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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 scala.reflect.ClassTag
import org.apache.spark.Logging
import org.apache.spark.annotation.DeveloperApi
import org.apache.spark.mllib.linalg.Vector
import org.apache.spark.streaming.StreamingContext._
import org.apache.spark.streaming.dstream.DStream
/**
* :: DeveloperApi ::
* StreamingLinearAlgorithm implements methods for continuously
* training a generalized linear model model on streaming data,
* and using it for prediction on (possibly different) streaming data.
*
* This class takes as type parameters a GeneralizedLinearModel,
* and a GeneralizedLinearAlgorithm, making it easy to extend to construct
* streaming versions of any analyses using GLMs.
* Initial weights must be set before calling trainOn or predictOn.
* Only weights will be updated, not an intercept. If the model needs
* an intercept, it should be manually appended to the input data.
*
* For example usage, see `StreamingLinearRegressionWithSGD`.
*
* NOTE(Freeman): In some use cases, the order in which trainOn and predictOn
* are called in an application will affect the results. When called on
* the same DStream, if trainOn is called before predictOn, when new data
* arrive the model will update and the prediction will be based on the new
* model. Whereas if predictOn is called first, the prediction will use the model
* from the previous update.
*
* NOTE(Freeman): It is ok to call predictOn repeatedly on multiple streams; this
* will generate predictions for each one all using the current model.
* It is also ok to call trainOn on different streams; this will update
* the model using each of the different sources, in sequence.
*
*/
@DeveloperApi
abstract class StreamingLinearAlgorithm[
M <: GeneralizedLinearModel,
A <: GeneralizedLinearAlgorithm[M]] extends Logging {
/** The model to be updated and used for prediction. */
protected var model: M
/** The algorithm to use for updating. */
protected val algorithm: A
/** Return the latest model. */
def latestModel(): M = {
model
}
/**
* Update the model by training on batches of data from a DStream.
* This operation registers a DStream for training the model,
* and updates the model based on every subsequent
* batch of data from the stream.
*
* @param data DStream containing labeled data
*/
def trainOn(data: DStream[LabeledPoint]) {
if (Option(model.weights) == None) {
logError("Initial weights must be set before starting training")
throw new IllegalArgumentException
}
data.foreachRDD { (rdd, time) =>
model = algorithm.run(rdd, model.weights)
logInfo("Model updated at time %s".format(time.toString))
val display = model.weights.size match {
case x if x > 100 => model.weights.toArray.take(100).mkString("[", ",", "...")
case _ => model.weights.toArray.mkString("[", ",", "]")
}
logInfo("Current model: weights, %s".format (display))
}
}
/**
* Use the model to make predictions on batches of data from a DStream
*
* @param data DStream containing feature vectors
* @return DStream containing predictions
*/
def predictOn(data: DStream[Vector]): DStream[Double] = {
if (Option(model.weights) == None) {
val msg = "Initial weights must be set before starting prediction"
logError(msg)
throw new IllegalArgumentException(msg)
}
data.map(model.predict)
}
/**
* Use the model to make predictions on the values of a DStream and carry over its keys.
* @param data DStream containing feature vectors
* @tparam K key type
* @return DStream containing the input keys and the predictions as values
*/
def predictOnValues[K: ClassTag](data: DStream[(K, Vector)]): DStream[(K, Double)] = {
if (Option(model.weights) == None) {
val msg = "Initial weights must be set before starting prediction"
logError(msg)
throw new IllegalArgumentException(msg)
}
data.mapValues(model.predict)
}
}
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