abacusai.feature_drift_summary — AbacusAI documentation

# `abacusai.feature_drift_summary`

## Module Contents

### Classes

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

class abacusai.feature_drift_summary.FeatureDriftSummary(client, featureIndex=None, name=None, distance=None, jsDistance=None, wsDistance=None, ksStatistic=None, predictionDrift=None, targetColumn=None, nullViolations={}, typeViolations={}, rangeViolations={}, catViolations={})

  Bases: `abacusai.return_class.AbstractApiClass`

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

  Parameters

    - client (ApiClient) – An authenticated API Client instance

    - featureIndex (list of json objects) – A list of dicts of eligible feature names and corresponding overall feature drift measures.

    - name (str) – Name of feature.

    - distance (float) – Symmetric sum of KL divergences between the training distribution and the range of values in the specified window.

    - jsDistance (float) – JS divergence between the training distribution and the range of values in the specified window.

    - wsDistance (float) – Wasserstein distance between the training distribution and the range of values in the specified window.

    - ksStatistic (float) – Kolmogorov-Smirnov statistic computed between the training distribution and the range of values in the specified window.

    - predictionDrift (float) – Drift for the target column.

    - targetColumn (str) – Target column name.

    - nullViolations (NullViolation) – A list of dicts of feature names and a description of corresponding null violations.

    - typeViolations (TypeViolation) – A list of dicts of feature names and corresponding type mismatches.

    - rangeViolations (RangeViolation) – A list of dicts of numerical feature names and corresponding prediction range discrepancies.

    - catViolations (CategoricalRangeViolation) – A list of dicts of categorical feature names and corresponding prediction range discrepancies.

  __repr__(self)

    Return repr(self).

  to_dict(self)

    Get a dict representation of the parameters in this class

    Returns

      The dict value representation of the class parameters

    Return type

      dict