Tuner¶

class pytorch_wrapper.tuner.AbstractTuner(hyper_parameter_generators, algorithm, fit_iterations)¶

Bases: abc.ABC

Objects of derived classes are used to tune a model using the Hyperopt library.

Parameters:	hyper_parameter_generators – Dict containing a hyperopt hyper-parameter generator for each hyper-parameter (e.g. {‘batch_size’: hp.choice(‘batch_size’, [32, 64])}) algorithm – Hyperopt’s tuning algorithm (e.g. hyperopt.rand.suggest, hyperopt.tpe.suggest). fit_iterations – Number of trials.

run(trials_load_path=None, trials_save_path=None)¶

Initiates the tuning algorithm.

Parameters:	trials_load_path – Path of a Trials object to load at the beginning of the tuning algorithm. If None the tuning algorithm will start from scratch. trials_save_path – Path where to save the Trials object after each iteration. If None the Trials object will not be saved.
Returns:	A sorted list of tuples [ (loss, {parameters}), … ].

class pytorch_wrapper.tuner.Tuner(hyper_parameter_generators, step_function, algorithm, fit_iterations)¶

Objects of this class are used to tune a model using the Hyperopt library.

Parameters:

hyper_parameter_generators – Dict containing a hyperopt hyper-parameter generator for each hyper-parameter (e.g. {‘batch_size’: hp.choice(‘batch_size’, [32, 64])})
step_function – callable that creates and evaluates a model using the provided hyper-parameters. A dict will be provided as an argument containing the chosen hyper-parameters for the current iteration. The key for each hyper-parameter is the same as its corresponding generator. It must return a numeric value representing the loss of the current iteration.
algorithm – Hyperopt’s tuning algorithm (e.g. hyperopt.rand.suggest, hyperopt.tpe.suggest).
fit_iterations – Number of trials.