UCI datasets

Regression datasets from the UCI machine learning repository prepared for benchmarking studies with test-train splits.

Installation

Install using pip (the download size is about 312 Mb):

python -m pip install git+https://github.com/treforevans/uci_datasets.git

Usage

The following code gets the first test-train split (i.e., split=0) of the challenger dataset:

from uci_datasets import Dataset
data = Dataset("challenger")
x_train, y_train, x_test, y_test = data.get_split(split=0)

There are 10 test-train splits for each dataset (as in 10-fold cross validation) with 90% of the dataset being training points and 10% being testing points in each split. The split parameter of the Dataset.get_split method accepts integers from 0 to 9 (inclusive).

Datasets

The below table contains the size (number of observations) and the number of input dimensions of each dataset. All datasets have a single output dimension.

Dataset name	Number of observations	Input dimension
3droad	434874	3
autompg	392	7
bike	17379	17
challenger	23	4
concreteslump	103	7
energy	768	8
forest	517	12
houseelectric	2049280	11
keggdirected	48827	20
kin40k	40000	8
parkinsons	5875	20
pol	15000	26
pumadyn32nm	8192	32
slice	53500	385
solar	1066	10
stock	536	11
yacht	308	6
airfoil	1503	5
autos	159	25
breastcancer	194	33
buzz	583250	77
concrete	1030	8
elevators	16599	18
fertility	100	9
gas	2565	128
housing	506	13
keggundirected	63608	27
machine	209	7
pendulum	630	9
protein	45730	9
servo	167	4
skillcraft	3338	19
sml	4137	26
song	515345	90
tamielectric	45781	3
wine	1599	11

Dataset information can be obtained from the all_datasets dictionary. For example, to obtain a list of all datasets with fewer than 1000 observations, execute the following:

from uci_datasets import all_datasets
[name for name, (n_observations, n_dimensions) in all_datasets.items() if n_observations < 1000]

Papers using these datasets

The following papers use the same datasets and test-train splits present in this repository.

• Yang, Zichao, et al. "A la carte–learning fast kernels." Artificial Intelligence and Statistics. 2015.
• Wilson, Andrew Gordon, et al. "Deep kernel learning." Artificial Intelligence and Statistics. 2016.
• Evans, Trefor W., and Prasanth B. Nair. "Scalable Gaussian processes with grid-structured eigenfunctions (GP-GRIEF)." International Conference on Machine Learning. 2018.
• Evans, Trefor W., and Prasanth B. Nair. "Discretely relaxing continuous variables for tractable variational inference." Neural Information Processing Systems. 2018.
• Audouze, Christophe, and Prasanth B. Nair. "Sparse low-rank separated representation models for learning from data." Proceedings of the Royal Society A 475.2221 (2019).
• Evans, Trefor W., and Prasanth B. Nair. "Quadruply stochastic gaussian processes." arXiv preprint arXiv:2006.03015 (2020).