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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.annotation.Since
import org.apache.spark.mllib.linalg.Vector
/**
* Train or predict a linear regression model on streaming data. Training uses
* Stochastic Gradient Descent to update the model based on each new batch of
* incoming data from a DStream (see `LinearRegressionWithSGD` for model equation)
*
* Each batch of data is assumed to be an RDD of LabeledPoints.
* The number of data points per batch can vary, but the number
* of features must be constant. An initial weight
* vector must be provided.
*
* Use a builder pattern to construct a streaming linear regression
* analysis in an application, like:
*
* val model = new StreamingLinearRegressionWithSGD()
* .setStepSize(0.5)
* .setNumIterations(10)
* .setInitialWeights(Vectors.dense(...))
* .trainOn(DStream)
*/
@Since("1.1.0")
class StreamingLinearRegressionWithSGD private[mllib] (
private var stepSize: Double,
private var numIterations: Int,
private var regParam: Double,
private var miniBatchFraction: Double)
extends StreamingLinearAlgorithm[LinearRegressionModel, LinearRegressionWithSGD]
with Serializable {
/**
* Construct a StreamingLinearRegression object with default parameters:
* {stepSize: 0.1, numIterations: 50, miniBatchFraction: 1.0}.
* Initial weights must be set before using trainOn or predictOn
* (see `StreamingLinearAlgorithm`)
*/
@Since("1.1.0")
def this() = this(0.1, 50, 0.0, 1.0)
@Since("1.1.0")
val algorithm = new LinearRegressionWithSGD(stepSize, numIterations, regParam, miniBatchFraction)
protected var model: Option[LinearRegressionModel] = None
/**
* Set the step size for gradient descent. Default: 0.1.
*/
@Since("1.1.0")
def setStepSize(stepSize: Double): this.type = {
this.algorithm.optimizer.setStepSize(stepSize)
this
}
/**
* Set the regularization parameter. Default: 0.0.
*/
@Since("2.0.0")
def setRegParam(regParam: Double): this.type = {
this.algorithm.optimizer.setRegParam(regParam)
this
}
/**
* Set the number of iterations of gradient descent to run per update. Default: 50.
*/
@Since("1.1.0")
def setNumIterations(numIterations: Int): this.type = {
this.algorithm.optimizer.setNumIterations(numIterations)
this
}
/**
* Set the fraction of each batch to use for updates. Default: 1.0.
*/
@Since("1.1.0")
def setMiniBatchFraction(miniBatchFraction: Double): this.type = {
this.algorithm.optimizer.setMiniBatchFraction(miniBatchFraction)
this
}
/**
* Set the initial weights.
*/
@Since("1.1.0")
def setInitialWeights(initialWeights: Vector): this.type = {
this.model = Some(algorithm.createModel(initialWeights, 0.0))
this
}
/**
* Set the convergence tolerance. Default: 0.001.
*/
@Since("1.5.0")
def setConvergenceTol(tolerance: Double): this.type = {
this.algorithm.optimizer.setConvergenceTol(tolerance)
this
}
}
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