在sklearn

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。

对于森林算法有一些称为特征重要性的东西，有什么相似之处吗？

Answer 1

是的，SVM分类器有属性coef_，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explanation。

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)

该函数的输出如下所示：

Answer 2

仅一行代码：

适合SVM模型：

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

并按照以下方式执行绘图：

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

结果将是：

the most contributing features of the SVM model in absolute values

Answer 3

我创建了一个基于Jakub Macina的代码段的解决方案，该解决方案也适用于Python 3。

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)

Answer 4

如果您使用 rbf（径向基函数）核，您可以使用 sklearn.inspection.permutation_importance 如下获取特征重要性。 [source]

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")