gradient_free_optimizers.optimizers.sequence_model.smbo.SMBO.__init__() - Code Metrics - Inspection of "rename to sequence model based optimization (as it..." - SimonBlanke/Gradient-Free-Optimizers - Measure and Improve Code Quality continuously with Scrutinizer

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by Simon

created 2020-11-05 15:53 UTC

SMBO.init() A

↳ Parent: gradient_free_optimizers.optimizers.sequence_model.smbo

Complexity

Conditions

Size

Total Lines	13
Code Lines	9

Duplication

Lines	0
Ratio	0 %

Importance

Changes

Metric	Value
cc	2
eloc	9
nop	3
dl	0
loc	13
rs	9.95
c	0
b	0
f	0

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



1			# Author: Simon Blanke
2			# Email: [email protected]
3			# License: MIT License
4
5
6			import numpy as np
7
8			np.seterr(divide="ignore", invalid="ignore")
9
10			from ..base_optimizer import BaseOptimizer
11			from ...search import Search
12
13
14			class SMBO(BaseOptimizer, Search):
15			def __init__(
16			self, search_space, warm_start_sbom=None,
17			):
18			super().__init__(search_space)
19			self.warm_start_sbom = warm_start_sbom
20
21			self.X_sample = []
22			self.Y_sample = []
23
24			if self.warm_start_sbom is not None:
25			(self.X_sample, self.Y_sample) = self.warm_start_sbom
26
27			self.all_pos_comb = self._all_possible_pos()
28
29			def track_X_sample(func):
30			def wrapper(self, args, *kwargs):
31			pos = func(self, args, *kwargs)
32			self.X_sample.append(pos)
33			return pos
34
35			return wrapper
36
37			def _all_possible_pos(self):
38			pos_space = []
39			for dim_ in self.max_positions:
40			pos_space.append(np.arange(dim_))
41
42			n_dim = len(pos_space)
43			return np.array(np.meshgrid(*pos_space)).T.reshape(-1, n_dim)
44
45			@track_X_sample
46			def init_pos(self, pos):
47			super().init_pos(pos)
48			return pos
49
50

SimonBlanke / Gradient-Free-Optimizers

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