gradient_free_optimizers.optimizer_search.pattern_search

Complexity

Total Complexity

1

Size/Duplication

Total Lines	66
Duplicated Lines	0 %

Importance

Changes

0

1 Method

Rating	Name	Duplication	Size	Complexity
A	PatternSearch.__init__()	0	24	1

# Author: Simon Blanke
# Email: simon.blanke@yahoo.com
# License: MIT License

from typing import List, Dict, Literal, Literal

from ..search import Search
from ..optimizers import PatternSearch as _PatternSearch


class PatternSearch(_PatternSearch, Search):
    """
    A class implementing the **pattern search** for the public API.
    Inheriting from the `Search`-class to get the `search`-method and from
    the `PatternSearch`-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.
    n_positions : int
        Number of positions that the pattern consists of.
    pattern_size : float
        The initial size of the patterns in percentage of the size of the search space in the corresponding dimension.
    reduction : float
        The factor that reduces the size of the pattern if no better position is found.
    """

    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,
        n_positions=4,
        pattern_size=0.25,
        reduction=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,
            n_positions=n_positions,
            pattern_size=pattern_size,
            reduction=reduction,
        )