diff options
3 files changed, 106 insertions, 4 deletions
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala b/mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala index b086cec083..426306d78c 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala @@ -283,6 +283,24 @@ private[python] class PythonMLLibAPI extends Serializable { } /** + * Java stub for Python mllib IsotonicRegression.run() + */ + def trainIsotonicRegressionModel( + data: JavaRDD[Vector], + isotonic: Boolean): JList[Object] = { + val isotonicRegressionAlg = new IsotonicRegression().setIsotonic(isotonic) + val input = data.rdd.map { x => + (x(0), x(1), x(2)) + }.persist(StorageLevel.MEMORY_AND_DISK) + try { + val model = isotonicRegressionAlg.run(input) + List[AnyRef](model.boundaryVector, model.predictionVector).asJava + } finally { + data.rdd.unpersist(blocking = false) + } + } + + /** * Java stub for Python mllib KMeans.run() */ def trainKMeansModel( diff --git a/mllib/src/main/scala/org/apache/spark/mllib/regression/IsotonicRegression.scala b/mllib/src/main/scala/org/apache/spark/mllib/regression/IsotonicRegression.scala index 1d7617046b..be2a00c2df 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/regression/IsotonicRegression.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/regression/IsotonicRegression.scala @@ -21,18 +21,20 @@ import java.io.Serializable import java.lang.{Double => JDouble} import java.util.Arrays.binarySearch +import scala.collection.JavaConverters._ import scala.collection.mutable.ArrayBuffer import org.json4s._ import org.json4s.JsonDSL._ import org.json4s.jackson.JsonMethods._ +import org.apache.spark.SparkContext import org.apache.spark.annotation.Experimental import org.apache.spark.api.java.{JavaDoubleRDD, JavaRDD} +import org.apache.spark.mllib.linalg.{Vector, Vectors} import org.apache.spark.mllib.util.{Loader, Saveable} import org.apache.spark.rdd.RDD -import org.apache.spark.SparkContext -import org.apache.spark.sql.{DataFrame, SQLContext} +import org.apache.spark.sql.SQLContext /** * :: Experimental :: @@ -57,6 +59,13 @@ class IsotonicRegressionModel ( assertOrdered(boundaries) assertOrdered(predictions)(predictionOrd) + /** A Java-friendly constructor that takes two Iterable parameters and one Boolean parameter. */ + def this(boundaries: java.lang.Iterable[Double], + predictions: java.lang.Iterable[Double], + isotonic: java.lang.Boolean) = { + this(boundaries.asScala.toArray, predictions.asScala.toArray, isotonic) + } + /** Asserts the input array is monotone with the given ordering. */ private def assertOrdered(xs: Array[Double])(implicit ord: Ordering[Double]): Unit = { var i = 1 @@ -132,6 +141,12 @@ class IsotonicRegressionModel ( } } + /** A convenient method for boundaries called by the Python API. */ + private[mllib] def boundaryVector: Vector = Vectors.dense(boundaries) + + /** A convenient method for boundaries called by the Python API. */ + private[mllib] def predictionVector: Vector = Vectors.dense(predictions) + override def save(sc: SparkContext, path: String): Unit = { IsotonicRegressionModel.SaveLoadV1_0.save(sc, path, boundaries, predictions, isotonic) } diff --git a/python/pyspark/mllib/regression.py b/python/pyspark/mllib/regression.py index 4bc6351bdf..41bde2ce3e 100644 --- a/python/pyspark/mllib/regression.py +++ b/python/pyspark/mllib/regression.py @@ -18,14 +18,16 @@ import numpy as np from numpy import array +from pyspark import RDD from pyspark.mllib.common import callMLlibFunc, _py2java, _java2py, inherit_doc -from pyspark.mllib.linalg import SparseVector, _convert_to_vector +from pyspark.mllib.linalg import SparseVector, Vectors, _convert_to_vector from pyspark.mllib.util import Saveable, Loader __all__ = ['LabeledPoint', 'LinearModel', 'LinearRegressionModel', 'LinearRegressionWithSGD', 'RidgeRegressionModel', 'RidgeRegressionWithSGD', - 'LassoModel', 'LassoWithSGD'] + 'LassoModel', 'LassoWithSGD', 'IsotonicRegressionModel', + 'IsotonicRegression'] class LabeledPoint(object): @@ -396,6 +398,73 @@ class RidgeRegressionWithSGD(object): return _regression_train_wrapper(train, RidgeRegressionModel, data, initialWeights) +class IsotonicRegressionModel(Saveable, Loader): + + """Regression model for isotonic regression. + + >>> data = [(1, 0, 1), (2, 1, 1), (3, 2, 1), (1, 3, 1), (6, 4, 1), (17, 5, 1), (16, 6, 1)] + >>> irm = IsotonicRegression.train(sc.parallelize(data)) + >>> irm.predict(3) + 2.0 + >>> irm.predict(5) + 16.5 + >>> irm.predict(sc.parallelize([3, 5])).collect() + [2.0, 16.5] + >>> import os, tempfile + >>> path = tempfile.mkdtemp() + >>> irm.save(sc, path) + >>> sameModel = IsotonicRegressionModel.load(sc, path) + >>> sameModel.predict(3) + 2.0 + >>> sameModel.predict(5) + 16.5 + >>> try: + ... os.removedirs(path) + ... except OSError: + ... pass + """ + + def __init__(self, boundaries, predictions, isotonic): + self.boundaries = boundaries + self.predictions = predictions + self.isotonic = isotonic + + def predict(self, x): + if isinstance(x, RDD): + return x.map(lambda v: self.predict(v)) + return np.interp(x, self.boundaries, self.predictions) + + def save(self, sc, path): + java_boundaries = _py2java(sc, self.boundaries.tolist()) + java_predictions = _py2java(sc, self.predictions.tolist()) + java_model = sc._jvm.org.apache.spark.mllib.regression.IsotonicRegressionModel( + java_boundaries, java_predictions, self.isotonic) + java_model.save(sc._jsc.sc(), path) + + @classmethod + def load(cls, sc, path): + java_model = sc._jvm.org.apache.spark.mllib.regression.IsotonicRegressionModel.load( + sc._jsc.sc(), path) + py_boundaries = _java2py(sc, java_model.boundaryVector()).toArray() + py_predictions = _java2py(sc, java_model.predictionVector()).toArray() + return IsotonicRegressionModel(py_boundaries, py_predictions, java_model.isotonic) + + +class IsotonicRegression(object): + """ + Run IsotonicRegression algorithm to obtain isotonic regression model. + + :param data: RDD of (label, feature, weight) tuples. + :param isotonic: Whether this is isotonic or antitonic. + """ + @classmethod + def train(cls, data, isotonic=True): + """Train a isotonic regression model on the given data.""" + boundaries, predictions = callMLlibFunc("trainIsotonicRegressionModel", + data.map(_convert_to_vector), bool(isotonic)) + return IsotonicRegressionModel(boundaries.toArray(), predictions.toArray(), isotonic) + + def _test(): import doctest from pyspark import SparkContext |