{"action":"created","comment":{"url":"https://api.github.com/repos/apache/flink/pulls/comments/51707018","id":51707018,"diff_hunk":"@@ -0,0 +1,282 @@\n+/*\n+ * Licensed to the Apache Software Foundation (ASF) under one\n+ * or more contributor license agreements.  See the NOTICE file\n+ * distributed with this work for additional information\n+ * regarding copyright ownership.  The ASF licenses this file\n+ * to you under the Apache License, Version 2.0 (the\n+ * \"License\"); you may not use this file except in compliance\n+ * with the License.  You may obtain a copy of the License at\n+ *\n+ *     http://www.apache.org/licenses/LICENSE-2.0\n+ *\n+ * Unless required by applicable law or agreed to in writing, software\n+ * distributed under the License is distributed on an \"AS IS\" BASIS,\n+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n+ * See the License for the specific language governing permissions and\n+ * limitations under the License.\n+ */\n+\n+package org.apache.flink.ml.regression\n+\n+import java.util.Arrays.binarySearch\n+\n+import org.apache.flink.api.common.functions.MapFunction\n+import org.apache.flink.api.scala._\n+import org.apache.flink.ml.common.{Parameter, ParameterMap}\n+import org.apache.flink.ml.pipeline.{FitOperation, PredictOperation, Predictor}\n+\n+import scala.collection.mutable.ArrayBuffer\n+\n+case class IsotonicRegressionModel(boundaries: Array[Double], predictions: Array[Double])\n+\n+/**\n+  * Isotonic regression.\n+  * Currently implemented using parallelized pool adjacent violators algorithm.\n+  * Only univariate (single feature) algorithm supported.\n+  *\n+  * Sequential PAV implementation based on:\n+  * Tibshirani, Ryan J., Holger Hoefling, and Robert Tibshirani.\n+  * \"Nearly-isotonic regression.\" Technometrics 53.1 (2011): 54-61.\n+  * Available from [[http://www.stat.cmu.edu/~ryantibs/papers/neariso.pdf]]\n+  *\n+  * Sequential PAV parallelization based on:\n+  * Kearsley, Anthony J., Richard A. Tapia, and Michael W. Trosset.\n+  * \"An approach to parallelizing isotonic regression.\"\n+  * Applied Mathematics and Parallel Computing. Physica-Verlag HD, 1996. 141-147.\n+  * Available from [[http://softlib.rice.edu/pub/CRPC-TRs/reports/CRPC-TR96640.pdf]]\n+  *\n+  * @see [[http://en.wikipedia.org/wiki/Isotonic_regression Isotonic regression (Wikipedia)]]\n+  *\n+  *      This is a port from the implementation in Apache Spark.\n+  * @example\n+  * {{{\n+  *             val ir = IsotonicRegression()\n+  *               .setIsotonic(true)\n+  *\n+  *             val trainingDS: DataSet[(Double,Double,Double)] = ...\n+  *             val testingDS: DataSet[(Double)] = ...\n+  *\n+  *             mlr.fit(trainingDS)\n+  *\n+  *             val predictions = mlr.predict(testingDS)\n+  *          }}}\n+  *\n+  * =Parameters=\n+  *\n+  * - [[org.apache.flink.ml.regression.IsotonicRegression.Isotonic]]:\n+  * true if labels shall be ascending, false if labels shall be descending.\n+  *\n+  */\n+class IsotonicRegression extends Predictor[IsotonicRegression] {\n+\n+  var isotonic = true\n+\n+  var model: Option[DataSet[IsotonicRegressionModel]] = None\n+\n+  def setIsotonic(isotonic: Boolean): this.type = {\n+    this.isotonic = isotonic\n+    this\n+  }\n+\n+}\n+\n+object IsotonicRegression {\n+\n+  // ====================================== Parameters ===========================================\n+\n+  case object Isotonic extends Parameter[Boolean] {\n+    val defaultValue = Some(true)\n+  }\n+\n+  // ======================================== Factory methods ====================================\n+\n+  def apply(): IsotonicRegression = {\n+    new IsotonicRegression()\n+  }\n+\n+  // ====================================== Operations ===========================================\n+\n+  class AdjacentPoolViolatersMapper extends MapFunction[Array[(Double, Double, Double)], Array[\n+    (Double, Double, Double)]] {\n+\n+    /**\n+      * Performs a pool adjacent violators algorithm (PAV).\n+      * Uses approach with single processing of data where violators\n+      * in previously processed data created by pooling are fixed immediately.\n+      * Uses optimization of discovering monotonicity violating sequences (blocks).\n+      *\n+      * @param input Input data of tuples (label, feature, weight).\n+      * @return Result tuples (label, feature, weight) where labels were updated\n+      *         to form a monotone sequence as per isotonic regression definition.\n+      */\n+    override def map(input: Array[(Double, Double, Double)]):\n+    Array[(Double, Double, Double)] = {\n+      if (input.isEmpty) {\n+        return Array.empty\n+      }\n+\n+      // Pools sub array within given bounds assigning weighted average value to all\n+      // elements.\n+      def pool(input: Array[(Double, Double, Double)], start: Int, end: Int): Unit = {\n+        val poolSubArray = input.slice(start, end + 1)\n+\n+        val weightedSum = poolSubArray.map(lp => lp._1 * lp._3).sum\n+        val weight = poolSubArray.map(_._3).sum\n+\n+        var i = start\n+        while (i <= end) {\n+          input(i) = (weightedSum / weight, input(i)._2, input(i)._3)\n+          i = i + 1\n+        }\n+      }\n+\n+      var i = 0\n+      val len = input.length\n+      while (i < len) {\n+        var j = i\n+\n+        // Find monotonicity violating sequence, if any.\n+        while (j < len - 1 && input(j)._1 > input(j + 1)._1) {\n+          j = j + 1\n+        }\n+\n+        // If monotonicity was not violated, move to next data point.\n+        if (i == j) {\n+          i = i + 1\n+        } else {\n+          // Otherwise pool the violating sequence\n+          // and check if pooling caused monotonicity violation in previously processed\n+          // points.\n+          while (i >= 0 && input(i)._1 > input(i + 1)._1) {\n+            pool(input, i, j)\n+            i = i - 1\n+          }\n+\n+          i = j\n+        }\n+      }\n+      // For points having the same prediction, we only keep two boundary points.\n+      val compressed = ArrayBuffer.empty[(Double, Double, Double)]\n+\n+      var (curLabel, curFeature, curWeight) = input.head\n+      var rightBound = curFeature\n+      def merge(): Unit = {\n+        compressed += ((curLabel, curFeature, curWeight))\n+        if (rightBound > curFeature) {\n+          compressed += ((curLabel, rightBound, 0.0))\n+        }\n+      }\n+      i = 1\n+      while (i < input.length) {\n+        val (label, feature, weight) = input(i)\n+        if (label == curLabel) {\n+          curWeight += weight\n+          rightBound = feature\n+        } else {\n+          merge()\n+          curLabel = label\n+          curFeature = feature\n+          curWeight = weight\n+          rightBound = curFeature\n+        }\n+        i += 1\n+      }\n+      merge()\n+\n+      compressed.toArray\n+    }\n+  }\n+\n+\n+  implicit val fitIR = new FitOperation[IsotonicRegression, (Double, Double, Double)] {\n+\n+    override def fit(instance: IsotonicRegression,\n+                     fitParameters: ParameterMap,\n+                     input: DataSet[(Double, Double, Double)]): Unit = {\n+\n+      val isotonic = instance.isotonic\n+\n+      val preprocessedInput = if (isotonic) {\n+        input\n+      } else {\n+        input.map(x => (-x._1, x._2, x._3))\n+      }\n+\n+      val parallelStepResult = preprocessedInput\n+        .partitionByRange(1)\n+        .mapPartition(partition => {\n+          val buffer = new ArrayBuffer[(Double, Double, Double)]\n+          buffer ++= partition\n+          Seq(buffer.sortBy(x => (x._2, x._1)).toArray)\n+        })\n+        .map(new AdjacentPoolViolatersMapper)\n+        .setParallelism(1)\n+        .mapPartition(partitions => {\n+          val buffer = new ArrayBuffer[(Double, Double, Double)]\n+          for (partition <- partitions) {\n+            buffer ++= partition\n+          }\n+          Seq(buffer.sortBy(x => (x._2, x._1)).toArray)\n+        })\n+        .map(new AdjacentPoolViolatersMapper)\n+        .map(arr => {\n+          val boundaries = new ArrayBuffer[Double]\n+          val predictions = new ArrayBuffer[Double]\n+          for (x <- arr) {\n+            predictions += (if (isotonic) x._1 else -x._1)\n+            boundaries += x._2\n+          }\n+          IsotonicRegressionModel(boundaries.toArray, predictions.toArray)\n+        })\n+\n+      instance.model = Some(parallelStepResult)\n+    }\n+  }\n+\n+  implicit val predictIR =\n+    new PredictOperation[IsotonicRegression, IsotonicRegressionModel, Double, Double] {\n+\n+      override def getModel(self: IsotonicRegression, predictParameters: ParameterMap):\n+      DataSet[IsotonicRegressionModel] =\n+        self.model match {\n+          case Some(model) => model\n+          case None => throw new RuntimeException(\"The IsotonicRegression has not \" +\n+            \"been fitted to the data. This is necessary to learn the model.\")\n+        }\n+\n+      override def predict(value: Double, model: IsotonicRegressionModel): Double = {\n+\n+        def linearInterpolation(x1: Double, y1: Double, x2: Double, y2: Double, x:\n+        Double):\n+        Double = 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