So I'm performing a feature selection using SVM with the mlxtend packege. X is a dataframe with the features, y is the target variable. This is part of my code.
from sklearn.svm import SVC
from mlxtend.feature_selection import SequentialFeatureSelector as SFS
def SFFS(X, y, C_GS, gamma_GS, kernel_GS):
sfs = SFS(SVC(kernel = kernel_GS, C = C_GS, gamma = gamma_GS),
k_features = (1, num_of_features),
forward= True,
floating = False,
verbose= 2,
scoring= 'roc_auc',
#scoring= 'accuracy',
cv = 10,
n_jobs= -1
).fit(X, y)
return sfs
def SFFS_lin(X, y, C_GS, kernel_GS):
sfs = SFS(SVC(kernel = kernel_GS, C = C_GS),
k_features = (1, num_of_features),
forward= True,
floating = False,
verbose= 2,
scoring= 'roc_auc',
cv = 10,
n_jobs= -1
).fit(X, y)
return sfs
def featureNames(sfs):
Feature_Names = sfs.k_feature_names_
return Feature_Names
sfs_lin = SFFS_lin(X, y, 1,'linear')
#sfs_rbf = SFFS(X, y, 1, 'auto', 'rbf')
names = featureNames(sfs_lin)
print(names)
The code starts running, but shortly it freezes here:
[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent
workers. [Parallel(n_jobs=-1)]: Done 28 out of 28 | elapsed: 2.5s
remaining: 0.0s [Parallel(n_jobs=-1)]: Done 28 out of 28 |
elapsed: 2.5s finished
[2021-01-24 00:01:57] Features: 1/28 -- score:
0.6146428161908037[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.
When using rbf kernel, the code runs beautifully. If I change the function to perform a backward elimination by setting the forward parameter to False, it runs beautifully
forward=False,
it runs beautifully. The freezing problem seems to appear when doing forward selection with linear kernel.
Is this a stupid bug or I'm missing something trivial?
System info:
Python 3.8.5
scikit-learn 0.24.1
mlxtend 0.18.0
question from:
https://stackoverflow.com/questions/65865518/mlxtend-feature-selection-forward-selection-not-working-with-svm-linear-kernel 
