def __init__()

in bayesmark/builtin_opt/opentuner_optimizer.py [0:0]

• 29 lines of code
• 2 McCabe index (conditional complexity)

    def __init__(self, api_config, techniques=DEFAULT_TECHNIQUES, n_suggestions=1):
        """Build wrapper class to use opentuner optimizer in benchmark.

        Parameters
        ----------
        api_config : dict-like of dict-like
            Configuration of the optimization variables. See API description.

        techniques : iterable of strings
            A list or tuple of techniques to use in opentuner. If the list
            has only one technique, then that technique will be used. If the
            list has multiple techniques a bandit over those techniques
            will be used.

        n_suggestions : int
            Default number of suggestions to be made in parallel.
        """
        AbstractOptimizer.__init__(self, api_config)

        # Opentuner requires DesiredResult to reference suggestion when making
        # its observation. x_to_dr maps the dict suggestion to DesiredResult.
        self.x_to_dr = {}
        # Keep last suggested x and repeat it whenever opentuner gives up.
        self.dummy_suggest = None

        """Setting up the arguments for opentuner. You can see all possible
        arguments using:
        ```
        >>> import opentuner
        >>> opentuner.default_argparser().parse_args(['-h'])
        ```
        We only change a few arguments (other arguments are set to defaults):
        * database = MEMORY_ONLY_DB: to use an in-memory sqlite database
        * parallelism = n_suggestions: num of suggestions to give in parallel
        * technique = techniques: a list of techniques to be used by opentuner
        * print_params = False: to avoid opentuner from exiting after printing
            param spaces
        """
        args = Namespace(
            bail_threshold=500,
            database=MEMORY_ONLY_DB,
            display_frequency=10,
            generate_bandit_technique=False,
            label=None,
            list_techniques=False,
            machine_class=None,
            no_dups=False,
            parallel_compile=False,
            parallelism=n_suggestions,
            pipelining=0,
            print_params=False,
            print_search_space_size=False,
            quiet=False,
            results_log=None,
            results_log_details=None,
            seed_configuration=[],
            stop_after=None,
            technique=techniques,
            test_limit=5000,
        )

        # Setup some dummy classes required by opentuner to actually run.
        manipulator = OpentunerOptimizer.build_manipulator(api_config)
        interface = DMI(args=args, manipulator=manipulator)
        self.api = TuningRunManager(interface, args)