Google Cloud Ai Platform V1 Client - Class FeatureStatsAnomaly (0.16.0)

Version 0.16.0

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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

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