Class: Google::Apis::AiplatformV1::GoogleCloudAiplatformV1FeatureStatsAnomaly

Inherits:

Object

• Object
• Google::Apis::AiplatformV1::GoogleCloudAiplatformV1FeatureStatsAnomaly

Includes:

Core::Hashable, Core::JsonObjectSupport

Defined in:

lib/google/apis/aiplatform_v1/classes.rb,
lib/google/apis/aiplatform_v1/representations.rb,
lib/google/apis/aiplatform_v1/representations.rb

Overview

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.

Instance Attribute Summary

• #anomaly_detection_threshold ⇒ Float

  This is the threshold used when detecting anomalies.

• #anomaly_uri ⇒ String

  Path of the anomaly file for current feature values in Cloud Storage bucket.

• #distribution_deviation ⇒ Float

  Deviation from the current stats to baseline stats.

• #end_time ⇒ String

  The end timestamp of window where stats were generated.

• #score ⇒ Float

  Feature importance score, only populated when cross-feature monitoring is enabled.

• #start_time ⇒ String

  The start timestamp of window where stats were generated.

• #stats_uri ⇒ String

  Path of the stats file for current feature values in Cloud Storage bucket.

Instance Method Summary

• #initialize(**args) ⇒ GoogleCloudAiplatformV1FeatureStatsAnomaly constructor

  A new instance of GoogleCloudAiplatformV1FeatureStatsAnomaly.

• #update!(**args) ⇒ Object

  Update properties of this object.

Constructor Details

#initialize(**args) ⇒ GoogleCloudAiplatformV1FeatureStatsAnomaly

Returns a new instance of GoogleCloudAiplatformV1FeatureStatsAnomaly.

# File 'lib/google/apis/aiplatform_v1/classes.rb', line 8218

def initialize(**args)
   update!(**args)
end

Instance Attribute Details

#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. Corresponds to the JSON property anomalyDetectionThreshold

Returns:

• (Float)

# File 'lib/google/apis/aiplatform_v1/classes.rb', line 8166

def anomaly_detection_threshold
  @anomaly_detection_threshold
end

#anomaly_uri ⇒ String

Path of the anomaly file for current feature values in Cloud Storage bucket. Format: gs:////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. Corresponds to the JSON property anomalyUri

Returns:

• (String)

# File 'lib/google/apis/aiplatform_v1/classes.rb', line 8176

def anomaly_uri
  @anomaly_uri
end

#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. Corresponds to the JSON property distributionDeviation

Returns:

• (Float)

# File 'lib/google/apis/aiplatform_v1/classes.rb', line 8183

def distribution_deviation
  @distribution_deviation
end

#end_time ⇒ String

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). Corresponds to the JSON property endTime

Returns:

• (String)

# File 'lib/google/apis/aiplatform_v1/classes.rb', line 8191

def end_time
  @end_time
end

#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. Corresponds to the JSON property score

Returns:

• (Float)

# File 'lib/google/apis/aiplatform_v1/classes.rb', line 8199

def score
  @score
end

#start_time ⇒ String

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). Corresponds to the JSON property startTime

Returns:

• (String)

# File 'lib/google/apis/aiplatform_v1/classes.rb', line 8207

def start_time
  @start_time
end

#stats_uri ⇒ String

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

Returns:

• (String)

# File 'lib/google/apis/aiplatform_v1/classes.rb', line 8216

def stats_uri
  @stats_uri
end

Instance Method Details

#update!(**args) ⇒ Object

Update properties of this object

# File 'lib/google/apis/aiplatform_v1/classes.rb', line 8223

def update!(**args)
  @anomaly_detection_threshold = args[:anomaly_detection_threshold] if args.key?(:anomaly_detection_threshold)
  @anomaly_uri = args[:anomaly_uri] if args.key?(:anomaly_uri)
  @distribution_deviation = args[:distribution_deviation] if args.key?(:distribution_deviation)
  @end_time = args[:end_time] if args.key?(:end_time)
  @score = args[:score] if args.key?(:score)
  @start_time = args[:start_time] if args.key?(:start_time)
  @stats_uri = args[:stats_uri] if args.key?(:stats_uri)
end