[GitHub] spark pull request: Streaming mllib [SPARK-2438][MLLIB]

[freeman-lab <gi...@git.apache.org>]

Github user freeman-lab commented on a diff in the pull request:

    https://github.com/apache/spark/pull/1361#discussion_r15682567
  
    --- Diff: mllib/src/main/scala/org/apache/spark/mllib/regression/StreamingRegression.scala ---
    @@ -0,0 +1,83 @@
    +/*
    + * 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.Logging
    +import org.apache.spark.annotation.{Experimental, DeveloperApi}
    +import org.apache.spark.streaming.dstream.DStream
    +
    +/**
    + * :: DeveloperApi ::
    + * StreamingRegression implements methods for training
    + * a linear regression model on streaming data, and using it
    + * for prediction on streaming data.
    + *
    + * This class takes as type parameters a GeneralizedLinearModel,
    + * and a GeneralizedLinearAlgorithm, making it easy to extend to construct
    + * streaming versions of arbitrary regression analyses. For example usage,
    + * see StreamingLinearRegressionWithSGD.
    + *
    + */
    +@DeveloperApi
    +@Experimental
    +abstract class StreamingRegression[
    +    M <: GeneralizedLinearModel,
    +    A <: GeneralizedLinearAlgorithm[M]] extends Logging {
    +
    +  /** The model to be updated and used for prediction. */
    +  var model: M
    +
    +  /** The algorithm to use for updating. */
    +  val algorithm: A
    +
    +  /** Return the latest model. */
    +  def latest(): 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 non-empty
    +   * batch of data from the stream.
    +   *
    +   * @param data DStream containing labeled data
    +   */
    +  def trainOn(data: DStream[LabeledPoint]) {
    +    data.foreachRDD{
    +      rdd =>
    +        if (rdd.count() > 0) {
    +          model = algorithm.run(rdd, model.weights)
    +          logInfo("Model updated")
    +        }
    +        logInfo("Current model: weights, %s".format(model.weights.toString))
    +        logInfo("Current model: intercept, %s".format(model.intercept.toString))
    --- End diff --
    
    Ok, good points, agreed it's safer. I'll make sure there's a note about this.


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