Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

feat(JAQPOT-127): jaqpotpy-datasets-refactoring #28

Merged
merged 52 commits into from
Jun 28, 2024

Conversation

periklis91
Copy link
Member

This branch does not only involve testing of datasets but also refactoring of the datasets class schema and classes themselves. Once all necessary changes are implemented, I will assign someone to review them

All test datasets should be concentrated at a single point and not in several subfolders within jaqpotpy
This file is going to be deleted once the new classes are up and running
This file is going to be deleted once the new dataset classes are ready
@periklis91 periklis91 changed the title Feat/jaqpot 127/jaqpotpy dataset tests feat(JAQPOT-127): jaqpotpy-datasets-refactoring Jun 12, 2024
This class was completely redundant
All commints after this commit will fix bugs
There was an error when the user was providing a list of a single string. Now it is fixed
Instead of taking different scenarios, just drop smiles into a list and work with this throughout the code
This consists of the first round of tests for dataset construction. Further tests need to be written, this is a start
Added a test that checks if a TypeError is raised in the case of the user providing both a path and a csv
Change all references of SmilesDataset and MolecularTabularDataset to JaqpotpyDataset
Removed the wildcard import from datasets.__inits__
doa_m was the assigned to doa.fit() so this was renamed accordingly
The changes on JaqpotpyDataset had created several compatibilty issues with the classes MolecularModel() and MolecularSKLearn which are now fixed. Next thing to fix is the .predict() method and also to checkout what is going on with the onnx framework and the predictions
Previously, once a preprocessor was defined, preprocessing of X and y were considered class attributes instead of object attributes, so no changes could be made. Now preprocessing of X and y are object attributes and as such are initialised and can be reset with each new object initilisation
models are now compatible with the new JaqpotpyDataset

@property
def task(self):
return self._task

@task.setter
def task(self, value):
if value is None:
Copy link
Contributor

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

Suggesting to reduce to a single if statement

if value is None or value.lower() not in ['regression', 'classification']:

@vassilismin vassilismin merged commit b143ecd into main Jun 28, 2024
3 checks passed
@vassilismin vassilismin deleted the feat/JAQPOT-127/jaqpotpy-dataset-tests branch June 28, 2024 14:54
Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment
Labels
None yet
Projects
None yet
Development

Successfully merging this pull request may close these issues.

None yet

2 participants