XGBClassifier.py

# recombine training and validation data
X_tv  = torch.cat((X_train.cpu(), X_val.cpu()))
y_tv = torch.cat((y_train.cpu(), y_val.cpu()))

np.random.seed(101)

sample_weights = compute_sample_weight(class_weight='balanced',y=y_tv)

model = XGBClassifier()
model.fit(X_tv, y_tv, sample_weight=sample_weights)

# make predictions for test data
y_pred = model.predict(X_test.cpu())

# evaluate predictions
accuracy = accuracy_score(y_test.cpu(), y_pred)
print("Accuracy: %.2f%%" % (accuracy * 100.0))

print(confusion_matrix(y_test.cpu(), y_pred))
print(classification_report(y_test.cpu(), y_pred, digits=4))