hyperactive.search_space.search_space.SearchSpace.create_kerasSearchSpace() - Code Metrics - Inspection of "Merge branch 'master' of https://github.com/SimonB..." - SimonBlanke/Hyperactive - Measure and Improve Code Quality continuously with Scrutinizer

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Push — master ( 57810b...fc102b )

by Simon

created 2019-09-14 15:52 UTC

SearchSpace.create_kerasSearchSpace() A

↳ Parent: hyperactive.search_space.search_space

Complexity

Conditions

Size

Total Lines	16
Code Lines	3

Duplication

Lines	0
Ratio	0 %

Importance

Changes

Metric	Value
eloc	3
dl	0
loc	16
rs	10
c	0
b	0
f	0
cc	1
nop	1

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


import numpy as np


class SearchSpace:
    def __init__(self, _config_):
        self.search_config = _config_.search_config
        self.warm_start = _config_.warm_start
        self.scatter_init = _config_.scatter_init

        self.memory = {}

    def pos_space_limit(self):
        dim = []

        for pos_key in self.para_space:
            dim.append(len(self.para_space[pos_key]) - 1)

        self.dim = np.array(dim)

    def create_kerasSearchSpace(self):
        """
        para_space = {}

        for para_key in search_config_temp.keys():

            for param_str in search_config_temp[para_key].keys():
                new_param_str = para_key + "." + param_str

                para_space[new_param_str] = search_config_temp[para_key][param_str]

        """

        self.para_space = self.search_config[list(self.search_config)[0]]

        self.pos_space_limit()

    def get_random_pos(self):
        pos_new = np.random.uniform(np.zeros(self.dim.shape), self.dim, self.dim.shape)
        pos = np.rint(pos_new)

        # n_zeros = [0] * len(self.dim)
        # pos = np.clip(pos_new_int, n_zeros, self.dim)
        return pos

    def pos2para(self, pos):
        if len(self.para_space.keys()) == pos.size:
            values_dict = {}
            for i, key in enumerate(self.para_space.keys()):
                pos_ = int(pos[i])
                values_dict[key] = list(self.para_space[key])[pos_]

            return values_dict


1			# Author: Simon Blanke
2			# Email: [email protected]
3			# License: MIT License
4
5
6			import numpy as np
7
8
9			class SearchSpace:
10			def __init__(self, _config_):
11			self.search_config = _config_.search_config
12			self.warm_start = _config_.warm_start
13			self.scatter_init = _config_.scatter_init
14
15			self.memory = {}
16
17			def pos_space_limit(self):
18			dim = []
19
20			for pos_key in self.para_space:
21			dim.append(len(self.para_space[pos_key]) - 1)
22
23			self.dim = np.array(dim)
24
25			def create_kerasSearchSpace(self):
26			"""
27			para_space = {}
28
29			for para_key in search_config_temp.keys():
30
31			for param_str in search_config_temp[para_key].keys():
32			new_param_str = para_key + "." + param_str
33
34			para_space[new_param_str] = search_config_temp[para_key][param_str]
35
36			"""
37
38			self.para_space = self.search_config[list(self.search_config)[0]]
39
40			self.pos_space_limit()
41
42			def get_random_pos(self):
43			pos_new = np.random.uniform(np.zeros(self.dim.shape), self.dim, self.dim.shape)
44			pos = np.rint(pos_new)
45
46			# n_zeros = [0] * len(self.dim)
47			# pos = np.clip(pos_new_int, n_zeros, self.dim)
48			return pos
49
50			def pos2para(self, pos):
51			if len(self.para_space.keys()) == pos.size:
52			values_dict = {}
53			for i, key in enumerate(self.para_space.keys()):
54			pos_ = int(pos[i])
55			values_dict[key] = list(self.para_space[key])[pos_]
56
57			return values_dict
58

SimonBlanke / Hyperactive

Push — master ( 57810b...fc102b )

SearchSpace.create_kerasSearchSpace() A

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