4 files changed, 258 insertions, 1 deletions
diff --git a/data/mllib/sample_isotonic_regression_data.txt b/data/mllib/sample_isotonic_regression_data.txt
new file mode 100644
index 0000000000..d257b509d4
--- /dev/null
+++ b/data/mllib/sample_isotonic_regression_data.txt
@@ -0,0 +1,100 @@
+0.24579296,0.01
+0.28505864,0.02
+0.31208567,0.03
+0.35900051,0.04
+0.35747068,0.05
+0.16675166,0.06
+0.17491076,0.07
+0.04181540,0.08
+0.04793473,0.09
+0.03926568,0.10
+0.12952575,0.11
+0.00000000,0.12
+0.01376849,0.13
+0.13105558,0.14
+0.08873024,0.15
+0.12595614,0.16
+0.15247323,0.17
+0.25956145,0.18
+0.20040796,0.19
+0.19581846,0.20
+0.15757267,0.21
+0.13717491,0.22
+0.19020908,0.23
+0.19581846,0.24
+0.20091790,0.25
+0.16879143,0.26
+0.18510964,0.27
+0.20040796,0.28
+0.29576747,0.29
+0.43396226,0.30
+0.53391127,0.31
+0.52116267,0.32
+0.48546660,0.33
+0.49209587,0.34
+0.54156043,0.35
+0.59765426,0.36
+0.56144824,0.37
+0.58592555,0.38
+0.52983172,0.39
+0.50178480,0.40
+0.52626211,0.41
+0.58286588,0.42
+0.64660887,0.43
+0.68077511,0.44
+0.74298827,0.45
+0.64864865,0.46
+0.67261601,0.47
+0.65782764,0.48
+0.69811321,0.49
+0.63029067,0.50
+0.61601224,0.51
+0.63233044,0.52
+0.65323814,0.53
+0.65323814,0.54
+0.67363590,0.55
+0.67006629,0.56
+0.51555329,0.57
+0.50892402,0.58
+0.33299337,0.59
+0.36206017,0.60
+0.43090260,0.61
+0.45996940,0.62
+0.56348802,0.63
+0.54920959,0.64
+0.48393677,0.65
+0.48495665,0.66
+0.46965834,0.67
+0.45181030,0.68
+0.45843957,0.69
+0.47118817,0.70
+0.51555329,0.71
+0.58031617,0.72
+0.55481897,0.73
+0.56297807,0.74
+0.56603774,0.75
+0.57929628,0.76
+0.64762876,0.77
+0.66241713,0.78
+0.69301377,0.79
+0.65119837,0.80
+0.68332483,0.81
+0.66598674,0.82
+0.73890872,0.83
+0.73992861,0.84
+0.84242733,0.85
+0.91330954,0.86
+0.88016318,0.87
+0.90719021,0.88
+0.93115757,0.89
+0.93115757,0.90
+0.91942886,0.91
+0.92911780,0.92
+0.95665477,0.93
+0.95002550,0.94
+0.96940337,0.95
+1.00000000,0.96
+0.89801122,0.97
+0.90311066,0.98
+0.90362060,0.99
+0.83477817,1.0
+\ No newline at end of file
diff --git a/docs/mllib-classification-regression.md b/docs/mllib-classification-regression.md
index 719cc95767..5b9b4dd83b 100644
--- a/docs/mllib-classification-regression.md
+++ b/docs/mllib-classification-regression.md
@@ -23,7 +23,7 @@ the supported algorithms for each type of problem.
       <td>Multiclass Classification</td><td>decision trees, naive Bayes</td>
     </tr>
     <tr>
-      <td>Regression</td><td>linear least squares, Lasso, ridge regression, decision trees</td>
+      <td>Regression</td><td>linear least squares, Lasso, ridge regression, decision trees, isotonic regression</td>
     </tr>
   </tbody>
 </table>
@@ -35,3 +35,4 @@ More details for these methods can be found here:
   * [linear regression (least squares, Lasso, ridge)](mllib-linear-methods.html#linear-least-squares-lasso-and-ridge-regression)
 * [Decision trees](mllib-decision-tree.html)
 * [Naive Bayes](mllib-naive-bayes.html)
+* [Isotonic regression](mllib-isotonic-regression.html)
diff --git a/docs/mllib-guide.md b/docs/mllib-guide.md
index 5091dbf329..fbe809b347 100644
--- a/docs/mllib-guide.md
+++ b/docs/mllib-guide.md
@@ -21,6 +21,7 @@ filtering, dimensionality reduction, as well as underlying optimization primitiv
   * [naive Bayes](mllib-naive-bayes.html)
   * [decision trees](mllib-decision-tree.html)
   * [ensembles of trees](mllib-ensembles.html) (Random Forests and Gradient-Boosted Trees)
+  * [isotonic regression](mllib-isotonic-regression.html)
 * [Collaborative filtering](mllib-collaborative-filtering.html)
   * alternating least squares (ALS)
 * [Clustering](mllib-clustering.html)
diff --git a/docs/mllib-isotonic-regression.md b/docs/mllib-isotonic-regression.md
new file mode 100644
index 0000000000..12fb29d426
--- /dev/null
+++ b/docs/mllib-isotonic-regression.md
@@ -0,0 +1,155 @@
+---
+layout: global
+title: Naive Bayes - MLlib
+displayTitle: <a href="mllib-guide.html">MLlib</a> - Regression
+---
+
+## Isotonic regression
+[Isotonic regression](http://en.wikipedia.org/wiki/Isotonic_regression)
+belongs to the family of regression algorithms. Formally isotonic regression is a problem where
+given a finite set of real numbers `$Y = {y_1, y_2, ..., y_n}$` representing observed responses
+and `$X = {x_1, x_2, ..., x_n}$` the unknown response values to be fitted
+finding a function that minimises
+
+`\begin{equation}
+  f(x) = \sum_{i=1}^n w_i (y_i - x_i)^2
+\end{equation}`
+
+with respect to complete order subject to
+`$x_1\le x_2\le ...\le x_n$` where `$w_i$` are positive weights.
+The resulting function is called isotonic regression and it is unique.
+It can be viewed as least squares problem under order restriction.
+Essentially isotonic regression is a
+[monotonic function](http://en.wikipedia.org/wiki/Monotonic_function)
+best fitting the original data points.
+
+MLlib supports a
+[pool adjacent violators algorithm](http://doi.org/10.1198/TECH.2010.10111)
+which uses an approach to
+[parallelizing isotonic regression](http://doi.org/10.1007/978-3-642-99789-1_10).
+The training input is a RDD of tuples of three double values that represent
+label, feature and weight in this order. Additionally IsotonicRegression algorithm has one
+optional parameter called $isotonic$ defaulting to true.
+This argument specifies if the isotonic regression is
+isotonic (monotonically increasing) or antitonic (monotonically decreasing).
+
+Training returns an IsotonicRegressionModel that can be used to predict
+labels for both known and unknown features. The result of isotonic regression
+is treated as piecewise linear function. The rules for prediction therefore are:
+
+* If the prediction input exactly matches a training feature
+  then associated prediction is returned. In case there are multiple predictions with the same
+  feature then one of them is returned. Which one is undefined
+  (same as java.util.Arrays.binarySearch).
+* If the prediction input is lower or higher than all training features
+  then prediction with lowest or highest feature is returned respectively.
+  In case there are multiple predictions with the same feature
+  then the lowest or highest is returned respectively.
+* If the prediction input falls between two training features then prediction is treated
+  as piecewise linear function and interpolated value is calculated from the
+  predictions of the two closest features. In case there are multiple values
+  with the same feature then the same rules as in previous point are used.
+
+### Examples
+
+<div class="codetabs">
+<div data-lang="scala" markdown="1">
+Data are read from a file where each line has a format label,feature
+i.e. 4710.28,500.00. The data are split to training and testing set.
+Model is created using the training set and a mean squared error is calculated from the predicted
+labels and real labels in the test set.
+
+{% highlight scala %}
+import org.apache.spark.mllib.regression.IsotonicRegression
+
+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)
+{% endhighlight %}
+</div>
+
+<div data-lang="java" markdown="1">
+Data are read from a file where each line has a format label,feature
+i.e. 4710.28,500.00. The data are split to training and testing set.
+Model is created using the training set and a mean squared error is calculated from the predicted
+labels and real labels in the test set.
+
+{% highlight java %}
+import org.apache.spark.SparkConf;
+import org.apache.spark.api.java.JavaDoubleRDD;
+import org.apache.spark.api.java.JavaPairRDD;
+import org.apache.spark.api.java.JavaRDD;
+import org.apache.spark.api.java.JavaSparkContext;
+import org.apache.spark.api.java.function.Function;
+import org.apache.spark.api.java.function.PairFunction;
+import org.apache.spark.mllib.regression.IsotonicRegressionModel;
+import scala.Tuple2;
+import scala.Tuple3;
+
+JavaRDD<String> 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.
+JavaRDD<Tuple3<Double, Double, Double>> parsedData = data.map(
+  new Function<String, Tuple3<Double, Double, Double>>() {
+    public Tuple3<Double, Double, Double> call(String line) {
+      String[] parts = line.split(",");
+      return new Tuple3<>(new Double(parts[0]), new Double(parts[1]), 1.0);
+    }
+  }
+);
+
+// Split data into training (60%) and test (40%) sets.
+JavaRDD<Tuple3<Double, Double, Double>>[] splits = parsedData.randomSplit(new double[] {0.6, 0.4}, 11L);
+JavaRDD<Tuple3<Double, Double, Double>> training = splits[0];
+JavaRDD<Tuple3<Double, Double, Double>> test = splits[1];
+
+// Create isotonic regression model from training data.
+// Isotonic parameter defaults to true so it is only shown for demonstration
+IsotonicRegressionModel model = new IsotonicRegression().setIsotonic(true).run(training);
+
+// Create tuples of predicted and real labels.
+JavaPairRDD<Double, Double> predictionAndLabel = test.mapToPair(
+  new PairFunction<Tuple3<Double, Double, Double>, Double, Double>() {
+    @Override public Tuple2<Double, Double> call(Tuple3<Double, Double, Double> point) {
+      Double predictedLabel = model.predict(point._2());
+      return new Tuple2<Double, Double>(predictedLabel, point._1());
+    }
+  }
+);
+
+// Calculate mean squared error between predicted and real labels.
+Double meanSquaredError = new JavaDoubleRDD(predictionAndLabel.map(
+  new Function<Tuple2<Double, Double>, Object>() {
+    @Override public Object call(Tuple2<Double, Double> pl) {
+      return Math.pow(pl._1() - pl._2(), 2);
+    }
+  }
+).rdd()).mean();
+
+System.out.println("Mean Squared Error = " + meanSquaredError);
+{% endhighlight %}
+</div>
+</div>
+\ No newline at end of file