Class: Google::Apis::AiplatformV1beta1::GoogleCloudAiplatformV1beta1FeatureStatsAnomaly
- Inherits:
-
Object
- Object
- Google::Apis::AiplatformV1beta1::GoogleCloudAiplatformV1beta1FeatureStatsAnomaly
- Includes:
- Core::Hashable, Core::JsonObjectSupport
- Defined in:
- lib/google/apis/aiplatform_v1beta1/classes.rb,
lib/google/apis/aiplatform_v1beta1/representations.rb,
lib/google/apis/aiplatform_v1beta1/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 collapse
-
#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 collapse
-
#initialize(**args) ⇒ GoogleCloudAiplatformV1beta1FeatureStatsAnomaly
constructor
A new instance of GoogleCloudAiplatformV1beta1FeatureStatsAnomaly.
-
#update!(**args) ⇒ Object
Update properties of this object.
Constructor Details
#initialize(**args) ⇒ GoogleCloudAiplatformV1beta1FeatureStatsAnomaly
Returns a new instance of GoogleCloudAiplatformV1beta1FeatureStatsAnomaly.
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# File 'lib/google/apis/aiplatform_v1beta1/classes.rb', line 7337 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
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# File 'lib/google/apis/aiplatform_v1beta1/classes.rb', line 7285 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
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# File 'lib/google/apis/aiplatform_v1beta1/classes.rb', line 7295 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
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# File 'lib/google/apis/aiplatform_v1beta1/classes.rb', line 7302 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
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# File 'lib/google/apis/aiplatform_v1beta1/classes.rb', line 7310 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
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# File 'lib/google/apis/aiplatform_v1beta1/classes.rb', line 7318 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
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# File 'lib/google/apis/aiplatform_v1beta1/classes.rb', line 7326 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
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# File 'lib/google/apis/aiplatform_v1beta1/classes.rb', line 7335 def stats_uri @stats_uri end |
Instance Method Details
#update!(**args) ⇒ Object
Update properties of this object
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# File 'lib/google/apis/aiplatform_v1beta1/classes.rb', line 7342 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 |