Calculation of expected_value in SHAP explanations of XGBoost Classifier

How do we make sense of SHAP explainer.expected_value? Why is it not the same with y_train.mean() after sigmoid transformation?

Below is a summary of the code for quick reference. Full code available in this notebook: https://github.com/MenaWANG/ML_toy_examples/blob/main/explain%20models/shap_XGB_classification.ipynb

model = xgb.XGBClassifier()
model.fit(X_train, y_train)
explainer = shap.Explainer(model)
shap_test = explainer(X_test)
shap_df = pd.DataFrame(shap_test.values)

#For each case, if we add up shap values across all features plus the expected value, we can get the margin for that case, which then can be transformed to return the predicted prob for that case:
np.isclose(model.predict(X_test, output_margin=True),explainer.expected_value + shap_df.sum(axis=1))
#True

But why isn't the below true? Why after sigmoid transformation, the explainer.expected_value is not the same with y_train.mean() for XGBoost classifiers?

expit(explainer.expected_value) == y_train.mean()
#False

SHAP is guaranteed to be additive in raw space (logits). To understand why additivity in raw scores doesn't extend to additivity in class predictions you may think for a while why exp(x+y) != exp(x) + exp(y)

Re: Just keen to understand how was explainer.expected_value calculated for XGBoost classifier. Do you happen to know?

As I stated in comments expected value comes either from the model trees or from your data.

Let's try reproducible:

from sklearn.model_selection import train_test_split
import xgboost
import shap

X, y = shap.datasets.adult()
X_display, y_display = shap.datasets.adult(display=True)

# create a train/test split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=7)
d_train = xgboost.DMatrix(X_train, label=y_train)
d_test = xgboost.DMatrix(X_test, label=y_test)

params = {
    "eta": 0.01,
    "objective": "binary:logistic",
    "subsample": 0.5,
    "base_score": np.mean(y_train),
    "eval_metric": "logloss",
}
model = xgboost.train(
    params,
    d_train,
    num_boost_round=5000,
    evals=[(d_test, "test")],
    verbose_eval=100,
    early_stopping_rounds=20,
)

Case 1. No data available, trees only.

explainer = shap.TreeExplainer(model)
ev_trees = explainer.expected_value[0]

from shap.explainers._tree import XGBTreeModelLoader

xgb_loader = XGBTreeModelLoader(model)
ts = xgb_loader.get_trees()

v = []
for t in ts:
    v.append(t.values[0][0])
sv = sum(v)

import struct
from scipy.special import logit
size = struct.calcsize('f')
buffer = model.save_raw().lstrip(b'binf')
v = struct.unpack('f', buffer[0:0+size])[0]
# if objective "binary:logistic" or "reg:logistic" 
bv = logit(v)

ev_trees_raw = sv+bv

np.isclose(ev_trees, ev_trees_raw)

---

True

Case 2. Background data set supplied.

background = X_train[:100]

explainer = shap.TreeExplainer(model, background)
ev_background = explainer.expected_value

Take a note that:

np.isclose(ev_trees, ev_background)

---

False

but

d_train_background = xgboost.DMatrix(background, y_train[:100])
preds = model.predict(d_train_background, pred_contribs = True)

np.isclose(ev_background, preds.sum(1).mean())

---

True

or simply

output_margin = model.predict(d_train_background, output_margin=True)
np.isclose(ev_background, output_margin.mean())

---

True