abacusai.feature_drift_summary
==============================

.. py:module:: abacusai.feature_drift_summary


Classes
-------

.. autoapisummary::

   abacusai.feature_drift_summary.FeatureDriftSummary


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

.. py:class:: FeatureDriftSummary(client, featureIndex=None, name=None, distance=None, jsDistance=None, wsDistance=None, ksStatistic=None, predictionDrift=None, targetColumn=None, dataIntegrityTimeseries=None, nestedSummary=None, psi=None, csi=None, chiSquare=None, nullViolations={}, rangeViolations={}, catViolations={})

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


   Summary of important model monitoring statistics for features available in a model monitoring instance

   :param client: An authenticated API Client instance
   :type client: ApiClient
   :param featureIndex: A list of dicts of eligible feature names and corresponding overall feature drift measures.
   :type featureIndex: list[dict]
   :param name: Name of feature.
   :type name: str
   :param distance: Symmetric sum of KL divergences between the training distribution and the range of values in the specified window.
   :type distance: float
   :param jsDistance: JS divergence between the training distribution and the range of values in the specified window.
   :type jsDistance: float
   :param wsDistance: Wasserstein distance between the training distribution and the range of values in the specified window.
   :type wsDistance: float
   :param ksStatistic: Kolmogorov-Smirnov statistic computed between the training distribution and the range of values in the specified window.
   :type ksStatistic: float
   :param predictionDrift: Drift for the target column.
   :type predictionDrift: float
   :param targetColumn: Target column name.
   :type targetColumn: str
   :param dataIntegrityTimeseries: Frequency vs Data Integrity Violation Charts.
   :type dataIntegrityTimeseries: dict
   :param nestedSummary: Summary of model monitoring statistics for nested features.
   :type nestedSummary: list[dict]
   :param psi: Population stability index computed between the training distribution and the range of values in the specified window.
   :type psi: float
   :param csi: Characteristic Stability Index computed between the training distribution and the range of values in the specified window.
   :type csi: float
   :param chiSquare: Chi-square statistic computed between the training distribution and the range of values in the specified window.
   :type chiSquare: float
   :param nullViolations: A list of dicts of feature names and a description of corresponding null violations.
   :type nullViolations: NullViolation
   :param rangeViolations: A list of dicts of numerical feature names and corresponding prediction range discrepancies.
   :type rangeViolations: RangeViolation
   :param catViolations: A list of dicts of categorical feature names and corresponding prediction range discrepancies.
   :type catViolations: CategoricalRangeViolation


   .. py:attribute:: feature_index
      :value: None



   .. py:attribute:: name
      :value: None



   .. py:attribute:: distance
      :value: None



   .. py:attribute:: js_distance
      :value: None



   .. py:attribute:: ws_distance
      :value: None



   .. py:attribute:: ks_statistic
      :value: None



   .. py:attribute:: prediction_drift
      :value: None



   .. py:attribute:: target_column
      :value: None



   .. py:attribute:: data_integrity_timeseries
      :value: None



   .. py:attribute:: nested_summary
      :value: None



   .. py:attribute:: psi
      :value: None



   .. py:attribute:: csi
      :value: None



   .. py:attribute:: chi_square
      :value: None



   .. py:attribute:: null_violations


   .. py:attribute:: range_violations


   .. py:attribute:: cat_violations


   .. py:attribute:: deprecated_keys


   .. py:method:: __repr__()


   .. 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