python - 随机森林多类 : what is shap_values[i]? 的 SHAP TreeExplainer

Question

我正在尝试绘制 SHAP
这是我的代码 rnd_clf是 RandomForestClassifier :

import shap 
explainer = shap.TreeExplainer(rnd_clf) 
shap_values = explainer.shap_values(X) 
shap.summary_plot(shap_values[1], X) 

我明白 shap_values[0]为负且 shap_values[1]是积极的。
但是对于多类 RandomForestClassifier 呢？我有 rnd_clf分类之一:

['Gusto','Kestrel 200 SCI Older Road Bike', 'Vilano Aluminum Road Bike 21 Speed Shimano', 'Fixie'].

我如何确定 shap_values[i] 的哪个索引对应于我输出的哪一类？

Answer 1

How do I determine which index of shap_values[i] corresponds to which class of my output?

shap_values[i]是第 i 个类的 SHAP 值。什么是第 i 个类更多的是您使用的编码模式的问题:LabelEncoder , pd.factorize ， 等等。
您可以尝试以下方法作为线索:

from sklearn.preprocessing import LabelEncoder

labels = [
    "Gusto",
    "Kestrel 200 SCI Older Road Bike",
    "Vilano Aluminum Road Bike 21 Speed Shimano",
    "Fixie",
]
le = LabelEncoder()
y = le.fit_transform(labels)
encoding_scheme = dict(zip(y, labels))
pprint(encoding_scheme)

{0: 'Fixie',
 1: 'Gusto',
 2: 'Kestrel 200 SCI Older Road Bike',
 3: 'Vilano Aluminum Road Bike 21 Speed Shimano'}

所以，例如 shap_values[3]对于这种特殊情况，适用于 'Vilano Aluminum Road Bike 21 Speed Shimano'为了进一步了解如何解释 SHAP 值，让我们为具有 100 个特征和 10 个类的多类分类准备一个合成数据集:

from sklearn.datasets import make_classification
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
from shap import TreeExplainer
from shap import summary_plot

X, y = make_classification(1000, 100, n_informative=8, n_classes=10)
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42)
print(X_train.shape)

(750, 100)

此时我们有 750 行、100 个特征和 10 个类的训练数据集。
让我们训练 RandomForestClassifier并将其提供给 TreeExplainer :

clf = RandomForestClassifier(n_estimators=100, max_depth=3)
clf.fit(X_train, y_train)
explainer = TreeExplainer(clf)
shap_values = np.array(explainer.shap_values(X_train))
print(shap_values.shape)

(10, 750, 100)

10 : number of classes. All SHAP values are organized into 10 arrays, 1 array per class.
750 : number of datapoints. We have local SHAP values per datapoint.
100 : number of features. We have SHAP value per every feature.

例如，对于 Class 3你将拥有:

print(shap_values[3].shape)

(750, 100)

750: SHAP values for every datapoint
100: SHAP value contributions for every feature

最后，您可以运行健全性检查以确保模型的真实预测与 shap 的预测相同。 .
为此，我们将 (1) 交换 shap_values 的前两个维度, (2) 将所有特征的每个类的 SHAP 值相加，(3) 将 SHAP 值添加到基值:

shap_values_ = shap_values.transpose((1,0,2))

np.allclose(
    clf.predict_proba(X_train),
    shap_values_.sum(2) + explainer.expected_value
)

True

然后你可以继续到summary_plot这将显示基于每个类的 SHAP 值的特征排名。对于第 3 类，这将是:

summary_plot(shap_values[3],X_train)

其解释如下:

• For class 3 most influential features based on SHAP contributions are 16,59,24

• For feature 15 lower values tend to result in higher SHAP values (hence higher probability of the class label)

• Features 50, 45, 48 are least influential out of 20 displayed