spark JavaIsotonicRegressionExample 源码
spark JavaIsotonicRegressionExample 代码
文件路径：/examples/src/main/java/org/apache/spark/examples/ml/JavaIsotonicRegressionExample.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;
// $example on$
import org.apache.spark.ml.regression.IsotonicRegression;
import org.apache.spark.ml.regression.IsotonicRegressionModel;
import org.apache.spark.sql.Dataset;
import org.apache.spark.sql.Row;
// $example off$
import org.apache.spark.sql.SparkSession;
/**
* An example demonstrating IsotonicRegression.
* Run with
* <pre>
* bin/run-example ml.JavaIsotonicRegressionExample
* </pre>
*/
public class JavaIsotonicRegressionExample {
public static void main(String[] args) {
// Create a SparkSession.
SparkSession spark = SparkSession
.builder()
.appName("JavaIsotonicRegressionExample")
.getOrCreate();
// $example on$
// Loads data.
Dataset<Row> dataset = spark.read().format("libsvm")
.load("data/mllib/sample_isotonic_regression_libsvm_data.txt");
// Trains an isotonic regression model.
IsotonicRegression ir = new IsotonicRegression();
IsotonicRegressionModel model = ir.fit(dataset);
System.out.println("Boundaries in increasing order: " + model.boundaries() + "\n");
System.out.println("Predictions associated with the boundaries: " + model.predictions() + "\n");
// Makes predictions.
model.transform(dataset).show();
// $example off$
spark.stop();
}
}
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