Interface FeatureStatsAndAnomalyOrBuilder (3.56.0)

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public interface FeatureStatsAndAnomalyOrBuilder extends MessageOrBuilder
MessageOrBuilder
public abstract double getDistributionDeviation()

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.

double distribution_deviation = 3;

Returns
Type Description
double

The distributionDeviation.

getDriftDetected()

public abstract boolean getDriftDetected()

If set to true, indicates current stats is detected as and comparing with baseline stats.

bool drift_detected = 5;

Returns
Type Description
boolean

The driftDetected.

getDriftDetectionThreshold()

public abstract double getDriftDetectionThreshold()

This is the threshold used when detecting drifts, which is set in FeatureMonitor.FeatureSelectionConfig.FeatureConfig.drift_threshold

double drift_detection_threshold = 4;

Returns
Type Description
double

The driftDetectionThreshold.

getFeatureId()

public abstract String getFeatureId()

Feature Id.

string feature_id = 1;

Returns
Type Description
String

The featureId.

getFeatureIdBytes()

public abstract ByteString getFeatureIdBytes()

Feature Id.

string feature_id = 1;

Returns
Type Description
ByteString

The bytes for featureId.

getFeatureMonitorId()

public abstract String getFeatureMonitorId()

The ID of the FeatureMonitor that this FeatureStatsAndAnomaly generated according to.

string feature_monitor_id = 8;

Returns
Type Description
String

The featureMonitorId.

getFeatureMonitorIdBytes()

public abstract ByteString getFeatureMonitorIdBytes()

The ID of the FeatureMonitor that this FeatureStatsAndAnomaly generated according to.

string feature_monitor_id = 8;

Returns
Type Description
ByteString

The bytes for featureMonitorId.

getFeatureMonitorJobId()

public abstract long getFeatureMonitorJobId()

The ID of the FeatureMonitorJob that generated this FeatureStatsAndAnomaly.

int64 feature_monitor_job_id = 7;

Returns
Type Description
long

The featureMonitorJobId.

getFeatureStats()

public abstract Value getFeatureStats()

Feature stats. e.g. histogram buckets. In the format of tensorflow.metadata.v0.DatasetFeatureStatistics.

.google.protobuf.Value feature_stats = 2;

Returns
Type Description
Value

The featureStats.

getFeatureStatsOrBuilder()

public abstract ValueOrBuilder getFeatureStatsOrBuilder()

Feature stats. e.g. histogram buckets. In the format of tensorflow.metadata.v0.DatasetFeatureStatistics.

.google.protobuf.Value feature_stats = 2;

Returns
Type Description
ValueOrBuilder

getStatsTime()

public abstract Timestamp getStatsTime()

The timestamp we take snapshot for feature values to generate stats.

.google.protobuf.Timestamp stats_time = 6;

Returns
Type Description
Timestamp

The statsTime.

getStatsTimeOrBuilder()

public abstract TimestampOrBuilder getStatsTimeOrBuilder()

The timestamp we take snapshot for feature values to generate stats.

.google.protobuf.Timestamp stats_time = 6;

Returns
Type Description
TimestampOrBuilder

hasFeatureStats()

public abstract boolean hasFeatureStats()

Feature stats. e.g. histogram buckets. In the format of tensorflow.metadata.v0.DatasetFeatureStatistics.

.google.protobuf.Value feature_stats = 2;

Returns
Type Description
boolean

Whether the featureStats field is set.

hasStatsTime()

public abstract boolean hasStatsTime()

The timestamp we take snapshot for feature values to generate stats.

.google.protobuf.Timestamp stats_time = 6;

Returns
Type Description
boolean

Whether the statsTime field is set.