org.apache.spark.mllib.classification.StreamingLogisticRegressionWithSGD.scala Maven / Gradle / Ivy
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* 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,
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* See the License for the specific language governing permissions and
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package org.apache.spark.mllib.classification
import org.apache.spark.annotation.Since
import org.apache.spark.mllib.linalg.Vector
import org.apache.spark.mllib.regression.StreamingLinearAlgorithm
/**
* Train or predict a logistic 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 `LogisticRegressionWithSGD` 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 logistic regression
* analysis in an application, like:
*
* {{{
* val model = new StreamingLogisticRegressionWithSGD()
* .setStepSize(0.5)
* .setNumIterations(10)
* .setInitialWeights(Vectors.dense(...))
* .trainOn(DStream)
* }}}
*/
@Since("1.3.0")
class StreamingLogisticRegressionWithSGD private[mllib] (
private var stepSize: Double,
private var numIterations: Int,
private var miniBatchFraction: Double,
private var regParam: Double)
extends StreamingLinearAlgorithm[LogisticRegressionModel, LogisticRegressionWithSGD]
with Serializable {
/**
* Construct a StreamingLogisticRegression object with default parameters:
* {stepSize: 0.1, numIterations: 50, miniBatchFraction: 1.0, regParam: 0.0}.
* Initial weights must be set before using trainOn or predictOn
* (see `StreamingLinearAlgorithm`)
*/
@Since("1.3.0")
def this() = this(0.1, 50, 1.0, 0.0)
protected val algorithm = new LogisticRegressionWithSGD(
stepSize, numIterations, regParam, miniBatchFraction)
protected var model: Option[LogisticRegressionModel] = None
/** Set the step size for gradient descent. Default: 0.1. */
@Since("1.3.0")
def setStepSize(stepSize: Double): this.type = {
this.algorithm.optimizer.setStepSize(stepSize)
this
}
/** Set the number of iterations of gradient descent to run per update. Default: 50. */
@Since("1.3.0")
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("1.3.0")
def setMiniBatchFraction(miniBatchFraction: Double): this.type = {
this.algorithm.optimizer.setMiniBatchFraction(miniBatchFraction)
this
}
/** Set the regularization parameter. Default: 0.0. */
@Since("1.3.0")
def setRegParam(regParam: Double): this.type = {
this.algorithm.optimizer.setRegParam(regParam)
this
}
/** Set the initial weights. Default: [0.0, 0.0]. */
@Since("1.3.0")
def setInitialWeights(initialWeights: Vector): this.type = {
this.model = Some(algorithm.createModel(initialWeights, 0.0))
this
}
}