Scikit learn 递归特征消除的RFE计算

我有一个名为“dataset\u con\u enc”的数据帧

我尝试对特征选择进行递归特征消除，因此：

# Load libraries
from sklearn.datasets import make_regression
from sklearn.feature_selection import RFECV
from sklearn import datasets, linear_model
import warnings

# Suppress an annoying but harmless warning
warnings.filterwarnings(action="ignore", module="scipy", message="^internal gelsd")
# Calculating RFE for non-discretised dataset, and graphing the Importance for each feature, per dataset
selector1 = RFECV(LogisticRegression(), step=1, cv=5, n_jobs=-1)
selector1 = selector1.fit(dataset_con_enc.drop('target', axis=1).values, dataset_con_enc['target'].values)

但我在最后一行代码中得到一个错误：

ImportError                               Traceback (most recent call last)
<ipython-input-509-5e50f1655a89> in <module>()
      3 # Calculating RFE for non-discretised dataset, and graphing the Importance for each feature, per dataset
      4 selector1 = RFECV(LogisticRegression(), step=1, cv=5, n_jobs=-1)
----> 5 selector1 = selector1.fit(dataset_con_enc.drop('target', axis=1).values, dataset_con_enc['target'].values)

~\AppData\Local\Continuum\anaconda3\lib\site-packages\sklearn\feature_selection\rfe.py in fit(self, X, y)
    434         scores = parallel(
    435             func(rfe, self.estimator, X, y, train, test, scorer)
--> 436             for train, test in cv.split(X, y))
    437 
    438         scores = np.sum(scores, axis=0)

~\AppData\Local\Continuum\anaconda3\lib\site-packages\sklearn\externals\joblib\parallel.py in __call__(self, iterable)
    747         self._aborting = False
    748         if not self._managed_backend:
--> 749             n_jobs = self._initialize_backend()
    750         else:
    751             n_jobs = self._effective_n_jobs()

~\AppData\Local\Continuum\anaconda3\lib\site-packages\sklearn\externals\joblib\parallel.py in _initialize_backend(self)
    545         try:
    546             n_jobs = self._backend.configure(n_jobs=self.n_jobs, parallel=self,
--> 547                                              **self._backend_args)
    548             if self.timeout is not None and not self._backend.supports_timeout:
    549                 warnings.warn(

~\AppData\Local\Continuum\anaconda3\lib\site-packages\sklearn\externals\joblib\_parallel_backends.py in configure(self, n_jobs, parallel, **backend_args)
    303         if already_forked:
    304             raise ImportError(
--> 305                 '[joblib] Attempting to do parallel computing '
    306                 'without protecting your import on a system that does '
    307                 'not support forking. To use parallel-computing in a '

ImportError: [joblib] Attempting to do parallel computing without protecting your import on a system that does not support forking. To use parallel-computing in a script, you must protect your main loop using "if __name__ == '__main__'". Please see the joblib documentation on Parallel for more information

ImportError回溯（最近一次调用）
在（）
3#计算非离散数据集的RFE，并绘制每个数据集每个特征的重要性
4选择器1=RFECV（逻辑回归（），步骤=1，cv=5，n_作业=-1）
---->5 selector1=selector1.fit（数据集\u con\u enc.drop（'target'，axis=1）。值，数据集\u con\u enc['target']。值）
~\AppData\Local\Continuum\anaconda3\lib\site packages\sklearn\feature\u selection\rfe.py合适（self，X，y）
434分=平行(
435 func（rfe、自估计器、X、y、训练、测试、记分器）
-->436对于列车，在等速拆分（X，y））中进行试验
437
438分=总分（分，轴=0）
调用中的~\AppData\Local\Continuum\anaconda3\lib\site packages\sklearn\externals\joblib\parallel.py（self，iterable）
747自动终止=错误
748如果不是自我管理的后端：
-->749 n_jobs=self._initialize_backend（）
750其他：
751 n_jobs=自我有效的n_jobs（）
~\AppData\Local\Continuum\anaconda3\lib\site packages\sklearn\externals\joblib\parallel.py in\u initialize\u backend（self）
545尝试：
546 n_jobs=self.\u backend.configure（n_jobs=self.n_jobs，parallel=self，
-->547**self.\u后端\u参数）
548如果self.timeout不是None且不是self.\u backend.supports\u timeout：
549.warn(
配置中的~\AppData\Local\Continuum\anaconda3\lib\site packages\sklearn\externals\joblib\\u parallel\u backends.py（self，n\u jobs，parallel，**backend\u args）
303如果已经分叉：
304引起恐怖(
-->305“[joblib]正在尝试进行并行计算”
306“在系统上不保护您的导入”
307“不支持分叉。要在
ImportError:[joblib]试图在不支持分叉的系统上执行并行计算而不保护您的导入。若要在脚本中使用并行计算，您必须使用“if _uname_uu=='_umain_uuu'”来保护主循环。有关详细信息，请参阅关于并行的joblib文档

你能帮我解决这个问题吗？

---

谢谢

显而易见的解决办法是将

n_jobs=1

（禁用并行计算）---但我不确定这是否是您正在寻找的解决方案。

您是如何运行上述代码的？我可以使用我的数据集在python2和python3中从终端和spyder运行代码，没有任何错误。请尝试将库更新到最新版本，谢谢，它可以工作

ImportError                               Traceback (most recent call last)
<ipython-input-509-5e50f1655a89> in <module>()
      3 # Calculating RFE for non-discretised dataset, and graphing the Importance for each feature, per dataset
      4 selector1 = RFECV(LogisticRegression(), step=1, cv=5, n_jobs=-1)
----> 5 selector1 = selector1.fit(dataset_con_enc.drop('target', axis=1).values, dataset_con_enc['target'].values)

~\AppData\Local\Continuum\anaconda3\lib\site-packages\sklearn\feature_selection\rfe.py in fit(self, X, y)
    434         scores = parallel(
    435             func(rfe, self.estimator, X, y, train, test, scorer)
--> 436             for train, test in cv.split(X, y))
    437 
    438         scores = np.sum(scores, axis=0)

~\AppData\Local\Continuum\anaconda3\lib\site-packages\sklearn\externals\joblib\parallel.py in __call__(self, iterable)
    747         self._aborting = False
    748         if not self._managed_backend:
--> 749             n_jobs = self._initialize_backend()
    750         else:
    751             n_jobs = self._effective_n_jobs()

~\AppData\Local\Continuum\anaconda3\lib\site-packages\sklearn\externals\joblib\parallel.py in _initialize_backend(self)
    545         try:
    546             n_jobs = self._backend.configure(n_jobs=self.n_jobs, parallel=self,
--> 547                                              **self._backend_args)
    548             if self.timeout is not None and not self._backend.supports_timeout:
    549                 warnings.warn(

~\AppData\Local\Continuum\anaconda3\lib\site-packages\sklearn\externals\joblib\_parallel_backends.py in configure(self, n_jobs, parallel, **backend_args)
    303         if already_forked:
    304             raise ImportError(
--> 305                 '[joblib] Attempting to do parallel computing '
    306                 'without protecting your import on a system that does '
    307                 'not support forking. To use parallel-computing in a '

ImportError: [joblib] Attempting to do parallel computing without protecting your import on a system that does not support forking. To use parallel-computing in a script, you must protect your main loop using "if __name__ == '__main__'". Please see the joblib documentation on Parallel for more information