hyperactive.search_space.search_space.SearchSpace.create_searchspace() - Code Metrics - Inspection of "cleanup opt-classes" - SimonBlanke/Hyperactive - Measure and Improve Code Quality continuously with Scrutinizer

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Push — master ( 9aa23e...87bd68 )

by Simon

created 2019-10-22 17:11 UTC

SearchSpace.create_searchspace() 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 random
import numpy as np


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

        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_searchspace(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)[self.model_nr]]

        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)

        return pos

    def get_random_pos_scalar(self, hyperpara_name):
        n_para_values = len(self.para_space[hyperpara_name])
        pos = random.randint(0, n_para_values - 1)

        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			import random
6			import numpy as np
7
8
9			class SearchSpace:
10			def __init__(self, _core_, model_nr):
11			self.search_config = _core_.search_config
12			self.warm_start = _core_.warm_start
13			self.scatter_init = _core_.scatter_init
14			self.model_nr = model_nr
15
16			self.memory = {}
17
18			def pos_space_limit(self):
19			dim = []
20
21			for pos_key in self.para_space:
22			dim.append(len(self.para_space[pos_key]) - 1)
23
24			self.dim = np.array(dim)
25
26			def create_searchspace(self):
27			"""
28			para_space = {}
29
30			for para_key in search_config_temp.keys():
31
32			for param_str in search_config_temp[para_key].keys():
33			new_param_str = para_key + "." + param_str
34
35			para_space[new_param_str] = search_config_temp[para_key][param_str]
36
37			"""
38
39			self.para_space = self.search_config[list(self.search_config)[self.model_nr]]
40
41			self.pos_space_limit()
42
43			def get_random_pos(self):
44			pos_new = np.random.uniform(np.zeros(self.dim.shape), self.dim, self.dim.shape)
45			pos = np.rint(pos_new)
46
47			return pos
48
49			def get_random_pos_scalar(self, hyperpara_name):
50			n_para_values = len(self.para_space[hyperpara_name])
51			pos = random.randint(0, n_para_values - 1)
52
53			return pos
54
55			def pos2para(self, pos):
56			if len(self.para_space.keys()) == pos.size:
57			values_dict = {}
58			for i, key in enumerate(self.para_space.keys()):
59			pos_ = int(pos[i])
60			values_dict[key] = list(self.para_space[key])[pos_]
61
62			return values_dict
63

SimonBlanke / Hyperactive

Push — master ( 9aa23e...87bd68 )

SearchSpace.create_searchspace() A

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