public static interface ModelMonitoringStatsDataPoint.TypedValue.DistributionDataValueOrBuilder
extends com.google.protobuf.MessageOrBuilder
| Modifier and Type | Method and Description |
|---|---|
com.google.protobuf.Value |
getDistribution()
Predictive monitoring drift distribution in
`tensorflow.metadata.v0.DatasetFeatureStatistics` format.
|
double |
getDistributionDeviation()
Distribution distance deviation from the current dataset's statistics
to baseline dataset's statistics
|
com.google.protobuf.ValueOrBuilder |
getDistributionOrBuilder()
Predictive monitoring drift distribution in
`tensorflow.metadata.v0.DatasetFeatureStatistics` format.
|
boolean |
hasDistribution()
Predictive monitoring drift distribution in
`tensorflow.metadata.v0.DatasetFeatureStatistics` format.
|
findInitializationErrors, getAllFields, getDefaultInstanceForType, getDescriptorForType, getField, getInitializationErrorString, getOneofFieldDescriptor, getRepeatedField, getRepeatedFieldCount, getUnknownFields, hasField, hasOneofboolean hasDistribution()
Predictive monitoring drift distribution in `tensorflow.metadata.v0.DatasetFeatureStatistics` format.
.google.protobuf.Value distribution = 1;com.google.protobuf.Value getDistribution()
Predictive monitoring drift distribution in `tensorflow.metadata.v0.DatasetFeatureStatistics` format.
.google.protobuf.Value distribution = 1;com.google.protobuf.ValueOrBuilder getDistributionOrBuilder()
Predictive monitoring drift distribution in `tensorflow.metadata.v0.DatasetFeatureStatistics` format.
.google.protobuf.Value distribution = 1;double getDistributionDeviation()
Distribution distance deviation from the current dataset's statistics
to baseline dataset's statistics.
* For categorical feature, the distribution distance is calculated
by L-inifinity norm or Jensen–Shannon divergence.
* For numerical feature, the distribution distance is calculated by
Jensen–Shannon divergence.
double distribution_deviation = 2;Copyright © 2025 Google LLC. All rights reserved.