def report(self, compute_time=False):
def show(metric):
if 'all' in self.show_metrics or metric in self.show_metrics or metric == 'exs':
return True
return False
show_metrics = self.opt.get('metrics', "all")
self.show_metrics = show_metrics.split(',')
metrics = {}
for a in self.agents:
if hasattr(a, 'report'):
m = a.report()
for k, v in m.items():
if k not in metrics:
# first agent gets priority in settings values for keys
# this way model can't e.g. override accuracy to 100%
if show(k):
metrics[k] = v
if metrics:
if compute_time and 'exs' in metrics:
self.total_exs += metrics['exs']
time_metrics = compute_time_metrics(self,
self.opt['max_train_time'])
metrics.update(time_metrics)
return metrics
def report(self, compute_time=False):
metrics = self.agents[0].report()
if compute_time:
self.total_exs += metrics['total']
time_metrics = compute_time_metrics(self, self.opt['max_train_time'])
metrics.update(time_metrics)
return metrics
def report(self, compute_time=False):
metrics = aggregate_metrics(self.worlds)
if compute_time:
self.total_exs += metrics['total']
time_metrics = compute_time_metrics(self, self.opt['max_train_time'])
metrics.update(time_metrics)
return metrics
def report(self, compute_time=False):
metrics = aggregate_metrics(self.worlds)
if compute_time:
self.total_exs += metrics['total']
time_metrics = compute_time_metrics(self, self.opt['max_train_time'])
metrics.update(time_metrics)
return metrics
def report(self, compute_time=False):
metrics = self.agents[0].report()
if compute_time:
self.total_exs += metrics['total']
time_metrics = compute_time_metrics(self, self.opt['max_train_time'])
metrics.update(time_metrics)
return metrics
def report(self, compute_time=False):
metrics = aggregate_metrics(self.worlds)
if compute_time:
self.total_exs += metrics['total']
if self.num_examples() is not None and self.num_examples() > 0:
self.total_epochs = int(self.total_exs / self.num_examples())
time_metrics = compute_time_metrics(self,
self.opt['max_train_time'])
metrics.update(time_metrics)
return metrics
def report(self, compute_time=False):
if hasattr(self.agents[0], 'report'):
metrics = self.agents[0].report()
if compute_time:
self.total_exs += metrics['total']
if self.num_examples() is not None and self.num_examples() > 0:
self.total_epochs = int(self.total_exs /
self.num_examples())
time_metrics = compute_time_metrics(self,
self.opt['max_train_time'])
metrics.update(time_metrics)
return metrics
def report(self, compute_time=False):
metrics = {}
for a in self.agents:
if hasattr(a, 'report'):
m = a.report()
for k, v in m.items():
if k not in metrics:
# first agent gets priority in settings values for keys
# this way model can't e.g. override accuracy to 100%
metrics[k] = v
if metrics:
if compute_time and 'exs' in metrics:
self.total_exs += metrics['exs']
time_metrics = compute_time_metrics(self, self.opt['max_train_time'])
metrics.update(time_metrics)
return metrics