Class DataDriftSpec (1.54.1)

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.

Class DataDriftSpec (1.54.1)

Attributes