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add dlib.global_function_search #34

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38 changes: 34 additions & 4 deletions humpday/optimizers/dlibcube.py
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Original file line number Diff line number Diff line change
Expand Up @@ -5,7 +5,7 @@
# This library also provides global_function_search which is pretty darn cool

try:
from dlib import find_min_global
from dlib import find_min_global, global_function_search, function_spec
using_dlib = True
except ImportError:
using_dlib = False
Expand Down Expand Up @@ -35,8 +35,38 @@ def dlib_curl2_cube(objective ,n_trials, n_dim, with_count):
# Meh
return curl_factory(optimizer=dlib_default_cube,objective=objective, n_trials=n_trials, n_dim=n_dim, with_count=with_count, d=2)

DLIB_OPTIMIZERS = [dlib_cube, dlib_default_cube ]
DLIB_TOP_OPTIMIZERS = [dlib_cube, dlib_default_cube ]
def dlib_gfs_default_cube(objective, n_trials, n_dim, with_count=False):
global feval_count
feval_count = 0
lb = [0.0 for _ in range(n_dim)]
ub = [1.0 for _ in range(n_dim)]
func_spec = function_spec(lb, ub)
search = global_function_search(func_spec)
best_val = float('inf')
best_x = None
for _ in range(n_trials):
next_x = search.get_next_x()

feval_count += 1
y = objective(list(next_x.x))

if y < best_val:
best_val = y
best_x = list(next_x.x)

next_x.set(-y)

return (best_val, best_x, feval_count) if with_count else (best_val, best_x)

def dlib_gfs_cube(objective, n_trials, n_dim, with_count):
return dlib_gfs_default_cube(objective=objective, n_trials=n_trials, n_dim=n_dim, with_count=with_count) # It is useful to have a clone of one of the better algos

def dlib_gfs_curl2_cube(objective, n_trials, n_dim, with_count):
# Meh
return curl_factory(optimizer=dlib_gfs_default_cube, objective=objective, n_trials=n_trials, n_dim=n_dim, with_count=with_count, d=2)

DLIB_OPTIMIZERS = [dlib_cube, dlib_default_cube, dlib_gfs_cube, dlib_gfs_default_cube]
DLIB_TOP_OPTIMIZERS = [dlib_cube, dlib_default_cube, dlib_gfs_cube, dlib_gfs_default_cube]
else:
DLIB_OPTIMIZERS = []
DLIB_TOP_OPTIMIZERS = []
Expand All @@ -49,4 +79,4 @@ def dlib_curl2_cube(objective ,n_trials, n_dim, with_count):
print(' ')
print(objective.__name__)
for optimizer in DLIB_OPTIMIZERS:
print(optimizer(objective, n_trials=500, n_dim=34, with_count=True))
print(optimizer(objective, n_trials=500, n_dim=34, with_count=True))