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| ''' | |
| Module of learners used to determine what parameters to try next given previous cost evaluations. | |
| Each learner is created and controlled by a controller. | |
| ''' | |
| from __future__ import absolute_import, division, print_function | |
| __metaclass__ = type | |
| import threading | |
| import numpy as np | |
| import numpy.random as nr | |
| import scipy.optimize as so | |
| import logging.handlers | |
| import datetime | |
| import os | |
| import mloop.utilities as mlu | |
| import mloop.localsklearn.gaussian_process as skg | |
| import mloop.localsklearn.gaussian_process.kernels as skk | |
| import mloop.localsklearn.preprocessing as skp | |
| import multiprocessing as mp | |
| learner_thread_count = 0 | |
| default_learner_archive_filename = 'learner_archive' | |
| default_learner_archive_file_type = 'txt' | |
| class LearnerInterrupt(Exception): | |
| ''' | |
| Exception that is raised when the learner is ended with the end flag or event. | |
| ''' | |
| def __init__(self): | |
| ''' | |
| Create LearnerInterrupt. | |
| ''' | |
| super(LearnerInterrupt,self).__init__() | |
| class Learner(): | |
| ''' | |
| Base class for all learners. Contains default boundaries and some useful functions that all learners use. | |
| The class that inherits from this class should also inherit from threading.Thread or multiprocessing.Process, depending if you need the learner to be a genuine parallel process or not. | |
| Keyword Args: | |
| num_params (Optional [int]): The number of parameters to be optimized. If None defaults to 1. Default None. | |
| min_boundary (Optional [array]): Array with minimimum values allowed for each parameter. Note if certain values have no minimum value you can set them to -inf for example [-1, 2, float('-inf')] is a valid min_boundary. If None sets all the boundaries to '-1'. Default None. | |
| max_boundary (Optional [array]): Array with maximum values allowed for each parameter. Note if certain values have no maximum value you can set them to +inf for example [0, float('inf'),3,-12] is a valid max_boundary. If None sets all the boundaries to '1'. Default None. | |
| learner_archive_filename (Optional [string]): Name for python archive of the learners current state. If None, no archive is saved. Default None. But this is typically overloaded by the child class. | |
| learner_archive_file_type (Optional [string]): File type for archive. Can be either 'txt' a human readable text file, 'pkl' a python dill file, 'mat' a matlab file or None if there is no archive. Default 'mat'. | |
| log_level (Optional [int]): Level for the learners logger. If None, set to warning. Default None. | |
| start_datetime (Optional [datetime]): Start date time, if None, is automatically generated. | |
| Attributes: | |
| params_out_queue (queue): Queue for parameters created by learner. | |
| costs_in_queue (queue): Queue for costs to be used by learner. | |
| end_event (event): Event to trigger end of learner. | |
| ''' | |
| def __init__(self, | |
| num_params=None, | |
| min_boundary=None, | |
| max_boundary=None, | |
| learner_archive_filename=default_learner_archive_filename, | |
| learner_archive_file_type=default_learner_archive_file_type, | |
| start_datetime=None, | |
| **kwargs): | |
| super(Learner,self).__init__() | |
| global learner_thread_count | |
| learner_thread_count += 1 | |
| self.log = logging.getLogger(__name__ + '.' + str(learner_thread_count)) | |
| self.learner_wait=float(1) | |
| self.remaining_kwargs = kwargs | |
| self.params_out_queue = mp.Queue() | |
| self.costs_in_queue = mp.Queue() | |
| self.end_event = mp.Event() | |
| if num_params is None: | |
| self.log.warning('num_params not provided, setting to default value of 1.') | |
| self.num_params = 1 | |
| else: | |
| self.num_params = int(num_params) | |
| if self.num_params <= 0: | |
| self.log.error('Number of parameters must be greater than zero:' + repr(self.num_params)) | |
| raise ValueError | |
| if min_boundary is None: | |
| self.min_boundary = np.full((self.num_params,), -1.0) | |
| else: | |
| self.min_boundary = np.array(min_boundary, dtype=np.float) | |
| if self.min_boundary.shape != (self.num_params,): | |
| self.log.error('min_boundary array the wrong shape:' + repr(self.min_boundary.shape)) | |
| raise ValueError | |
| if max_boundary is None: | |
| self.max_boundary = np.full((self.num_params,), 1.0) | |
| else: | |
| self.max_boundary = np.array(max_boundary, dtype=np.float) | |
| if self.max_boundary.shape != (self.num_params,): | |
| self.log.error('max_boundary array the wrong shape:' + self.min_boundary.shape) | |
| raise ValueError | |
| self.diff_boundary = self.max_boundary - self.min_boundary | |
| if not np.all(self.diff_boundary>0.0): | |
| self.log.error('All elements of max_boundary are not larger than min_boundary') | |
| raise ValueError | |
| if start_datetime is None: | |
| self.start_datetime = datetime.datetime.now() | |
| else: | |
| self.start_datetime = start_datetime | |
| if mlu.check_file_type_supported(learner_archive_file_type): | |
| self.learner_archive_file_type = learner_archive_file_type | |
| else: | |
| self.log.error('File in type is not supported:' + learner_archive_file_type) | |
| raise ValueError | |
| if learner_archive_filename is None: | |
| self.learner_archive_filename = None | |
| else: | |
| if not os.path.exists(mlu.archive_foldername): | |
| os.makedirs(mlu.archive_foldername) | |
| self.learner_archive_filename =str(learner_archive_filename) | |
| self.total_archive_filename = mlu.archive_foldername + self.learner_archive_filename + '_' + mlu.datetime_to_string(self.start_datetime) + '.' + self.learner_archive_file_type | |
| self.archive_dict = {'archive_type':'learner', | |
| 'num_params':self.num_params, | |
| 'min_boundary':self.min_boundary, | |
| 'max_boundary':self.max_boundary, | |
| 'start_datetime':mlu.datetime_to_string(self.start_datetime)} | |
| self.log.debug('Learner init completed.') | |
| def check_num_params(self,param): | |
| ''' | |
| Check the number of parameters is right. | |
| ''' | |
| return param.shape == (self.num_params,) | |
| def check_in_boundary(self,param): | |
| ''' | |
| Check give parameters are within stored boundaries | |
| Args: | |
| param (array): array of parameters | |
| Returns: | |
| bool : True if the parameters are within boundaries, False otherwise. | |
| ''' | |
| param = np.array(param) | |
| testbool = np.all(param >= self.min_boundary) and np.all(param <= self.max_boundary) | |
| return testbool | |
| def check_in_diff_boundary(self,param): | |
| ''' | |
| Check given distances are less than the boundaries | |
| Args: | |
| param (array): array of distances | |
| Returns: | |
| bool : True if the distances are smaller or equal to boundaries, False otherwise. | |
| ''' | |
| param = np.array(param) | |
| testbool = np.all(param<=self.diff_boundary) | |
| return testbool | |
| def put_params_and_get_cost(self, params, **kwargs): | |
| ''' | |
| Send parameters to queue and whatever additional keywords. Saves sent variables in appropriate storage arrays. | |
| Args: | |
| params (array) : array of values to be sent to file | |
| Returns: | |
| cost from the cost queue | |
| ''' | |
| #self.log.debug('Learner params='+repr(params)) | |
| if not self.check_num_params(params): | |
| self.log.error('Incorrect number of parameters sent to queue.Params' + repr(params)) | |
| raise ValueError | |
| if not self.check_in_boundary(params): | |
| self.log.warning('Parameters sent to queue are not within boundaries. Params:' + repr(params)) | |
| #self.log.debug('Learner puts params.') | |
| self.params_out_queue.put(params) | |
| #self.log.debug('Learner waiting for costs.') | |
| self.save_archive() | |
| while not self.end_event.is_set(): | |
| try: | |
| cost = self.costs_in_queue.get(True, self.learner_wait) | |
| except mlu.empty_exception: | |
| continue | |
| else: | |
| break | |
| else: | |
| self.log.debug('Learner end signal received. Ending') | |
| raise LearnerInterrupt | |
| #self.log.debug('Learner cost='+repr(cost)) | |
| return cost | |
| def save_archive(self): | |
| ''' | |
| Save the archive associated with the learner class. Only occurs if the filename for the archive is not None. Saves with the format previously set. | |
| ''' | |
| self.update_archive() | |
| if self.learner_archive_filename is not None: | |
| mlu.save_dict_to_file(self.archive_dict, self.total_archive_filename, self.learner_archive_file_type) | |
| def update_archive(self): | |
| ''' | |
| Abstract method for update to the archive. To be implemented by child class. | |
| ''' | |
| pass | |
| def _set_trust_region(self,trust_region): | |
| ''' | |
| Sets trust region properties for learner that have this. Common function for learners with trust regions. | |
| Args: | |
| trust_region (float or array): Property defines the trust region. | |
| ''' | |
| if trust_region is None: | |
| self.trust_region = float('nan') | |
| self.has_trust_region = False | |
| else: | |
| self.has_trust_region = True | |
| if isinstance(trust_region , float): | |
| if trust_region > 0 and trust_region < 1: | |
| self.trust_region = trust_region * self.diff_boundary | |
| else: | |
| self.log.error('Trust region, when a float, must be between 0 and 1: '+repr(trust_region)) | |
| raise ValueError | |
| else: | |
| self.trust_region = np.array(trust_region, dtype=float) | |
| if self.has_trust_region: | |
| if not self.check_num_params(self.trust_region): | |
| self.log.error('Shape of the trust_region does not match the number of parameters:' + repr(self.trust_region)) | |
| raise ValueError | |
| if not np.all(self.trust_region>0): | |
| self.log.error('All trust_region values must be positive:' + repr(self.trust_region)) | |
| raise ValueError | |
| if not self.check_in_diff_boundary(self.trust_region): | |
| self.log.error('The trust_region must be smaller than the range of the boundaries:' + repr(self.trust_region)) | |
| raise ValueError | |
| def _shut_down(self): | |
| ''' | |
| Shut down and perform one final save of learner. | |
| ''' | |
| self.log.debug('Performing shut down of learner.') | |
| self.save_archive() | |
| class RandomLearner(Learner, threading.Thread): | |
| ''' | |
| Random learner. Simply generates new parameters randomly with a uniform distribution over the boundaries. Learner is perhaps a misnomer for this class. | |
| Args: | |
| **kwargs (Optional dict): Other values to be passed to Learner. | |
| Keyword Args: | |
| min_boundary (Optional [array]): If set to None, overrides default learner values and sets it to a set of value 0. Default None. | |
| max_boundary (Optional [array]): If set to None overides default learner values and sets it to an array of value 1. Default None. | |
| trust_region (Optional [float or array]): The trust region defines the maximum distance the learner will travel from the current best set of parameters. If None, the learner will search everywhere. If a float, this number must be between 0 and 1 and defines maximum distance the learner will venture as a percentage of the boundaries. If it is an array, it must have the same size as the number of parameters and the numbers define the maximum absolute distance that can be moved along each direction. | |
| ''' | |
| def __init__(self, | |
| trust_region=None, | |
| first_params=None, | |
| **kwargs): | |
| super(RandomLearner,self).__init__(**kwargs) | |
| if not np.all(self.diff_boundary>0.0): | |
| self.log.error('All elements of max_boundary are not larger than min_boundary') | |
| raise ValueError | |
| if ((np.all(np.isfinite(self.min_boundary))&np.all(np.isfinite(self.max_boundary)))==False): | |
| self.log.error('Minimum and/or maximum boundaries are NaN or inf. Must both be finite for random learner. Min boundary:' + repr(self.min_boundary) +'. Max bounday:' + repr(self.max_boundary)) | |
| raise ValueError | |
| if first_params is None: | |
| self.first_params = None | |
| else: | |
| self.first_params = np.array(first_params, dtype=float) | |
| if not self.check_num_params(self.first_params): | |
| self.log.error('first_params has the wrong number of parameters:' + repr(self.first_params)) | |
| raise ValueError | |
| if not self.check_in_boundary(self.first_params): | |
| self.log.error('first_params is not in the boundary:' + repr(self.first_params)) | |
| raise ValueError | |
| self._set_trust_region(trust_region) | |
| self.archive_dict.update({'archive_type':'random_learner'}) | |
| self.log.debug('Random learner init completed.') | |
| def run(self): | |
| ''' | |
| Puts the next parameters on the queue which are randomly picked from a uniform distribution between the minimum and maximum boundaries when a cost is added to the cost queue. | |
| ''' | |
| self.log.debug('Starting Random Learner') | |
| if self.first_params is None: | |
| next_params = self.min_boundary + nr.rand(self.num_params) * self.diff_boundary | |
| else: | |
| next_params = self.first_params | |
| while not self.end_event.is_set(): | |
| try: | |
| centre_params = self.put_params_and_get_cost(next_params) | |
| except LearnerInterrupt: | |
| break | |
| else: | |
| if self.has_trust_region: | |
| temp_min = np.maximum(self.min_boundary,centre_params - self.trust_region) | |
| temp_max = np.minimum(self.max_boundary,centre_params + self.trust_region) | |
| next_params = temp_min + nr.rand(self.num_params) * (temp_max - temp_min) | |
| else: | |
| next_params = self.min_boundary + nr.rand(self.num_params) * self.diff_boundary | |
| self._shut_down() | |
| self.log.debug('Ended Random Learner') | |
| class NelderMeadLearner(Learner, threading.Thread): | |
| ''' | |
| Nelder-Mead learner. Executes the Nelder-Mead learner algorithm and stores the needed simplex to estimate the next points. | |
| Args: | |
| params_out_queue (queue): Queue for parameters from controller. | |
| costs_in_queue (queue): Queue for costs for nelder learner. The queue should be populated with cost (float) corresponding to the last parameter sent from the Nelder-Mead Learner. Can be a float('inf') if it was a bad run. | |
| end_event (event): Event to trigger end of learner. | |
| Keyword Args: | |
| initial_simplex_corner (Optional [array]): Array for the initial set of parameters, which is the lowest corner of the initial simplex. If None the initial parameters are randomly sampled if the boundary conditions are provided, or all are set to 0 if boundary conditions are not provided. | |
| initial_simplex_displacements (Optional [array]): Array used to construct the initial simplex. Each array is the positive displacement of the parameters above the init_params. If None and there are no boundary conditions, all are set to 1. If None and there are boundary conditions assumes the initial conditions are scaled. Default None. | |
| initial_simplex_scale (Optional [float]): Creates a simplex using a the boundary conditions and the scaling factor provided. If None uses the init_simplex if provided. If None and init_simplex is not provided, but boundary conditions are is set to 0.5. Default None. | |
| Attributes: | |
| init_simplex_corner (array): Parameters for the corner of the initial simple used. | |
| init_simplex_disp (array): Parameters for the displacements about the simplex corner used to create the initial simple. | |
| simplex_params (array): Parameters of the current simplex | |
| simplex_costs (array): Costs associated with the parameters of the current simplex | |
| ''' | |
| def __init__(self, | |
| initial_simplex_corner=None, | |
| initial_simplex_displacements=None, | |
| initial_simplex_scale=None, | |
| **kwargs): | |
| super(NelderMeadLearner,self).__init__(**kwargs) | |
| self.num_boundary_hits = 0 | |
| self.rho = 1 | |
| self.chi = 2 | |
| self.psi = 0.5 | |
| self.sigma = 0.5 | |
| if initial_simplex_displacements is None and initial_simplex_scale is None: | |
| self.init_simplex_disp = self.diff_boundary * 0.6 | |
| self.init_simplex_disp[self.init_simplex_disp==float('inf')] = 1 | |
| elif initial_simplex_scale is not None: | |
| initial_simplex_scale = float(initial_simplex_scale) | |
| if initial_simplex_scale>1 or initial_simplex_scale<=0: | |
| self.log.error('initial_simplex_scale must be bigger than 0 and less than 1') | |
| raise ValueError | |
| self.init_simplex_disp = self.diff_boundary * initial_simplex_scale | |
| elif initial_simplex_displacements is not None: | |
| self.init_simplex_disp = np.array(initial_simplex_displacements, dtype=float) | |
| else: | |
| self.log.error('initial_simplex_displacements and initial_simplex_scale can not both be provided simultaneous.') | |
| if not self.check_num_params(self.init_simplex_disp): | |
| self.log.error('There is the wrong number of elements in the initial simplex displacement:' + repr(self.init_simplex_disp)) | |
| raise ValueError | |
| if np.any(self.init_simplex_disp<0): | |
| self.log.error('initial simplex displacements generated from configuration must all be positive') | |
| raise ValueError | |
| if not self.check_in_diff_boundary(self.init_simplex_disp): | |
| self.log.error('Initial simplex displacements must be within boundaries. init_simplex_disp:'+ repr(self.init_simplex_disp) + '. diff_boundary:' +repr(self.diff_boundary)) | |
| raise ValueError | |
| if initial_simplex_corner is None: | |
| diff_roll = (self.diff_boundary - self.init_simplex_disp) * nr.rand(self.num_params) | |
| diff_roll[diff_roll==float('+inf')]= 0 | |
| self.init_simplex_corner = self.min_boundary | |
| self.init_simplex_corner[self.init_simplex_corner==float('-inf')]=0 | |
| self.init_simplex_corner += diff_roll | |
| else: | |
| self.init_simplex_corner = np.array(initial_simplex_corner, dtype=float) | |
| if not self.check_num_params(self.init_simplex_corner): | |
| self.log.error('There is the wrong number of elements in the initial simplex corner:' + repr(self.init_simplex_corner)) | |
| if not self.check_in_boundary(self.init_simplex_corner): | |
| self.log.error('Initial simplex corner outside of boundaries:' + repr(self.init_simplex_corner)) | |
| raise ValueError | |
| if not np.all(np.isfinite(self.init_simplex_corner + self.init_simplex_disp)): | |
| self.log.error('Initial simplex corner and simplex are not finite numbers. init_simplex_corner:'+ repr(self.init_simplex_corner) + '. init_simplex_disp:' +repr(self.init_simplex_disp)) | |
| raise ValueError | |
| if not self.check_in_boundary(self.init_simplex_corner + self.init_simplex_disp): | |
| self.log.error('Largest boundary of simplex not inside the boundaries:' + repr(self.init_simplex_corner + self.init_simplex_disp)) | |
| raise ValueError | |
| self.simplex_params = np.zeros((self.num_params + 1, self.num_params), dtype=float) | |
| self.simplex_costs = np.zeros((self.num_params + 1,), dtype=float) | |
| self.archive_dict.update({'archive_type':'nelder_mead_learner', | |
| 'initial_simplex_corner':self.init_simplex_corner, | |
| 'initial_simplex_displacements':self.init_simplex_disp}) | |
| self.log.debug('Nelder-Mead learner init completed.') | |
| def run(self): | |
| ''' | |
| Runs Nelder-Mead algorithm to produce new parameters given costs, until end signal is given. | |
| ''' | |
| self.log.info('Starting Nelder Mead Learner') | |
| N = int(self.num_params) | |
| one2np1 = list(range(1, N + 1)) | |
| self.simplex_params[0] = self.init_simplex_corner | |
| try: | |
| self.simplex_costs[0] = self.put_params_and_get_cost(self.init_simplex_corner) | |
| except ValueError: | |
| self.log.error('Outside of boundary on initial condition. THIS SHOULD NOT HAPPEN') | |
| raise | |
| except LearnerInterrupt: | |
| self.log.info('Ended Nelder-Mead before end of simplex') | |
| return | |
| for k in range(0, N): | |
| y = np.array(self.init_simplex_corner, copy=True) | |
| y[k] = y[k] + self.init_simplex_disp[k] | |
| self.simplex_params[k + 1] = y | |
| try: | |
| f = self.put_params_and_get_cost(y) | |
| except ValueError: | |
| self.log.error('Outside of boundary on initial condition. THIS SHOULD NOT HAPPEN') | |
| raise | |
| except LearnerInterrupt: | |
| self.log.info('Ended Nelder-Mead before end of simplex') | |
| return | |
| self.simplex_costs[k + 1] = f | |
| ind = np.argsort(self.simplex_costs) | |
| self.simplex_costs = np.take(self.simplex_costs, ind, 0) | |
| # sort so sim[0,:] has the lowest function value | |
| self.simplex_params = np.take(self.simplex_params, ind, 0) | |
| while not self.end_event.is_set(): | |
| xbar = np.add.reduce(self.simplex_params[:-1], 0) / N | |
| xr = (1 +self.rho) * xbar -self.rho * self.simplex_params[-1] | |
| if self.check_in_boundary(xr): | |
| try: | |
| fxr = self.put_params_and_get_cost(xr) | |
| except ValueError: | |
| self.log.error('Outside of boundary on first reduce. THIS SHOULD NOT HAPPEN') | |
| raise | |
| except LearnerInterrupt: | |
| break | |
| else: | |
| #Hit boundary so set the cost to positive infinite to ensure reflection | |
| fxr = float('inf') | |
| self.num_boundary_hits+=1 | |
| self.log.debug('Hit boundary (reflect): '+str(self.num_boundary_hits)+' times.') | |
| doshrink = 0 | |
| if fxr < self.simplex_costs[0]: | |
| xe = (1 +self.rho *self.chi) * xbar -self.rho *self.chi * self.simplex_params[-1] | |
| if self.check_in_boundary(xe): | |
| try: | |
| fxe = self.put_params_and_get_cost(xe) | |
| except ValueError: | |
| self.log.error('Outside of boundary when it should not be. THIS SHOULD NOT HAPPEN') | |
| raise | |
| except LearnerInterrupt: | |
| break | |
| else: | |
| #Hit boundary so set the cost above maximum this ensures the algorithm does a contracting reflection | |
| fxe = fxr+1.0 | |
| self.num_boundary_hits+=1 | |
| self.log.debug('Hit boundary (expand): '+str(self.num_boundary_hits)+' times.') | |
| if fxe < fxr: | |
| self.simplex_params[-1] = xe | |
| self.simplex_costs[-1] = fxe | |
| else: | |
| self.simplex_params[-1] = xr | |
| self.simplex_costs[-1] = fxr | |
| else: # fsim[0] <= fxr | |
| if fxr < self.simplex_costs[-2]: | |
| self.simplex_params[-1] = xr | |
| self.simplex_costs[-1] = fxr | |
| else: # fxr >= fsim[-2] | |
| # Perform contraction | |
| if fxr < self.simplex_costs[-1]: | |
| xc = (1 +self.psi *self.rho) * xbar -self.psi *self.rho * self.simplex_params[-1] | |
| try: | |
| fxc = self.put_params_and_get_cost(xc) | |
| except ValueError: | |
| self.log.error('Outside of boundary on contraction: THIS SHOULD NOT HAPPEN') | |
| raise | |
| except LearnerInterrupt: | |
| break | |
| if fxc <= fxr: | |
| self.simplex_params[-1] = xc | |
| self.simplex_costs[-1] = fxc | |
| else: | |
| doshrink = 1 | |
| else: | |
| # Perform an inside contraction | |
| xcc = (1 -self.psi) * xbar +self.psi * self.simplex_params[-1] | |
| try: | |
| fxcc = self.put_params_and_get_cost(xcc) | |
| except ValueError: | |
| self.log.error('Outside of boundary on inside contraction: THIS SHOULD NOT HAPPEN') | |
| raise | |
| except LearnerInterrupt: | |
| break | |
| if fxcc < self.simplex_costs[-1]: | |
| self.simplex_params[-1] = xcc | |
| self.simplex_costs[-1] = fxcc | |
| else: | |
| doshrink = 1 | |
| if doshrink: | |
| for j in one2np1: | |
| self.simplex_params[j] = self.simplex_params[0] +self.sigma * (self.simplex_params[j] - self.simplex_params[0]) | |
| try: | |
| self.simplex_costs[j] = self.put_params_and_get_cost(self.simplex_params[j]) | |
| except ValueError: | |
| self.log.error('Outside of boundary on shrink contraction: THIS SHOULD NOT HAPPEN') | |
| raise | |
| except LearnerInterrupt: | |
| break | |
| ind = np.argsort(self.simplex_costs) | |
| self.simplex_params = np.take(self.simplex_params, ind, 0) | |
| self.simplex_costs = np.take(self.simplex_costs, ind, 0) | |
| self._shut_down() | |
| self.log.info('Ended Nelder-Mead') | |
| def update_archive(self): | |
| ''' | |
| Update the archive. | |
| ''' | |
| self.archive_dict.update({'archive_type':'nelder_mead_learner', | |
| 'simplex_parameters':self.simplex_params, | |
| 'simplex_costs':self.simplex_costs}) | |
| class GaussianProcessLearner(Learner, mp.Process): | |
| ''' | |
| Gaussian process learner. Generats new parameters based on a gaussian process fitted to all previous data. | |
| Args: | |
| params_out_queue (queue): Queue for parameters sent to controller. | |
| costs_in_queue (queue): Queue for costs for gaussian process. This must be tuple | |
| end_event (event): Event to trigger end of learner. | |
| Keyword Args: | |
| length_scale (Optional [array]): The initial guess for length scale(s) of the gaussian process. The array can either of size one or the number of parameters or None. If it is size one, it is assumed all the correlation lengths are the same. If it is the number of the parameters then all the parameters have their own independent length scale. If it is None, it is assumed all the length scales should be independent and they are all given an initial value of 1. Default None. | |
| cost_has_noise (Optional [bool]): If true the learner assumes there is common additive white noise that corrupts the costs provided. This noise is assumed to be on top of the uncertainty in the costs (if it is provided). If false, it is assumed that there is no noise in the cost (or if uncertainties are provided no extra noise beyond the uncertainty). Default True. | |
| noise_level (Optional [float]): The initial guess for the noise level in the costs, is only used if cost_has_noise is true. Default 1.0. | |
| update_hyperparameters (Optional [bool]): Whether the length scales and noise estimate should be updated when new data is provided. Is set to true by default. | |
| trust_region (Optional [float or array]): The trust region defines the maximum distance the learner will travel from the current best set of parameters. If None, the learner will search everywhere. If a float, this number must be between 0 and 1 and defines maximum distance the learner will venture as a percentage of the boundaries. If it is an array, it must have the same size as the number of parameters and the numbers define the maximum absolute distance that can be moved along each direction. | |
| default_bad_cost (Optional [float]): If a run is reported as bad and default_bad_cost is provided, the cost for the bad run is set to this default value. If default_bad_cost is None, then the worst cost received is set to all the bad runs. Default None. | |
| default_bad_uncertainty (Optional [float]): If a run is reported as bad and default_bad_uncertainty is provided, the uncertainty for the bad run is set to this default value. If default_bad_uncertainty is None, then the uncertainty is set to a tenth of the best to worst cost range. Default None. | |
| minimum_uncertainty (Optional [float]): The minimum uncertainty associated with provided costs. Must be above zero to avoid fitting errors. Default 1e-8. | |
| predict_global_minima_at_end (Optional [bool]): If True finds the global minima when the learner is ended. Does not if False. Default True. | |
| predict_local_minima_at_end (Optional [bool]): If True finds the all minima when the learner is ended. Does not if False. Default False. | |
| Attributes: | |
| all_params (array): Array containing all parameters sent to learner. | |
| all_costs (array): Array containing all costs sent to learner. | |
| all_uncers (array): Array containing all uncertainties sent to learner. | |
| scaled_costs (array): Array contaning all the costs scaled to have zero mean and a standard deviation of 1. Needed for training the gaussian process. | |
| bad_run_indexs (list): list of indexes to all runs that were marked as bad. | |
| best_cost (float): Minimum received cost, updated during execution. | |
| best_params (array): Parameters of best run. (reference to element in params array). | |
| best_index (int): index of the best cost and params. | |
| worst_cost (float): Maximum received cost, updated during execution. | |
| worst_index (int): index to run with worst cost. | |
| cost_range (float): Difference between worst_cost and best_cost | |
| generation_num (int): Number of sets of parameters to generate each generation. Set to 5. | |
| length_scale_history (list): List of length scales found after each fit. | |
| noise_level_history (list): List of noise levels found after each fit. | |
| fit_count (int): Counter for the number of times the gaussian process has been fit. | |
| cost_count (int): Counter for the number of costs, parameters and uncertainties added to learner. | |
| params_count (int): Counter for the number of parameters asked to be evaluated by the learner. | |
| gaussian_process (GaussianProcessRegressor): Gaussian process that is fitted to data and used to make predictions | |
| cost_scaler (StandardScaler): Scaler used to normalize the provided costs. | |
| has_trust_region (bool): Whether the learner has a trust region. | |
| ''' | |
| def __init__(self, | |
| length_scale = None, | |
| update_hyperparameters = True, | |
| cost_has_noise=True, | |
| noise_level=1.0, | |
| trust_region=None, | |
| default_bad_cost = None, | |
| default_bad_uncertainty = None, | |
| minimum_uncertainty = 1e-8, | |
| gp_training_filename =None, | |
| gp_training_file_type ='txt', | |
| predict_global_minima_at_end = True, | |
| predict_local_minima_at_end = False, | |
| **kwargs): | |
| if gp_training_filename is not None: | |
| gp_training_filename = str(gp_training_filename) | |
| gp_training_file_type = str(gp_training_file_type) | |
| if not mlu.check_file_type_supported(gp_training_file_type): | |
| self.log.error('GP training file type not supported' + repr(gp_training_file_type)) | |
| self.training_dict = mlu.get_dict_from_file(gp_training_filename, gp_training_file_type) | |
| #Basic optimization settings | |
| num_params = int(self.training_dict['num_params']) | |
| min_boundary = np.squeeze(np.array(self.training_dict['min_boundary'], dtype=float)) | |
| max_boundary = np.squeeze(np.array(self.training_dict['max_boundary'], dtype=float)) | |
| #Configuration of the learner | |
| self.cost_has_noise = bool(self.training_dict['cost_has_noise']) | |
| self.length_scale = np.squeeze(np.array(self.training_dict['length_scale'])) | |
| self.length_scale_history = list(self.training_dict['length_scale_history']) | |
| self.noise_level = float(self.training_dict['noise_level']) | |
| if isinstance(self.training_dict['noise_level_history'], np.ndarray): | |
| self.noise_level_history = list(np.squeeze(self.training_dict['noise_level_history'])) | |
| else: | |
| self.noise_level_history = list( self.training_dict['noise_level_history']) | |
| #Counters | |
| self.costs_count = int(self.training_dict['costs_count']) | |
| self.fit_count = int(self.training_dict['fit_count']) | |
| self.params_count = int(self.training_dict['params_count']) | |
| #Data from previous experiment | |
| self.all_params = np.array(self.training_dict['all_params'], dtype=float) | |
| self.all_costs = np.squeeze(np.array(self.training_dict['all_costs'], dtype=float)) | |
| self.all_uncers = np.squeeze(np.array(self.training_dict['all_uncers'], dtype=float)) | |
| if isinstance(self.training_dict['bad_run_indexs'], np.ndarray): | |
| self.bad_run_indexs = list(np.squeeze(self.training_dict['bad_run_indexs'])) | |
| else: | |
| self.bad_run_indexs = list(self.training_dict['bad_run_indexs']) | |
| #Derived properties | |
| self.best_cost = float(self.training_dict['best_cost']) | |
| self.best_params = np.squeeze(np.array(self.training_dict['best_params'], dtype=float)) | |
| self.best_index = int(self.training_dict['best_index']) | |
| self.worst_cost = float(self.training_dict['worst_cost']) | |
| self.worst_index = int(self.training_dict['worst_index']) | |
| self.cost_range = float(self.training_dict['cost_range']) | |
| try: | |
| self.predicted_best_parameters = np.squeeze(np.array(self.training_dict['predicted_best_parameters'])) | |
| self.predicted_best_cost = float(self.training_dict['predicted_best_cost']) | |
| self.predicted_best_uncertainty = float(self.training_dict['predicted_best_uncertainty']) | |
| self.has_global_minima = True | |
| except KeyError: | |
| self.has_global_minima = False | |
| try: | |
| self.local_minima_parameters = list(self.training_dict['local_minima_parameters']) | |
| if isinstance(self.training_dict['local_minima_costs'], np.ndarray): | |
| self.local_minima_costs = list(np.squeeze(self.training_dict['local_minima_costs'])) | |
| else: | |
| self.local_minima_costs = list(self.training_dict['local_minima_costs']) | |
| if isinstance(self.training_dict['local_minima_uncers'], np.ndarray): | |
| self.local_minima_uncers = list(np.squeeze(self.training_dict['local_minima_uncers'])) | |
| else: | |
| self.local_minima_uncers = list(self.training_dict['local_minima_uncers']) | |
| self.has_local_minima = True | |
| except KeyError: | |
| self.has_local_minima = False | |
| super(GaussianProcessLearner,self).__init__(num_params=num_params, | |
| min_boundary=min_boundary, | |
| max_boundary=max_boundary, | |
| **kwargs) | |
| else: | |
| super(GaussianProcessLearner,self).__init__(**kwargs) | |
| #Storage variables, archived | |
| self.all_params = np.array([], dtype=float) | |
| self.all_costs = np.array([], dtype=float) | |
| self.all_uncers = np.array([], dtype=float) | |
| self.bad_run_indexs = [] | |
| self.best_cost = float('inf') | |
| self.best_params = float('nan') | |
| self.best_index = 0 | |
| self.worst_cost = float('-inf') | |
| self.worst_index = 0 | |
| self.cost_range = float('inf') | |
| self.length_scale_history = [] | |
| self.noise_level_history = [] | |
| self.costs_count = 0 | |
| self.fit_count = 0 | |
| self.params_count = 0 | |
| self.has_local_minima = False | |
| self.has_global_minima = False | |
| #Optional user set variables | |
| if length_scale is None: | |
| self.length_scale = np.ones((self.num_params,)) | |
| else: | |
| self.length_scale = np.array(length_scale, dtype=float) | |
| self.noise_level = float(noise_level) | |
| self.cost_has_noise = bool(cost_has_noise) | |
| #Multiprocessor controls | |
| self.new_params_event = mp.Event() | |
| #Storage variables and counters | |
| self.search_params = [] | |
| self.scaled_costs = None | |
| self.cost_bias = None | |
| self.uncer_bias = None | |
| #Internal variable for bias function | |
| self.bias_func_cycle = 4 | |
| self.bias_func_cost_factor = [1.0,1.0,1.0,1.0] | |
| self.bias_func_uncer_factor =[0.0,1.0,2.0,3.0] | |
| self.generation_num = self.bias_func_cycle | |
| if self.generation_num < 3: | |
| self.log.error('Number in generation must be larger than 2.') | |
| raise ValueError | |
| #Constants, limits and tolerances | |
| self.search_precision = 1.0e-6 | |
| self.parameter_searches = max(10,self.num_params) | |
| self.hyperparameter_searches = max(10,self.num_params) | |
| self.bad_uncer_frac = 0.1 #Fraction of cost range to set a bad run uncertainty | |
| #Optional user set variables | |
| self.update_hyperparameters = bool(update_hyperparameters) | |
| self.predict_global_minima_at_end = bool(predict_global_minima_at_end) | |
| self.predict_local_minima_at_end = bool(predict_local_minima_at_end) | |
| if default_bad_cost is not None: | |
| self.default_bad_cost = float(default_bad_cost) | |
| else: | |
| self.default_bad_cost = None | |
| if default_bad_uncertainty is not None: | |
| self.default_bad_uncertainty = float(default_bad_uncertainty) | |
| else: | |
| self.default_bad_uncertainty = None | |
| self.minimum_uncertainty = float(minimum_uncertainty) | |
| self._set_trust_region(trust_region) | |
| #Checks of variables | |
| if self.length_scale.size == 1: | |
| self.length_scale = float(self.length_scale) | |
| elif not self.check_num_params(self.length_scale): | |
| self.log.error('Correlation lengths not the right size and shape, must be one or the number of parameters:' + repr(self.length_scale)) | |
| raise ValueError | |
| if not np.all(self.length_scale >0): | |
| self.log.error('Correlation lengths must all be positive numbers:' + repr(self.length_scale)) | |
| raise ValueError | |
| if self.noise_level < 0: | |
| self.log.error('noise_level must be greater or equal to zero:' +repr(self.noise_level)) | |
| raise ValueError | |
| if self.default_bad_uncertainty is not None: | |
| if self.default_bad_uncertainty < 0: | |
| self.log.error('Default bad uncertainty must be positive.') | |
| raise ValueError | |
| if (self.default_bad_cost is None) and (self.default_bad_cost is None): | |
| self.bad_defaults_set = False | |
| elif (self.default_bad_cost is not None) and (self.default_bad_cost is not None): | |
| self.bad_defaults_set = True | |
| else: | |
| self.log.error('Both the default cost and uncertainty must be set for a bad run or they must both be set to None.') | |
| raise ValueError | |
| if self.minimum_uncertainty <= 0: | |
| self.log.error('Minimum uncertainty must be larger than zero for the learner.') | |
| raise ValueError | |
| self.create_gaussian_process() | |
| #Search bounds | |
| self.search_min = self.min_boundary | |
| self.search_max = self.max_boundary | |
| self.search_diff = self.search_max - self.search_min | |
| self.search_region = list(zip(self.search_min, self.search_max)) | |
| self.cost_scaler = skp.StandardScaler() | |
| self.archive_dict.update({'archive_type':'gaussian_process_learner', | |
| 'cost_has_noise':self.cost_has_noise, | |
| 'length_scale_history':self.length_scale_history, | |
| 'noise_level_history':self.noise_level_history, | |
| 'bad_run_indexs':self.bad_run_indexs, | |
| 'bias_func_cycle':self.bias_func_cycle, | |
| 'bias_func_cost_factor':self.bias_func_cost_factor, | |
| 'bias_func_uncer_factor':self.bias_func_uncer_factor, | |
| 'generation_num':self.generation_num, | |
| 'search_precision':self.search_precision, | |
| 'parameter_searches':self.parameter_searches, | |
| 'hyperparameter_searches':self.hyperparameter_searches, | |
| 'bad_uncer_frac':self.bad_uncer_frac, | |
| 'trust_region':self.trust_region, | |
| 'has_trust_region':self.has_trust_region, | |
| 'predict_global_minima_at_end':self.predict_global_minima_at_end, | |
| 'predict_local_minima_at_end':self.predict_local_minima_at_end}) | |
| #Remove logger so gaussian process can be safely picked for multiprocessing on Windows | |
| self.log = None | |
| def create_gaussian_process(self): | |
| ''' | |
| Create the initial Gaussian process. | |
| ''' | |
| if self.cost_has_noise: | |
| gp_kernel = skk.RBF(length_scale=self.length_scale) + skk.WhiteKernel(noise_level=self.noise_level) | |
| else: | |
| gp_kernel = skk.RBF(length_scale=self.length_scale) | |
| if self.update_hyperparameters: | |
| self.gaussian_process = skg.GaussianProcessRegressor(kernel=gp_kernel,n_restarts_optimizer=self.hyperparameter_searches) | |
| else: | |
| self.gaussian_process = skg.GaussianProcessRegressor(kernel=gp_kernel,optimizer=None) | |
| def wait_for_new_params_event(self): | |
| ''' | |
| Waits for a new parameters event and starts a new parameter generation cycle. Also checks end event and will break if it is triggered. | |
| ''' | |
| while not self.end_event.is_set(): | |
| if self.new_params_event.wait(timeout=self.learner_wait): | |
| self.new_params_event.clear() | |
| break | |
| else: | |
| continue | |
| else: | |
| self.log.debug('GaussianProcessLearner end signal received. Ending') | |
| raise LearnerInterrupt | |
| def get_params_and_costs(self): | |
| ''' | |
| Get the parameters and costs from the queue and place in their appropriate all_[type] arrays. Also updates bad costs, best parameters, and search boundaries given trust region. | |
| ''' | |
| if self.costs_in_queue.empty(): | |
| self.log.error('Gaussian process asked for new parameters but no new costs were provided.') | |
| raise ValueError | |
| new_params = [] | |
| new_costs = [] | |
| new_uncers = [] | |
| new_bads = [] | |
| new_costs_count = 0 | |
| update_bads_flag = False | |
| while not self.costs_in_queue.empty(): | |
| (param, cost, uncer, bad) = self.costs_in_queue.get_nowait() | |
| if bad: | |
| new_bads.append(self.data_count) | |
| if self.bad_defaults_set: | |
| cost = self.default_bad_cost | |
| uncer = self.default_bad_uncertainty | |
| else: | |
| cost = self.worst_cost | |
| uncer = self.cost_range*self.bad_uncer_frac | |
| param = np.array(param, dtype=float) | |
| if not self.check_num_params(param): | |
| self.log.error('Incorrect number of parameters provided to Gaussian process learner:' + repr(param) + '. Number of parameters:' + str(self.num_params)) | |
| raise ValueError | |
| if not self.check_in_boundary(param): | |
| self.log.warning('Parameters provided to Gaussian process learner not in boundaries:' + repr(param)) | |
| cost = float(cost) | |
| if uncer < 0: | |
| self.log.error('Provided uncertainty must be larger or equal to zero:' + repr(uncer)) | |
| uncer = max(float(uncer), self.minimum_uncertainty) | |
| new_costs_count += 1 | |
| self.costs_count +=1 | |
| cost_change_flag = False | |
| if cost > self.worst_cost: | |
| self.worst_cost = cost | |
| self.worst_index = self.costs_count | |
| cost_change_flag = True | |
| if cost < self.best_cost: | |
| self.best_cost = cost | |
| self.best_params = param | |
| self.best_index = self.costs_count | |
| cost_change_flag = True | |
| if cost_change_flag: | |
| self.cost_range = self.worst_cost - self.best_cost | |
| if not self.bad_defaults_set: | |
| update_bads_flag = True | |
| new_params.append(param) | |
| new_costs.append(cost) | |
| new_uncers.append(uncer) | |
| if self.all_params.size==0: | |
| self.all_params = np.array(new_params, dtype=float) | |
| self.all_costs = np.array(new_costs, dtype=float) | |
| self.all_uncers = np.array(new_uncers, dtype=float) | |
| else: | |
| self.all_params = np.concatenate((self.all_params, np.array(new_params, dtype=float))) | |
| self.all_costs = np.concatenate((self.all_costs, np.array(new_costs, dtype=float))) | |
| self.all_uncers = np.concatenate((self.all_uncers, np.array(new_uncers, dtype=float))) | |
| if self.all_params.shape != (self.costs_count,self.num_params): | |
| self.log('Saved GP params are the wrong size. THIS SHOULD NOT HAPPEN:' + repr(self.all_params)) | |
| if self.all_costs.shape != (self.costs_count,): | |
| self.log('Saved GP costs are the wrong size. THIS SHOULD NOT HAPPEN:' + repr(self.all_costs)) | |
| if self.all_uncers.shape != (self.costs_count,): | |
| self.log('Saved GP uncertainties are the wrong size. THIS SHOULD NOT HAPPEN:' + repr(self.all_uncers)) | |
| if update_bads_flag: | |
| self.update_bads() | |
| self.update_search_region() | |
| def update_bads(self): | |
| ''' | |
| Best and/or worst costs have changed, update the values associated with bad runs accordingly. | |
| ''' | |
| for index in self.bad_run_indexs: | |
| self.all_costs[index] = self.worst_cost | |
| self.all_uncers[index] = self.cost_range*self.bad_uncer_frac | |
| def update_search_region(self): | |
| ''' | |
| If trust boundaries is not none, updates the search boundaries based on the defined trust region. | |
| ''' | |
| if self.has_trust_region: | |
| self.search_min = np.maximum(self.best_params - self.trust_region, self.min_boundary) | |
| self.search_max = np.minimum(self.best_params + self.trust_region, self.max_boundary) | |
| self.search_diff = self.search_max - self.search_min | |
| self.search_region = list(zip(self.search_min, self.search_max)) | |
| def update_search_params(self): | |
| ''' | |
| Update the list of parameters to use for the next search. | |
| ''' | |
| self.search_params = [] | |
| self.search_params.append(self.best_params) | |
| for _ in range(self.parameter_searches): | |
| self.search_params.append(self.search_min + nr.uniform(size=self.num_params) * self.search_diff) | |
| def update_archive(self): | |
| ''' | |
| Update the archive. | |
| ''' | |
| self.archive_dict.update({'all_params':self.all_params, | |
| 'all_costs':self.all_costs, | |
| 'all_uncers':self.all_uncers, | |
| 'best_cost':self.best_cost, | |
| 'best_params':self.best_params, | |
| 'best_index':self.best_index, | |
| 'worst_cost':self.worst_cost, | |
| 'worst_index':self.worst_index, | |
| 'cost_range':self.cost_range, | |
| 'fit_count':self.fit_count, | |
| 'costs_count':self.costs_count, | |
| 'params_count':self.params_count, | |
| 'update_hyperparameters':self.update_hyperparameters, | |
| 'length_scale':self.length_scale, | |
| 'noise_level':self.noise_level}) | |
| def fit_gaussian_process(self): | |
| ''' | |
| Fit the Gaussian process to the current data | |
| ''' | |
| self.log.debug('Fitting Gaussian process.') | |
| if self.all_params.size==0 or self.all_costs.size==0 or self.all_uncers.size==0: | |
| self.log.error('Asked to fit GP but no data is in all_costs, all_params or all_uncers.') | |
| raise ValueError | |
| self.scaled_costs = self.cost_scaler.fit_transform(self.all_costs[:,np.newaxis])[:,0] | |
| self.scaled_uncers = self.all_uncers * self.cost_scaler.scale_ | |
| self.gaussian_process.alpha_ = self.scaled_uncers | |
| self.gaussian_process.fit(self.all_params,self.scaled_costs) | |
| if self.update_hyperparameters: | |
| self.fit_count += 1 | |
| self.gaussian_process.kernel = self.gaussian_process.kernel_ | |
| last_hyperparameters = self.gaussian_process.kernel.get_params() | |
| if self.cost_has_noise: | |
| self.length_scale = last_hyperparameters['k1__length_scale'] | |
| if isinstance(self.length_scale, float): | |
| self.length_scale = np.array([self.length_scale]) | |
| self.length_scale_history.append(self.length_scale) | |
| self.noise_level = last_hyperparameters['k2__noise_level'] | |
| self.noise_level_history.append(self.noise_level) | |
| else: | |
| self.length_scale = last_hyperparameters['length_scale'] | |
| self.length_scale_history.append(self.length_scale) | |
| def update_bias_function(self): | |
| ''' | |
| Set the constants for the cost bias function. | |
| ''' | |
| self.cost_bias = self.bias_func_cost_factor[self.params_count%self.bias_func_cycle] | |
| self.uncer_bias = self.bias_func_uncer_factor[self.params_count%self.bias_func_cycle] | |
| def predict_biased_cost(self,params): | |
| ''' | |
| Predicts the biased cost at the given parameters. The bias function is: | |
| biased_cost = cost_bias*pred_cost - uncer_bias*pred_uncer | |
| Returns: | |
| pred_bias_cost (float): Biased cost predicted at the given parameters | |
| ''' | |
| (pred_cost, pred_uncer) = self.gaussian_process.predict(params[np.newaxis,:], return_std=True) | |
| return self.cost_bias*pred_cost - self.uncer_bias*pred_uncer | |
| def find_next_parameters(self): | |
| ''' | |
| Returns next parameters to find. Increments counters and bias function appropriately. | |
| Return: | |
| next_params (array): Returns next parameters from biased cost search. | |
| ''' | |
| self.params_count += 1 | |
| self.update_bias_function() | |
| self.update_search_params() | |
| next_params = None | |
| next_cost = float('inf') | |
| for start_params in self.search_params: | |
| result = so.minimize(self.predict_biased_cost, start_params, bounds = self.search_region, tol=self.search_precision) | |
| if result.fun < next_cost: | |
| next_params = result.x | |
| next_cost = result.fun | |
| return next_params | |
| def run(self): | |
| ''' | |
| Starts running the Gaussian process learner. When the new parameters event is triggered, reads the cost information provided and updates the Gaussian process with the information. Then searches the Gaussian process for new optimal parameters to test based on the biased cost. Parameters to test next are put on the output parameters queue. | |
| ''' | |
| #logging to the main log file from a process (as apposed to a thread) in cpython is currently buggy on windows and/or python 2.7 | |
| #current solution is to only log to the console for warning and above from a process | |
| self.log = mp.log_to_stderr(logging.WARNING) | |
| try: | |
| while not self.end_event.is_set(): | |
| #self.log.debug('Learner waiting for new params event') | |
| self.save_archive() | |
| self.wait_for_new_params_event() | |
| #self.log.debug('Gaussian process learner reading costs') | |
| self.get_params_and_costs() | |
| self.fit_gaussian_process() | |
| for _ in range(self.generation_num): | |
| self.log.debug('Gaussian process learner generating parameter:'+ str(self.params_count+1)) | |
| next_params = self.find_next_parameters() | |
| self.params_out_queue.put(next_params) | |
| if self.end_event.is_set(): | |
| raise LearnerInterrupt() | |
| except LearnerInterrupt: | |
| pass | |
| end_dict = {} | |
| if self.predict_global_minima_at_end: | |
| self.find_global_minima() | |
| end_dict.update({'predicted_best_parameters':self.predicted_best_parameters, | |
| 'predicted_best_cost':self.predicted_best_cost, | |
| 'predicted_best_uncertainty':self.predicted_best_uncertainty}) | |
| if self.predict_local_minima_at_end: | |
| self.find_local_minima() | |
| end_dict.update({'local_minima_parameters':self.local_minima_parameters, | |
| 'local_minima_costs':self.local_minima_costs, | |
| 'local_minima_uncers':self.local_minima_uncers}) | |
| self.params_out_queue.put(end_dict) | |
| self._shut_down() | |
| self.log.debug('Ended Gaussian Process Learner') | |
| def predict_cost(self,params): | |
| ''' | |
| Produces a prediction of cost from the gaussian process at params. | |
| Returns: | |
| float : Predicted cost at paramters | |
| ''' | |
| return self.gaussian_process.predict(params[np.newaxis,:]) | |
| def find_global_minima(self): | |
| ''' | |
| Performs a quick search for the predicted global minima from the learner. Does not return any values, but creates the following attributes. | |
| Attributes: | |
| predicted_best_parameters (array): the parameters for the predicted global minima | |
| predicted_best_cost (float): the cost at the predicted global minima | |
| predicted_best_uncertainty (float): the uncertainty of the predicted global minima | |
| ''' | |
| self.log.debug('Started search for predicted global minima.') | |
| self.predicted_best_parameters = None | |
| self.predicted_best_scaled_cost = float('inf') | |
| self.predicted_best_scaled_uncertainty = None | |
| search_params = [] | |
| search_params.append(self.best_params) | |
| for _ in range(self.parameter_searches): | |
| search_params.append(self.min_boundary + nr.uniform(size=self.num_params) * self.diff_boundary) | |
| search_bounds = list(zip(self.min_boundary, self.max_boundary)) | |
| for start_params in search_params: | |
| result = so.minimize(self.predict_cost, start_params, bounds = search_bounds, tol=self.search_precision) | |
| curr_best_params = result.x | |
| (curr_best_cost,curr_best_uncer) = self.gaussian_process.predict(curr_best_params[np.newaxis,:],return_std=True) | |
| if curr_best_cost<self.predicted_best_scaled_cost: | |
| self.predicted_best_parameters = curr_best_params | |
| self.predicted_best_scaled_cost = curr_best_cost | |
| self.predicted_best_scaled_uncertainty = curr_best_uncer | |
| self.predicted_best_cost = self.cost_scaler.inverse_transform(self.predicted_best_scaled_cost) | |
| self.predicted_best_uncertainty = self.predicted_best_scaled_uncertainty / self.cost_scaler.scale_ | |
| self.archive_dict.update({'predicted_best_parameters':self.predicted_best_parameters, | |
| 'predicted_best_scaled_cost':self.predicted_best_scaled_cost, | |
| 'predicted_best_scaled_uncertainty':self.predicted_best_scaled_uncertainty, | |
| 'predicted_best_cost':self.predicted_best_cost, | |
| 'predicted_best_uncertainty':self.predicted_best_uncertainty}) | |
| self.has_global_minima = True | |
| self.log.debug('Predicted global minima found.') | |
| def find_local_minima(self): | |
| ''' | |
| Performs a comprehensive search of the learner for all predicted local minima (and hence the global as well) in the landscape. Note, this can take a very long time. | |
| Attributes: | |
| local_minima_parameters (list): list of all the parameters for local minima. | |
| local_minima_costs (list): list of all the costs at local minima. | |
| local_minima_uncers (list): list of all the uncertainties at local minima. | |
| ''' | |
| self.log.info('Searching for all minima.') | |
| self.minima_tolerance = 10*self.search_precision | |
| self.number_of_local_minima = 0 | |
| self.local_minima_parameters = [] | |
| self.local_minima_costs = [] | |
| self.local_minima_uncers = [] | |
| search_bounds = list(zip(self.min_boundary, self.max_boundary)) | |
| for start_params in self.all_params: | |
| result = so.minimize(self.predict_cost, start_params, bounds = search_bounds, tol=self.search_precision) | |
| curr_minima_params = result.x | |
| (curr_minima_cost,curr_minima_uncer) = self.gaussian_process.predict(curr_minima_params[np.newaxis,:],return_std=True) | |
| if all( not np.all( np.abs(params - curr_minima_params) < self.minima_tolerance ) for params in self.local_minima_parameters): | |
| #Non duplicate point so add to the list | |
| self.number_of_local_minima += 1 | |
| self.local_minima_parameters.append(curr_minima_params) | |
| self.local_minima_costs.append(curr_minima_cost) | |
| self.local_minima_uncers.append(curr_minima_uncer) | |
| self.archive_dict.update({'number_of_local_minima':self.number_of_local_minima, | |
| 'local_minima_parameters':self.local_minima_parameters, | |
| 'local_minima_costs':self.local_minima_costs, | |
| 'local_minima_uncers':self.local_minima_uncers}) | |
| self.has_local_minima = True | |
| self.log.info('Search completed') | |