multiml.agent.basic package

Submodules

• multiml.agent.basic.base module
  • BaseAgent
    • BaseAgent.__init__()
    • BaseAgent.execute()
    • BaseAgent.finalize()
    • BaseAgent.execute_finalize()
    • BaseAgent.storegate
    • BaseAgent.saver
    • BaseAgent.task_scheduler
    • BaseAgent.metric
• multiml.agent.basic.connection_grid_search module
  • ConnectionGridSearchAgent
    • ConnectionGridSearchAgent.__init__()
    • ConnectionGridSearchAgent.execute()
    • ConnectionGridSearchAgent.finalize()
    • ConnectionGridSearchAgent.get_best_result()
• multiml.agent.basic.connection_random_search module
  • ConnectionRandomSearchAgent
    • ConnectionRandomSearchAgent.__init__()
    • ConnectionRandomSearchAgent.execute()
    • ConnectionRandomSearchAgent.connectiontask_name_with_jobid()
• multiml.agent.basic.grid_search module
  • GridSearchAgent
    • GridSearchAgent.__init__()
    • GridSearchAgent.execute()
• multiml.agent.basic.random_search module
  • RandomSearchAgent
    • RandomSearchAgent.__init__()
    • RandomSearchAgent.history
    • RandomSearchAgent.execute()
    • RandomSearchAgent.finalize()
    • RandomSearchAgent.execute_jobs()
    • RandomSearchAgent.execute_pool_jobs()
    • RandomSearchAgent.execute_wrapper()
• multiml.agent.basic.sequential module
  • SequentialAgent
    • SequentialAgent.__init__()
    • SequentialAgent.result
    • SequentialAgent.execute()
    • SequentialAgent.finalize()
    • SequentialAgent.execute_subtasktuples()
    • SequentialAgent.execute_pipeline()
    • SequentialAgent.execute_differentiable()

Module contents

class multiml.agent.basic.BaseAgent(saver=None, storegate=None, task_scheduler=None, metric=None, metric_args=None)

Bases: Agent

Base class of agent.

All agent class need to inherit this BaseAgent class.

__init__(saver=None, storegate=None, task_scheduler=None, metric=None, metric_args=None)

Initialize base agent.

Parameters:

• saver (Saver or str) – Saver class instance. If saver is None, Saver class instance is created without any args. If If saver is str, Saver` class instance is created with given save_dir.

• storegate (StoreGate or dict) – StoreGate class instance. If dict is given, StoreGate class instance is created with given dict args.

• task_scheduler (TaskScheduler or list) – TaskScheduler class instance. If ordered tasks (list) are given, TaskScheduler is initialized with ordered tasks. Please see TaskScheduler class for details.

• metric (str or BaseMetric) – str or Metric class instance. If str is given, Metric class is searched from multiml.agent.metric, and initialized with metric_args below.

• metric_args (dict) – arbitrary args of Metric class. This option is valid only if metric is str.

execute()

Execute base agent.

Users need to implement algorithms.

finalize()

Finalize base agent.

execute_finalize()

Execute and finalize base agent.

property storegate

Return storegate of base agent.

property saver

Return saver of base agent.

property task_scheduler

Return task_scheduler of base agent.

property metric

Return metric of base agent.

class multiml.agent.basic.SequentialAgent(differentiable=None, diff_pretrain=False, diff_task_args=None, num_trials=None, **kwargs)

Bases: BaseAgent

Agent execute sequential tasks.

Examples

>>> task0 = your_task0
>>> task1 = your_task1
>>> task2 = your_task2
>>>
>>> agent = SequentialAgent(storegate=storegate,
>>>                         task_scheduler=[task0, task1, task2],
>>>                         metric=your_metric)
>>> agent.execute()
>>> agent.finalize()

__init__(differentiable=None, diff_pretrain=False, diff_task_args=None, num_trials=None, **kwargs)

Initialize sequential agent.

Parameters:

• differentiable (str) – keras or pytorch. If differentiable is given, ConnectionTask() is created based on sequential tasks. If differentiable is None (default), sequential tasks are executed step by step.

• diff_pretrain (bool) – If True, each subtask is trained before creating ConnectionTask()`.

• diff_task_args (dict) – arbitrary args passed to ConnectionTask().

• num_trials (ine) – number of trials. Average value of trials is used as final metric.

property result

Return result of execution.

execute()

Execute sequential agent.

finalize()

Finalize sequential agent.

execute_subtasktuples(subtasktuples, counter)

Execute given subtasktuples.

execute_pipeline(subtasktuples, counter, trial=None)

Execute pipeline.

execute_differentiable(subtasktuples, counter, trial=None)

Execute connection model.

class multiml.agent.basic.RandomSearchAgent(samplings=None, seed=0, metric_type=None, num_workers=None, context='spawn', dump_all_results=False, disable_tqdm=True, **kwargs)

Bases: SequentialAgent

Agent executing random search..

__init__(samplings=None, seed=0, metric_type=None, num_workers=None, context='spawn', dump_all_results=False, disable_tqdm=True, **kwargs)

Initialize simple agent.

Parameters:

• samplings (int or list) – If int, number of random samplings. If list, indexes of combination.

• seed (int) – seed of random samplings.

• metric_type (str) – ‘min’ or ‘max’ for indicating direction of metric optimization. If it is None, type is retrieved from metric class instance.

• num_workers (int or list) – number of workers for multiprocessing or lsit of GPU ids. If num_workers is given, multiprocessing is enabled.

• context (str) – fork (default) or spawn.

• dump_all_results (bool) – dump all results or not.

• disable_tqdm (bool) – enable tqdm bar.

property history

Return history of execution.

execute()

Execute simple agent.

finalize()

Finalize grid scan agent.

execute_jobs(ctx, queue, args)

(expert method) Execute multiprocessing jobs.

execute_pool_jobs(ctx, queue, args)

(expert method) Execute multiprocessing pool jobs.

execute_wrapper(queue, subtasktuples, counter, cuda_id)

(expert method) Wrapper method to execute multiprocessing pipeline.

class multiml.agent.basic.GridSearchAgent(**kwargs)

Bases: RandomSearchAgent

Agent scanning all possible subtasks and hyper parameters.

__init__(**kwargs)

Initialize grid scan agent.

Parameters:

kwargs (dict) – arbitrary kwargs passed to RandomSearchAgent class.

execute()

Execute grid scan agent.

class multiml.agent.basic.ConnectionRandomSearchAgent(freeze_model_weights=False, do_pretraining=True, connectiontask_name=None, connectiontask_args={}, **kwargs)

Bases: RandomSearchAgent

Agent executing with only one possible hyperparameter combination.

__init__(freeze_model_weights=False, do_pretraining=True, connectiontask_name=None, connectiontask_args={}, **kwargs)

Parameters:

• freeze_model_weights (bool) – Fix model trainable weights after pre-training

• do_pretraining (bool) – run pre-training before model connection

• connectiontask_name (str) – Task name for ModelConnectionTask

• connectiontask_args (dict) – args for ModelConnectionTask

• **kwargs – Arbitrary keyword arguments

execute()

Execute.

connectiontask_name_with_jobid(job_id)

Returns a formatted name for ConnectionTask. job_id is used as a suffix.

Returns:

formatted name for the ConnectionTask

Return type:

str

class multiml.agent.basic.ConnectionGridSearchAgent(reuse_pretraining=False, **kwargs)

Bases: GridSearchAgent, ConnectionRandomSearchAgent

Agent executing with all possible hyperparameter combination.

__init__(reuse_pretraining=False, **kwargs)

Initialize.

Parameters:

• reuse_pretraining (bool) – Use common model weights for pretraining

• **kwargs – Arbitrary keyword arguments

execute()

Execute.

finalize()

Finalize.

get_best_result()

Returns the best combination as a result of agent execution.

Returns:

best result dict: auxiliary value of the best result

Return type:

dict