[SPARK-14812][ML][MLLIB][PYTHON] Experimental, DeveloperApi annotation audit for ML

## What changes were proposed in this pull request?

General decisions to follow, except where noted:
* spark.mllib, pyspark.mllib: Remove all Experimental annotations.  Leave DeveloperApi annotations alone.
* spark.ml, pyspark.ml
** Annotate Estimator-Model pairs of classes and companion objects the same way.
** For all algorithms marked Experimental with Since tag <= 1.6, remove Experimental annotation.
** For all algorithms marked Experimental with Since tag = 2.0, leave Experimental annotation.
* DeveloperApi annotations are left alone, except where noted.
* No changes to which types are sealed.

Exceptions where I am leaving items Experimental in spark.ml, pyspark.ml, mainly because the items are new:
* Model Summary classes
* MLWriter, MLReader, MLWritable, MLReadable
* Evaluator and subclasses: There is discussion of changes around evaluating multiple metrics at once for efficiency.
* RFormula: Its behavior may need to change slightly to match R in edge cases.
* AFTSurvivalRegression
* MultilayerPerceptronClassifier

DeveloperApi changes:
* ml.tree.Node, ml.tree.Split, and subclasses should no longer be DeveloperApi

## How was this patch tested?

N/A

Note to reviewers:
* spark.ml.clustering.LDA underwent significant changes (additional methods), so let me know if you want me to leave it Experimental.
* Be careful to check for cases where a class should no longer be Experimental but has an Experimental method, val, or other feature.  I did not find such cases, but please verify.

Author: Joseph K. Bradley <joseph@databricks.com>

Closes #14147 from jkbradley/experimental-audit.

1 files changed, 0 insertions, 12 deletions

diff --git a/python/pyspark/mllib/tree.py b/python/pyspark/mllib/tree.py
index 8be76fcefe..b3011d42e5 100644
--- a/python/pyspark/mllib/tree.py
+++ b/python/pyspark/mllib/tree.py
@@ -76,8 +76,6 @@ class TreeEnsembleModel(JavaModelWrapper, JavaSaveable):
 
 class DecisionTreeModel(JavaModelWrapper, JavaSaveable, JavaLoader):
     """
-    .. note:: Experimental
-
     A decision tree model for classification or regression.
 
     .. versionadded:: 1.1.0
@@ -130,8 +128,6 @@ class DecisionTreeModel(JavaModelWrapper, JavaSaveable, JavaLoader):
 
 class DecisionTree(object):
     """
-    .. note:: Experimental
-
     Learning algorithm for a decision tree model for classification or
     regression.
 
@@ -283,8 +279,6 @@ class DecisionTree(object):
 @inherit_doc
 class RandomForestModel(TreeEnsembleModel, JavaLoader):
     """
-    .. note:: Experimental
-
     Represents a random forest model.
 
     .. versionadded:: 1.2.0
@@ -297,8 +291,6 @@ class RandomForestModel(TreeEnsembleModel, JavaLoader):
 
 class RandomForest(object):
     """
-    .. note:: Experimental
-
     Learning algorithm for a random forest model for classification or
     regression.
 
@@ -486,8 +478,6 @@ class RandomForest(object):
 @inherit_doc
 class GradientBoostedTreesModel(TreeEnsembleModel, JavaLoader):
     """
-    .. note:: Experimental
-
     Represents a gradient-boosted tree model.
 
     .. versionadded:: 1.3.0
@@ -500,8 +490,6 @@ class GradientBoostedTreesModel(TreeEnsembleModel, JavaLoader):
 
 class GradientBoostedTrees(object):
     """
-    .. note:: Experimental
-
     Learning algorithm for a gradient boosted trees model for
     classification or regression.