Class ModelMonitoringObjectiveConfig.TrainingPredictionSkewDetectionConfig (3.23.0)

public static final class ModelMonitoringObjectiveConfig.TrainingPredictionSkewDetectionConfig extends GeneratedMessageV3 implements ModelMonitoringObjectiveConfig.TrainingPredictionSkewDetectionConfigOrBuilder

The config for Training & Prediction data skew detection. It specifies the training dataset sources and the skew detection parameters.

Protobuf type google.cloud.aiplatform.v1.ModelMonitoringObjectiveConfig.TrainingPredictionSkewDetectionConfig

Inheritance

Object > AbstractMessageLite<MessageType,BuilderType> > AbstractMessage > GeneratedMessageV3 > ModelMonitoringObjectiveConfig.TrainingPredictionSkewDetectionConfig

Static Fields

ATTRIBUTION_SCORE_SKEW_THRESHOLDS_FIELD_NUMBER

public static final int ATTRIBUTION_SCORE_SKEW_THRESHOLDS_FIELD_NUMBER
Field Value
TypeDescription
int

DEFAULT_SKEW_THRESHOLD_FIELD_NUMBER

public static final int DEFAULT_SKEW_THRESHOLD_FIELD_NUMBER
Field Value
TypeDescription
int

SKEW_THRESHOLDS_FIELD_NUMBER

public static final int SKEW_THRESHOLDS_FIELD_NUMBER
Field Value
TypeDescription
int

Static Methods

getDefaultInstance()

public static ModelMonitoringObjectiveConfig.TrainingPredictionSkewDetectionConfig getDefaultInstance()
Returns
TypeDescription
ModelMonitoringObjectiveConfig.TrainingPredictionSkewDetectionConfig

getDescriptor()

public static final Descriptors.Descriptor getDescriptor()
Returns
TypeDescription
Descriptor

newBuilder()

public static ModelMonitoringObjectiveConfig.TrainingPredictionSkewDetectionConfig.Builder newBuilder()
Returns
TypeDescription
ModelMonitoringObjectiveConfig.TrainingPredictionSkewDetectionConfig.Builder

newBuilder(ModelMonitoringObjectiveConfig.TrainingPredictionSkewDetectionConfig prototype)

public static ModelMonitoringObjectiveConfig.TrainingPredictionSkewDetectionConfig.Builder newBuilder(ModelMonitoringObjectiveConfig.TrainingPredictionSkewDetectionConfig prototype)
Parameter
NameDescription
prototypeModelMonitoringObjectiveConfig.TrainingPredictionSkewDetectionConfig
Returns
TypeDescription
ModelMonitoringObjectiveConfig.TrainingPredictionSkewDetectionConfig.Builder

parseDelimitedFrom(InputStream input)

public static ModelMonitoringObjectiveConfig.TrainingPredictionSkewDetectionConfig parseDelimitedFrom(InputStream input)
Parameter
NameDescription
inputInputStream
Returns
TypeDescription
ModelMonitoringObjectiveConfig.TrainingPredictionSkewDetectionConfig
Exceptions
TypeDescription
IOException

parseDelimitedFrom(InputStream input, ExtensionRegistryLite extensionRegistry)

public static ModelMonitoringObjectiveConfig.TrainingPredictionSkewDetectionConfig parseDelimitedFrom(InputStream input, ExtensionRegistryLite extensionRegistry)
Parameters
NameDescription
inputInputStream
extensionRegistryExtensionRegistryLite
Returns
TypeDescription
ModelMonitoringObjectiveConfig.TrainingPredictionSkewDetectionConfig
Exceptions
TypeDescription
IOException

parseFrom(byte[] data)

public static ModelMonitoringObjectiveConfig.TrainingPredictionSkewDetectionConfig parseFrom(byte[] data)
Parameter
NameDescription
databyte[]
Returns
TypeDescription
ModelMonitoringObjectiveConfig.TrainingPredictionSkewDetectionConfig
Exceptions
TypeDescription
InvalidProtocolBufferException

parseFrom(byte[] data, ExtensionRegistryLite extensionRegistry)

public static ModelMonitoringObjectiveConfig.TrainingPredictionSkewDetectionConfig parseFrom(byte[] data, ExtensionRegistryLite extensionRegistry)
Parameters
NameDescription
databyte[]
extensionRegistryExtensionRegistryLite
Returns
TypeDescription
ModelMonitoringObjectiveConfig.TrainingPredictionSkewDetectionConfig
Exceptions
TypeDescription
InvalidProtocolBufferException

parseFrom(ByteString data)

public static ModelMonitoringObjectiveConfig.TrainingPredictionSkewDetectionConfig parseFrom(ByteString data)
Parameter
NameDescription
dataByteString
Returns
TypeDescription
ModelMonitoringObjectiveConfig.TrainingPredictionSkewDetectionConfig
Exceptions
TypeDescription
InvalidProtocolBufferException

parseFrom(ByteString data, ExtensionRegistryLite extensionRegistry)

public static ModelMonitoringObjectiveConfig.TrainingPredictionSkewDetectionConfig parseFrom(ByteString data, ExtensionRegistryLite extensionRegistry)
Parameters
NameDescription
dataByteString
extensionRegistryExtensionRegistryLite
Returns
TypeDescription
ModelMonitoringObjectiveConfig.TrainingPredictionSkewDetectionConfig
Exceptions
TypeDescription
InvalidProtocolBufferException

parseFrom(CodedInputStream input)

public static ModelMonitoringObjectiveConfig.TrainingPredictionSkewDetectionConfig parseFrom(CodedInputStream input)
Parameter
NameDescription
inputCodedInputStream
Returns
TypeDescription
ModelMonitoringObjectiveConfig.TrainingPredictionSkewDetectionConfig
Exceptions
TypeDescription
IOException

parseFrom(CodedInputStream input, ExtensionRegistryLite extensionRegistry)

public static ModelMonitoringObjectiveConfig.TrainingPredictionSkewDetectionConfig parseFrom(CodedInputStream input, ExtensionRegistryLite extensionRegistry)
Parameters
NameDescription
inputCodedInputStream
extensionRegistryExtensionRegistryLite
Returns
TypeDescription
ModelMonitoringObjectiveConfig.TrainingPredictionSkewDetectionConfig
Exceptions
TypeDescription
IOException

parseFrom(InputStream input)

public static ModelMonitoringObjectiveConfig.TrainingPredictionSkewDetectionConfig parseFrom(InputStream input)
Parameter
NameDescription
inputInputStream
Returns
TypeDescription
ModelMonitoringObjectiveConfig.TrainingPredictionSkewDetectionConfig
Exceptions
TypeDescription
IOException

parseFrom(InputStream input, ExtensionRegistryLite extensionRegistry)

public static ModelMonitoringObjectiveConfig.TrainingPredictionSkewDetectionConfig parseFrom(InputStream input, ExtensionRegistryLite extensionRegistry)
Parameters
NameDescription
inputInputStream
extensionRegistryExtensionRegistryLite
Returns
TypeDescription
ModelMonitoringObjectiveConfig.TrainingPredictionSkewDetectionConfig
Exceptions
TypeDescription
IOException

parseFrom(ByteBuffer data)

public static ModelMonitoringObjectiveConfig.TrainingPredictionSkewDetectionConfig parseFrom(ByteBuffer data)
Parameter
NameDescription
dataByteBuffer
Returns
TypeDescription
ModelMonitoringObjectiveConfig.TrainingPredictionSkewDetectionConfig
Exceptions
TypeDescription
InvalidProtocolBufferException

parseFrom(ByteBuffer data, ExtensionRegistryLite extensionRegistry)

public static ModelMonitoringObjectiveConfig.TrainingPredictionSkewDetectionConfig parseFrom(ByteBuffer data, ExtensionRegistryLite extensionRegistry)
Parameters
NameDescription
dataByteBuffer
extensionRegistryExtensionRegistryLite
Returns
TypeDescription
ModelMonitoringObjectiveConfig.TrainingPredictionSkewDetectionConfig
Exceptions
TypeDescription
InvalidProtocolBufferException

parser()

public static Parser<ModelMonitoringObjectiveConfig.TrainingPredictionSkewDetectionConfig> parser()
Returns
TypeDescription
Parser<TrainingPredictionSkewDetectionConfig>

Methods

containsAttributionScoreSkewThresholds(String key)

public boolean containsAttributionScoreSkewThresholds(String key)

Key is the feature name and value is the threshold. The threshold here is against attribution score distance between the training and prediction feature.

map<string, .google.cloud.aiplatform.v1.ThresholdConfig> attribution_score_skew_thresholds = 2;

Parameter
NameDescription
keyString
Returns
TypeDescription
boolean

containsSkewThresholds(String key)

public boolean containsSkewThresholds(String key)

Key is the feature name and value is the threshold. If a feature needs to be monitored for skew, a value threshold must be configured for that feature. The threshold here is against feature distribution distance between the training and prediction feature.

map<string, .google.cloud.aiplatform.v1.ThresholdConfig> skew_thresholds = 1;

Parameter
NameDescription
keyString
Returns
TypeDescription
boolean

equals(Object obj)

public boolean equals(Object obj)
Parameter
NameDescription
objObject
Returns
TypeDescription
boolean
Overrides

getAttributionScoreSkewThresholds()

public Map<String,ThresholdConfig> getAttributionScoreSkewThresholds()
Returns
TypeDescription
Map<String,ThresholdConfig>

getAttributionScoreSkewThresholdsCount()

public int getAttributionScoreSkewThresholdsCount()

Key is the feature name and value is the threshold. The threshold here is against attribution score distance between the training and prediction feature.

map<string, .google.cloud.aiplatform.v1.ThresholdConfig> attribution_score_skew_thresholds = 2;

Returns
TypeDescription
int

getAttributionScoreSkewThresholdsMap()

public Map<String,ThresholdConfig> getAttributionScoreSkewThresholdsMap()

Key is the feature name and value is the threshold. The threshold here is against attribution score distance between the training and prediction feature.

map<string, .google.cloud.aiplatform.v1.ThresholdConfig> attribution_score_skew_thresholds = 2;

Returns
TypeDescription
Map<String,ThresholdConfig>

getAttributionScoreSkewThresholdsOrDefault(String key, ThresholdConfig defaultValue)

public ThresholdConfig getAttributionScoreSkewThresholdsOrDefault(String key, ThresholdConfig defaultValue)

Key is the feature name and value is the threshold. The threshold here is against attribution score distance between the training and prediction feature.

map<string, .google.cloud.aiplatform.v1.ThresholdConfig> attribution_score_skew_thresholds = 2;

Parameters
NameDescription
keyString
defaultValueThresholdConfig
Returns
TypeDescription
ThresholdConfig

getAttributionScoreSkewThresholdsOrThrow(String key)

public ThresholdConfig getAttributionScoreSkewThresholdsOrThrow(String key)

Key is the feature name and value is the threshold. The threshold here is against attribution score distance between the training and prediction feature.

map<string, .google.cloud.aiplatform.v1.ThresholdConfig> attribution_score_skew_thresholds = 2;

Parameter
NameDescription
keyString
Returns
TypeDescription
ThresholdConfig

getDefaultInstanceForType()

public ModelMonitoringObjectiveConfig.TrainingPredictionSkewDetectionConfig getDefaultInstanceForType()
Returns
TypeDescription
ModelMonitoringObjectiveConfig.TrainingPredictionSkewDetectionConfig

getDefaultSkewThreshold()

public ThresholdConfig getDefaultSkewThreshold()

Skew anomaly detection threshold used by all features. When the per-feature thresholds are not set, this field can be used to specify a threshold for all features.

.google.cloud.aiplatform.v1.ThresholdConfig default_skew_threshold = 6;

Returns
TypeDescription
ThresholdConfig

The defaultSkewThreshold.

getDefaultSkewThresholdOrBuilder()

public ThresholdConfigOrBuilder getDefaultSkewThresholdOrBuilder()

Skew anomaly detection threshold used by all features. When the per-feature thresholds are not set, this field can be used to specify a threshold for all features.

.google.cloud.aiplatform.v1.ThresholdConfig default_skew_threshold = 6;

Returns
TypeDescription
ThresholdConfigOrBuilder

getParserForType()

public Parser<ModelMonitoringObjectiveConfig.TrainingPredictionSkewDetectionConfig> getParserForType()
Returns
TypeDescription
Parser<TrainingPredictionSkewDetectionConfig>
Overrides

getSerializedSize()

public int getSerializedSize()
Returns
TypeDescription
int
Overrides

getSkewThresholds()

public Map<String,ThresholdConfig> getSkewThresholds()
Returns
TypeDescription
Map<String,ThresholdConfig>

getSkewThresholdsCount()

public int getSkewThresholdsCount()

Key is the feature name and value is the threshold. If a feature needs to be monitored for skew, a value threshold must be configured for that feature. The threshold here is against feature distribution distance between the training and prediction feature.

map<string, .google.cloud.aiplatform.v1.ThresholdConfig> skew_thresholds = 1;

Returns
TypeDescription
int

getSkewThresholdsMap()

public Map<String,ThresholdConfig> getSkewThresholdsMap()

Key is the feature name and value is the threshold. If a feature needs to be monitored for skew, a value threshold must be configured for that feature. The threshold here is against feature distribution distance between the training and prediction feature.

map<string, .google.cloud.aiplatform.v1.ThresholdConfig> skew_thresholds = 1;

Returns
TypeDescription
Map<String,ThresholdConfig>

getSkewThresholdsOrDefault(String key, ThresholdConfig defaultValue)

public ThresholdConfig getSkewThresholdsOrDefault(String key, ThresholdConfig defaultValue)

Key is the feature name and value is the threshold. If a feature needs to be monitored for skew, a value threshold must be configured for that feature. The threshold here is against feature distribution distance between the training and prediction feature.

map<string, .google.cloud.aiplatform.v1.ThresholdConfig> skew_thresholds = 1;

Parameters
NameDescription
keyString
defaultValueThresholdConfig
Returns
TypeDescription
ThresholdConfig

getSkewThresholdsOrThrow(String key)

public ThresholdConfig getSkewThresholdsOrThrow(String key)

Key is the feature name and value is the threshold. If a feature needs to be monitored for skew, a value threshold must be configured for that feature. The threshold here is against feature distribution distance between the training and prediction feature.

map<string, .google.cloud.aiplatform.v1.ThresholdConfig> skew_thresholds = 1;

Parameter
NameDescription
keyString
Returns
TypeDescription
ThresholdConfig

hasDefaultSkewThreshold()

public boolean hasDefaultSkewThreshold()

Skew anomaly detection threshold used by all features. When the per-feature thresholds are not set, this field can be used to specify a threshold for all features.

.google.cloud.aiplatform.v1.ThresholdConfig default_skew_threshold = 6;

Returns
TypeDescription
boolean

Whether the defaultSkewThreshold field is set.

hashCode()

public int hashCode()
Returns
TypeDescription
int
Overrides

internalGetFieldAccessorTable()

protected GeneratedMessageV3.FieldAccessorTable internalGetFieldAccessorTable()
Returns
TypeDescription
FieldAccessorTable
Overrides

internalGetMapField(int number)

protected MapField internalGetMapField(int number)
Parameter
NameDescription
numberint
Returns
TypeDescription
MapField
Overrides

isInitialized()

public final boolean isInitialized()
Returns
TypeDescription
boolean
Overrides

newBuilderForType()

public ModelMonitoringObjectiveConfig.TrainingPredictionSkewDetectionConfig.Builder newBuilderForType()
Returns
TypeDescription
ModelMonitoringObjectiveConfig.TrainingPredictionSkewDetectionConfig.Builder

newBuilderForType(GeneratedMessageV3.BuilderParent parent)

protected ModelMonitoringObjectiveConfig.TrainingPredictionSkewDetectionConfig.Builder newBuilderForType(GeneratedMessageV3.BuilderParent parent)
Parameter
NameDescription
parentBuilderParent
Returns
TypeDescription
ModelMonitoringObjectiveConfig.TrainingPredictionSkewDetectionConfig.Builder
Overrides

newInstance(GeneratedMessageV3.UnusedPrivateParameter unused)

protected Object newInstance(GeneratedMessageV3.UnusedPrivateParameter unused)
Parameter
NameDescription
unusedUnusedPrivateParameter
Returns
TypeDescription
Object
Overrides

toBuilder()

public ModelMonitoringObjectiveConfig.TrainingPredictionSkewDetectionConfig.Builder toBuilder()
Returns
TypeDescription
ModelMonitoringObjectiveConfig.TrainingPredictionSkewDetectionConfig.Builder

writeTo(CodedOutputStream output)

public void writeTo(CodedOutputStream output)
Parameter
NameDescription
outputCodedOutputStream
Overrides
Exceptions
TypeDescription
IOException