mlos_bench.optimizers.manual_optimizer
======================================

.. py:module:: mlos_bench.optimizers.manual_optimizer

.. autoapi-nested-parse::

   Manual config suggester (Optimizer) for mlos_bench that proposes an explicit sequence of
   configurations.

   This is useful for testing and validation, as it allows you to run a sequence of
   configurations in a cyclic fashion.

   .. rubric:: Examples

   >>> # Load tunables from a JSON string.
   >>> # Note: normally these would be automatically loaded from the Environment(s)'s
   >>> # `include_tunables` config parameter.
   >>> #
   >>> import json5 as json
   >>> from mlos_bench.environments.status import Status
   >>> from mlos_bench.services.config_persistence import ConfigPersistenceService
   >>> service = ConfigPersistenceService()
   >>> json_config = '''
   ... {
   ...   "group_1": {
   ...     "cost": 1,
   ...     "params": {
   ...       "colors": {
   ...         "type": "categorical",
   ...         "values": ["red", "blue", "green"],
   ...         "default": "green",
   ...       },
   ...       "int_param": {
   ...         "type": "int",
   ...         "range": [1, 3],
   ...         "default": 2,
   ...       },
   ...       "float_param": {
   ...         "type": "float",
   ...         "range": [0, 1],
   ...         "default": 0.5,
   ...         // Quantize the range into 3 bins
   ...         "quantization_bins": 3,
   ...       }
   ...     }
   ...   }
   ... }
   ... '''
   >>> tunables = service.load_tunables(jsons=[json_config])
   >>> # Check the defaults:
   >>> tunables.get_param_values()
   {'colors': 'green', 'int_param': 2, 'float_param': 0.5}

   >>> # Now create a ManualOptimizer from a JSON config string.
   >>> optimizer_json_config = '''
   ... {
   ...   "class": "mlos_bench.optimizers.manual_optimizer.ManualOptimizer",
   ...   "description": "ManualOptimizer",
   ...     "config": {
   ...         "max_cycles": 3,
   ...         "tunable_values_cycle": [
   ...             {"colors": "red", "int_param": 1, "float_param": 0.0},
   ...             {"colors": "blue", "int_param": 3, "float_param": 1.0},
   ...             // special case: {} - represents the defaults, without
   ...             // having to copy them from the tunables JSON
   ...             // (which is presumably specified elsewhere)
   ...             {},
   ...         ],
   ...     }
   ... }
   ... '''
   >>> config = json.loads(optimizer_json_config)
   >>> optimizer = service.build_optimizer(
   ...   tunables=tunables,
   ...   service=service,
   ...   config=config,
   ... )
   >>> # Run the optimizer.
   >>> # Note that the cycle will repeat 3 times, as specified in the config.
   >>> while optimizer.not_converged():
   ...     suggestion = optimizer.suggest()
   ...     print(suggestion.get_param_values())
   {'colors': 'red', 'int_param': 1, 'float_param': 0.0}
   {'colors': 'blue', 'int_param': 3, 'float_param': 1.0}
   {'colors': 'green', 'int_param': 2, 'float_param': 0.5}
   {'colors': 'red', 'int_param': 1, 'float_param': 0.0}
   {'colors': 'blue', 'int_param': 3, 'float_param': 1.0}
   {'colors': 'green', 'int_param': 2, 'float_param': 0.5}
   {'colors': 'red', 'int_param': 1, 'float_param': 0.0}
   {'colors': 'blue', 'int_param': 3, 'float_param': 1.0}
   {'colors': 'green', 'int_param': 2, 'float_param': 0.5}



Classes
-------

.. autoapisummary::

   mlos_bench.optimizers.manual_optimizer.ManualOptimizer


Module Contents
---------------

.. py:class:: ManualOptimizer(tunables: mlos_bench.tunables.tunable_groups.TunableGroups, config: dict, global_config: dict | None = None, service: mlos_bench.services.base_service.Service | None = None)

   Bases: :py:obj:`mlos_bench.optimizers.mock_optimizer.MockOptimizer`


   Optimizer that proposes an explicit sequence of tunable values.

   Create a new optimizer for the given configuration space defined by the
   tunables.

   :param tunables: The tunables to optimize.
   :type tunables: TunableGroups
   :param config: Free-format key/value pairs of configuration parameters to pass to the optimizer.
   :type config: dict
   :param global_config:
   :type global_config: dict | None
   :param service:
   :type service: Service | None


   .. py:method:: suggest() -> mlos_bench.tunables.tunable_groups.TunableGroups

      Always produce the same sequence of explicit suggestions, in a cycle.



   .. py:property:: supports_preload
      :type: bool


      Return True if the optimizer supports pre-loading the data from previous
      experiments.