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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 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 imputer = new Imputer()
.setInputCols(new String[]{"a", "b"})
.setOutputCols(new String[]{"out_a", "out_b"});
ImputerModel model = imputer.fit(df);
model.transform(df).show();
// $example off$
spark.stop();
}
}
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