gradient_free_optimizers.optimizers.population.base_population_optimizer.BasePopulationOptimizer._create_population() - Code Metrics - Inspection of "refactor method" - SimonBlanke/Gradient-Free-Optimizers - Measure and Improve Code Quality continuously with Scrutinizer

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Push — master ( 19dcde...f6c7e6 )

by Simon

created 2021-07-26 12:45 UTC

BasePopulationOptimizer._create_population() A

↳ Parent: gradient_free_optimizers.optimizers.population.base_population_optimizer

Complexity

Conditions

Size

Total Lines	18
Code Lines	13

Duplication

Lines	0
Ratio	0 %

Importance

Changes

Metric	Value
cc	4
eloc	13
nop	2
dl	0
loc	18
rs	9.75
c	0
b	0
f	0

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

from ...converter import Converter
from ...results_manager import ResultsManager
from ...optimizers.base_optimizer import get_n_inits


class BasePopulationOptimizer:
    def __init__(
        self,
        search_space,
        initialize={"grid": 4, "random": 2, "vertices": 4},
    ):
        super().__init__()
        self.conv = Converter(search_space)
        self.results_mang = ResultsManager(self.conv)
        self.initialize = initialize

        self.eval_times = []
        self.iter_times = []

    def _iterations(self, positioners):
        nth_iter = 0
        for p in positioners:
            nth_iter = nth_iter + len(p.pos_new_list)

        return nth_iter

    def _create_population(self, Optimizer):
        if isinstance(self.population, int):
            population = []
            for pop_ in range(self.population):
                population.append(
                    Optimizer(self.conv.search_space, rand_rest_p=self.rand_rest_p)
                )
        else:
            population = self.population

        n_inits = get_n_inits(self.initialize)

        if n_inits < len(population):
            print("\n Warning: Not enough initial positions for population size")
            print(" Population size is reduced to", n_inits)
            population = population[:n_inits]

        return population

    def finish_initialization(self):
        pass


1			# Author: Simon Blanke
2			# Email: [email protected]
3			# License: MIT License
4
5			from ...converter import Converter
6			from ...results_manager import ResultsManager
7			from ...optimizers.base_optimizer import get_n_inits
8
9
10			class BasePopulationOptimizer:
11			def __init__(
12			self,
13			search_space,
14			initialize={"grid": 4, "random": 2, "vertices": 4},
15			):
16			super().__init__()
17			self.conv = Converter(search_space)
18			self.results_mang = ResultsManager(self.conv)
19			self.initialize = initialize
20
21			self.eval_times = []
22			self.iter_times = []
23
24			def _iterations(self, positioners):
25			nth_iter = 0
26			for p in positioners:
27			nth_iter = nth_iter + len(p.pos_new_list)
28
29			return nth_iter
30
31			def _create_population(self, Optimizer):
32			if isinstance(self.population, int):
33			population = []
34			for pop_ in range(self.population):
35			population.append(
36			Optimizer(self.conv.search_space, rand_rest_p=self.rand_rest_p)
37			)
38			else:
39			population = self.population
40
41			n_inits = get_n_inits(self.initialize)
42
43			if n_inits < len(population):
44			print("\n Warning: Not enough initial positions for population size")
45			print(" Population size is reduced to", n_inits)
46			population = population[:n_inits]
47
48			return population
49
50			def finish_initialization(self):
51			pass
52

SimonBlanke / Gradient-Free-Optimizers

Push — master ( 19dcde...f6c7e6 )

BasePopulationOptimizer._create_population() A

Complexity

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