Class DataDriftSpec (1.53.0)

Version 1.53.0

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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.