Links

Experiment Update

https://api.sigopt.com/v1/experiments/EXPERIMENT_ID
Updates an existing Experiment.
Request Method: PUT

Parameters

Name
Type
Required?
Description
metadata
Metadata
N
Optional user-provided object. See Using Metadata for more information.
metrics
array<Metric>
N
An array of Metric objects. Only the threshold field of metrics can be updated. Use the threshold value null to remove a threshold.
name
string
N
A user-specified name for this experiment.
observation_budget
int
N
The number of Observations you plan to create for this experiment. This can be thought of as a lower bound on the number of observations you will create. Failing to reach this value may result in suboptimal performance for your experiment. For experiments with multiple metrics, this cannot be updated.
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.
parameters
array<Parameter>
N
An array of Parameter objects. Only the bounds, categorical_values, precision, and default_value fields on parameters can be updated.
state
string
N
The state of this experiment. Can be active (for experiments that are currently running), or deleted (for experiments that have been deleted).

Response

Experiment object.

Example Request

Python
Bash
Java
experiment = conn.experiments(EXPERIMENT_ID).update(
name="Support Vector Classifier Accuracy"
)
EXPERIMENT=`curl -s -X PUT https://api.sigopt.com/v1/experiments/EXPERIMENT_ID -u "$SIGOPT_API_TOKEN": \
-H 'Content-Type: application/json' \
-d "{\"name\":\"Suppport Vector Classifier Accuracy\"}"`
Experiment experiment = Experiment.update(EXPERIMENT_ID)
.data(
new Experiment.Builder()
.name("Support Vector Classifier Accuracy")
.build()
)
.call();
Response
{
"client": "1",
"conditionals": [],
"created": 1414800000,
"development": false,
"id": "1",
"linear_constraints": [],
"metadata": null,
"metric": {
"name": "Accuracy",
"object": "metric",
"objective": "maximize",
"strategy": "optimize",
"threshold": null
},
"metrics": [
{
"name": "Accuracy",
"object": "metric",
"objective": "maximize",
"strategy": "optimize",
"threshold": null
}
],
"name": "Support Vector Classifier Accuracy",
"num_solutions": null,
"object": "experiment",
"observation_budget": 60,
"parallel_bandwidth": null,
"parameters": [
{
"bounds": {
"max": 5,
"min": 1,
"object": "bounds"
},
"categorical_values": null,
"conditions": {},
"default_value": null,
"name": "degree",
"object": "parameter",
"precision": null,
"prior": null,
"transformation": null,
"tunable": true,
"type": "int"
},
{
"bounds": {
"max": 1,
"min": 0.001,
"object": "bounds"
},
"categorical_values": null,
"conditions": {},
"default_value": null,
"name": "gamma",
"object": "parameter",
"precision": null,
"prior": null,
"transformation": null,
"tunable": true,
"type": "double"
},
{
"bounds": null,
"categorical_values": [
{
"enum_index": 1,
"name": "rbf",
"object": "categorical_value"
},
{
"enum_index": 2,
"name": "poly",
"object": "categorical_value"
},
{
"enum_index": 3,
"name": "sigmoid",
"object": "categorical_value"
}
],
"conditions": {},
"default_value": null,
"name": "kernel",
"object": "parameter",
"precision": null,
"prior": null,
"transformation": null,
"tunable": true,
"type": "categorical"
}
],
"progress": null,
"project": "classification-models",
"runs_only": false,
"state": "active",
"type": "offline",
"updated": 1446422400,
"user": null
}