tests/test_optimizer_parameter/test_Bayesian.py 1 location
|
@@ 24-38 (lines=15) @@
|
| 21 |
|
_base_test(opt, n_iter, opt_para=opt_para) |
| 22 |
|
|
| 23 |
|
|
| 24 |
|
def test_warm_start_smbo(): |
| 25 |
|
gpr_X, gpr_y = [], [] |
| 26 |
|
for _ in range(10): |
| 27 |
|
pos_ = np.random.randint(0, high=9) |
| 28 |
|
pos = np.array([pos_]) |
| 29 |
|
|
| 30 |
|
para = { |
| 31 |
|
"x1": pos_, |
| 32 |
|
} |
| 33 |
|
gpr_X.append(pos) |
| 34 |
|
gpr_y.append(get_score(para)) |
| 35 |
|
|
| 36 |
|
for warm_start_smbo in [None, (gpr_X, gpr_y)]: |
| 37 |
|
opt_para = {"warm_start_smbo": warm_start_smbo} |
| 38 |
|
_base_test(opt, n_iter, opt_para=opt_para) |
| 39 |
|
|
| 40 |
|
|
| 41 |
|
def test_max_sample_size(): |
tests/test_optimizer_parameter/test_DecisionTree.py 1 location
|
@@ 18-32 (lines=15) @@
|
| 15 |
|
return -(para["x1"] * para["x1"]) |
| 16 |
|
|
| 17 |
|
|
| 18 |
|
def test_warm_start_smbo(): |
| 19 |
|
gpr_X, gpr_y = [], [] |
| 20 |
|
for _ in range(10): |
| 21 |
|
pos_ = np.random.randint(0, high=9) |
| 22 |
|
pos = np.array([pos_]) |
| 23 |
|
|
| 24 |
|
para = { |
| 25 |
|
"x1": pos_, |
| 26 |
|
} |
| 27 |
|
gpr_X.append(pos) |
| 28 |
|
gpr_y.append(get_score(para)) |
| 29 |
|
|
| 30 |
|
for warm_start_smbo in [None, (gpr_X, gpr_y)]: |
| 31 |
|
opt_para = {"warm_start_smbo": warm_start_smbo} |
| 32 |
|
_base_test(opt, n_iter, opt_para=opt_para) |
| 33 |
|
|
| 34 |
|
|
| 35 |
|
def test_max_sample_size(): |
tests/test_optimizer_parameter/test_TPE.py 1 location
|
@@ 18-32 (lines=15) @@
|
| 15 |
|
return -(para["x1"] * para["x1"]) |
| 16 |
|
|
| 17 |
|
|
| 18 |
|
def test_warm_start_smbo(): |
| 19 |
|
gpr_X, gpr_y = [], [] |
| 20 |
|
for _ in range(10): |
| 21 |
|
pos_ = np.random.randint(0, high=9) |
| 22 |
|
pos = np.array([pos_]) |
| 23 |
|
|
| 24 |
|
para = { |
| 25 |
|
"x1": pos_, |
| 26 |
|
} |
| 27 |
|
gpr_X.append(pos) |
| 28 |
|
gpr_y.append(get_score(para)) |
| 29 |
|
|
| 30 |
|
for warm_start_smbo in [None, (gpr_X, gpr_y)]: |
| 31 |
|
opt_para = {"warm_start_smbo": warm_start_smbo} |
| 32 |
|
_base_test(opt, n_iter, opt_para=opt_para) |
| 33 |
|
|
| 34 |
|
|
tests/test_optimizer_parameter/test_EnsembleOptimizer.py 1 location
|
@@ 18-32 (lines=15) @@
|
| 15 |
|
return -(para["x1"] * para["x1"]) |
| 16 |
|
|
| 17 |
|
|
| 18 |
|
def test_warm_start_smbo(): |
| 19 |
|
gpr_X, gpr_y = [], [] |
| 20 |
|
for _ in range(10): |
| 21 |
|
pos_ = np.random.randint(0, high=9) |
| 22 |
|
pos = np.array([pos_]) |
| 23 |
|
|
| 24 |
|
para = { |
| 25 |
|
"x1": pos_, |
| 26 |
|
} |
| 27 |
|
gpr_X.append(pos) |
| 28 |
|
gpr_y.append(get_score(para)) |
| 29 |
|
|
| 30 |
|
for warm_start_smbo in [None, (gpr_X, gpr_y)]: |
| 31 |
|
opt_para = {"warm_start_smbo": warm_start_smbo} |
| 32 |
|
_base_test(opt, n_iter, opt_para=opt_para) |
| 33 |
|
|
SimonBlanke /
Gradient-Free-Optimizers
