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Configurable RetainCompletenessRule #564

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May 6, 2024
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Configurable RetainCompletenessRule #564

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3b41e4c
Configurable RetainCompletenessRule
zeotuan Apr 19, 2024
ac337ea
Add doc string
zeotuan Apr 19, 2024
db9b764
Add default completeness const
zeotuan May 1, 2024
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8 changes: 5 additions & 3 deletions src/main/scala/com/amazon/deequ/suggestions/rules/RetainCompletenessRule.scala
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Original file line number Diff line number Diff line change
Expand Up @@ -26,11 +26,13 @@ import scala.math.BigDecimal.RoundingMode
/**
* If a column is incomplete in the sample, we model its completeness as a binomial variable,
* estimate a confidence interval and use this to define a lower bound for the completeness
*
* @param minCompleteness : minimum completeness threshold to determine if rule should be applied
* @param maxCompleteness : maximum completeness threshold to determine if rule should be applied
*/
case class RetainCompletenessRule() extends ConstraintRule[ColumnProfile] {

case class RetainCompletenessRule(minCompleteness: Double = 0.2, maxCompleteness: Double = 1.0) extends ConstraintRule[ColumnProfile] {
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Decided not to Parameterize z-value likes in original implementation. Due to the fact that it is related to a specific Interval Calculation Techniques. If possible, we can work into parameterize the strategy use to calculating the interval #563

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Thanks @zeotuan
Can you trim this line to below 120 characters? It is failing checkstyle and failing the build.

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Can we also store the values 0.2 and 1.0 as constants ?

override def shouldBeApplied(profile: ColumnProfile, numRecords: Long): Boolean = {
profile.completeness > 0.2 && profile.completeness < 1.0
profile.completeness > minCompleteness && profile.completeness < maxCompleteness
}

override def candidate(profile: ColumnProfile, numRecords: Long): ConstraintSuggestion = {
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25 changes: 25 additions & 0 deletions src/test/scala/com/amazon/deequ/suggestions/rules/ConstraintRulesTest.scala
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Expand Up @@ -130,9 +130,14 @@ class ConstraintRulesTest extends WordSpec with FixtureSupport with SparkContext
"be applied correctly" in {

val complete = StandardColumnProfile("col1", 1.0, 100, String, false, Map.empty, None)
val tenPercent = StandardColumnProfile("col1", 0.1, 100, String, false, Map.empty, None)
val incomplete = StandardColumnProfile("col1", .25, 100, String, false, Map.empty, None)

assert(!RetainCompletenessRule().shouldBeApplied(complete, 1000))
assert(!RetainCompletenessRule(0.05, 0.9).shouldBeApplied(complete, 1000))
assert(RetainCompletenessRule(0.05, 0.9).shouldBeApplied(tenPercent, 1000))
assert(RetainCompletenessRule(0.0).shouldBeApplied(tenPercent, 1000))
assert(RetainCompletenessRule(0.0).shouldBeApplied(incomplete, 1000))
assert(RetainCompletenessRule().shouldBeApplied(incomplete, 1000))
}

Expand Down Expand Up @@ -183,6 +188,26 @@ class ConstraintRulesTest extends WordSpec with FixtureSupport with SparkContext

assert(metricResult.value.isSuccess)
}

"return evaluable constraint candidates with custom min/max completeness" in
withSparkSession { session =>

val dfWithColumnCandidate = getDfFull(session)

val fakeColumnProfile = getFakeColumnProfileWithNameAndCompleteness("att1", 0.5)

val check = Check(CheckLevel.Warning, "some")
.addConstraint(RetainCompletenessRule(0.4, 0.6).candidate(fakeColumnProfile, 100).constraint)

val verificationResult = VerificationSuite()
.onData(dfWithColumnCandidate)
.addCheck(check)
.run()

val metricResult = verificationResult.metrics.head._2

assert(metricResult.value.isSuccess)
}
}

"UniqueIfApproximatelyUniqueRule" should {
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