5

下面是一个非常愚蠢的例子，它基本上是我真实世界用例的简化

import pandas as pd
from scipy.optimize import differential_evolution
import time

def optimizer_function(x, cost_name):
    print(cost_name)
    a = df_generator(x)
    return cost_function(a,cost_name)

def df_generator(x):
    return pd.DataFrame({"a":[x**2+3*x+8 for i in range(-int(x),int(x))]})

def cost_function(df, name):
    cost = min(df["a"])
    if name == "distance":
        cost = 2*cost
    return cost


def optimiser(criteria):
    print("start optimization")
    start = time.process_time()
    print(start)
    result = differential_evolution(func=optimizer_function, bounds=[(-100,100)], 
                                    args=(criteria))
    end = time.process_time()
    print(end)
    print(end - start)
    print("end optimization")


if __name__ == '__main__':
    optimiser("distance")
    print("all good")

我无法让它工作。我希望它能给我x最小化的值，cost_function但我收到以下错误消息：

RuntimeError：类似地图的可调用对象必须是 f(func, iterable) 的形式，返回与“可迭代”长度相同的数字序列

听起来我没有正确设置问题，或者我的函数没有返回正确类型的数据differential_evolution以正确解释，但我不知道应该在哪里更正。

欢迎任何帮助

这是堆栈

  File "H:/projects/decay-optimizer/test_diff_evo.py", line 33, in <module>
    optimiser("distance")
  File "H:/projects/decay-optimizer/test_diff_evo.py", line 25, in optimiser
    args=(criteria))
  File "H:\projects\decay-optimizer\venv\lib\site-packages\scipy\optimize\_differentialevolution.py", line 276, in differential_evolution
    ret = solver.solve()
  File "H:\projects\decay-optimizer\venv\lib\site-packages\scipy\optimize\_differentialevolution.py", line 688, in solve
    self.population)
  File "H:\projects\decay-optimizer\venv\lib\site-packages\scipy\optimize\_differentialevolution.py", line 794, in _calculate_population_energies
    raise RuntimeError("The map-like callable must be of the"
RuntimeError: The map-like callable must be of the form f(func, iterable), returning a sequence of numbers the same length as 'iterable'
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1 回答 1

3

args=(criteria,) 来自@hpaulj 的回答。

于 2020-10-08T00:50:37.987 回答