Jiashuo Liu^†, Tianyu Wang^†, Henry Lam, Hongseok Namkoong, Jose Blanchet
_{† equal contributions (α-β order)}

dro is a python package that implements typical DRO methods on linear loss (SVM, logistic regression, and linear regression) for supervised learning tasks. It is built based on the convex optimization solver cvxpy. The dro package supports different kinds of distance metrics $d(\cdot,\cdot)$ as well as different kinds of base models (e.g., linear regression, logistic regression, SVM, neural networks...). Furthermore, it integrates different synthetic data generating mechanisms from recent research papers.

Without specified, our DRO model is to solve the following optimization problem: $$\min_{\theta} \max_{P: P \in U} E_{(X,Y) \sim P}[\ell(\theta;(X, Y))],$$ where $U$ is the so-called ambiguity set and typically of the form $U = \{P: d(P, \hat P) \leq \epsilon\}$ and $\hat P := \frac{1}{n}\sum_{i = 1}^n \delta_{(X_i, Y_i)}$ is the empirical distribution of training samples ${(X_i, Y_i)}_{i = 1}^n$. And $\epsilon$ is the hyperparameter.

Our package is built upon cvxpy and mosek (by default), where the latter needs the license file. The steps are as follows:

• Request license at Official Website, and then the license mosek.lic will be emailed to you.

• Put your license in your home directory as follows:

  cd
  mkdir mosek
  mv /path_to_license/mosek.lic  mosek/
  

To install dro package, you can simply run:

pip install dro

And it will install all required packages.

A simple user example is as follows:

from dro.src.data.dataloader_classification import classification_basic
from dro.src.data.draw_utils import draw_classification
from dro.src.linear_model.chi2_dro import Chi2DRO

# Data generating
X, y = classification_basic(d = 2, num_samples = 100, radius = 2, visualize = True)

# Chi-square DRO 
clf_model = Chi2DRO(input_dim=2, model_type = 'logistic')
clf_model.update({'eps': 0.1})
print(clf_model.fit(X, y))

For more examples, please refer to our examples.

As for the latest v0.2.2 version, dro supports:

The models listed below are solved by exact solvers from cvxpy.

The models listed below are solved by gradient descent (Pytorch).

The models listed below are solved by function approximation (xgboost, lightgbm).

In linear DRO models, we provide additional interfaces for understanding the worst-case model performance and evaluating the true model performance.

For more details, please refer to https://python-dro.org for more details!

ps: our logo is generated via GPT:)