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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_scatter_init():
from hyperactive import HillClimbingOptimizer
scatter_init_list = [False, 2, 3, 4]
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_HillClimbingOptimizer():
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, y)
opt1 = HillClimbingOptimizer(
search_config,
n_iter_1,
random_state=random_state,
verbosity=0,
cv=cv,
n_jobs=n_jobs,
)
opt1.fit(X, y)
assert opt0.score_best < opt1.score_best
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_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_verbosity():
from hyperactive import HillClimbingOptimizer
verbosity_list = [0, 1, 2]
for verbosity in verbosity_list:
opt = HillClimbingOptimizer(search_config, 1, verbosity=verbosity)
opt.search(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.search(X, y)
opt.predict(X)
opt.score(X, y)
def test_keras():
from hyperactive import HillClimbingOptimizer
opt = HillClimbingOptimizer(search_config, 1)
opt.search(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.search(X, y)
opt.predict(X)
opt.score(X, y)