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Add adaptive stdev scaling to local search neighborhood sampling #954 #1303

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Add adaptive stdev scaling to local search neighborhood sampling #954 #1303

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34 changes: 32 additions & 2 deletions smac/acquisition/maximizer/local_search.py
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Original file line number Diff line number Diff line change
Expand Up @@ -318,6 +318,13 @@ def _search(

hp_names = list(start_points[0].config_space.keys())

# Initial standard deviation used when sampling continuous neighbors.
stdev_init = 0.05
# Lower bound to prevent the search radius from collapsing.
stdev_min = 5e-3
# Upper bound to prevent the search radius from exploding.
stdev_max = stdev_init * 8

candidates = start_points
# Compute the acquisition value of the candidates
num_candidates = len(candidates)
Expand All @@ -338,14 +345,20 @@ def _search(
local_search_steps = [0] * num_candidates
# tracking the number of neighbors looked at for logging purposes
neighbors_looked_at = [0] * num_candidates
# tracking the number of neighbors generated for logging purposse
# tracking the number of neighbors generated for logging purposes
neighbors_generated = [0] * num_candidates
# how many neighbors were obtained for the i-th local search. Important to map the individual acquisition
# function values to the correct local search run
obtain_n = [self._vectorization_min_obtain] * num_candidates
# Tracking the time it takes to compute the acquisition function
times = []

# Tracks consecutive improvements for each local search.
# Used to adapt the neighborhood sampling radius.
improvement_count = [0] * num_candidates
# Current neighborhood sampling standard deviation for each local search.
stdev = [stdev_init] * num_candidates

# Set up the neighborhood generators
neighborhood_iterators = []
for i, inc in enumerate(candidates):
Expand All @@ -359,7 +372,7 @@ def _search(
# Sequential Model-Based Optimization for General Algorithm Configuration
# In Proceedings of the conference on Learning and Intelligent
# Optimization(LION 5)
get_one_exchange_neighbourhood(inc, seed=self._rng.randint(low=0, high=100000))
get_one_exchange_neighbourhood(inc, seed=self._rng.randint(low=0, high=100000), stdev=stdev[i])
)
local_search_steps[i] += 1

Expand Down Expand Up @@ -506,6 +519,17 @@ def _search(
obtain_n[i] = min(obtain_n[i], self._vectorization_max_obtain)

if new_neighborhood[i]:

if improved[i]:
improvement_count[i] += 1
else:
improvement_count[i] = 0

# Increase exploration radius if the search improves consistently.
if improvement_count[i] >= 3 and n_no_plateau_walk[i] == 0:
stdev[i] = min(stdev[i] * 2, stdev_max)
improvement_count[i] = 0

if not improved[i] and n_no_plateau_walk[i] < self._n_steps_plateau_walk:
if len(neighbors_w_equal_acq[i]) != 0:
candidates[i] = neighbors_w_equal_acq[i][0]
Expand All @@ -514,13 +538,19 @@ def _search(
for s in visited_values[i].values():
s.clear()
n_no_plateau_walk[i] += 1
# Reduce exploration radius during plateau walking to refine the
# search locally around the current candidate.
if n_no_plateau_walk[i] % 3 == 0:
stdev[i] = max(stdev[i] * 0.5, stdev_min)

if n_no_plateau_walk[i] >= self._n_steps_plateau_walk:
active[i] = False
continue

neighborhood_iterators[i] = get_one_exchange_neighbourhood(
candidates[i],
seed=self._rng.randint(low=0, high=100000),
stdev=stdev[i],
)

logger.debug(
Expand Down
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