spark JavaBinarizerExample 源码

2022-10-20
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spark JavaBinarizerExample 代码

文件路径：/examples/src/main/java/org/apache/spark/examples/ml/JavaBinarizerExample.java

/*
 * 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.Dataset;
import org.apache.spark.sql.SparkSession;

// $example on$
import java.util.Arrays;
import java.util.List;

import org.apache.spark.ml.feature.Binarizer;
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 JavaBinarizerExample {
  public static void main(String[] args) {
    SparkSession spark = SparkSession
      .builder()
      .appName("JavaBinarizerExample")
      .getOrCreate();

    // $example on$
    List<Row> data = Arrays.asList(
      RowFactory.create(0, 0.1),
      RowFactory.create(1, 0.8),
      RowFactory.create(2, 0.2)
    );
    StructType schema = new StructType(new StructField[]{
      new StructField("id", DataTypes.IntegerType, false, Metadata.empty()),
      new StructField("feature", DataTypes.DoubleType, false, Metadata.empty())
    });
    Dataset<Row> continuousDataFrame = spark.createDataFrame(data, schema);

    Binarizer binarizer = new Binarizer()
      .setInputCol("feature")
      .setOutputCol("binarized_feature")
      .setThreshold(0.5);

    Dataset<Row> binarizedDataFrame = binarizer.transform(continuousDataFrame);

    System.out.println("Binarizer output with Threshold = " + binarizer.getThreshold());
    binarizedDataFrame.show();
    // $example off$

    spark.stop();
  }
}

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