abacusai.feature_drift_summary

Classes

FeatureDriftSummary

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

Module Contents

class abacusai.feature_drift_summary.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: 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[dict]) – 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.

  • dataIntegrityTimeseries (dict) – Frequency vs Data Integrity Violation Charts.

  • nestedSummary (list[dict]) – Summary of model monitoring statistics for nested features.

  • psi (float) – Population stability index computed between the training distribution and the range of values in the specified window.

  • csi (float) – Characteristic Stability Index computed between the training distribution and the range of values in the specified window.

  • chiSquare (float) – Chi-square statistic computed between the training distribution and the range of values in the specified window.

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

  • 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.

feature_index = None
name = None
distance = None
js_distance = None
ws_distance = None
ks_statistic = None
prediction_drift = None
target_column = None
data_integrity_timeseries = None
nested_summary = None
psi = None
csi = None
chi_square = None
null_violations
range_violations
cat_violations
deprecated_keys
__repr__()
to_dict()

Get a dict representation of the parameters in this class

Returns:

The dict value representation of the class parameters

Return type:

dict