def test_data_not_loaded(self):
"""
Test case for check correct handling when data is not loaded
"""
with pytest.raises(DataNotLoadedError):
opt = SmartOptimizer()
opt._validate_data_files()
def test_data_size(self):
"""
Test case for check the size of dataset
"""
opt = SmartOptimizer()
opt.load('trucks.csv', 'cargo.csv')
assert (opt._is_valid_data_size())
def test_data_loaded(self):
"""
Test case for check if data was loaded
"""
opt = SmartOptimizer()
opt.load('trucks.csv', 'cargo.csv')
assert (opt._is_data_loaded())
def test_display(self):
"""
Test case for display function
"""
op = SmartOptimizer()
op.load('trucks.csv', 'cargo.csv')
df_distance, row_ind, col_ind = op.optimizer()
app.display_result(df_distance, row_ind, col_ind)
def test_build_cost_matrix(self, dataset):
"""
Test case for check build cost matrix
"""
trucks = dataset.get_truck()
cargo = dataset.get_cargo()
opt = SmartOptimizer()
df = opt._build_cost_matrix(trucks, cargo)
info = df.loc["Hartford Plastics Incartford",
"Light bulbs_Sikeston_Grapevine"]
assert round(info.values[0][0]) == 288
def test_transform_quadratic_matrix(self, dataset):
"""
Test case for check quadratic matrix generator
"""
trucks = dataset.get_truck()
cargo = dataset.get_cargo()
cargo["product_city"] = cargo["product"] + "_" + \
cargo["origin_city"] + "_" + cargo["destination_city"]
df_distances = pd.DataFrame(index=[trucks["truck"]],
columns=[cargo["product_city"]])
opt = SmartOptimizer()
df_distances = opt._transform_quadratic_matrix(df_distances, trucks,
cargo)
assert df_distances.shape == (3, 3)
def test_build_cost_matrix(self, dataset):
"""
Test case for check build cost matrix
"""
opt = SmartOptimizer()
opt.load('trucks.csv', 'cargo.csv')
row_ind_resp = [
0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18,
19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,
36, 37, 38, 39, 40, 41, 42, 43
]
col_ind_resp = [
34, 31, 27, 23, 15, 12, 13, 14, 0, 16, 17, 18, 19, 20, 21, 22, 4,
24, 25, 26, 5, 28, 29, 30, 1, 32, 33, 2, 35, 36, 37, 38, 39, 40,
41, 42, 43, 11, 10, 9, 8, 7, 6, 3
]
df_distance, row_ind, col_ind = opt.optimizer()
assert (sum(row_ind_resp - row_ind) == 0)
assert (sum(col_ind_resp - col_ind) == 0)
def test_data_invalid_size(self):
"""
Test case for check correct handling when data size is not valid
"""
with pytest.raises(InvalidDataSizeError):
opt = SmartOptimizer()
opt.load('trucks_invalid_size.csv', 'cargo.csv')
opt._validate_data_files()
def test_smart_optimizer_selection(self):
BED, _ = self.sample_BED_data()
opt = SmartOptimizer().build(BED, capacity = 100, max_time = 585)
self.assertEqual(opt.get_type(), "Heuristic", msg="Given enough time heuristic should be chosen")
opt = SmartOptimizer().build(BED, capacity = 100, max_time = 300)
self.assertEqual(opt.get_type(), "Linear", msg="With limited time LP with estimation should be chosen")