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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 spark.mllib.regression
import spark.{Logging, RDD, SparkContext}
import spark.mllib.optimization._
import spark.mllib.util.MLUtils
import org.jblas.DoubleMatrix
/**
* Regression model trained using Lasso.
*
* @param weights Weights computed for every feature.
* @param intercept Intercept computed for this model.
*/
class LassoModel(
override val weights: Array[Double],
override val intercept: Double)
extends GeneralizedLinearModel(weights, intercept)
with RegressionModel with Serializable {
override def predictPoint(dataMatrix: DoubleMatrix, weightMatrix: DoubleMatrix,
intercept: Double) = {
dataMatrix.dot(weightMatrix) + intercept
}
}
/**
* Train a regression model with L1-regularization using Stochastic Gradient Descent.
*/
class LassoWithSGD private (
var stepSize: Double,
var numIterations: Int,
var regParam: Double,
var miniBatchFraction: Double,
var addIntercept: Boolean)
extends GeneralizedLinearAlgorithm[LassoModel]
with Serializable {
val gradient = new SquaredGradient()
val updater = new L1Updater()
val optimizer = new GradientDescent(gradient, updater).setStepSize(stepSize)
.setNumIterations(numIterations)
.setRegParam(regParam)
.setMiniBatchFraction(miniBatchFraction)
/**
* Construct a Lasso object with default parameters
*/
def this() = this(1.0, 100, 1.0, 1.0, true)
def createModel(weights: Array[Double], intercept: Double) = {
new LassoModel(weights, intercept)
}
}
/**
* Top-level methods for calling Lasso.
*/
object LassoWithSGD {
/**
* Train a Lasso model given an RDD of (label, features) pairs. We run a fixed number
* of iterations of gradient descent using the specified step size. Each iteration uses
* `miniBatchFraction` fraction of the data to calculate the gradient. The weights used in
* gradient descent are initialized using the initial weights provided.
*
* @param input RDD of (label, array of features) pairs.
* @param numIterations Number of iterations of gradient descent to run.
* @param stepSize Step size to be used for each iteration of gradient descent.
* @param regParam Regularization parameter.
* @param miniBatchFraction Fraction of data to be used per iteration.
* @param initialWeights Initial set of weights to be used. Array should be equal in size to
* the number of features in the data.
*/
def train(
input: RDD[LabeledPoint],
numIterations: Int,
stepSize: Double,
regParam: Double,
miniBatchFraction: Double,
initialWeights: Array[Double])
: LassoModel =
{
new LassoWithSGD(stepSize, numIterations, regParam, miniBatchFraction, true).run(input,
initialWeights)
}
/**
* Train a Lasso model given an RDD of (label, features) pairs. We run a fixed number
* of iterations of gradient descent using the specified step size. Each iteration uses
* `miniBatchFraction` fraction of the data to calculate the gradient.
*
* @param input RDD of (label, array of features) pairs.
* @param numIterations Number of iterations of gradient descent to run.
* @param stepSize Step size to be used for each iteration of gradient descent.
* @param regParam Regularization parameter.
* @param miniBatchFraction Fraction of data to be used per iteration.
*/
def train(
input: RDD[LabeledPoint],
numIterations: Int,
stepSize: Double,
regParam: Double,
miniBatchFraction: Double)
: LassoModel =
{
new LassoWithSGD(stepSize, numIterations, regParam, miniBatchFraction, true).run(input)
}
/**
* Train a Lasso model given an RDD of (label, features) pairs. We run a fixed number
* of iterations of gradient descent using the specified step size. We use the entire data set to
* update the gradient in each iteration.
*
* @param input RDD of (label, array of features) pairs.
* @param stepSize Step size to be used for each iteration of Gradient Descent.
* @param regParam Regularization parameter.
* @param numIterations Number of iterations of gradient descent to run.
* @return a LassoModel which has the weights and offset from training.
*/
def train(
input: RDD[LabeledPoint],
numIterations: Int,
stepSize: Double,
regParam: Double)
: LassoModel =
{
train(input, numIterations, stepSize, regParam, 1.0)
}
/**
* Train a Lasso model given an RDD of (label, features) pairs. We run a fixed number
* of iterations of gradient descent using a step size of 1.0. We use the entire data set to
* update the gradient in each iteration.
*
* @param input RDD of (label, array of features) pairs.
* @param numIterations Number of iterations of gradient descent to run.
* @return a LassoModel which has the weights and offset from training.
*/
def train(
input: RDD[LabeledPoint],
numIterations: Int)
: LassoModel =
{
train(input, numIterations, 1.0, 1.0, 1.0)
}
def main(args: Array[String]) {
if (args.length != 5) {
println("Usage: Lasso <master> <input_dir> <step_size> <regularization_parameter> <niters>")
System.exit(1)
}
val sc = new SparkContext(args(0), "Lasso")
val data = MLUtils.loadLabeledData(sc, args(1))
val model = LassoWithSGD.train(data, args(4).toInt, args(2).toDouble, args(3).toDouble)
sc.stop()
}
}
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