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

Code Duplication Length = 55-56 lines in 4 locations

tests/test_optimizers/test_early_stop.py 2 locations


    assert n_performed_iter == (n_iter_no_change + 1)


@pytest.mark.parametrize(*optimizers)
def test_early_stop_6(Optimizer):
    def objective_function(para):
        return para["x1"]

    search_space = {
        "x1": np.arange(0, 100, 0.01),
    }

    n_iter_no_change = 5
    early_stopping = {
        "n_iter_no_change": 5,
        "tol_abs": None,
        "tol_rel": 10,
    }

    start1 = {"x1": 1}
    start2 = {"x1": 1.1}
    start3 = {"x1": 1.22}
    start4 = {"x1": 1.35}
    start5 = {"x1": 1.48}

    warm_start_l = [
        start1,
        start1,
        start1,
        start1,
        start1,
        start2,
        start2,
        start2,
        start3,
        start3,
        start3,
        start4,
        start4,
        start4,
        start5,
        start5,
        start5,
    ]
    n_iter = len(warm_start_l)

    opt = Optimizer(search_space, initialize={"warm_start": warm_start_l})
    opt.search(
        objective_function,
        n_iter=n_iter,
        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


@pytest.mark.parametrize(*optimizers)

    assert n_performed_iter == (n_iter_no_change + 2)


@pytest.mark.parametrize(*optimizers)
def test_early_stop_4(Optimizer):
    def objective_function(para):
        return para["x1"]

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

    n_iter_no_change = 5
    early_stopping = {
        "n_iter_no_change": 5,
        "tol_abs": 0.1,
        "tol_rel": None,
    }

    start1 = {"x1": 0}
    start2 = {"x1": 0.1}
    start3 = {"x1": 0.2}
    start4 = {"x1": 0.3}
    start5 = {"x1": 0.4}

    warm_start_l = [
        start1,
        start1,
        start1,
        start1,
        start1,
        start2,
        start2,
        start2,
        start3,
        start3,
        start3,
        start4,
        start4,
        start4,
        start5,
        start5,
        start5,
    ]
    n_iter = len(warm_start_l)

    opt = Optimizer(search_space, initialize={"warm_start": warm_start_l})
    opt.search(
        objective_function,
        n_iter=n_iter,
        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


@pytest.mark.parametrize(*optimizers)

tests/test_early_stop.py 2 locations


    assert n_performed_iter == (n_iter_no_change + 1)


def test_early_stop_6():
    def objective_function(para):
        return para["x1"]

    search_space = {
        "x1": np.arange(0, 100, 0.01),
    }

    n_iter_no_change = 5
    early_stopping = {
        "n_iter_no_change": 5,
        "tol_abs": None,
        "tol_rel": 10,
    }

    start1 = {"x1": 1}
    start2 = {"x1": 1.1}
    start3 = {"x1": 1.22}
    start4 = {"x1": 1.35}
    start5 = {"x1": 1.48}

    warm_start_l = [
        start1,
        start1,
        start1,
        start1,
        start1,
        start2,
        start2,
        start2,
        start3,
        start3,
        start3,
        start4,
        start4,
        start4,
        start5,
        start5,
        start5,
    ]
    n_iter = len(warm_start_l)

    opt = HillClimbingOptimizer(search_space, initialize={"warm_start": warm_start_l})
    opt.search(
        objective_function,
        n_iter=n_iter,
        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


def test_early_stop_7():

    assert n_performed_iter == (n_iter_no_change + 1)


def test_early_stop_4():
    def objective_function(para):
        return para["x1"]

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

    n_iter_no_change = 5
    early_stopping = {
        "n_iter_no_change": 5,
        "tol_abs": 1,
        "tol_rel": None,
    }

    start1 = {"x1": 0}
    start2 = {"x1": 1}
    start3 = {"x1": 2}
    start4 = {"x1": 3}
    start5 = {"x1": 4}

    warm_start_l = [
        start1,
        start1,
        start1,
        start1,
        start1,
        start2,
        start2,
        start2,
        start3,
        start3,
        start3,
        start4,
        start4,
        start4,
        start5,
        start5,
        start5,
    ]
    n_iter = len(warm_start_l)

    opt = HillClimbingOptimizer(search_space, initialize={"warm_start": warm_start_l})
    opt.search(
        objective_function,
        n_iter=n_iter,
        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


def test_early_stop_5():

		@@ 206-261 (lines=56) @@
203		assert n_performed_iter == (n_iter_no_change + 1)
204
205
206		@pytest.mark.parametrize(*optimizers)
207		def test_early_stop_6(Optimizer):
208		def objective_function(para):
209		return para["x1"]
210
211		search_space = {
212		"x1": np.arange(0, 100, 0.01),
213		}
214
215		n_iter_no_change = 5
216		early_stopping = {
217		"n_iter_no_change": 5,
218		"tol_abs": None,
219		"tol_rel": 10,
220		}
221
222		start1 = {"x1": 1}
223		start2 = {"x1": 1.1}
224		start3 = {"x1": 1.22}
225		start4 = {"x1": 1.35}
226		start5 = {"x1": 1.48}
227
228		warm_start_l = [
229		start1,
230		start1,
231		start1,
232		start1,
233		start1,
234		start2,
235		start2,
236		start2,
237		start3,
238		start3,
239		start3,
240		start4,
241		start4,
242		start4,
243		start5,
244		start5,
245		start5,
246		]
247		n_iter = len(warm_start_l)
248
249		opt = Optimizer(search_space, initialize={"warm_start": warm_start_l})
250		opt.search(
251		objective_function,
252		n_iter=n_iter,
253		early_stopping=early_stopping,
254		)
255		search_data = opt.search_data
256		n_performed_iter = len(search_data)
257
258		print("\n n_performed_iter \n", n_performed_iter)
259		print("\n n_iter_no_change \n", n_iter_no_change)
260
261		assert n_performed_iter == n_iter
262
263
264		@pytest.mark.parametrize(*optimizers)
		@@ 98-153 (lines=56) @@
95		assert n_performed_iter == (n_iter_no_change + 2)
96
97
98		@pytest.mark.parametrize(*optimizers)
99		def test_early_stop_4(Optimizer):
100		def objective_function(para):
101		return para["x1"]
102
103		search_space = {
104		"x1": np.arange(0, 100, 0.1),
105		}
106
107		n_iter_no_change = 5
108		early_stopping = {
109		"n_iter_no_change": 5,
110		"tol_abs": 0.1,
111		"tol_rel": None,
112		}
113
114		start1 = {"x1": 0}
115		start2 = {"x1": 0.1}
116		start3 = {"x1": 0.2}
117		start4 = {"x1": 0.3}
118		start5 = {"x1": 0.4}
119
120		warm_start_l = [
121		start1,
122		start1,
123		start1,
124		start1,
125		start1,
126		start2,
127		start2,
128		start2,
129		start3,
130		start3,
131		start3,
132		start4,
133		start4,
134		start4,
135		start5,
136		start5,
137		start5,
138		]
139		n_iter = len(warm_start_l)
140
141		opt = Optimizer(search_space, initialize={"warm_start": warm_start_l})
142		opt.search(
143		objective_function,
144		n_iter=n_iter,
145		early_stopping=early_stopping,
146		)
147		search_data = opt.search_data
148		n_performed_iter = len(search_data)
149
150		print("\n n_performed_iter \n", n_performed_iter)
151		print("\n n_iter_no_change \n", n_iter_no_change)
152
153		assert n_performed_iter == n_iter
154
155
156		@pytest.mark.parametrize(*optimizers)

		@@ 203-257 (lines=55) @@
200		assert n_performed_iter == (n_iter_no_change + 1)
201
202
203		def test_early_stop_6():
204		def objective_function(para):
205		return para["x1"]
206
207		search_space = {
208		"x1": np.arange(0, 100, 0.01),
209		}
210
211		n_iter_no_change = 5
212		early_stopping = {
213		"n_iter_no_change": 5,
214		"tol_abs": None,
215		"tol_rel": 10,
216		}
217
218		start1 = {"x1": 1}
219		start2 = {"x1": 1.1}
220		start3 = {"x1": 1.22}
221		start4 = {"x1": 1.35}
222		start5 = {"x1": 1.48}
223
224		warm_start_l = [
225		start1,
226		start1,
227		start1,
228		start1,
229		start1,
230		start2,
231		start2,
232		start2,
233		start3,
234		start3,
235		start3,
236		start4,
237		start4,
238		start4,
239		start5,
240		start5,
241		start5,
242		]
243		n_iter = len(warm_start_l)
244
245		opt = HillClimbingOptimizer(search_space, initialize={"warm_start": warm_start_l})
246		opt.search(
247		objective_function,
248		n_iter=n_iter,
249		early_stopping=early_stopping,
250		)
251		search_data = opt.search_data
252		n_performed_iter = len(search_data)
253
254		print("\n n_performed_iter \n", n_performed_iter)
255		print("\n n_iter_no_change \n", n_iter_no_change)
256
257		assert n_performed_iter == n_iter
258
259
260		def test_early_stop_7():
		@@ 97-151 (lines=55) @@
94		assert n_performed_iter == (n_iter_no_change + 1)
95
96
97		def test_early_stop_4():
98		def objective_function(para):
99		return para["x1"]
100
101		search_space = {
102		"x1": np.arange(0, 100, 0.1),
103		}
104
105		n_iter_no_change = 5
106		early_stopping = {
107		"n_iter_no_change": 5,
108		"tol_abs": 1,
109		"tol_rel": None,
110		}
111
112		start1 = {"x1": 0}
113		start2 = {"x1": 1}
114		start3 = {"x1": 2}
115		start4 = {"x1": 3}
116		start5 = {"x1": 4}
117
118		warm_start_l = [
119		start1,
120		start1,
121		start1,
122		start1,
123		start1,
124		start2,
125		start2,
126		start2,
127		start3,
128		start3,
129		start3,
130		start4,
131		start4,
132		start4,
133		start5,
134		start5,
135		start5,
136		]
137		n_iter = len(warm_start_l)
138
139		opt = HillClimbingOptimizer(search_space, initialize={"warm_start": warm_start_l})
140		opt.search(
141		objective_function,
142		n_iter=n_iter,
143		early_stopping=early_stopping,
144		)
145		search_data = opt.search_data
146		n_performed_iter = len(search_data)
147
148		print("\n n_performed_iter \n", n_performed_iter)
149		print("\n n_iter_no_change \n", n_iter_no_change)
150
151		assert n_performed_iter == n_iter
152
153
154		def test_early_stop_5():

SimonBlanke / Gradient-Free-Optimizers

Code Duplication Length = 55-56 lines in 4 locations

tests/test_optimizers/test_early_stop.py 2 locations

tests/test_early_stop.py 2 locations