python - TPOT：使用 TPOTRegressor 时出现酸洗错误

Question

我有一个称为 DataFrameX和一组称为Y.

对于我的大多数模型，我会做这样的事情（只是一个例子）：

from sklearn.linear_model import LassoCV
clf = LassoCV()
score = cross_val_score(estimator = clf, X = X, y = Y, cv = KFold(n_splits = 3, random_state = 100), n_jobs = -1, \
                        scoring = "neg_mean_squared_error")
np.mean([np.sqrt(-x) for x in score])

我正在尝试以类似的方式使用 TPOT，如下所示：

from tpot import TPOTRegressor
tpot = TPOTRegressor(generations=20, population_size=100, verbosity=2)

score = cross_val_score(estimator = tpot, X = X, y = Y, cv = KFold(n_splits = 3, random_state = 100), n_jobs = -1, \
                        scoring = "neg_mean_squared_error")
np.mean([np.sqrt(-x) for x in score])

TPOT 启动但随后给我一个酸洗错误，如下所示：

PicklingError: Can't pickle <type 'instancemethod'>: it's not found as __builtin__.instancemethod

知道为什么会发生这种情况/如何让 TPOT 发挥出色吗？

谢谢！

Answer 1

如果您使用的是 Python 2，请尝试：

import dill  

这样 lambda 函数可以被腌制......为我工作......

在 Python 3 中，您可能需要：

import dill as pickle

Answer 2

尝试使用：tpot.fitted_pipeline_

from tpot import TPOTRegressor
tpot = TPOTRegressor(generations=20, population_size=100, verbosity=2)

score = cross_val_score(estimator = tpot.fitted_pipeline_, X = X, y = Y, cv = KFold(n_splits = 3, random_state = 100), n_jobs = -1, \
                        scoring = "neg_mean_squared_error")
np.mean([np.sqrt(-x) for x in score])