Machine Learning regression and classification for mosquito gene drive datasets generated by MGDrivE and pre-processed by MoNeT_DA. For more information on the experiments and the datasets, follow these links:
- DSDP: Data Scholars Discovery Program
- PYF: Onetahi pgSIT
- tGD: Second iteration of the tGD
- STP: São Tomé and Príncipe linked drive
- Push to the repo often!
- Python code (as PEP8 compliant as possible)
- Document your code in MD files and comments
- Git commits start with the 3 letter code of the project (PYF, STP, etc.)
- You can use Jupyter notebooks for exploration but the end result should be a
.pyset of files that can be run from the terminal - These
pyfiles will be called from abashcommand to create pipelines - Functions definitions should have their own separate file(s)
- Have separate files for: data cleaning, training, testing, evaluation
- Have paths as clearly stated variables that can be changed easily
- Datasets will be synched through Mega for the time being
- Have a file with the re-scaling constants
- Return some type of uncertainty estimate
- Lead: Héctor M. Sánchez C.
- Contributors: Elijah Bartolome, Ana L. Dueñas C., Xingli Yu, Lillian Weng, Joanna Yoo, Ayden Salazar
- Former Contributors: Christopher De Leon, Juán J. Olivera L., Guillermo O. Cota M.
- Collaborators: Benjamín Valdés
- PI: John M. Marshall
