Regression Data

dro.src.data.dataloader_regression.regression_basic(num_samples=100, d=1, noise=0.1, seed=42)

Basic regression setting.

Args:

num_samples (int): The number of samples d (int): The dimension of covariates noise (float): The variance of the noise term seed (int): Random seed.

Returns:
tuple: (covariate, target) where:

  • X (numpy.ndarray): A numpy array containing the generated covariate data

  • y (numpy.ndarray): A numpy array containing the generated target data

dro.src.data.dataloader_regression.regression_DN20_1(num_samples, d=5, noise=0.01, seed=42)

Following Section 3.1.2 of “Learning Models with Uniform Performance via Distributionally Robust Optimization” link: https://arxiv.org/pdf/1810.08750

Args:

num_samples (int): The number of samples d (int): The dimension of covariates noise (float): The variance of the noise term seed (int): Random seed.

Returns:
tuple: (covariate, target) where:

  • X (numpy.ndarray): A numpy array containing the generated covariate data

  • y (numpy.ndarray): A numpy array containing the generated target data

dro.src.data.dataloader_regression.regression_DN20_2(num_samples, prob=0.1, noise=0.01, seed=42)

Following Section 3.1.3 of “Learning Models with Uniform Performance via Distributionally Robust Optimization” link: https://arxiv.org/pdf/1810.08750

Args:

num_samples (int): The number of samples prob (float): the minority group ratio in (0,1) noise (float): The variance of the noise term seed (int): Random seed.

Returns:
tuple: (covariate, target) where:

  • X (numpy.ndarray): A numpy array containing the generated covariate data

  • y (numpy.ndarray): A numpy array containing the generated target data

dro.src.data.dataloader_regression.regression_DN20_3(save_dir='./data/', download=True)

Following Section 3.3 of “Learning Models with Uniform Performance via Distributionally Robust Optimization” link: https://arxiv.org/pdf/1810.08750

Data is from UCI repository: https://archive.ics.uci.edu/dataset/183/communities+and+crime

Args:

save_dir (str): The path to save the data download (bool): Whether to download the data. If not, will load the data according to the save_dir

Returns:
tuple: (covariate, target) where:

  • X (numpy.ndarray): A numpy array containing the generated covariate data

  • y (numpy.ndarray): A numpy array containing the generated target data

dro.src.data.dataloader_regression.regression_LWLC(n1=100000, n2=1000, ps=5, pvb=1, pv=4, r=1.7, scramble=False)

Following Section 4.1 (Regression) of “Distributionally Robust Optimization with Data Geometry” link: https://proceedings.neurips.cc/paper_files/paper/2022/file/da535999561b932f56efdd559498282e-Paper-Conference.pdf

Args:

n1 (int): The total number of samples in the pool n2 (int): The number of samples required ps (int): The dimension of feature S pvb (int): The dimension of feature Vb pv (int): The dimension of other features in V (except for Vb) r (float): The adjustment parameter to control the spurious correlation with abs(r)>1. Higher abs(r) denotes stronger spurious correlation, and sign(r) controls the direction of spurious correlation scramble (bool): Whether to mix the features S and V.

Returns:
tuple: (covariate, target) where:

  • X (numpy.ndarray): A numpy array containing the generated covariate data

  • y (numpy.ndarray): A numpy array containing the generated target data