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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. |