private[suggestions] def profileAndSuggest()

in src/main/scala/com/amazon/deequ/suggestions/ConstraintSuggestionRunner.scala [161:209]
38 lines of code
1 McCabe index (conditional complexity)

  private[suggestions] def profileAndSuggest(
      trainingData: DataFrame,
      constraintRules: Seq[ConstraintRule[ColumnProfile]],
      restrictToColumns: Option[Seq[String]],
      lowCardinalityHistogramThreshold: Int,
      printStatusUpdates: Boolean,
      metricsRepositoryOptions: ConstraintSuggestionMetricsRepositoryOptions,
      kllParameters: Option[KLLParameters],
      predefinedTypes: Map[String, DataTypeInstances.Value])
    : (ColumnProfiles, Seq[ConstraintSuggestion]) = {

    var columnProfilerRunner = ColumnProfilerRunner()
      .onData(trainingData)
      .printStatusUpdates(printStatusUpdates)
      .withLowCardinalityHistogramThreshold(lowCardinalityHistogramThreshold)

    restrictToColumns.foreach { restrictToColumns =>
      columnProfilerRunner = columnProfilerRunner.restrictToColumns(restrictToColumns)
    }

    columnProfilerRunner = columnProfilerRunner.setKLLParameters(kllParameters)

    columnProfilerRunner =
      columnProfilerRunner.setPredefinedTypes(predefinedTypes)

    metricsRepositoryOptions.metricsRepository.foreach { metricsRepository =>
      var columnProfilerRunnerWithRepository = columnProfilerRunner.useRepository(metricsRepository)

      metricsRepositoryOptions.reuseExistingResultsKey.foreach { reuseExistingResultsKey =>
        columnProfilerRunnerWithRepository = columnProfilerRunnerWithRepository
          .reuseExistingResultsForKey(reuseExistingResultsKey,
            metricsRepositoryOptions.failIfResultsForReusingMissing)
      }

      metricsRepositoryOptions.saveOrAppendResultsKey.foreach { saveOrAppendResultsKey =>
        columnProfilerRunnerWithRepository = columnProfilerRunnerWithRepository
          .saveOrAppendResult(saveOrAppendResultsKey)
      }

      columnProfilerRunner = columnProfilerRunnerWithRepository
    }

    val profiles = columnProfilerRunner.run()

    val relevantColumns = getRelevantColumns(trainingData.schema, restrictToColumns)
    val suggestions = applyRules(constraintRules, profiles, relevantColumns)

    (profiles, suggestions)
  }