def test_Int():
rg = hp_module.Int('rg', min_value=5, max_value=9, step=1, default=6)
rg = hp_module.Int.from_config(rg.get_config())
assert rg.default == 6
assert 5 <= rg.random_sample() <= 9
assert isinstance(rg.random_sample(), int)
assert rg.random_sample(123) == rg.random_sample(123)
# No default
rg = hp_module.Int('rg', min_value=5, max_value=9, step=1)
assert rg.default == 5
def test_sampling_arg():
f = hp_module.Float('f', 1e-20, 1e10, sampling='log')
f = hp_module.Float.from_config(f.get_config())
assert f.sampling == 'log'
i = hp_module.Int('i', 0, 10, sampling='linear')
i = hp_module.Int.from_config(i.get_config())
assert i.sampling == 'linear'
with pytest.raises(ValueError, match='`sampling` must be one of'):
hp_module.Int('j', 0, 10, sampling='invalid')
def test_bayesian_oracle_with_zero_y(tmp_dir):
hp_list = [hp_module.Choice('a', [1, 2], default=1),
hp_module.Int('b', 3, 10, default=3),
hp_module.Float('c', 0, 1, 0.1, default=0),
hp_module.Fixed('d', 7),
hp_module.Choice('e', [9, 0], default=9)]
oracle = bo_module.BayesianOptimizationOracle()
for i in range(100):
oracle.populate_space(str(i), hp_list)
oracle.result(str(i), 0)
def test_save_before_result(tmp_dir):
hp_list = [hp_module.Choice('a', [1, 2], default=1),
hp_module.Int('b', 3, 10, default=3),
hp_module.Float('c', 0, 1, 0.1, default=0),
hp_module.Fixed('d', 7),
hp_module.Choice('e', [9, 0], default=9)]
oracle = bo_module.BayesianOptimizationOracle()
oracle.populate_space(str(1), hp_list)
oracle.save(os.path.join(tmp_dir, 'temp_oracle'))
oracle.result(str(1), 0)
def test_sampling_arg():
f = hp_module.Float("f", 1e-20, 1e10, sampling="log")
f = hp_module.Float.from_config(f.get_config())
assert f.sampling == "log"
i = hp_module.Int("i", 0, 10, sampling="linear")
i = hp_module.Int.from_config(i.get_config())
assert i.sampling == "linear"
with pytest.raises(ValueError, match="`sampling` must be one of"):
hp_module.Int("j", 0, 10, sampling="invalid")
with pytest.raises(
ValueError,
match="`sampling` `min_value` 1 is greater than the `max_value` 0",
):
hp_module.Int("k", 1, 0, sampling="linear")
with pytest.raises(
ValueError,
match="`sampling` `min_value` 1 is greater than the `max_value` 0",
):
hp_module.Int("k", 1, 0, sampling="linear")
def test_bayesian_dynamic_space(tmp_dir):
hp_list = [hp_module.Choice('a', [1, 2], default=1)]
oracle = bo_module.BayesianOptimizationOracle()
for i in range(10):
oracle.populate_space(str(i), hp_list)
oracle.result(str(i), i)
hp_list.append(hp_module.Int('b', 3, 10, default=3))
assert 'b' in oracle.populate_space('1_0', hp_list)['values']
hp_list.append(hp_module.Float('c', 0, 1, 0.1, default=0))
assert 'c' in oracle.populate_space('1_1', hp_list)['values']
hp_list.append(hp_module.Fixed('d', 7))
assert 'd' in oracle.populate_space('1_2', hp_list)['values']
hp_list.append(hp_module.Choice('e', [9, 0], default=9))
assert 'e' in oracle.populate_space('1_3', hp_list)['values']
def test_int_proto():
hp = hp_module.Int('a', 1, 100, sampling='log')
proto = hp.to_proto()
assert proto.name == 'a'
assert proto.min_value == 1
assert proto.max_value == 100
assert proto.sampling == kerastuner_pb2.Sampling.LOG
# Proto stores the implicit default.
assert proto.default == 1
assert proto.step == 1
new_hp = hp_module.Int.from_proto(proto)
assert new_hp._default == 1
# Pop the implicit default for comparison purposes.
new_hp._default = None
assert new_hp.get_config() == hp.get_config()
def assemble(self, input_node):
block = hyperblock.ImageBlock()
if max(self._shape[0], self._shape[1]) < 32:
if self._num_samples < 10000:
self.hps.append(hp_module.Choice(
block.name + '_resnet/v1/conv4_depth', [6],
default=6))
self.hps.append(hp_module.Choice(
block.name + '_resnet/v2/conv4_depth', [6],
default=6))
self.hps.append(hp_module.Choice(
block.name + '_resnet/next/conv4_depth', [6],
default=6))
self.hps.append(hp_module.Int(
block.name + '_xception/num_residual_blocks', 2, 4,
default=4))
return block(input_node)
