[SPARK-10286][ML][PYSPARK][DOCS] Add @since annotation to pyspark.ml.param and pyspark.ml.*

Author: lihao <lihaowhu@gmail.com>

Closes #9275 from lidinghao/SPARK-10286.

1 files changed, 30 insertions, 0 deletions

diff --git a/python/pyspark/ml/pipeline.py b/python/pyspark/ml/pipeline.py
index 312a8502b3..4475451edb 100644
--- a/python/pyspark/ml/pipeline.py
+++ b/python/pyspark/ml/pipeline.py
@@ -17,6 +17,7 @@
 
 from abc import ABCMeta, abstractmethod
 
+from pyspark import since
 from pyspark.ml.param import Param, Params
 from pyspark.ml.util import keyword_only
 from pyspark.mllib.common import inherit_doc
@@ -26,6 +27,8 @@ from pyspark.mllib.common import inherit_doc
 class Estimator(Params):
     """
     Abstract class for estimators that fit models to data.
+
+    .. versionadded:: 1.3.0
     """
 
     __metaclass__ = ABCMeta
@@ -42,6 +45,7 @@ class Estimator(Params):
         """
         raise NotImplementedError()
 
+    @since("1.3.0")
     def fit(self, dataset, params=None):
         """
         Fits a model to the input dataset with optional parameters.
@@ -73,6 +77,8 @@ class Transformer(Params):
     """
     Abstract class for transformers that transform one dataset into
     another.
+
+    .. versionadded:: 1.3.0
     """
 
     __metaclass__ = ABCMeta
@@ -88,6 +94,7 @@ class Transformer(Params):
         """
         raise NotImplementedError()
 
+    @since("1.3.0")
     def transform(self, dataset, params=None):
         """
         Transforms the input dataset with optional parameters.
@@ -113,6 +120,8 @@ class Transformer(Params):
 class Model(Transformer):
     """
     Abstract class for models that are fitted by estimators.
+
+    .. versionadded:: 1.4.0
     """
 
     __metaclass__ = ABCMeta
@@ -136,6 +145,8 @@ class Pipeline(Estimator):
     consists of fitted models and transformers, corresponding to the
     pipeline stages. If there are no stages, the pipeline acts as an
     identity transformer.
+
+    .. versionadded:: 1.3.0
     """
 
     @keyword_only
@@ -151,6 +162,7 @@ class Pipeline(Estimator):
         kwargs = self.__init__._input_kwargs
         self.setParams(**kwargs)
 
+    @since("1.3.0")
     def setStages(self, value):
         """
         Set pipeline stages.
@@ -161,6 +173,7 @@ class Pipeline(Estimator):
         self._paramMap[self.stages] = value
         return self
 
+    @since("1.3.0")
     def getStages(self):
         """
         Get pipeline stages.
@@ -169,6 +182,7 @@ class Pipeline(Estimator):
             return self._paramMap[self.stages]
 
     @keyword_only
+    @since("1.3.0")
     def setParams(self, stages=None):
         """
         setParams(self, stages=None)
@@ -204,7 +218,14 @@ class Pipeline(Estimator):
                 transformers.append(stage)
         return PipelineModel(transformers)
 
+    @since("1.4.0")
     def copy(self, extra=None):
+        """
+        Creates a copy of this instance.
+
+        :param extra: extra parameters
+        :returns: new instance
+        """
         if extra is None:
             extra = dict()
         that = Params.copy(self, extra)
@@ -216,6 +237,8 @@ class Pipeline(Estimator):
 class PipelineModel(Model):
     """
     Represents a compiled pipeline with transformers and fitted models.
+
+    .. versionadded:: 1.3.0
     """
 
     def __init__(self, stages):
@@ -227,7 +250,14 @@ class PipelineModel(Model):
             dataset = t.transform(dataset)
         return dataset
 
+    @since("1.4.0")
     def copy(self, extra=None):
+        """
+        Creates a copy of this instance.
+
+        :param extra: extra parameters
+        :returns: new instance
+        """
         if extra is None:
             extra = dict()
         stages = [stage.copy(extra) for stage in self.stages]