SHAP: exporting shap values from KernelExplainer to pandas dataframe

Question

I am working on a binary classification and using kernelExplainer to explain the results of my model (logistic regression).

My code is as follows

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
})

But I got the below error first. So, I updated the last line to 'shap_values': pd.DataFrame(sv).values[:,1].flatten() but I got the second error shown below

numpy.ndarray has no attribute values

ValueError: All arrays must be of the same length

wrt to datatypes, my X_train is a dataframe and sv is numpy.ndarray

I expect my output to be like as below (ignore the changes in base value. It should be constant). But the output structure is like below

Answer 1

The following will do:

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

Note the shape of sv:

sv.shape

---

(398, 30)

which means:

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