[SPARK-10024][PYSPARK] Python API RF and GBT related params clear up

implement {RandomForest, GBT, TreeEnsemble, TreeClassifier, TreeRegressor}Params for Python API
in pyspark/ml/{classification, regression}.py

Author: vectorijk <jiangkai@gmail.com>

Closes #9233 from vectorijk/spark-10024.

2 files changed, 168 insertions, 338 deletions

diff --git a/python/pyspark/ml/classification.py b/python/pyspark/ml/classification.py
index 88815e561f..4cbe7fbd48 100644
--- a/python/pyspark/ml/classification.py
+++ b/python/pyspark/ml/classification.py
@@ -19,7 +19,7 @@ from pyspark.ml.util import keyword_only
 from pyspark.ml.wrapper import JavaEstimator, JavaModel
 from pyspark.ml.param.shared import *
 from pyspark.ml.regression import (
-    RandomForestParams, DecisionTreeModel, TreeEnsembleModels)
+    RandomForestParams, TreeEnsembleParams, DecisionTreeModel, TreeEnsembleModels)
 from pyspark.mllib.common import inherit_doc
 
 
@@ -205,8 +205,34 @@ class TreeClassifierParams(object):
     """
     supportedImpurities = ["entropy", "gini"]
 
+    # a placeholder to make it appear in the generated doc
+    impurity = Param(Params._dummy(), "impurity",
+                     "Criterion used for information gain calculation (case-insensitive). " +
+                     "Supported options: " +
+                     ", ".join(supportedImpurities))
+
+    def __init__(self):
+        super(TreeClassifierParams, self).__init__()
+        #: param for Criterion used for information gain calculation (case-insensitive).
+        self.impurity = Param(self, "impurity", "Criterion used for information " +
+                              "gain calculation (case-insensitive). Supported options: " +
+                              ", ".join(self.supportedImpurities))
+
+    def setImpurity(self, value):
+        """
+        Sets the value of :py:attr:`impurity`.
+        """
+        self._paramMap[self.impurity] = value
+        return self
 
-class GBTParams(object):
+    def getImpurity(self):
+        """
+        Gets the value of impurity or its default value.
+        """
+        return self.getOrDefault(self.impurity)
+
+
+class GBTParams(TreeEnsembleParams):
     """
     Private class to track supported GBT params.
     """
@@ -216,7 +242,7 @@ class GBTParams(object):
 @inherit_doc
 class DecisionTreeClassifier(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredictionCol,
                              HasProbabilityCol, HasRawPredictionCol, DecisionTreeParams,
-                             HasCheckpointInterval):
+                             TreeClassifierParams, HasCheckpointInterval):
     """
     `http://en.wikipedia.org/wiki/Decision_tree_learning Decision tree`
     learning algorithm for classification.
@@ -250,11 +276,6 @@ class DecisionTreeClassifier(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPred
     1.0
     """
 
-    # a placeholder to make it appear in the generated doc
-    impurity = Param(Params._dummy(), "impurity",
-                     "Criterion used for information gain calculation (case-insensitive). " +
-                     "Supported options: " + ", ".join(TreeClassifierParams.supportedImpurities))
-
     @keyword_only
     def __init__(self, featuresCol="features", labelCol="label", predictionCol="prediction",
                  probabilityCol="probability", rawPredictionCol="rawPrediction",
@@ -269,11 +290,6 @@ class DecisionTreeClassifier(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPred
         super(DecisionTreeClassifier, self).__init__()
         self._java_obj = self._new_java_obj(
             "org.apache.spark.ml.classification.DecisionTreeClassifier", self.uid)
-        #: param for Criterion used for information gain calculation (case-insensitive).
-        self.impurity = \
-            Param(self, "impurity",
-                  "Criterion used for information gain calculation (case-insensitive). " +
-                  "Supported options: " + ", ".join(TreeClassifierParams.supportedImpurities))
         self._setDefault(maxDepth=5, maxBins=32, minInstancesPerNode=1, minInfoGain=0.0,
                          maxMemoryInMB=256, cacheNodeIds=False, checkpointInterval=10,
                          impurity="gini")
@@ -299,19 +315,6 @@ class DecisionTreeClassifier(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPred
     def _create_model(self, java_model):
         return DecisionTreeClassificationModel(java_model)
 
-    def setImpurity(self, value):
-        """
-        Sets the value of :py:attr:`impurity`.
-        """
-        self._paramMap[self.impurity] = value
-        return self
-
-    def getImpurity(self):
-        """
-        Gets the value of impurity or its default value.
-        """
-        return self.getOrDefault(self.impurity)
-
 
 @inherit_doc
 class DecisionTreeClassificationModel(DecisionTreeModel):
@@ -323,7 +326,7 @@ class DecisionTreeClassificationModel(DecisionTreeModel):
 @inherit_doc
 class RandomForestClassifier(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredictionCol, HasSeed,
                              HasRawPredictionCol, HasProbabilityCol,
-                             DecisionTreeParams, HasCheckpointInterval):
+                             RandomForestParams, TreeClassifierParams, HasCheckpointInterval):
     """
     `http://en.wikipedia.org/wiki/Random_forest  Random Forest`
     learning algorithm for classification.
@@ -357,19 +360,6 @@ class RandomForestClassifier(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPred
     1.0
     """
 
-    # a placeholder to make it appear in the generated doc
-    impurity = Param(Params._dummy(), "impurity",
-                     "Criterion used for information gain calculation (case-insensitive). " +
-                     "Supported options: " + ", ".join(TreeClassifierParams.supportedImpurities))
-    subsamplingRate = Param(Params._dummy(), "subsamplingRate",
-                            "Fraction of the training data used for learning each decision tree, " +
-                            "in range (0, 1].")
-    numTrees = Param(Params._dummy(), "numTrees", "Number of trees to train (>= 1)")
-    featureSubsetStrategy = \
-        Param(Params._dummy(), "featureSubsetStrategy",
-              "The number of features to consider for splits at each tree node. Supported " +
-              "options: " + ", ".join(RandomForestParams.supportedFeatureSubsetStrategies))
-
     @keyword_only
     def __init__(self, featuresCol="features", labelCol="label", predictionCol="prediction",
                  probabilityCol="probability", rawPredictionCol="rawPrediction",
@@ -386,23 +376,6 @@ class RandomForestClassifier(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPred
         super(RandomForestClassifier, self).__init__()
         self._java_obj = self._new_java_obj(
             "org.apache.spark.ml.classification.RandomForestClassifier", self.uid)
-        #: param for Criterion used for information gain calculation (case-insensitive).
-        self.impurity = \
-            Param(self, "impurity",
-                  "Criterion used for information gain calculation (case-insensitive). " +
-                  "Supported options: " + ", ".join(TreeClassifierParams.supportedImpurities))
-        #: param for Fraction of the training data used for learning each decision tree,
-        #  in range (0, 1]
-        self.subsamplingRate = Param(self, "subsamplingRate",
-                                     "Fraction of the training data used for learning each " +
-                                     "decision tree, in range (0, 1].")
-        #: param for Number of trees to train (>= 1)
-        self.numTrees = Param(self, "numTrees", "Number of trees to train (>= 1)")
-        #: param for The number of features to consider for splits at each tree node
-        self.featureSubsetStrategy = \
-            Param(self, "featureSubsetStrategy",
-                  "The number of features to consider for splits at each tree node. Supported " +
-                  "options: " + ", ".join(RandomForestParams.supportedFeatureSubsetStrategies))
         self._setDefault(maxDepth=5, maxBins=32, minInstancesPerNode=1, minInfoGain=0.0,
                          maxMemoryInMB=256, cacheNodeIds=False, checkpointInterval=10, seed=None,
                          impurity="gini", numTrees=20, featureSubsetStrategy="auto")
@@ -429,58 +402,6 @@ class RandomForestClassifier(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPred
     def _create_model(self, java_model):
         return RandomForestClassificationModel(java_model)
 
-    def setImpurity(self, value):
-        """
-        Sets the value of :py:attr:`impurity`.
-        """
-        self._paramMap[self.impurity] = value
-        return self
-
-    def getImpurity(self):
-        """
-        Gets the value of impurity or its default value.
-        """
-        return self.getOrDefault(self.impurity)
-
-    def setSubsamplingRate(self, value):
-        """
-        Sets the value of :py:attr:`subsamplingRate`.
-        """
-        self._paramMap[self.subsamplingRate] = value
-        return self
-
-    def getSubsamplingRate(self):
-        """
-        Gets the value of subsamplingRate or its default value.
-        """
-        return self.getOrDefault(self.subsamplingRate)
-
-    def setNumTrees(self, value):
-        """
-        Sets the value of :py:attr:`numTrees`.
-        """
-        self._paramMap[self.numTrees] = value
-        return self
-
-    def getNumTrees(self):
-        """
-        Gets the value of numTrees or its default value.
-        """
-        return self.getOrDefault(self.numTrees)
-
-    def setFeatureSubsetStrategy(self, value):
-        """
-        Sets the value of :py:attr:`featureSubsetStrategy`.
-        """
-        self._paramMap[self.featureSubsetStrategy] = value
-        return self
-
-    def getFeatureSubsetStrategy(self):
-        """
-        Gets the value of featureSubsetStrategy or its default value.
-        """
-        return self.getOrDefault(self.featureSubsetStrategy)
-
 
 class RandomForestClassificationModel(TreeEnsembleModels):
     """
@@ -490,7 +411,7 @@ class RandomForestClassificationModel(TreeEnsembleModels):
 
 @inherit_doc
 class GBTClassifier(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredictionCol, HasMaxIter,
-                    DecisionTreeParams, HasCheckpointInterval):
+                    GBTParams, HasCheckpointInterval, HasStepSize, HasSeed):
     """
     `http://en.wikipedia.org/wiki/Gradient_boosting Gradient-Boosted Trees (GBTs)`
     learning algorithm for classification.
@@ -522,12 +443,6 @@ class GBTClassifier(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredictionCol
     lossType = Param(Params._dummy(), "lossType",
                      "Loss function which GBT tries to minimize (case-insensitive). " +
                      "Supported options: " + ", ".join(GBTParams.supportedLossTypes))
-    subsamplingRate = Param(Params._dummy(), "subsamplingRate",
-                            "Fraction of the training data used for learning each decision tree, " +
-                            "in range (0, 1].")
-    stepSize = Param(Params._dummy(), "stepSize",
-                     "Step size (a.k.a. learning rate) in interval (0, 1] for shrinking the " +
-                     "contribution of each estimator")
 
     @keyword_only
     def __init__(self, featuresCol="features", labelCol="label", predictionCol="prediction",
@@ -547,15 +462,6 @@ class GBTClassifier(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredictionCol
         self.lossType = Param(self, "lossType",
                               "Loss function which GBT tries to minimize (case-insensitive). " +
                               "Supported options: " + ", ".join(GBTParams.supportedLossTypes))
-        #: Fraction of the training data used for learning each decision tree, in range (0, 1].
-        self.subsamplingRate = Param(self, "subsamplingRate",
-                                     "Fraction of the training data used for learning each " +
-                                     "decision tree, in range (0, 1].")
-        #: Step size (a.k.a. learning rate) in interval (0, 1] for shrinking the contribution of
-        #  each estimator
-        self.stepSize = Param(self, "stepSize",
-                              "Step size (a.k.a. learning rate) in interval (0, 1] for shrinking " +
-                              "the contribution of each estimator")
         self._setDefault(maxDepth=5, maxBins=32, minInstancesPerNode=1, minInfoGain=0.0,
                          maxMemoryInMB=256, cacheNodeIds=False, checkpointInterval=10,
                          lossType="logistic", maxIter=20, stepSize=0.1)
@@ -593,32 +499,6 @@ class GBTClassifier(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredictionCol
         """
         return self.getOrDefault(self.lossType)
 
-    def setSubsamplingRate(self, value):
-        """
-        Sets the value of :py:attr:`subsamplingRate`.
-        """
-        self._paramMap[self.subsamplingRate] = value
-        return self
-
-    def getSubsamplingRate(self):
-        """
-        Gets the value of subsamplingRate or its default value.
-        """
-        return self.getOrDefault(self.subsamplingRate)
-
-    def setStepSize(self, value):
-        """
-        Sets the value of :py:attr:`stepSize`.
-        """
-        self._paramMap[self.stepSize] = value
-        return self
-
-    def getStepSize(self):
-        """
-        Gets the value of stepSize or its default value.
-        """
-        return self.getOrDefault(self.stepSize)
-
 
 class GBTClassificationModel(TreeEnsembleModels):
     """
diff --git a/python/pyspark/ml/regression.py b/python/pyspark/ml/regression.py
index eb5f4bd6d7..eeb18b3e9d 100644
--- a/python/pyspark/ml/regression.py
+++ b/python/pyspark/ml/regression.py
@@ -260,21 +260,127 @@ class IsotonicRegressionModel(JavaModel):
         return self._call_java("predictions")
 
 
-class TreeRegressorParams(object):
+class TreeEnsembleParams(DecisionTreeParams):
+    """
+    Mixin for Decision Tree-based ensemble algorithms parameters.
+    """
+
+    # a placeholder to make it appear in the generated doc
+    subsamplingRate = Param(Params._dummy(), "subsamplingRate", "Fraction of the training data " +
+                            "used for learning each decision tree, in range (0, 1].")
+
+    def __init__(self):
+        super(TreeEnsembleParams, self).__init__()
+        #: param for Fraction of the training data, in range (0, 1].
+        self.subsamplingRate = Param(self, "subsamplingRate", "Fraction of the training data " +
+                                     "used for learning each decision tree, in range (0, 1].")
+
+    @since("1.4.0")
+    def setSubsamplingRate(self, value):
+        """
+        Sets the value of :py:attr:`subsamplingRate`.
+        """
+        self._paramMap[self.subsamplingRate] = value
+        return self
+
+    @since("1.4.0")
+    def getSubsamplingRate(self):
+        """
+        Gets the value of subsamplingRate or its default value.
+        """
+        return self.getOrDefault(self.subsamplingRate)
+
+
+class TreeRegressorParams(Params):
     """
     Private class to track supported impurity measures.
     """
+
     supportedImpurities = ["variance"]
+    # a placeholder to make it appear in the generated doc
+    impurity = Param(Params._dummy(), "impurity",
+                     "Criterion used for information gain calculation (case-insensitive). " +
+                     "Supported options: " +
+                     ", ".join(supportedImpurities))
 
+    def __init__(self):
+        super(TreeRegressorParams, self).__init__()
+        #: param for Criterion used for information gain calculation (case-insensitive).
+        self.impurity = Param(self, "impurity", "Criterion used for information " +
+                              "gain calculation (case-insensitive). Supported options: " +
+                              ", ".join(self.supportedImpurities))
 
-class RandomForestParams(object):
+    @since("1.4.0")
+    def setImpurity(self, value):
+        """
+        Sets the value of :py:attr:`impurity`.
+        """
+        self._paramMap[self.impurity] = value
+        return self
+
+    @since("1.4.0")
+    def getImpurity(self):
+        """
+        Gets the value of impurity or its default value.
+        """
+        return self.getOrDefault(self.impurity)
+
+
+class RandomForestParams(TreeEnsembleParams):
     """
     Private class to track supported random forest parameters.
     """
+
     supportedFeatureSubsetStrategies = ["auto", "all", "onethird", "sqrt", "log2"]
+    # a placeholder to make it appear in the generated doc
+    numTrees = Param(Params._dummy(), "numTrees", "Number of trees to train (>= 1).")
+    featureSubsetStrategy = \
+        Param(Params._dummy(), "featureSubsetStrategy",
+              "The number of features to consider for splits at each tree node. Supported " +
+              "options: " + ", ".join(supportedFeatureSubsetStrategies))
+
+    def __init__(self):
+        super(RandomForestParams, self).__init__()
+        #: param for Number of trees to train (>= 1).
+        self.numTrees = Param(self, "numTrees", "Number of trees to train (>= 1).")
+        #: param for The number of features to consider for splits at each tree node.
+        self.featureSubsetStrategy = \
+            Param(self, "featureSubsetStrategy",
+                  "The number of features to consider for splits at each tree node. Supported " +
+                  "options: " + ", ".join(self.supportedFeatureSubsetStrategies))
+
+    @since("1.4.0")
+    def setNumTrees(self, value):
+        """
+        Sets the value of :py:attr:`numTrees`.
+        """
+        self._paramMap[self.numTrees] = value
+        return self
+
+    @since("1.4.0")
+    def getNumTrees(self):
+        """
+        Gets the value of numTrees or its default value.
+        """
+        return self.getOrDefault(self.numTrees)
 
+    @since("1.4.0")
+    def setFeatureSubsetStrategy(self, value):
+        """
+        Sets the value of :py:attr:`featureSubsetStrategy`.
+        """
+        self._paramMap[self.featureSubsetStrategy] = value
+        return self
 
-class GBTParams(object):
+    @since("1.4.0")
+    def getFeatureSubsetStrategy(self):
+        """
+        Gets the value of featureSubsetStrategy or its default value.
+        """
+        return self.getOrDefault(self.featureSubsetStrategy)
+
+
+class GBTParams(TreeEnsembleParams):
     """
     Private class to track supported GBT params.
     """
@@ -283,7 +389,7 @@ class GBTParams(object):
 
 @inherit_doc
 class DecisionTreeRegressor(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredictionCol,
-                            DecisionTreeParams, HasCheckpointInterval):
+                            DecisionTreeParams, TreeRegressorParams, HasCheckpointInterval):
     """
     `http://en.wikipedia.org/wiki/Decision_tree_learning Decision tree`
     learning algorithm for regression.
@@ -309,11 +415,6 @@ class DecisionTreeRegressor(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredi
     .. versionadded:: 1.4.0
     """
 
-    # a placeholder to make it appear in the generated doc
-    impurity = Param(Params._dummy(), "impurity",
-                     "Criterion used for information gain calculation (case-insensitive). " +
-                     "Supported options: " + ", ".join(TreeRegressorParams.supportedImpurities))
-
     @keyword_only
     def __init__(self, featuresCol="features", labelCol="label", predictionCol="prediction",
                  maxDepth=5, maxBins=32, minInstancesPerNode=1, minInfoGain=0.0,
@@ -326,11 +427,6 @@ class DecisionTreeRegressor(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredi
         super(DecisionTreeRegressor, self).__init__()
         self._java_obj = self._new_java_obj(
             "org.apache.spark.ml.regression.DecisionTreeRegressor", self.uid)
-        #: param for Criterion used for information gain calculation (case-insensitive).
-        self.impurity = \
-            Param(self, "impurity",
-                  "Criterion used for information gain calculation (case-insensitive). " +
-                  "Supported options: " + ", ".join(TreeRegressorParams.supportedImpurities))
         self._setDefault(maxDepth=5, maxBins=32, minInstancesPerNode=1, minInfoGain=0.0,
                          maxMemoryInMB=256, cacheNodeIds=False, checkpointInterval=10,
                          impurity="variance")
@@ -355,21 +451,6 @@ class DecisionTreeRegressor(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredi
     def _create_model(self, java_model):
         return DecisionTreeRegressionModel(java_model)
 
-    @since("1.4.0")
-    def setImpurity(self, value):
-        """
-        Sets the value of :py:attr:`impurity`.
-        """
-        self._paramMap[self.impurity] = value
-        return self
-
-    @since("1.4.0")
-    def getImpurity(self):
-        """
-        Gets the value of impurity or its default value.
-        """
-        return self.getOrDefault(self.impurity)
-
 
 @inherit_doc
 class DecisionTreeModel(JavaModel):
@@ -422,7 +503,7 @@ class DecisionTreeRegressionModel(DecisionTreeModel):
 
 @inherit_doc
 class RandomForestRegressor(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredictionCol, HasSeed,
-                            DecisionTreeParams, HasCheckpointInterval):
+                            RandomForestParams, TreeRegressorParams, HasCheckpointInterval):
     """
     `http://en.wikipedia.org/wiki/Random_forest  Random Forest`
     learning algorithm for regression.
@@ -447,54 +528,26 @@ class RandomForestRegressor(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredi
     .. versionadded:: 1.4.0
     """
 
-    # a placeholder to make it appear in the generated doc
-    impurity = Param(Params._dummy(), "impurity",
-                     "Criterion used for information gain calculation (case-insensitive). " +
-                     "Supported options: " + ", ".join(TreeRegressorParams.supportedImpurities))
-    subsamplingRate = Param(Params._dummy(), "subsamplingRate",
-                            "Fraction of the training data used for learning each decision tree, " +
-                            "in range (0, 1].")
-    numTrees = Param(Params._dummy(), "numTrees", "Number of trees to train (>= 1)")
-    featureSubsetStrategy = \
-        Param(Params._dummy(), "featureSubsetStrategy",
-              "The number of features to consider for splits at each tree node. Supported " +
-              "options: " + ", ".join(RandomForestParams.supportedFeatureSubsetStrategies))
-
     @keyword_only
     def __init__(self, featuresCol="features", labelCol="label", predictionCol="prediction",
                  maxDepth=5, maxBins=32, minInstancesPerNode=1, minInfoGain=0.0,
-                 maxMemoryInMB=256, cacheNodeIds=False, checkpointInterval=10, impurity="variance",
-                 numTrees=20, featureSubsetStrategy="auto", seed=None):
+                 maxMemoryInMB=256, cacheNodeIds=False, checkpointInterval=10,
+                 impurity="variance", subsamplingRate=1.0, seed=None, numTrees=20,
+                 featureSubsetStrategy="auto"):
         """
         __init__(self, featuresCol="features", labelCol="label", predictionCol="prediction", \
                  maxDepth=5, maxBins=32, minInstancesPerNode=1, minInfoGain=0.0, \
                  maxMemoryInMB=256, cacheNodeIds=False, checkpointInterval=10, \
-                 impurity="variance", numTrees=20, \
-                 featureSubsetStrategy="auto", seed=None)
+                 impurity="variance", subsamplingRate=1.0, seed=None, numTrees=20, \
+                 featureSubsetStrategy="auto")
         """
         super(RandomForestRegressor, self).__init__()
         self._java_obj = self._new_java_obj(
             "org.apache.spark.ml.regression.RandomForestRegressor", self.uid)
-        #: param for Criterion used for information gain calculation (case-insensitive).
-        self.impurity = \
-            Param(self, "impurity",
-                  "Criterion used for information gain calculation (case-insensitive). " +
-                  "Supported options: " + ", ".join(TreeRegressorParams.supportedImpurities))
-        #: param for Fraction of the training data used for learning each decision tree,
-        #  in range (0, 1]
-        self.subsamplingRate = Param(self, "subsamplingRate",
-                                     "Fraction of the training data used for learning each " +
-                                     "decision tree, in range (0, 1].")
-        #: param for Number of trees to train (>= 1)
-        self.numTrees = Param(self, "numTrees", "Number of trees to train (>= 1)")
-        #: param for The number of features to consider for splits at each tree node
-        self.featureSubsetStrategy = \
-            Param(self, "featureSubsetStrategy",
-                  "The number of features to consider for splits at each tree node. Supported " +
-                  "options: " + ", ".join(RandomForestParams.supportedFeatureSubsetStrategies))
         self._setDefault(maxDepth=5, maxBins=32, minInstancesPerNode=1, minInfoGain=0.0,
-                         maxMemoryInMB=256, cacheNodeIds=False, checkpointInterval=10, seed=None,
-                         impurity="variance", numTrees=20, featureSubsetStrategy="auto")
+                         maxMemoryInMB=256, cacheNodeIds=False, checkpointInterval=10,
+                         impurity="variance", subsamplingRate=1.0, seed=None, numTrees=20,
+                         featureSubsetStrategy="auto")
         kwargs = self.__init__._input_kwargs
         self.setParams(**kwargs)
 
@@ -502,13 +555,15 @@ class RandomForestRegressor(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredi
     @since("1.4.0")
     def setParams(self, featuresCol="features", labelCol="label", predictionCol="prediction",
                   maxDepth=5, maxBins=32, minInstancesPerNode=1, minInfoGain=0.0,
-                  maxMemoryInMB=256, cacheNodeIds=False, checkpointInterval=10, seed=None,
-                  impurity="variance", numTrees=20, featureSubsetStrategy="auto"):
+                  maxMemoryInMB=256, cacheNodeIds=False, checkpointInterval=10,
+                  impurity="variance", subsamplingRate=1.0, seed=None, numTrees=20,
+                  featureSubsetStrategy="auto"):
         """
         setParams(self, featuresCol="features", labelCol="label", predictionCol="prediction", \
                   maxDepth=5, maxBins=32, minInstancesPerNode=1, minInfoGain=0.0, \
-                  maxMemoryInMB=256, cacheNodeIds=False, checkpointInterval=10, seed=None, \
-                  impurity="variance", numTrees=20, featureSubsetStrategy="auto")
+                  maxMemoryInMB=256, cacheNodeIds=False, checkpointInterval=10, \
+                  impurity="variance", subsamplingRate=1.0, seed=None, numTrees=20, \
+                  featureSubsetStrategy="auto")
         Sets params for linear regression.
         """
         kwargs = self.setParams._input_kwargs
@@ -517,66 +572,6 @@ class RandomForestRegressor(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredi
     def _create_model(self, java_model):
         return RandomForestRegressionModel(java_model)
 
-    @since("1.4.0")
-    def setImpurity(self, value):
-        """
-        Sets the value of :py:attr:`impurity`.
-        """
-        self._paramMap[self.impurity] = value
-        return self
-
-    @since("1.4.0")
-    def getImpurity(self):
-        """
-        Gets the value of impurity or its default value.
-        """
-        return self.getOrDefault(self.impurity)
-
-    @since("1.4.0")
-    def setSubsamplingRate(self, value):
-        """
-        Sets the value of :py:attr:`subsamplingRate`.
-        """
-        self._paramMap[self.subsamplingRate] = value
-        return self
-
-    @since("1.4.0")
-    def getSubsamplingRate(self):
-        """
-        Gets the value of subsamplingRate or its default value.
-        """
-        return self.getOrDefault(self.subsamplingRate)
-
-    @since("1.4.0")
-    def setNumTrees(self, value):
-        """
-        Sets the value of :py:attr:`numTrees`.
-        """
-        self._paramMap[self.numTrees] = value
-        return self
-
-    @since("1.4.0")
-    def getNumTrees(self):
-        """
-        Gets the value of numTrees or its default value.
-        """
-        return self.getOrDefault(self.numTrees)
-
-    @since("1.4.0")
-    def setFeatureSubsetStrategy(self, value):
-        """
-        Sets the value of :py:attr:`featureSubsetStrategy`.
-        """
-        self._paramMap[self.featureSubsetStrategy] = value
-        return self
-
-    @since("1.4.0")
-    def getFeatureSubsetStrategy(self):
-        """
-        Gets the value of featureSubsetStrategy or its default value.
-        """
-        return self.getOrDefault(self.featureSubsetStrategy)
-
 
 class RandomForestRegressionModel(TreeEnsembleModels):
     """
@@ -588,7 +583,7 @@ class RandomForestRegressionModel(TreeEnsembleModels):
 
 @inherit_doc
 class GBTRegressor(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredictionCol, HasMaxIter,
-                   DecisionTreeParams, HasCheckpointInterval):
+                   GBTParams, HasCheckpointInterval, HasStepSize, HasSeed):
     """
     `http://en.wikipedia.org/wiki/Gradient_boosting Gradient-Boosted Trees (GBTs)`
     learning algorithm for regression.
@@ -617,23 +612,17 @@ class GBTRegressor(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredictionCol,
     lossType = Param(Params._dummy(), "lossType",
                      "Loss function which GBT tries to minimize (case-insensitive). " +
                      "Supported options: " + ", ".join(GBTParams.supportedLossTypes))
-    subsamplingRate = Param(Params._dummy(), "subsamplingRate",
-                            "Fraction of the training data used for learning each decision tree, " +
-                            "in range (0, 1].")
-    stepSize = Param(Params._dummy(), "stepSize",
-                     "Step size (a.k.a. learning rate) in interval (0, 1] for shrinking the " +
-                     "contribution of each estimator")
 
     @keyword_only
     def __init__(self, featuresCol="features", labelCol="label", predictionCol="prediction",
                  maxDepth=5, maxBins=32, minInstancesPerNode=1, minInfoGain=0.0,
-                 maxMemoryInMB=256, cacheNodeIds=False, checkpointInterval=10, lossType="squared",
-                 maxIter=20, stepSize=0.1):
+                 maxMemoryInMB=256, cacheNodeIds=False, subsamplingRate=1.0,
+                 checkpointInterval=10, lossType="squared", maxIter=20, stepSize=0.1):
         """
         __init__(self, featuresCol="features", labelCol="label", predictionCol="prediction", \
                  maxDepth=5, maxBins=32, minInstancesPerNode=1, minInfoGain=0.0, \
-                 maxMemoryInMB=256, cacheNodeIds=False, checkpointInterval=10, \
-                 lossType="squared", maxIter=20, stepSize=0.1)
+                 maxMemoryInMB=256, cacheNodeIds=False, subsamplingRate=1.0, \
+                 checkpointInterval=10, lossType="squared", maxIter=20, stepSize=0.1)
         """
         super(GBTRegressor, self).__init__()
         self._java_obj = self._new_java_obj("org.apache.spark.ml.regression.GBTRegressor", self.uid)
@@ -641,18 +630,9 @@ class GBTRegressor(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredictionCol,
         self.lossType = Param(self, "lossType",
                               "Loss function which GBT tries to minimize (case-insensitive). " +
                               "Supported options: " + ", ".join(GBTParams.supportedLossTypes))
-        #: Fraction of the training data used for learning each decision tree, in range (0, 1].
-        self.subsamplingRate = Param(self, "subsamplingRate",
-                                     "Fraction of the training data used for learning each " +
-                                     "decision tree, in range (0, 1].")
-        #: Step size (a.k.a. learning rate) in interval (0, 1] for shrinking the contribution of
-        #  each estimator
-        self.stepSize = Param(self, "stepSize",
-                              "Step size (a.k.a. learning rate) in interval (0, 1] for shrinking " +
-                              "the contribution of each estimator")
         self._setDefault(maxDepth=5, maxBins=32, minInstancesPerNode=1, minInfoGain=0.0,
-                         maxMemoryInMB=256, cacheNodeIds=False, checkpointInterval=10,
-                         lossType="squared", maxIter=20, stepSize=0.1)
+                         maxMemoryInMB=256, cacheNodeIds=False, subsamplingRate=1.0,
+                         checkpointInterval=10, lossType="squared", maxIter=20, stepSize=0.1)
         kwargs = self.__init__._input_kwargs
         self.setParams(**kwargs)
 
@@ -660,13 +640,13 @@ class GBTRegressor(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredictionCol,
     @since("1.4.0")
     def setParams(self, featuresCol="features", labelCol="label", predictionCol="prediction",
                   maxDepth=5, maxBins=32, minInstancesPerNode=1, minInfoGain=0.0,
-                  maxMemoryInMB=256, cacheNodeIds=False, checkpointInterval=10,
-                  lossType="squared", maxIter=20, stepSize=0.1):
+                  maxMemoryInMB=256, cacheNodeIds=False, subsamplingRate=1.0,
+                  checkpointInterval=10, lossType="squared", maxIter=20, stepSize=0.1):
         """
         setParams(self, featuresCol="features", labelCol="label", predictionCol="prediction", \
                   maxDepth=5, maxBins=32, minInstancesPerNode=1, minInfoGain=0.0, \
-                  maxMemoryInMB=256, cacheNodeIds=False, checkpointInterval=10, \
-                  lossType="squared", maxIter=20, stepSize=0.1)
+                  maxMemoryInMB=256, cacheNodeIds=False, subsamplingRate=1.0, \
+                  checkpointInterval=10, lossType="squared", maxIter=20, stepSize=0.1)
         Sets params for Gradient Boosted Tree Regression.
         """
         kwargs = self.setParams._input_kwargs
@@ -690,36 +670,6 @@ class GBTRegressor(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredictionCol,
         """
         return self.getOrDefault(self.lossType)
 
-    @since("1.4.0")
-    def setSubsamplingRate(self, value):
-        """
-        Sets the value of :py:attr:`subsamplingRate`.
-        """
-        self._paramMap[self.subsamplingRate] = value
-        return self
-
-    @since("1.4.0")
-    def getSubsamplingRate(self):
-        """
-        Gets the value of subsamplingRate or its default value.
-        """
-        return self.getOrDefault(self.subsamplingRate)
-
-    @since("1.4.0")
-    def setStepSize(self, value):
-        """
-        Sets the value of :py:attr:`stepSize`.
-        """
-        self._paramMap[self.stepSize] = value
-        return self
-
-    @since("1.4.0")
-    def getStepSize(self):
-        """
-        Gets the value of stepSize or its default value.
-        """
-        return self.getOrDefault(self.stepSize)
-
 
 class GBTRegressionModel(TreeEnsembleModels):
     """
@@ -783,7 +733,7 @@ class AFTSurvivalRegression(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredi
         __init__(self, featuresCol="features", labelCol="label", predictionCol="prediction", \
                  fitIntercept=True, maxIter=100, tol=1E-6, censorCol="censor", \
                  quantileProbabilities=[0.01, 0.05, 0.1, 0.25, 0.5, 0.75, 0.9, 0.95, 0.99], \
-                 quantilesCol=None):
+                 quantilesCol=None)
         """
         super(AFTSurvivalRegression, self).__init__()
         self._java_obj = self._new_java_obj(