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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.
*/
// scalastyle:off println
package org.apache.spark.examples.mllib
import org.apache.spark.{SparkConf, SparkContext}
// $example on$
import org.apache.spark.mllib.classification.{NaiveBayes, NaiveBayesModel}
import org.apache.spark.mllib.util.MLUtils
// $example off$
object NaiveBayesExample {
def main(args: Array[String]): Unit = {
val conf = new SparkConf().setAppName("NaiveBayesExample")
val sc = new SparkContext(conf)
// $example on$
// Load and parse the data file.
val data = MLUtils.loadLibSVMFile(sc, "data/mllib/sample_libsvm_data.txt")
// Split data into training (60%) and test (40%).
val Array(training, test) = data.randomSplit(Array(0.6, 0.4))
val model = NaiveBayes.train(training, lambda = 1.0, modelType = "multinomial")
val predictionAndLabel = test.map(p => (model.predict(p.features), p.label))
val accuracy = 1.0 * predictionAndLabel.filter(x => x._1 == x._2).count() / test.count()
// Save and load model
model.save(sc, "target/tmp/myNaiveBayesModel")
val sameModel = NaiveBayesModel.load(sc, "target/tmp/myNaiveBayesModel")
// $example off$
sc.stop()
}
}
// scalastyle:on println
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