def test_bad_numeric_init():
u = VMAP(init=42)
assert_raises(ValueError, u.fit, nn_data)
def test_negative_sample_rate():
u = VMAP(negative_sample_rate=-1)
assert_raises(ValueError, u.fit, nn_data)
def test_bad_init():
u = VMAP(init="foobar")
assert_raises(ValueError, u.fit, nn_data)
def test_negative_learning_rate():
u = VMAP(learning_rate=-1.5)
assert_raises(ValueError, u.fit, nn_data)
def test_negative_repulsion():
u = VMAP(repulsion_strength=-0.5)
assert_raises(ValueError, u.fit, nn_data)
def test_negative_nneighbors():
u = VMAP(n_neighbors=-1)
assert_raises(ValueError, u.fit, nn_data)
def test_bad_metric():
u = VMAP(metric=45)
assert_raises(ValueError, u.fit, nn_data)
def test_non_integer_ncomponents():
u = VMAP(n_components=1.5)
assert_raises(ValueError, u.fit, nn_data)
def test_too_small_nneighbors():
u = VMAP(n_neighbors=0.5)
assert_raises(ValueError, u.fit, nn_data)
def test_negative_ncomponents():
u = VMAP(n_components=-1)
assert_raises(ValueError, u.fit, nn_data)
def test_negative_min_dist():
u = VMAP(min_dist=-1)
assert_raises(ValueError, u.fit, nn_data)
def test_too_large_op():
u = VMAP(set_op_mix_ratio=1.5)
assert_raises(ValueError, u.fit, nn_data)
def test_negative_op():
u = VMAP(set_op_mix_ratio=-1.0)
assert_raises(ValueError, u.fit, nn_data)
def test_blobs_cluster():
data, labels = datasets.make_blobs(n_samples=500, n_features=10, centers=5)
embedding = VMAP().fit_transform(data)
assert_equal(adjusted_rand_score(labels,
KMeans(5).fit_predict(embedding)), 1.0)