我正在研究二元分類并kernelExplainer用來解釋我的模型的結果（邏輯回歸）。
我的代碼如下
X_train, X_test, y_train, y_test = train_test_split(X, y,test_size=0.30, random_state=42)
lr = LogisticRegression() # fit and predict statements not shown
masker = Independent(X_train, max_samples=100)
explainer = KernelExplainer(lr.predict,X_train)
bv = explainer.expected_value
sv = explainer.shap_values(X_train)
sdf_train = pd.DataFrame({
'row_id': X_train.index.values.repeat(X_train.shape[1]),
'feature': X_train.columns.to_list() * X_train.shape[0],
'feature_value': X_train.values.flatten(),
'base_value': bv,
'shap_values': sv.values[:,:,1].flatten() #error here I guess
})
但我首先得到了以下錯誤。因此，我將最后一行更新為，'shap_values': pd.DataFrame(sv).values[:,1].flatten()但出現如下所示的第二個錯誤
numpy.ndarray 沒有屬性值
ValueError：所有陣列的長度必須相同
對于資料型別，我X_train是一個資料框，并且sv是numpy.ndarray
我希望我的輸出如下所示（忽略基值的變化。它應該是恒定的）。但輸出結構如下
uj5u.com熱心網友回復：
以下將做：
from sklearn.linear_model import LogisticRegression
from sklearn.datasets import load_breast_cancer
from sklearn.model_selection import train_test_split
from shap import KernelExplainer
from shap import sample
X, y = load_breast_cancer(return_X_y=True, as_frame=True)
X_train, X_test, y_train, y_test = train_test_split(X, y,test_size=0.30, random_state=42)
lr = LogisticRegression(max_iter=10000).fit(X_train, y_train)
background = sample(X_train, 100)
explainer = KernelExplainer(lr.predict, background)
sv = explainer.shap_values(X_train)
bv = explainer.expected_value
注意 sv 的形狀：
sv.shape
(398, 30)
意思是：
sdf_train = pd.DataFrame({
'row_id': X_train.index.values.repeat(X_train.shape[1]),
'feature': X_train.columns.to_list() * X_train.shape[0],
'feature_value': X_train.values.flatten(),
'base_value': bv,
'shap_values': sv.flatten() #error here I guess
})
sdf_train
row_id feature feature_value base_value shap_values
0 149 mean radius 13.74000 0.67 0.000000
1 149 mean texture 17.91000 0.67 -0.014988
2 149 mean perimeter 88.12000 0.67 0.060759
3 149 mean area 585.00000 0.67 0.028677
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