def create_trials(losses, statuses, vals, scope_keys):
trials = Trials()
tids = trials.new_trial_ids(len(losses))
specs = [None for x in range(len(tids))]
results = []
miscs = []
for i in range(len(tids)):
idxs_content = [[i] for key in scope_keys]
idxs_vals_content = []
for key in scope_keys:
idxs_vals_content.append([vals[key][i]])
results.append(dict(loss=losses[i], status=statuses[i]))
miscs.append(
dict(tid=tids[i],
cmd=None,
idxs=dict(zip(scope_keys, idxs_content)),
vals=dict(zip(scope_keys, idxs_vals_content))))
trials.insert_trial_docs(
trials.new_trial_docs(
tids,
specs,
results,
miscs,
))
trials.refresh()
return trials
def create_trials(self, complete, losses):
if len(complete) > 0:
trials = Trials()
hist = self.create_history(complete)
index = 0
for c in complete:
if c >= len(losses):
error(
"Index {} is larger than the size of losses {}".format(
c, len(losses)))
loss = losses[c]
rval_specs = [None]
new_id = index
rval_results = []
rval_results.append(create_ok_result(loss, c))
rval_miscs = []
rval_miscs.append(self.create_misc(index, hist))
hyperopt_trial = trials.new_trial_docs([new_id], rval_specs,
rval_results,
rval_miscs)[0]
index += 1
if self.response_shaping is True:
# transform log applied loss for enhancing optimization performance
#debug("before scaling: {}".format(loss))
if self.shaping_func == "log_err":
loss = apply_log_err(loss)
elif self.shaping_func == "hybrid_log":
loss = apply_hybrid_log(loss)
else:
debug("Invalid shaping function: {}".format(
self.shaping_func))
hyperopt_trial['result'] = {
'loss': float(loss),
'status': STATUS_OK
}
hyperopt_trial['state'] = JOB_STATE_DONE
#debug("History appended: {}-{}".format(c, loss))
trials.insert_trial_doc(hyperopt_trial)
trials.refresh()
return trials
else:
return Trials()
def create_trials(self, completed, losses):
if len(completed) > 0:
trials = Trials()
hist = self.create_history(completed)
#index = 0
#for c in completed:
for index in range(len(completed)):
c = completed[index]
loss = losses[index]
rval_specs = [None]
new_id = index
rval_results = [ ]
rval_results.append(create_ok_result(loss, c))
rval_miscs = [ ]
rval_miscs.append(self.create_misc(index, hist))
hopt_trial = trials.new_trial_docs([new_id], rval_specs, rval_results, rval_miscs)[0]
if self.response_shaping is True:
# transform log applied loss for enhancing optimization performance
#debug("before scaling: {}".format(loss))
if self.shaping_func == "log_err":
loss = apply_log_err(loss)
elif self.shaping_func == "hybrid_log":
loss = apply_hybrid_log(loss)
else:
debug("Invalid shaping function: {}".format(self.shaping_func))
if loss != None:
hopt_trial['result'] = {'loss': float(loss), 'status': STATUS_OK}
hopt_trial['state'] = JOB_STATE_DONE
#debug("History appended: {}-{}".format(c, loss))
trials.insert_trial_doc(hopt_trial)
trials.refresh()
return trials
else:
return Trials()
