Struct FeatureStatsAnomaly 

#[non_exhaustive]
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>,
    /* private fields */
}

Available on crate features feature-registry-service or featurestore-service or job-service only.

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 (Non-exhaustive)

Non-exhaustive structs could have additional fields added in future. Therefore, non-exhaustive structs cannot be constructed in external crates using the traditional Struct { .. } syntax; cannot be matched against without a wildcard ..; and struct update syntax will not work.

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 and 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.

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.

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).

Implementations

impl FeatureStatsAnomaly

pub fn new() -> Self

pub fn set_score<T: Into<f64>>(self, v: T) -> Self

Sets the value of score.

Example

let x = FeatureStatsAnomaly::new().set_score(42.0);

pub fn set_stats_uri<T: Into<String>>(self, v: T) -> Self

Sets the value of stats_uri.

Example

let x = FeatureStatsAnomaly::new().set_stats_uri("example");

pub fn set_anomaly_uri<T: Into<String>>(self, v: T) -> Self

Sets the value of anomaly_uri.

Example

let x = FeatureStatsAnomaly::new().set_anomaly_uri("example");

pub fn set_distribution_deviation<T: Into<f64>>(self, v: T) -> Self

Sets the value of distribution_deviation.

Example

let x = FeatureStatsAnomaly::new().set_distribution_deviation(42.0);

pub fn set_anomaly_detection_threshold<T: Into<f64>>(self, v: T) -> Self

Sets the value of anomaly_detection_threshold.

Example

let x = FeatureStatsAnomaly::new().set_anomaly_detection_threshold(42.0);

pub fn set_start_time<T>(self, v: T) -> Self

where T: Into<Timestamp>,

Sets the value of start_time.

Example

use wkt::Timestamp;
let x = FeatureStatsAnomaly::new().set_start_time(Timestamp::default()/* use setters */);

pub fn set_or_clear_start_time<T>(self, v: Option<T>) -> Self

where T: Into<Timestamp>,

Sets or clears the value of start_time.

Example

use wkt::Timestamp;
let x = FeatureStatsAnomaly::new().set_or_clear_start_time(Some(Timestamp::default()/* use setters */));
let x = FeatureStatsAnomaly::new().set_or_clear_start_time(None::<Timestamp>);

pub fn set_end_time<T>(self, v: T) -> Self

where T: Into<Timestamp>,

Sets the value of end_time.

Example

use wkt::Timestamp;
let x = FeatureStatsAnomaly::new().set_end_time(Timestamp::default()/* use setters */);

pub fn set_or_clear_end_time<T>(self, v: Option<T>) -> Self

where T: Into<Timestamp>,

Sets or clears the value of end_time.

Example

use wkt::Timestamp;
let x = FeatureStatsAnomaly::new().set_or_clear_end_time(Some(Timestamp::default()/* use setters */));
let x = FeatureStatsAnomaly::new().set_or_clear_end_time(None::<Timestamp>);

Trait Implementations

impl Clone for FeatureStatsAnomaly

fn clone(&self) -> FeatureStatsAnomaly

Returns a duplicate 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() -> FeatureStatsAnomaly

Returns the “default value” for a type.

impl Message for FeatureStatsAnomaly

fn typename() -> &'static str

The typename of this message.

impl PartialEq for FeatureStatsAnomaly

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

Tests for self and other values to be equal, and is used by ==.

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

Tests for !=. The default implementation is almost always sufficient, and should not be overridden without very good reason.

impl StructuralPartialEq for FeatureStatsAnomaly