Python 如何将 SHAP 本地解释导出到数据框

如果你有这样的模型:

import xgboost
import shap
import warnings
warnings.filterwarnings("ignore")

# train XGBoost model
X,y = shap.datasets.boston()
model = xgboost.XGBRegressor().fit(X, y)

# explain the model's predictions using SHAP values
# (same syntax works for LightGBM, CatBoost, and scikit-learn models)
background = shap.maskers.Independent(X, max_samples=100)
explainer = shap.Explainer(model, background, algorithm="tree")
sv = explainer(X)

你可以这样分解你的结果:

sv.base_values[0]

22.342787810446044

sv.values[0]

array([-7.68297079e-01, -4.38205232e-02,  3.46814548e-01, -4.06731364e-03,
       -3.17875379e-01, -5.37296545e-01,  2.68567768e-01, -1.30198611e+00,
       -4.83524088e-01, -4.39375216e-01,  2.94188969e-01,  2.43096180e-02,
        4.63890554e+00])

model.predict(X.iloc[[0]])

array([24.019339], dtype=float32)

这正好等于:

sv.base_values[0] + sum(sv.values[0])

24.01933200249436

如果你想把结果放在数据库中:

pd.DataFrame(sv.values[0], index = X.columns)

         0
CRIM    -0.768297
ZN      -0.043821
INDUS    0.346815
CHAS    -0.004067
NOX     -0.317875
RM      -0.537297
AGE      0.268568
DIS     -1.301986
RAD     -0.483524
TAX     -0.439375
PTRATIO  0.294189
B        0.024310
LSTAT    4.638906

或者，如果您希望一切按行排列:

pd.DataFrame(
    np.c_[sv.base_values, sv.values],
    columns = ["bv"] + list(X.columns)
)