Class DataDriftSpec (1.56.0)

Version 1.56.0

• 1.73.0 (latest)
• 1.72.0
• 1.71.1
• 1.70.0
• 1.69.0
• 1.68.0
• 1.67.1
• 1.66.0
• 1.65.0
• 1.63.0
• 1.62.0
• 1.60.0
• 1.59.0
• 1.58.0
• 1.57.0
• 1.56.0
• 1.55.0
• 1.54.1
• 1.53.0
• 1.52.0
• 1.51.0
• 1.50.0
• 1.49.0
• 1.48.0
• 1.47.0
• 1.46.0
• 1.45.0
• 1.44.0
• 1.43.0
• 1.39.0
• 1.38.1
• 1.37.0
• 1.36.4
• 1.35.0
• 1.34.0
• 1.33.1
• 1.32.0
• 1.31.1
• 1.30.1
• 1.29.0
• 1.28.1
• 1.27.1
• 1.26.1
• 1.25.0
• 1.24.1
• 1.23.0
• 1.22.1
• 1.21.0
• 1.20.0
• 1.19.1
• 1.18.3
• 1.17.1
• 1.16.1
• 1.15.1
• 1.14.0
• 1.13.1
• 1.12.1
• 1.11.0
• 1.10.0
• 1.9.0
• 1.8.1
• 1.7.1
• 1.6.2
• 1.5.0
• 1.4.3
• 1.3.0
• 1.2.0
• 1.1.1
• 1.0.1
• 0.9.0
• 0.8.0
• 0.7.1
• 0.6.0
• 0.5.1
• 0.4.0
• 0.3.1

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.