Github user sethah commented on a diff in the pull request:
https://github.com/apache/spark/pull/16715#discussion_r100422395
--- Diff:
examples/src/main/python/ml/bucketed_random_projection_lsh_example.py ---
@@ -0,0 +1,86 @@
+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements. See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+
+from __future__ import print_function
+
+# $example on$
+from pyspark.ml.feature import BucketedRandomProjectionLSH
+from pyspark.ml.linalg import Vectors
+# $example off$
+from pyspark.sql import SparkSession
+
+"""
+An example demonstrating BucketedRandomProjectionLSH.
+Run with:
+ bin/spark-submit
examples/src/main/python/ml/bucketed_random_projection_lsh_example.py
+"""
+
+if __name__ == "__main__":
+ spark = SparkSession \
+ .builder \
+ .appName("BucketedRandomProjectionLSHExample") \
+ .getOrCreate()
+
+ # $example on$
+ dataA = [(0, Vectors.dense([1.0, 1.0]),),
+ (1, Vectors.dense([1.0, -1.0]),),
+ (2, Vectors.dense([-1.0, -1.0]),),
+ (3, Vectors.dense([-1.0, 1.0]),)]
+ dfA = spark.createDataFrame(dataA, ["id", "features"])
+
+ dataB = [(4, Vectors.dense([1.0, 0.0]),),
+ (5, Vectors.dense([-1.0, 0.0]),),
+ (6, Vectors.dense([0.0, 1.0]),),
+ (7, Vectors.dense([0.0, -1.0]),)]
+ dfB = spark.createDataFrame(dataB, ["id", "features"])
+
+ key = Vectors.dense([1.0, 0.0])
+
+ brp = BucketedRandomProjectionLSH(inputCol="features",
outputCol="hashes", bucketLength=2.0,
+ numHashTables=3)
+ model = brp.fit(dfA)
+
+ # Feature Transformation
+ print("The hashed dataset where hashed values are stored in the column
'values':")
+ model.transform(dfA).show()
+ # Cache the transformed columns
+ transformedA = model.transform(dfA).cache()
+ transformedB = model.transform(dfB).cache()
+
+ # Approximate similarity join
+ print("Approximately joining dfA and dfB on distance smaller than
1.5:")
+ model.approxSimilarityJoin(dfA, dfB, 1.5)\
+ .select("datasetA.id", "datasetB.id", "distCol").show()
+ print("Joining cached datasets to avoid recomputing the hash values:")
+ model.approxSimilarityJoin(transformedA, transformedB, 1.5)\
+ .select("datasetA.id", "datasetB.id", "distCol").show()
+
+ # Self Join
+ print("Approximately self join of dfB on distance smaller than 2.5:")
+ model.approxSimilarityJoin(dfA, dfA, 2.5).filter("datasetA.id <
datasetB.id")\
--- End diff --
I really do not think this is necessary. If you can do an approx similarity
join between two datasets it will hopefully be obvious that you can do a self
join. This is also confusing, since the name of the input is `dfA` in both
cases, but the filter and select logic reference a `datasetB` and a `datasetA`.
At first I thought it was wrong, then I realized that those are the default
names, but that's not clear unless you read the API docs.
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