/* * 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.OneHotEncoder; import org.apache.spark.ml.feature.StringIndexer; import org.apache.spark.ml.feature.StringIndexerModel; 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 JavaOneHotEncoderExample { public static void main(String[] args) { SparkSession spark = SparkSession .builder() .appName("JavaOneHotEncoderExample") .getOrCreate(); // $example on$ List<Row> data = Arrays.asList( RowFactory.create(0, "a"), RowFactory.create(1, "b"), RowFactory.create(2, "c"), RowFactory.create(3, "a"), RowFactory.create(4, "a"), RowFactory.create(5, "c") ); StructType schema = new StructType(new StructField[]{ new StructField("id", DataTypes.IntegerType, false, Metadata.empty()), new StructField("category", DataTypes.StringType, false, Metadata.empty()) }); Dataset<Row> df = spark.createDataFrame(data, schema); StringIndexerModel indexer = new StringIndexer() .setInputCol("category") .setOutputCol("categoryIndex") .fit(df); Dataset<Row> indexed = indexer.transform(df); OneHotEncoder encoder = new OneHotEncoder() .setInputCol("categoryIndex") .setOutputCol("categoryVec"); Dataset<Row> encoded = encoder.transform(indexed); encoded.show(); // $example off$ spark.stop(); } }