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