A toolset for black-box hyperparameter optimisation.
Reporter instance can be loaded with data from the database of another reporter using a from_database() method.Reporter instance can be exported into a HistoryPoint list to load into an optimiser.BayesianOptimisation raises ExhaustedSearchSpaceError if a discrete domain is exhausted.Domain is moved from hypertunity.optimisation to the hypertunity package.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.BayesianOptimisation sample conversion for nested dictionaries.BayesianOptimisation type preserving between the domain and the sample value.Tensorboard reporter for real intervals with integer boundaries.Reporter for not using the default metric name during logging.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.Job from a script with command line arguments can now be run with
named arguments passed as a dictionary instead of a tuple.log() or a Reporter object.Optimiser. Example use-case would be to warm-start
BayesianOptimisation from the history of the quicker RandomSearch.Reporter instance has a primary_metric attribute, which is an argument to __init__.Domain is not allowing for intervals with more than 2 numbers.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.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.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.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.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.optimisation.bo package has been removed. Instead a single bo.py
module supports the only BO backend---GPyOpt, as of now.