Loading a dataset

August 29, 2022 ยท View on GitHub

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common-datasets: common machine learning datasets #################################################

This package provides an unofficial collection of datasets widely used in the evaluation of machine learning techniques, mainly small and imbalanced datasets for binary, multiclass classification and regression. The datasets are provided in the usual sklearn.datasets format, with missing data imputation and the encoding of category and ordinal features. The authors of this repository do not own any licenses for the datasets, the goal of the project is to provide a stanardized collection of datasets for research purposes.

PLEASE DO NOT CITE OR REFER TO THIS PACKAGE IN ANY FORM!

If you use data through this repository, please cite the original works publishing and specifying these datasets:

.. code-block:: BibTex

@article{keel, author={Alcala-Fdez, J. and Fernandez, A. and Luengo, J. and Derrac, J. and Garcia, S. and Sanchez, L. and Herrera, F.}, title={KEEL Data-Mining Software Tool: Data Set Repository, Integration of Algorithms and Experimental Analysis Framework}, journal={Journal of Multiple-Valued Logic and Soft Computing}, volume={17}, number={2-3}, year={2011}, pages={255-287}}

@misc{uci, author = "Dua, Dheeru and Karra Taniskidou, Efi", year = "2017", title = "{UCI} Machine Learning Repository", url = "http://archive.ics.uci.edu/ml", institution = "University of California, Irvine, School of Information and Computer Sciences"}

@article{krnn, author={X. J. Zhang and Z. Tari and M. Cheriet}, title={{KRNN}: k {Rare-class Nearest Neighbor} classification}, journal={Pattern Recognition}, year={2017}, volume={62}, number={2}, pages={33--44} }

For each individual dataset the citation key referring to its publisher or a relevant publication in which the dataset in the given configuration has been used is provided as part of the dataset. For example:

.. code-block:: python

binary classification

import common_datasets.binary_classification as binclas

dataset = bin_clas.load_abalone19() dataset['citation_key'] 'keel'

Introduction

The package contains 119 binary classification, 23 multiclass classification and 23 regression datasets.

Installation

The package can be cloned from GitHub in the usual way, and the latest stable version is also available in the PyPI repository:

.. code-block:: bash

pip install common_datasets

Use cases

Loading a dataset

.. code-block:: python

binary classification

import common_datasets.binary_classification as binclas

dataset = binclas.load_abalone19()

multiclass classification

import common_datasets.multiclass_classification as multclas

dataset = multclas.load_abalone()

regression

from common_datasets import regression

dataset = regression.load_treasury()

Querying all dataset loaders and loading a dataset

.. code-block:: python

binary classification

import common_datasets.binary_classification as binclas

data_loaders = binclas.get_data_loaders()

dataset_0 = data_loaders0

multiclass classification

import common_datasets.multiclass_classification as multclas

data_loaders = multclas.get_data_loaders()

dataset_0 = data_loaders0

regression

from common_datasets import regression

data_loaders = regression.get_data_loaders()

dataset_0 = data_loaders0

Querying the loaders of the 5 smallest datasets regarding the total number of records

.. code-block:: python

binary classification

import common_datasets.binary_classification as binclas

data_loaders = binclas.get_filtered_data_loaders(n_smallest=5, sorting='n')

dataset_0 = data_loaders0

multiclass classification

import common_datasets.multiclass_classification as multclas

data_loaders = multclas.get_data_loaders(n_smallest=5, sorting='n')

dataset_0 = data_loaders0

regression

from common_datasets import regression

data_loaders = regression.get_data_loaders(n_smallest=5, sorting='n')

dataset_0 = data_loaders0

Documentation