spark OneVsRestExample 源码
spark OneVsRestExample 代码
文件路径：/examples/src/main/scala/org/apache/spark/examples/ml/OneVsRestExample.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.{LogisticRegression, OneVsRest}
import org.apache.spark.ml.evaluation.MulticlassClassificationEvaluator
// $example off$
import org.apache.spark.sql.SparkSession
/**
* An example of Multiclass to Binary Reduction with One Vs Rest,
* using Logistic Regression as the base classifier.
* Run with
* {{{
* ./bin/run-example ml.OneVsRestExample
* }}}
*/
object OneVsRestExample {
def main(args: Array[String]): Unit = {
val spark = SparkSession
.builder
.appName(s"OneVsRestExample")
.getOrCreate()
// $example on$
// load data file.
val inputData = spark.read.format("libsvm")
.load("data/mllib/sample_multiclass_classification_data.txt")
// generate the train/test split.
val Array(train, test) = inputData.randomSplit(Array(0.8, 0.2))
// instantiate the base classifier
val classifier = new LogisticRegression()
.setMaxIter(10)
.setTol(1E-6)
.setFitIntercept(true)
// instantiate the One Vs Rest Classifier.
val ovr = new OneVsRest().setClassifier(classifier)
// train the multiclass model.
val ovrModel = ovr.fit(train)
// score the model on test data.
val predictions = ovrModel.transform(test)
// obtain evaluator.
val evaluator = new MulticlassClassificationEvaluator()
.setMetricName("accuracy")
// compute the classification error on test data.
val accuracy = evaluator.evaluate(predictions)
println(s"Test Error = ${1 - accuracy}")
// $example off$
spark.stop()
}
}
// scalastyle:on println
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