gradient_free_optimizers.optimizers.sequence_model.smbo

Complexity

Total Complexity

6

Size/Duplication

Total Lines	49
Duplicated Lines	0 %

Importance

Changes

0

4 Methods

Rating	Name	Duplication	Size	Complexity
A	SMBO.init_pos()	0	4	1
A	SMBO.track_X_sample()	0	7	1
A	SMBO._all_possible_pos()	0	7	2
A	SMBO.__init__()	0	13	2

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


import numpy as np

np.seterr(divide="ignore", invalid="ignore")

from ..base_optimizer import BaseOptimizer
from ...search import Search


class SMBO(BaseOptimizer, Search):
    def __init__(
        self, search_space, warm_start_sbom=None,
    ):
        super().__init__(search_space)
        self.warm_start_sbom = warm_start_sbom

        self.X_sample = []
        self.Y_sample = []

        if self.warm_start_sbom is not None:
            (self.X_sample, self.Y_sample) = self.warm_start_sbom

        self.all_pos_comb = self._all_possible_pos()

    def track_X_sample(func):
        def wrapper(self, *args, **kwargs):
            pos = func(self, *args, **kwargs)
            self.X_sample.append(pos)
            return pos

        return wrapper

    def _all_possible_pos(self):
        pos_space = []
        for dim_ in self.max_positions:
            pos_space.append(np.arange(dim_))

        n_dim = len(pos_space)
        return np.array(np.meshgrid(*pos_space)).T.reshape(-1, n_dim)

    @track_X_sample
    def init_pos(self, pos):
        super().init_pos(pos)
        return pos