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

Code Duplication Length = 24-27 lines in 2 locations

src/gradient_free_optimizers/optimizers/global_opt/direct_algorithm.py 1 location


    optimizer_type = "sequential"
    computationally_expensive = True

    def __init__(
        self,
        search_space,
        initialize={"grid": 4, "random": 2, "vertices": 4},
        constraints=[],
        random_state=None,
        rand_rest_p=0,
        nth_process=None,
        warm_start_smbo=None,
        max_sample_size=10000000,
        sampling={"random": 1000000},
        replacement=True,
    ):
        super().__init__(
            search_space=search_space,
            initialize=initialize,
            constraints=constraints,
            random_state=random_state,
            rand_rest_p=rand_rest_p,
            nth_process=nth_process,
            warm_start_smbo=warm_start_smbo,
            max_sample_size=max_sample_size,
            sampling=sampling,
            replacement=replacement,
        )

        self.subspace_l = []

    def select_next_subspace(self):
        for subspace in self.subspace_l:

src/gradient_free_optimizers/optimizers/global_opt/lipschitz_optimization.py 1 location


    optimizer_type = "sequential"
    computationally_expensive = True

    def __init__(
        self,
        search_space,
        initialize={"grid": 4, "random": 2, "vertices": 4},
        constraints=[],
        random_state=None,
        rand_rest_p=0,
        nth_process=None,
        warm_start_smbo=None,
        max_sample_size=10000000,
        sampling={"random": 1000000},
        replacement=True,
    ):
        super().__init__(
            search_space=search_space,
            initialize=initialize,
            constraints=constraints,
            random_state=random_state,
            rand_rest_p=rand_rest_p,
            nth_process=nth_process,
            warm_start_smbo=warm_start_smbo,
            max_sample_size=max_sample_size,
            sampling=sampling,
            replacement=replacement,
        )

    def finish_initialization(self):

		@@ 77-103 (lines=27) @@
74		optimizer_type = "sequential"
75		computationally_expensive = True
76
77		def __init__(
78		self,
79		search_space,
80		initialize={"grid": 4, "random": 2, "vertices": 4},
81		constraints=[],
82		random_state=None,
83		rand_rest_p=0,
84		nth_process=None,
85		warm_start_smbo=None,
86		max_sample_size=10000000,
87		sampling={"random": 1000000},
88		replacement=True,
89		):
90		super().__init__(
91		search_space=search_space,
92		initialize=initialize,
93		constraints=constraints,
94		random_state=random_state,
95		rand_rest_p=rand_rest_p,
96		nth_process=nth_process,
97		warm_start_smbo=warm_start_smbo,
98		max_sample_size=max_sample_size,
99		sampling=sampling,
100		replacement=replacement,
101		)
102
103		self.subspace_l = []
104
105		def select_next_subspace(self):
106		for subspace in self.subspace_l:

		@@ 60-83 (lines=24) @@
57		optimizer_type = "sequential"
58		computationally_expensive = True
59
60		def __init__(
61		self,
62		search_space,
63		initialize={"grid": 4, "random": 2, "vertices": 4},
64		constraints=[],
65		random_state=None,
66		rand_rest_p=0,
67		nth_process=None,
68		warm_start_smbo=None,
69		max_sample_size=10000000,
70		sampling={"random": 1000000},
71		replacement=True,
72		):
73		super().__init__(
74		search_space=search_space,
75		initialize=initialize,
76		constraints=constraints,
77		random_state=random_state,
78		rand_rest_p=rand_rest_p,
79		nth_process=nth_process,
80		warm_start_smbo=warm_start_smbo,
81		max_sample_size=max_sample_size,
82		sampling=sampling,
83		replacement=replacement,
84		)
85
86		def finish_initialization(self):

SimonBlanke / Gradient-Free-Optimizers

Code Duplication Length = 24-27 lines in 2 locations

src/gradient_free_optimizers/optimizers/global_opt/direct_algorithm.py 1 location

src/gradient_free_optimizers/optimizers/global_opt/lipschitz_optimization.py 1 location