def get_suggestions(self, iteration_config=None):
"""Return a list of suggestions based on random search.
Params:
matrix: `dict` representing the {hyperparam: hyperparam matrix config}.
n_suggestions: number of suggestions to make.
"""
matrix = self.hptuning_config.matrix
n_suggestions = self.hptuning_config.random_search.n_experiments
seed = self.hptuning_config.seed
return get_random_suggestions(matrix=matrix, n_suggestions=n_suggestions, seed=seed)
def get_suggestions(self, iteration_config=None):
"""Return a list of suggestions based on random search.
Params:
matrix: `dict` representing the {hyperparam: hyperparam matrix config}.
n_suggestions: number of suggestions to make.
"""
matrix = self.hptuning_config.matrix
n_suggestions = self.hptuning_config.random_search.n_experiments
seed = self.hptuning_config.seed
return get_random_suggestions(matrix=matrix,
n_suggestions=n_suggestions,
seed=seed)
def get_suggestions(self, iteration_config=None):
"""Return a list of suggestions/arms based on hyperband."""
if not iteration_config or not isinstance(iteration_config, HyperbandIterationConfig):
raise ValueError('Hyperband get suggestions requires an iteration.')
bracket = self.get_bracket(iteration=iteration_config.iteration)
n_configs = self.get_n_configs(bracket=bracket)
n_resources = self.get_n_resources_for_iteration(
iteration=iteration_config.iteration,
bracket_iteration=iteration_config.bracket_iteration)
n_resources = self.params_config.hyperband.resource.cast_value(n_resources)
suggestion_params = {
self.params_config.hyperband.resource.name: n_resources
}
return get_random_suggestions(matrix=self.params_config.matrix,
n_suggestions=n_configs,
suggestion_params=suggestion_params,
seed=self.params_config.seed)
def get_suggestions(self, iteration_config=None):
"""Return a list of suggestions/arms based on hyperband."""
if not iteration_config or not isinstance(iteration_config, HyperbandIterationConfig):
raise ValueError('Hyperband get suggestions requires an iteration.')
bracket = self.get_bracket(iteration=iteration_config.iteration)
n_configs = self.get_n_configs(bracket=bracket)
n_resources = self.get_n_resources_for_iteration(
iteration=iteration_config.iteration,
bracket_iteration=iteration_config.bracket_iteration)
n_resources = self.hptuning_config.hyperband.resource.cast_value(n_resources)
suggestion_params = {
self.hptuning_config.hyperband.resource.name: n_resources
}
return get_random_suggestions(matrix=self.hptuning_config.matrix,
n_suggestions=n_configs,
suggestion_params=suggestion_params,
seed=self.hptuning_config.seed)
def get_suggestions(self, iteration_config=None):
if not iteration_config:
return get_random_suggestions(matrix=self.hptuning_config.matrix,
n_suggestions=self.n_initial_trials,
seed=self.hptuning_config.seed)
# Use the iteration_config to construct observed point and metrics
experiments_configs = dict(
iteration_config.combined_experiments_configs)
experiments_metrics = dict(
iteration_config.combined_experiments_metrics)
configs = []
metrics = []
for key in experiments_metrics.keys():
configs.append(experiments_configs[key])
metrics.append(experiments_metrics[key])
optimizer = BOOptimizer(hptuning_config=self.hptuning_config)
optimizer.add_observations(configs=configs, metrics=metrics)
suggestion = optimizer.get_suggestion()
return [suggestion] if suggestion else None
