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/*
* 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.SparkContext
import org.apache.spark.annotation.Since
import org.apache.spark.mllib.linalg.Vector
import org.apache.spark.mllib.optimization._
import org.apache.spark.mllib.pmml.PMMLExportable
import org.apache.spark.mllib.regression.impl.GLMRegressionModel
import org.apache.spark.mllib.util.{Loader, Saveable}
/**
* Regression model trained using RidgeRegression.
*
* @param weights Weights computed for every feature.
* @param intercept Intercept computed for this model.
*
*/
@Since("0.8.0")
class RidgeRegressionModel @Since("1.1.0") (
@Since("1.0.0") override val weights: Vector,
@Since("0.8.0") override val intercept: Double)
extends GeneralizedLinearModel(weights, intercept)
with RegressionModel with Serializable with Saveable with PMMLExportable {
override protected def predictPoint(
dataMatrix: Vector,
weightMatrix: Vector,
intercept: Double): Double = {
weightMatrix.asBreeze.dot(dataMatrix.asBreeze) + intercept
}
@Since("1.3.0")
override def save(sc: SparkContext, path: String): Unit = {
GLMRegressionModel.SaveLoadV1_0.save(sc, path, this.getClass.getName, weights, intercept)
}
}
@Since("1.3.0")
object RidgeRegressionModel extends Loader[RidgeRegressionModel] {
@Since("1.3.0")
override def load(sc: SparkContext, path: String): RidgeRegressionModel = {
val (loadedClassName, version, metadata) = Loader.loadMetadata(sc, path)
// Hard-code class name string in case it changes in the future
val classNameV1_0 = "org.apache.spark.mllib.regression.RidgeRegressionModel"
(loadedClassName, version) match {
case (className, "1.0") if className == classNameV1_0 =>
val numFeatures = RegressionModel.getNumFeatures(metadata)
val data = GLMRegressionModel.SaveLoadV1_0.loadData(sc, path, classNameV1_0, numFeatures)
new RidgeRegressionModel(data.weights, data.intercept)
case _ => throw new Exception(
s"RidgeRegressionModel.load did not recognize model with (className, format version):" +
s"($loadedClassName, $version). Supported:\n" +
s" ($classNameV1_0, 1.0)")
}
}
}
/**
* Train a regression model with L2-regularization using Stochastic Gradient Descent.
* This solves the l2-regularized least squares regression formulation
* f(weights) = 1/2n ||A weights-y||^2^ + regParam/2 ||weights||^2^
* Here the data matrix has n rows, and the input RDD holds the set of rows of A, each with
* its corresponding right hand side label y.
* See also the documentation for the precise formulation.
*/
@Since("0.8.0")
class RidgeRegressionWithSGD private[mllib] (
private var stepSize: Double,
private var numIterations: Int,
private var regParam: Double,
private var miniBatchFraction: Double)
extends GeneralizedLinearAlgorithm[RidgeRegressionModel] with Serializable {
private val gradient = new LeastSquaresGradient()
private val updater = new SquaredL2Updater()
@Since("0.8.0")
override val optimizer = new GradientDescent(gradient, updater)
.setStepSize(stepSize)
.setNumIterations(numIterations)
.setRegParam(regParam)
.setMiniBatchFraction(miniBatchFraction)
override protected def createModel(weights: Vector, intercept: Double) = {
new RidgeRegressionModel(weights, intercept)
}
}
