Github user mengxr commented on a diff in the pull request:
https://github.com/apache/spark/pull/1361#discussion_r15724803
--- Diff:
mllib/src/main/scala/org/apache/spark/mllib/regression/StreamingLinearAlgorithm.scala
---
@@ -0,0 +1,94 @@
+/*
+ * 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.DeveloperApi
+import org.apache.spark.Logging
+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. 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]) {
+ data.foreachRDD { (rdd, time) =>
+ model = algorithm.run(rdd, model.weights)
+ logInfo("Model updated at time %s".format(time.toString))
+ logInfo("Current model: weights, %s".format(
+ model.weights.toArray.take(100).mkString("[", ",", "]")))
--- End diff --
If the number of features is greater than 100, we should output `, ...`
instead of `]`
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