Struct googapis::google::cloud::aiplatform::v1::FeatureStatsAnomaly

pub struct FeatureStatsAnomaly {
    pub score: f64,
    pub stats_uri: String,
    pub anomaly_uri: String,
    pub distribution_deviation: f64,
    pub anomaly_detection_threshold: f64,
    pub start_time: Option<Timestamp>,
    pub end_time: Option<Timestamp>,
}

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.

Fields

score: f64

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].

stats_uri: String

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).

anomaly_uri: String

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).

distribution_deviation: f64

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.

anomaly_detection_threshold: f64

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].

start_time: Option<Timestamp>

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).

end_time: Option<Timestamp>

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).

Trait Implementations

impl Clone for FeatureStatsAnomaly

fn clone(&self) -> FeatureStatsAnomaly

Returns a copy of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl Debug for FeatureStatsAnomaly

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.

impl Default for FeatureStatsAnomaly

fn default() -> Self

Returns the “default value” for a type.

impl Message for FeatureStatsAnomaly

fn encode_raw<B>(&self, buf: &mut B) where
    B: BufMut, 

fn merge_field<B>(
    &mut self,
    tag: u32,
    wire_type: WireType,
    buf: &mut B,
    ctx: DecodeContext
) -> Result<(), DecodeError> where
    B: Buf, 

fn encoded_len(&self) -> usize

Returns the encoded length of the message without a length delimiter.

fn clear(&mut self)

Clears the message, resetting all fields to their default.

fn encode<B>(&self, buf: &mut B) -> Result<(), EncodeError> where
    B: BufMut, 

Encodes the message to a buffer.

fn encode_to_vec(&self) -> Vec<u8, Global>

Encodes the message to a newly allocated buffer.

fn encode_length_delimited<B>(&self, buf: &mut B) -> Result<(), EncodeError> where
    B: BufMut, 

Encodes the message with a length-delimiter to a buffer.

fn encode_length_delimited_to_vec(&self) -> Vec<u8, Global>

Encodes the message with a length-delimiter to a newly allocated buffer.

fn decode<B>(buf: B) -> Result<Self, DecodeError> where
    Self: Default,
    B: Buf, 

Decodes an instance of the message from a buffer.

fn decode_length_delimited<B>(buf: B) -> Result<Self, DecodeError> where
    Self: Default,
    B: Buf, 

Decodes a length-delimited instance of the message from the buffer.

fn merge<B>(&mut self, buf: B) -> Result<(), DecodeError> where
    B: Buf, 

Decodes an instance of the message from a buffer, and merges it into self.

fn merge_length_delimited<B>(&mut self, buf: B) -> Result<(), DecodeError> where
    B: Buf, 

Decodes a length-delimited instance of the message from buffer, and merges it into self.

impl PartialEq<FeatureStatsAnomaly> for FeatureStatsAnomaly

fn eq(&self, other: &FeatureStatsAnomaly) -> bool

This method tests for self and other values to be equal, and is used by ==.

fn ne(&self, other: &FeatureStatsAnomaly) -> bool

This method tests for !=.

impl StructuralPartialEq for FeatureStatsAnomaly