Class FeatureStatsAndAnomaly (1.91.0)

Version 1.91.0

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FeatureStatsAndAnomaly(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Stats and Anomaly generated by FeatureMonitorJobs. Anomaly only includes Drift.

Attributes
Name	Description
feature_id	str Feature Id.
feature_stats	google.protobuf.struct_pb2.Value Feature stats. e.g. histogram buckets. In the format of tensorflow.metadata.v0.DatasetFeatureStatistics.
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.
drift_detection_threshold	float This is the threshold used when detecting drifts, which is set in FeatureMonitor.FeatureSelectionConfig.FeatureConfig.drift_threshold
drift_detected	bool If set to true, indicates current stats is detected as and comparing with baseline stats.
stats_time	google.protobuf.timestamp_pb2.Timestamp The timestamp we take snapshot for feature values to generate stats.
feature_monitor_job_id	int The ID of the FeatureMonitorJob that generated this FeatureStatsAndAnomaly.
feature_monitor_id	str The ID of the FeatureMonitor that this FeatureStatsAndAnomaly generated according to.

Methods

FeatureStatsAndAnomaly

FeatureStatsAndAnomaly(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Stats and Anomaly generated by FeatureMonitorJobs. Anomaly only includes Drift.