Github user mengxr commented on a diff in the pull request:
https://github.com/apache/spark/pull/3519#discussion_r23275406
--- Diff:
mllib/src/main/scala/org/apache/spark/mllib/regression/IsotonicRegression.scala
---
@@ -0,0 +1,235 @@
+/*
+ * 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.
+ */
+
+package org.apache.spark.mllib.regression
+
+import org.apache.spark.api.java.{JavaRDD, JavaPairRDD}
+import org.apache.spark.rdd.RDD
+
+/**
+ * Regression model for Isotonic regression
+ *
+ * @param predictions Weights computed for every feature.
+ * @param isotonic isotonic (increasing) or antitonic (decreasing) sequence
+ */
+class IsotonicRegressionModel (
+ val predictions: Seq[(Double, Double, Double)],
+ val isotonic: Boolean)
+ extends Serializable {
+
+ /**
+ * Predict labels for provided features
+ *
+ * @param testData features to be labeled
+ * @return predicted labels
+ */
+ def predict(testData: RDD[Double]): RDD[Double] =
+ testData.map(predict)
+
+ /**
+ * Predict labels for provided features
+ *
+ * @param testData features to be labeled
+ * @return predicted labels
+ */
+ def predict(testData: JavaRDD[java.lang.Double]):
JavaRDD[java.lang.Double] =
+ testData.rdd.map(_.doubleValue()).map(predict).map(new
java.lang.Double(_))
+
+ /**
+ * Predict a single label
+ *
+ * @param testData feature to be labeled
+ * @return predicted label
+ */
+ def predict(testData: Double): Double =
+ // Take the highest of data points smaller than our feature or data
point with lowest feature
+ (predictions.head +: predictions.filter(y => y._2 <= testData)).last._1
+}
+
+/**
+ * Base representing algorithm for isotonic regression
+ */
+trait IsotonicRegressionAlgorithm
+ extends Serializable {
+
+ /**
+ * Creates isotonic regression model with given parameters
+ *
+ * @param predictions labels estimated using isotonic regression
algorithm.
+ * Used for predictions on new data points.
+ * @param isotonic isotonic (increasing) or antitonic (decreasing)
sequence
+ * @return isotonic regression model
+ */
+ protected def createModel(
+ predictions: Seq[(Double, Double, Double)],
+ isotonic: Boolean): IsotonicRegressionModel
+
+ /**
+ * Run algorithm to obtain isotonic regression model
+ *
+ * @param input (label, feature, weight)
+ * @param isotonic isotonic (increasing) or antitonic (decreasing)
sequence
+ * @return isotonic regression model
+ */
+ def run(
+ input: RDD[(Double, Double, Double)],
+ isotonic: Boolean): IsotonicRegressionModel
+}
+
+/**
+ * Parallel pool adjacent violators algorithm for monotone regression
+ */
+class PoolAdjacentViolators private [mllib]
--- End diff --
Shall we cite the paper, which the implementation is based on?
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