In the LightGBM documentation it is stated that one can set predict_contrib=True to predict the SHAP-values.
How do we extract the SHAP-values (apart from using the shap package)?
I have tried
model = LGBM(objective="binary",is_unbalance=True,predict_contrib=True)
model.fit(X_train,y_train)
pred_shap = opt_model.predict(X_train) #Does not get SHAP-values
which does not seem to work
Shap values the LGBM way with pred_contrib=True:
from lightgbm.sklearn import LGBMClassifier
from sklearn.datasets import load_iris
X,y = load_iris(return_X_y=True)
lgbm = LGBMClassifier()
lgbm.fit(X,y)
lgbm_shap = lgbm.predict(X, pred_contrib=True)
# Shape of returned LGBM shap values: 4 features x 3 classes + 3 expected values over the training dataset
print(lgbm_shap.shape)
# 0th row of LGBM shap values for 0th feature
print(lgbm_shap[0,:4])
Output:
(150, 15)
[-0.0176954 0.50644615 5.56584344 3.43032313]
Shap values from shap:
import shap
explainer = shap.TreeExplainer(lgbm)
shap_values = explainer.shap_values(X)
# num of predicted classes
print(len(shap_values))
# shap values for 0th class for 0th row
print(shap_values[0][0])
Output:
3
array([-0.0176954 , 0.50644615, 5.56584344, 3.43032313])
Looks the same to me.