Developer documentation > API reference > Python API client > Mitigation > Challenger models

Challenger¶

class datarobot.models.deployment.challenger.Challenger

A challenger is an alternative model being compared to the model currently deployed

Variables

Attribute	Type	Description
`id`	`str`	The ID of the challenger.
`deployment_id`	`str`	The ID of the deployment.
`name`	`str`	The name of the challenger.
`model`	`dict`	The model of the challenger.
`model_package`	`dict`	The model package of the challenger.
`prediction_environment`	`dict`	The prediction environment of the challenger.

create()¶

classmethod create()

Create a challenger for a deployment

Parameters

Parameter	Type	Description
`deployment_id`	`str`	The ID of the deployment
`model_package_id`	`str`	The model package id of the challenger model
`prediction_environment_id`	`str`	The prediction environment id of the challenger model
`name`	`str`	The name of the challenger model
`max_wait`	`Optional[int]`	The amount of seconds to wait for successful resolution of a challenger creation job.

Return type: Challenger

Examples

from datarobot import Challenger
challenger = Challenger.create(
    deployment_id="5c939e08962d741e34f609f0",
    name="Elastic-Net Classifier",
    model_package_id="5c0a969859b00004ba52e41b",
    prediction_environment_id="60b012436635fc00909df555"
)

get()¶

classmethod get()

Get a challenger for a deployment

Parameters

Parameter	Type	Description
`deployment_id`	`str`	The ID of the deployment
`challenger_id`	`str`	The ID of the challenger

Returns

Returns	Description
The challenger object

Return type: Challenger

Examples

from datarobot import Challenger
challenger = Challenger.get(
    deployment_id="5c939e08962d741e34f609f0",
    challenger_id="5c939e08962d741e34f609f0"
)

challenger.id
>>>'5c939e08962d741e34f609f0'
challenger.model_package['name']
>>> 'Elastic-Net Classifier'

list()¶

classmethod list()

List all challengers for a deployment

Parameters

Parameter	Type	Description
`deployment_id`	`str`	The ID of the deployment

Returns

Returns	Description
challengers	A list of challenger objects

Return type: list

Examples

from datarobot import Challenger
challengers = Challenger.list(deployment_id="5c939e08962d741e34f609f0")

challengers[0].id
>>>'5c939e08962d741e34f609f0'
challengers[0].model_package['name']
>>> 'Elastic-Net Classifier'

delete()¶

method delete()

Delete a challenger for a deployment

Return type: None

update()¶

method update()

Update name and prediction environment of a challenger

Parameters

Parameter	Type	Description
`name`	`Optional[str]`	The name of the challenger model
`prediction_environment_id`	`Optional[str]`	The prediction environment id of the challenger model

Return type: None

ChampionModelPackage¶

class datarobot.models.deployment.champion_model_package.ChampionModelPackage

Represents a champion model package.

Parameters

Parameter	Type	Description
`id`	`str`	The ID of the registered model version.
`registered_model_id`	`str`	The ID of the parent registered model.
`registered_model_version`	`int`	The version of the registered model.
`name`	`str`	The name of the registered model version.
`model_id`	`str`	The ID of the model.
`model_execution_type`	`str`	The type of model package (version). dedicated (native DataRobot models) and custom_inference_model` (user added inference models) both execute on DataRobot prediction servers, while external does not.
`is_archived`	`bool`	Whether the model package (version) is permanently archived (cannot be used in deployment or replacement).
`import_meta`	`ImportMeta`	Information from when this model package (version) was first saved.
`source_meta`	`SourceMeta`	Meta information from where the model was generated.
`model_kind`	`ModelKind`	Model attribute information.
`target`	`Target`	Target information for the registered model version.
`model_description`	`ModelDescription`	Model description information.
`datasets`	`Dataset`	Dataset information for the registered model version.
`timeseries`	`Timeseries`	Time series information for the registered model version.
`bias_and_fairness`	`BiasAndFairness`	Bias and fairness information for the registered model version.
`is_deprecated`	`bool`	Whether the model package (version) is deprecated (cannot be used in deployment or replacement).
`build_status`	`str` or `None`	Model package (version) build status. One of complete, inProgress, failed.
`user_provided_id`	`str` or `None`	User provided ID for the registered model version.
`updated_at`	`str` or `None`	The time the registered model version was last updated.
`updated_by`	`UserMetadata` or `None`	The user who last updated the registered model version.
`tags`	`List[TagWithId]` or `None`	The tags associated with the registered model version.
`mlpkg_file_contents`	`str` or `None`	The contents of the model package file.