bayesoptmodule.py

Go to the documentation of this file.

#!/usr/bin/env python


# ----------------------------------------------------------------------------
#    This file is part of BayesOptimization, an efficient C++ library for 
#    Bayesian optimization.
#
#    Copyright (C) 2011-2015 Ruben Martinez-Cantin <rmcantin@unizar.es>
#
#    BayesOptimization is free software: you can redistribute it and/or modify
#    it under the terms of the GNU Affero General Public License as published by
#    the Free Software Foundation, either version 3 of the License, or
#    (at your option) any later version.
#
#    BayesOptimization is distributed in the hope that it will be useful,
#    but WITHOUT ANY WARRANTY; without even the implied warranty of
#    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
#    GNU Affero General Public License for more details.
#
#    You should have received a copy of the GNU Affero General Public License
#    along with BayesOptimization. If not, see <http://www.gnu.org/licenses/>.
# ----------------------------------------------------------------------------

import numpy as np
import bayesopt as bo




class BayesOptContinuous(object):
    
    
    def __init__(self,n_dim):
        
        self.params = {}
        
        self.n_dim = n_dim
        
        self.lb = np.zeros((self.n_dim,))
        
        self.ub = np.ones((self.n_dim,))

    @property
    def parameters(self):
        return self.params

    @parameters.setter
    def parameters(self,params):
        self.params = params

    @property
    def lower_bound(self):
        return self.lb

    @lower_bound.setter
    def lower_bound(self,lb):
        self.lb = lb

    @property
    def upper_bound(self):
        return self.ub

    @upper_bound.setter
    def upper_bound(self,ub):
        self.ub = ub

    
    def evaluateSample(self, x_in):
        raise NotImplementedError("Please Implement this method")

    
    def optimize(self):
        min_val, x_out, error = bo.optimize(self.evaluateSample, self.n_dim,
                                            self.lb, self.ub,
                                            self.params)
        
        return min_val, x_out, error



class BayesOptDiscrete:
    
    
    def __init__(self, x_set=None, n_dim=None, n_samples=None):
        
        self.params = {} #bo.initialize_params()
        if x_set is None:
            
            if n_dim is None or n_samples is None:
                raise ValueError
            else:
                self.x_set = np.random.rand(n_samples, n_dim)

    @property
    def parameters(self):
        return self.params

    @parameters.setter
    def parameters(self,params):
        self.params = params

        
    
    def evaluateSample(self, x_in):
        raise NotImplementedError("Please Implement this method")

    
    def optimize(self):
        min_val, x_out, error = bo.optimize_discrete(self.evaluateSample,
                                                    self.x_set,
                                                    self.params)
        
        return min_val, x_out, error



class BayesOptCategorical:
    
    
    def __init__(self, categories=None):
        
        self.params = {}
        self.categories = categories

    @property
    def parameters(self):
        return self.params

    @parameters.setter
    def parameters(self,params):
        self.params = params

        
    
    def evaluateSample(self, x_in):
        raise NotImplementedError("Please Implement this method")
    
    
    def optimize(self):
        min_val, x_out, error = bo.optimize_categorical(self.evaluateSample,
                                                        self.categories,
                                                        self.params)
        
        return min_val, x_out, error


    
if __name__ == "__main__":
    BO = BayesOptContinuous()
    __value__, __x__, __err__ = BO.optimize()
    print("Result", __x__)