sklearn VotingClassifier with RandomizedSearchCV给出pickle错误

Question

我试图通过调整sklearn文档中给出的示例，使随机超参数搜索与sklearn的投票分类器一起使用。

我见过这个最小的工作示例，但使用我的sklearn版本，它在很多方面都有所突破。

下面是一个简单的例子：

import numpy as np
from sklearn import __version__ as skv
from sklearn.ensemble import RandomForestClassifier as RFClassi
from sklearn.ensemble import HistGradientBoostingClassifier as HGBClassi
from sklearn.tree import DecisionTreeClassifier as DTClassi
from sklearn.model_selection import RandomizedSearchCV
from sklearn.ensemble import VotingClassifier
from sklearn.datasets import load_iris
print(f"sklearn version: {skv}")
df_X, target = load_iris(return_X_y=True, as_frame=True)
ensemble = ['rf','dtree','hgb']
hy_pa_grid = {
'hgb': dict(learning_rate = list(np.linspace(0.01,0.5,10).round(3))),
'rf':dict(criterion = ['gini', 'entropy']),
'dtree':dict(criterion = ['gini', 'entropy']),
}
clfs = {'hgb' : HGBClassi(), 'rf': RFClassi(), 'dtree' : DTClassi()}
vc = VotingClassifier(estimators = clfs.items(), voting = 'soft')
params = {
f"{c}__{p}" : hy_pa_grid[c][p]
for c in ensemble
for p in hy_pa_grid[c].keys()
}
print("n".join(map(str,params.items())))
clf = RandomizedSearchCV(estimator = vc, param_distributions = params)
clf.fit(df_X,target)

我得到的输出是：

sklearn version: 1.1.3
{'rf__criterion': ['gini', 'entropy'], 'dtree__criterion': ['gini', 'entropy'], 'hgb__learning_rate': [0.01, 0.064, 0.119, 0.173, 0.228, 0.282, 0.337, 0.391, 0.446, 0.5]}
Traceback (most recent call last):
File "vc.py", line 34, in <module>
clf.fit(df_X,target)                
File "/home/USER/.local/lib/python3.8/site-packages/sklearn/model_selection/_search.py", line 789, in fit
base_estimator = clone(self.estimator)
File "/home/USER/.local/lib/python3.8/site-packages/sklearn/base.py", line 87, in clone
new_object_params[name] = clone(param, safe=False)
File "/home/USER/.local/lib/python3.8/site-packages/sklearn/base.py", line 68, in clone
return copy.deepcopy(estimator)
File "/usr/lib/python3.8/copy.py", line 161, in deepcopy
rv = reductor(4)
TypeError: cannot pickle 'dict_items' object

有什么办法绕过这个吗？我也试着用GridSearchCV来做这件事，就像在例子中一样，但我得到了同样的错误。

Answer 1

哎呀，原来问题出在中

estimators = clfs.items()

一旦我将它封装在tuple()中，使其成为一个实际的元组而不是生成器，一切都很好。