Ecnet

- Build/installation now uses `pyproject.toml` instead of the deprecated `setup.py`
- Added GitHub workflows for PyPI publishing and unit testing
- Unit tests now use `pytest` instead of `unittest`

Update to package dependencies, notably PyTorch 1.8.0 -> 2.0.0. ECNet now requires Python 3.11+.

- Added option to shuffle training/validation subsets every epoch
- Update to docstrings/documentation
- Added a "getting started" notebook in the examples directory
- New argument format for ABC-based parameter tuning

- ECNet now leverages the PyTorch package for ML operations
- This change presented an opportunity to overhaul ECNet from the ground up, allowing us to think about _how_ the user will interact with this package. Ultimately, we wanted to make interactions easier.
- Custom data structures were weird, and didn't belong in a ML toolkit. Instead, we offer PyTorch-based data structures, adjusted to house chemical data. Users can obtain SMILES strings and property values, or a ML-ready structure ready to be passed to ECNet for training.
- All these changes require documentation, so full API documentation is available. We also have an example script, and would like to include more examples in the future.

Per ECabc's API changes in its 3.0.0 update, this ECNet update incorporates these changes into all relevant functions.

- _ecnet.models.mlp.MultilayerPerceptron_'s implementation now makes sense, and leads to faster training times
- some database cleanup
- in case ECNet is not utilized to use a pre-trained project, input QSPR descriptor names are also saved inside the project (DataFrame object not required)