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Remove sampleContributionsPerPartition because it is not used anywhere anymore. #346

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Remove sampleContributionsPerPartition because it is not used anywhere anymore. #346

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65 changes: 23 additions & 42 deletions pipelinedp4j/main/com/google/privacy/differentialprivacy/pipelinedp4j/api/AggregationSpec.kt
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Original file line number Diff line number Diff line change
Expand Up @@ -148,58 +148,20 @@ internal fun AggregationSpec.getFeatureId(): String {
}
}

internal fun List<AggregationSpec>.metrics(): List<MetricDefinition> = buildList {
for (aggregation in this@metrics) {
when (aggregation) {
// Count and PrivacyIdCount do not aggregate any specific value, therefore they are handled
// differently.
is PrivacyIdCount ->
add(
MetricDefinition(
MetricType.PRIVACY_ID_COUNT,
aggregation.budget?.toInternalBudgetPerOpSpec(),
)
)
is Count ->
add(MetricDefinition(MetricType.COUNT, aggregation.budget?.toInternalBudgetPerOpSpec()))
is ValueAggregations<*> -> {
for (valueAggregationSpec in aggregation.valueAggregationSpecs) {
add(
MetricDefinition(
valueAggregationSpec.metricType,
valueAggregationSpec.budget?.toInternalBudgetPerOpSpec(),
)
)
}
}
is VectorAggregations<*> -> {
for (vectorAggregationSpec in aggregation.vectorAggregationSpecs) {
add(
MetricDefinition(
vectorAggregationSpec.metricType,
vectorAggregationSpec.budget?.toInternalBudgetPerOpSpec(),
)
)
}
}
}
}
}

internal fun List<AggregationSpec>.outputColumnNamesWithMetricTypes():
List<Pair<String, MetricType>> = buildList {
for (aggregation in this@outputColumnNamesWithMetricTypes) {
when (aggregation) {
is PrivacyIdCount -> add(aggregation.outputColumnName to MetricType.PRIVACY_ID_COUNT)
is Count -> add(aggregation.outputColumnName to MetricType.COUNT)
is PrivacyIdCount -> add(Pair(aggregation.outputColumnName, MetricType.PRIVACY_ID_COUNT))
is Count -> add(Pair(aggregation.outputColumnName, MetricType.COUNT))
is ValueAggregations<*> -> {
for (valueAggregationSpec in aggregation.valueAggregationSpecs) {
add(valueAggregationSpec.outputColumnName to valueAggregationSpec.metricType)
add(Pair(valueAggregationSpec.outputColumnName, valueAggregationSpec.metricType))
}
}
is VectorAggregations<*> -> {
for (vectorAggregationSpec in aggregation.vectorAggregationSpecs) {
add(vectorAggregationSpec.outputColumnName to vectorAggregationSpec.metricType)
add(Pair(vectorAggregationSpec.outputColumnName, vectorAggregationSpec.metricType))
}
}
}
Expand Down Expand Up @@ -227,3 +189,22 @@ internal fun List<AggregationSpec>.outputColumnNameToFeatureIdMap(): Map<String,

internal fun List<AggregationSpec>.outputColumnNames(): List<String> =
outputColumnNamesWithMetricTypes().map { it.first }

internal fun AggregationSpec.toNonFeatureMetricDefinition(): MetricDefinition {
val (metricType, budget) =
when (this) {
is Count -> Pair(MetricType.COUNT, this.budget)
is PrivacyIdCount -> Pair(MetricType.PRIVACY_ID_COUNT, this.budget)
else ->
throw IllegalArgumentException("Unsupported AggregationSpec type for non feature metrics")
}
return MetricDefinition(metricType, budget?.toInternalBudgetPerOpSpec())
}

internal fun ValueAggregationSpec.toMetricDefinition(): MetricDefinition {
return MetricDefinition(this.metricType, this.budget?.toInternalBudgetPerOpSpec())
}

internal fun VectorAggregationSpec.toMetricDefinition(): MetricDefinition {
return MetricDefinition(this.metricType, this.budget?.toInternalBudgetPerOpSpec())
}
56 changes: 46 additions & 10 deletions pipelinedp4j/main/com/google/privacy/differentialprivacy/pipelinedp4j/api/Query.kt
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Expand Up @@ -23,11 +23,14 @@ import com.google.privacy.differentialprivacy.pipelinedp4j.core.DpEngine
import com.google.privacy.differentialprivacy.pipelinedp4j.core.DpEngineBudgetSpec
import com.google.privacy.differentialprivacy.pipelinedp4j.core.Encoder
import com.google.privacy.differentialprivacy.pipelinedp4j.core.EncoderFactory
import com.google.privacy.differentialprivacy.pipelinedp4j.core.FeatureSpec
import com.google.privacy.differentialprivacy.pipelinedp4j.core.FeatureValuesExtractor
import com.google.privacy.differentialprivacy.pipelinedp4j.core.FrameworkCollection
import com.google.privacy.differentialprivacy.pipelinedp4j.core.FrameworkTable
import com.google.privacy.differentialprivacy.pipelinedp4j.core.MetricType
import com.google.privacy.differentialprivacy.pipelinedp4j.core.ScalarFeatureSpec
import com.google.privacy.differentialprivacy.pipelinedp4j.core.SelectPartitionsParams
import com.google.privacy.differentialprivacy.pipelinedp4j.core.VectorFeatureSpec
import com.google.privacy.differentialprivacy.pipelinedp4j.proto.DpAggregates
import com.google.privacy.differentialprivacy.pipelinedp4j.proto.PerFeature
import com.google.privacy.differentialprivacy.pipelinedp4j.proto.copy
Expand Down Expand Up @@ -494,22 +497,53 @@ protected constructor(
valueAggregations: ValueAggregations<*>?,
vectorAggregations: VectorAggregations<*>?,
): AggregationParams {
val valueContributionBounds = valueAggregations?.contributionBounds
val vectorContributionBounds = vectorAggregations?.vectorContributionBounds
val nonFeatureMetrics =
aggregationSpecs
.filter { it is Count || it is PrivacyIdCount }
.map { it.toNonFeatureMetricDefinition() }
val features =
buildList<FeatureSpec> {
if (valueAggregations != null) {
val valueContributionBounds = valueAggregations.contributionBounds
add(
ScalarFeatureSpec(
featureId = valueAggregations.getFeatureId(),
metrics =
valueAggregations.valueAggregationSpecs
.map { it.toMetricDefinition() }
.toImmutableList(),
minValue = valueContributionBounds.valueBounds?.minValue,
maxValue = valueContributionBounds.valueBounds?.maxValue,
minTotalValue = valueContributionBounds.totalValueBounds?.minValue,
maxTotalValue = valueContributionBounds.totalValueBounds?.maxValue,
)
)
}
if (vectorAggregations != null) {
val vectorContributionBounds = vectorAggregations.vectorContributionBounds
add(
VectorFeatureSpec(
featureId = vectorAggregations.getFeatureId(),
metrics =
vectorAggregations.vectorAggregationSpecs
.map { it.toMetricDefinition() }
.toImmutableList(),
vectorSize = vectorAggregations.vectorSize,
normKind = vectorContributionBounds.maxVectorTotalNorm.normKind.toInternalNormKind(),
vectorMaxTotalNorm = vectorContributionBounds.maxVectorTotalNorm.value,
)
)
}
}

return AggregationParams(
metrics = ImmutableList.copyOf(aggregationSpecs.metrics()),
nonFeatureMetrics = nonFeatureMetrics.toImmutableList(),
features = features.toImmutableList(),
noiseKind =
checkNotNull(noiseKind) { "noiseKind cannot be null if there are aggregations." }
.toInternalNoiseKind(),
maxPartitionsContributed = contributionBoundingLevel.getMaxPartitionsContributed(),
maxContributionsPerPartition = contributionBoundingLevel.getMaxContributionsPerPartition(),
minValue = valueContributionBounds?.valueBounds?.minValue,
maxValue = valueContributionBounds?.valueBounds?.maxValue,
minTotalValue = valueContributionBounds?.totalValueBounds?.minValue,
maxTotalValue = valueContributionBounds?.totalValueBounds?.maxValue,
vectorNormKind = vectorContributionBounds?.maxVectorTotalNorm?.normKind?.toInternalNormKind(),
vectorMaxTotalNorm = vectorContributionBounds?.maxVectorTotalNorm?.value,
vectorSize = vectorAggregations?.vectorSize,
partitionSelectionBudget = groupsType.getBudget()?.toInternalBudgetPerOpSpec(),
preThreshold = groupsType.getPreThreshold(),
contributionBoundingLevel = contributionBoundingLevel.toInternalContributionBoundingLevel(),
Expand All @@ -534,3 +568,5 @@ protected constructor(
}
}
}

private fun <T : Any> Iterable<T>.toImmutableList(): ImmutableList<T> = ImmutableList.copyOf(this)
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