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- """Basic Checkpoint class implementation
- """
- # main imports
- import os
- import logging
- import numpy as np
- # module imports
- from macop.callbacks.Callback import Callback
- from macop.utils.color import macop_text, macop_line
- class SurrogateCheckpoint(Callback):
- """
- SurrogateCheckpoint is used for logging training data information about surrogate
- 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 best solution
- solution = self._algo._bestSolution
- surrogate_analyser = self._algo._surrogate_analyser
- # Do nothing is surrogate analyser does not exist
- if surrogate_analyser is None:
- return
- currentEvaluation = self._algo.getGlobalEvaluation()
- # backup if necessary
- if currentEvaluation % self._every == 0:
- logging.info(f"Surrogate analysis checkpoint is done into {self._filepath}")
- solutionData = ""
- solutionSize = len(solution._data)
- for index, val in enumerate(solution._data):
- solutionData += str(val)
- if index < solutionSize - 1:
- solutionData += ' '
- line = str(currentEvaluation) + ';' + str(surrogate_analyser._every_ls) + ';' + str(surrogate_analyser._time) + ';' + str(surrogate_analyser._r2) \
- + ';' + solutionData + ';' + str(solution.fitness()) + ';\n'
- # check if file exists
- if not os.path.exists(self._filepath):
- with open(self._filepath, 'w') as f:
- f.write(line)
- else:
- with open(self._filepath, 'a') as f:
- f.write(line)
- def load(self):
- """
- Load nothing there, as we only log surrogate training information
- """
- logging.info("No loading to do with surrogate checkpoint")
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