Hypertunity

Changed
- some code style related changes are applied, such as import sorting and line length shortening.
- refactoring in tests to use pytest parameterisation and fixtures.

Fixed
- issue with running callables from script thanks to David Turner (https://github.com/gdikov/hypertunity/pull/43).
- issue with tensorflow version comparison in the tensorboard reporter.

Added
- `Reporter` instance can be loaded with data from the database of another reporter using a `from_database()` method.
- data from a `Reporter` instance can be exported into a `HistoryPoint` list to load into an optimiser.
- compiled documentation and logo.
- `BayesianOptimisation` raises `ExhaustedSearchSpaceError` if a discrete domain is exhausted.

Changed
- minor fixes in documentation typos, argument names and tests.
- `Domain` is moved from `hypertunity.optimisation` to the `hypertunity` package.
- rename `TableReporter` to `Table` and `TensorboardReporter` to `Tensorboard`.
- `ExhaustedSearchSpaceError` is moved from `optimisation.exhastive` to `optimisation.base` module.
- `Trial` running a task from a job is now done with dict as input keyword arguments or named command line arguments.

Fixed
- bug in `BayesianOptimisation` sample conversion for nested dictionaries.
- bug in `BayesianOptimisation` type preserving between the domain and the sample value.
- bug in `Tensorboard` reporter for real intervals with integer boundaries.
- bug in `Reporter` for not using the default metric name during logging.

Added
- `Trial` a wrapper class for high-level usage, which runs the optimiser, evaluates the objective
by scheduling jobs, updates the optimiser and summarises the results.
- a `Job` from a script with command line arguments can now be run with
named arguments passed as a dictionary instead of a tuple.
- checkpointing of results on disk when calling `log()` or a `Reporter` object.
- optimisation history can now be loaded into an `Optimiser`. Example use-case would be to warm-start
`BayesianOptimisation` from the history of the quicker `RandomSearch`.

Changed
- every `Reporter` instance has a `primary_metric` attribute, which is an argument to `__init__`.

Fixed
- validation of `Domain` is not allowing for intervals with more than 2 numbers.
- minor bugs in tests.

Fixed
- `Optimiser.update()` now accepts evaluation arguments that are float, `EvaluationScore` or a dict
with metric names and floats or `EvaluationScore`s. This is valid for all optimisers.

Added
- `Job` can now be scheduled locally to run command line scripts with arguments.
- `BayesianOptimisation.run_step` can pass arguments to the backend for better customisation.

Changed
- any `Reporter` object can be fed with data from a tuple of a
`Sample` object and a score, which can be a float or an `EvaluationScore`.
- `BayesianOptimisation` optimiser can be updated with a `Sample` and
a float or `EvaluationScore` objective evaluation types.
- a discrete/categorical `Domain` is defined with a set literal instead of a tuple.
- `Job` supports running functions from within a script by specifying 'script_path::func_name'.
- `batch_size` is no more an attribute of an `Optimiser` but an argument to `run_step`.
- `minimise` is no more an attribute of `BayesianOptimisation` but an argument to `run_step`.

Added
- `Scheduler` to run jobs locally using joblib.
- `SlurmJob` and `Job` dataclasses defining the tasks to be scheduled.
- `Result` dataclass encapsulating the results from the tasks.
- `TableReporter` class for reporting results in tabular format.
- `Reporter` base class for extending reporters.

Changed
- `Base`-prefix is removed from all base classes which reside
in `base.py` modules.
- `split_by_type` is now a method of the `Domain` class.
- `Optimiser` has a `batch_size` attribute accessible as a property.

Removed
- `optimisation.bo` package has been removed. Instead a single `bo.py`
module supports the only BO backend---GPyOpt, as of now.
- prefix for the file encoding (default is utf-8).