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/*
* 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.
*/
package org.apache.spark.examples.ml;
import org.apache.spark.sql.SparkSession;
// $example on$
import java.util.Arrays;
import java.util.List;
import org.apache.spark.ml.feature.MinHashLSH;
import org.apache.spark.ml.feature.MinHashLSHModel;
import org.apache.spark.ml.linalg.Vector;
import org.apache.spark.ml.linalg.VectorUDT;
import org.apache.spark.ml.linalg.Vectors;
import org.apache.spark.sql.Dataset;
import org.apache.spark.sql.Row;
import org.apache.spark.sql.RowFactory;
import org.apache.spark.sql.types.DataTypes;
import org.apache.spark.sql.types.Metadata;
import org.apache.spark.sql.types.StructField;
import org.apache.spark.sql.types.StructType;
import static org.apache.spark.sql.functions.col;
// $example off$
/**
* An example demonstrating MinHashLSH.
* Run with:
* bin/run-example org.apache.spark.examples.ml.JavaMinHashLSHExample
*/
public class JavaMinHashLSHExample {
public static void main(String[] args) {
SparkSession spark = SparkSession
.builder()
.appName("JavaMinHashLSHExample")
.getOrCreate();
// $example on$
List<Row> dataA = Arrays.asList(
RowFactory.create(0, Vectors.sparse(6, new int[]{0, 1, 2}, new double[]{1.0, 1.0, 1.0})),
RowFactory.create(1, Vectors.sparse(6, new int[]{2, 3, 4}, new double[]{1.0, 1.0, 1.0})),
RowFactory.create(2, Vectors.sparse(6, new int[]{0, 2, 4}, new double[]{1.0, 1.0, 1.0}))
);
List<Row> dataB = Arrays.asList(
RowFactory.create(0, Vectors.sparse(6, new int[]{1, 3, 5}, new double[]{1.0, 1.0, 1.0})),
RowFactory.create(1, Vectors.sparse(6, new int[]{2, 3, 5}, new double[]{1.0, 1.0, 1.0})),
RowFactory.create(2, Vectors.sparse(6, new int[]{1, 2, 4}, new double[]{1.0, 1.0, 1.0}))
);
StructType schema = new StructType(new StructField[]{
new StructField("id", DataTypes.IntegerType, false, Metadata.empty()),
new StructField("features", new VectorUDT(), false, Metadata.empty())
});
Dataset<Row> dfA = spark.createDataFrame(dataA, schema);
Dataset<Row> dfB = spark.createDataFrame(dataB, schema);
int[] indices = {1, 3};
double[] values = {1.0, 1.0};
Vector key = Vectors.sparse(6, indices, values);
MinHashLSH mh = new MinHashLSH()
.setNumHashTables(5)
.setInputCol("features")
.setOutputCol("hashes");
MinHashLSHModel model = mh.fit(dfA);
// Feature Transformation
System.out.println("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)`
System.out.println("Approximately joining dfA and dfB on Jaccard distance smaller than 0.6:");
model.approxSimilarityJoin(dfA, dfB, 0.6, "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.
System.out.println("Approximately searching dfA for 2 nearest neighbors of the key:");
model.approxNearestNeighbors(dfA, key, 2).show();
// $example off$
spark.stop();
}
}
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