如何使用Shap从ML模型中提取最重要的功能 - 为什么我的所有列名称为空？

Question

我想使用Shap在模型中找到最重要的功能。

我有此代码：

from sklearn.model_selection import train_test_split
from sklearn.datasets import load_breast_cancer
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import KFold
import shap
import pandas as pd
import numpy as np


#loading and preparing the data
iris = load_breast_cancer()
X = iris.data
y = iris.target
columns = iris.feature_names
#if you don't shuffle you wont need to keep track of test_index, but I think 
#it is always good practice to shuffle your data
kf = KFold(n_splits=2,shuffle=True)

list_shap_values = list()
list_test_sets = list()
for train_index, test_index in kf.split(X):
    X_train, X_test = X[train_index], X[test_index]
    y_train, y_test = y[train_index], y[test_index]
    X_train = pd.DataFrame(X_train,columns=columns)
    X_test = pd.DataFrame(X_test,columns=columns)

    #training model
    clf = RandomForestClassifier(random_state=0)
    clf.fit(X_train, y_train)

    #explaining model
    explainer = shap.TreeExplainer(clf)
    shap_values = explainer.shap_values(X_test)
    #for each iteration we save the test_set index and the shap_values
    list_shap_values.append(shap_values)
    list_test_sets.append(test_index)


#combining results from all iterations
test_set = list_test_sets[0]
shap_values = np.array(list_shap_values[0])
for i in range(1,len(list_test_sets)):
    test_set = np.concatenate((test_set,list_test_sets[i]),axis=0)
    shap_values = np.concatenate((shap_values,np.array(list_shap_values[i])),axis=1)
#bringing back variable names    
X_test = pd.DataFrame(X[test_set],columns=columns)

#creating explanation plot for the whole experiment, the first dimension from shap_values indicate the class we are predicting (0=0, 1=1)
#shap.summary_plot(shap_values[1], X_test)

shap_sum = np.abs(shap_values).mean(axis=0)
#columns = full_X_train.columns
X_test = pd.DataFrame(X[test_set],columns=columns)
importance_df = pd.DataFrame([X_test.columns.tolist(),shap_sum.tolist()]).T
importance_df.columns = ['column_name','shap_importance']
importance_df = importance_df.sort_values('shap_importance',ascending=False)
print(importance_df)

输出是：

390        None  [0.07973283098297632, 0.012745693741197047, 0....
477        None  [0.07639585953247056, 0.012705549054148915, 0....
542        None  [0.07263038600009886, 0.004509187889530952, 0....
359        None  [0.07006782821092902, 0.008022265024270826, 0....
292        None  [0.06501143916982145, 0.014648801487419996, 0....
..          ...                                                ...
129        None  [0.001207252383050206, 0.005154096692481416, 0...
68         None  [0.000537261423323933, 0.000554437257101772, 0...
229        None  [0.00046312350178067416, 0.0171676941721087, 0...
94         None  [0.00016002701188627102, 0.015384623641506117,...
97         None  [0.0001434577248065334, 0.01162161896706629, 0...

这是不正确的，列的名称全部不正确，而且我不清楚塑形值是什么（我期望每个列的数字从顶部最重要的功能排名正在打印的内容 - 不是列表）。

我希望能有更多类似的东西：

Column      Shap value
Age         0.3 
Gender      0.2

有人可以向我展示我出错的地方，以及如何使用此方法列出我的模型的重要功能？

Answer 1

如果您检查：

shap_values.shape

---

(2, 569, 30)

并且

X_test.shape

---

(569, 30)

您会发现最后2个轴上的一个令人惊讶的巧合，这不是偶然发生的：

1. shap值中的第一个轴是类别的
2. 第二个轴，用于数据集中的数据集数量，您在第三轴上迭代了
3. 第三个轴对于实际的形状值。

然后，问一个问题“平均而言，最有影响力的功能是由沙普利贡献所判断的最有影响力的功能”，您会得到：

sv = np.abs(shap_values[1,:,:]).mean(0)
cols = X_test.columns
importance_df = pd.DataFrame({
    "column_name": cols,
    "shap_values": sv
})
#expected result
importance_df.sort_values("shap_values", ascending=False)

---

    column_name shap_values
22  worst perimeter 0.078686
27  worst concave points    0.059845
20  worst radius    0.051551
23  worst area  0.044879
7   mean concave points 0.042114
6   mean concavity  0.033258
2   mean perimeter  0.032346
...

<

shap.plots.bar(shap.Explanation(shap_values[1], feature_names=X_test.columns))

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