Vertex AI V1 API - Class Google::Cloud::AIPlatform::V1::FeatureStatsAnomaly (v0.11.0)

Reference documentation and code samples for the Vertex AI V1 API class Google::Cloud::AIPlatform::V1::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.

Inherits

  • Object

Extended By

  • Google::Protobuf::MessageExts::ClassMethods

Includes

  • Google::Protobuf::MessageExts

Methods

#anomaly_detection_threshold

def anomaly_detection_threshold() -> ::Float
Returns
  • (::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.

#anomaly_detection_threshold=

def anomaly_detection_threshold=(value) -> ::Float
Parameter
  • value (::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.
Returns
  • (::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.

#anomaly_uri

def anomaly_uri() -> ::String
Returns
  • (::String) — Path of the anomaly file for current feature values in Cloud Storage bucket. Format: gs://

#anomaly_uri=

def anomaly_uri=(value) -> ::String
Parameter
  • value (::String) — Path of the anomaly file for current feature values in Cloud Storage bucket. Format: gs://
Returns
  • (::String) — Path of the anomaly file for current feature values in Cloud Storage bucket. Format: gs://

#distribution_deviation

def distribution_deviation() -> ::Float
Returns
  • (::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.

#distribution_deviation=

def distribution_deviation=(value) -> ::Float
Parameter
  • value (::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.
Returns
  • (::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.

#end_time

def end_time() -> ::Google::Protobuf::Timestamp
Returns
  • (::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).

#end_time=

def end_time=(value) -> ::Google::Protobuf::Timestamp
Parameter
  • value (::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).
Returns
  • (::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).

#score

def score() -> ::Float
Returns

#score=

def score=(value) -> ::Float
Parameter
Returns

#start_time

def start_time() -> ::Google::Protobuf::Timestamp
Returns
  • (::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).

#start_time=

def start_time=(value) -> ::Google::Protobuf::Timestamp
Parameter
  • value (::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).
Returns
  • (::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).

#stats_uri

def stats_uri() -> ::String
Returns
  • (::String) — Path of the stats file for current feature values in Cloud Storage bucket. Format: gs://

#stats_uri=

def stats_uri=(value) -> ::String
Parameter
  • value (::String) — Path of the stats file for current feature values in Cloud Storage bucket. Format: gs://
Returns
  • (::String) — Path of the stats file for current feature values in Cloud Storage bucket. Format: gs://