[SPARK-10284][ML][PySpark][Docs] Add @SInCE annotation to pyspark.ml.…

…tuning

python/pyspark/ml/tuning.py

@@ -18,6 +18,7 @@
 import itertools
 import numpy as np
 
+from pyspark import since
 from pyspark.ml.param import Params, Param
 from pyspark.ml import Estimator, Model
 from pyspark.ml.util import keyword_only
@@ -47,11 +48,14 @@ class ParamGridBuilder(object):
     True
     >>> all([m in expected for m in output])
     True
+
+    .. addedversion:: 1.4.0
     """
 
     def __init__(self):
         self._param_grid = {}
 
+    @since("1.4.0")
     def addGrid(self, param, values):
         """
         Sets the given parameters in this grid to fixed values.
@@ -60,6 +64,7 @@ def addGrid(self, param, values):
 
         return self
 
+    @since("1.4.0")
     def baseOn(self, *args):
         """
         Sets the given parameters in this grid to fixed values.
@@ -104,6 +109,8 @@ class CrossValidator(Estimator):
     >>> cvModel = cv.fit(dataset)
     >>> evaluator.evaluate(cvModel.transform(dataset))
     0.8333...
+
+    .. addedversion:: 1.4.0
     """
 
     # a placeholder to make it appear in the generated doc
@@ -142,6 +149,7 @@ def __init__(self, estimator=None, estimatorParamMaps=None, evaluator=None, numF
         self._set(**kwargs)
 
     @keyword_only
+    @since("1.4.0")
     def setParams(self, estimator=None, estimatorParamMaps=None, evaluator=None, numFolds=3):
         """
         setParams(self, estimator=None, estimatorParamMaps=None, evaluator=None, numFolds=3):
@@ -150,52 +158,60 @@ def setParams(self, estimator=None, estimatorParamMaps=None, evaluator=None, num
         kwargs = self.setParams._input_kwargs
         return self._set(**kwargs)
 
+    @since("1.4.0")
     def setEstimator(self, value):
         """
         Sets the value of :py:attr:`estimator`.
         """
         self._paramMap[self.estimator] = value
         return self
 
+    @since("1.4.0")
     def getEstimator(self):
         """
         Gets the value of estimator or its default value.
         """
         return self.getOrDefault(self.estimator)
 
+    @since("1.4.0")
     def setEstimatorParamMaps(self, value):
         """
         Sets the value of :py:attr:`estimatorParamMaps`.
         """
         self._paramMap[self.estimatorParamMaps] = value
         return self
 
+    @since("1.4.0")
     def getEstimatorParamMaps(self):
         """
         Gets the value of estimatorParamMaps or its default value.
         """
         return self.getOrDefault(self.estimatorParamMaps)
 
+    @since("1.4.0")
     def setEvaluator(self, value):
         """
         Sets the value of :py:attr:`evaluator`.
         """
         self._paramMap[self.evaluator] = value
         return self
 
+    @since("1.4.0")
     def getEvaluator(self):
         """
         Gets the value of evaluator or its default value.
         """
         return self.getOrDefault(self.evaluator)
 
+    @since("1.4.0")
     def setNumFolds(self, value):
         """
         Sets the value of :py:attr:`numFolds`.
         """
         self._paramMap[self.numFolds] = value
         return self
 
+    @since("1.4.0")
     def getNumFolds(self):
         """
         Gets the value of numFolds or its default value.
@@ -231,7 +247,15 @@ def _fit(self, dataset):
         bestModel = est.fit(dataset, epm[bestIndex])
         return CrossValidatorModel(bestModel)
 
+    @since("1.4.0")
     def copy(self, extra=None):
+        """
+        Creates a copy of this instance with a randomly generated uid
+        and some extra params. This copies creates a deep copy of
+        the embedded paramMap, and copies the embedded and extra parameters over.
+        :param extra: Extra parameters to copy to the new instance
+        :return: Copy of this instance
+        """
         if extra is None:
             extra = dict()
         newCV = Params.copy(self, extra)
@@ -246,6 +270,8 @@ def copy(self, extra=None):
 class CrossValidatorModel(Model):
     """
     Model from k-fold cross validation.
+
+    .. addedversion:: 1.4.0
     """
 
     def __init__(self, bestModel):
@@ -256,6 +282,7 @@ def __init__(self, bestModel):
     def _transform(self, dataset):
         return self.bestModel.transform(dataset)
 
+    @since("1.4.0")
     def copy(self, extra=None):
         """
         Creates a copy of this instance with a randomly generated uid