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:
# read data for each line
for i, line in enumerate(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
current_data = list(map(int, data[-1].split(' ')))
self.algo.result[i].data = np.array(current_data)
self.algo.result[i].scores = scores
macop_text(
self.algo,
f'Load of available pareto front backup from `{ self._filepath}`'
)
else:
macop_text(
self.algo,
'No pareto front found... Start running algorithm with new pareto front population.'
)
logging.info("No pareto front backup used...")
macop_line(self.algo)
def load(self):
"""
Load backup lines as rewards and occurrences for UCB
"""
if os.path.exists(self._filepath):
logging.info('Load UCB data')
with open(self._filepath) as f:
lines = f.readlines()
# read data for each line
rewardsLine = lines[0].replace('\n', '')
occurrencesLine = lines[1].replace('\n', '')
self.algo.policy.rewards = [
float(f) for f in rewardsLine.split(';')
]
self.algo.policy.occurences = [
float(f) for f in occurrencesLine.split(';')
]
macop_text(
self.algo,
f'Load of available UCB policy data from `{self._filepath}`')
else:
macop_text(self.algo, 'No UCB data found, use default UCB policy')
logging.info("No UCB data found...")
macop_line(self.algo)
def end(self):
"""Display end message into `run` method
"""
macop_text(
self,
f'({type(self).__name__}) Found after {self._numberOfEvaluations} evaluations \n - {self._bestSolution}'
)
macop_line(self)
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:
# read data for each line
for i, line in enumerate(f.readlines()):
data = line.replace(';\n', '').split(';')
# only the first time
if i == 0:
# get evaluation information
globalEvaluation = int(data[0])
if self.algo.getParent() is not None:
self.algo.getParent(
)._numberOfEvaluations = globalEvaluation
else:
self.algo._numberOfEvaluations = globalEvaluation
nObjectives = len(self.algo.evaluator)
scores = [float(s) for s in data[1:nObjectives + 1]]
# get best solution data information
current_data = list(map(int, data[-1].split(' ')))
# initialise and fill with data
self.algo.population[i] = self.algo.initialiser()
self.algo.population[i].data = np.array(current_data)
self.algo.population[i].scores = scores
self.algo.result.append(self.algo.population[i])
macop_line(self.algo)
macop_text(
self.algo,
f'Load of available population from `{self._filepath}`')
macop_text(
self.algo,
f'Restart algorithm from evaluation {self.algo._numberOfEvaluations}.'
)
else:
macop_text(
self.algo,
'No backup found... Start running algorithm from evaluation 0.'
)
logging.info(
"Can't load backup... Backup filepath not valid in Checkpoint")
macop_line(self.algo)
def end(self):
"""Display end message into `run` method
"""
macop_text(
self,
f'({type(self).__name__}) Found after {self._numberOfEvaluations} evaluations'
)
for i, solution in enumerate(self._pfPop):
macop_text(self, f' - [{i}] {solution.scores} : {solution}')
macop_line(self)
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.getParent() is not None:
self.algo.getParent().setEvaluation(globalEvaluation)
else:
self.algo.setEvaluation(globalEvaluation)
# get best solution data information
solution_data = list(map(int, data[1].split(' ')))
if self.algo.result is None:
self.algo.result = self.algo.initialiser()
self.algo.result.data = np.array(solution_data)
self.algo.result.fitness = float(data[2])
macop_line(self.algo)
macop_text(self.algo,
f'Checkpoint found from `{self._filepath}` file.')
macop_text(
self.algo,
f'Restart algorithm from evaluation {self.algo.getEvaluation()}.'
)
else:
macop_text(
self.algo,
'No backup found... Start running algorithm from evaluation 0.'
)
logging.info(
"Can't load backup... Backup filepath not valid in Checkpoint")
macop_line(self.algo)