test.py

import numpy as np
import matplotlib.pyplot as plt
from sklearn.svm import SVC
from sklearn.model_selection import train_test_split
from mlxtend.evaluate import feature_importance_permutation

from sklearn.datasets import make_classification
from sklearn.ensemble import RandomForestClassifier

# Build a classification task using 3 informative features
X, y = make_classification(n_samples=10000,
                           n_features=10,
                           n_informative=3,
                           n_redundant=0,
                           n_repeated=0,
                           n_classes=2,
                           random_state=0,
                           shuffle=False)

X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.3, random_state=1, stratify=y)


svm = SVC(C=1.0, kernel='rbf')
svm.fit(X_train, y_train)

print('Training accuracy', np.mean(svm.predict(X_train) == y_train)*100)
print('Test accuracy', np.mean(svm.predict(X_test) == y_test)*100)

imp_vals, imp_all = feature_importance_permutation(
    predict_method=svm.predict, 
    X=X_test,
    y=y_test,
    metric='accuracy',
    num_rounds=10,
    seed=1)

print(f"imp_vals-> {imp_vals}")

std = np.std(imp_all, axis=1)
indices = np.argsort(imp_vals)[::-1]
print(f"indices-> {indices}")


plt.figure()
plt.title("SVM feature importance via permutation importance")
plt.bar(range(X.shape[1]), imp_vals[indices],
        yerr=std[indices])
plt.xticks(range(X.shape[1]), indices)
plt.xlim([-1, X.shape[1]])
plt.show()