Release 1.0.1

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 (#43).
• issue with tensorflow version comparison in the tensorboard reporter.

Release 1.0.0

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.

Release 0.4.0

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.

Release 0.3.1

Fixed:

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

Release 0.3.0

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.

Release 0.2.0

Features

• 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).

Release 0.1.0

Features

• Support for discrete, continuous and categorical variables
• Bayesian Optimisation using GP regression, powered by GPyOpt
• Native support for random search
• Native support for grid search
• Tensorboard HParams visualisation