IterationInput.md

IterationInput

Properties

Name	Type	Description	Notes
hypothesis	str	The expected outcome of this experiment
can_reshuffle_traffic	bool	Whether to allow the experiment to reassign traffic to different variations when you increase or decrease the traffic in your experiment audience (true) or keep all traffic assigned to its initial variation (false). Defaults to true.	[optional]
metrics	List[MetricInput]
primary_single_metric_key	str	The key of the primary metric for this experiment. Either <code>primarySingleMetricKey</code> or <code>primaryFunnelKey</code> must be present.	[optional]
primary_funnel_key	str	The key of the primary funnel group for this experiment. Either <code>primarySingleMetricKey</code> or <code>primaryFunnelKey</code> must be present.	[optional]
treatments	List[TreatmentInput]
flags	Dict[str, FlagInput]
randomization_unit	str	The unit of randomization for this iteration. Defaults to user.	[optional]
attributes	List[str]	The attributes that this iteration's results can be sliced by	[optional]

Example

from launchdarkly_api.models.iteration_input import IterationInput

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

# convert the object into a dict
iteration_input_dict = iteration_input_instance.to_dict()
# create an instance of IterationInput from a dict
iteration_input_from_dict = IterationInput.from_dict(iteration_input_dict)

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