gradient_free_optimizers.optimizer_search.differential_evolution.DifferentialEvolutionOptimizer.__init__() - Code Metrics - Inspection of "change type hint for 'sampling'" - SimonBlanke/Gradient-Free-Optimizers - Measure and Improve Code Quality continuously with Scrutinizer

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created 2024-12-07 10:43 UTC

DifferentialEvolutionOptimizer.init() A

↳ Parent: gradient_free_optimizers.optimizer_search.differential_evolution

Complexity

Conditions

Size

Total Lines	24
Code Lines	23

Duplication

Lines	24
Ratio	100 %

Importance

Changes

Metric	Value
eloc	23
dl	24
loc	24
rs	9.328
c	0
b	0
f	0
cc	1
nop	10

How to fix Many Parameters

# Author: Simon Blanke
# Email: [email protected]
# License: MIT License

from typing import List, Dict, Literal, Literal

from ..search import Search
from ..optimizers import (
    DifferentialEvolutionOptimizer as _DifferentialEvolutionOptimizer,
)


class DifferentialEvolutionOptimizer(_DifferentialEvolutionOptimizer, Search):

    """
    A class implementing the **differential evolution** for the public API.
    Inheriting from the `Search`-class to get the `search`-method and from
    the `DifferentialEvolutionOptimizer`-backend to get the underlying algorithm.

    Parameters
    ----------
    search_space : dict[str, list]
        The search space to explore. A dictionary with parameter
        names as keys and a numpy array as values.
    initialize : dict[str, int]
        The method to generate initial positions. A dictionary with
        the following key literals and the corresponding value type:
        {"grid": int, "vertices": int, "random": int, "warm_start": list[dict]}
    constraints : list[callable]
        A list of constraints, where each constraint is a callable.
        The callable returns `True` or `False` dependend on the input parameters.
    random_state : None, int
        If None, create a new random state. If int, create a new random state
        seeded with the value.
    rand_rest_p : float
        The probability of a random iteration during the the search process.
    population  : int
        The number of individuals in the population.
    mutation_rate : float
        The mutation rate.
    crossover_rate : float
        The crossover rate.
    """

    def __init__(
        self,
        search_space: Dict[str, list],
        initialize: Dict[
            Literal["grid", "vertices", "random", "warm_start"], int | List
        ] = {"grid": 4, "random": 2, "vertices": 4},
        constraints: List[callable] = [],
        random_state: int = None,
        rand_rest_p: float = 0,
        nth_process: int = 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,
            population=population,
            mutation_rate=mutation_rate,
            crossover_rate=crossover_rate,
        )


1		# Author: Simon Blanke
2		# Email: [email protected]
3		# License: MIT License
4
5		from typing import List, Dict, Literal, Literal
6
7		from ..search import Search
8		from ..optimizers import (
9		DifferentialEvolutionOptimizer as _DifferentialEvolutionOptimizer,
10		)
11
12
13	View Code Duplication	class DifferentialEvolutionOptimizer(_DifferentialEvolutionOptimizer, Search):
		0 ignored issues – show Duplication introduced 2024-12-07 10:49 UTC by Report Bug Copy Issue Report This code seems to be duplicated in your project. Loading history...
14		"""
15		A class implementing the differential evolution for the public API.
16		Inheriting from the `Search`-class to get the `search`-method and from
17		the `DifferentialEvolutionOptimizer`-backend to get the underlying algorithm.
18
19		Parameters
20		----------
21		search_space : dict[str, list]
22		The search space to explore. A dictionary with parameter
23		names as keys and a numpy array as values.
24		initialize : dict[str, int]
25		The method to generate initial positions. A dictionary with
26		the following key literals and the corresponding value type:
27		{"grid": int, "vertices": int, "random": int, "warm_start": list[dict]}
28		constraints : list[callable]
29		A list of constraints, where each constraint is a callable.
30		The callable returns `True` or `False` dependend on the input parameters.
31		random_state : None, int
32		If None, create a new random state. If int, create a new random state
33		seeded with the value.
34		rand_rest_p : float
35		The probability of a random iteration during the the search process.
36		population : int
37		The number of individuals in the population.
38		mutation_rate : float
39		The mutation rate.
40		crossover_rate : float
41		The crossover rate.
42		"""
43
44		def __init__(
45		self,
46		search_space: Dict[str, list],
47		initialize: Dict[
48		Literal["grid", "vertices", "random", "warm_start"], int \| List
49		] = {"grid": 4, "random": 2, "vertices": 4},
50		constraints: List[callable] = [],
51		random_state: int = None,
52		rand_rest_p: float = 0,
53		nth_process: int = None,
54		population=10,
55		mutation_rate=0.9,
56		crossover_rate=0.9,
57		):
58		super().__init__(
59		search_space=search_space,
60		initialize=initialize,
61		constraints=constraints,
62		random_state=random_state,
63		rand_rest_p=rand_rest_p,
64		nth_process=nth_process,
65		population=population,
66		mutation_rate=mutation_rate,
67		crossover_rate=crossover_rate,
68		)
69

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DifferentialEvolutionOptimizer.__init__() A

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