当我使用scikit-optimize 0.7.4版来优化scikit-learn 0.23模型时:
rf = BayesSearchCV(
RandomForestClassifier(
min_samples_leaf=0.01, oob_score=True
), {
'n_estimators': Integer(30, 200),
'max_depth': Integer(10, 150),
'min_samples_split': Real(0.05, 0.3),
}, n_iter=32
)当我运行rf.fit时,它会说,
File "C:\Users\cloudy\PyCharmProjects\clixove\venv\lib\site-packages\skopt\space\space.py", line 764, in rvs
if sp_version < (0, 16):
TypeError: '<' not supported between instances of 'Version' and 'tuple'但是,当我简单地使用RandomForestClassifier并对其进行拟合时,错误不会发生。那么,如何避免这个问题呢?谢谢!
完整的回溯如下。
Traceback (most recent call last):
File "C:/Users/cloudy/PyCharmProjects/clixove/BasicML/classifier.py", line 106, in <module>
rf.fit(clf.data['X_train'], clf.data['Y_train'])
File "C:\Users\cloudy\PyCharmProjects\clixove\venv\lib\site-packages\skopt\searchcv.py", line 678, in fit
optim_result = self._step(
File "C:\Users\cloudy\PyCharmProjects\clixove\venv\lib\site-packages\skopt\searchcv.py", line 552, in _step
params = optimizer.ask(n_points=n_points)
File "C:\Users\cloudy\PyCharmProjects\clixove\venv\lib\site-packages\skopt\optimizer\optimizer.py", line 360, in ask
x = opt.ask()
File "C:\Users\cloudy\PyCharmProjects\clixove\venv\lib\site-packages\skopt\optimizer\optimizer.py", line 332, in ask
return self._ask()
File "C:\Users\cloudy\PyCharmProjects\clixove\venv\lib\site-packages\skopt\optimizer\optimizer.py", line 398, in _ask
return self.space.rvs(random_state=self.rng)[0]
File "C:\Users\cloudy\PyCharmProjects\clixove\venv\lib\site-packages\skopt\space\space.py", line 764, in rvs
if sp_version < (0, 16):
TypeError: '<' not supported between instances of 'Version' and 'tuple'发布于 2020-09-04 17:50:35
scikit learn >= 0.23的问题已在0.8.1版中修复
以下是PIP的安装:
pip install scikit-optimize==0.8.1发布于 2020-08-15 03:38:16
如果scikit learn版本在您的问题中不重要,您可以通过pip install -U scikit-learn == 0.20.3将scikit learn版本降级到‘0.20.3
发布于 2020-08-15 18:31:17
我已经解决了修改skopt/space/space.py 763-768行的问题
for dim in self.dimensions:
if sp_version < (0, 16):
columns.append(dim.rvs(n_samples=n_samples))
else:
columns.append(dim.rvs(n_samples=n_samples, random_state=rng))变成
for dim in self.dimensions:
try:
columns.append(dim.rvs(n_samples=n_samples, random_state=rng))
except:
columns.append(dim.rvs(n_samples=n_samples))https://stackoverflow.com/questions/63353842
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