def config_generator(search_space, max_search, rng, shuffle=True):
        """Generates config dicts from the given search space

        Args:
            search_space: (dict) A dictionary of parameters to search over.
                See note below for more details.
            max_search: (int) The maximum number of configurations to search.
                If max_search is None, do a full grid search of all discrete
                    parameters, filling in range parameters as needed.
                Otherwise, do a full grid search of all discrete
                    parameters and then cycle through again filling in new
                    range parameters values; if there are no range parameters,
                    stop after yielding the full cross product of parameters
                    once.
            shuffle: (bool) If True, shuffle the order of generated configs

        Yields:
            configs: each config is a dict of parameter values based on the
                provided search space

        The search_space dictionary may consist of two types of parameters:
            --discrete: a discrete parameter is either a single value or a
                list of values. Use single values, for example, to override
                a default model parameter or set a flag such as 'verbose'=True.
            --range: a range parameter is a dict of the form:
                {'range': [<min>, <max>], 'scale': <scale>}
                where <min> and <max> are the min/max values to search between
                and scale is one of ['linear', 'log'] (defaulting to 'linear')
                representing the scale to use when searching the given range

        Example:
            search_space = {
                'verbose': True,                              # discrete
                'n_epochs': 100,                              # discrete
                'momentum': [0.0, 0.9, 0.99],                       # discrete
                'l2': {'range': [0.0001, 10]}                 # linear range
                'lr': {'range': [0.001, 1], 'scale': 'log'},  # log range
            }
            If max_search is None, this will return 3 configurations (enough to
                just cover the full cross-product of discrete values, filled
                in with sampled range values)
            Otherewise, this will return max_search configurations
                (cycling through the discrete value combinations multiple
                time if necessary)
        """

        def dict_product(d):
            keys = d.keys()
            for element in product(*d.values()):
                yield dict(zip(keys, element))

        def range_param_func(v):
            scale = v.get("scale", "linear")
            mini = min(v["range"])
            maxi = max(v["range"])
            if scale == "linear":
                func = lambda rand: mini + (maxi - mini) * rand
            elif scale == "log":
                mini = np.log(mini)
                maxi = np.log(maxi)
                func = lambda rand: np.exp(mini + (maxi - mini) * rand)
            else:
                raise ValueError(f"Unrecognized scale '{scale}' for " "parameter {k}")
            return func

        discretes = {}
        ranges = {}
        for k, v in search_space.items():
            if isinstance(v, dict):
                ranges[k] = range_param_func(v)
            elif isinstance(v, list):
                discretes[k] = v
            else:
                discretes[k] = [v]

        discrete_configs = list(dict_product(discretes))

        if shuffle:
            rng.shuffle(discrete_configs)

        # If there are range parameters and a non-None max_search, cycle
        # through the discrete_configs (with new range values) until
        # max_search is met
        if ranges and max_search:
            discrete_configs = cycle(discrete_configs)

        for i, config in enumerate(discrete_configs):
            # We may see the same config twice due to cycle
            config = config.copy()
            if max_search and i == max_search:
                break
            for k, v in ranges.items():
                config[k] = float(v(rng.random()))
            yield config
def config_generator ( search_space , max_search , rng , shuffle = True ) : def dict_product ( d ) : keys = d . keys ( ) for element in product ( * d . values ( ) ) : yield dict ( zip ( keys , element ) ) def range_param_func ( v ) : scale = v . get ( "scale" , "linear" ) mini = min ( v [ "range" ] ) maxi = max ( v [ "range" ] ) if scale == "linear" : func = lambda rand : mini + ( maxi - mini ) * rand elif scale == "log" : mini = np . log ( mini ) maxi = np . log ( maxi ) func = lambda rand : np . exp ( mini + ( maxi - mini ) * rand ) else : raise ValueError ( f"Unrecognized scale '{scale}' for " "parameter {k}" ) return func discretes = { } ranges = { } for k , v in search_space . items ( ) : if isinstance ( v , dict ) : ranges [ k ] = range_param_func ( v ) elif isinstance ( v , list ) : discretes [ k ] = v else : discretes [ k ] = [ v ] discrete_configs = list ( dict_product ( discretes ) ) if shuffle : rng . shuffle ( discrete_configs ) if ranges and max_search : discrete_configs = cycle ( discrete_configs ) for i , config in enumerate ( discrete_configs ) : config = config . copy ( ) if max_search and i == max_search : break for k , v in ranges . items ( ) : config [ k ] = float ( v ( rng . random ( ) ) ) yield config
Generates config dicts from the given search space

        Args:
            search_space: (dict) A dictionary of parameters to search over.
                See note below for more details.
            max_search: (int) The maximum number of configurations to search.
                If max_search is None, do a full grid search of all discrete
                    parameters, filling in range parameters as needed.
                Otherwise, do a full grid search of all discrete
                    parameters and then cycle through again filling in new
                    range parameters values; if there are no range parameters,
                    stop after yielding the full cross product of parameters
                    once.
            shuffle: (bool) If True, shuffle the order of generated configs

        Yields:
            configs: each config is a dict of parameter values based on the
                provided search space

        The search_space dictionary may consist of two types of parameters:
            --discrete: a discrete parameter is either a single value or a
                list of values. Use single values, for example, to override
                a default model parameter or set a flag such as 'verbose'=True.
            --range: a range parameter is a dict of the form:
                {'range': [<min>, <max>], 'scale': <scale>}
                where <min> and <max> are the min/max values to search between
                and scale is one of ['linear', 'log'] (defaulting to 'linear')
                representing the scale to use when searching the given range

        Example:
            search_space = {
                'verbose': True,                              # discrete
                'n_epochs': 100,                              # discrete
                'momentum': [0.0, 0.9, 0.99],                       # discrete
                'l2': {'range': [0.0001, 10]}                 # linear range
                'lr': {'range': [0.001, 1], 'scale': 'log'},  # log range
            }
            If max_search is None, this will return 3 configurations (enough to
                just cover the full cross-product of discrete values, filled
                in with sampled range values)
            Otherewise, this will return max_search configurations
                (cycling through the discrete value combinations multiple
                time if necessary)