Github user yanboliang commented on a diff in the pull request:
https://github.com/apache/spark/pull/19204#discussion_r139312034
--- Diff: python/pyspark/ml/evaluation.py ---
@@ -328,6 +329,86 @@ def setParams(self, predictionCol="prediction",
labelCol="label",
kwargs = self._input_kwargs
return self._set(**kwargs)
+
+@inherit_doc
+class ClusteringEvaluator(JavaEvaluator, HasPredictionCol, HasFeaturesCol,
+ JavaMLReadable, JavaMLWritable):
+ """
+ .. note:: Experimental
+
+ Evaluator for Clustering results, which expects two input
+ columns: prediction and features.
+
+ >>> from sklearn import datasets
+ >>> from pyspark.sql.types import *
+ >>> from pyspark.ml.linalg import Vectors, VectorUDT
+ >>> from pyspark.ml.evaluation import ClusteringEvaluator
+ ...
+ >>> iris = datasets.load_iris()
--- End diff --
Please don't involves other libraries if not necessary, here the doc test
is used to show how to use ```ClusteringEvaluator``` to fresh users, so we
should focus on evaluator and keep it as simple as possible. You can refer
other evaluator to construct simple dataset.
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