REST Resource: projects.locations.featureGroups.features

Resource: Feature

feature metadata information. For example, color is a feature that describes an apple.

Fields
name string

Immutable. name of the feature. Format: projects/{project}/locations/{location}/featurestores/{featurestore}/entityTypes/{entityType}/features/{feature} projects/{project}/locations/{location}/featureGroups/{featureGroup}/features/{feature}

The last part feature is assigned by the client. The feature can be up to 64 characters long and can consist only of ASCII Latin letters A-Z and a-z, underscore(_), and ASCII digits 0-9 starting with a letter. The value will be unique given an entity type.

description string

description of the feature.

valueType enum (ValueType)

Immutable. Only applicable for Vertex AI feature Store (Legacy). type of feature value.

createTime string (Timestamp format)

Output only. Only applicable for Vertex AI feature Store (Legacy). timestamp when this EntityType was created.

A timestamp in RFC3339 UTC "Zulu" format, with nanosecond resolution and up to nine fractional digits. Examples: "2014-10-02T15:01:23Z" and "2014-10-02T15:01:23.045123456Z".

updateTime string (Timestamp format)

Output only. Only applicable for Vertex AI feature Store (Legacy). timestamp when this EntityType was most recently updated.

A timestamp in RFC3339 UTC "Zulu" format, with nanosecond resolution and up to nine fractional digits. Examples: "2014-10-02T15:01:23Z" and "2014-10-02T15:01:23.045123456Z".

labels map (key: string, value: string)

Optional. The labels with user-defined metadata to organize your Features.

label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed.

See https://goo.gl/xmQnxf for more information on and examples of labels. No more than 64 user labels can be associated with one feature (System labels are excluded)." System reserved label keys are prefixed with "aiplatform.googleapis.com/" and are immutable.

etag string

Used to perform a consistent read-modify-write updates. If not set, a blind "overwrite" update happens.

disableMonitoring boolean

Optional. Only applicable for Vertex AI feature Store (Legacy). If not set, use the monitoringConfig defined for the EntityType this feature belongs to. Only Features with type (feature.ValueType) BOOL, STRING, DOUBLE or INT64 can enable monitoring.

If set to true, all types of data monitoring are disabled despite the config on EntityType.

monitoringStatsAnomalies[] object (MonitoringStatsAnomaly)

Output only. Only applicable for Vertex AI feature Store (Legacy). The list of historical stats and anomalies with specified objectives.

versionColumnName string

Only applicable for Vertex AI feature Store. The name of the BigQuery Table/View column hosting data for this version. If no value is provided, will use featureId.

pointOfContact string

Entity responsible for maintaining this feature. Can be comma separated list of email addresses or URIs.

JSON representation 
{
  "name": string,
  "description": string,
  "valueType": enum (ValueType),
  "createTime": string,
  "updateTime": string,
  "labels": {
    string: string,
    ...
  },
  "etag": string,
  "disableMonitoring": boolean,
  "monitoringStatsAnomalies": [
    {
      object (MonitoringStatsAnomaly)
    }
  ],
  "versionColumnName": string,
  "pointOfContact": string
}

ValueType

Only applicable for Vertex AI Legacy feature Store. An enum representing the value type of a feature.

Enums
VALUE_TYPE_UNSPECIFIED The value type is unspecified.
BOOL Used for feature that is a boolean.
BOOL_ARRAY Used for feature that is a list of boolean.
DOUBLE Used for feature that is double.
DOUBLE_ARRAY Used for feature that is a list of double.
INT64 Used for feature that is INT64.
INT64_ARRAY Used for feature that is a list of INT64.
STRING Used for feature that is string.
STRING_ARRAY Used for feature that is a list of String.
BYTES Used for feature that is bytes.
STRUCT Used for feature that is struct.

MonitoringStatsAnomaly

A list of historical SnapshotAnalysis or ImportFeaturesAnalysis stats requested by user, sorted by FeatureStatsAnomaly.start_time descending.

Fields
objective enum (Objective)

Output only. The objective for each stats.

featureStatsAnomaly object (FeatureStatsAnomaly)

Output only. The stats and anomalies generated at specific timestamp.

JSON representation 
{
  "objective": enum (Objective),
  "featureStatsAnomaly": {
    object (FeatureStatsAnomaly)
  }
}

Objective

If the objective in the request is both Import feature Analysis and Snapshot Analysis, this objective could be one of them. Otherwise, this objective should be the same as the objective in the request.

Enums
OBJECTIVE_UNSPECIFIED If it's OBJECTIVE_UNSPECIFIED, monitoringStats will be empty.
IMPORT_FEATURE_ANALYSIS Stats are generated by Import feature Analysis.
SNAPSHOT_ANALYSIS Stats are generated by Snapshot Analysis.

FeatureStatsAnomaly

Stats and Anomaly generated at specific timestamp for specific feature. The startTime and endTime 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, startTime = endTime. timestamp of the stats and anomalies always refers to endTime. Raw stats and anomalies are stored in statsUri or anomalyUri in the tensorflow defined protos. Field dataStats contains almost identical information with the raw stats in Vertex AI defined proto, for UI to display.

Fields
score number

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.

statsUri string

Path of the stats file for current feature values in Cloud Storage bucket. Format: gs:////stats. Example: gs://monitoring_bucket/featureName/stats. Stats are stored as binary format with Protobuf message tensorflow.metadata.v0.FeatureNameStatistics.

anomalyUri string

Path of the anomaly file for current feature values in Cloud Storage bucket. Format: gs:////anomalies. Example: gs://monitoring_bucket/featureName/anomalies. Stats are stored as binary format with Protobuf message Anoamlies are stored as binary format with Protobuf message tensorflow.metadata.v0.AnomalyInfo.

distributionDeviation number

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.

anomalyDetectionThreshold number

This is the threshold used when detecting anomalies. The threshold can be changed by user, so this one might be different from ThresholdConfig.value.

startTime string (Timestamp format)

The start timestamp of window where stats were generated. For objectives where time window doesn't make sense (e.g. Featurestore Snapshot Monitoring), startTime is only used to indicate the monitoring intervals, so it always equals to (endTime - monitoringInterval).

A timestamp in RFC3339 UTC "Zulu" format, with nanosecond resolution and up to nine fractional digits. Examples: "2014-10-02T15:01:23Z" and "2014-10-02T15:01:23.045123456Z".

endTime string (Timestamp format)

The end timestamp of window where stats were generated. For objectives where time window doesn't make sense (e.g. Featurestore Snapshot Monitoring), endTime indicates the timestamp of the data used to generate stats (e.g. timestamp we take snapshots for feature values).

A timestamp in RFC3339 UTC "Zulu" format, with nanosecond resolution and up to nine fractional digits. Examples: "2014-10-02T15:01:23Z" and "2014-10-02T15:01:23.045123456Z".

JSON representation 
{
  "score": number,
  "statsUri": string,
  "anomalyUri": string,
  "distributionDeviation": number,
  "anomalyDetectionThreshold": number,
  "startTime": string,
  "endTime": string
}

Methods

batchCreate

 Creates a batch of Features in a given FeatureGroup.

create

 Creates a new Feature in a given FeatureGroup.

delete

 Deletes a single Feature.

get

 Gets details of a single Feature.

list

 Lists Features in a given FeatureGroup.

patch

 Updates the parameters of a single Feature.