# Source code for macop.callbacks.ParetoCheckpoint

    """Pareto front Checkpoint class implementation
    """

    # main imports
    import os
    import logging
    import numpy as np
    import sys

    # module imports
    from .Callback import Callback
    from ..utils.color import macop_text, macop_line
    from ..utils.modules import load_class

    class ParetoCheckpoint(Callback):
        """
        Pareto checkpoint is used for loading previous computations and start again after loading checkpoint

        Attributes:
            algo: {Algorithm} -- main algorithm instance reference
            every: {int} -- checkpoint frequency used (based on number of evaluations)
            filepath: {str} -- file path where checkpoints will be saved
        """
        def run(self):
            """
            Check if necessary to do backup based on `every` variable
            """
            # get current population
            pfPop = self.algo.pfPop

            currentEvaluation = self.algo.getGlobalEvaluation()

            # backup if necessary
            if currentEvaluation % self.every == 0:

                logging.info("Checkpoint is done into " + self.filepath)

                with open(self.filepath, 'w') as f:

                    for solution in pfPop:
                        solutionData = ""
                        solutionSize = len(solution.data)

                        for index, val in enumerate(solution.data):
                            solutionData += str(val)

                            if index < solutionSize - 1:
                                solutionData += ' '

                        line = ''

                        for i in range(len(self.algo.evaluator)):
                            line += str(solution.scores[i]) + ';'

                        line += solutionData + ';\n'

                        f.write(line)

        def load(self):
            """
            Load backup lines as population and set algorithm state (population and pareto front) at this backup
            """
            if os.path.exists(self.filepath):

                logging.info('Load best solution from last checkpoint')
                with open(self.filepath) as f:

                    # reinit pf population
                    self.algo.pfPop = []

                    # retrieve class name from algo
                    class_name = type(self.algo.population[0]).__name__

                    # dynamically load solution class if unknown
                    if class_name not in sys.modules:
                        load_class(class_name, globals())

                    # read data for each line
                    for line in f.readlines():

                        data = line.replace(';\n', '').split(';')

                        nObjectives = len(self.algo.evaluator)
                        scores = [float(s) for s in data[0:nObjectives]]

                        # get best solution data information
                        solutionData = list(map(int, data[-1].split(' ')))

                        newSolution = getattr(
                            globals()['macop.solutions.' + class_name],
                            class_name)(solutionData, len(solutionData))
                        newSolution.scores = scores

                        self.algo.pfPop.append(newSolution)

                print(
                    macop_text(
                        'Load of available pareto front backup from `{}`'.format(
                            self.filepath)))

            else:
                print(
                    macop_text(
                        'No pareto front found... Start running algorithm with new pareto front population.'
                    ))
                logging.info("No pareto front backup used...")

            print(macop_line())