├── requirements-nocuda.txt ├── requirements.txt ├── evoltier ├── __init__.py ├── model │ ├── __init__.py │ ├── probability_distribution.py │ ├── bernoulli.py │ └── multi_variable_gassian.py ├── optimizers │ ├── __init__.py │ ├── bernoulli_natural_gradient.py │ ├── gaussian_natural_gradient.py │ └── cma_es.py ├── selection │ ├── __init__.py │ ├── cma_selection.py │ ├── nes_selection.py │ ├── cma_large_popsize_selection.py │ └── pbil_selection.py ├── test │ ├── __init__.py │ ├── make_test.py │ ├── test_multi_variable_gaussian.py │ └── test_probability_distribution.py ├── problem.py ├── optimizer.py ├── utils.py ├── weight.py └── updater.py ├── .coveragerc ├── .travis.yml ├── README.md ├── main.py ├── .gitignore └── LICENSE.txt /requirements-nocuda.txt: -------------------------------------------------------------------------------- 1 | numpy>=1.13.1 2 | six>=1.10.0 3 | -------------------------------------------------------------------------------- /requirements.txt: -------------------------------------------------------------------------------- 1 | cupy>=2.4.0 2 | numpy>=1.13.1 3 | six>=1.10.0 4 | -------------------------------------------------------------------------------- /evoltier/__init__.py: -------------------------------------------------------------------------------- 1 | # load modules 2 | from . import model 3 | from . import test 4 | from . import optimizers 5 | from . import selection 6 | -------------------------------------------------------------------------------- /evoltier/model/__init__.py: -------------------------------------------------------------------------------- 1 | from .probability_distribution import ProbabilityDistribution 2 | from .multi_variable_gassian import MultiVariableGaussian 3 | from .bernoulli import Bernoulli 4 | -------------------------------------------------------------------------------- /evoltier/optimizers/__init__.py: -------------------------------------------------------------------------------- 1 | from .gaussian_natural_gradient import GaussianNaturalGradientOptimizer 2 | from .cma_es import CMAES 3 | from .bernoulli_natural_gradient import BernoulliNaturalGradientOptimizer 4 | -------------------------------------------------------------------------------- /.coveragerc: -------------------------------------------------------------------------------- 1 | [report] 2 | include = 3 | evoltier/*.py 4 | omit = 5 | evoltier/__init__.py 6 | evoltier/model/__init__.py 7 | evoltier/optimizers/__init__.py 8 | evoltier/test/__init__.py 9 | 10 | -------------------------------------------------------------------------------- /evoltier/selection/__init__.py: -------------------------------------------------------------------------------- 1 | from .pbil_selection import PBILSelection 2 | from .cma_large_popsize_selection import CMALargePopSizeSelection 3 | from .cma_selection import CMASelection 4 | from .nes_selection import NESSelection 5 | -------------------------------------------------------------------------------- /evoltier/test/__init__.py: -------------------------------------------------------------------------------- 1 | from evoltier.test import test_probability_distribution 2 | from evoltier.test import test_multi_variable_gaussian 3 | 4 | TestProbabilityDistribution = test_probability_distribution.TestProbabilityDistribution 5 | TestMultiVariableGaussian = test_multi_variable_gaussian.TestMultiVariableGaussian 6 | 7 | -------------------------------------------------------------------------------- /evoltier/problem.py: -------------------------------------------------------------------------------- 1 | 2 | class Problem(object): 3 | """ 4 | Abstact class of a target problem or a objective function. 5 | """ 6 | 7 | def __init__(self, *args, **kwargs): 8 | raise NotImplementedError 9 | 10 | def __call__(self, *args, **kwargs): 11 | raise NotImplementedError 12 | 13 | -------------------------------------------------------------------------------- /evoltier/test/make_test.py: -------------------------------------------------------------------------------- 1 | import unittest 2 | 3 | 4 | def suite(): 5 | test_suite = unittest.TestSuite() 6 | all_test_suite = unittest.defaultTestLoader.discover(".", pattern="test_*.py") 7 | for test in all_test_suite: 8 | test_suite.addTest(test) 9 | return test_suite 10 | 11 | if __name__ == "__main__": 12 | suites = suite() 13 | unittest.TextTestRunner().run(suites) -------------------------------------------------------------------------------- /.travis.yml: -------------------------------------------------------------------------------- 1 | language: python 2 | 3 | python: 4 | - 2.7 5 | - 3.6 6 | 7 | install: 8 | - pip install -r requirements-nocuda.txt 9 | - pip install coverage coveralls 10 | 11 | script: 12 | - python evoltier/test/make_test.py 13 | 14 | after_success: 15 | - coverage run evoltier/test/make_test.py 16 | - coverage report 17 | - coveralls 18 | 19 | #notifications: 20 | # slack: 21 | # secure: your_token_key 22 | -------------------------------------------------------------------------------- /evoltier/selection/cma_selection.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | 3 | from ..weight import RankingBasedSelection 4 | 5 | class CMASelection(RankingBasedSelection): 6 | """ 7 | This selection scheme is Non-increasing transformation as CMA-ES weight. See also, 8 | [Hansen & Auger, 2014] 9 | """ 10 | 11 | def transform(self, rank_based_vals, xp=np): 12 | lam = len(rank_based_vals) 13 | weight = xp.maximum(0, xp.log((lam + 1) / 2) - xp.log(rank_based_vals)) 14 | weight /= weight.sum() 15 | return weight 16 | -------------------------------------------------------------------------------- /evoltier/selection/nes_selection.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | 3 | from ..weight import RankingBasedSelection 4 | 5 | class NESSelection(RankingBasedSelection): 6 | """ 7 | This selection scheme is Non-increasing transformation as NES weight. See also, 8 | [Wierstra et. al., 2014] 9 | """ 10 | 11 | def transform(self, rank_based_vals, xp=np): 12 | lam = len(rank_based_vals) 13 | weight = xp.maximum(0, xp.log((lam / 2) + 1) - xp.log(rank_based_vals)) 14 | weight /= weight.sum() 15 | return weight - 1. / lam 16 | -------------------------------------------------------------------------------- /evoltier/selection/cma_large_popsize_selection.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | 3 | from ..weight import QuantileBasedSelection 4 | 5 | class CMALargePopSizeSelection(QuantileBasedSelection): 6 | """ 7 | This selection scheme is Non-increasing transformation as CMA-ES weight which is 8 | considered for large population size. See also, 9 | [Shirakawa et al. 2015 (GECCO2015)] 10 | """ 11 | 12 | def transform(self, rank_based_vals, xp=np): 13 | weight = -2. * xp.log(2. * rank_based_vals) 14 | weight[rank_based_vals > 0.5] = 0 15 | return weight 16 | -------------------------------------------------------------------------------- /evoltier/test/test_multi_variable_gaussian.py: -------------------------------------------------------------------------------- 1 | import unittest 2 | import numpy as np 3 | import itertools 4 | 5 | from evoltier.model import MultiVariableGaussian 6 | 7 | 8 | class TestMultiVariableGaussian(unittest.TestCase): 9 | 10 | def setUp(self): 11 | self.dim = 3 12 | self.gaussian = MultiVariableGaussian(self.dim) 13 | 14 | def tearDown(self): 15 | self.gaussian = None 16 | 17 | def test_sampling(self): 18 | pop_size = 100 19 | sample = self.gaussian.sampling(pop_size) 20 | self.assertTrue(sample.shape, (pop_size, self.dim)) 21 | 22 | if __name__ == '__main__': 23 | unittest.main() 24 | -------------------------------------------------------------------------------- /evoltier/model/probability_distribution.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | 3 | 4 | class ProbabilityDistribution(object): 5 | def __init__(self, xp=np): 6 | self.xp = xp 7 | 8 | def sampling(self, pop_size): 9 | raise NotImplementedError() 10 | 11 | def get_info(self): 12 | raise NotImplementedError() 13 | 14 | def get_info_dict(self): 15 | raise NotImplementedError() 16 | 17 | def generate_header(self): 18 | raise NotImplementedError() 19 | 20 | def use_gpu(self): 21 | try: 22 | import cupy as cp 23 | self.xp = cp 24 | except ImportError: 25 | raise ImportError() 26 | -------------------------------------------------------------------------------- /evoltier/optimizer.py: -------------------------------------------------------------------------------- 1 | class Optimizer(object): 2 | """ 3 | Base class of all optimizer. 4 | """ 5 | 6 | def __init__(self, distribution, weight_function, lr): 7 | self.target = distribution 8 | self.t = 0 9 | self.w_func = weight_function 10 | self.lr = lr 11 | 12 | if not callable(self.w_func): 13 | raise TypeError('weight function is NOT callable.') 14 | 15 | def update(self, evals, sample): 16 | raise NotImplementedError() 17 | 18 | def get_info_dict(self): 19 | raise NotImplementedError() 20 | 21 | def generate_header(self): 22 | raise NotImplementedError() 23 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | [![Build Status](https://travis-ci.org/satuma777/evoltier.svg?branch=master)](https://travis-ci.org/satuma777/evoltier) 2 | [![Coverage Status](https://coveralls.io/repos/github/satuma777/evoltier/badge.svg?branch=master)](https://coveralls.io/github/satuma777/evoltier?branch=master) 3 | [![Maintainability](https://api.codeclimate.com/v1/badges/50f59641ed78e7991754/maintainability)](https://codeclimate.com/github/satuma777/evoltier/maintainability) 4 | [![Codacy Badge](https://api.codacy.com/project/badge/Grade/b8bd0ae067f94a7eaee8a26ec10a1dc3)](https://www.codacy.com/app/satuma777/evoltier?utm_source=github.com&utm_medium=referral&utm_content=satuma777/evoltier&utm_campaign=Badge_Grade) 5 | 6 | # evoltier 7 | Python implementation of evolution strategy based on Information Geometry. This library includes CMA-ES, NES, CompactGA and PBIL. 8 | -------------------------------------------------------------------------------- /evoltier/test/test_probability_distribution.py: -------------------------------------------------------------------------------- 1 | import unittest 2 | 3 | from evoltier.model import ProbabilityDistribution 4 | 5 | 6 | class TestProbabilityDistribution(unittest.TestCase): 7 | 8 | def setUp(self): 9 | self.distribution = ProbabilityDistribution() 10 | 11 | def tearDown(self): 12 | self.distribution = None 13 | 14 | def test_sampling(self): 15 | with self.assertRaises(NotImplementedError): 16 | self.distribution.sampling() 17 | 18 | def test_get_info(self): 19 | with self.assertRaises(NotImplementedError): 20 | self.distribution.get_info() 21 | 22 | def test_get_info_dict(self): 23 | with self.assertRaises(NotImplementedError): 24 | self.distribution.get_info_dict() 25 | 26 | def test_generate_header(self): 27 | with self.assertRaises(NotImplementedError): 28 | self.distribution.generate_header() 29 | 30 | def test_use_gpu(self): 31 | try: 32 | import cupy 33 | except ImportError: 34 | # this case is using by cpu only. 35 | with self.assertRaises(ImportError): 36 | self.distribution.use_gpu() 37 | else: 38 | # this case is using by gpu. 39 | self.assertTrue(isinstance(self.distribution.xp, cupy)) 40 | 41 | 42 | if __name__ == '__main__': 43 | unittest.main() 44 | -------------------------------------------------------------------------------- /evoltier/optimizers/bernoulli_natural_gradient.py: -------------------------------------------------------------------------------- 1 | from __future__ import print_function, division 2 | 3 | from evoltier.optimizer import Optimizer 4 | from evoltier.model.bernoulli import Bernoulli 5 | 6 | 7 | class BernoulliNaturalGradientOptimizer(Optimizer): 8 | def __init__(self, weight_function, lr, distribution=None, dim=None): 9 | if dim is None and distribution is None: 10 | print('Need to set argument "dim" or "distribution"') 11 | raise 12 | dist = distribution if distribution is not None else Bernoulli(dim=dim) 13 | super(BernoulliNaturalGradientOptimizer, self).__init__(dist, weight_function, lr) 14 | 15 | def update(self, evals, sample): 16 | self.t += 1 17 | weights = self.w_func(evals, xp=self.target.xp) 18 | self.lr.set_parameters(weights, xp=self.target.xp) 19 | 20 | grad_theta = self.compute_natural_grad(weights, sample, self.target.theta) 21 | self.target.theta += self.lr.eta * grad_theta 22 | 23 | def compute_natural_grad(self, weights, sample, theta): 24 | xp = self.target.xp 25 | grad_theta = xp.sum(weights[:, None] * (sample - theta), axis=0) 26 | return grad_theta 27 | 28 | def get_info_dict(self): 29 | info = {'LearningRate': self.lr.eta} 30 | return info 31 | 32 | def generate_header(self): 33 | header = ['LearningRate'] 34 | return header 35 | 36 | -------------------------------------------------------------------------------- /evoltier/selection/pbil_selection.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | from math import floor 3 | 4 | from ..weight import RankingBasedSelection 5 | 6 | 7 | class PBILSelection(RankingBasedSelection): 8 | """ 9 | This selection scheme is used by PBIL and compact GA. 10 | Also, PBIL selection scheme also used in natural gradient update 11 | with Bernoulli distribution. See also, 12 | [Shirakawa et al. 2018 (AAAI-2018)] 13 | """ 14 | def __init__(self, selection_rate=0.5, is_use_negative=True, is_minimize=True, is_normalize=False): 15 | super(PBILSelection, self).__init__(is_minimize, is_normalize) 16 | self.selection_rate = selection_rate 17 | self.is_use_negative = is_use_negative 18 | 19 | def transform(self, rank_based_vals, xp=np): 20 | weights = xp.zeros_like(rank_based_vals) 21 | worst_rank = len(rank_based_vals) 22 | idx_sorted_rank = xp.argsort(rank_based_vals) 23 | 24 | if self.is_use_negative: 25 | half_num_weight = floor(worst_rank * self.selection_rate / 2.) 26 | # the best floor(lam * selection_rate / 2) samples get the positive weights 27 | idx_positive = idx_sorted_rank[:half_num_weight] 28 | weights[idx_positive] = 1 29 | # the worst floor(lam * selection_rate / 2) samples get the negative weights 30 | idx_negative = idx_sorted_rank[-half_num_weight:] 31 | weights[idx_negative] = -1 32 | else: 33 | # the best floor(lam * selection_rate) samples get the positive weights 34 | num_weight = floor(worst_rank * self.selection_rate) 35 | idx_positive = idx_sorted_rank[:num_weight] 36 | weights[idx_positive] = 1 37 | 38 | return weights 39 | -------------------------------------------------------------------------------- /evoltier/model/bernoulli.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | 3 | from evoltier.model import ProbabilityDistribution 4 | 5 | 6 | class Bernoulli(ProbabilityDistribution): 7 | def __init__(self, dim, theta=None, upper=None, lower=None, xp=np): 8 | self.dim = dim 9 | self.xp = xp 10 | self.upper = upper if upper is not None else 1. - 1. / self.dim 11 | self.lower = lower if lower is not None else 1. / self.dim 12 | self.theta = theta if theta is not None else 0.5 * self.xp.ones(self.dim) 13 | self.model_class = 'Bernoulli' 14 | 15 | def sampling(self, pop_size): 16 | xp = self.xp 17 | size = (pop_size, self.dim) 18 | samples = xp.random.binomial(n=1, p=self.theta, size=size) 19 | return samples 20 | 21 | def get_info(self): 22 | mean, var, median, mini, maxi = self._calculate_stat() 23 | string_info = 'Mean: {:.2e}, Variance: {:.2e}, Median: {:.2e}, Min: {:.2e}, Max: {:.2e}'.format(mean, var, median, mini, maxi) 24 | return string_info 25 | 26 | def get_info_dict(self): 27 | mean, var, median, mini, maxi = self._calculate_stat() 28 | dict_info = {'Mean': mean, 'Variance': var, 'Median': median, 'Min': mini, 'Max': maxi} 29 | return dict_info 30 | 31 | def generate_header(self): 32 | return ['Mean', 'Variance', 'Median', 'Min', 'Max'] 33 | 34 | def _calculate_stat(self): 35 | xp = self.xp 36 | mean = xp.mean(self.theta) 37 | var = xp.var(self.theta) 38 | median = xp.median(self.theta) 39 | mini = xp.min(self.theta) 40 | maxi = xp.max(self.theta) 41 | return mean, var, median, mini, maxi 42 | 43 | @property 44 | def theta(self): 45 | return self.__theta 46 | 47 | @theta.setter 48 | def theta(self, new_theta): 49 | xp = self.xp 50 | self. __theta = xp.minimum(xp.maximum(new_theta, self.lower), self.upper) 51 | -------------------------------------------------------------------------------- /main.py: -------------------------------------------------------------------------------- 1 | from __future__ import print_function 2 | 3 | from evoltier.optimizers import CMAES, GaussianNaturalGradientOptimizer, BernoulliNaturalGradientOptimizer 4 | from evoltier import updater 5 | from evoltier import weight 6 | from evoltier import model 7 | from evoltier.selection import PBILSelection, CMALargePopSizeSelection, CMASelection, NESSelection 8 | from evoltier.utils import CMAESParameters, HyperParameters 9 | 10 | 11 | def quad(x): 12 | # implementation of quadratic function. 13 | # global minima is zero. 14 | return (x * x).sum(axis=1) 15 | 16 | 17 | def negative_quad(x): 18 | # implementation of quadratic function. 19 | # global mixima is zero. 20 | return - (x * x).sum(axis=1) 21 | 22 | 23 | def onemax(x): 24 | return x.sum(axis=1) 25 | 26 | 27 | def leading_ones(x): 28 | import numpy as np 29 | return np.sum(np.dot(x, np.tri(x.shape[1])), axis=1) 30 | 31 | 32 | def main(gpuID=-1): 33 | dim = 100 34 | # set probability distribution 35 | gaussian = model.MultiVariableGaussian(dim=dim) 36 | #gaussian = model.Bernoulli(dim=dim) 37 | if gpuID >= 0: 38 | gaussian.use_gpu() 39 | 40 | # set utility function 41 | w = NESSelection(is_minimize=True) 42 | #w = PBILSelection(is_minimize=True, selection_rate=0.5) 43 | 44 | # set learning rate of distribution parameters 45 | lr = CMAESParameters(dim=dim) 46 | #lr = HyperParameters({'eta': 1 / dim}) 47 | 48 | # set optimizer 49 | opt = CMAES(w, lr, dim=dim) 50 | #opt = BernoulliNaturalGradientOptimizer(w, lr, dim=dim) 51 | 52 | # set updater 53 | upd = updater.Updater(optimizer=opt, obj_func=quad, pop_size=100, threshold=float('-inf'), 54 | out='result', max_iter=10000, logging=True) 55 | 56 | # run IGO and print result 57 | print(upd.run()) 58 | 59 | 60 | if __name__ == '__main__': 61 | import argparse 62 | parser = argparse.ArgumentParser(description='Evoltier Example') 63 | parser.add_argument('--gpu', '-g', type=int, default=-1, help='GPU ID (negative value indicates CPU)') 64 | args = parser.parse_args() 65 | main(args.gpu) 66 | -------------------------------------------------------------------------------- /evoltier/optimizers/gaussian_natural_gradient.py: -------------------------------------------------------------------------------- 1 | from __future__ import print_function, division 2 | 3 | from evoltier.optimizer import Optimizer 4 | from evoltier.model.multi_variable_gassian import MultiVariableGaussian 5 | 6 | 7 | class GaussianNaturalGradientOptimizer(Optimizer): 8 | def __init__(self, weight_function, lr, distribution=None, dim=None): 9 | if dim is None and distribution is None: 10 | print('Need to set argument "dim" or "distribution"') 11 | raise 12 | dist = distribution if distribution is not None else MultiVariableGaussian(dim=dim) 13 | super(GaussianNaturalGradientOptimizer, self).__init__(dist, weight_function, lr) 14 | 15 | def update(self, evals, sample): 16 | self.t += 1 17 | xp = self.target.xp 18 | weights = self.w_func(evals, xp=xp) 19 | self.lr.set_parameters(weights, xp=xp) 20 | 21 | mean, cov, sigma = self.target.mean, self.target.cov, self.target.sigma 22 | grad_m, grad_cov = self.compute_natural_grad(weights, sample, mean, cov, sigma) 23 | 24 | self.target.mean += self.lr.c_m * grad_m 25 | self.target.cov += self.lr.c_C * grad_cov 26 | 27 | def compute_natural_grad(self, weight, sample, mean, cov, sigma): 28 | xp = self.target.xp 29 | derivation = (sample - mean) / sigma 30 | w_der = weight * derivation.T 31 | grad_m = sigma * w_der.sum(axis=1) 32 | 33 | if self.target.model_class == 'Isotropic': 34 | norm_w_der = xp.diag(xp.dot(w_der, w_der.T)) 35 | grad_cov = (xp.sum(weight * norm_w_der) / self.target.dim) - (xp.sum(weight) * cov) 36 | elif self.target.model_class == 'Separable': 37 | grad_cov = (w_der * derivation.T).sum(axis=1) - (weight.sum() * cov) 38 | else: 39 | grad_cov = xp.dot(w_der, derivation) - weight.sum() * cov 40 | 41 | return grad_m, grad_cov 42 | 43 | def generate_header(self): 44 | header = ['LearningRateMean', 'LearningRateCov'] 45 | return header 46 | 47 | def get_info_dict(self): 48 | info = {'LearningRateMean': self.lr.c_m, 49 | 'LearningRateCov': self.lr.c_C} 50 | return info 51 | -------------------------------------------------------------------------------- /evoltier/model/multi_variable_gassian.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | from six.moves import range 3 | 4 | from evoltier.model import ProbabilityDistribution 5 | 6 | 7 | class MultiVariableGaussian(ProbabilityDistribution): 8 | def __init__(self, dim, mean=None, cov=None, sigma=None, xp=np): 9 | self.xp = xp 10 | self.dim = dim 11 | self.sigma = sigma if sigma is not None else 0.3 12 | self.mean = mean if mean is not None else self.xp.random.random(self.dim) 13 | self.cov = cov if cov is not None else self.xp.identity(self.dim) 14 | self.model_class = 'FullCovariance' 15 | 16 | def sampling(self, pop_size): 17 | xp = self.xp 18 | sqrtC = xp.sqrt(self.eigan_vals)[:, None] * self.B.T 19 | samples = self.mean + self.sigma * xp.dot(xp.random.randn(pop_size, self.dim), sqrtC) 20 | return samples 21 | 22 | def get_info(self): 23 | return 'MaxEigenValue: {:.2e}, MinEigenValue: {:.2e},'.format(self.var_max, self.var_min) 24 | 25 | def get_info_dict(self): 26 | return {'MaxEigenValue': self.var_max, 'MinEigenValue': self.var_min} 27 | 28 | def generate_header(self): 29 | return ['MaxEigenValue', 'MinEigenValue'] 30 | 31 | def eigan_decomp_cov(self, cov): 32 | xp = self.xp 33 | if xp == np: 34 | from numpy.dual import eigh 35 | eigan_vals, B = eigh(cov) 36 | else: 37 | eigan_vals, B = xp.linalg.eigh(cov) 38 | 39 | if eigan_vals.min() < 0: 40 | print('Wanning: Minimum eigan value is negative.') 41 | return False 42 | 43 | self.eigan_vals = eigan_vals 44 | self.B = B 45 | self.var_max = (self.sigma ** 2) * eigan_vals.max() 46 | self.var_min = (self.sigma ** 2) * eigan_vals.min() 47 | return True 48 | 49 | def use_gpu(self): 50 | super(MultiVariableGaussian, self).use_gpu() 51 | self.cov = self.xp.array(self.cov) 52 | self.mean = self.xp.array(self.mean) 53 | 54 | @property 55 | def cov(self): 56 | return self.__cov 57 | 58 | @cov.setter 59 | def cov(self, new_cov): 60 | if self.eigan_decomp_cov(new_cov): 61 | self.__cov = new_cov 62 | -------------------------------------------------------------------------------- /evoltier/utils.py: -------------------------------------------------------------------------------- 1 | from math import sqrt, floor, log 2 | import numpy as np 3 | 4 | 5 | class HyperParameters(object): 6 | def __init__(self, dict_params): 7 | self.dict_params = dict_params 8 | 9 | def set_parameters(self, *args, **kargs): 10 | for name, value in self.dict_params.items(): 11 | setattr(self, name, value) 12 | return True 13 | 14 | 15 | class CMAESParameters(object): 16 | def __init__(self, dim, alpha_cov=2.): 17 | self.dim = dim 18 | self.alpha_cov = alpha_cov 19 | 20 | def set_parameters(self, weights, xp=np): 21 | dim = self.dim 22 | alpha_cov = self.alpha_cov 23 | 24 | self.mu_eff = self.compute_mu_eff(weights, xp=xp) 25 | 26 | # Hyperparameters for the covariance matrix adaptation 27 | self.c_C = self.compute_c_C(self.mu_eff, dim) 28 | self.c_1 = self.compute_c_1(self.mu_eff, dim, alpha_cov=alpha_cov) 29 | self.c_mu = self.compute_c_mu(self.mu_eff, dim, self.c_1, alpha_cov=alpha_cov, xp=xp) 30 | 31 | # Hyperparameters for step-size control 32 | self.c_sigma = self.compute_c_sigma(self.mu_eff, dim) 33 | self.d_sigma = self.compute_d_sigma(self.mu_eff, dim, self.c_sigma) 34 | 35 | # Hyperparameter for the mean vector update 36 | self.c_m = self.compute_c_m() 37 | 38 | return True 39 | 40 | def get_population_size(self, scale=1): 41 | return scale * (4 + floor(3 * log(self.dim))) 42 | 43 | @staticmethod 44 | def compute_mu_eff(weights, xp=np): 45 | return xp.linalg.norm(weights, ord=1) / xp.linalg.norm(weights) 46 | 47 | @staticmethod 48 | def compute_c_C(mu_eff, dim): 49 | return (4 + mu_eff / dim) / (dim + 4 + 2 * mu_eff / dim) 50 | 51 | @staticmethod 52 | def compute_c_1(mu_eff, dim, alpha_cov=2.): 53 | return alpha_cov / ((dim + 1.3) ** 2 + mu_eff) 54 | 55 | @staticmethod 56 | def compute_c_mu(mu_eff, dim, c_1, alpha_cov=2., xp=np): 57 | return xp.min([1 - c_1, alpha_cov * (mu_eff - 2 + 1 / mu_eff) / ((dim + 2) ** 2 + alpha_cov * mu_eff / 2)]) 58 | 59 | @staticmethod 60 | def compute_c_sigma(mu_eff, dim): 61 | return (mu_eff + 2) / (dim + mu_eff + 5) 62 | 63 | @staticmethod 64 | def compute_d_sigma(mu_eff, dim, c_sigma): 65 | return 1 + 2 * max([0, sqrt((mu_eff - 1) / (dim + 1)) - 1]) + c_sigma 66 | 67 | @staticmethod 68 | def compute_c_m(): 69 | return 1 70 | -------------------------------------------------------------------------------- /evoltier/weight.py: -------------------------------------------------------------------------------- 1 | from __future__ import division 2 | import numpy as np 3 | 4 | 5 | class QuantileBasedSelection(object): 6 | def __init__(self, is_minimize=True, is_normalize=False): 7 | self.is_minimize = is_minimize 8 | self.is_normalize = is_normalize 9 | 10 | def __call__(self, evals, coefficient=None, xp=np): 11 | quantiles = self.compute_quantiles(evals, coefficient=None, xp=xp) 12 | weight = self.transform(quantiles, xp=xp) 13 | if self.is_normalize: 14 | weight /= xp.linalg.norm(weight, ord=1) 15 | return weight 16 | 17 | def compute_quantiles(self, evals, coefficient=None, xp=np, rank_rule='upper'): 18 | pop_size = evals.shape[0] 19 | if coefficient is None: 20 | coefficient = xp.ones(pop_size) 21 | sorter = xp.argsort(evals) 22 | if self.is_minimize is False: 23 | sorter = sorter[::-1] 24 | 25 | # set label sequentially that minimum eval = 0 , ... , maximum eval = pop_size - 1 26 | # --- Example --- 27 | # eval = [12, 13, 10] 28 | # inv = [ 1, 2, 0] 29 | inv = xp.empty(sorter.size, dtype=xp.integer) 30 | inv[sorter] = xp.arange(sorter.size, dtype=xp.integer) 31 | 32 | arr = evals[sorter] 33 | obs = xp.r_[True, arr[1:] != arr[:-1]] 34 | dense = xp.cumsum(obs)[inv] 35 | 36 | # cumulative counts of likelihood ratio 37 | count = xp.r_[False, xp.cumsum(coefficient[sorter])] 38 | 39 | if rank_rule == 'upper': 40 | cum_llr = count[dense] 41 | elif rank_rule == 'lower': 42 | cum_llr = count[dense - 1] 43 | 44 | quantile = cum_llr / pop_size 45 | return quantile 46 | 47 | def transform(self, rank_based_vals, xp=np): 48 | raise NotImplementedError() 49 | 50 | 51 | class RankingBasedSelection(QuantileBasedSelection): 52 | def __init__(self, is_minimize=True, is_normalize=False): 53 | super(RankingBasedSelection, self).__init__(is_minimize, is_normalize) 54 | 55 | def __call__(self, evals, coefficient=None, xp=np,): 56 | ranking = self.compute_ranking(evals, coefficient=coefficient, xp=xp) 57 | weight = self.transform(ranking, xp=xp) 58 | if self.is_minimize: 59 | weight /= xp.linalg.norm(weight, ord=1) 60 | return weight 61 | 62 | def compute_ranking(self, evals, coefficient=None, xp=np): 63 | return self.compute_quantiles(evals, coefficient=coefficient, xp=xp) * len(evals) 64 | -------------------------------------------------------------------------------- /evoltier/optimizers/cma_es.py: -------------------------------------------------------------------------------- 1 | from __future__ import print_function, division 2 | 3 | from evoltier.optimizers.gaussian_natural_gradient import GaussianNaturalGradientOptimizer 4 | 5 | 6 | class CMAES(GaussianNaturalGradientOptimizer): 7 | def __init__(self, weight_function, lr, distribution=None, dim=None): 8 | super(CMAES, self).__init__(weight_function, lr, distribution, dim) 9 | 10 | xp = self.target.xp 11 | dim = self.target.dim 12 | self.p_c = xp.zeros(dim) 13 | self.p_sigma = xp.zeros(dim) 14 | self.ex_norm = xp.sqrt(dim) * (1. - 1. / (4. * dim) + 1. / (21. * (dim ** 2))) 15 | 16 | def update(self, evals, sample): 17 | xp = self.target.xp 18 | self.t += 1 19 | weights = self.w_func(evals, xp=xp) 20 | self.lr.set_parameters(weights, xp=xp) 21 | 22 | mean, cov, sigma = self.target.mean, self.target.cov, self.target.sigma 23 | mu_eff = self.lr.mu_eff 24 | c_C, c_1, c_mu = self.lr.c_C, self.lr.c_1, self.lr.c_mu 25 | c_sigma, d_sigma = self.lr.c_sigma, self.lr.d_sigma 26 | c_m = self.lr.c_m 27 | 28 | grad_m, grad_cov = self.compute_natural_grad(weights, sample, mean, cov, sigma) 29 | 30 | h_sigma = self.update_evolution_path(mu_eff, grad_m / sigma, c_sigma, c_C) 31 | delta = (1. - h_sigma) * c_C * (2. - c_C) 32 | 33 | self.target.mean += c_m * grad_m 34 | self.target.cov += c_1 * (cov * (delta - 1) + xp.outer(self.p_c, self.p_c)) + c_mu * (grad_cov - xp.sum(weights) * cov) 35 | self.target.sigma *= self.compute_step_size(c_sigma, d_sigma) 36 | 37 | def update_evolution_path(self, mu_eff, y_w, c_sigma, c_C): 38 | xp = self.target.xp 39 | 40 | # compute new p_sigma 41 | D_inv = xp.reciprocal(xp.sqrt(self.target.eigan_vals))[:, None] 42 | inv_sqrtC = xp.dot(self.target.B * D_inv, self.target.B.T) 43 | self.p_sigma += xp.sqrt(c_sigma * (2. - c_sigma) * mu_eff) * xp.dot(y_w, inv_sqrtC) - c_sigma * self.p_sigma 44 | 45 | # compute new p_c 46 | p_sigma_norm_scaled = xp.linalg.norm(self.p_sigma) / xp.sqrt(1. - ((1. - c_sigma) ** (2 * (self.t + 1)))) 47 | h_sigma = (1.4 + 2. / (self.target.dim + 1)) * self.ex_norm < p_sigma_norm_scaled 48 | self.p_c += h_sigma * xp.sqrt(c_C * (2. - c_C) * mu_eff) * y_w - c_C * self.p_c 49 | return h_sigma 50 | 51 | def compute_step_size(self, c_sigma, d_sigma): 52 | xp = self.target.xp 53 | control_factor = xp.exp((c_sigma / d_sigma) * (-1. + xp.linalg.norm(self.p_sigma) / self.ex_norm)) 54 | return control_factor 55 | -------------------------------------------------------------------------------- /.gitignore: -------------------------------------------------------------------------------- 1 | *.log 2 | *.pot 3 | *.pyc 4 | __pycache__/ 5 | media 6 | 7 | ### OSX ### 8 | *.DS_Store 9 | .AppleDouble 10 | .LSOverride 11 | 12 | # Icon must end with two \r 13 | Icon 14 | 15 | 16 | # Thumbnails 17 | ._* 18 | 19 | # Files that might appear in the root of a volume 20 | .DocumentRevisions-V100 21 | .fseventsd 22 | .Spotlight-V100 23 | .TemporaryItems 24 | .Trashes 25 | .VolumeIcon.icns 26 | .com.apple.timemachine.donotpresent 27 | 28 | # Directories potentially created on remote AFP share 29 | .AppleDB 30 | .AppleDesktop 31 | Network Trash Folder 32 | Temporary Items 33 | .apdisk 34 | 35 | ### PyCharm ### 36 | # Covers JetBrains IDEs: IntelliJ, RubyMine, PhpStorm, AppCode, PyCharm, CLion, Android Studio and Webstorm 37 | # Reference: https://intellij-support.jetbrains.com/hc/en-us/articles/206544839 38 | 39 | # User-specific stuff: 40 | .idea/ 41 | 42 | ## File-based project format: 43 | *.iws 44 | 45 | ## Plugin-specific files: 46 | 47 | # IntelliJ 48 | /out/ 49 | 50 | # mpeltonen/sbt-idea plugin 51 | .idea_modules/ 52 | 53 | # JIRA plugin 54 | atlassian-ide-plugin.xml 55 | 56 | # Crashlytics plugin (for Android Studio and IntelliJ) 57 | com_crashlytics_export_strings.xml 58 | crashlytics.properties 59 | crashlytics-build.properties 60 | fabric.properties 61 | 62 | ### PyCharm Patch ### 63 | # Comment Reason: https://github.com/joeblau/gitignore.io/issues/186#issuecomment-215987721 64 | 65 | # *.iml 66 | # modules.xml 67 | # .idea/misc.xml 68 | # *.ipr 69 | 70 | ### Python ### 71 | # Byte-compiled / optimized / DLL files 72 | *.py[cod] 73 | *$py.class 74 | 75 | # C extensions 76 | *.so 77 | 78 | # Distribution / packaging 79 | .Python 80 | env/ 81 | build/ 82 | develop-eggs/ 83 | dist/ 84 | downloads/ 85 | eggs/ 86 | .eggs/ 87 | lib/ 88 | lib64/ 89 | parts/ 90 | sdist/ 91 | var/ 92 | wheels/ 93 | *.egg-info/ 94 | .installed.cfg 95 | *.egg 96 | 97 | # PyInstaller 98 | # Usually these files are written by a python script from a template 99 | # before PyInstaller builds the exe, so as to inject date/other infos into it. 100 | *.manifest 101 | *.spec 102 | 103 | # Installer logs 104 | pip-log.txt 105 | pip-delete-this-directory.txt 106 | 107 | # Unit test / coverage reports 108 | htmlcov/ 109 | .tox/ 110 | .coverage 111 | .coverage.* 112 | .cache 113 | nosetests.xml 114 | coverage.xml 115 | *,cover 116 | .hypothesis/ 117 | 118 | # Translations 119 | *.mo 120 | 121 | # Django stuff: 122 | 123 | # Flask stuff: 124 | instance/ 125 | .webassets-cache 126 | 127 | # Scrapy stuff: 128 | .scrapy 129 | 130 | # Sphinx documentation 131 | docs/_build/ 132 | 133 | # PyBuilder 134 | target/ 135 | 136 | # Jupyter Notebook 137 | .ipynb_checkpoints 138 | 139 | # celery beat schedule file 140 | celerybeat-schedule 141 | 142 | # dotenv 143 | .env 144 | 145 | # virtualenv 146 | .venv 147 | venv/ 148 | ENV/ 149 | 150 | # Spyder project settings 151 | .spyderproject 152 | 153 | # Rope project settings 154 | .ropeproject 155 | 156 | ### VirtualEnv ### 157 | # Virtualenv 158 | # http://iamzed.com/2009/05/07/a-primer-on-virtualenv/ 159 | [Bb]in 160 | [Ii]nclude 161 | [Ll]ib 162 | [Ll]ib64 163 | [Ll]ocal 164 | [Ss]cripts 165 | pyvenv.cfg 166 | pip-selfcheck.json 167 | *.csv 168 | *.pyc 169 | *.pyx 170 | *.lprof 171 | -------------------------------------------------------------------------------- /evoltier/updater.py: -------------------------------------------------------------------------------- 1 | from six.moves import range 2 | import csv 3 | import os 4 | 5 | 6 | class Updater(object): 7 | ''' 8 | Abstraction of main loop. 9 | ''' 10 | def __init__(self, optimizer, obj_func, pop_size=1, threshold=None, max_iter=10000, out='result', 11 | logging=False): 12 | self.opt = optimizer 13 | self.obj_func = obj_func 14 | self.pop_size = pop_size 15 | self.threshold = threshold 16 | self.max_iter = max_iter 17 | self.min = optimizer.w_func.is_minimize 18 | self.out = out 19 | self.logging = logging 20 | 21 | if self.logging: 22 | if not os.path.isdir(out): 23 | os.makedirs(out) 24 | 25 | with open(out+'/log.csv', 'w') as log_file: 26 | self.header = ['Generation', 'BestEval'] + self.opt.generate_header() + self.opt.target.generate_header() 27 | csv_writer = csv.DictWriter(log_file, fieldnames=self.header) 28 | csv_writer.writeheader() 29 | 30 | if self.threshold is None and self.min: 31 | self.threshold = 1e-6 32 | elif self.threshold is None: 33 | self.threshold = 1e+6 34 | 35 | def run(self): 36 | best_solution = None 37 | success = False 38 | if self.min: 39 | best_eval = float('inf') 40 | for i in range(1, self.max_iter + 1): 41 | sample = self.opt.target.sampling(pop_size=self.pop_size) 42 | evals = self.obj_func(sample) 43 | self.opt.update(evals=evals, sample=sample) 44 | 45 | if evals.min() < best_eval: 46 | best_eval = evals.min() 47 | best_solution = sample[evals.argmin()] 48 | 49 | self.print_log(i, best_eval) 50 | 51 | if self.logging: 52 | self.write_csv_log(i, best_eval) 53 | 54 | if best_eval < self.threshold: 55 | success = True 56 | break 57 | else: 58 | best_eval = float('-inf') 59 | for i in range(1, self.max_iter + 1): 60 | sample = self.opt.target.sampling(pop_size=self.pop_size) 61 | evals = self.obj_func(sample) 62 | self.opt.update(evals=evals, sample=sample) 63 | 64 | if evals.max() > best_eval: 65 | best_eval = evals.max() 66 | best_solution = sample[evals.argmax()] 67 | 68 | self.print_log(i, best_eval) 69 | 70 | if self.logging: 71 | self.write_csv_log(i, best_eval) 72 | 73 | if best_eval > self.threshold: 74 | success = True 75 | break 76 | 77 | return best_solution, best_eval, success 78 | 79 | def print_log(self, i, best_eval): 80 | updater_info = 'Generation: {}\t BestEval: {}\t '.format(i, best_eval) 81 | distribution_info = self.opt.target.get_info() 82 | 83 | print(updater_info + distribution_info) 84 | 85 | def write_csv_log(self, i, best_eval): 86 | info = {'Generation': i, 'BestEval': best_eval} 87 | opt_info = self.opt.get_info_dict() 88 | info.update(opt_info) 89 | 90 | distribution_info = self.opt.target.get_info_dict() 91 | info.update(distribution_info) 92 | 93 | with open(self.out+'/log.csv', 'a') as csv_file: 94 | csv_writer = csv.DictWriter(csv_file, fieldnames=self.header) 95 | csv_writer.writerows([info]) 96 | -------------------------------------------------------------------------------- /LICENSE.txt: -------------------------------------------------------------------------------- 1 | GNU GENERAL PUBLIC LICENSE 2 | Version 3, 29 June 2007 3 | 4 | Copyright (C) 2007 Free Software Foundation, Inc. 5 | Everyone is permitted to copy and distribute verbatim copies 6 | of this license document, but changing it is not allowed. 7 | 8 | Preamble 9 | 10 | The GNU General Public License is a free, copyleft license for 11 | software and other kinds of works. 12 | 13 | The licenses 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No Surrender of Others' Freedom. 541 | 542 | If conditions are imposed on you (whether by court order, agreement or 543 | otherwise) that contradict the conditions of this License, they do not 544 | excuse you from the conditions of this License. If you cannot convey a 545 | covered work so as to satisfy simultaneously your obligations under this 546 | License and any other pertinent obligations, then as a consequence you may 547 | not convey it at all. For example, if you agree to terms that obligate you 548 | to collect a royalty for further conveying from those to whom you convey 549 | the Program, the only way you could satisfy both those terms and this 550 | License would be to refrain entirely from conveying the Program. 551 | 552 | 13. Use with the GNU Affero General Public License. 553 | 554 | Notwithstanding any other provision of this License, you have 555 | permission to link or combine any covered work with a work licensed 556 | under version 3 of the GNU Affero General Public License into a single 557 | combined work, and to convey the resulting work. The terms of this 558 | License will continue to apply to the part which is the covered work, 559 | but the special requirements of the GNU Affero General Public License, 560 | section 13, concerning interaction through a network will apply to the 561 | combination as such. 562 | 563 | 14. Revised Versions of this License. 564 | 565 | The Free Software Foundation may publish revised and/or new versions of 566 | the GNU General Public License from time to time. Such new versions will 567 | be similar in spirit to the present version, but may differ in detail to 568 | address new problems or concerns. 569 | 570 | Each version is given a distinguishing version number. If the 571 | Program specifies that a certain numbered version of the GNU General 572 | Public License "or any later version" applies to it, you have the 573 | option of following the terms and conditions either of that numbered 574 | version or of any later version published by the Free Software 575 | Foundation. If the Program does not specify a version number of the 576 | GNU General Public License, you may choose any version ever published 577 | by the Free Software Foundation. 578 | 579 | If the Program specifies that a proxy can decide which future 580 | versions of the GNU General Public License can be used, that proxy's 581 | public statement of acceptance of a version permanently authorizes you 582 | to choose that version for the Program. 583 | 584 | Later license versions may give you additional or different 585 | permissions. However, no additional obligations are imposed on any 586 | author or copyright holder as a result of your choosing to follow a 587 | later version. 588 | 589 | 15. Disclaimer of Warranty. 590 | 591 | THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY 592 | APPLICABLE LAW. EXCEPT WHEN OTHERWISE STATED IN WRITING THE COPYRIGHT 593 | HOLDERS AND/OR OTHER PARTIES PROVIDE THE PROGRAM "AS IS" WITHOUT WARRANTY 594 | OF ANY KIND, EITHER EXPRESSED OR IMPLIED, INCLUDING, BUT NOT LIMITED TO, 595 | THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR 596 | PURPOSE. THE ENTIRE RISK AS TO THE QUALITY AND PERFORMANCE OF THE PROGRAM 597 | IS WITH YOU. SHOULD THE PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF 598 | ALL NECESSARY SERVICING, REPAIR OR CORRECTION. 599 | 600 | 16. Limitation of Liability. 601 | 602 | IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING 603 | WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS 604 | THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY 605 | GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE 606 | USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF 607 | DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD 608 | PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS), 609 | EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF 610 | SUCH DAMAGES. 611 | 612 | 17. Interpretation of Sections 15 and 16. 613 | 614 | If the disclaimer of warranty and limitation of liability provided 615 | above cannot be given local legal effect according to their terms, 616 | reviewing courts shall apply local law that most closely approximates 617 | an absolute waiver of all civil liability in connection with the 618 | Program, unless a warranty or assumption of liability accompanies a 619 | copy of the Program in return for a fee. 620 | 621 | END OF TERMS AND CONDITIONS 622 | 623 | How to Apply These Terms to Your New Programs 624 | 625 | If you develop a new program, and you want it to be of the greatest 626 | possible use to the public, the best way to achieve this is to make it 627 | free software which everyone can redistribute and change under these terms. 628 | 629 | To do so, attach the following notices to the program. It is safest 630 | to attach them to the start of each source file to most effectively 631 | state the exclusion of warranty; and each file should have at least 632 | the "copyright" line and a pointer to where the full notice is found. 633 | 634 | {one line to give the program's name and a brief idea of what it does.} 635 | Copyright (C) {year} {name of author} 636 | 637 | This program is free software: you can redistribute it and/or modify 638 | it under the terms of the GNU General Public License as published by 639 | the Free Software Foundation, either version 3 of the License, or 640 | (at your option) any later version. 641 | 642 | This program is distributed in the hope that it will be useful, 643 | but WITHOUT ANY WARRANTY; without even the implied warranty of 644 | MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 645 | GNU General Public License for more details. 646 | 647 | You should have received a copy of the GNU General Public License 648 | along with this program. If not, see . 649 | 650 | Also add information on how to contact you by electronic and paper mail. 651 | 652 | If the program does terminal interaction, make it output a short 653 | notice like this when it starts in an interactive mode: 654 | 655 | {project} Copyright (C) {year} {fullname} 656 | This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. 657 | This is free software, and you are welcome to redistribute it 658 | under certain conditions; type `show c' for details. 659 | 660 | The hypothetical commands `show w' and `show c' should show the appropriate 661 | parts of the General Public License. Of course, your program's commands 662 | might be different; for a GUI interface, you would use an "about box". 663 | 664 | You should also get your employer (if you work as a programmer) or school, 665 | if any, to sign a "copyright disclaimer" for the program, if necessary. 666 | For more information on this, and how to apply and follow the GNU GPL, see 667 | . 668 | 669 | The GNU General Public License does not permit incorporating your program 670 | into proprietary programs. If your program is a subroutine library, you 671 | may consider it more useful to permit linking proprietary applications with 672 | the library. If this is what you want to do, use the GNU Lesser General 673 | Public License instead of this License. But first, please read 674 | . 675 | --------------------------------------------------------------------------------