确定sklearn中SVM分类器的最有用特征

Question

我有一个数据集,我想在这些数据上训练我的模型.在训练之后,我需要知道SVM分类器分类中主要贡献者的特征.

对森林算法有一些称为特征重要性的东西,有什么类似的吗？

Answer 1

是的,coef_SVM分类器有属性,但它仅适用于带线性内核的 SVM .对于其他内核,它是不可能的,因为数据被内核方法转换到另一个与输入空间无关的空间,请检查解释.

from matplotlib import pyplot as plt
from sklearn import svm

def f_importances(coef, names):
    imp = coef
    imp,names = zip(*sorted(zip(imp,names)))
    plt.barh(range(len(names)), imp, align='center')
    plt.yticks(range(len(names)), names)
    plt.show()

features_names = ['input1', 'input2']
svm = svm.SVC(kernel='linear')
svm.fit(X, Y)
f_importances(svm.coef_, features_names)

函数的输出如下所示:

Answer 2

如果您使用rbf（径向基函数）kernal，则可以sklearn.inspection.permutation_importance按如下方式使用来获取特征重要性。[文档]

from sklearn.inspection import permutation_importance
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline

svc =  SVC(kernel='rbf', C=2)
svc.fit(X_train, y_train)

perm_importance = permutation_importance(svc, X_test, y_test)

feature_names = ['feature1', 'feature2', 'feature3', ...... ]
features = np.array(feature_names)

sorted_idx = perm_importance.importances_mean.argsort()
plt.barh(features[sorted_idx], perm_importance.importances_mean[sorted_idx])
plt.xlabel("Permutation Importance")

Answer 3

仅在一行代码中：

拟合 SVM 模型：

from sklearn import svm
svm = svm.SVC(gamma=0.001, C=100., kernel = 'linear')

并按如下方式实施情节：

pd.Series(abs(svm.coef_[0]), index=features.columns).nlargest(10).plot(kind='barh')

结果将是：

SVM 模型在绝对值中最有贡献的特征

Answer 4

我创建了一个也适用于 Python 3 的解决方案，并且基于 Jakub Macina 的代码片段。

from matplotlib import pyplot as plt
from sklearn import svm

def f_importances(coef, names, top=-1):
    imp = coef
    imp, names = zip(*sorted(list(zip(imp, names))))

    # Show all features
    if top == -1:
        top = len(names)

    plt.barh(range(top), imp[::-1][0:top], align='center')
    plt.yticks(range(top), names[::-1][0:top])
    plt.show()

# whatever your features are called
features_names = ['input1', 'input2', ...] 
svm = svm.SVC(kernel='linear')
svm.fit(X_train, y_train)

# Specify your top n features you want to visualize.
# You can also discard the abs() function 
# if you are interested in negative contribution of features
f_importances(abs(clf.coef_[0]), feature_names, top=10)