AutoMLModel#

class datarobotx.AutoMLModel(name=None, **kwargs)#

AutoML orchestrator.

Trains challenger models asynchronously and exposes the present champion for predictions or deployment. Training is performed within an automatically created DataRobot project.

Parameters:

name (str, optional) – Name to use for the DataRobot project that will be created. Alias for the DR ‘project_name’ configuration parameter.
**kwargs – Additional DataRobot configuration parameters for project creation and autopilot execution. See the DRConfig docs for usage examples.

See also

DRConfig: Configuration object for DataRobot project and autopilot settings, also includes detailed examples of usage

Inherited attributes:

`dr_model`	DataRobot python client datarobot.Model object for the present champion.
`dr_project`	DataRobot python client datarobot.Project object.

Methods:

fit(X, target, **kwargs)

Fit challenger models using DataRobot.

Inherited methods:

`deploy`([wait_for_autopilot, name])	Deploy the model into ML Ops.
`from_project_id`(project_id)	Class method to create from an existing project id.
`from_url`(url)	Class method to initialize from a URL string.
`get_params`()	Retrieve configuration parameters for the model.
`predict`(X[, wait_for_autopilot])	Make batch predictions using the present champion.
`predict_proba`(X[, wait_for_autopilot])	Calculate class probabilities using the present champion.
`set_params`(**kwargs)	Set or update configuration parameters for the model.
`share`(emails)	Share a project with other users.

deploy(wait_for_autopilot=False, name=None)#

Deploy the model into ML Ops.

Return type:

Deployment

Returns:

Deployment – Resulting ML Ops deployment
wait_for_autopilot (bool, optional, default=False) – If True, wait for autopilot to complete before deploying the model In non-notebook environments, fit() will always block until complete
name (str, optional, default=None) – Name for the deployment. If None, a name will be generated

property dr_model: Model#

DataRobot python client datarobot.Model object for the present champion.

Returns:: datarobot.Model object associated with this drx model
Return type:: datarobot.Model

property dr_project: Project#

DataRobot python client datarobot.Project object.

Returns:: datarobot.Project object associated with this drx.Model
Return type:: datarobot.Project

fit(X, target, **kwargs)#

Fit challenger models using DataRobot.

Creates a new DataRobot project, uploads X to DataRobot and starts Autopilot. Exposes the present champion model for making predictions or deployment; asynchronously and automatically updates the champion model.

Parameters:

X (pandas.DataFrame) – Training dataset for challenger models. If str, can be AI catalog dataset id or name (if unambiguous)
target (str) – Column name from the dataset to be used as the target variable
**kwargs – Additional optional fit-time parameters to pass to DataRobot i.e. ‘weights’

Return type:

AutoMLModel

See also

DRConfig: Configuration object for DataRobot project and autopilot settings, also includes detailed examples of usage.

classmethod from_project_id(project_id)#

Class method to create from an existing project id.

Initializes a new object from the provided project_id. Configuration parameters originally used to create the project and start Autopilot may not be recoverable.

Parameters:: project_id (str, optional) – DataRobot id for the project from which to initialize the object
Returns:: model – New AutopilotModel instance
Return type:: AutopilotModel

Examples

>>> from datarobotx.models.autopilot import AutopilotModel
>>> my_model = AutopilotModel.from_project_id('62f14505bab13ab73593d69e')

classmethod from_url(url)#

Class method to initialize from a URL string.

Useful for copy and pasting between GUI and notebook environments

Parameters:: url (str) – URL of a DataRobot GUI page related to the project of interest
Returns:: model – The constructed AutopilotModel object
Return type:: AutopilotModel

get_params()#

Retrieve configuration parameters for the model.

Note that some parameters may be initialized or materialized server-side after creating a project or starting Autopilot. get_params() only returns the client-side parameters which will be (or were) passed to DataRobot.

Returns:: config – Configuration object containing the parameters to be used with DataRobot
Return type:: DRConfig

predict(X, wait_for_autopilot=False, **kwargs)#

Make batch predictions using the present champion.

Predictions are calculated asynchronously - returns immediately but reinitializes the returned DataFrame with data once predictions are completed.

Predictions are made within the project containing the model using modeling workers. For real-time predictions, first deploy the model.

Parameters:

X (pandas.DataFrame or str) – Dataset to be scored - target column can be included or omitted. If str, can be AI catalog dataset id or name (if unambiguous)
wait_for_autopilot (bool, optional, default=False) – If True, wait for autopilot to complete before making predictions In non-notebook environments, fit() will always block until complete
**kwargs (Any) – Other key word arguments to pass to the _predict function

Returns:

Resulting predictions (contained in the column ‘predictions’) Returned immediately, updated automatically when results are completed.

Return type:

pandas.DataFrame

predict_proba(X, wait_for_autopilot=False, **kwargs)#

Calculate class probabilities using the present champion.

Only available for classifier and clustering models.

Parameters:

X (pandas.DataFrame or str) – Dataset to be scored - target column can be included or omitted. If str, can be AI catalog dataset id or name (if unambiguous)
wait_for_autopilot (bool, optional, default=False) – If True, wait for autopilot to complete before making predictions In non-notebook environments, fit() will always block until complete
**kwargs (Any) – Other key word arguments to pass to the _predict function

Returns:

Resulting predictions; probabilities for each label are contained in the column ‘class_{label}’; returned immediately, updated automatically when results are completed.

Return type:

pandas.DataFrame