Code Duplication - SimonBlanke/Gradient-Free-Optimizers - Measure and Improve Code Quality continuously with Scrutinizer

Code Duplication Length = 27-28 lines in 2 locations

tests/test_optimizers/test_early_stop.py 1 location


    )


@pytest.mark.parametrize(*optimizers)
def test_early_stop_3(Optimizer):
    def objective_function(para):
        score = -para["x1"] * para["x1"]
        return score

    search_space = {
        "x1": np.arange(0, 1000, 0.01),
    }

    n_iter_no_change = 5
    early_stopping = {
        "n_iter_no_change": n_iter_no_change,
    }

    opt = Optimizer(search_space, initialize={"warm_start": [{"x1": 0}], "random": 1})
    opt.search(
        objective_function,
        n_iter=100000,
        early_stopping=early_stopping,
    )
    search_data = opt.search_data
    n_performed_iter = len(search_data)

    print("\n n_performed_iter \n", n_performed_iter)
    print("\n n_iter_no_change \n", n_iter_no_change)

    assert n_performed_iter == (n_iter_no_change + 2)


@pytest.mark.parametrize(*optimizers)

tests/test_early_stop.py 1 location


    )


def test_early_stop_3():
    def objective_function(para):
        score = -para["x1"] * para["x1"]
        return score

    search_space = {
        "x1": np.arange(0, 100, 0.1),
    }

    n_iter_no_change = 5
    early_stopping = {
        "n_iter_no_change": n_iter_no_change,
    }

    opt = HillClimbingOptimizer(search_space, initialize={"warm_start": [{"x1": 0}]})
    opt.search(
        objective_function,
        n_iter=100000,
        early_stopping=early_stopping,
    )
    search_data = opt.search_data
    n_performed_iter = len(search_data)

    print("\n n_performed_iter \n", n_performed_iter)
    print("\n n_iter_no_change \n", n_iter_no_change)

    assert n_performed_iter == (n_iter_no_change + 1)


def test_early_stop_4():

		@@ 68-95 (lines=28) @@
65		)
66
67
68		@pytest.mark.parametrize(*optimizers)
69		def test_early_stop_3(Optimizer):
70		def objective_function(para):
71		score = -para["x1"] * para["x1"]
72		return score
73
74		search_space = {
75		"x1": np.arange(0, 1000, 0.01),
76		}
77
78		n_iter_no_change = 5
79		early_stopping = {
80		"n_iter_no_change": n_iter_no_change,
81		}
82
83		opt = Optimizer(search_space, initialize={"warm_start": [{"x1": 0}], "random": 1})
84		opt.search(
85		objective_function,
86		n_iter=100000,
87		early_stopping=early_stopping,
88		)
89		search_data = opt.search_data
90		n_performed_iter = len(search_data)
91
92		print("\n n_performed_iter \n", n_performed_iter)
93		print("\n n_iter_no_change \n", n_iter_no_change)
94
95		assert n_performed_iter == (n_iter_no_change + 2)
96
97
98		@pytest.mark.parametrize(*optimizers)

		@@ 68-94 (lines=27) @@
65		)
66
67
68		def test_early_stop_3():
69		def objective_function(para):
70		score = -para["x1"] * para["x1"]
71		return score
72
73		search_space = {
74		"x1": np.arange(0, 100, 0.1),
75		}
76
77		n_iter_no_change = 5
78		early_stopping = {
79		"n_iter_no_change": n_iter_no_change,
80		}
81
82		opt = HillClimbingOptimizer(search_space, initialize={"warm_start": [{"x1": 0}]})
83		opt.search(
84		objective_function,
85		n_iter=100000,
86		early_stopping=early_stopping,
87		)
88		search_data = opt.search_data
89		n_performed_iter = len(search_data)
90
91		print("\n n_performed_iter \n", n_performed_iter)
92		print("\n n_iter_no_change \n", n_iter_no_change)
93
94		assert n_performed_iter == (n_iter_no_change + 1)
95
96
97		def test_early_stop_4():

SimonBlanke / Gradient-Free-Optimizers

Code Duplication Length = 27-28 lines in 2 locations

tests/test_optimizers/test_early_stop.py 1 location

tests/test_early_stop.py 1 location