def get_suggestions(self, params: Dict = None) -> List[Dict]:
if not self.config.num_runs:
raise ValueError("This search strategy requires `num_runs`.")
suggestions = []
params = params or {}
rand_generator = get_random_generator(seed=self.config.seed)
# Validate number of suggestions and total space
all_discrete = True
for v in self.config.params.values():
if v.is_continuous:
all_discrete = False
break
num_runs = self.config.num_runs
if all_discrete:
space = reduce(
mul, [get_length(v) for v in self.config.params.values()])
num_runs = self.config.num_runs if self.config.num_runs <= space else space
while num_runs > 0:
suggestion_params = copy.deepcopy(params)
suggestion_params.update({
k: sample(v, rand_generator=rand_generator)
for k, v in self.config.params.items()
})
suggestion = SuggestionSpec(params=suggestion_params)
if suggestion not in suggestions:
suggestions.append(suggestion)
num_runs -= 1
return [suggestion.params for suggestion in suggestions]
def test_matrix_pchoice_option(self):
config_dict = {"kind": "pchoice", "value": [(1, 0.1), (2, 0.3), (3, 6)]}
with self.assertRaises(ValidationError):
V1HpPChoice.from_dict(config_dict)
config_dict["value"] = [(1, 0.1), (2, 0.3), (3, 0.8)]
with self.assertRaises(ValidationError):
V1HpPChoice.from_dict(config_dict)
config_dict["value"] = [(1, 0.1), (2, 0.3), (3, -0.6)]
with self.assertRaises(ValidationError):
V1HpPChoice.from_dict(config_dict)
config_dict["value"] = ["ok", "nook"]
with self.assertRaises(ValidationError):
V1HpPChoice.from_dict(config_dict)
# Pass for correct config
config_dict["value"] = [(1, 0.1), (2, 0.1), (3, 0.8)]
config = V1HpPChoice.from_dict(config_dict)
assert config.to_dict() == config_dict
with self.assertRaises(ValidationError):
to_numpy(config)
assert sample(config) in [1, 2, 3]
assert get_length(config) == 3
assert config.is_categorical is False
assert config.is_distribution is True
assert config.is_range is False
assert config.is_uniform is False
assert config.is_discrete is True
assert config.is_continuous is False
assert get_min(config) is None
assert get_max(config) is None
def test_matrix_values_option(self):
config_dict = {"kind": "choice", "value": [1, 2, 3]}
config = V1HpChoice.from_dict(config_dict)
assert config.to_dict() == config_dict
assert to_numpy(config) == config_dict["value"]
assert sample(config) in [1, 2, 3]
assert get_length(config) == 3
assert config.is_categorical is False
assert config.is_distribution is False
assert config.is_range is False
assert config.is_uniform is False
assert config.is_discrete is True
assert config.is_continuous is False
assert get_min(config) == 1
assert get_max(config) == 3
config_dict["value"] = ["ok", "nook"]
config = V1HpChoice.from_dict(config_dict)
assert config.to_dict() == config_dict
assert to_numpy(config) == config_dict["value"]
assert sample(config) in ["ok", "nook"]
assert get_length(config) == 2
assert config.is_categorical is True
assert config.is_distribution is False
assert config.is_range is False
assert config.is_uniform is False
assert config.is_discrete is True
assert config.is_continuous is False
assert get_min(config) is None
assert get_max(config) is None
config_dict["value"] = [[1, 2], [2, 4]]
config = V1HpChoice.from_dict(config_dict)
assert config.to_dict() == config_dict
assert to_numpy(config) == config_dict["value"]
assert sample(config) in [[1, 2], [2, 4]]
assert get_length(config) == 2
assert config.is_categorical is True
assert config.is_distribution is False
assert config.is_range is False
assert config.is_uniform is False
assert config.is_discrete is True
assert config.is_continuous is False
assert get_min(config) is None
assert get_max(config) is None
def assert_equal(config, v1, v2, v3):
result = {"start": v1, "stop": v2, "num": v3}
assert config.to_dict()["value"] == result
np.testing.assert_array_equal(to_numpy(config), np.geomspace(**result))
assert get_length(config) == len(np.geomspace(**result))
assert sample(config) in np.geomspace(**result)
assert config.is_categorical is False
assert config.is_distribution is False
assert config.is_range is True
assert config.is_uniform is False
assert config.is_discrete is True
assert config.is_continuous is False
assert get_min(config) == v1
assert get_max(config) == v2
def assert_equal(config, v1, v2, q, v3=None):
result = {"loc": v1, "scale": v2, "q": q}
if v3:
result["size"] = v3
assert config.to_dict()["value"] == result
with self.assertRaises(ValidationError):
to_numpy(config)
with self.assertRaises(ValidationError):
get_length(config)
assert isinstance(sample(config), float)
assert config.is_categorical is False
assert config.is_distribution is True
assert config.is_range is False
assert config.is_uniform is False
assert config.is_discrete is False
assert config.is_continuous is True
assert get_min(config) is None
assert get_max(config) is None
def assert_equal(config, v1, v2, v3=None):
result = {"low": v1, "high": v2}
if v3:
result["size"] = v3
assert config.to_dict()["value"] == result
with self.assertRaises(ValidationError):
to_numpy(config)
with self.assertRaises(ValidationError):
to_numpy(config)
with self.assertRaises(ValidationError):
get_length(config)
assert v1 <= sample(config) <= v2
assert config.is_categorical is False
assert config.is_distribution is True
assert config.is_range is False
assert config.is_uniform is True
assert config.is_discrete is False
assert config.is_continuous is True
assert get_min(config) == v1
assert get_max(config) == v2
