Github user hhbyyh commented on a diff in the pull request: https://github.com/apache/spark/pull/17324#discussion_r108234190 --- Diff: examples/src/main/java/org/apache/spark/examples/ml/JavaImputerExample.java --- @@ -0,0 +1,72 @@ +/* + * 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; + +// $example on$ +import java.util.Arrays; +import java.util.List; + +import org.apache.spark.ml.feature.Imputer; +import org.apache.spark.ml.feature.ImputerModel; +import org.apache.spark.sql.Dataset; +import org.apache.spark.sql.Row; +import org.apache.spark.sql.RowFactory; +import org.apache.spark.sql.SparkSession; +import org.apache.spark.sql.types.*; +// $example off$ + +import static org.apache.spark.sql.types.DataTypes.*; + +/** + * An example demonstrating Imputer. + * Run with: + * bin/run-example ml.JavaImputerExample + */ +public class JavaImputerExample { + public static void main(String[] args) { + SparkSession spark = SparkSession + .builder() + .appName("JavaImputerExample") + .getOrCreate(); + + // $example on$ + List<Row> data = Arrays.asList( + RowFactory.create(1.0, Double.NaN), + RowFactory.create(2.0, Double.NaN), + RowFactory.create(Double.NaN, 3.0), + RowFactory.create(4.0, 4.0), + RowFactory.create(5.0, 5.0) + ); + StructType schema = new StructType(new StructField[]{ + createStructField("a", DoubleType, false), + createStructField("b", DoubleType, false) + }); + Dataset<Row> df = spark.createDataFrame(data, schema); + + Imputer imputerModel = new Imputer() + .setStrategy("mean") --- End diff -- For the example code, can we keep it to introduce the primary API or important paramters?
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