gradient_free_optimizers.optimizer_search.lipschitz_optimizer.LipschitzOptimizer.__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

LipschitzOptimizer.init() A

↳ Parent: gradient_free_optimizers.optimizer_search.lipschitz_optimizer

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

from ..search import Search
from ..optimizers import LipschitzOptimizer as _LipschitzOptimizer


class LipschitzOptimizer(_LipschitzOptimizer, Search):

    """
    A class implementing the **lipschitz optimizer** for the public API.
    Inheriting from the `Search`-class to get the `search`-method and from
    the `LipschitzOptimizer`-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.
    warm_start_smbo
        The warm start for SMBO.
    max_sample_size : int
        The maximum number of points to sample.
    sampling : dict
        The sampling method to use.
    replacement : bool
        Whether to sample with replacement.
    """

    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,
        warm_start_smbo=None,
        max_sample_size: int = 10000000,
        sampling: Dict[Literal["random"], int] = {"random": 1000000},
        replacement: bool = 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,
        )


1		# Author: Simon Blanke
2		# Email: [email protected]
3		# License: MIT License
4
5		from typing import List, Dict, Literal
6
7		from ..search import Search
8		from ..optimizers import LipschitzOptimizer as _LipschitzOptimizer
9
10
11	View Code Duplication	class LipschitzOptimizer(_LipschitzOptimizer, 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...
12		"""
13		A class implementing the lipschitz optimizer for the public API.
14		Inheriting from the `Search`-class to get the `search`-method and from
15		the `LipschitzOptimizer`-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		warm_start_smbo
35		The warm start for SMBO.
36		max_sample_size : int
37		The maximum number of points to sample.
38		sampling : dict
39		The sampling method to use.
40		replacement : bool
41		Whether to sample with replacement.
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		warm_start_smbo=None,
55		max_sample_size: int = 10000000,
56		sampling: Dict[Literal["random"], int] = {"random": 1000000},
57		replacement: bool = True,
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		warm_start_smbo=warm_start_smbo,
67		max_sample_size=max_sample_size,
68		sampling=sampling,
69		replacement=replacement,
70		)
71

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