def test_VectorSpaceData_instantiation():
"""Test VectorSpaceData initialization"""
# unlabeled, no domain
ds = smlb.VectorSpaceData(dimensions=3)
validate_data_interface(ds)
assert ds.dimensions == 3 and ds.domain is None
assert not ds.is_labeled and not ds.is_finite
assert (ds.samples([[1, 2, 3], [4, 5, 6]]) == [[1, 2, 3], [4, 5, 6]]).all()
with pytest.raises(smlb.BenchmarkError):
ds.labels()
# unlabeled, domain
ds = smlb.VectorSpaceData(dimensions=3, domain=(1, 5))
validate_data_interface(ds)
assert ds.dimensions == 3 and len(ds.domain) == 3
assert not ds.is_labeled and not ds.is_finite
assert (ds.samples([[1, 2, 3], [4, 5, 5]]) == [[1, 2, 3], [4, 5, 5]]).all()
with pytest.raises(smlb.InvalidParameterError):
ds.samples([[1, 2, 3], [4, 5, 6]])
with pytest.raises(smlb.BenchmarkError):
ds.labels()
# labeled, no domain
f = lambda arg: np.sum(arg, axis=1) # noqa: E731
ds = smlb.VectorSpaceData(dimensions=2, function=f)
validate_data_interface(ds)
assert ds.dimensions == 2 and ds.domain is None
assert ds.is_labeled and not ds.is_finite
assert (ds.labels([[1, 2], [3, 4]]) == [3, 7]).all()
def test_RandomVectorSampler_1():
"""Simple test cases."""
# without labels
ds = smlb.VectorSpaceData(dimensions=2)
ss = smlb.RandomVectorSampler(size=30, rng=0,
domain=[[0, 1], [-3, 2]]).fit(ds).apply(ds)
assert ss.samples().shape == (30, 2)
assert (ss.samples()[:, 0] >= 0).all() and (ss.samples()[:, 1] >= -3).all()
assert (ss.samples()[:, 0] <= 1).all() and (ss.samples()[:, 1] <= 2).all()
ss = smlb.RandomVectorSampler(size=3, rng=0,
domain=[[0, 1], [0, 1]]).fit(ds).apply(ds)
ss2 = smlb.RandomVectorSampler(size=3, rng=0).fit(ds).apply(ds)
assert ss2.samples().shape == (3, 2)
assert (ss.samples() == ss2.samples()).all()
ss1 = smlb.RandomVectorSampler(size=3, rng=0).fit(ds).apply(ds)
ss2 = smlb.RandomVectorSampler(size=3, rng=0).fit(ds).apply(ds)
assert (ss1.samples() == ss2.samples()).all()
ss3 = smlb.RandomVectorSampler(size=3, rng=1).fit(ds).apply(ds)
assert (ss1.samples() != ss3.samples()).any()
# with labels
with pytest.raises(smlb.BenchmarkError):
ds = smlb.VectorSpaceData(dimensions=2,
function=lambda x: np.sum(x, axis=1),
domain=[-np.inf, np.inf])
smlb.RandomVectorSampler(size=3, rng=0).fit(ds).apply(ds)
ds = smlb.VectorSpaceData(dimensions=2,
function=lambda x: np.sum(x, axis=1),
domain=[0, 1])
ss = smlb.RandomVectorSampler(size=3, rng=0).fit(ds).apply(ds)
assert ss.samples().shape == (3, 2)
ss1 = smlb.RandomVectorSampler(size=3, rng=0).fit(ds).apply(ds)
ss2 = smlb.RandomVectorSampler(size=3, rng=0).fit(ds).apply(ds)
assert (ss1.samples() == ss2.samples()).all() and (ss1.labels()
== ss2.labels()).all()
ss3 = smlb.RandomVectorSampler(size=3, rng=1).fit(ds).apply(ds)
assert (ss1.samples() != ss3.samples()).any()
def fixture_VectorSpaceData_parabola_1d():
f = lambda v: v[0]**2
ds = smlb.VectorSpaceData(dimensions=1, function=f, domain=[-2, 2])
return ds
def _create_ds_ss(dim, size=0, rng=0):
f = lambda v: np.sum(np.power(v, 2), axis=1) # noqa E731
dataset = smlb.VectorSpaceData(dim, f, [-2, 2])
sampler = smlb.GridSampler(size, rng=rng)
return dataset, sampler