Model#

class datarobotx.Model(project_id=None, model_id=None)[source]#

DataRobot training model

Represents a model on a project leaderboard. Implements prediction and deployment asynchronously.

Parameters:

model_id (str, optional) – DataRobot id for the model from which to initialize the object
project_id (str, optional) – DataRobot id for the project containing the model from which to initialize the object

Attributes:

`dr_model`	DataRobot python client datarobot.Model object
`dr_project`	DataRobot python client datarobot.Project object

Methods:

`deploy`([name])	Deploy the model into ML Ops
`from_url`([url])	Class method to initialize from a URL string
`predict`(X)	Make batch predictions using the model
`predict_proba`(X)	Calculate class probabilities using the model

deploy(name=None)[source]#

Deploy the model into ML Ops

Parameters:: name (str, optional, default=None) – Name for the deployment. If None, a name will be generated
Returns:: Resulting ML Ops deployment
Return type:: Deployment

property dr_model: datarobot.Model#

DataRobot python client datarobot.Model object

property dr_project: datarobot.Project#

DataRobot python client datarobot.Project object

classmethod from_url(url=None)[source]#

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 model of interest
Returns:: model – The constructed Model object
Return type:: Model

predict(X)[source]#

Make batch predictions using the model

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)
Returns:: Resulting predictions (contained in the column ‘predictions’) Returned immediately, updated automatically when results are completed. If attribute access is attempted, will block until results are completed.
Return type:: FutureDataFrame

predict_proba(X)[source]#

Calculate class probabilities using the model

Only available for classifier models.

Parameters:: X (pandas.DataFrame or str) – Dataset to compute class probabilities on; target column can be included or omitted. If str, can be AI catalog dataset id or name (if unambiguous)
Returns:: Resulting predictions; probabilities for each label are contained in the column ‘class_{label}’; returned immediately, updated automatically when results are completed. If attribute access is attempted, will block until results are completed.
Return type:: FutureDataFrame