Fseval

Minor release.
- [x] Adds README information to PyPi page

- [x] Bugfixes
- [x] Fix PyPi distribution

Completely rewritten framework, integrating [wandb](https://wandb.ai/), [hydra](https://hydra.cc/) and [sklearn](https://scikit-learn.org/) for easily configurable and flexible feature selection benchmarking.

- [x] Beautifully visualize benchmarking results inside a wandb dashboard ✨✨
- [x] Ability to enqueue jobs in a Redis database using Hydra's [RQ Launcher](https://hydra.cc/docs/plugins/rq_launcher/).
- [x] Both classification **and** regression datasets supported.
- [x] Multi-label classification and multi-output classification- and regression all supported.
- [x] Flexible dataset loading using [adapters](https://github.com/dunnkers/fseval/tree/master/fseval/adapters): adapters for [OpenML](https://www.openml.org/search?type=data) and [wandb artifacts](https://docs.wandb.ai/guides/artifacts) are included.
- [x] Ability to dynamically define a feature importances ground-truth on any dataset - can be used to evaluate feature rankings.
- [x] Sophisticated evaluation metrics are used to evaluate the quality of a feature ranking.

🙌🏻

- [x] Benchmark feature selection algorithms
- [x] Complete pipeline for benchmarking: reading datasets, performing feature selection, computing evaluation metrics, plotting results
- [x] Written from the ground up to be parallel-compatible
- [x] SLURM interoperability (e.g. University of Groningen's Peregrine cluster)
- [x] CLI tool for submitting jobs