Code enters infinite loop when trying to select features

When you perform svm, because it is distance based, it makes sense to scale your feature variables, especially in your case when they are huge. you can also check out this intro to svm. Using an example dataset:

from sklearn.datasets import make_blobs
import seaborn as sns
import numpy as np
from sklearn.preprocessing import StandardScaler

Scaler =  StandardScaler()

X, y = make_blobs(n_samples=5000, centers=3, shuffle=False,random_state=42)
X = np.concatenate((X,np.random.randint(0,363175645.191632,size=(5000,35))),axis=1)
y = (y==1).astype('int')

X_scaled = Scaler.fit_transform(X)

This dataset has only 2 useful variables in the first two columns, as you can see from the plot:

plt.scatter(x=X_scaled[:,0],y=X_scaled[:,1],c=['k' if i else 'b' for i in y])

Now we run rfe on scaled data and we can see it returns the first two columns as top variables:

from sklearn.svm import SVC
from sklearn.model_selection import StratifiedKFold
from sklearn.feature_selection import RFECV

svc = SVC(kernel="linear")
rfecv = RFECV(estimator=svc, step=1, cv=StratifiedKFold(2),scoring='accuracy')
rfecv.fit(X_scaled, y)

rfecv.ranking_

array([ 1,  2, 17, 28, 33, 22, 23, 26,  6, 19, 20,  4, 10, 25,  3, 27, 11,
        8, 18,  5, 29, 14,  7, 21,  9, 13, 24, 30, 35, 31, 32, 34, 16, 36,
       37, 12, 15])