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author | Feynman Liang <feynman.liang@gmail.com> | 2015-11-30 15:38:44 -0800 |
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committer | Xiangrui Meng <meng@databricks.com> | 2015-11-30 15:38:44 -0800 |
commit | 55358889309cf2d856b72e72e0f3081dfdf61cfa (patch) | |
tree | c52e01ffa7276e514bfb622f704afa6b3be264d3 /docs | |
parent | de64b65f7cf2ac58c1abc310ba547637fdbb8557 (diff) | |
download | spark-55358889309cf2d856b72e72e0f3081dfdf61cfa.tar.gz spark-55358889309cf2d856b72e72e0f3081dfdf61cfa.tar.bz2 spark-55358889309cf2d856b72e72e0f3081dfdf61cfa.zip |
[SPARK-11960][MLLIB][DOC] User guide for streaming tests
CC jkbradley mengxr josepablocam
Author: Feynman Liang <feynman.liang@gmail.com>
Closes #10005 from feynmanliang/streaming-test-user-guide.
Diffstat (limited to 'docs')
-rw-r--r-- | docs/mllib-guide.md | 1 | ||||
-rw-r--r-- | docs/mllib-statistics.md | 25 |
2 files changed, 26 insertions, 0 deletions
diff --git a/docs/mllib-guide.md b/docs/mllib-guide.md index 54e35fcbb1..43772adcf2 100644 --- a/docs/mllib-guide.md +++ b/docs/mllib-guide.md @@ -34,6 +34,7 @@ We list major functionality from both below, with links to detailed guides. * [correlations](mllib-statistics.html#correlations) * [stratified sampling](mllib-statistics.html#stratified-sampling) * [hypothesis testing](mllib-statistics.html#hypothesis-testing) + * [streaming significance testing](mllib-statistics.html#streaming-significance-testing) * [random data generation](mllib-statistics.html#random-data-generation) * [Classification and regression](mllib-classification-regression.html) * [linear models (SVMs, logistic regression, linear regression)](mllib-linear-methods.html) diff --git a/docs/mllib-statistics.md b/docs/mllib-statistics.md index ade5b0768a..de209f68e1 100644 --- a/docs/mllib-statistics.md +++ b/docs/mllib-statistics.md @@ -521,6 +521,31 @@ print(testResult) # summary of the test including the p-value, test statistic, </div> </div> +### Streaming Significance Testing +MLlib provides online implementations of some tests to support use cases +like A/B testing. These tests may be performed on a Spark Streaming +`DStream[(Boolean,Double)]` where the first element of each tuple +indicates control group (`false`) or treatment group (`true`) and the +second element is the value of an observation. + +Streaming significance testing supports the following parameters: + +* `peacePeriod` - The number of initial data points from the stream to +ignore, used to mitigate novelty effects. +* `windowSize` - The number of past batches to perform hypothesis +testing over. Setting to `0` will perform cumulative processing using +all prior batches. + + +<div class="codetabs"> +<div data-lang="scala" markdown="1"> +[`StreamingTest`](api/scala/index.html#org.apache.spark.mllib.stat.test.StreamingTest) +provides streaming hypothesis testing. + +{% include_example scala/org/apache/spark/examples/mllib/StreamingTestExample.scala %} +</div> +</div> + ## Random data generation |