Github user feynmanliang commented on a diff in the pull request:
https://github.com/apache/spark/pull/4716#discussion_r39897100
--- Diff:
mllib/src/main/scala/org/apache/spark/mllib/stat/test/StreamingTest.scala ---
@@ -0,0 +1,145 @@
+/*
+ * 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.stat.test
+
+import org.apache.spark.Logging
+import org.apache.spark.annotation.{Experimental, Since}
+import org.apache.spark.streaming.dstream.DStream
+import org.apache.spark.util.StatCounter
+
+/**
+ * :: Experimental ::
+ * Performs online 2-sample significance testing for a stream of (Boolean,
Double) pairs. The
+ * Boolean identifies which sample each observation comes from, and the
Double is the numeric value
+ * of the observation.
+ *
+ * To address novelty affects, the `peacePeriod` specifies a set number of
initial
+ * [[org.apache.spark.rdd.RDD]] batches of the [[DStream]] to be dropped
from significance testing.
+ *
+ * The `windowSize` sets the number of batches each significance test is
to be performed over. The
+ * window is sliding with a stride length of 1 batch. Setting windowSize
to 0 will perform
+ * cumulative processing, using all batches seen so far.
+ *
+ * Different tests may be used for assessing statistical significance
depending on assumptions
+ * satisfied by data. For more details, see [[StreamingTestMethod]]. The
`testMethod` specifies
+ * which test will be used.
+ *
+ * Use a builder pattern to construct a streaming test in an application,
for example:
+ * ```
+ * val model = new OnlineABTest()
+ * .setPeacePeriod(10)
+ * .setWindowSize(0)
+ * .setTestMethod("welch")
+ * .registerStream(DStream)
+ * ```
+ */
+@Experimental
+@Since("1.6.0")
+class StreamingTest(
+ @Since("1.6.0") var peacePeriod: Int = 0,
+ @Since("1.6.0") var windowSize: Int = 0,
+ @Since("1.6.0") var testMethod: StreamingTestMethod = WelchTTest)
+ extends Logging with Serializable {
+
+ /** Set the number of initial batches to ignore. */
+ @Since("1.6.0")
+ def setPeacePeriod(peacePeriod: Int): this.type = {
+ this.peacePeriod = peacePeriod
+ this
+ }
+
+ /**
+ * Set the number of batches to compute significance tests over.
+ * A value of 0 will use all batches seen so far.
+ */
+ @Since("1.6.0")
+ def setWindowSize(windowSize: Int): this.type = {
+ this.windowSize = windowSize
+ this
+ }
+
+ /** Set the statistical method used for significance testing. */
+ @Since("1.6.0")
+ def setTestMethod(method: String): this.type = {
+ this.testMethod = StreamingTestMethod.getTestMethodFromName(method)
+ this
+ }
+
+ /**
+ * Register a [[DStream]] of values for significance testing.
+ *
+ * @param data stream of (key,value) pairs where the key is the group
membership (control or
+ * treatment) and the value is the numerical metric to test
for significance
+ * @return stream of significance testing results
+ */
+ @Since("1.6.0")
+ def registerStream(data: DStream[(Boolean, Double)]):
DStream[StreamingTestResult] = {
+ val dataAfterPeacePeriod = dropPeacePeriod(data)
+ val summarizedData = summarizeByKeyAndWindow(dataAfterPeacePeriod)
+ val pairedSummaries = pairSummaries(summarizedData)
+ val testResults = testMethod.doTest(pairedSummaries)
+
+ testResults
+ }
+
+ /** Drop all batches inside the peace period. */
+ private[stat] def dropPeacePeriod(
+ data: DStream[(Boolean, Double)]): DStream[(Boolean, Double)] = {
+ data.transform { (rdd, time) =>
+ if (time.milliseconds > data.slideDuration.milliseconds *
peacePeriod) {
+ rdd
+ } else {
+ rdd.filter(_ => false) // TODO: Is there a better way to drop a
RDD from a DStream?
+ }
+ }
+ }
+
+ /** Compute summary statistics over each key and the specified test
window size. */
+ private[stat] def summarizeByKeyAndWindow(
+ data: DStream[(Boolean, Double)]): DStream[(Boolean, StatCounter)] =
{
+ if (this.windowSize == 0) {
+ data.updateStateByKey[StatCounter](
+ (newValues: Seq[Double], oldSummary: Option[StatCounter]) => {
+ val newSummary = oldSummary.getOrElse(new StatCounter())
+ newSummary.merge(newValues)
+ Some(newSummary)
+ })
+ } else {
+ val windowDuration = data.slideDuration * this.windowSize
+ data
+ .groupByKeyAndWindow(windowDuration)
+ .mapValues { values =>
+ val summary = new StatCounter()
+ values.foreach(value => summary.merge(value))
+ summary
+ }
+ }
+ }
+
+ /**
+ * Transform a stream of summaries into pairs representing summary
statistics for group A and
+ * group B up to this batch.
+ */
+ private[stat] def pairSummaries(summarizedData: DStream[(Boolean,
StatCounter)])
+ : DStream[(StatCounter, StatCounter)] = {
+ summarizedData
+ .map[(Int, StatCounter)](x => (0, x._2))
+ .groupByKey() // Iterable[StatCounter] should be length two, one
for each A/B group
--- End diff --
OK
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