Experiment Update

https://api.sigopt.com/v1/experiments/EXPERIMENT_ID

Updates an existing Experiment.

Request Method: PUT

Parameters

NameTypeRequired?Description
metadataMetadataNOptional user-provided object. See Using Metadata for more information.
metricsarray<Metric>NAn array of Metric objects. Only the threshold field of metrics can be updated. Use the threshold value null to remove a threshold.
namestringNA user-specified name for this experiment.
observation_budgetintNThe 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_bandwidthintNThe 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.
parametersarray<Parameter>NAn array of Parameter objects. Only the bounds, categorical_values, precision, and default_value fields on parameters can be updated.
statestringNThe 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

experiment = conn.experiments(EXPERIMENT_ID).update(
  name="Support Vector Classifier Accuracy"
  )
Response
{
  "client": "1",
  "conditionals": [],
  "created": 1565896407,
  "development": false,
  "id": "1",
  "linear_constraints": [],
  "metadata": null,
  "metric": {
    "name": "Accuracy",
    "object": "metric",
    "objective": "maximize",
    "threshold": null
  },
  "metrics": [
    {
      "name": "Accuracy",
      "object": "metric",
      "objective": "maximize",
      "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,
      "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,
      "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,
      "tunable": true,
      "type": "categorical"
    }
  ],
  "progress": {
    "best_observation": null,
    "first_observation": null,
    "last_observation": null,
    "object": "progress",
    "observation_budget_consumed": 0,
    "observation_count": 0
  },
  "project": "classification-models",
  "state": "active",
  "type": "offline",
  "updated": 1565896407,
  "user": null
}