def __init__(self,
min_records,
trials,
num_halving_rounds=float('inf'),
eta=0,
bracket_id=None,
goal_maximize=False,
pending_trials=None,
fix_quantiles=False):
self._min_records = min_records
self._max_records = min_records * eta**num_halving_rounds
self._num_halving_rounds = num_halving_rounds
self._eta = eta
self._bracket_id = bracket_id
self._goal_maximize = goal_maximize
# Total budget
self._total_records = 0
self._ladder = [[]]
self._fix_quantiles = fix_quantiles
self._pending_trials = {}
for t in pending_trials:
self._pending_trials[(_get_metadata(t, 'hyperband_id'),
_get_metadata(t,
'termination_record'))] = True
self._populate_bracket(trials)
def GetNewSuggestions(self, num_suggestions_hint, completed_trials,
pending_trials):
# brackets reconstructed each time, probably can modify with each new result
brackets, next_hyperband_id = self._construct_brackets(
completed_trials, pending_trials)
trials = []
for _ in range(num_suggestions_hint):
bracket_budget = [(idx,
numpy.floor(bracket.num_fully_trained *
(bracket.num_halving_rounds + 1) /
(self._max_halving_rounds + 1)))
for idx, bracket in enumerate(brackets)]
bracket_id = sorted(bracket_budget, key=lambda x: x[1] * 10 - x[0])
print(bracket_id)
bracket_id = bracket_id[0][0]
trial = brackets[bracket_id].get_trial()
if _get_metadata(trial, 'hyperband_id') < 0:
# new trial
parameters = self._delegate_policy.GetNewSuggestions(
1, completed_trials, pending_trials)[0].parameters
trial.parameters = parameters
_set_metadata(trial, 'hyperband_id', next_hyperband_id)
next_hyperband_id += 1
trials.append(trial)
return trials
def bracket_string(self):
brk_str = ''
def smean(S):
if len(S) == 0:
return float('NaN')
else:
return numpy.mean(S)
brk_str += 'r_i\tn_i\tbest objective_value\n---------------------\n'
for idx, rung in enumerate(self._ladder):
vals = []
if rung and self._goal_maximize:
vals = sorted([
-max(x.objective_value for x in trial.measurements)
for trial in rung if trial.measurements
])
vals = ['%.2f' % -x for x in vals]
elif rung:
vals = sorted([
min(x.objective_value for x in trial.measurements)
for trial in rung if trial.measurements
])
vals = ['%.2f' % x for x in vals]
brk_str += '%.0f/%.0f, %d, %s\n' % (
round(self._min_records * self._eta**idx),
smean([
trial.measurements[-1].steps
for trial in rung if trial.measurements
]), len(vals), str(vals))
if len(self._ladder[-1]):
brk_str += 'best trial is: %d\n' % (_get_metadata(
self._ladder[-1][0], 'hyperband_id'))
return brk_str
def get_trial(self):
# identify the best rung to advance a trial from
trial_to_advance = None
#bracket_depth = min(len(self._ladder), self._num_halving_rounds)
bracket_depth = len(self._ladder)
trials_already_advanced = {}
if len(self._ladder) > self._num_halving_rounds:
trials_already_advanced.update({
_get_metadata(trial, 'hyperband_id'): True
for trial in self._ladder[-1]
})
for rung in reversed(range(bracket_depth)):
candidate_trials = self._ladder[rung]
if len(candidate_trials) >= self._eta:
#each rung already sorted when populating brackets
candidate_trials = candidate_trials[
0:int(len(self._ladder[rung]) / self._eta)]
#print([_get_metadata(t,'hyperband_id') for t in candidate_trials])
while candidate_trials:
trial = candidate_trials.pop(0)
hyperband_id = _get_metadata(trial, 'hyperband_id')
if not trials_already_advanced.get(
hyperband_id, False) and (
hyperband_id,
_get_metadata(trial, 'termination_record') *
self._eta) not in self._pending_trials:
trial_to_advance = trial
break
if trial_to_advance is not None:
break
trials_already_advanced.update({
_get_metadata(trial, 'hyperband_id'): True
for trial in self._ladder[rung]
})
# advance an existing trial or initialize a new one
if trial_to_advance:
parameters = trial_to_advance.parameters
measurements = trial_to_advance.measurements
bracket_id = _get_metadata(trial_to_advance, 'bracket_id')
hyperband_id = _get_metadata(trial_to_advance, 'hyperband_id')
termination_record = self._eta * _get_metadata(
trial_to_advance, 'termination_record')
else:
parameters = []
measurements = []
bracket_id = self._bracket_id
hyperband_id = -1
termination_record = self._min_records
trial = Trial.Trial()
trial.status = Trial.REQUESTED
trial.parameters = parameters
trial.measurements.extend(measurements)
_set_metadata(trial, 'bracket_id', bracket_id)
_set_metadata(trial, 'hyperband_id', hyperband_id)
_set_metadata(trial, 'termination_record', termination_record)
self._total_records += termination_record
return trial
def _construct_brackets(self, completed_trials, pending_trials):
all_trials = set(completed_trials).union(set(pending_trials))
num_brackets = self._max_halving_rounds + 1
next_hyperband_id = -1
stratified_trials = [[] for _ in range(num_brackets)]
for trial in all_trials:
stratified_trials[_get_metadata(trial, 'bracket_id')].append(trial)
next_hyperband_id = max(next_hyperband_id,
_get_metadata(trial, 'hyperband_id'))
next_hyperband_id += 1
brackets = []
for s in range(num_brackets):
bracket = Bracket(
self._min_records * self._eta**(self._max_halving_rounds - s),
stratified_trials[s], s, self._eta, s, self._goal_maximize, [
trial for trial in pending_trials
if _get_metadata(trial, 'bracket_id') == s
])
brackets.append(bracket)
return brackets, next_hyperband_id
def _construct_brackets(self, completed_trials, pending_trials):
all_trials = set(completed_trials).union(set(pending_trials))
next_hyperband_id = -1
if len(all_trials) > 0:
next_hyperband_id = max(
[_get_metadata(t, 'hyperband_id') for t in all_trials])
next_hyperband_id += 1
s = self._halving_rounds
bracket = Bracket(self._min_records, all_trials, s, self._eta, s,
self._goal_maximize, pending_trials)
return bracket, next_hyperband_id
def _populate_bracket(self, trials):
for trial in trials:
if trial.measurements:
records = trial.measurements[-1].steps
self._total_records += records
rung = int(
round(log(records / self._min_records) / log(self._eta)))
while len(self._ladder) <= rung:
self._ladder.append([])
self._ladder[rung].append(trial)
for rung in range(len(self._ladder)):
candidate_trials = [
trial for trial in self._ladder[rung] if
trial.status == Trial.COMPLETE and not trial.trial_infeasible
]
if self._goal_maximize:
candidate_trials = sorted(
candidate_trials,
key=lambda x: -max(m.objective_value
for m in x.measurements))
else:
candidate_trials = sorted(
candidate_trials,
key=lambda x: min(m.objective_value
for m in x.measurements))
self._ladder[rung] = candidate_trials
if self._fix_quantiles and rung >= 1:
trials_below = self._ladder[
rung - 1][0:int(len(self._ladder[rung - 1]) / self._eta)]
ids_to_advance = [
_get_metadata(trial, 'hyperband_id')
for trial in trials_below
]
for t in reversed(range(len(self._ladder[rung]))):
trial = self._ladder[rung][t]
if _get_metadata(trial,
'hyperband_id') not in ids_to_advance:
#print("Deleting configuration %d from rung %d" % (_get_metadata(trial,'hyperband_id'),rung))
#print(len(self._ladder[rung]))
del self._ladder[rung][t]
def GetNewSuggestions(self, num_suggestions_hint, completed_trials,
pending_trials):
# brackets reconstructed each time, probably can modify with each new result
bracket, next_hyperband_id = self._construct_brackets(
completed_trials, pending_trials)
trials = []
for _ in range(num_suggestions_hint):
trial = bracket.get_trial()
if _get_metadata(trial, 'hyperband_id') < 0:
# new trial
parameters = self._delegate_policy.GetNewSuggestions(
1, completed_trials, pending_trials)[0].parameters
trial.parameters = parameters
_set_metadata(trial, 'hyperband_id', next_hyperband_id)
#print(next_hyperband_id, _get_metadata(trial,'termination_record'))
next_hyperband_id += 1
trials.append(trial)
return trials
