def test_ltm_10():
path = None
model_name = "test_ltm_0"
def objective_function(opt):
score = -opt["x1"] * opt["x1"]
return score
search_space = {
"x1": list(range(0, 3, 1)),
}
memory = LongTermMemory(model_name, path=path)
hyper = Hyperactive()
hyper.add_search(
objective_function, search_space, n_iter=25, long_term_memory=memory
)
hyper.run()
hyper = Hyperactive()
hyper.add_search(
objective_function, search_space, n_iter=25, long_term_memory=memory
)
hyper.run()
memory.remove_model_data()
def test_ltm_list():
path = "./"
model_name = "test_ltm_list"
dict_ = {"x1": [[1, 1, 1], [1, 2, 1], [1, 1, 2]]}
results1 = pd.DataFrame(dict_)
print("\n results1 \n", results1)
memory = LongTermMemory(model_name, path=path)
memory.save(results1, objective_function)
results2 = memory.load()
memory.remove_model_data()
print("\n results2 \n", results2)
for list_1 in list(results2.values.flatten()):
assert_ = False
for list_2 in list(dict_.values())[0]:
print("\n list_1 ", list_1)
print("list_2 ", list_2)
print(list_1 == list_2)
if list_1 == list_2:
assert_ = True
break
assert assert_
def test_ltm_0(objective_function, search_space, path):
(search_space, compare) = search_space
print("\n objective_function \n", objective_function)
print("\n search_space \n", search_space)
print("\n compare \n", compare)
print("\n path \n", path)
model_name = str(objective_function.__name__)
hyper = Hyperactive()
hyper.add_search(objective_function,
search_space,
n_iter=10,
initialize={"random": 1})
hyper.run()
results1 = hyper.results(objective_function)
memory = LongTermMemory(model_name, path=path)
memory.save(results1, objective_function)
results2 = memory.load()
print("\n results1 \n", results1)
print("\n results2 \n", results2)
memory.remove_model_data()
compare(results1, results2)
def test_ltm_1(objective_function, search_space, path):
(search_space, compare) = search_space
print("\n objective_function \n", objective_function)
print("\n search_space \n", search_space)
print("\n compare \n", compare)
print("\n path \n", path)
model_name = str(objective_function.__name__)
memory = LongTermMemory(model_name, path=path)
hyper1 = Hyperactive()
hyper1.add_search(
objective_function,
search_space,
n_iter=10,
initialize={"random": 1},
long_term_memory=memory,
)
hyper1.run()
results1 = hyper1.results(objective_function)
hyper2 = Hyperactive()
hyper2.add_search(
objective_function,
search_space,
n_iter=10,
initialize={"random": 1},
long_term_memory=memory,
)
hyper2.run()
results2 = hyper2.results(objective_function)
memory.remove_model_data()
print("\n results1 \n", results1)
print("\n results2 \n", results2)
def test_ltm_int():
path = "./"
model_name = "test_ltm_int"
array = np.arange(3 * 10).reshape(10, 3)
results1 = pd.DataFrame(array, columns=["x1", "x2", "x3"])
memory = LongTermMemory(model_name, path=path)
memory.save(results1, objective_function)
results2 = memory.load()
memory.remove_model_data()
assert results1.equals(results2)
def test_ltm_str():
path = "./"
model_name = "test_ltm_str"
array = ["str1", "str2", "str3"]
results1 = pd.DataFrame(
[array, array, array, array, array], columns=["x1", "x2", "x3"]
)
memory = LongTermMemory(model_name, path=path)
memory.save(results1, objective_function)
results2 = memory.load()
memory.remove_model_data()
assert results1.equals(results2)
def test_ltm_obj():
path = "./"
model_name = "test_ltm_obj"
array = [class1_(), class2_(), class3_()]
results1 = pd.DataFrame(
[array, array, array, array, array], columns=["x1", "x2", "x3"]
)
memory = LongTermMemory(model_name, path=path)
memory.save(results1, objective_function)
results2 = memory.load()
memory.remove_model_data()
for obj_1 in list(results2.values.flatten()):
assert_ = False
for obj_2 in array:
if obj_1.name == obj_2.name:
assert_ = True
break
assert assert_
def test_ltm_func():
path = "./"
model_name = "test_ltm_func"
array = [func1, func2, func3]
results1 = pd.DataFrame(
[array, array, array, array, array], columns=["x1", "x2", "x3"]
)
memory = LongTermMemory(model_name, path=path)
memory.save(results1, objective_function)
results2 = memory.load()
memory.remove_model_data()
func_str_list = []
for func_ in array:
func_str_list.append(inspect.getsource(func_))
for func_ in list(results2.values.flatten()):
func_str_ = inspect.getsource(func_)
if func_str_ not in func_str_list:
assert False