The dead salmon brain is an open source project for industrial A/B statistical analysis and reporting. The core library extends Apache Spark functionality, making it easy to integrate into your data processing pipelines and providing scalable implementations of common online experimentation techniques and mathematical methods.
Java/Scala/Python
1) Clone git repository
2) Check spark,scala,hadoop and set correct in 3 step
3) bash setup.sh spark=3.1.2 scala=2.12.11 hadoop=3.2 is_build=true is_install_python_lib=false1) Clone git repository
2) Check version,spark,scala,hadoop and set correct in 3 step
3) bash setup.sh version=0.0.4 spark=3.1.2 scala=2.12.11 hadoop=3.2 is_build=false is_install_python_lib=falsecase class ExpData(
timestamp: Long, //metric timestamp
variantId: String, //(treatment and control)
entityUid: String, //unique entity id
experimentUid: String, //unique experiment id
metricValue: Double, //numeric metric value
metricName: String, //unique metric name in metricSource space
categoryName: String, //entity category name (i.e gender)
categoryValue: String, // entity category value (i.e male, female, other)
metricSource: String, //metric source
isAdditive: Boolean // is additive metric or not
)import org.apache.commons.math3.distribution.NormalDistribution
import org.apache.spark.ml.Pipeline
import ai.salmonbrain.computing.{ExpData, CumulativeMetricTransformer, OutlierRemoveTransformer, AutoStatisticsTransformer}
val control = new NormalDistribution(1, 1)
.sample(1000)
.zipWithIndex.map { case (value, idx) =>
ExpData(System.currentTimeMillis(), "control", idx.toString, "exp", value, "timeSpent")
}
val treatment = new NormalDistribution(2, 4)
.sample(1000)
.zipWithIndex.map { case (value, idx) =>
ExpData(System.currentTimeMillis(), "treatment", idx.toString, "exp", value, "timeSpent")
}
val model = new Pipeline().setStages(
Array(
new CumulativeMetricTransformer(), // aggregate all metrics
new OutlierRemoveTransformer() // remove outliers by percentile
.setLowerPercentile(0.01)
.setUpperPercentile(0.99),
new AutoStatisticsTransformer() // auto choose and compute Welch or MannWhitney test
.setAlpha(0.05)
.setBeta(0.2)
)
)
val report = model.fit(data).transform(data)
report.select(
"metricName",
"experimentUid",
"statisticsData.srm",
"statisticsData.testType",
"statisticsData.statResult.pValue",
"statisticsData.statResult.requiredSampleSizeByVariant",
"statisticsData.statResult.percentageLeft",
"statisticsData.statResult.percentageRight"
).show
+----------+-------------+-----+--------+-------+---------------------------+-----------------+-----------------+
|metricName|experimentUid| srm|testType|pValue |requiredSampleSizeByVariant| percentageLeft| percentageRight|
+----------+-------------+-----+--------+-------+---------------------------+-----------------+-----------------+
| timeSpent| exp|false| WELCH|1.3e-21| 82| 94.6294| 148.8822|
+----------+-------------+-----+--------+-------+---------------------------+-----------------+-----------------+srm if true than your test is invalid
pValue if less than 0.05 or 0.01 then great
requiredSampleSizeByVariant estimated required sample size by each variant for observed test and data parameters
percentageLeft lower percent confidence interval
percentageRight upper percent confidence interval
import ai.salmon.computing.RatioMetricData
val cum = new CumulativeMetricTransformer()
.setNumBuckets(256) // you can split your data by buckets and use buckets like new entity
.setNumeratorNames(Array("clicks")) // you can set numerator
.setDenominatorNames(Array("views")) // and denominator
.setRatioNames(Array("ctr")) // and create new ratio metricPull requests are welcome.