abacusai.monitor_drift_and_distributions — AbacusAI documentation

# abacusai.monitor_drift_and_distributions

## Classes

`EmbeddingFeatureDriftDistribution` | Feature distribution for embeddings
`ForecastingMonitorSummary` | Forecasting Monitor Summary of the latest version of the data.
`AbstractApiClass` |
`MonitorDriftAndDistributions` | Summary of important model monitoring statistics for features available in a model monitoring instance

## Module Contents

class abacusai.monitor_drift_and_distributions.EmbeddingFeatureDriftDistribution(client, distance=None, jsDistance=None, wsDistance=None, ksStatistic=None, psi=None, csi=None, chiSquare=None, averageDrift={})

  Bases: `abacusai.return_class.AbstractApiClass`

  Feature distribution for embeddings

  Parameters:

    - client (ApiClient) – An authenticated API Client instance

    - distance (list) – Histogram data of KL divergences between the training distribution and the range of values in the specified window.

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

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

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

    - psi (list) – Histogram data of Population stability index computed between the training distribution and the range of values in the specified window.

    - csi (list) – Histogram data of Characteristic Stability Index computed between the training distribution and the range of values in the specified window.

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

    - averageDrift (DriftTypesValue) – Average drift embedding for each type of drift

  __repr__()

    Return repr(self).

  to_dict()

    Get a dict representation of the parameters in this class

    Returns:

      The dict value representation of the class parameters

    Return type:

      dict

class abacusai.monitor_drift_and_distributions.ForecastingMonitorSummary(client, predictionTimestampCol=None, predictionTargetCol=None, trainingTimestampCol=None, trainingTargetCol=None, predictionItemId=None, trainingItemId=None, forecastFrequency=None, trainingTargetAcrossTime={}, predictionTargetAcrossTime={}, actualsHistogram={}, predictionsHistogram={}, trainHistoryData={}, predictHistoryData={}, targetDrift={}, historyDrift={})

  Bases: `abacusai.return_class.AbstractApiClass`

  Forecasting Monitor Summary of the latest version of the data.

  Parameters:

    - client (ApiClient) – An authenticated API Client instance

    - predictionTimestampCol (str) – Feature in the data that represents the timestamp column.

    - predictionTargetCol (str) – Feature in the data that represents the target.

    - trainingTimestampCol (str) – Feature in the data that represents the timestamp column.

    - trainingTargetCol (str) – Feature in the data that represents the target.

    - predictionItemId (str) – Feature in the data that represents the item id.

    - trainingItemId (str) – Feature in the data that represents the item id.

    - forecastFrequency (str) – Frequency of data, could be hourly, daily, weekly, monthly, quarterly or yearly.

    - trainingTargetAcrossTime (ForecastingAnalysisGraphData) – Data showing average, p10, p90, median sales across time

    - predictionTargetAcrossTime (ForecastingAnalysisGraphData) – Data showing average, p10, p90, median sales across time

    - actualsHistogram (ForecastingAnalysisGraphData) – Data showing actuals histogram

    - predictionsHistogram (ForecastingAnalysisGraphData) – Data showing predictions histogram

    - trainHistoryData (ForecastingAnalysisGraphData) – Data showing length of history distribution

    - predictHistoryData (ForecastingAnalysisGraphData) – Data showing length of history distribution

    - targetDrift (FeatureDriftRecord) – Data showing drift of the target for all drift types: distance (KL divergence), js_distance, ws_distance, ks_statistic, psi, csi, chi_square

    - historyDrift (FeatureDriftRecord) – Data showing drift of the history for all drift types: distance (KL divergence), js_distance, ws_distance, ks_statistic, psi, csi, chi_square

  __repr__()

    Return repr(self).

  to_dict()

    Get a dict representation of the parameters in this class

    Returns:

      The dict value representation of the class parameters

    Return type:

      dict

class abacusai.monitor_drift_and_distributions.AbstractApiClass(client, id)
  __eq__(other)

    Return self==value.

  _get_attribute_as_dict(attribute)

class abacusai.monitor_drift_and_distributions.MonitorDriftAndDistributions(client, featureDrifts=None, featureDistributions=None, nestedDrifts=None, forecastingMonitorSummary={}, embeddingsDistribution={})

  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

    - featureDrifts (list[dict]) – A list of dicts of eligible feature names and corresponding overall feature drift measures.

    - featureDistributions (list[dict]) – A list of dicts of feature names and corresponding feature distributions.

    - nestedDrifts (list[dict]) – A list of dicts of nested feature names and corresponding overall feature drift measures.

    - forecastingMonitorSummary (ForecastingMonitorSummary) – Summary of important model monitoring statistics for features available in a model monitoring instance

    - embeddingsDistribution (EmbeddingFeatureDriftDistribution) – Summary of important model monitoring statistics for features available in a model monitoring instance

  __repr__()

    Return repr(self).

  to_dict()

    Get a dict representation of the parameters in this class

    Returns:

      The dict value representation of the class parameters

    Return type:

      dict