计算递归特征消除的 RFE [关闭]

Question

我有一个名为“dataset_con_enc”的数据框。

dataset_con_enc.head()
    OFFER_TYPE_PROXY    OPPLINE_PRODUCT_BU  OPPLINE_PRODUCT_FAMILY  OPP_FLAG_LED_BY_PARTNER     OPP_SOURCE          target
0   0   0   8   0   19  2   1   11  137     1   ...     0   0   5   8   13  578     1   100     100                     1

....

我尝试为特征选择做递归特征消除，所以：

# 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遇到错误：

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

你能帮我解决这个问题吗？ 谢谢你

Answer 1

明显的解决方法是输入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。