def test_HillClimbingOptimizer():
from hyperactive import HillClimbingOptimizer
opt0 = HillClimbingOptimizer(
search_config,
n_iter_0,
random_state=random_state,
verbosity=1,
cv=cv,
n_jobs=1,
warm_start=warm_start,
)
opt0.fit(X, y)
opt1 = HillClimbingOptimizer(
search_config,
n_iter_1,
random_state=random_state,
verbosity=1,
cv=cv,
n_jobs=n_jobs,
warm_start=warm_start,
)
opt1.fit(X, y)
assert opt0.score_best < opt1.score_best
def test_keras():
from hyperactive import HillClimbingOptimizer
opt = HillClimbingOptimizer(search_config, 1)
opt.fit(X, y)
opt.predict(X)
opt.score(X, y)
def test_keras_warm_start():
from hyperactive import HillClimbingOptimizer
warm_start = {
"keras.compile.0": {
"loss": ["binary_crossentropy"],
"optimizer": ["adam"]
},
"keras.fit.0": {
"epochs": [1],
"batch_size": [300],
"verbose": [0]
},
"keras.layers.Dense.1": {
"units": [1],
"activation": ["softmax"]
},
}
warm_start_list = [None, warm_start]
for warm_start in warm_start_list:
opt = HillClimbingOptimizer(search_config, 1, warm_start=warm_start)
opt.fit(X, y)
opt.predict(X)
opt.score(X, y)
def test_keras_n_jobs():
from hyperactive import HillClimbingOptimizer
n_jobs_list = [1, 2]
for n_jobs in n_jobs_list:
opt = HillClimbingOptimizer(search_config, 1, n_jobs=n_jobs)
opt.fit(X, y)
opt.predict(X)
opt.score(X, y)
def test_keras_memory():
from hyperactive import HillClimbingOptimizer
memory_list = [False, True]
for memory in memory_list:
opt = HillClimbingOptimizer(search_config, 1, memory=memory)
opt.fit(X, y)
opt.predict(X)
opt.score(X, y)
def test_keras_verbosity():
from hyperactive import HillClimbingOptimizer
verbosity_list = [0, 1]
for verbosity in verbosity_list:
opt = HillClimbingOptimizer(search_config, 1, verbosity=verbosity)
opt.fit(X, y)
opt.predict(X)
opt.score(X, y)
def test_keras_cv():
from hyperactive import HillClimbingOptimizer
cv_list = [0.1, 0.5, 0.9, 2]
for cv in cv_list:
opt = HillClimbingOptimizer(search_config, 1, cv=cv)
opt.fit(X, y)
opt.predict(X)
opt.score(X, y)
def test_keras_n_iter():
from hyperactive import HillClimbingOptimizer
n_iter_list = [0, 1, 2]
for n_iter in n_iter_list:
opt = HillClimbingOptimizer(search_config, n_iter)
opt.fit(X, y)
opt.predict(X)
opt.score(X, y)
def test_keras_scatter_init():
from hyperactive import HillClimbingOptimizer
scatter_init_list = [False, 2]
for scatter_init in scatter_init_list:
opt = HillClimbingOptimizer(search_config,
1,
scatter_init=scatter_init)
opt.fit(X, y)
opt.predict(X)
opt.score(X, y)
def test_keras_random_state():
from hyperactive import HillClimbingOptimizer
random_state_list = [None, 0, 1]
for random_state in random_state_list:
opt = HillClimbingOptimizer(search_config,
1,
random_state=random_state)
opt.fit(X, y)
opt.predict(X)
opt.score(X, y)
def test_data():
from hyperactive import HillClimbingOptimizer
opt0 = HillClimbingOptimizer(
search_config, n_iter_0, random_state=random_state, verbosity=0, cv=cv, n_jobs=1
)
opt0.fit(X_np, y_np)
opt1 = HillClimbingOptimizer(
search_config, n_iter_0, random_state=random_state, verbosity=0, cv=cv, n_jobs=1
)
opt1.fit(X_pd, y_pd)
def test_HillClimbingOptimizer_args():
from hyperactive import HillClimbingOptimizer
opt = HillClimbingOptimizer(search_config, 3, eps=2)
opt.fit(X, y)