pyrelational is a python active learning library for rapidly implementing active learning pipelines from data management, model development (and Bayesian approximation), to creating novel active learning strategies.
This PR focuses on the suite of datasets part of the library.
It first refactors the existing datasets interface, breaking down the large files into smaller ones to facilitate maintenance.
Second, it adds DrugCombDataset object that interface with the drugcomb database of drug combinations.
What are the changes implemented in this PR?
breaks down classification.py and regression.py into multiple files to facilitate maintenance.
abstracts away some common functionalities
expose the random seed parameter for all datasets to ensure reproducibility
What is the goal of this PR?
This PR focuses on the suite of datasets part of the library.
It first refactors the existing datasets interface, breaking down the large files into smaller ones to facilitate maintenance. Second, it adds DrugCombDataset object that interface with the drugcomb database of drug combinations.
What are the changes implemented in this PR?
classification.pyandregression.pyinto multiple files to facilitate maintenance.