#

# 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.

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# limitations under the License.

#

"""

An example demonstrating MinHashLSH.

Run with:

bin/spark-submit examples/src/main/python/ml/min_hash_lsh_example.py

"""

# $example on$

from pyspark.ml.feature import MinHashLSH

from pyspark.ml.linalg import Vectors

from pyspark.sql.functions import col

# $example off$

from pyspark.sql import SparkSession

if __name__ == "__main__":

spark = SparkSession \

.builder \

.appName("MinHashLSHExample") \

.getOrCreate()

# $example on$

dataA = [(0, Vectors.sparse(6, [0, 1, 2], [1.0, 1.0, 1.0]),),

(1, Vectors.sparse(6, [2, 3, 4], [1.0, 1.0, 1.0]),),

(2, Vectors.sparse(6, [0, 2, 4], [1.0, 1.0, 1.0]),)]

dfA = spark.createDataFrame(dataA, ["id", "features"])

dataB = [(3, Vectors.sparse(6, [1, 3, 5], [1.0, 1.0, 1.0]),),

(4, Vectors.sparse(6, [2, 3, 5], [1.0, 1.0, 1.0]),),

(5, Vectors.sparse(6, [1, 2, 4], [1.0, 1.0, 1.0]),)]

dfB = spark.createDataFrame(dataB, ["id", "features"])

key = Vectors.sparse(6, [1, 3], [1.0, 1.0])

mh = MinHashLSH(inputCol="features", outputCol="hashes", numHashTables=5)

model = mh.fit(dfA)

# Feature Transformation

print("The hashed dataset where hashed values are stored in the column 'hashes':")

model.transform(dfA).show()

# Compute the locality sensitive hashes for the input rows, then perform approximate

# similarity join.

# We could avoid computing hashes by passing in the already-transformed dataset, e.g.

# `model.approxSimilarityJoin(transformedA, transformedB, 0.6)`

print("Approximately joining dfA and dfB on distance smaller than 0.6:")

model.approxSimilarityJoin(dfA, dfB, 0.6, distCol="JaccardDistance")\

.select(col("datasetA.id").alias("idA"),

col("datasetB.id").alias("idB"),

col("JaccardDistance")).show()

# Compute the locality sensitive hashes for the input rows, then perform approximate nearest

# neighbor search.

# We could avoid computing hashes by passing in the already-transformed dataset, e.g.

# `model.approxNearestNeighbors(transformedA, key, 2)`

# It may return less than 2 rows when not enough approximate near-neighbor candidates are

# found.

print("Approximately searching dfA for 2 nearest neighbors of the key:")

model.approxNearestNeighbors(dfA, key, 2).show()

# $example off$

spark.stop()