Shap值维度在随机森林和XGBoost中不同，原因是什么？有什么方法可以解决吗？

Question

从树解释器的.shap_values(some_data)返回的SHAP值，对于XGB和随机森林的结果维度/结果不同。我已经尝试过了解原因，但似乎找不到原因或方法，也没有在任何Slundberg（SHAP专家）的教程中找到解释。

• 我是否错过了某些原因？
• 是否有一些标志可以返回像其他模型那样每类的XGB SHAP值，这不那么明显或者说我错过了吗？

下面是一些示例代码！

import xgboost.sklearn as xgb
import pandas as pd
from sklearn.ensemble import RandomForestClassifier
from sklearn.datasets import load_breast_cancer
from sklearn.model_selection import train_test_split
import shap

bc = load_breast_cancer()
cancer_df = pd.DataFrame(bc['data'], columns=bc['feature_names'])
cancer_df['target'] = bc['target']
cancer_df = cancer_df.iloc[0:50, :]
target = cancer_df['target']
cancer_df.drop(['target'], inplace=True, axis=1)

X_train, X_test, y_train, y_test = train_test_split(cancer_df, target, test_size=0.33, random_state = 42)

xg = xgb.XGBClassifier()
xg.fit(X_train, y_train)
rf = RandomForestClassifier()
rf.fit(X_train, y_train)

xg_pred = xg.predict(X_test)
rf_pred = rf.predict(X_test)

rf_explainer = shap.TreeExplainer(rf, X_train)
xg_explainer = shap.TreeExplainer(xg, X_train)

rf_vals = rf_explainer.shap_values(X_train)
xg_vals = xg_explainer.shap_values(X_train)

print('Random Forest')
print(type(rf_vals))
print(type(rf_vals[0]))
print(rf_vals[0].shape)
print(rf_vals[1].shape)

print('XGBoost')
print(type(xg_vals))
print(xg_vals.shape)

输出：

Random Forest
<class 'list'>
<class 'numpy.ndarray'>
(33, 30)
(33, 30)
XGBoost
<class 'numpy.ndarray'>
(33, 30)

- D00dood

Answer 1

针对二分类问题：

• XGBClassifier（sklearn API）的SHAP值是针对类别1的原始值（一维数据）
• RandomForestClassifier的SHAP值是适用于类别0和1的概率（二维数据）

DEMO

from xgboost import XGBClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.datasets import load_breast_cancer
from sklearn.model_selection import train_test_split
from shap import TreeExplainer
from scipy.special import expit

X, y = load_breast_cancer(return_X_y=True)
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42)

xgb = XGBClassifier(
    max_depth=5, n_estimators=100, eval_metric="logloss", use_label_encoder=False
).fit(X_train, y_train)
xgb_exp = TreeExplainer(xgb)
xgb_sv = np.array(xgb_exp.shap_values(X_test))
xgb_ev = np.array(xgb_exp.expected_value)

print("Shape of XGB SHAP values:", xgb_sv.shape)

rf = RandomForestClassifier(max_depth=5, n_estimators=100).fit(X_train, y_train)
rf_exp = TreeExplainer(rf)
rf_sv = np.array(rf_exp.shap_values(X_test))
rf_ev = np.array(rf_exp.expected_value)

print("Shape of RF SHAP values:", rf_sv.shape)

---

Shape of XGB SHAP values: (143, 30)
Shape of RF SHAP values: (2, 143, 30)

翻译如下：

解释： - XGBoost（143,30）维度： - 143：测试样本数量 - 30：特征数 - RF（2,143,30）维度： - 2：输出类别数 - 143：样本数 - 30：特征数

为了比较 xgboost 的 SHAP 值和预测的概率（以及类别），您可以尝试将 SHAP 值添加到基础（期望）值中。对于测试集中的第一个数据点，它将是：

xgb_pred = expit(xgb_sv[0,:].sum() + xgb_ev)
assert np.isclose(xgb_pred, xgb.predict_proba(X_test)[0,1])

将RF SHAP值与第0个数据点的预测概率进行比较：

rf_pred = rf_sv[1,0,:].sum() + rf_ev[1]
assert np.isclose(rf_pred, rf.predict_proba(X_test)[0,1])

注意，此分析适用于（i）sklearn API和（ii）二元分类。

- Sergey Bushmanov