Class FeatureStatsAndAnomaly (1.157.0)

1.157.0 (latest) 1.156.0 1.155.0 1.154.0 1.153.1 1.152.0 1.151.0 1.150.0 1.149.0 1.148.1 1.147.0 1.146.0 1.145.0 1.144.0 1.143.0 1.142.0 1.141.0 1.140.0 1.139.0 1.138.0 1.137.0 1.136.0 1.135.0 1.134.0 1.133.0 1.132.0 1.131.0 1.130.0 1.129.0 1.122.0 1.121.0 1.120.0 1.119.0 1.118.0 1.117.0 1.116.0 1.115.0 1.114.0 1.113.0 1.112.0 1.111.0 1.110.0 1.109.0 1.108.0 1.107.0 1.106.0 1.105.0 1.104.0 1.103.0 1.102.0 1.101.0 1.100.0 1.99.0 1.98.0 1.97.0 1.96.0 1.95.1 1.94.0 1.93.1 1.92.0 1.91.0 1.90.0 1.89.0 1.88.0 1.87.0 1.86.0 1.85.0 1.84.0 1.83.0 1.82.0 1.81.0 1.80.0 1.79.0 1.78.0 1.77.0 1.76.0 1.75.0 1.74.0 1.73.0 1.72.0 1.71.1 1.70.0 1.69.0 1.68.0 1.67.1 1.66.0 1.65.0 1.63.0 1.62.0 1.60.0 1.59.0 1.58.0 1.57.0 1.56.0 1.55.0 1.54.1 1.53.0 1.52.0 1.51.0 1.50.0 1.49.0 1.48.0 1.47.0 1.46.0 1.45.0 1.44.0 1.43.0 1.39.0 1.38.1 1.37.0 1.36.4 1.35.0 1.34.0 1.33.1 1.32.0 1.31.1 1.30.1 1.29.0 1.28.1 1.27.1 1.26.1 1.25.0 1.24.1 1.23.0 1.22.1 1.21.0 1.20.0 1.19.1 1.18.3 1.17.1 1.16.1 1.15.1 1.14.0 1.13.1 1.12.1 1.11.0 1.10.0 1.9.0 1.8.1 1.7.1 1.6.2 1.5.0 1.4.3 1.3.0 1.2.0 1.1.1 1.0.1 0.9.0 0.8.0 0.7.1 0.6.0 0.5.1 0.4.0 0.3.1

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.