Github user feynmanliang commented on a diff in the pull request:
https://github.com/apache/spark/pull/8022#discussion_r38242939
--- Diff:
mllib/src/main/scala/org/apache/spark/mllib/regression/StreamingDecay.scala ---
@@ -0,0 +1,116 @@
+/*
+ * 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
+
+/**
+ * :: Experimental ::
+ * Supplies an interface for the discount value in
+ * the forgetful update rule in StreamingLinearAlgorithm.
+ * Actual implementation is provided in StreamingDecaySetter[T].
+ */
+@Experimental
+trait StreamingDecay {
+ /**
+ * Derive the discount factor.
+ *
+ * @param numNewDataPoints number of data points for the RDD arriving at
time t.
+ * @return Discount factor
+ */
+ def getDiscount(numNewDataPoints: Long): Double
+}
+
+/**
+ * :: Experimental ::
+ * StreamingDecaySetter provides the concrete implementation
+ * of getDiscount in StreamingDecay and setters for decay factor
+ * and half-life.
+ */
+@Experimental
+private[mllib] trait StreamingDecaySetter[T] extends Logging {
+ self: T =>
+ private var decayFactor: Double = 0
+ private var timeUnit: String = StreamingDecay.BATCHES
+
+ /**
+ * Set the decay factor for the forgetful algorithms.
+ * The decay factor should be between 0 and 1, inclusive.
+ * decayFactor = 0: only the data from the most recent RDD will be used.
+ * decayFactor = 1: all data since the beginning of the DStream will be
used.
+ * decayFactor is default to zero.
+ *
+ * @param decayFactor the decay factor
+ */
+ def setDecayFactor(decayFactor: Double): T = {
+ this.decayFactor = decayFactor
+ this
+ }
+
+
+ /**
+ * Set the half life and time unit ("batches" or "points") for the
forgetful algorithm.
+ * The half life along with the time unit provides an alternative way to
specify decay factor.
+ * The decay factor is calculated such that, for data acquired at time t,
+ * its contribution by time t + halfLife will have dropped to 0.5.
+ * The unit of time can be specified either as batches or points;
+ * see StreamingDecay companion object.
+ *
+ * @param halfLife the half life
+ * @param timeUnit the time unit
+ */
+ def setHalfLife(halfLife: Double, timeUnit: String): T = {
+ if (timeUnit != StreamingDecay.BATCHES && timeUnit !=
StreamingDecay.POINTS) {
+ throw new IllegalArgumentException("Invalid time unit for decay: " +
timeUnit)
+ }
+ this.decayFactor = math.exp(math.log(0.5) / halfLife)
+ logInfo("Setting decay factor to: %g ".format (this.decayFactor))
+ this.timeUnit = timeUnit
+ this
+ }
+
+ /**
+ * Derive the discount factor.
+ *
+ * @param numNewDataPoints number of data points for the RDD arriving at
time t.
+ * @return Discount factor
+ */
+ def getDiscount(numNewDataPoints: Long): Double = timeUnit match {
+ case StreamingDecay.BATCHES => decayFactor
+ case StreamingDecay.POINTS => math.pow(decayFactor, numNewDataPoints)
+ }
+}
+
+/**
+ * :: Experimental ::
+ * Provides the String constants for allowed time unit in the forgetful
algorithm.
+ */
+@Experimental
+object StreamingDecay {
+ /**
+ * Each RDD in the DStream will be treated as 1 time unit.
+ *
+ */
+ final val BATCHES = "batches"
--- End diff --
I think that would be much better but it might be useful to ping whoever
implemented the StreamingKMeans API first just to check if they did that for
some particular reason that I am unaware of.
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