ODTlearn API¶

The software architecture of ODTlearn is motivated by the optimal decision tree literature in which researchers have recently proposed numerous new problem classes, reformulation strategies, and solution approaches. With this in mind, we have created a class structure for our package that emphasizes structuring classes such that they are easy to maintain and extend. The following Figure shows the inheritance diagram in ODTlearn.

The classes in ODTlearn are derived from our abstract base class OptimalDecisionTree. This class provides a standardized interface for the two types of trees currently supported (classification and prescription) while keeping their implementation details separate from one another. OptimalClassificationTree and OptimalPrescriptiveTree extend OptimalDecisionTree by specifying problem-specific methods for traversing and visualizing the decision tree. The structure prevents unnecessary code duplication within each child class. Next, the children of OptimalClassificationTree and OptimalPrescriptiveTree implement methods for creating the decision variables, the constraints, and the objective function necessary for constructing the optimization problem of interest. The separate classes for each of the variations of the MIO formulations ensure that any changes to the structure of one problem formulation do not affect any of the other problem formulations. Finally, the leaf-node classes in the class diagram implement user-facing methods such as fit and predict.

This class structure is meant to encourage other researchers and practitioners to build upon ODTlearn by augmenting it with more features (e.g., different objectives or additional constraints) or even build new types of trees altogether. We provide an example of how to extend the ODTlearn API in our user guide. The example notebooks and user guide for usage examples and additional information about the MIO formulations.