Fixes to k-fold fingerprint splitting

[chertianser]

Pull Request Template

Description

There are two issues when attempting to k-fold split via fingerprint similarity:

1. If a dataset of size N does not divide evenly into k folds (i.e N mod k != 0) the extra data points are discarded. As test_size != 0 when N mod k != 0, there is an attempted second split for the test indices. k_fold_split for FingerprintSplitter only collects training and validation data, and therefore the test indices are lost. The proposed fix by adding checks on frac_valid and frac_test ensure that the extra data points are merged appropriately into the test and validation sets respectively.
2. There is a division-by-zero error when attempting to k-fold split by fingerprint similarity. At the final kth fold, split calls with arguments frac_train=1.0, frac_valid=0.0 and frac_test=0.0. _split_fingerprints is then called with size1=N/k and size2=0, which will fail as the grouping algorithm attempts to divide by zero. A straightforward fix is to return the identity (i.e all of the unsplit indices), because the final fold is already considered split by process of elimination.

To test on both these issues, I have also written a unit test that checks for both issues.

Extra line changes were caused by running yapf on both splitter.py and test_splitter.py.

Type of change

Please check the option that is related to your PR.

• Bug fix (non-breaking change which fixes an issue)
• New feature (non-breaking change which adds functionality)
• Breaking change (fix or feature that would cause existing functionality to not work as expected)
  • In this case, we recommend to discuss your modification on GitHub issues before creating the PR
• Documentations (modification for documents)

Checklist

• My code follows the style guidelines of this project
  • Run yapf -i <modified file> and check no errors (yapf version must be 0.32.0)
  • Run mypy -p deepchem and check no errors
  • Run flake8 <modified file> --count and check no errors
  • Run python -m doctest <modified file> and check no errors
• I have performed a self-review of my own code
• I have commented my code, particularly in hard-to-understand areas
• I have made corresponding changes to the documentation
• I have added tests that prove my fix is effective or that my feature works
• New unit tests pass locally with my changes
• I have checked my code and corrected any misspellings

[arunppsg]

Looks great. Thank you @chertianser