spark JavaNaiveBayesExample 源码
spark JavaNaiveBayesExample 代码
文件路径：/examples/src/main/java/org/apache/spark/examples/ml/JavaNaiveBayesExample.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.classification.NaiveBayes;
import org.apache.spark.ml.classification.NaiveBayesModel;
import org.apache.spark.ml.evaluation.MulticlassClassificationEvaluator;
import org.apache.spark.sql.Dataset;
import org.apache.spark.sql.Row;
import org.apache.spark.sql.SparkSession;
// $example off$
/**
* An example for Naive Bayes Classification.
*/
public class JavaNaiveBayesExample {
public static void main(String[] args) {
SparkSession spark = SparkSession
.builder()
.appName("JavaNaiveBayesExample")
.getOrCreate();
// $example on$
// Load training data
Dataset<Row> dataFrame =
spark.read().format("libsvm").load("data/mllib/sample_libsvm_data.txt");
// Split the data into train and test
Dataset<Row>[] splits = dataFrame.randomSplit(new double[]{0.6, 0.4}, 1234L);
Dataset<Row> train = splits[0];
Dataset<Row> test = splits[1];
// create the trainer and set its parameters
NaiveBayes nb = new NaiveBayes();
// train the model
NaiveBayesModel model = nb.fit(train);
// Select example rows to display.
Dataset<Row> predictions = model.transform(test);
predictions.show();
// compute accuracy on the test set
MulticlassClassificationEvaluator evaluator = new MulticlassClassificationEvaluator()
.setLabelCol("label")
.setPredictionCol("prediction")
.setMetricName("accuracy");
double accuracy = evaluator.evaluate(predictions);
System.out.println("Test set accuracy = " + accuracy);
// $example off$
spark.stop();
}
}
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