问题遇到的现象和发生背景
在对随机森林调参的时候，出现了TypeError: 'float' object is not subscriptable
问题相关代码，请勿粘贴截图
代码如下
import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestRegressor
import matplotlib.pyplot as plt
data = pd.read_csv("x.csv")
y = pd.read_csv("y.csv")
feature = ['surface area(approx)','surface area(grid)','volume','hydration energy(kcal/mol)','logP','refractivity','polarizability','mass']
x = data[feature]
y = y['pIC50']
train_x, test_x, train_y, test_y = train_test_split(x, y,random_state=0,test_size=7)
model = RandomForestRegressor()
model = model.fit(train_x, train_y)
score = model.score(test_x, test_y)
print('默认参数下测试集评分：')
print(score)
def black_box_function(n_estimators, min_samples_split, max_features, max_depth):
res = RandomForestRegressor(n_estimators=int(n_estimators),
min_samples_split=int(min_samples_split),
max_features=min(max_features, 0.999), # float
max_depth=int(max_depth),
random_state=0
).fit(train_x, train_y).score(test_x, test_y)
return res
#确定域空间
pbounds= {'n_estimators': (1, 250),
'min_samples_split': (2, 25),
'max_features': (0.1, 0.999),
'max_depth': (5, 25)}
from bayes_opt import BayesianOptimization
optimizer = BayesianOptimization(
f=black_box_function,
pbounds=pbounds,
verbose=2, # verbose = 1 prints only when a maximum is observed, verbose = 0 is silent
random_state=0,
)
optimizer.maximize(
init_points=5, #执行随机搜索的步数
n_iter=25, #执行贝叶斯优化的步数
)
运行结果及报错内容
| iter | target | max_depth | max_fe... | min_sa... | n_esti... |
| 1 | -0.8974 | 15.98 | 0.743 | 15.86 | 136.7 |
| 2 | -0.7852 | 13.47 | 0.6807 | 12.06 | 223.1 |
| 3 | -1.096 | 24.27 | 0.4447 | 20.21 | 132.7 |
| 4 | -1.25 | 16.36 | 0.9321 | 3.634 | 22.7 |
| 5 | -1.08 | 5.404 | 0.8485 | 19.9 | 217.6 |
StopIteration Traceback (most recent call last)
File D:\python\lib\site-packages\bayes_opt\bayesian_optimization.py:179, in BayesianOptimization.maximize(self, init_points, n_iter, acq, kappa, kappa_decay, kappa_decay_delay, xi, **gp_params)
178 try:
--> 179 x_probe = next(self._queue)
180 except StopIteration:
File D:\python\lib\site-packages\bayes_opt\bayesian_optimization.py:25, in Queue.__next__(self)
24 if self.empty:
---> 25 raise StopIteration("Queue is empty, no more objects to retrieve.")
26 obj = self._queue[0]
StopIteration: Queue is empty, no more objects to retrieve.
During handling of the above exception, another exception occurred:
TypeError Traceback (most recent call last)
Input In [9], in <cell line: 1>()
----> 1 optimizer.maximize(
2 init_points=5, #执行随机搜索的步数
3 n_iter=25, #执行贝叶斯优化的步数
4 )
File D:\python\lib\site-packages\bayes_opt\bayesian_optimization.py:182, in BayesianOptimization.maximize(self, init_points, n_iter, acq, kappa, kappa_decay, kappa_decay_delay, xi, **gp_params)
180 except StopIteration:
181 util.update_params()
--> 182 x_probe = self.suggest(util)
183 iteration += 1
185 self.probe(x_probe, lazy=False)
File D:\python\lib\site-packages\bayes_opt\bayesian_optimization.py:131, in BayesianOptimization.suggest(self, utility_function)
128 self._gp.fit(self._space.params, self._space.target)
130 # Finding argmax of the acquisition function.
--> 131 suggestion = acq_max(
132 ac=utility_function.utility,
133 gp=self._gp,
134 y_max=self._space.target.max(),
135 bounds=self._space.bounds,
136 random_state=self._random_state
137 )
139 return self._space.array_to_params(suggestion)
File D:\python\lib\site-packages\bayes_opt\util.py:65, in acq_max(ac, gp, y_max, bounds, random_state, n_warmup, n_iter)
62 continue
64 # Store it if better than previous minimum(maximum).
---> 65 if max_acq is None or -res.fun[0] >= max_acq:
66 x_max = res.x
67 max_acq = -res.fun[0]
TypeError: 'float' object is not subscriptable
我的解答思路和尝试过的方法
只出现了5组数据
