evolve.py 文件源码

def _to_param_meta(param_grid, control):
    '''Acquire parameter metadata such as bounds that are useful for sampling'''
    choice_params = {k: v for k, v in param_grid.items()
                     if not hasattr(v, 'rvs')}
    distributions = {k: v for k, v in param_grid.items()
                     if k not in choice_params}
    pg_list = list(ParameterGrid(choice_params))
    choices, low, high, param_order, is_int = [], [], [], [], []
    is_continuous = lambda v: isinstance(v, numbers.Real)
    while len(pg_list):
        pg2 = pg_list.pop(0)
        for k, v in pg2.items():
            if k in param_order:
                idx = param_order.index(k)
            else:
                idx = len(param_order)
                param_order.append(k)
                low.append(v)
                high.append(v)
                choices.append([v])
                is_int.append(not is_continuous(v))
                continue
            if v not in choices[idx]:
                choices[idx].append(v)
            if is_continuous(v):
                is_int[idx] = False
                if v < low[idx]:
                    low[idx] = v
                if v > high[idx]:
                    high[idx] = v
            else:
                is_int[idx] = True
                low[idx] = high[idx] = v

    for k, v in distributions.items():
        choices.append(v)
        low.append(None)
        high.append(None)
        is_int.append(False)
        param_order.append(k)
    param_meta = dict(control=control, high=high, low=low,
                      choices=choices, is_int=is_int,
                      param_order=param_order)
    return param_meta