examples/ml/JavaCorrelationExample.java

0001 /*
0002  * Licensed to the Apache Software Foundation (ASF) under one or more
0003  * contributor license agreements.  See the NOTICE file distributed with
0004  * this work for additional information regarding copyright ownership.
0005  * The ASF licenses this file to You under the Apache License, Version 2.0
0006  * (the "License"); you may not use this file except in compliance with
0007  * the License.  You may obtain a copy of the License at
0008  *
0009  *    http://www.apache.org/licenses/LICENSE-2.0
0010  *
0011  * Unless required by applicable law or agreed to in writing, software
0012  * distributed under the License is distributed on an "AS IS" BASIS,
0013  * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
0014  * See the License for the specific language governing permissions and
0015  * limitations under the License.
0016  */
0017
0018 package org.apache.spark.examples.ml;
0019
0020 import org.apache.spark.sql.SparkSession;
0021
0022 // $example on$
0023 import java.util.Arrays;
0024 import java.util.List;
0025
0026 import org.apache.spark.ml.linalg.Vectors;
0027 import org.apache.spark.ml.linalg.VectorUDT;
0028 import org.apache.spark.ml.stat.Correlation;
0029 import org.apache.spark.sql.Dataset;
0030 import org.apache.spark.sql.Row;
0031 import org.apache.spark.sql.RowFactory;
0032 import org.apache.spark.sql.types.*;
0033 // $example off$
0034
0035 /**
0036  * An example for computing correlation matrix.
0037  * Run with
0038  * <pre>
0039  * bin/run-example ml.JavaCorrelationExample
0040  * </pre>
0041  */
0042 public class JavaCorrelationExample {
0043
0044   public static void main(String[] args) {
0045     SparkSession spark = SparkSession
0046       .builder()
0047       .appName("JavaCorrelationExample")
0048       .getOrCreate();
0049
0050     // $example on$
0051     List<Row> data = Arrays.asList(
0052       RowFactory.create(Vectors.sparse(4, new int[]{0, 3}, new double[]{1.0, -2.0})),
0053       RowFactory.create(Vectors.dense(4.0, 5.0, 0.0, 3.0)),
0054       RowFactory.create(Vectors.dense(6.0, 7.0, 0.0, 8.0)),
0055       RowFactory.create(Vectors.sparse(4, new int[]{0, 3}, new double[]{9.0, 1.0}))
0056     );
0057
0058     StructType schema = new StructType(new StructField[]{
0059       new StructField("features", new VectorUDT(), false, Metadata.empty()),
0060     });
0061
0062     Dataset<Row> df = spark.createDataFrame(data, schema);
0063     Row r1 = Correlation.corr(df, "features").head();
0064     System.out.println("Pearson correlation matrix:\n" + r1.get(0).toString());
0065
0066     Row r2 = Correlation.corr(df, "features", "spearman").head();
0067     System.out.println("Spearman correlation matrix:\n" + r2.get(0).toString());
0068     // $example off$
0069
0070     spark.stop();
0071   }
0072 }