Class FeatureStatsAnomaly.Builder (2.4.0)

Version 2.4.0

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public static final class FeatureStatsAnomaly.Builder extends GeneratedMessageV3.Builder<FeatureStatsAnomaly.Builder> implements FeatureStatsAnomalyOrBuilder

Stats and Anomaly generated at specific timestamp for specific Feature. The start_time and end_time are used to define the time range of the dataset that current stats belongs to, e.g. prediction traffic is bucketed into prediction datasets by time window. If the Dataset is not defined by time window, start_time = end_time. Timestamp of the stats and anomalies always refers to end_time. Raw stats and anomalies are stored in stats_uri or anomaly_uri in the tensorflow defined protos. Field data_stats contains almost identical information with the raw stats in Vertex AI defined proto, for UI to display.

Protobuf type google.cloud.aiplatform.v1beta1.FeatureStatsAnomaly

Inheritance

Object > AbstractMessageLite.Builder<MessageType,BuilderType> > AbstractMessage.Builder<BuilderType> > GeneratedMessageV3.Builder > FeatureStatsAnomaly.Builder

Implements

FeatureStatsAnomalyOrBuilder

Inherited Members

AbstractMessage.Builder.findInitializationErrors()

AbstractMessage.Builder.getInitializationErrorString()

AbstractMessage.Builder.internalMergeFrom(AbstractMessageLite)

AbstractMessage.Builder.mergeDelimitedFrom(InputStream)

AbstractMessage.Builder.mergeDelimitedFrom(InputStream,ExtensionRegistryLite)

AbstractMessage.Builder.mergeFrom(byte[])

AbstractMessage.Builder.mergeFrom(byte[],ExtensionRegistryLite)

AbstractMessage.Builder.mergeFrom(byte[],int,int)

AbstractMessage.Builder.mergeFrom(byte[],int,int,ExtensionRegistryLite)

AbstractMessage.Builder.mergeFrom(ByteString)

AbstractMessage.Builder.mergeFrom(ByteString,ExtensionRegistryLite)

AbstractMessage.Builder.mergeFrom(CodedInputStream)

AbstractMessage.Builder.mergeFrom(CodedInputStream,ExtensionRegistryLite)

AbstractMessage.Builder.mergeFrom(Message)

AbstractMessage.Builder.mergeFrom(InputStream)

AbstractMessage.Builder.mergeFrom(InputStream,ExtensionRegistryLite)

AbstractMessage.Builder.newUninitializedMessageException(Message)

AbstractMessage.Builder.toString()

AbstractMessageLite.Builder.<T>addAll(Iterable<T>,Collection<? super T>)

AbstractMessageLite.Builder.<T>addAll(Iterable<T>,List<? super T>)

AbstractMessageLite.Builder.internalMergeFrom(MessageType)

AbstractMessageLite.Builder.mergeFrom(MessageLite)

AbstractMessageLite.Builder.newUninitializedMessageException(MessageLite)

GeneratedMessageV3.Builder.addRepeatedField(Descriptors.FieldDescriptor,Object)

GeneratedMessageV3.Builder.clear()

GeneratedMessageV3.Builder.clearField(Descriptors.FieldDescriptor)

GeneratedMessageV3.Builder.clearOneof(Descriptors.OneofDescriptor)

GeneratedMessageV3.Builder.clone()

GeneratedMessageV3.Builder.getAllFields()

GeneratedMessageV3.Builder.getDescriptorForType()

GeneratedMessageV3.Builder.getField(Descriptors.FieldDescriptor)

GeneratedMessageV3.Builder.getFieldBuilder(Descriptors.FieldDescriptor)

GeneratedMessageV3.Builder.getOneofFieldDescriptor(Descriptors.OneofDescriptor)

GeneratedMessageV3.Builder.getParentForChildren()

GeneratedMessageV3.Builder.getRepeatedField(Descriptors.FieldDescriptor,int)

GeneratedMessageV3.Builder.getRepeatedFieldBuilder(Descriptors.FieldDescriptor,int)

GeneratedMessageV3.Builder.getRepeatedFieldCount(Descriptors.FieldDescriptor)

GeneratedMessageV3.Builder.getUnknownFields()

GeneratedMessageV3.Builder.hasField(Descriptors.FieldDescriptor)

GeneratedMessageV3.Builder.hasOneof(Descriptors.OneofDescriptor)

GeneratedMessageV3.Builder.internalGetFieldAccessorTable()

GeneratedMessageV3.Builder.internalGetMapField(int)

GeneratedMessageV3.Builder.internalGetMutableMapField(int)

GeneratedMessageV3.Builder.isClean()

GeneratedMessageV3.Builder.isInitialized()

GeneratedMessageV3.Builder.markClean()

GeneratedMessageV3.Builder.mergeUnknownFields(UnknownFieldSet)

GeneratedMessageV3.Builder.newBuilderForField(Descriptors.FieldDescriptor)

GeneratedMessageV3.Builder.onBuilt()

GeneratedMessageV3.Builder.onChanged()

GeneratedMessageV3.Builder.setField(Descriptors.FieldDescriptor,Object)

GeneratedMessageV3.Builder.setRepeatedField(Descriptors.FieldDescriptor,int,Object)

GeneratedMessageV3.Builder.setUnknownFields(UnknownFieldSet)

GeneratedMessageV3.Builder.setUnknownFieldsProto3(UnknownFieldSet)

Object.equals(Object)

Object.finalize()

Object.getClass()

Object.hashCode()

Object.notify()

Object.notifyAll()

Object.wait()

Object.wait(long)

Object.wait(long,int)

Methods

addRepeatedField(Descriptors.FieldDescriptor field, Object value)

public FeatureStatsAnomaly.Builder addRepeatedField(Descriptors.FieldDescriptor field, Object value)

Parameters

Name	Description
field	FieldDescriptor
value	Object

Returns

Type	Description
FeatureStatsAnomaly.Builder

Overrides

GeneratedMessageV3.Builder<BuilderType>.addRepeatedField(Descriptors.FieldDescriptor field, Object value)

build()

public FeatureStatsAnomaly build()

Returns

Type	Description
FeatureStatsAnomaly

buildPartial()

public FeatureStatsAnomaly buildPartial()

Returns

Type	Description
FeatureStatsAnomaly

clear()

public FeatureStatsAnomaly.Builder clear()

Returns

Type	Description
FeatureStatsAnomaly.Builder

Overrides

GeneratedMessageV3.Builder<BuilderType>.clear()

clearAnomalyDetectionThreshold()

public FeatureStatsAnomaly.Builder clearAnomalyDetectionThreshold()

This is the threshold used when detecting anomalies. The threshold can be changed by user, so this one might be different from ThresholdConfig.value.

double anomaly_detection_threshold = 9;

Returns

Type	Description
FeatureStatsAnomaly.Builder	This builder for chaining.

clearAnomalyUri()

public FeatureStatsAnomaly.Builder clearAnomalyUri()

Path of the anomaly file for current feature values in Cloud Storage bucket. Format: gs://<bucket_name>/<object_name>/anomalies. Example: gs://monitoring_bucket/feature_name/anomalies. Stats are stored as binary format with Protobuf message Anoamlies are stored as binary format with Protobuf message tensorflow.metadata.v0.AnomalyInfo.

string anomaly_uri = 4;

Returns

Type	Description
FeatureStatsAnomaly.Builder	This builder for chaining.

clearDistributionDeviation()

public FeatureStatsAnomaly.Builder clearDistributionDeviation()

Deviation from the current stats to baseline stats.

1. For categorical feature, the distribution distance is calculated by L-inifinity norm.
2. For numerical feature, the distribution distance is calculated by Jensen\u2013Shannon divergence.

double distribution_deviation = 5;

Returns

Type	Description
FeatureStatsAnomaly.Builder	This builder for chaining.

clearEndTime()

public FeatureStatsAnomaly.Builder clearEndTime()

The end timestamp of window where stats were generated. For objectives where time window doesn't make sense (e.g. Featurestore Snapshot Monitoring), end_time indicates the timestamp of the data used to generate stats (e.g. timestamp we take snapshots for feature values).

.google.protobuf.Timestamp end_time = 8;

Returns

Type	Description
FeatureStatsAnomaly.Builder

clearField(Descriptors.FieldDescriptor field)

public FeatureStatsAnomaly.Builder clearField(Descriptors.FieldDescriptor field)

Parameter

Name	Description
field	FieldDescriptor

Returns

Type	Description
FeatureStatsAnomaly.Builder

Overrides

GeneratedMessageV3.Builder<BuilderType>.clearField(Descriptors.FieldDescriptor field)

clearOneof(Descriptors.OneofDescriptor oneof)

public FeatureStatsAnomaly.Builder clearOneof(Descriptors.OneofDescriptor oneof)

Parameter

Name	Description
oneof	OneofDescriptor

Returns

Type	Description
FeatureStatsAnomaly.Builder

Overrides

GeneratedMessageV3.Builder<BuilderType>.clearOneof(Descriptors.OneofDescriptor oneof)

clearScore()

public FeatureStatsAnomaly.Builder clearScore()

Feature importance score, only populated when cross-feature monitoring is enabled. For now only used to represent feature attribution score within range [0, 1] for ModelDeploymentMonitoringObjectiveType.FEATURE_ATTRIBUTION_SKEW and ModelDeploymentMonitoringObjectiveType.FEATURE_ATTRIBUTION_DRIFT.

double score = 1;

Returns

Type	Description
FeatureStatsAnomaly.Builder	This builder for chaining.

clearStartTime()

public FeatureStatsAnomaly.Builder clearStartTime()

The start timestamp of window where stats were generated. For objectives where time window doesn't make sense (e.g. Featurestore Snapshot Monitoring), start_time is only used to indicate the monitoring intervals, so it always equals to (end_time - monitoring_interval).

.google.protobuf.Timestamp start_time = 7;

Returns

Type	Description
FeatureStatsAnomaly.Builder

clearStatsUri()

public FeatureStatsAnomaly.Builder clearStatsUri()

Path of the stats file for current feature values in Cloud Storage bucket. Format: gs://<bucket_name>/<object_name>/stats. Example: gs://monitoring_bucket/feature_name/stats. Stats are stored as binary format with Protobuf message tensorflow.metadata.v0.FeatureNameStatistics.

string stats_uri = 3;

Returns

Type	Description
FeatureStatsAnomaly.Builder	This builder for chaining.

clone()

public FeatureStatsAnomaly.Builder clone()

Returns

Type	Description
FeatureStatsAnomaly.Builder

Overrides

GeneratedMessageV3.Builder<BuilderType>.clone()

getAnomalyDetectionThreshold()

public double getAnomalyDetectionThreshold()

This is the threshold used when detecting anomalies. The threshold can be changed by user, so this one might be different from ThresholdConfig.value.

double anomaly_detection_threshold = 9;

Returns

Type	Description
double	The anomalyDetectionThreshold.

getAnomalyUri()

public String getAnomalyUri()

Path of the anomaly file for current feature values in Cloud Storage bucket. Format: gs://<bucket_name>/<object_name>/anomalies. Example: gs://monitoring_bucket/feature_name/anomalies. Stats are stored as binary format with Protobuf message Anoamlies are stored as binary format with Protobuf message tensorflow.metadata.v0.AnomalyInfo.

string anomaly_uri = 4;

Returns

Type	Description
String	The anomalyUri.

getAnomalyUriBytes()

public ByteString getAnomalyUriBytes()

Path of the anomaly file for current feature values in Cloud Storage bucket. Format: gs://<bucket_name>/<object_name>/anomalies. Example: gs://monitoring_bucket/feature_name/anomalies. Stats are stored as binary format with Protobuf message Anoamlies are stored as binary format with Protobuf message tensorflow.metadata.v0.AnomalyInfo.

string anomaly_uri = 4;

Returns

Type	Description
ByteString	The bytes for anomalyUri.

getDefaultInstanceForType()

public FeatureStatsAnomaly getDefaultInstanceForType()

Returns

Type	Description
FeatureStatsAnomaly

getDescriptor()

public static final Descriptors.Descriptor getDescriptor()

Returns

Type	Description
Descriptor

getDescriptorForType()

public Descriptors.Descriptor getDescriptorForType()

Returns

Type	Description
Descriptor

Overrides

GeneratedMessageV3.Builder<BuilderType>.getDescriptorForType()

getDistributionDeviation()

public double getDistributionDeviation()

Deviation from the current stats to baseline stats.

1. For categorical feature, the distribution distance is calculated by L-inifinity norm.
2. For numerical feature, the distribution distance is calculated by Jensen\u2013Shannon divergence.

double distribution_deviation = 5;

Returns

Type	Description
double	The distributionDeviation.

getEndTime()

public Timestamp getEndTime()

The end timestamp of window where stats were generated. For objectives where time window doesn't make sense (e.g. Featurestore Snapshot Monitoring), end_time indicates the timestamp of the data used to generate stats (e.g. timestamp we take snapshots for feature values).

.google.protobuf.Timestamp end_time = 8;

Returns

Type	Description
Timestamp	The endTime.

getEndTimeBuilder()

public Timestamp.Builder getEndTimeBuilder()

The end timestamp of window where stats were generated. For objectives where time window doesn't make sense (e.g. Featurestore Snapshot Monitoring), end_time indicates the timestamp of the data used to generate stats (e.g. timestamp we take snapshots for feature values).

.google.protobuf.Timestamp end_time = 8;

Returns

Type	Description
Builder

getEndTimeOrBuilder()

public TimestampOrBuilder getEndTimeOrBuilder()

The end timestamp of window where stats were generated. For objectives where time window doesn't make sense (e.g. Featurestore Snapshot Monitoring), end_time indicates the timestamp of the data used to generate stats (e.g. timestamp we take snapshots for feature values).

.google.protobuf.Timestamp end_time = 8;

Returns

Type	Description
TimestampOrBuilder

getScore()

public double getScore()

Feature importance score, only populated when cross-feature monitoring is enabled. For now only used to represent feature attribution score within range [0, 1] for ModelDeploymentMonitoringObjectiveType.FEATURE_ATTRIBUTION_SKEW and ModelDeploymentMonitoringObjectiveType.FEATURE_ATTRIBUTION_DRIFT.

double score = 1;

Returns

Type	Description
double	The score.

getStartTime()

public Timestamp getStartTime()

The start timestamp of window where stats were generated. For objectives where time window doesn't make sense (e.g. Featurestore Snapshot Monitoring), start_time is only used to indicate the monitoring intervals, so it always equals to (end_time - monitoring_interval).

.google.protobuf.Timestamp start_time = 7;

Returns

Type	Description
Timestamp	The startTime.

getStartTimeBuilder()

public Timestamp.Builder getStartTimeBuilder()

The start timestamp of window where stats were generated. For objectives where time window doesn't make sense (e.g. Featurestore Snapshot Monitoring), start_time is only used to indicate the monitoring intervals, so it always equals to (end_time - monitoring_interval).

.google.protobuf.Timestamp start_time = 7;

Returns

Type	Description
Builder

getStartTimeOrBuilder()

public TimestampOrBuilder getStartTimeOrBuilder()

The start timestamp of window where stats were generated. For objectives where time window doesn't make sense (e.g. Featurestore Snapshot Monitoring), start_time is only used to indicate the monitoring intervals, so it always equals to (end_time - monitoring_interval).

.google.protobuf.Timestamp start_time = 7;

Returns

Type	Description
TimestampOrBuilder

getStatsUri()

public String getStatsUri()

Path of the stats file for current feature values in Cloud Storage bucket. Format: gs://<bucket_name>/<object_name>/stats. Example: gs://monitoring_bucket/feature_name/stats. Stats are stored as binary format with Protobuf message tensorflow.metadata.v0.FeatureNameStatistics.

string stats_uri = 3;

Returns

Type	Description
String	The statsUri.

getStatsUriBytes()

public ByteString getStatsUriBytes()

Path of the stats file for current feature values in Cloud Storage bucket. Format: gs://<bucket_name>/<object_name>/stats. Example: gs://monitoring_bucket/feature_name/stats. Stats are stored as binary format with Protobuf message tensorflow.metadata.v0.FeatureNameStatistics.

string stats_uri = 3;

Returns

Type	Description
ByteString	The bytes for statsUri.

hasEndTime()

public boolean hasEndTime()

The end timestamp of window where stats were generated. For objectives where time window doesn't make sense (e.g. Featurestore Snapshot Monitoring), end_time indicates the timestamp of the data used to generate stats (e.g. timestamp we take snapshots for feature values).

.google.protobuf.Timestamp end_time = 8;

Returns

Type	Description
boolean	Whether the endTime field is set.

hasStartTime()

public boolean hasStartTime()

The start timestamp of window where stats were generated. For objectives where time window doesn't make sense (e.g. Featurestore Snapshot Monitoring), start_time is only used to indicate the monitoring intervals, so it always equals to (end_time - monitoring_interval).

.google.protobuf.Timestamp start_time = 7;

Returns

Type	Description
boolean	Whether the startTime field is set.

internalGetFieldAccessorTable()

protected GeneratedMessageV3.FieldAccessorTable internalGetFieldAccessorTable()

Returns

Type	Description
FieldAccessorTable

Overrides

GeneratedMessageV3.Builder<BuilderType>.internalGetFieldAccessorTable()

isInitialized()

public final boolean isInitialized()

Returns

Type	Description
boolean

Overrides

GeneratedMessageV3.Builder<BuilderType>.isInitialized()

mergeEndTime(Timestamp value)

public FeatureStatsAnomaly.Builder mergeEndTime(Timestamp value)

The end timestamp of window where stats were generated. For objectives where time window doesn't make sense (e.g. Featurestore Snapshot Monitoring), end_time indicates the timestamp of the data used to generate stats (e.g. timestamp we take snapshots for feature values).

.google.protobuf.Timestamp end_time = 8;

Parameter

Name	Description
value	Timestamp

Returns

Type	Description
FeatureStatsAnomaly.Builder

mergeFrom(FeatureStatsAnomaly other)

public FeatureStatsAnomaly.Builder mergeFrom(FeatureStatsAnomaly other)

Parameter

Name	Description
other	FeatureStatsAnomaly

Returns

Type	Description
FeatureStatsAnomaly.Builder

mergeFrom(CodedInputStream input, ExtensionRegistryLite extensionRegistry)

public FeatureStatsAnomaly.Builder mergeFrom(CodedInputStream input, ExtensionRegistryLite extensionRegistry)

Parameters

Name	Description
input	CodedInputStream
extensionRegistry	ExtensionRegistryLite

Returns

Type	Description
FeatureStatsAnomaly.Builder

Overrides

AbstractMessage.Builder<BuilderType>.mergeFrom(CodedInputStream input, ExtensionRegistryLite extensionRegistry)

Exceptions

Type	Description
IOException

mergeFrom(Message other)

public FeatureStatsAnomaly.Builder mergeFrom(Message other)

Parameter

Name	Description
other	Message

Returns

Type	Description
FeatureStatsAnomaly.Builder

Overrides

AbstractMessage.Builder<BuilderType>.mergeFrom(Message other)

mergeStartTime(Timestamp value)

public FeatureStatsAnomaly.Builder mergeStartTime(Timestamp value)

The start timestamp of window where stats were generated. For objectives where time window doesn't make sense (e.g. Featurestore Snapshot Monitoring), start_time is only used to indicate the monitoring intervals, so it always equals to (end_time - monitoring_interval).

.google.protobuf.Timestamp start_time = 7;

Parameter

Name	Description
value	Timestamp

Returns

Type	Description
FeatureStatsAnomaly.Builder

mergeUnknownFields(UnknownFieldSet unknownFields)

public final FeatureStatsAnomaly.Builder mergeUnknownFields(UnknownFieldSet unknownFields)

Parameter

Name	Description
unknownFields	UnknownFieldSet

Returns

Type	Description
FeatureStatsAnomaly.Builder

Overrides

GeneratedMessageV3.Builder<BuilderType>.mergeUnknownFields(UnknownFieldSet unknownFields)

setAnomalyDetectionThreshold(double value)

public FeatureStatsAnomaly.Builder setAnomalyDetectionThreshold(double value)

This is the threshold used when detecting anomalies. The threshold can be changed by user, so this one might be different from ThresholdConfig.value.

double anomaly_detection_threshold = 9;

Parameter

Name	Description
value	double The anomalyDetectionThreshold to set.

Returns

Type	Description
FeatureStatsAnomaly.Builder	This builder for chaining.

setAnomalyUri(String value)

public FeatureStatsAnomaly.Builder setAnomalyUri(String value)

Path of the anomaly file for current feature values in Cloud Storage bucket. Format: gs://<bucket_name>/<object_name>/anomalies. Example: gs://monitoring_bucket/feature_name/anomalies. Stats are stored as binary format with Protobuf message Anoamlies are stored as binary format with Protobuf message tensorflow.metadata.v0.AnomalyInfo.

string anomaly_uri = 4;

Parameter

Name	Description
value	String The anomalyUri to set.

Returns

Type	Description
FeatureStatsAnomaly.Builder	This builder for chaining.

setAnomalyUriBytes(ByteString value)

public FeatureStatsAnomaly.Builder setAnomalyUriBytes(ByteString value)

Path of the anomaly file for current feature values in Cloud Storage bucket. Format: gs://<bucket_name>/<object_name>/anomalies. Example: gs://monitoring_bucket/feature_name/anomalies. Stats are stored as binary format with Protobuf message Anoamlies are stored as binary format with Protobuf message tensorflow.metadata.v0.AnomalyInfo.

string anomaly_uri = 4;

Parameter

Name	Description
value	ByteString The bytes for anomalyUri to set.

Returns

Type	Description
FeatureStatsAnomaly.Builder	This builder for chaining.

setDistributionDeviation(double value)

public FeatureStatsAnomaly.Builder setDistributionDeviation(double value)

Deviation from the current stats to baseline stats.

1. For categorical feature, the distribution distance is calculated by L-inifinity norm.
2. For numerical feature, the distribution distance is calculated by Jensen\u2013Shannon divergence.

double distribution_deviation = 5;

Parameter

Name	Description
value	double The distributionDeviation to set.

Returns

Type	Description
FeatureStatsAnomaly.Builder	This builder for chaining.

setEndTime(Timestamp value)

public FeatureStatsAnomaly.Builder setEndTime(Timestamp value)

The end timestamp of window where stats were generated. For objectives where time window doesn't make sense (e.g. Featurestore Snapshot Monitoring), end_time indicates the timestamp of the data used to generate stats (e.g. timestamp we take snapshots for feature values).

.google.protobuf.Timestamp end_time = 8;

Parameter

Name	Description
value	Timestamp

Returns

Type	Description
FeatureStatsAnomaly.Builder

setEndTime(Timestamp.Builder builderForValue)

public FeatureStatsAnomaly.Builder setEndTime(Timestamp.Builder builderForValue)

The end timestamp of window where stats were generated. For objectives where time window doesn't make sense (e.g. Featurestore Snapshot Monitoring), end_time indicates the timestamp of the data used to generate stats (e.g. timestamp we take snapshots for feature values).

.google.protobuf.Timestamp end_time = 8;

Parameter

Name	Description
builderForValue	Builder

Returns

Type	Description
FeatureStatsAnomaly.Builder

setField(Descriptors.FieldDescriptor field, Object value)

public FeatureStatsAnomaly.Builder setField(Descriptors.FieldDescriptor field, Object value)

Parameters

Name	Description
field	FieldDescriptor
value	Object

Returns

Type	Description
FeatureStatsAnomaly.Builder

Overrides

GeneratedMessageV3.Builder<BuilderType>.setField(Descriptors.FieldDescriptor field, Object value)

setRepeatedField(Descriptors.FieldDescriptor field, int index, Object value)

public FeatureStatsAnomaly.Builder setRepeatedField(Descriptors.FieldDescriptor field, int index, Object value)

Parameters

Name	Description
field	FieldDescriptor
index	int
value	Object

Returns

Type	Description
FeatureStatsAnomaly.Builder

Overrides

GeneratedMessageV3.Builder<BuilderType>.setRepeatedField(Descriptors.FieldDescriptor field, int index, Object value)

setScore(double value)

public FeatureStatsAnomaly.Builder setScore(double value)

Feature importance score, only populated when cross-feature monitoring is enabled. For now only used to represent feature attribution score within range [0, 1] for ModelDeploymentMonitoringObjectiveType.FEATURE_ATTRIBUTION_SKEW and ModelDeploymentMonitoringObjectiveType.FEATURE_ATTRIBUTION_DRIFT.

double score = 1;

Parameter

Name	Description
value	double The score to set.

Returns

Type	Description
FeatureStatsAnomaly.Builder	This builder for chaining.

setStartTime(Timestamp value)

public FeatureStatsAnomaly.Builder setStartTime(Timestamp value)

The start timestamp of window where stats were generated. For objectives where time window doesn't make sense (e.g. Featurestore Snapshot Monitoring), start_time is only used to indicate the monitoring intervals, so it always equals to (end_time - monitoring_interval).

.google.protobuf.Timestamp start_time = 7;

Parameter

Name	Description
value	Timestamp

Returns

Type	Description
FeatureStatsAnomaly.Builder

setStartTime(Timestamp.Builder builderForValue)

public FeatureStatsAnomaly.Builder setStartTime(Timestamp.Builder builderForValue)

The start timestamp of window where stats were generated. For objectives where time window doesn't make sense (e.g. Featurestore Snapshot Monitoring), start_time is only used to indicate the monitoring intervals, so it always equals to (end_time - monitoring_interval).

.google.protobuf.Timestamp start_time = 7;

Parameter

Name	Description
builderForValue	Builder

Returns

Type	Description
FeatureStatsAnomaly.Builder

setStatsUri(String value)

public FeatureStatsAnomaly.Builder setStatsUri(String value)

Path of the stats file for current feature values in Cloud Storage bucket. Format: gs://<bucket_name>/<object_name>/stats. Example: gs://monitoring_bucket/feature_name/stats. Stats are stored as binary format with Protobuf message tensorflow.metadata.v0.FeatureNameStatistics.

string stats_uri = 3;

Parameter

Name	Description
value	String The statsUri to set.

Returns

Type	Description
FeatureStatsAnomaly.Builder	This builder for chaining.

setStatsUriBytes(ByteString value)

public FeatureStatsAnomaly.Builder setStatsUriBytes(ByteString value)

Path of the stats file for current feature values in Cloud Storage bucket. Format: gs://<bucket_name>/<object_name>/stats. Example: gs://monitoring_bucket/feature_name/stats. Stats are stored as binary format with Protobuf message tensorflow.metadata.v0.FeatureNameStatistics.

string stats_uri = 3;

Parameter

Name	Description
value	ByteString The bytes for statsUri to set.

Returns

Type	Description
FeatureStatsAnomaly.Builder	This builder for chaining.

setUnknownFields(UnknownFieldSet unknownFields)

public final FeatureStatsAnomaly.Builder setUnknownFields(UnknownFieldSet unknownFields)

Parameter

Name	Description
unknownFields	UnknownFieldSet

Returns

Type	Description
FeatureStatsAnomaly.Builder

Overrides

GeneratedMessageV3.Builder<BuilderType>.setUnknownFields(UnknownFieldSet unknownFields)