AiConfigsMetricDenominatorRep.md

AiConfigsMetricDenominatorRep

Properties

Name	Type	Description	Notes
event_name	str	The warehouse event column for the denominator	[optional]
is_numeric	bool	Whether the denominator aggregates a numeric value	[optional]
unit_aggregation_type	str	How individual unit values are aggregated for the denominator	[optional]
unit_aggregation_field	str	The column to count distinct values of; required when unitAggregationType is count_distinct	[optional]
data_source	AiConfigsMetricDataSourceRefRep		[optional]
filters	AiConfigsFilter		[optional]
window_start_offset	int	Start of the measurement window in milliseconds	[optional]
window_end_offset	int	End of the measurement window in milliseconds	[optional]
winsor_lower_percentile	float	Lower winsorization percentile in the open interval (0, 100)	[optional]
winsor_upper_percentile	float	Upper winsorization percentile in the open interval (0, 100)	[optional]
winsor_exclude_imputed	bool	Deprecated and ignored. Use winsorIncludeImputed instead.	[optional]
winsor_include_imputed	bool	When true, the percentile bound calculation includes imputed zeros	[optional]

Example

from launchdarkly_api.models.ai_configs_metric_denominator_rep import AiConfigsMetricDenominatorRep

# TODO update the JSON string below
json = "{}"
# create an instance of AiConfigsMetricDenominatorRep from a JSON string
ai_configs_metric_denominator_rep_instance = AiConfigsMetricDenominatorRep.from_json(json)
# print the JSON string representation of the object
print(AiConfigsMetricDenominatorRep.to_json())

# convert the object into a dict
ai_configs_metric_denominator_rep_dict = ai_configs_metric_denominator_rep_instance.to_dict()
# create an instance of AiConfigsMetricDenominatorRep from a dict
ai_configs_metric_denominator_rep_from_dict = AiConfigsMetricDenominatorRep.from_dict(ai_configs_metric_denominator_rep_dict)

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