X_test = pd.read_csv(PATH_DATA + "x_test.csv")
y_train = pd.read_csv(PATH_DATA + "y_train.csv")
y_test = pd.read_csv(PATH_DATA + "y_test.csv")
return X_train, X_test, y_train, y_test
def get_model(PARAMS):
'''Get model according to parameters'''
model = XGBClassifier()
model.set_params(**PARAMS)
return model
def run(X_train, X_test, y_train, y_test, model):
'''Train model and predict result'''
model.fit(X_train, y_train)
score = roc_auc_score(y_test, model.predict_proba(X_test)[:, 1])
nni.report_final_result(score)
if __name__ == "__main__":
X_train, X_test, y_train, y_test = load_data()
# get parameters from tuner
RECEIVED_PARAMS = nni.get_next_parameter()
print(RECEIVED_PARAMS)
PARAMS = XGBClassifier().get_params()
PARAMS.update(RECEIVED_PARAMS)
model = get_model(PARAMS)
run(X_train, X_test, y_train, y_test, model)
