abacusai.model_monitor_version
==============================

.. py:module:: abacusai.model_monitor_version


Classes
-------

.. autoapisummary::

   abacusai.model_monitor_version.ModelMonitorVersion


Module Contents
---------------

.. py:class:: ModelMonitorVersion(client, modelMonitorVersion=None, status=None, modelMonitorId=None, monitoringStartedAt=None, monitoringCompletedAt=None, trainingFeatureGroupVersion=None, predictionFeatureGroupVersion=None, error=None, pendingDeploymentIds=None, failedDeploymentIds=None, metricConfigs=None, featureGroupMonitorConfigs=None, metricTypes=None, modelVersion=None, batchPredictionVersion=None, edaConfigs=None, trainingForecastConfig=None, predictionForecastConfig=None, forecastFrequency=None, monitorDriftConfig=None, predictionDataUseMappings=None, trainingDataUseMappings=None)

   Bases: :py:obj:`abacusai.return_class.AbstractApiClass`


   A version of a model monitor

   :param client: An authenticated API Client instance
   :type client: ApiClient
   :param modelMonitorVersion: The unique identifier of a model monitor version.
   :type modelMonitorVersion: str
   :param status: The current status of the model.
   :type status: str
   :param modelMonitorId: A reference to the model monitor this version belongs to.
   :type modelMonitorId: str
   :param monitoringStartedAt: The start time and date of the monitoring process.
   :type monitoringStartedAt: str
   :param monitoringCompletedAt: The end time and date of the monitoring process.
   :type monitoringCompletedAt: str
   :param trainingFeatureGroupVersion: Feature group version IDs that this refresh pipeline run is monitoring.
   :type trainingFeatureGroupVersion: list[str]
   :param predictionFeatureGroupVersion: Feature group version IDs that this refresh pipeline run is monitoring.
   :type predictionFeatureGroupVersion: list[str]
   :param error: Relevant error if the status is FAILED.
   :type error: str
   :param pendingDeploymentIds: List of deployment IDs where deployment is pending.
   :type pendingDeploymentIds: list
   :param failedDeploymentIds: List of failed deployment IDs.
   :type failedDeploymentIds: list
   :param metricConfigs: List of metric configs for the model monitor instance.
   :type metricConfigs: list[dict]
   :param featureGroupMonitorConfigs: Configurations for feature group monitor
   :type featureGroupMonitorConfigs: dict
   :param metricTypes: List of metric types.
   :type metricTypes: list
   :param modelVersion: Model version IDs that this refresh pipeline run is monitoring.
   :type modelVersion: list[str]
   :param batchPredictionVersion: The batch prediction version this model monitor is monitoring
   :type batchPredictionVersion: str
   :param edaConfigs: The list of eda configs for the version
   :type edaConfigs: list
   :param trainingForecastConfig: The training forecast config for the monitor version
   :type trainingForecastConfig: dict
   :param predictionForecastConfig: The prediction forecast config for the monitor version
   :type predictionForecastConfig: dict
   :param forecastFrequency: The forecast frequency for the monitor version
   :type forecastFrequency: str
   :param monitorDriftConfig: The monitor drift config for the monitor version
   :type monitorDriftConfig: dict
   :param predictionDataUseMappings: The mapping of prediction data use to feature group version
   :type predictionDataUseMappings: dict
   :param trainingDataUseMappings: The mapping of training data use to feature group version
   :type trainingDataUseMappings: dict


   .. py:method:: __repr__()

      Return repr(self).



   .. py:method:: to_dict()

      Get a dict representation of the parameters in this class

      :returns: The dict value representation of the class parameters
      :rtype: dict



   .. py:method:: get_prediction_drift()

      Gets the label and prediction drifts for a model monitor.

      :param model_monitor_version: Unique string identifier for a model monitor version created under the project.
      :type model_monitor_version: str

      :returns: Object describing training and prediction output label and prediction distributions.
      :rtype: DriftDistributions



   .. py:method:: refresh()

      Calls describe and refreshes the current object's fields

      :returns: The current object
      :rtype: ModelMonitorVersion



   .. py:method:: describe()

      Retrieves a full description of the specified model monitor version.

      :param model_monitor_version: The unique version ID of the model monitor version.
      :type model_monitor_version: str

      :returns: A model monitor version.
      :rtype: ModelMonitorVersion



   .. py:method:: delete()

      Deletes the specified model monitor version.

      :param model_monitor_version: Unique identifier of the model monitor version to delete.
      :type model_monitor_version: str



   .. py:method:: metric_data(metric_type, actual_values_to_detail = None)

      Provides the data needed for decile metrics associated with the model monitor.

      :param metric_type: The type of metric to get data for.
      :type metric_type: str
      :param actual_values_to_detail: The actual values to detail.
      :type actual_values_to_detail: list

      :returns: Data associated with the metric.
      :rtype: ModelMonitorVersionMetricData



   .. py:method:: list_monitor_alert_versions_for_monitor_version()

      Retrieves the list of monitor alert versions for a specified monitor instance.

      :param model_monitor_version: The unique ID associated with the model monitor.
      :type model_monitor_version: str

      :returns: A list of monitor alert versions.
      :rtype: list[MonitorAlertVersion]



   .. py:method:: get_drift_for_feature(feature_name, nested_feature_name = None)

      Gets the feature drift associated with a single feature in an output feature group from a prediction.

      :param feature_name: Name of the feature to view the distribution of.
      :type feature_name: str
      :param nested_feature_name: Optionally, the name of the nested feature that the feature is in.
      :type nested_feature_name: str

      :returns: An object describing the training and prediction output feature distributions.
      :rtype: FeatureDistribution



   .. py:method:: get_outliers_for_feature(feature_name = None, nested_feature_name = None)

      Gets a list of outliers measured by a single feature (or overall) in an output feature group from a prediction.

      :param feature_name: Name of the feature to view the distribution of.
      :type feature_name: str
      :param nested_feature_name: Optionally, the name of the nested feature that the feature is in.
      :type nested_feature_name: str



   .. py:method:: wait_for_monitor(timeout=1200)

      A waiting call until model monitor version is ready.

      :param timeout: The waiting time given to the call to finish, if it doesn't finish by the allocated time, the call is said to be timed out.
      :type timeout: int



   .. py:method:: get_status()

      Gets the status of the model monitor version.

      :returns: A string describing the status of the model monitor version, for e.g., pending, complete, etc.
      :rtype: str



