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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.regression.{IsotonicRegression, IsotonicRegressionModel}
import org.apache.spark.mllib.util.MLUtils
// $example off$
object IsotonicRegressionExample {
def main(args: Array[String]): Unit = {
val conf = new SparkConf().setAppName("IsotonicRegressionExample")
val sc = new SparkContext(conf)
// $example on$
val data = MLUtils.loadLibSVMFile(sc,
"data/mllib/sample_isotonic_regression_libsvm_data.txt").cache()
// Create label, feature, weight tuples from input data with weight set to default value 1.0.
val parsedData = data.map { labeledPoint =>
(labeledPoint.label, labeledPoint.features(0), 1.0)
}
// Split data into training (60%) and test (40%) sets.
val splits = parsedData.randomSplit(Array(0.6, 0.4), seed = 11L)
val training = splits(0)
val test = splits(1)
// Create isotonic regression model from training data.
// Isotonic parameter defaults to true so it is only shown for demonstration
val model = new IsotonicRegression().setIsotonic(true).run(training)
// Create tuples of predicted and real labels.
val predictionAndLabel = test.map { point =>
val predictedLabel = model.predict(point._2)
(predictedLabel, point._1)
}
// Calculate mean squared error between predicted and real labels.
val meanSquaredError = predictionAndLabel.map { case (p, l) => math.pow((p - l), 2) }.mean()
println("Mean Squared Error = " + meanSquaredError)
// Save and load model
model.save(sc, "target/tmp/myIsotonicRegressionModel")
val sameModel = IsotonicRegressionModel.load(sc, "target/tmp/myIsotonicRegressionModel")
// $example off$
}
}
// scalastyle:on println
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