This repository contains a software package implementing RandALO, a fast randomized method for risk estimation of machine learning models, as described in the paper,
P. T. Nobel, D. LeJeune, E. J. Candès. RandALO: Out-of-sample risk estimation in no time flat. 2024. arXiv:2409.09781.
Note: the experiments in the paper were performed in an earlier version of the codebase available in the paper-code branch.
In a folder run the following:
git clone [email protected]:cvxgrp/randalo.git
cd randalo
# create a new environment with Python >= 3.10 (could also use venv or similar)
conda create -n randalo python=3.12
# install requirements and randalo
pip install -r requirements.txtThe simplest way to use RandALO is with linear models from scikit-learn. See a longer demonstration in a notebook here.
from torch import nn
from sklearn.linear_model import Lasso
from randalo import RandALO
X, y = ... # load data as np.ndarrays as usual
model = Lasso(1.0).fit(X, y) # fit the model
alo = RandALO.from_sklearn(model, X, y) # set up the Jacobian
mse_estimate = alo.evaluate(nn.MSELoss()) # estimate riskWe currently support the following models:
LinearRegressionRidgeLassoLassoLarsElasticNetLogisticRegression
If you prefer to use other solvers for fitting your models than scikit-learn, or if you wish to extend to other models than the ones listed above, you can still use RandALO by instantiating the Jacobian yourself. You need only be careful to ensure that you scale the regularizer correctly for your problem formulation.
from torch import nn
from sklearn.linear_model import Lasso
from randalo import RandALO, MSELoss, L1Regularizer, Jacobian
X, y = ... # load data as np.ndarrays as usual
model = Lasso(1.0).fit(X, y) # fit the model
# instantiate RandALO by creating a Jacobian object
loss = MSELoss()
reg = 2.0 * model.alpha * L1Regularizer() # scale the regularizer appropriately
y_hat = model.predict(X)
solution_func = lambda: model.coef_
jac = Jacobian(y, X, solution_func, loss, reg)
alo = RandALO(loss, jac, y, y_hat)
mse_estimate = alo.evaluate(nn.MSELoss()) # estimate riskPlease refer to our scikit-learn integration source code for more examples.