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Diffstat (limited to 'examples/src/main/scala/org/apache/spark/examples/ml/MinHashLSHExample.scala')
-rw-r--r-- | examples/src/main/scala/org/apache/spark/examples/ml/MinHashLSHExample.scala | 77 |
1 files changed, 77 insertions, 0 deletions
diff --git a/examples/src/main/scala/org/apache/spark/examples/ml/MinHashLSHExample.scala b/examples/src/main/scala/org/apache/spark/examples/ml/MinHashLSHExample.scala new file mode 100644 index 0000000000..f4fc3cf411 --- /dev/null +++ b/examples/src/main/scala/org/apache/spark/examples/ml/MinHashLSHExample.scala @@ -0,0 +1,77 @@ +/* + * 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. + */ + +// scalastyle:off println +package org.apache.spark.examples.ml + +// $example on$ +import org.apache.spark.ml.feature.MinHashLSH +import org.apache.spark.ml.linalg.Vectors +// $example off$ +import org.apache.spark.sql.SparkSession + +object MinHashLSHExample { + def main(args: Array[String]): Unit = { + // Creates a SparkSession + val spark = SparkSession + .builder + .appName("MinHashLSHExample") + .getOrCreate() + + // $example on$ + val dfA = spark.createDataFrame(Seq( + (0, Vectors.sparse(6, Seq((0, 1.0), (1, 1.0), (2, 1.0)))), + (1, Vectors.sparse(6, Seq((2, 1.0), (3, 1.0), (4, 1.0)))), + (2, Vectors.sparse(6, Seq((0, 1.0), (2, 1.0), (4, 1.0)))) + )).toDF("id", "keys") + + val dfB = spark.createDataFrame(Seq( + (3, Vectors.sparse(6, Seq((1, 1.0), (3, 1.0), (5, 1.0)))), + (4, Vectors.sparse(6, Seq((2, 1.0), (3, 1.0), (5, 1.0)))), + (5, Vectors.sparse(6, Seq((1, 1.0), (2, 1.0), (4, 1.0)))) + )).toDF("id", "keys") + + val key = Vectors.sparse(6, Seq((1, 1.0), (3, 1.0))) + + val mh = new MinHashLSH() + .setNumHashTables(3) + .setInputCol("keys") + .setOutputCol("values") + + val model = mh.fit(dfA) + + // Feature Transformation + model.transform(dfA).show() + // Cache the transformed columns + val transformedA = model.transform(dfA).cache() + val transformedB = model.transform(dfB).cache() + + // Approximate similarity join + model.approxSimilarityJoin(dfA, dfB, 0.6).show() + model.approxSimilarityJoin(transformedA, transformedB, 0.6).show() + // Self Join + model.approxSimilarityJoin(dfA, dfA, 0.6).filter("datasetA.id < datasetB.id").show() + + // Approximate nearest neighbor search + model.approxNearestNeighbors(dfA, key, 2).show() + model.approxNearestNeighbors(transformedA, key, 2).show() + // $example off$ + + spark.stop() + } +} +// scalastyle:on println |