Java tutorial
/* * 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 com.sdw.dream.spark.examples.ml; import org.apache.spark.SparkConf; import org.apache.spark.api.java.JavaSparkContext; import org.apache.spark.sql.SQLContext; // $example on$ import java.util.Arrays; import org.apache.spark.api.java.JavaRDD; import org.apache.spark.ml.feature.Bucketizer; import org.apache.spark.sql.DataFrame; 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; // $example off$ public class JavaBucketizerExample { public static void main(String[] args) { SparkConf conf = new SparkConf().setAppName("JavaBucketizerExample"); JavaSparkContext jsc = new JavaSparkContext(conf); SQLContext jsql = new SQLContext(jsc); // $example on$ double[] splits = { Double.NEGATIVE_INFINITY, -0.5, 0.0, 0.5, Double.POSITIVE_INFINITY }; JavaRDD<Row> data = jsc.parallelize(Arrays.asList(RowFactory.create(-0.5), RowFactory.create(-0.3), RowFactory.create(0.0), RowFactory.create(0.2))); StructType schema = new StructType( new StructField[] { new StructField("features", DataTypes.DoubleType, false, Metadata.empty()) }); DataFrame dataFrame = jsql.createDataFrame(data, schema); Bucketizer bucketizer = new Bucketizer().setInputCol("features").setOutputCol("bucketedFeatures") .setSplits(splits); // Transform original data into its bucket index. DataFrame bucketedData = bucketizer.transform(dataFrame); bucketedData.show(); // $example off$ jsc.stop(); } }