com.google.cloud.aiplatform.v1beta1

Interface ModelMonitoringStatsDataPoint.TypedValue.DistributionDataValueOrBuilder

All Superinterfaces:

com.google.protobuf.MessageLiteOrBuilder, com.google.protobuf.MessageOrBuilder

All Known Implementing Classes:

ModelMonitoringStatsDataPoint.TypedValue.DistributionDataValue, ModelMonitoringStatsDataPoint.TypedValue.DistributionDataValue.Builder

Enclosing class:

ModelMonitoringStatsDataPoint.TypedValue

public static interface ModelMonitoringStatsDataPoint.TypedValue.DistributionDataValueOrBuilder
extends com.google.protobuf.MessageOrBuilder

Method Summary

Modifier and Type	Method and Description
com.google.protobuf.Value	getDistribution() 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() tensorflow.metadata.v0.DatasetFeatureStatistics format.
boolean	hasDistribution() tensorflow.metadata.v0.DatasetFeatureStatistics format.

Methods inherited from interface com.google.protobuf.MessageOrBuilder

findInitializationErrors, getAllFields, getDefaultInstanceForType, getDescriptorForType, getField, getInitializationErrorString, getOneofFieldDescriptor, getRepeatedField, getRepeatedFieldCount, getUnknownFields, hasField, hasOneof

Methods inherited from interface com.google.protobuf.MessageLiteOrBuilder

isInitialized

Method Detail

hasDistribution

boolean hasDistribution()

 tensorflow.metadata.v0.DatasetFeatureStatistics format.
 

.google.protobuf.Value distribution = 1;

Returns:

Whether the distribution field is set.

getDistribution

com.google.protobuf.Value getDistribution()

 tensorflow.metadata.v0.DatasetFeatureStatistics format.
 

.google.protobuf.Value distribution = 1;

Returns:

The distribution.

getDistributionOrBuilder

com.google.protobuf.ValueOrBuilder getDistributionOrBuilder()

 tensorflow.metadata.v0.DatasetFeatureStatistics format.
 

.google.protobuf.Value distribution = 1;

getDistributionDeviation

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;

Returns:

The distributionDeviation.