#!/usr/bin/env python

import numpy as np
from olympus.objects                   import ParameterVector
from olympus.planners.abstract_planner import AbstractPlanner
from olympus import Logger


class Grid(AbstractPlanner):

    def __init__(self, goal='minimize', levels=2, budget=None, exceed_budget=True, shuffle=False, random_seed=None):
        """Grid search.

        Note that the number of samples grow exponentially with the number of dimensions. E.g. for a 2-dimensional
        parameter space, with 2 levels, the grid will contain 4 samples; for a 3-dimensional space, it will contain
        8 samples; for a 6-dimensional space, 64 samples.

        Args:
            goal (str): The optimization goal, either 'minimize' or 'maximize'. Default is 'minimize'.
            levels (int, list): How many locations in parameter space to sample per parameter/dimension. If an integer
                is provided, the same number of levels will be used for each dimension. Provide a list to use different
                levels for different dimensions. Default is 2.
            budget (int): Maximum number of samples you want to evaluate. From the specified ``budget`` an adequate
                value for the levels will be derived. Note that, if provided, the argument ``levels`` will be discarded.
                Default is None.
            exceed_budget (bool): Whether to allow building the grid with more samples then ``budget``.
                This means some points might not be evaluated, but ensures there will be enough grid points to run as
                many evaluations as defined in ``budget``. If False, the number of grid points will be less or equal to
                ``budget``; this guarantees the budget is enough to guarantee the exploration of the whole grid.
                Default is True.
            shuffle (bool): Whether to randomize the order of the samples in the grid. Default is False.
            random_seed (int): Random seed. Set a random seed for a reproducible randomization of the grid if ``shuffle``
                was set to True.
        """
        AbstractPlanner.__init__(**locals())
        self.grid_created = False

    def reset(self):
        """Clears the remaining samples in the grid and prepares the planner for re-initialisation."""
        self.samples_loc = None
        self.samples = None
        self.grid_created = False

    def _set_param_space(self, param_space):
        self.param_space = param_space
        self.dims = len(self.param_space)
        self.bounds = self.param_space.param_bounds

        # if budget provided, define levels
        if self.budget is not None:
            self._get_approximate_levels()

        # allow providing a list of levels to tune the budget
        if isinstance(self.levels, int):
            self._levels = [self.levels] * self.dims
        elif isinstance(self.levels, list) or isinstance(self.levels, np.ndarray):
            if len(self.levels) != self.dims:
                message = (f'The number of levels provided ({len(self.levels)}) does not match dimensionality of the '
                           f'parameter space ({self.dimlists}).')
                Logger.log(message, 'ERROR')
            self._levels = list(self.levels)
        else:
            raise ValueError('Argument `level` can only be a integer or a list.')

    def _tell(self, observations):
        # grid search does not care about previous observations
        pass

    def _create_grid(self):
        self.samples_loc = []  # sample locations by dimension

        # define location of grid samples for each dimension
        for param, level in zip(self.param_space, self._levels):
            loc = np.linspace(start=param.low, stop=param.high, num=level)
            self.samples_loc.append(loc)

        meshgrid = np.stack(np.meshgrid(*self.samples_loc), len(self.samples_loc))  # make grid
        num_samples = np.prod(np.shape(meshgrid)[:-1])  # number of samples in grid

        # all grid samples in a 2D array
        self.samples = np.reshape(meshgrid, newshape=(num_samples, len(self.samples_loc)))

        # shuffle is we are sampling these points at random
        if self.shuffle is True:
            np.random.seed(self.random_seed)
            np.random.shuffle(self.samples)

        self.samples = list(self.samples)
        self.grid_created = True

    def _get_approximate_levels(self):
        # initialise with lowest number of samples that does not exceed budget
        base_level = int(np.floor(self.budget ** (1. / self.dims)))
        self.levels = np.array([base_level] * self.dims)
        effective_budget = np.prod(self.levels)  # actual number of samples created
        excess = int(self.budget - effective_budget)  # excess budget left

        # now add one level at a time to each dimension
        counter = 0
        while excess > 0:  # add levels until we exhaust all excess budget samples
            # if we want to have samples <= budget (i.e. do not exceed budget)
            if self.exceed_budget is False:
                # add 1 to last level as this should never get to receive a +1 anyway
                effective_budget_lookahead = np.prod(self.levels[:-1]) * (self.levels[-1] + 1)
                if effective_budget_lookahead > self.budget:
                    break
            # increment one level at a time
            i = counter % self.dims
            self.levels[i] += 1
            counter += 1
            # update effective budget and excess
            effective_budget = np.prod(self.levels)
            excess = int(self.budget - effective_budget)

        if excess >= 0:
            excess_type = 'budgeted evaluations'
        else:
            excess_type = 'grid points'
        message = (f'Budget provided, discarding argument `levels`. Given a budget of {self.budget} and a '
                   f'parameter space of dimensionality {self.dims}, we will explore {self.levels} levels. '
                   f'Given an excess of {np.abs(excess)} {excess_type}, the effective number '
                   f'of evaluations required is {effective_budget}.')
        Logger.log(message, 'INFO')

    def _ask(self):
        if self.grid_created is False:
            self._create_grid()
        param = self.samples.pop(0)

        if len(self.samples) == 0:
            message = 'Last parameter being provided - there will not be any more available samples in the grid.'
            Logger.log(message, 'INFO')

        return ParameterVector(array=param, param_space=self.param_space)




#!/usr/bin/env python

import numpy as np
from olympus.objects                   import ParameterVector
from olympus.planners.abstract_planner import AbstractPlanner
from olympus import Logger


class Grid(AbstractPlanner):

    def __init__(self, goal='minimize', levels=2, budget=None, exceed_budget=True, shuffle=False, random_seed=None):
        """Grid search.

        Note that the number of samples grow exponentially with the number of dimensions. E.g. for a 2-dimensional
        parameter space, with 2 levels, the grid will contain 4 samples; for a 3-dimensional space, it will contain
        8 samples; for a 6-dimensional space, 64 samples.

        Args:
            goal (str): The optimization goal, either 'minimize' or 'maximize'. Default is 'minimize'.
            levels (int, list): How many locations in parameter space to sample per parameter/dimension. If an integer
                is provided, the same number of levels will be used for each dimension. Provide a list to use different
                levels for different dimensions. Default is 2.
            budget (int): Maximum number of samples you want to evaluate. From the specified ``budget`` an adequate
                value for the levels will be derived. Note that, if provided, the argument ``levels`` will be discarded.
                Default is None.
            exceed_budget (bool): Whether to allow building the grid with more samples then ``budget``.
                This means some points might not be evaluated, but ensures there will be enough grid points to run as
                many evaluations as defined in ``budget``. If False, the number of grid points will be less or equal to
                ``budget``; this guarantees the budget is enough to guarantee the exploration of the whole grid.
                Default is True.
            shuffle (bool): Whether to randomize the order of the samples in the grid. Default is False.
            random_seed (int): Random seed. Set a random seed for a reproducible randomization of the grid if ``shuffle``
                was set to True.
        """
        AbstractPlanner.__init__(**locals())
        self.grid_created = False

    def reset(self):
        """Clears the remaining samples in the grid and prepares the planner for re-initialisation."""
        self.samples_loc = None
        self.samples = None
        self.grid_created = False

    def _set_param_space(self, param_space):
        self.param_space = param_space
        self.dims = len(self.param_space)
        self.bounds = self.param_space.param_bounds

        # if budget provided, define levels
        if self.budget is not None:
            self._get_approximate_levels()

        # allow providing a list of levels to tune the budget
        if isinstance(self.levels, int):
            self._levels = [self.levels] * self.dims
        elif isinstance(self.levels, list) or isinstance(self.levels, np.ndarray):
            if len(self.levels) != self.dims:
                message = (f'The number of levels provided ({len(self.levels)}) does not match dimensionality of the '
                           f'parameter space ({self.dimlists}).')
                Logger.log(message, 'ERROR')
            self._levels = list(self.levels)
        else:
            raise ValueError('Argument `level` can only be a integer or a list.')

    def _tell(self, observations):
        # grid search does not care about previous observations
        pass

    def _create_grid(self):
        self.samples_loc = []  # sample locations by dimension

        # define location of grid samples for each dimension
        for param, level in zip(self.param_space, self._levels):
            loc = np.linspace(start=param.low, stop=param.high, num=level)
            self.samples_loc.append(loc)

        meshgrid = np.stack(np.meshgrid(*self.samples_loc), len(self.samples_loc))  # make grid
        num_samples = np.prod(np.shape(meshgrid)[:-1])  # number of samples in grid

        # all grid samples in a 2D array
        self.samples = np.reshape(meshgrid, newshape=(num_samples, len(self.samples_loc)))

        # shuffle is we are sampling these points at random
        if self.shuffle is True:
            np.random.seed(self.random_seed)
            np.random.shuffle(self.samples)

        self.samples = list(self.samples)
        self.grid_created = True

    def _get_approximate_levels(self):
        # initialise with lowest number of samples that does not exceed budget
        base_level = int(np.floor(self.budget ** (1. / self.dims)))
        self.levels = np.array([base_level] * self.dims)
        effective_budget = np.prod(self.levels)  # actual number of samples created
        excess = int(self.budget - effective_budget)  # excess budget left

        # now add one level at a time to each dimension
        counter = 0
        while excess > 0:  # add levels until we exhaust all excess budget samples
            # if we want to have samples <= budget (i.e. do not exceed budget)
            if self.exceed_budget is False:
                # add 1 to last level as this should never get to receive a +1 anyway
                effective_budget_lookahead = np.prod(self.levels[:-1]) * (self.levels[-1] + 1)
                if effective_budget_lookahead > self.budget:
                    break
            # increment one level at a time
            i = counter % self.dims
            self.levels[i] += 1
            counter += 1
            # update effective budget and excess
            effective_budget = np.prod(self.levels)
            excess = int(self.budget - effective_budget)

        if excess >= 0:
            excess_type = 'budgeted evaluations'
        else:
            excess_type = 'grid points'
        message = (f'Budget provided, discarding argument `levels`. Given a budget of {self.budget} and a '
                   f'parameter space of dimensionality {self.dims}, we will explore {self.levels} levels. '
                   f'Given an excess of {np.abs(excess)} {excess_type}, the effective number '
                   f'of evaluations required is {effective_budget}.')
        Logger.log(message, 'INFO')

    def _ask(self):
        if self.grid_created is False:
            self._create_grid()
        param = self.samples.pop(0)

        if len(self.samples) == 0:
            message = 'Last parameter being provided - there will not be any more available samples in the grid.'
            Logger.log(message, 'INFO')

        return ParameterVector(array=param, param_space=self.param_space)




#!/usr/bin/env python Grid search.          Note that the number of samples grow exponentially with the number of dimensions. E.g. for a 2-dimensional         parameter space, with 2 levels, the grid will contain 4 samples; for a 3-dimensional space, it will contain         8 samples; for a 6-dimensional space, 64 samples.          Args:             goal (str): The optimization goal, either 'minimize' or 'maximize'. Default is 'minimize'.             levels (int, list): How many locations in parameter space to sample per parameter/dimension. If an integer                 is provided, the same number of levels will be used for each dimension. Provide a list to use different                 levels for different dimensions. Default is 2.             budget (int): Maximum number of samples you want to evaluate. From the specified ``budget`` an adequate                 value for the levels will be derived. Note that, if provided, the argument ``levels`` will be discarded.                 Default is None.             exceed_budget (bool): Whether to allow building the grid with more samples then ``budget``.                 This means some points might not be evaluated, but ensures there will be enough grid points to run as                 many evaluations as defined in ``budget``. If False, the number of grid points will be less or equal to                 ``budget``; this guarantees the budget is enough to guarantee the exploration of the whole grid.                 Default is True.             shuffle (bool): Whether to randomize the order of the samples in the grid. Default is False.             random_seed (int): Random seed. Set a random seed for a reproducible randomization of the grid if ``shuffle``                 was set to True. Clears the remaining samples in the grid and prepares the planner for re-initialisation. # if budget provided, define levels # allow providing a list of levels to tune the budget # grid search does not care about previous observations # sample locations by dimension # define location of grid samples for each dimension # make grid # number of samples in grid # all grid samples in a 2D array # shuffle is we are sampling these points at random # initialise with lowest number of samples that does not exceed budget # actual number of samples created # excess budget left # now add one level at a time to each dimension # add levels until we exhaust all excess budget samples # if we want to have samples <= budget (i.e. do not exceed budget) # add 1 to last level as this should never get to receive a +1 anyway # increment one level at a time # update effective budget and excess