Github user mengxr commented on a diff in the pull request:
https://github.com/apache/spark/pull/3519#discussion_r23594884
--- Diff:
mllib/src/main/scala/org/apache/spark/mllib/regression/IsotonicRegression.scala
---
@@ -0,0 +1,238 @@
+/*
+ * 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 java.io.Serializable
+import java.util.Arrays.binarySearch
+
+import org.apache.spark.api.java.{JavaDoubleRDD, JavaRDD}
+import org.apache.spark.rdd.RDD
+
+/**
+ * Regression model for Isotonic regression
+ *
+ * @param features Array of features.
+ * @param labels Array of labels associated to the features at the same
index.
+ */
+class IsotonicRegressionModel (
+ features: Array[Double],
+ val labels: Array[Double])
+ extends Serializable {
+
+ /**
+ * Predict labels for provided features
+ * Using a piecewise constant function
+ *
+ * @param testData features to be labeled
+ * @return predicted labels
+ */
+ def predict(testData: RDD[Double]): RDD[Double] =
+ testData.map(predict)
+
+ /**
+ * Predict labels for provided features
+ * Using a piecewise constant function
+ *
+ * @param testData features to be labeled
+ * @return predicted labels
+ */
+ def predict(testData: JavaRDD[java.lang.Double]): JavaDoubleRDD =
+ JavaDoubleRDD.fromRDD(predict(testData.rdd.asInstanceOf[RDD[Double]]))
+
+ /**
+ * Predict a single label
+ * Using a piecewise constant function
+ *
+ * @param testData feature to be labeled
+ * @return predicted label
+ */
+ def predict(testData: Double): Double = {
+ val result = binarySearch(features, testData)
+
+ val index =
+ if (result == -1) {
+ 0
+ } else if (result < 0) {
+ -result - 2
+ } else {
+ result
+ }
+
+ labels(index)
+ }
+}
+
+/**
+ * Isotonic regression
+ * Currently implemented using parallel pool adjacent violators algorithm
+ */
+class IsotonicRegression
+ extends Serializable {
+
+ /**
+ * 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 = true): IsotonicRegressionModel = {
+ createModel(
+ parallelPoolAdjacentViolators(input, isotonic),
+ isotonic)
+ }
+
+ /**
+ * 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: Array[(Double, Double, Double)],
+ isotonic: Boolean): IsotonicRegressionModel = {
+
+ val labels = predictions.map(_._1)
+ val features = predictions.map(_._2)
+
+ new IsotonicRegressionModel(features, labels)
+ }
+
+ /**
+ * Performs a pool adjacent violators algorithm (PAVA)
+ * Uses approach with single processing of data where violators
+ * in previously processed data created by pooling are fixed
immediatelly.
+ * Uses optimization of discovering monotonicity violating sequences
(blocks)
+ * Method in situ mutates input array
+ *
+ * @param in input data
--- End diff --
`in` -> `input`?
---
If your project is set up for it, you can reply to this email and have your
reply appear on GitHub as well. If your project does not have this feature
enabled and wishes so, or if the feature is enabled but not working, please
contact infrastructure at infrastruct...@apache.org or file a JIRA ticket
with INFRA.
---
---------------------------------------------------------------------
To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org
For additional commands, e-mail: reviews-h...@spark.apache.org
