def test_attributes_result_errors_0(): with pytest.raises(ValueError): hyper = Hyperactive() hyper.add_search(objective_function, search_space, n_iter=15) hyper.run() hyper.best_para(objective_function1)
def test_search_space_3(): def func1(): pass def func2(): pass def func3(): pass def objective_function(opt): score = -opt["x1"] * opt["x1"] return score search_space = { "x1": list(range(0, 100, 1)), "func1": [func1, func2, func3], } hyper = Hyperactive() hyper.add_search( objective_function, search_space, n_iter=15, ) hyper.run() assert isinstance(hyper.results(objective_function), pd.DataFrame) assert (hyper.best_para(objective_function)["func1"] in search_space["func1"])
def test_search_space_4(): class class1: pass class class2: pass class class3: pass def objective_function(opt): score = -opt["x1"] * opt["x1"] return score search_space = { "x1": list(range(0, 100, 1)), "class1": [class1, class2, class3], } hyper = Hyperactive() hyper.add_search( objective_function, search_space, n_iter=15, ) hyper.run() assert isinstance(hyper.results(objective_function), pd.DataFrame) assert (hyper.best_para(objective_function)["class1"] in search_space["class1"])
def test_attributes_best_para_objective_function_0(): hyper = Hyperactive() hyper.add_search( objective_function, search_space, n_iter=15, ) hyper.run() assert isinstance(hyper.best_para(objective_function), dict)
def test_attributes_best_para_search_id_1(): hyper = Hyperactive() hyper.add_search( objective_function, search_space, search_id="1", n_iter=15, ) hyper.add_search( objective_function1, search_space, search_id="2", n_iter=15, ) hyper.run() assert isinstance(hyper.best_para("1"), dict)
def test_initialize_warm_start_1(): search_space = { "x1": np.arange(-10, 10, 1), } init = { "x1": -10, } initialize = {"warm_start": [init]} hyper = Hyperactive() hyper.add_search( objective_function, search_space, n_iter=1, initialize=initialize, ) hyper.run() assert hyper.best_para(objective_function) == init
def test_search_space_0(): def objective_function(opt): score = -opt["x1"] * opt["x1"] return score search_space = { "x1": list(range(0, 3, 1)), } hyper = Hyperactive() hyper.add_search( objective_function, search_space, n_iter=15, ) hyper.run() assert isinstance(hyper.results(objective_function), pd.DataFrame) assert hyper.best_para(objective_function)["x1"] in search_space["x1"]
def test_best_results_1(Optimizer, search_space, objective): search_space = search_space objective_function = objective initialize = {"vertices": 2} hyper = Hyperactive() hyper.add_search( objective_function, search_space, optimizer=Optimizer(), n_iter=10, memory=False, initialize=initialize, ) hyper.run() assert hyper.best_para(objective_function)["x1"] in list( hyper.results(objective_function)["x1"])
def test_best_results_0(Optimizer, objective): search_space = objective[1] objective_function = objective[0] initialize = {"vertices": 2} hyper = Hyperactive() hyper.add_search( objective_function, search_space, optimizer=Optimizer(), n_iter=30, memory=False, initialize=initialize, ) hyper.run() assert hyper.best_score(objective_function) == objective_function( hyper.best_para(objective_function) )
def test_best_results_1(Optimizer): search_space = {"x1": np.arange(-100, 101, 1)} def objective_function(opt): score = -opt["x1"] * opt["x1"] return score initialize = {"vertices": 2} hyper = Hyperactive() hyper.add_search( objective_function, search_space, optimizer=Optimizer(), n_iter=30, memory=False, initialize=initialize, ) hyper.run() assert hyper.best_para(objective_function)["x1"] in list( hyper.results(objective_function)["x1"] )