spark NaiveBayesExample 源码
spark NaiveBayesExample 代码
文件路径：/examples/src/main/scala/org/apache/spark/examples/ml/NaiveBayesExample.scala
/*
* 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.
*/
// scalastyle:off println
package org.apache.spark.examples.ml
// $example on$
import org.apache.spark.ml.classification.NaiveBayes
import org.apache.spark.ml.evaluation.MulticlassClassificationEvaluator
// $example off$
import org.apache.spark.sql.SparkSession
object NaiveBayesExample {
def main(args: Array[String]): Unit = {
val spark = SparkSession
.builder
.appName("NaiveBayesExample")
.getOrCreate()
// $example on$
// Load the data stored in LIBSVM format as a DataFrame.
val data = spark.read.format("libsvm").load("data/mllib/sample_libsvm_data.txt")
// Split the data into training and test sets (30% held out for testing)
val Array(trainingData, testData) = data.randomSplit(Array(0.7, 0.3), seed = 1234L)
// Train a NaiveBayes model.
val model = new NaiveBayes()
.fit(trainingData)
// Select example rows to display.
val predictions = model.transform(testData)
predictions.show()
// Select (prediction, true label) and compute test error
val evaluator = new MulticlassClassificationEvaluator()
.setLabelCol("label")
.setPredictionCol("prediction")
.setMetricName("accuracy")
val accuracy = evaluator.evaluate(predictions)
println(s"Test set accuracy = $accuracy")
// $example off$
spark.stop()
}
}
// scalastyle:on println
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