Culebra is a DEAP-based evolutionary computation library designed to solve feature selection problems.
It provides several individual representations, such as bitvectors and sets of feature indices, several fitness functions and several wrapper algorithms.
Experiments and experiment batches are automatized by means of the Experiment and Batch classes.
Culebra requires Python 3. It also depends on the following Python packages:
Culebra is fully documented in its github-pages. You can also generate its docs from the source code. Simply change directory to the doc subfolder and type in make html, the documentation will be under build/html. You will need Sphinx to build the documentation.
The examples subfolder contains several examples that show the basic usage of culebra.
This work is supported by projects New Computing Paradigms and Heterogeneous Parallel Architectures for High-Performance and Energy Efficiency of Classification and Optimization Tasks on Biomedical Engineering Applications (HPEE-COBE), with reference PGC2018-098813-B-C31, and Advanced Methods of Biomedical Data Analysis and Brain Modeling Optimized for High-Performance and Energy-Efficient Computing (HPEEC-BIOBRAIN), with reference PID2022-137461NB-C31, both funded by the Spanish Ministerio de Ciencia, Innovación y Universidades, and by the European Regional Development Fund (ERDF).
Culebra © 2020-2023 EFFICOMP.