Please note: The code in these repos is sourced from the DataRobot user community and is not owned or maintained by DataRobot, Inc. You may need to make edits or updates for this code to function properly in your environment.

API Samples for Data Scientists

This repository contains Python notebooks and R Markdown guides for achieving specific tasks using the API.

Start learning with the API Training module.

Additional code examples can be accessed from the following locations:

• Tutorials for data scientists repo
• AI Engineering repo
• docs.datarobot.com
• R Vignettes included in the R client
• Examples included in the Python client

Usage

For each respective guide, follow the instructions in its own .ipynb or .Rmd file.

Please pay attention to the different DataRobot API Endpoints

The API endpoint you specify for accessing DataRobot is dependent on the deployment environment, as follows:

• AI Platform Trial—https://app2.datarobot.com/api/v2
• US Managed AI Cloud—https://app.datarobot.com/api/v2
• EU Managed AI Cloud—https://app.eu.datarobot.com/api/v2
• On-Premise—https://{datarobot.example.com}/api/v2 (replacing {datarobot.example.com} with your specific deployment endpoint

The DataRobot API Endpoint is used to connect your IDE to DataRobot.

Important Links

• To learn how to use DataRobot and gain practical data science education, visit DataRobot University
• To ask questions and get help from DataRobot experts and peers, visit DataRobot Community

Contents

Advanced Tuning

• Advanced Tuning: how to do advanced tuning. Python R

• Datetime Partitioning: how to do datetime partitioning. Python R

• AdvancedOptions object: how to use AdvancedOptions object. Python

Compliance Documentation

• Getting Compliance Documentation: how to get Compliance Documentation documents. Python R

Feature Lists Manipulation

• Feature Selection using Feature Importance Rank Ensembling: This notebook shows the benefits of advanced feature selection that uses median rank agggregation of feature impacts across several models created during a run of DataRobot autopilot. Python

• Advanced Feature Selection: how to do advanced feature selection using all of the models created during a run of DataRobot autopilot. Python R

• Feature Lists Manipulation: how to create and manipulate custom feature lists and use it for training. Python R

• Transforming Feature Type: how to transform feature types. Python R

Helper Functions

• Modeling/Python: A function that helps you search for specific blueprints within a DataRobot's project's repository and then initiates all of these models. Python

• Time Series/Python: a set of custom functions for AutoTS projects (advanced settings, data quality, filling dates, preprocessing, brute force, cloning, accuracy metrics, modeling, project lists). Python

Initiating Projects

• Starting a Binary Classification Project: how to initiate a DataRobot project for a Binary Classification target. Python R

• Starting a Multiclass Project: how to initiate a DataRobot project for a Multiclass Classification target. Python R

• Starting a Project with Selected Blueprints: how to initiate a DataRobot project manually where the user has the option to choose which models/blueprints to initiate. Python R

• Starting a Regression Project: how to initiate a DataRobot project for a numerical target. Python R

• Starting a Time Series Project: how to initiate a DataRobot project for a Time Series problem. This notebook also covers calendars and feature settings for time series projects. Python R

Making Predictions

• Getting Predictions and Prediction Explanations: how to get predictions and prediction explanations out of a trained model. Python R

• Batch Prediction API: how to use DataRobot's batch prediction API to get predictions out of a DataRobot deployed model. Python

• Prediction Explanation Clustering: creating clusters of prediction explanations to better understand patterns in your data. R

Model Evaluation

• Getting Confusion Chart: how to get the Confusion Matrix Chart. Python R

• Getting Feature Impact: how to get the Feature Impact scores. Python R

• Getting Lift Chart: how to get the lift chart. Python R

• Getting Partial Dependence: how to get partial dependence.Python R

• Getting ROC Curve: how to get the ROC Curve data. Python R

• Getting SHAP Values: how to get SHAP values. Python

• Getting Word Cloud: how to pull the word cloud data. Python R

• Plotting Prediction Intervals: how to plot prediction intervals for time series projects (single and multi series). Python

Model Management

• Model Management and Monitoring: how to manage models through the API. This includes deployment, replacement, deletion, and monitoring capabilities. Python R

• Sharing Projects: how to share projects with colleagues. Python

• Uploading Actuals to a DataRobot Deployment: how to upload actuals into the DataRobot platform in order to calculate accuracy metrics Python

AI Catalog

• AI Catalog API Demo: how to create and share datasets in AI Catalog and use them to create projects and run predictions. Python

DataRobot Data Prep

• DataRobot Data Prep: A collection of functions to interact with DataRobot Data Prep. Python

Rating Tables

• • Rating Tables*: A script that allows you to transform rating tables coming from Generalized Additive 2 Models to Rating Tables. Python

Development and Contributing

If you'd like to report an issue or bug, suggest improvements, or contribute code to this project, please refer to CONTRIBUTING.md.

Code of Conduct

This project has adopted the Contributor Covenant for its Code of Conduct. See CODE_OF_CONDUCT.md to read it in full.

License

Licensed under the Apache License 2.0. See LICENSE to read it in full.