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From mengxr <...@git.apache.org>
Subject [GitHub] spark pull request: [SPARK-11383][Docs] Replaced example code in m...
Date Fri, 30 Oct 2015 06:09:44 GMT
Github user mengxr commented on a diff in the pull request:

    https://github.com/apache/spark/pull/9353#discussion_r43474263
  
    --- Diff: examples/src/main/scala/org/apache/spark/examples/mllib/IsotonicRegressionExample.scala
---
    @@ -0,0 +1,66 @@
    +/*
    + * 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
    +
    +// $example on$
    +import org.apache.spark.mllib.regression.{IsotonicRegression, IsotonicRegressionModel}
    +// $example off$
    +import org.apache.spark.{SparkConf, SparkContext}
    +
    +object IsotonicRegressionExample {
    +
    +  def main(args: Array[String]) {
    +
    +    val conf = new SparkConf().setAppName("IsotonicRegressionExample")
    +    val sc = new SparkContext(conf)
    +    // $example on$
    +    val data = sc.textFile("data/mllib/sample_isotonic_regression_data.txt")
    +
    +    // Create label, feature, weight tuples from input data with weight set to default
value 1.0.
    +    val parsedData = data.map { line =>
    +      val parts = line.split(',').map(_.toDouble)
    +      (parts(0), parts(1), 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, "myModelPath")
    +    val sameModel = IsotonicRegressionModel.load(sc, "myModelPath")
    --- End diff --
    
    same here


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