Create Experiment

cloudwatchevidently_create_experiment

R Documentation

Creates an Evidently experiment

Description

Creates an Evidently experiment. Before you create an experiment, you must create the feature to use for the experiment.

An experiment helps you make feature design decisions based on evidence and data. An experiment can test as many as five variations at once. Evidently collects experiment data and analyzes it by statistical methods, and provides clear recommendations about which variations perform better.

You can optionally specify a segment to have the experiment consider only certain audience types in the experiment, such as using only user sessions from a certain location or who use a certain internet browser.

Don't use this operation to update an existing experiment. Instead, use update_experiment.

Usage

cloudwatchevidently_create_experiment(description, metricGoals, name,
  onlineAbConfig, project, randomizationSalt, samplingRate, segment, tags,
  treatments)

Arguments

description

An optional description of the experiment.

metricGoals

[required] An array of structures that defines the metrics used for the experiment, and whether a higher or lower value for each metric is the goal.

name

[required] A name for the new experiment.

onlineAbConfig

A structure that contains the configuration of which variation to use as the "control" version. tThe "control" version is used for comparison with other variations. This structure also specifies how much experiment traffic is allocated to each variation.

project

[required] The name or ARN of the project that you want to create the new experiment in.

randomizationSalt

When Evidently assigns a particular user session to an experiment, it must use a randomization ID to determine which variation the user session is served. This randomization ID is a combination of the entity ID and randomizationSalt. If you omit randomizationSalt, Evidently uses the experiment name as the randomizationSalt.

samplingRate

The portion of the available audience that you want to allocate to this experiment, in thousandths of a percent. The available audience is the total audience minus the audience that you have allocated to overrides or current launches of this feature.

This is represented in thousandths of a percent. For example, specify 10,000 to allocate 10% of the available audience.

segment

Specifies an audience segment to use in the experiment. When a segment is used in an experiment, only user sessions that match the segment pattern are used in the experiment.

tags

Assigns one or more tags (key-value pairs) to the experiment.

Tags can help you organize and categorize your resources. You can also use them to scope user permissions by granting a user permission to access or change only resources with certain tag values.

Tags don't have any semantic meaning to Amazon Web Services and are interpreted strictly as strings of characters.

You can associate as many as 50 tags with an experiment.

For more information, see Tagging Amazon Web Services resources.

treatments

[required] An array of structures that describe the configuration of each feature variation used in the experiment.

Value

A list with the following syntax:

list(
  experiment = list(
    arn = "string",
    createdTime = as.POSIXct(
      "2015-01-01"
    ),
    description = "string",
    execution = list(
      endedTime = as.POSIXct(
        "2015-01-01"
      ),
      startedTime = as.POSIXct(
        "2015-01-01"
      )
    ),
    lastUpdatedTime = as.POSIXct(
      "2015-01-01"
    ),
    metricGoals = list(
      list(
        desiredChange = "INCREASE"|"DECREASE",
        metricDefinition = list(
          entityIdKey = "string",
          eventPattern = "string",
          name = "string",
          unitLabel = "string",
          valueKey = "string"
        )
      )
    ),
    name = "string",
    onlineAbDefinition = list(
      controlTreatmentName = "string",
      treatmentWeights = list(
        123
      )
    ),
    project = "string",
    randomizationSalt = "string",
    samplingRate = 123,
    schedule = list(
      analysisCompleteTime = as.POSIXct(
        "2015-01-01"
      )
    ),
    segment = "string",
    status = "CREATED"|"UPDATING"|"RUNNING"|"COMPLETED"|"CANCELLED",
    statusReason = "string",
    tags = list(
      "string"
    ),
    treatments = list(
      list(
        description = "string",
        featureVariations = list(
          "string"
        ),
        name = "string"
      )
    ),
    type = "aws.evidently.onlineab"
  )
)

Request syntax

svc$create_experiment(
  description = "string",
  metricGoals = list(
    list(
      desiredChange = "INCREASE"|"DECREASE",
      metricDefinition = list(
        entityIdKey = "string",
        eventPattern = "string",
        name = "string",
        unitLabel = "string",
        valueKey = "string"
      )
    )
  ),
  name = "string",
  onlineAbConfig = list(
    controlTreatmentName = "string",
    treatmentWeights = list(
      123
    )
  ),
  project = "string",
  randomizationSalt = "string",
  samplingRate = 123,
  segment = "string",
  tags = list(
    "string"
  ),
  treatments = list(
    list(
      description = "string",
      feature = "string",
      name = "string",
      variation = "string"
    )
  )
)