def run_randomForestRegressor(single_time):
save_folder = 'RandomForestRegressor'
model = RandomForestRegressor(n_estimators=100)
if single_time:
comparison_algorithm.training_test_with_sklearnmodel(
save_folder=save_folder, model=model)
else:
save_folder += '10t'
comparison_algorithm.training_test_10times_sklearnmodel(
save_folder=save_folder, model=model)
def run_LassoLars(single_time):
save_folder = 'LassoLars'
model = LassoLars(alpha=1.0)
if single_time:
comparison_algorithm.training_test_with_sklearnmodel(
save_folder=save_folder, model=model)
else:
save_folder += '10t'
comparison_algorithm.training_test_10times_sklearnmodel(
save_folder=save_folder, model=model)
def run_MLPRegressor(single_time):
save_folder = 'MLPRegressor'
model = MLPRegressor(hidden_layer_sizes=3,
activation='logistic',
max_iter=1000)
if single_time:
comparison_algorithm.training_test_with_sklearnmodel(
save_folder=save_folder, model=model)
else:
save_folder += '10t'
comparison_algorithm.training_test_10times_sklearnmodel(
save_folder=save_folder, model=model)
def run_RRgcv_model(single_time):
save_folder = 'RidgeGCV'
alpha = list(np.linspace(start=0.1, stop=1000, num=10000))
# alpha = [0.001, 0.01, 0.1, 1, 5, 10, 15, 20, 25, 30, 50, 100, 300, 500, 1000]
model = RidgeCV(alphas=alpha)
if single_time:
comparison_algorithm.training_test_with_sklearnmodel(
save_folder=save_folder, model=model)
else:
save_folder += '10t'
comparison_algorithm.training_test_10times_sklearnmodel(
save_folder=save_folder, model=model)
def run_linearRegresion_model():
save_folder = 'LinearRegression'
model = LinearRegression()
comparison_algorithm.training_test_with_sklearnmodel(
save_folder=save_folder, model=model)