autoflow.tuner package

Submodules

autoflow.tuner module

class autoflow.tuner.Tuner(evaluator: Union[Callable, str] = 'TrainEvaluator', search_method: str = 'smac', run_limit: int = 100, initial_runs: int = 20, search_method_params: dict = <frozendict {}>, n_jobs: int = 1, exit_processes: Optional[int] = None, limit_resource: bool = True, per_run_time_limit: float = 60, per_run_memory_limit: float = 3072, time_left_for_this_task: float = None, n_jobs_in_algorithm=1, debug=False)

Bases: autoflow.utils.klass.StrSignatureMixin

Tuner if class who agent an abstract search process.

Parameters

• evaluator (callable, str) –

  evaluator is a function or callable class (implement magic method __call__) or string-indicator.

  evaluator can receive a shp(SMAC Hyper Param, ConfigSpace.ConfigurationSpace),

  and return a dict ,which contains such keys:

  • loss, you can think of it as negative reward.

  • status, a string , SUCCESS means fine, FAILED means crashed.

  As default, “TrainEvaluator” is the string-indicator of autoflow.evaluation.train_evaluator.TrainEvaluator .

• search_method (str) –

  Specific searching method, random, smac, grid are available.

  • random Random Search Algorithm,

  • grid Grid Search Algorithm,

  • smac Bayes Search by SMAC Algorithm.

• run_limit (int) – Limitation of running step.

• initial_runs (int) –

  If you choose smac algorithm,

  you should realize the SMAC algorithm has a initialize procedure,

  The algorithm needs enough initial runs to get enough experience.

  This param will be omitted if random or grid is selected.

• search_method_params (dict) – Configuration for specific search method.

• n_jobs (int) – n_jobs searching process will start.

• exit_processes (int) –

• limit_resource (bool) – If limit_resource = True, a searching trial will be killed if it use more CPU times or memory.

• per_run_time_limit (float) –

  will active if limit_resource = True.

  a searching trial will be killed if it use CPU times more than per_run_time_limit.

• per_run_memory_limit (float) –

  will active if limit_resource = True.

  a searching trial will be killed if it use memory more than per_run_memory_limit.

• time_left_for_this_task (float) –

  will active if limit_resource = True.

  a searching task will be killed if it’s totally run time more than time_left_for_this_task.

• debug (bool) –

  For debug mode.

  Exception will be re-raised if debug = True

design_initial_configs(n_jobs)

get_run_limit()

hdl2shps(hdl: Dict)

match_cs_key(step_name: str, config: Union[ConfigSpace.configuration_space.Configuration, ConfigSpace.configuration_space.ConfigurationSpace])

prepare_beam_search_configs(config_space: ConfigSpace.configuration_space.ConfigurationSpace, cs_keys: List[str], search_ranges: List[List[Any]])

run(initial_configs, evaluator_params=<frozendict {}>, instance_id='', rh_db_type='sqlite', rh_db_params=<frozendict {}>, rh_db_table_name='runhistory')

set_beam_search_result_to_cs_default(config_space: ConfigSpace.configuration_space.ConfigurationSpace, beam_result: dict)

set_data_manager(data_manager: autoflow.data_manager.DataManager)

set_hdl(hdl: Dict)

set_random_state(random_state)

set_resource_manager(resource_manager: autoflow.resource_manager.base.ResourceManager)

set_task(ml_task: autoflow.utils.ml_task.MLTask)

Module contents

class autoflow.tuner.Tuner(evaluator: Union[Callable, str] = 'TrainEvaluator', search_method: str = 'smac', run_limit: int = 100, initial_runs: int = 20, search_method_params: dict = <frozendict {}>, n_jobs: int = 1, exit_processes: Optional[int] = None, limit_resource: bool = True, per_run_time_limit: float = 60, per_run_memory_limit: float = 3072, time_left_for_this_task: float = None, n_jobs_in_algorithm=1, debug=False)

Bases: autoflow.utils.klass.StrSignatureMixin

Tuner if class who agent an abstract search process.

Parameters

• evaluator (callable, str) –

  evaluator is a function or callable class (implement magic method __call__) or string-indicator.

  evaluator can receive a shp(SMAC Hyper Param, ConfigSpace.ConfigurationSpace),

  and return a dict ,which contains such keys:

  • loss, you can think of it as negative reward.

  • status, a string , SUCCESS means fine, FAILED means crashed.

  As default, “TrainEvaluator” is the string-indicator of autoflow.evaluation.train_evaluator.TrainEvaluator .

• search_method (str) –

  Specific searching method, random, smac, grid are available.

  • random Random Search Algorithm,

  • grid Grid Search Algorithm,

  • smac Bayes Search by SMAC Algorithm.

• run_limit (int) – Limitation of running step.

• initial_runs (int) –

  If you choose smac algorithm,

  you should realize the SMAC algorithm has a initialize procedure,

  The algorithm needs enough initial runs to get enough experience.

  This param will be omitted if random or grid is selected.

• search_method_params (dict) – Configuration for specific search method.

• n_jobs (int) – n_jobs searching process will start.

• exit_processes (int) –

• limit_resource (bool) – If limit_resource = True, a searching trial will be killed if it use more CPU times or memory.

• per_run_time_limit (float) –

  will active if limit_resource = True.

  a searching trial will be killed if it use CPU times more than per_run_time_limit.

• per_run_memory_limit (float) –

  will active if limit_resource = True.

  a searching trial will be killed if it use memory more than per_run_memory_limit.

• time_left_for_this_task (float) –

  will active if limit_resource = True.

  a searching task will be killed if it’s totally run time more than time_left_for_this_task.

• debug (bool) –

  For debug mode.

  Exception will be re-raised if debug = True

design_initial_configs(n_jobs)

get_run_limit()

hdl2shps(hdl: Dict)

match_cs_key(step_name: str, config: Union[ConfigSpace.configuration_space.Configuration, ConfigSpace.configuration_space.ConfigurationSpace])

prepare_beam_search_configs(config_space: ConfigSpace.configuration_space.ConfigurationSpace, cs_keys: List[str], search_ranges: List[List[Any]])

run(initial_configs, evaluator_params=<frozendict {}>, instance_id='', rh_db_type='sqlite', rh_db_params=<frozendict {}>, rh_db_table_name='runhistory')

set_beam_search_result_to_cs_default(config_space: ConfigSpace.configuration_space.ConfigurationSpace, beam_result: dict)

set_data_manager(data_manager: autoflow.data_manager.DataManager)

set_hdl(hdl: Dict)

set_random_state(random_state)

set_resource_manager(resource_manager: autoflow.resource_manager.base.ResourceManager)

set_task(ml_task: autoflow.utils.ml_task.MLTask)