test_size=test_size,
random_state=seed)
#
# # fit model no training data
# model = XGBRegressor(objective='reg:linear', colsample_bytree=0.3, learning_rate=0.1,
# max_depth=5, alpha=10, n_estimators=10)
# model.fit(X_train, y_train)
#
# model.save_model(str(time.localtime()))
#
# y_pred = model.predict(X_test)
# print(y_pred)
# predictions = [round(value) for value in y_pred]
# mse = math.sqrt(np.mean(y_pred - y_test) ** 2)
# print(mse)
model = OneVsRestClassifier(
XGBClassifier(n_jobs=-1,
max_depth=4,
num_class=5,
objective="multi:softmax"))
model.fit(X_train, y_train)
model.save_model("classifier " + str(time.localtime()))
y_pred = model.predict(X_test)
error = np.where(y_pred != y_test, 1, 0)
print(np.sum(error) / len(y_test))
