Github user feynmanliang commented on a diff in the pull request:
https://github.com/apache/spark/pull/8022#discussion_r38676215
--- Diff:
mllib/src/main/scala/org/apache/spark/mllib/regression/StreamingDecay.scala ---
@@ -0,0 +1,99 @@
+/*
+ * 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
+import org.apache.spark.mllib.regression.StreamingDecay.{TimeUnit,
BATCHES, POINTS}
+
+/**
+ * :: Experimental ::
+ * Supply the discount value for the
+ * forgetful update rule in [[StreamingLinearAlgorithm]];
+ * The degree of forgetfulness can be specified by the decay factor
+ * or the half life.
+ *
+ */
+@Experimental
+private[mllib] trait StreamingDecay extends Logging{
+
+ private[this] var decayFactor: Double = 0
+ private[this] var timeUnit: TimeUnit = 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): this.type = {
+ 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 by 0.5.
+ * The unit of time can be specified either as batches or points.
+ *
+ * @param halfLife the half life
+ * @param timeUnit the time unit
+ */
+ def setHalfLife(halfLife: Double, timeUnit: TimeUnit): this.type = {
+ 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
+ */
+ private[mllib] def getDiscount(numNewDataPoints: Long): Double =
timeUnit match {
+ case BATCHES => decayFactor
--- End diff --
Since `timeUnit` affects the behavior `decayFactor` as well, what do you
think about either:
1. adding a `timeUnit` param to `decayFactor`
2. removing the `timeUnit` param from `setHalfLife` and introducing a
`setTimeUnit`
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