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140 lines (103 loc) · 4.3 KB
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import unittest
import pytest
@pytest.mark.hps
class HpProblemTest(unittest.TestCase):
def test_add_good_dim(self):
import ConfigSpace as cs
import ConfigSpace.hyperparameters as csh
from deephyper.problem import HpProblem
pb = HpProblem()
p0 = pb.add_hyperparameter((-10, 10), "p0")
p0_csh = csh.UniformIntegerHyperparameter(
name="p0", lower=-10, upper=10, log=False
)
assert p0 == p0_csh
p1 = pb.add_hyperparameter((1, 100, "log-uniform"), "p1")
p1_csh = csh.UniformIntegerHyperparameter(
name="p1", lower=1, upper=100, log=True
)
assert p1 == p1_csh
p2 = pb.add_hyperparameter((-10.0, 10.0), "p2")
p2_csh = csh.UniformFloatHyperparameter(
name="p2", lower=-10.0, upper=10.0, log=False
)
assert p2 == p2_csh
p3 = pb.add_hyperparameter((1.0, 100.0, "log-uniform"), "p3")
p3_csh = csh.UniformFloatHyperparameter(
name="p3", lower=1.0, upper=100.0, log=True
)
assert p3 == p3_csh
p4 = pb.add_hyperparameter([1, 2, 3, 4], "p4")
p4_csh = csh.OrdinalHyperparameter(name="p4", sequence=[1, 2, 3, 4])
assert p4 == p4_csh
p5 = pb.add_hyperparameter([1.0, 2.0, 3.0, 4.0], "p5")
p5_csh = csh.OrdinalHyperparameter(name="p5", sequence=[1.0, 2.0, 3.0, 4.0])
assert p5 == p5_csh
p6 = pb.add_hyperparameter(["cat0", "cat1"], "p6")
p6_csh = csh.CategoricalHyperparameter(name="p6", choices=["cat0", "cat1"])
assert p6 == p6_csh
p7 = pb.add_hyperparameter({"mu": 0, "sigma": 1}, "p7")
p7_csh = csh.NormalIntegerHyperparameter(name="p7", mu=0, sigma=1)
assert p7 == p7_csh
if cs.__version__ > "0.4.20":
p8 = pb.add_hyperparameter(
{"mu": 0, "sigma": 1, "lower": -5, "upper": 5}, "p8"
)
p8_csh = csh.NormalIntegerHyperparameter(
name="p8", mu=0, sigma=1, lower=-5, upper=5
)
assert p8 == p8_csh
p9 = pb.add_hyperparameter({"mu": 0.0, "sigma": 1.0}, "p9")
p9_csh = csh.NormalFloatHyperparameter(name="p9", mu=0, sigma=1)
assert p9 == p9_csh
def test_kwargs(self):
from deephyper.problem import HpProblem
pb = HpProblem()
pb.add_hyperparameter(value=(-10, 10), name="dim0")
def test_dim_with_wrong_name(self):
from deephyper.core.exceptions.problem import SpaceDimNameOfWrongType
from deephyper.problem import HpProblem
pb = HpProblem()
with pytest.raises(SpaceDimNameOfWrongType):
pb.add_hyperparameter((-10, 10), 0)
def test_config_space_hp(self):
import ConfigSpace.hyperparameters as csh
from deephyper.problem import HpProblem
alpha = csh.UniformFloatHyperparameter(name="alpha", lower=0, upper=1)
beta = csh.UniformFloatHyperparameter(name="beta", lower=0, upper=1)
pb = HpProblem()
pb.add_hyperparameters([alpha, beta])
@pytest.mark.nas
class TestNaProblem(unittest.TestCase):
def test_search_space(self):
from deephyper.nas.spacelib.tabular import OneLayerSpace
from deephyper.problem import NaProblem
pb = NaProblem()
with pytest.raises(TypeError):
pb.search_space(space_class="a")
pb.search_space(OneLayerSpace)
def test_full_problem(self):
from deephyper.core.exceptions.problem import NaProblemError
from deephyper.nas.preprocessing import minmaxstdscaler
from deephyper.nas.spacelib.tabular import OneLayerSpace
from deephyper.problem import NaProblem
pb = NaProblem()
def load_data(prop):
return ([[10]], [1]), ([10], [1])
pb.load_data(load_data, prop=1.0)
pb.preprocessing(minmaxstdscaler)
pb.search_space(OneLayerSpace)
pb.hyperparameters(
batch_size=64,
learning_rate=0.001,
optimizer="adam",
num_epochs=10,
loss_metric="mse",
)
with pytest.raises(NaProblemError):
pb.objective("r2")
pb.loss("mse")
pb.metrics(["r2"])
possible_objective = ["loss", "val_loss", "r2", "val_r2"]
for obj in possible_objective:
pb.objective(obj)