hyperactive.search_space.SearchSpace.__init__() - Code Metrics - Inspection of "Merge branch 'dev'" - SimonBlanke/Hyperactive - Measure and Improve Code Quality continuously with Scrutinizer

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by Simon

created 2020-01-15 17:42 UTC

hyperactive.search_space.SearchSpace.init() A

↳ Parent: hyperactive.search_space

Complexity

Conditions

Size

Total Lines	14
Code Lines	11

Duplication

Lines	0
Ratio	0 %

Importance

Changes

Metric	Value
cc	4
eloc	11
nop	3
dl	0
loc	14
rs	9.85
c	0
b	0
f	0

# 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_space = _core_.search_config[list(_core_.search_config)[model_nr]]
        self.pos_space_limit()
        self.init_type = None

        self.para_names = list(self.search_space.keys())

        if _core_.init_config:
            self.init_para = _core_.init_config[list(_core_.init_config)[model_nr]]

            if list(self.init_para.keys())[0] == list(self.search_space.keys())[0]:
                self.init_type = "warm_start"
            elif list(self.init_para.keys())[0] == "scatter_init":
                self.init_type = "scatter_init"

    def pos_space_limit(self):
        dim = []

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

        self.dim = np.array(dim)

    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).astype(int)

        return pos

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

        return pos

    def pos2para(self, pos):
        if len(self.search_space.keys()) == pos.size:
            values_dict = {}
            for i, key in enumerate(self.search_space.keys()):
                pos_ = int(pos[i])
                values_dict[key] = list(self.search_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_space = _core_.search_config[list(_core_.search_config)[model_nr]]
12			self.pos_space_limit()
13			self.init_type = None
14
15			self.para_names = list(self.search_space.keys())
16
17			if _core_.init_config:
18			self.init_para = _core_.init_config[list(_core_.init_config)[model_nr]]
19
20			if list(self.init_para.keys())[0] == list(self.search_space.keys())[0]:
21			self.init_type = "warm_start"
22			elif list(self.init_para.keys())[0] == "scatter_init":
23			self.init_type = "scatter_init"
24
25			def pos_space_limit(self):
26			dim = []
27
28			for pos_key in self.search_space:
29			dim.append(len(self.search_space[pos_key]) - 1)
30
31			self.dim = np.array(dim)
32
33			def get_random_pos(self):
34			pos_new = np.random.uniform(np.zeros(self.dim.shape), self.dim, self.dim.shape)
35			pos = np.rint(pos_new).astype(int)
36
37			return pos
38
39			def get_random_pos_scalar(self, hyperpara_name):
40			n_para_values = len(self.search_space[hyperpara_name])
41			pos = random.randint(0, n_para_values - 1)
42
43			return pos
44
45			def pos2para(self, pos):
46			if len(self.search_space.keys()) == pos.size:
47			values_dict = {}
48			for i, key in enumerate(self.search_space.keys()):
49			pos_ = int(pos[i])
50			values_dict[key] = list(self.search_space[key])[pos_]
51
52			return values_dict
53

SimonBlanke / Hyperactive

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