def test_too_many_neighbors_warns():
u = UMAP(a=1.2, b=1.75, n_neighbors=2000, n_epochs=11, init="random")
u.fit(
nn_data[:100,]
)
assert_equal(u._a, 1.2)
assert_equal(u._b, 1.75)
def test_umap_fit_params():
# x and y are required to be the same length
u = UMAP()
x = np.random.uniform(0, 1, (256, 10))
y = np.random.randint(10, size=(257,))
assert_raises(ValueError, u.fit, x, y)
u = UMAP()
x = np.random.uniform(0, 1, (256, 10))
y = np.random.randint(10, size=(255,))
assert_raises(ValueError, u.fit, x, y)
u = UMAP()
x = np.random.uniform(0, 1, (256, 10))
assert_raises(ValueError, u.fit, x, [])
u = UMAP()
x = np.random.uniform(0, 1, (256, 10))
y = np.random.randint(10, size=(256,))
res = u.fit(x, y)
assert isinstance(res, UMAP)
u = UMAP()
x = np.random.uniform(0, 1, (256, 10))
res = u.fit(x)
assert isinstance(res, UMAP)
def test_umap_bad_parameters():
u = UMAP(set_op_mix_ratio=-1.0)
assert_raises(ValueError, u.fit, nn_data)
u = UMAP(set_op_mix_ratio=1.5)
assert_raises(ValueError, u.fit, nn_data)
u = UMAP(min_dist=2.0)
assert_raises(ValueError, u.fit, nn_data)
u = UMAP(min_dist=-1)
assert_raises(ValueError, u.fit, nn_data)
u = UMAP(n_components=-1)
assert_raises(ValueError, u.fit, nn_data)
u = UMAP(n_components=1.5)
assert_raises(ValueError, u.fit, nn_data)
u = UMAP(n_neighbors=0.5)
assert_raises(ValueError, u.fit, nn_data)
u = UMAP(n_neighbors=-1)
assert_raises(ValueError, u.fit, nn_data)
u = UMAP(metric=45)
assert_raises(ValueError, u.fit, nn_data)
u = UMAP(learning_rate=-1.5)
assert_raises(ValueError, u.fit, nn_data)
u = UMAP(repulsion_strength=-0.5)
assert_raises(ValueError, u.fit, nn_data)
u = UMAP(negative_sample_rate=-1)
assert_raises(ValueError, u.fit, nn_data)
u = UMAP(init="foobar")
assert_raises(ValueError, u.fit, nn_data)
u = UMAP(init=42)
assert_raises(ValueError, u.fit, nn_data)
u = UMAP(init=np.array([[0, 0, 0], [0, 0, 0]]))
assert_raises(ValueError, u.fit, nn_data)
u = UMAP(n_epochs=-2)
assert_raises(ValueError, u.fit, nn_data)
u = UMAP(target_n_neighbors=1)
assert_raises(ValueError, u.fit, nn_data)
u = UMAP(a=1.2, b=1.75, n_neighbors=2000)
u.fit(nn_data)
assert_equal(u._a, 1.2)
assert_equal(u._b, 1.75)
# assert_raises(ValueError, u.fit, nn_data) we simply warn now
assert_raises(ValueError, nearest_neighbors, nn_data, 10, 42, {}, False,
np.random)
assert_raises(
ValueError,
nearest_neighbors,
sparse_nn_data,
10,
"seuclidean",
{},
False,
np.random,
)
