gradient_free_optimizers.optimizer_search.downhill_simplex.DownhillSimplexOptimizer.__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

DownhillSimplexOptimizer.init() A

↳ Parent: gradient_free_optimizers.optimizer_search.downhill_simplex

Complexity

Conditions

Size

Total Lines	26
Code Lines	25

Duplication

Lines	26
Ratio	100 %

Importance

Changes

Metric	Value
eloc	25
dl	26
loc	26
rs	9.28
c	0
b	0
f	0
cc	1
nop	11

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 DownhillSimplexOptimizer as _DownhillSimplexOptimizer


class DownhillSimplexOptimizer(_DownhillSimplexOptimizer, Search):
    """
    A class implementing the **downhill simplex optimizer** for the public API.
    Inheriting from the `Search`-class to get the `search`-method and from
    the `DownhillSimplexOptimizer`-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.
    alpha : float
        The reflection parameter of the simplex algorithm.
    gamma : float
        The expansion parameter of the simplex algorithm.
    beta : float
        The contraction parameter of the simplex algorithm.
    sigma : float
        The shrinking parameter of the simplex algorithm.
    """

    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,
        alpha: float = 1,
        gamma: float = 2,
        beta: float = 0.5,
        sigma: float = 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,
            alpha=alpha,
            gamma=gamma,
            beta=beta,
            sigma=sigma,
        )


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 DownhillSimplexOptimizer as _DownhillSimplexOptimizer
9
10
11		class DownhillSimplexOptimizer(_DownhillSimplexOptimizer, Search):
12		"""
13		A class implementing the downhill simplex optimizer for the public API.
14		Inheriting from the `Search`-class to get the `search`-method and from
15		the `DownhillSimplexOptimizer`-backend to get the underlying algorithm.
16
17		Parameters
18		----------
19		search_space : dict[str, list]
20		The search space to explore. A dictionary with parameter
21		names as keys and a numpy array as values.
22		initialize : dict[str, int]
23		The method to generate initial positions. A dictionary with
24		the following key literals and the corresponding value type:
25		{"grid": int, "vertices": int, "random": int, "warm_start": list[dict]}
26		constraints : list[callable]
27		A list of constraints, where each constraint is a callable.
28		The callable returns `True` or `False` dependend on the input parameters.
29		random_state : None, int
30		If None, create a new random state. If int, create a new random state
31		seeded with the value.
32		rand_rest_p : float
33		The probability of a random iteration during the the search process.
34		alpha : float
35		The reflection parameter of the simplex algorithm.
36		gamma : float
37		The expansion parameter of the simplex algorithm.
38		beta : float
39		The contraction parameter of the simplex algorithm.
40		sigma : float
41		The shrinking parameter of the simplex algorithm.
42		"""
43
44	View Code Duplication	def __init__(
		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...
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		alpha: float = 1,
55		gamma: float = 2,
56		beta: float = 0.5,
57		sigma: float = 0.5,
58		):
59		super().__init__(
60		search_space=search_space,
61		initialize=initialize,
62		constraints=constraints,
63		random_state=random_state,
64		rand_rest_p=rand_rest_p,
65		nth_process=nth_process,
66		alpha=alpha,
67		gamma=gamma,
68		beta=beta,
69		sigma=sigma,
70		)
71

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