[SPARK-17197][ML][PYSPARK] PySpark LiR/LoR supports tree aggregation level configurable.

## What changes were proposed in this pull request?
[SPARK-17090](https://issues.apache.org/jira/browse/SPARK-17090) makes tree aggregation level in LiR/LoR configurable, this PR makes PySpark support this function.

## How was this patch tested?
Since ```aggregationDepth``` is an expert param, I'm not prefer to test it in doctest which is also used for example. Here is the offline test result:
![image](https://cloud.githubusercontent.com/assets/1962026/17879457/f83d7760-68a6-11e6-9936-d0a884d5d6ec.png)

Author: Yanbo Liang <[email protected]>

Closes apache#14766 from yanboliang/spark-17197.

Author: yanboliang

python/pyspark/ml/classification.py

@@ -64,7 +64,7 @@ def numClasses(self):
 class LogisticRegression(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredictionCol, HasMaxIter,
                          HasRegParam, HasTol, HasProbabilityCol, HasRawPredictionCol,
                          HasElasticNetParam, HasFitIntercept, HasStandardization, HasThresholds,
-                         HasWeightCol, JavaMLWritable, JavaMLReadable):
+                         HasWeightCol, HasAggregationDepth, JavaMLWritable, JavaMLReadable):
     """
     Logistic regression.
     Currently, this class only supports binary classification.
@@ -121,12 +121,14 @@ class LogisticRegression(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredicti
     def __init__(self, featuresCol="features", labelCol="label", predictionCol="prediction",
                  maxIter=100, regParam=0.0, elasticNetParam=0.0, tol=1e-6, fitIntercept=True,
                  threshold=0.5, thresholds=None, probabilityCol="probability",
-                 rawPredictionCol="rawPrediction", standardization=True, weightCol=None):
+                 rawPredictionCol="rawPrediction", standardization=True, weightCol=None,
+                 aggregationDepth=2):
         """
         __init__(self, featuresCol="features", labelCol="label", predictionCol="prediction", \
                  maxIter=100, regParam=0.0, elasticNetParam=0.0, tol=1e-6, fitIntercept=True, \
                  threshold=0.5, thresholds=None, probabilityCol="probability", \
-                 rawPredictionCol="rawPrediction", standardization=True, weightCol=None)
+                 rawPredictionCol="rawPrediction", standardization=True, weightCol=None, \
+                 aggregationDepth=2)
         If the threshold and thresholds Params are both set, they must be equivalent.
         """
         super(LogisticRegression, self).__init__()
@@ -142,12 +144,14 @@ def __init__(self, featuresCol="features", labelCol="label", predictionCol="pred
     def setParams(self, featuresCol="features", labelCol="label", predictionCol="prediction",
                   maxIter=100, regParam=0.0, elasticNetParam=0.0, tol=1e-6, fitIntercept=True,
                   threshold=0.5, thresholds=None, probabilityCol="probability",
-                  rawPredictionCol="rawPrediction", standardization=True, weightCol=None):
+                  rawPredictionCol="rawPrediction", standardization=True, weightCol=None,
+                  aggregationDepth=2):
         """
         setParams(self, featuresCol="features", labelCol="label", predictionCol="prediction", \
                   maxIter=100, regParam=0.0, elasticNetParam=0.0, tol=1e-6, fitIntercept=True, \
                   threshold=0.5, thresholds=None, probabilityCol="probability", \
-                  rawPredictionCol="rawPrediction", standardization=True, weightCol=None)
+                  rawPredictionCol="rawPrediction", standardization=True, weightCol=None, \
+                  aggregationDepth=2)
         Sets params for logistic regression.
         If the threshold and thresholds Params are both set, they must be equivalent.
         """

python/pyspark/ml/param/_shared_params_code_gen.py

@@ -147,7 +147,9 @@ def get$Name(self):
         ("solver", "the solver algorithm for optimization. If this is not set or empty, " +
          "default value is 'auto'.", "'auto'", "TypeConverters.toString"),
         ("varianceCol", "column name for the biased sample variance of prediction.",
-         None, "TypeConverters.toString")]
+         None, "TypeConverters.toString"),
+        ("aggregationDepth", "suggested depth for treeAggregate (>= 2).", "2",
+         "TypeConverters.toInt")]
 
     code = []
     for name, doc, defaultValueStr, typeConverter in shared:

python/pyspark/ml/param/shared.py

@@ -560,6 +560,30 @@ def getVarianceCol(self):
         return self.getOrDefault(self.varianceCol)
 
 
+class HasAggregationDepth(Params):
+    """
+    Mixin for param aggregationDepth: suggested depth for treeAggregate (>= 2).
+    """
+
+    aggregationDepth = Param(Params._dummy(), "aggregationDepth", "suggested depth for treeAggregate (>= 2).", typeConverter=TypeConverters.toInt)
+
+    def __init__(self):
+        super(HasAggregationDepth, self).__init__()
+        self._setDefault(aggregationDepth=2)
+
+    def setAggregationDepth(self, value):
+        """
+        Sets the value of :py:attr:`aggregationDepth`.
+        """
+        return self._set(aggregationDepth=value)
+
+    def getAggregationDepth(self):
+        """
+        Gets the value of aggregationDepth or its default value.
+        """
+        return self.getOrDefault(self.aggregationDepth)
+
+
 class DecisionTreeParams(Params):
     """
     Mixin for Decision Tree parameters.

python/pyspark/ml/regression.py

@@ -39,7 +39,8 @@
 @inherit_doc
 class LinearRegression(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredictionCol, HasMaxIter,
                        HasRegParam, HasTol, HasElasticNetParam, HasFitIntercept,
-                       HasStandardization, HasSolver, HasWeightCol, JavaMLWritable, JavaMLReadable):
+                       HasStandardization, HasSolver, HasWeightCol, HasAggregationDepth,
+                       JavaMLWritable, JavaMLReadable):
     """
     Linear regression.
 
@@ -97,11 +98,11 @@ class LinearRegression(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPrediction
     @keyword_only
     def __init__(self, featuresCol="features", labelCol="label", predictionCol="prediction",
                  maxIter=100, regParam=0.0, elasticNetParam=0.0, tol=1e-6, fitIntercept=True,
-                 standardization=True, solver="auto", weightCol=None):
+                 standardization=True, solver="auto", weightCol=None, aggregationDepth=2):
         """
         __init__(self, featuresCol="features", labelCol="label", predictionCol="prediction", \
                  maxIter=100, regParam=0.0, elasticNetParam=0.0, tol=1e-6, fitIntercept=True, \
-                 standardization=True, solver="auto", weightCol=None)
+                 standardization=True, solver="auto", weightCol=None, aggregationDepth=2)
         """
         super(LinearRegression, self).__init__()
         self._java_obj = self._new_java_obj(
@@ -114,11 +115,11 @@ def __init__(self, featuresCol="features", labelCol="label", predictionCol="pred
     @since("1.4.0")
     def setParams(self, featuresCol="features", labelCol="label", predictionCol="prediction",
                   maxIter=100, regParam=0.0, elasticNetParam=0.0, tol=1e-6, fitIntercept=True,
-                  standardization=True, solver="auto", weightCol=None):
+                  standardization=True, solver="auto", weightCol=None, aggregationDepth=2):
         """
         setParams(self, featuresCol="features", labelCol="label", predictionCol="prediction", \
                   maxIter=100, regParam=0.0, elasticNetParam=0.0, tol=1e-6, fitIntercept=True, \
-                  standardization=True, solver="auto", weightCol=None)
+                  standardization=True, solver="auto", weightCol=None, aggregationDepth=2)
         Sets params for linear regression.
         """
         kwargs = self.setParams._input_kwargs