Github user mengxr commented on a diff in the pull request:
https://github.com/apache/spark/pull/9353#discussion_r43474262
--- 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")
--- End diff --
Maybe it useful to change it to `"target/tmp/myIsotonicRegressionModel"`
because users might run in under `spark/` folder.
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