Python client v3.0¶
Now generally available, DataRobot has released version 3.0 of the Python client. This version introduces significant changes to common methods and usage of the client. Many prominent changes are listed below, but view the changelog for a complete list of changes introduced in version 3.0.
A summary of some new features for version 3.0 are outlined below:
- Version 3.0 of the Python client does not support Python 3.6 and earlier versions. Version 3.0 currently supports Python 3.7+.
- The default Autopilot mode for the
project.start_autopilotmethod has changed to
- Pass a file, file path, or DataFrame to a deployment to easily make batch predictions and return the results as a DataFrame using the new method
- You can use a new method to retrieve the canonical URI for a project, model, deployment, or dataset:
New methods for DataRobot projects¶
Review the new methods available for
Project.get_optionsallows you to retrieve saved modeling options.
AdvancedOptionsvalues for use in modeling.
Project.analyze_and_modelinitiates Autopilot or data analysis using data that has been uploaded to DataRobot.
Project.get_datasetretrieves the dataset used to create the project.
Project.set_partitioning_methodcreates the correct Partition class for a regular project, based on input arguments.
Project.set_datetime_partitioningcreates the correct Partition class for a time series project.
Project.get_top_modelreturns the highest scoring model for a metric of your choice.
Review the deprecations introduced in version 3.0:
Project.set_targethas been deprecated. Use
PredictJob.createhas been removed. Use
Model.get_leaderboard_ui_permalinkhas been removed. Use
Project.open_leaderboard_browserhas been removed. Use
ComplianceDocumentationhas been removed. Use
The table below outlines the notebooks available that use version 3.0 of DataRobot's Python client.
|Insurance claim triage||Evaluate the severity of an insurance claim in order to triage it effectively.|
|Large scale demand forecasting||Learn about an end-to-end demand forecasting use case that uses DataRobot's Python package.|
|Predict fraudulent medical claims||The identification of fraudulent medical claims using the DataRobot Python package.|
|Predict customer churn||How to predict customers that are at risk to churn and when to intervene to prevent it.|
|Generate SHAP-based Prediction Explanations||How to use DataRobot's SHAP Prediction Explanations to determine what qualities of a home drive sale value.|
|Configure datetime partitioning||How to use datetime partitioning to guard a project against time-based target leakage.|
|Generate advanced model insights||How to generate the model insights available for DataRobot's Python client.|
|Migrate models||How to transfer models from one DataRobot cluster to another as an .mlpkg file.|
|Create and schedule JDBC prediction jobs||How to use DataRobot's Python client to schedule prediction jobs and write them to a JDBC database.|