Catlearn

- Genetic algorithm feature selection can parallelize over population within each generation.
- Default fingerprinter function sets accessible using `catlearn.fingerprint.setup.default_fingerprinters`
- New surrogate model utility
- New utility for evaluating cutoff radii for connectivity based fingerprinting.
- `default_catlearn_radius` improved.

- AtoML renamed to CatLearn and moved to Github.
- Adsorbate fingerprinting again parallelizable.
- Adsorbate fingerprinting use atoms.tags to get layers if present.
- Adsorbate fingerprinting relies on connectivity matrix before neighborlist.
- New bond-electronegativity centered fingerprints for adsorbates.
- Fixed a bug that caused the negative log marginal likelihood to be attached to the gp class.
- Small speed improvement for initialize and updates to `GaussianProcess`.

- Added `autogen_info` function for list of atoms objects representing adsorbates.
- This can auto-generate all atomic group information and attach it to `atoms.info`.
- Parallelized fingerprinting is not yet supported for output from `autogen_info`.
- Added `database_to_list` for import of atoms objects from ase.db with formatted metadata.
- Added function to translate a connection matrix to a formatted neighborlist dict.
- `periodic_table_data.list_mendeleev_params` now returns a numpy array.
- Magpie api added, allows for Voronoi and prototype feature generation.
- A genetic algorithm added for feature optimization.
- Parallelism updated to be compatable with Python2.
- Added in better neighborlist generation.
- Updated wrapper for ase neighborlist.
- Updated CatLearn neighborlist generator.
- Defaults cutoffs changed to `atomic_radius` plus a relative tolerance.
- Added basic NetworkX api.
- Added some general functions to clean data and build a GP.
- Added a test for dependencies. Will raise a warning in the CI if things get out of date.
- Added a custom docker image for the tests. This is compiled in the `setup/` directory in root.
- Modified uncertainty output. The user can ask for the uncertainty with and without adding noise parameter (regularization).
- Clean up some bits of code, fix some bugs.

- Added a parallel version of the greedy feature selection. **Python3 only!**
- Updated the k-fold cross-validation function to handle features and targets explicitly.
- Added some basic read/write functionality to the k-fold CV.
- A number of minor bugs have been fixed.

- Update the fingerprint generator functions so there is now a `FeatureGenerator` class that wraps round all type specific generators.
- Feature generation can now be performed in parallel, setting `nprocs` variable in the `FeatureGenerator` class. **Python3 only!**
- Add better handling when passing variable length/composition data objects to the feature generators.
- More acquisition functions added.
- Penalty functions added.
- Started adding a general api for ASE.
- Added some more test and changed the way test are called/handled.
- A number of minor bugs have been fixed.

- Update functions to compile features allowing for variable length of atoms objects.
- Added some tutorials for hierarchy cross-validation and prediction on organic molecules.