dbx.compute.spark.jobs.ml.JavaVectorSlicerExample.java Source code

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Here is the source code for dbx.compute.spark.jobs.ml.JavaVectorSlicerExample.java

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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 dbx.compute.spark.jobs.ml;

import org.apache.spark.sql.SparkSession;

// $example on$
import java.util.List;

import com.google.common.collect.Lists;

import org.apache.spark.ml.attribute.Attribute;
import org.apache.spark.ml.attribute.AttributeGroup;
import org.apache.spark.ml.attribute.NumericAttribute;
import org.apache.spark.ml.feature.VectorSlicer;
import org.apache.spark.mllib.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.*;
// $example off$

public class JavaVectorSlicerExample {
    public static void main(String[] args) {
        SparkSession spark = SparkSession.builder().appName("JavaVectorSlicerExample").getOrCreate();

        // $example on$
        Attribute[] attrs = new Attribute[] { NumericAttribute.defaultAttr().withName("f1"),
                NumericAttribute.defaultAttr().withName("f2"), NumericAttribute.defaultAttr().withName("f3") };
        AttributeGroup group = new AttributeGroup("userFeatures", attrs);

        List<Row> data = Lists.newArrayList(
                RowFactory.create(Vectors.sparse(3, new int[] { 0, 1 }, new double[] { -2.0, 2.3 })),
                RowFactory.create(Vectors.dense(-2.0, 2.3, 0.0)));

        Dataset<Row> dataset = spark.createDataFrame(data, (new StructType()).add(group.toStructField()));

        VectorSlicer vectorSlicer = new VectorSlicer().setInputCol("userFeatures").setOutputCol("features");

        vectorSlicer.setIndices(new int[] { 1 }).setNames(new String[] { "f3" });
        // or slicer.setIndices(new int[]{1, 2}), or slicer.setNames(new String[]{"f2", "f3"})

        Dataset<Row> output = vectorSlicer.transform(dataset);

        System.out.println(output.select("userFeatures", "features").first());
        // $example off$
        spark.stop();
    }
}