Google Cloud Ai Platform V1 Client - Class FeatureStatsAnomaly (1.2.0)

Reference documentation and code samples for the Google Cloud Ai Platform V1 Client class FeatureStatsAnomaly.

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.

Generated from protobuf message google.cloud.aiplatform.v1.FeatureStatsAnomaly

Namespace

Google \ Cloud \ AIPlatform \ V1

Methods

__construct

Constructor.

Parameters
Name Description
data array

Optional. Data for populating the Message object.
↳ score float

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.
↳ distribution_deviation float

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 float

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 Google\Protobuf\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 Google\Protobuf\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).

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.

Returns
Type Description
float

setScore

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.

Parameter
Name Description
var float
Returns
Type Description
$this

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.

Returns
Type Description
string

setStatsUri

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.

Parameter
Name Description
var string
Returns
Type Description
$this

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.

Returns
Type Description
string

setAnomalyUri

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.

Parameter
Name Description
var string
Returns
Type Description
$this

getDistributionDeviation

Deviation from the current stats to baseline stats.

  1. For categorical feature, the distribution distance is calculated by L-inifinity norm.
    1. For numerical feature, the distribution distance is calculated by Jensen–Shannon divergence.
Returns
Type Description
float

setDistributionDeviation

Deviation from the current stats to baseline stats.

  1. For categorical feature, the distribution distance is calculated by L-inifinity norm.
    1. For numerical feature, the distribution distance is calculated by Jensen–Shannon divergence.
Parameter
Name Description
var float
Returns
Type Description
$this

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.

Returns
Type Description
float

setAnomalyDetectionThreshold

This is the threshold used when detecting anomalies.

The threshold can be changed by user, so this one might be different from ThresholdConfig.value.

Parameter
Name Description
var float
Returns
Type Description
$this

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).

Returns
Type Description
Google\Protobuf\Timestamp|null

hasStartTime

clearStartTime

setStartTime

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).

Parameter
Name Description
var Google\Protobuf\Timestamp
Returns
Type Description
$this

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).

Returns
Type Description
Google\Protobuf\Timestamp|null

hasEndTime

clearEndTime

setEndTime

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).

Parameter
Name Description
var Google\Protobuf\Timestamp
Returns
Type Description
$this