Package com.google.cloud.aiplatform.v1
Interface FeatureStatsAnomalyOrBuilder
- All Superinterfaces:
com.google.protobuf.MessageLiteOrBuilder,com.google.protobuf.MessageOrBuilder
- All Known Implementing Classes:
FeatureStatsAnomaly,FeatureStatsAnomaly.Builder
@Generated
public interface FeatureStatsAnomalyOrBuilder
extends com.google.protobuf.MessageOrBuilder
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Method Summary
Modifier and TypeMethodDescriptiondoubleThis is the threshold used when detecting anomalies.Path of the anomaly file for current feature values in Cloud Storage bucket.com.google.protobuf.ByteStringPath of the anomaly file for current feature values in Cloud Storage bucket.doubleDeviation from the current stats to baseline stats. 1.com.google.protobuf.TimestampThe end timestamp of window where stats were generated.com.google.protobuf.TimestampOrBuilderThe end timestamp of window where stats were generated.doublegetScore()Feature importance score, only populated when cross-feature monitoring is enabled.com.google.protobuf.TimestampThe start timestamp of window where stats were generated.com.google.protobuf.TimestampOrBuilderThe start timestamp of window where stats were generated.Path of the stats file for current feature values in Cloud Storage bucket.com.google.protobuf.ByteStringPath of the stats file for current feature values in Cloud Storage bucket.booleanThe end timestamp of window where stats were generated.booleanThe start timestamp of window where stats were generated.Methods inherited from interface com.google.protobuf.MessageLiteOrBuilder
isInitializedMethods inherited from interface com.google.protobuf.MessageOrBuilder
findInitializationErrors, getAllFields, getDefaultInstanceForType, getDescriptorForType, getField, getInitializationErrorString, getOneofFieldDescriptor, getRepeatedField, getRepeatedFieldCount, getUnknownFields, hasField, hasOneof
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Method Details
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getScore
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][google.cloud.aiplatform.v1.ModelDeploymentMonitoringObjectiveType.FEATURE_ATTRIBUTION_SKEW] and [ModelDeploymentMonitoringObjectiveType.FEATURE_ATTRIBUTION_DRIFT][google.cloud.aiplatform.v1.ModelDeploymentMonitoringObjectiveType.FEATURE_ATTRIBUTION_DRIFT].
double score = 1;- Returns:
- The score.
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getStatsUri
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](https://github.com/tensorflow/metadata/blob/master/tensorflow_metadata/proto/v0/statistics.proto).
string stats_uri = 3;- Returns:
- The statsUri.
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getStatsUriBytes
com.google.protobuf.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](https://github.com/tensorflow/metadata/blob/master/tensorflow_metadata/proto/v0/statistics.proto).
string stats_uri = 3;- Returns:
- The bytes for statsUri.
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getAnomalyUri
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] (https://github.com/tensorflow/metadata/blob/master/tensorflow_metadata/proto/v0/anomalies.proto).
string anomaly_uri = 4;- Returns:
- The anomalyUri.
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getAnomalyUriBytes
com.google.protobuf.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] (https://github.com/tensorflow/metadata/blob/master/tensorflow_metadata/proto/v0/anomalies.proto).
string anomaly_uri = 4;- Returns:
- The bytes for anomalyUri.
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getDistributionDeviation
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–Shannon divergence.
double distribution_deviation = 5;- Returns:
- The distributionDeviation.
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getAnomalyDetectionThreshold
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][google.cloud.aiplatform.v1.ThresholdConfig.value].
double anomaly_detection_threshold = 9;- Returns:
- The anomalyDetectionThreshold.
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hasStartTime
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:
- Whether the startTime field is set.
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getStartTime
com.google.protobuf.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:
- The startTime.
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getStartTimeOrBuilder
com.google.protobuf.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; -
hasEndTime
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:
- Whether the endTime field is set.
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getEndTime
com.google.protobuf.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:
- The endTime.
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getEndTimeOrBuilder
com.google.protobuf.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;
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