abacusai.forecasting_monitor_summary — AbacusAI documentation

# abacusai.forecasting_monitor_summary

## Classes

`FeatureDriftRecord` | Value of each type of drift
`ForecastingAnalysisGraphData` | Forecasting Analysis Graph Data representation.
`AbstractApiClass` |
`ForecastingMonitorSummary` | Forecasting Monitor Summary of the latest version of the data.

## Module Contents

class abacusai.forecasting_monitor_summary.FeatureDriftRecord(client, name=None, distance=None, jsDistance=None, wsDistance=None, ksStatistic=None, psi=None, csi=None, chiSquare=None)

  Bases: `abacusai.return_class.AbstractApiClass`

  Value of each type of drift

  Parameters:

    - client (ApiClient) – An authenticated API Client instance

    - 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.

    - 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.

  __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.forecasting_monitor_summary.ForecastingAnalysisGraphData(client, data=None, xAxis=None, yAxis=None, dataColumns=None, chartName=None, chartTypes=None, itemStatistics={}, chartDescriptions={})

  Bases: `abacusai.return_class.AbstractApiClass`

  Forecasting Analysis Graph Data representation.

  Parameters:

    - client (ApiClient) – An authenticated API Client instance

    - data (list) – List of graph data

    - xAxis (str) – Feature that represents the x axis

    - yAxis (str) – Feature that represents the y axis

    - dataColumns (list) – Ordered name of the column for each rowwise data

    - chartName (str) – Name of the chart represented by the data

    - chartTypes (list) – Type of charts in that can exist in the current data.

    - itemStatistics (ItemStatistics) – In item wise charts, gives the mean, median, count, missing_percent, p10, p90, standard_deviation, min, max

    - chartDescriptions (EdaChartDescription) – List of descriptions of what the chart contains

  __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.forecasting_monitor_summary.AbstractApiClass(client, id)
  __eq__(other)

    Return self==value.

  _get_attribute_as_dict(attribute)

class abacusai.forecasting_monitor_summary.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