyspark/ml/regression.py b/python/pyspark/ml/regression.py
index c064fe500c..3c7852526a 100644
--- a/python/pyspark/ml/regression.py
+++ b/python/pyspark/ml/regression.py
@@ -902,7 +902,8 @@ class RandomForestRegressionModel(TreeEnsembleModels, JavaMLWritable, JavaMLRead
 
 @inherit_doc
 class GBTRegressor(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredictionCol, HasMaxIter,
-                   GBTParams, HasCheckpointInterval, HasStepSize, HasSeed):
+                   GBTParams, HasCheckpointInterval, HasStepSize, HasSeed, JavaMLWritable,
+                   JavaMLReadable):
     """
     `http://en.wikipedia.org/wiki/Gradient_boosting Gradient-Boosted Trees (GBTs)`
     learning algorithm for regression.
@@ -925,6 +926,18 @@ class GBTRegressor(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredictionCol,
     >>> test1 = sqlContext.createDataFrame([(Vectors.sparse(1, [0], [1.0]),)], ["features"])
     >>> model.transform(test1).head().prediction
     1.0
+    >>> gbtr_path = temp_path + "gbtr"
+    >>> gbt.save(gbtr_path)
+    >>> gbt2 = GBTRegressor.load(gbtr_path)
+    >>> gbt2.getMaxDepth()
+    2
+    >>> model_path = temp_path + "gbtr_model"
+    >>> model.save(model_path)
+    >>> model2 = GBTRegressionModel.load(model_path)
+    >>> model.featureImportances == model2.featureImportances
+    True
+    >>> model.treeWeights == model2.treeWeights
+    True
 
     .. versionadded:: 1.4.0
     """
@@ -989,7 +1002,7 @@ class GBTRegressor(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredictionCol,
         return self.getOrDefault(self.lossType)
 
 
-class GBTRegressionModel(TreeEnsembleModels):
+class GBTRegressionModel(TreeEnsembleModels, JavaMLWritable, JavaMLReadable):
     """
     Model fitted by GBTRegressor.