Experiment Create
Last updated
Last updated
https://api.sigopt.com/v1/experiments
Creates a new Experiment.
Request Method: POST
Name | Type | Required? | Description |
---|---|---|---|
These parameters should no longer be used because there are better alternatives.
Name | Type | Required? | Description |
---|---|---|---|
Experiment object.
name
string
Y
A user-specified name for this experiment.
parameters
array<
Parameter
>
Y
An array of Parameter objects.
conditionals
array<
Conditional
>
N
See conditionals.
linear_constraints
array<
Parameter Constraint
>
N
See constraints.
metadata
N
Optional user-provided object. See Using Metadata for more information.
metrics
array<
Metric
>
N
An array of Metric objects to be optimized or stored for analysis. If the array is of length one, the standard optimization problem is conducted. This array can have no more than 2 optimized entries and no more than 50 entries in total.
num_solutions
int
N
The number of (diverse) solutions SigOpt will search for. This feature is only available for special plans, and does not need to be set unless the desired number of solutions is greater than 1. An observation budget is required if the number of solutions is greater than 1. No categorical variables are allowed in multiple solution experiments.
observation_budget
int
N
The number of Observations you plan to create for this experiment. This is required when the length of metrics is greater than 1, and optional for a single metric experiment. Deviating from this value, especially by failing to reach it, may result in suboptimal performance for your experiment.
parallel_bandwidth
int
N
The number of simultaneously open Suggestions you plan to maintain during this experiment. The default value for this is 1, i.e., a sequential experiment. The maximum value for this is dependent on your plan. This field is optional, but setting it correctly may improve performance.
type
string
N
A type for this experiment. Used for experimental and alpha features only.
metric
N
Information about the metric that this experiment is optimizing.