Experiment Update
https://api.sigopt.com/v1/experiments/EXPERIMENT_ID
Request Method: PUT
Name | Type | Required? | Description |
---|---|---|---|
metadata | N | ||
metrics | 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 | 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). |
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();
{
"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
}
Last modified 1yr ago