Class DataDriftSpec (1.65.0)
 
 
 
 
 
 
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DataDriftSpec(
 features: typing.Optional[typing.List[str]] = None,
 categorical_metric_type: typing.Optional[str] = "l_infinity",
 numeric_metric_type: typing.Optional[str] = "jensen_shannon_divergence",
 default_categorical_alert_threshold: typing.Optional[float] = None,
 default_numeric_alert_threshold: typing.Optional[float] = None,
 feature_alert_thresholds: typing.Optional[typing.Dict[str, float]] = None,
)Data drift monitoring spec.
Data drift measures the distribution distance between the current dataset and a baseline dataset. A typical use case is to detect data drift between the recent production serving dataset and the training dataset, or to compare the recent production dataset with a dataset from a previous period.
.. rubric:: Example
feature_drift_spec=DataDriftSpec( features=["feature1"] categorical_metric_type="l_infinity", numeric_metric_type="jensen_shannon_divergence", default_categorical_alert_threshold=0.01, default_numeric_alert_threshold=0.02, feature_alert_thresholds={"feature1":0.02, "feature2":0.01}, )
Attributes | 
 |
|---|---|
| Name | Description | 
features | 
 
 List[str]
 Optional. Feature names / Prediction output names interested in monitoring. These should be a subset of the input feature names or prediction output names specified in the monitoring schema. If not specified, all features / prediction outputs outlied in the monitoring schema will be used.  | 
 
categorical_metric_type | 
 
 str
 Optional. Supported metrics type: l_infinity, jensen_shannon_divergence  | 
 
numeric_metric_type | 
 
 str
 Optional. Supported metrics type: jensen_shannon_divergence  | 
 
default_categorical_alert_threshold | 
 
 float
 Optional. Default alert threshold for all the categorical features.  | 
 
default_numeric_alert_threshold | 
 
 float
 Optional. Default alert threshold for all the numeric features.  | 
 
feature_alert_thresholds | 
 
 Dict[str, float]
 Optional. Per feature alert threshold will override default alert threshold.  |