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

Code Duplication Length = 29-35 lines in 3 locations

src/gradient_free_optimizers/optimizers/pop_opt/genetic_algorithm.py 1 location


    optimizer_type = "population"
    computationally_expensive = False

    def __init__(
        self,
        search_space,
        initialize={"grid": 4, "random": 2, "vertices": 4},
        constraints=[],
        random_state=None,
        rand_rest_p=0,
        nth_process=None,
        population=10,
        offspring=10,
        crossover="discrete-recombination",
        n_parents=2,
        mutation_rate=0.5,
        crossover_rate=0.5,
    ):
        super().__init__(
            search_space=search_space,
            initialize=initialize,
            constraints=constraints,
            random_state=random_state,
            rand_rest_p=rand_rest_p,
            nth_process=nth_process,
        )

        self.population = population
        self.offspring = offspring
        self.crossover = crossover
        self.n_parents = n_parents
        self.mutation_rate = mutation_rate
        self.crossover_rate = crossover_rate

        self.individuals = self._create_population(Individual)
        self.optimizers = self.individuals

        self.offspring_l = []

    def fittest_parents(self):
        fittest_parents_f = 0.5

src/gradient_free_optimizers/optimizers/pop_opt/evolution_strategy.py 1 location


    optimizer_type = "population"
    computationally_expensive = False

    def __init__(
        self,
        search_space,
        initialize={"grid": 4, "random": 2, "vertices": 4},
        constraints=[],
        random_state=None,
        rand_rest_p=0,
        nth_process=None,
        population=10,
        offspring=20,
        replace_parents=False,
        mutation_rate=0.7,
        crossover_rate=0.3,
    ):
        super().__init__(
            search_space=search_space,
            initialize=initialize,
            constraints=constraints,
            random_state=random_state,
            rand_rest_p=rand_rest_p,
            nth_process=nth_process,
        )

        self.population = population
        self.offspring = offspring
        self.replace_parents = replace_parents
        self.mutation_rate = mutation_rate
        self.crossover_rate = crossover_rate

        self.individuals = self._create_population(Individual)
        self.optimizers = self.individuals

    def _cross(self):
        while True:

src/gradient_free_optimizers/optimizers/pop_opt/differential_evolution.py 1 location


    optimizer_type = "population"
    computationally_expensive = False

    def __init__(
        self,
        search_space,
        initialize={"grid": 4, "random": 2, "vertices": 4},
        constraints=[],
        random_state=None,
        rand_rest_p=0,
        nth_process=None,
        population=10,
        mutation_rate=0.9,
        crossover_rate=0.9,
    ):
        super().__init__(
            search_space=search_space,
            initialize=initialize,
            constraints=constraints,
            random_state=random_state,
            rand_rest_p=rand_rest_p,
            nth_process=nth_process,
        )

        self.population = population
        self.mutation_rate = mutation_rate
        self.crossover_rate = crossover_rate

        self.individuals = self._create_population(Individual)
        self.optimizers = self.individuals

        self.offspring_l = []

    def mutation(self, f=1):
        ind_selected = random.sample(self.individuals, 3)

		@@ 20-54 (lines=35) @@
17		optimizer_type = "population"
18		computationally_expensive = False
19
20		def __init__(
21		self,
22		search_space,
23		initialize={"grid": 4, "random": 2, "vertices": 4},
24		constraints=[],
25		random_state=None,
26		rand_rest_p=0,
27		nth_process=None,
28		population=10,
29		offspring=10,
30		crossover="discrete-recombination",
31		n_parents=2,
32		mutation_rate=0.5,
33		crossover_rate=0.5,
34		):
35		super().__init__(
36		search_space=search_space,
37		initialize=initialize,
38		constraints=constraints,
39		random_state=random_state,
40		rand_rest_p=rand_rest_p,
41		nth_process=nth_process,
42		)
43
44		self.population = population
45		self.offspring = offspring
46		self.crossover = crossover
47		self.n_parents = n_parents
48		self.mutation_rate = mutation_rate
49		self.crossover_rate = crossover_rate
50
51		self.individuals = self._create_population(Individual)
52		self.optimizers = self.individuals
53
54		self.offspring_l = []
55
56		def fittest_parents(self):
57		fittest_parents_f = 0.5

		@@ 20-48 (lines=29) @@
17		optimizer_type = "population"
18		computationally_expensive = False
19
20		def __init__(
21		self,
22		search_space,
23		initialize={"grid": 4, "random": 2, "vertices": 4},
24		constraints=[],
25		random_state=None,
26		rand_rest_p=0,
27		nth_process=None,
28		population=10,
29		mutation_rate=0.9,
30		crossover_rate=0.9,
31		):
32		super().__init__(
33		search_space=search_space,
34		initialize=initialize,
35		constraints=constraints,
36		random_state=random_state,
37		rand_rest_p=rand_rest_p,
38		nth_process=nth_process,
39		)
40
41		self.population = population
42		self.mutation_rate = mutation_rate
43		self.crossover_rate = crossover_rate
44
45		self.individuals = self._create_population(Individual)
46		self.optimizers = self.individuals
47
48		self.offspring_l = []
49
50		def mutation(self, f=1):
51		ind_selected = random.sample(self.individuals, 3)

		@@ 20-50 (lines=31) @@
17		optimizer_type = "population"
18		computationally_expensive = False
19
20		def __init__(
21		self,
22		search_space,
23		initialize={"grid": 4, "random": 2, "vertices": 4},
24		constraints=[],
25		random_state=None,
26		rand_rest_p=0,
27		nth_process=None,
28		population=10,
29		offspring=20,
30		replace_parents=False,
31		mutation_rate=0.7,
32		crossover_rate=0.3,
33		):
34		super().__init__(
35		search_space=search_space,
36		initialize=initialize,
37		constraints=constraints,
38		random_state=random_state,
39		rand_rest_p=rand_rest_p,
40		nth_process=nth_process,
41		)
42
43		self.population = population
44		self.offspring = offspring
45		self.replace_parents = replace_parents
46		self.mutation_rate = mutation_rate
47		self.crossover_rate = crossover_rate
48
49		self.individuals = self._create_population(Individual)
50		self.optimizers = self.individuals
51
52		def _cross(self):
53		while True:

SimonBlanke / Gradient-Free-Optimizers

Code Duplication Length = 29-35 lines in 3 locations

src/gradient_free_optimizers/optimizers/pop_opt/genetic_algorithm.py 1 location

src/gradient_free_optimizers/optimizers/pop_opt/evolution_strategy.py 1 location

src/gradient_free_optimizers/optimizers/pop_opt/differential_evolution.py 1 location