"""Basic Checkpoint class implementation
"""
# main imports
import os
import logging
import numpy as np
# module imports
from .Callback import Callback
from ..utils.color import macop_text, macop_line
[docs]class BasicCheckpoint(Callback):
"""
BasicCheckpoint 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
"""
[docs] def run(self):
"""
Check if necessary to do backup based on `every` variable
"""
# get current best solution
solution = self.algo.bestSolution
currentEvaluation = self.algo.getGlobalEvaluation()
# backup if necessary
if currentEvaluation % self.every == 0:
logging.info("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) + ';' + 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)
[docs] def load(self):
"""
Load last backup line of solution and set algorithm state (best solution and evaluations) at this backup
"""
if os.path.exists(self.filepath):
logging.info('Load best solution from last checkpoint')
with open(self.filepath) as f:
# get last line and read data
lastline = f.readlines()[-1]
data = lastline.split(';')
# get evaluation information
globalEvaluation = int(data[0])
if self.algo.parent is not None:
self.algo.parent.numberOfEvaluations = globalEvaluation
else:
self.algo.numberOfEvaluations = globalEvaluation
# get best solution data information
solutionData = list(map(int, data[1].split(' ')))
self.algo.bestSolution.data = np.array(solutionData)
self.algo.bestSolution.score = float(data[2])
print(
macop_text('Restart algorithm from evaluation {}.'.format(
self.algo.numberOfEvaluations)))
else:
print(
macop_text(
'No backup found... Start running algorithm from evaluation 0.'
))
logging.info(
"Can't load backup... Backup filepath not valid in Checkpoint")
print(macop_line())