/* * 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.QuantileDiscretizer; 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; // $example off$ public class JavaQuantileDiscretizerExample { public static void main(String[] args) { SparkSession spark = SparkSession .builder() .appName("JavaQuantileDiscretizerExample") .getOrCreate(); // $example on$ List<Row> data = Arrays.asList( RowFactory.create(0, 18.0), RowFactory.create(1, 19.0), RowFactory.create(2, 8.0), RowFactory.create(3, 5.0), RowFactory.create(4, 2.2) ); StructType schema = new StructType(new StructField[]{ new StructField("id", DataTypes.IntegerType, false, Metadata.empty()), new StructField("hour", DataTypes.DoubleType, false, Metadata.empty()) }); Dataset<Row> df = spark.createDataFrame(data, schema); // $example off$ // Output of QuantileDiscretizer for such small datasets can depend on the number of // partitions. Here we force a single partition to ensure consistent results. // Note this is not necessary for normal use cases df = df.repartition(1); // $example on$ QuantileDiscretizer discretizer = new QuantileDiscretizer() .setInputCol("hour") .setOutputCol("result") .setNumBuckets(3); Dataset<Row> result = discretizer.fit(df).transform(df); result.show(); // $example off$ spark.stop(); } }