def test_map_overlap_value_error():
data = np.array([[0., 0.], [0.25, 0.25], [0.5, 0.5], [0.75, 0.75],
[1., 1.], [1., 0.], [0.25, 0.75], [0.75, 0.25], [0., 1.]])
t = Topology()
t.fit_transform(data, metric=None, lens=None)
with pytest.raises(ValueError):
t.map(resolution=10, overlap=-0.1, clusterer=None)
def test_transform_multi_lens():
data = np.array([[0., 0.], [0., 1.], [1., 1.]])
t = Topology()
metric = Distance(metric="hamming")
lens = [L1Centrality(), GaussianDensity(h=0.25)]
t.fit_transform(data, metric=metric, lens=lens)
test_data = np.array([[1., 0.], [0., 1.], [1., 0.]])
assert_array_equal(t.pointcloud, test_data)
def test_color_dtype():
data = np.array([[0., 0.], [0.1, 0.1], [0.2, 0.2], [0.2, 0.8], [0.1, 0.9],
[0., 1.], [0.8, 0.8], [0.9, 0.9], [1., 1.]])
target = np.array([[0], [0], [0], [1], [1], [1], [2], [2], [2]])
t = Topology()
t.fit_transform(data, metric=None, lens=None)
t.map(resolution=2, overlap=0.3, clusterer=None)
with pytest.raises(Exception):
t.color(target, dtype="somecategory", ctype="rgb", normalized=False)
def test_color_different_size_input():
data = np.array([[0., 0.], [0.1, 0.1], [0.2, 0.2], [0.2, 0.8], [0.1, 0.9],
[0., 1.], [0.8, 0.8], [0.9, 0.9], [1., 1.]])
target = np.array([0, 1, 2])
t = Topology()
t.fit_transform(data, metric=None, lens=None)
t.map(resolution=2, overlap=0.3, clusterer=None)
with pytest.raises(ValueError):
t.color(target, dtype="categorical", ctype="rgb", normalized=False)
def test_transform_none_none():
data = np.array([[0., 0.], [1., 1.]])
t = Topology()
metric = None
lens = None
t.fit_transform(data, metric=metric, lens=lens)
test_data = np.array([[0., 0.], [1., 1.]])
assert_array_equal(t.pointcloud, test_data)
def test_transform_none_pca():
data = np.array([[0., 1.], [1., 0.]])
t = Topology()
metric = None
lens = [PCA(components=[0])]
t.fit_transform(data, metric=metric, lens=lens)
test_data = np.array([0., 1.])
test_data = test_data.reshape(test_data.shape[0], 1)
assert_array_equal(t.pointcloud, test_data)
def test_color_numerical_rgb():
data = np.array([[0., 0.], [0.1, 0.1], [0.2, 0.2], [0.2, 0.8], [0.1, 0.9],
[0., 1.], [0.8, 0.8], [0.9, 0.9], [1., 1.]])
target = np.array([[0], [0], [0], [1], [1.1], [0.9], [2], [2], [2]])
t = Topology()
t.fit_transform(data, metric=None, lens=None)
clusterer = DBSCAN(eps=0.2, min_samples=3)
t.map(resolution=2, overlap=0.3, clusterer=clusterer)
t.color(target, dtype="numerical", ctype="rgb", normalized=False)
test_color = ['#0000b2', '#00b200', '#b2b200']
assert t.colorlist == test_color
def test_map():
data = np.array([[0., 0.], [0.25, 0.25], [0.5, 0.5], [0.75, 0.75],
[1., 1.], [1., 0.], [0.25, 0.75], [0.75, 0.25], [0., 1.]])
t = Topology()
t.fit_transform(data, metric=None, lens=None)
clusterer = DBSCAN(eps=0.4, min_samples=3)
t.map(resolution=2, overlap=0.3, clusterer=clusterer)
test_nodes = np.array([[0.25, 0.25], [0.25, 0.75], [0.75, 0.25],
[0.75, 0.75]])
test_edges = np.array([[0, 1], [0, 2], [0, 3], [1, 2], [1, 3], [2, 3]])
assert_array_equal(t.nodes, test_nodes)
assert_array_equal(t.edges, test_edges)
def test_color_categorical_gray():
data = np.array([[0., 0.], [0.1, 0.1], [0.2, 0.2], [0.2, 0.8], [0.1, 0.9],
[0., 1.], [0.8, 0.8], [0.9, 0.9], [1., 1.]])
target = np.array([[0], [0], [0], [1], [1], [1], [2], [2], [2]])
t = Topology()
t.fit_transform(data, metric=None, lens=None)
clusterer = DBSCAN(eps=0.2, min_samples=3)
t.map(resolution=2, overlap=0.3, clusterer=clusterer)
t.color(target, dtype="categorical", ctype="gray", normalized=False)
test_color = ['#dcdcdc', '#787878', '#464646']
assert t.colorlist == test_color
def test_transform_3darray_input():
data = np.array([[[]]])
t = Topology()
with pytest.raises(ValueError):
t.fit_transform(data)
def test_transform_none_input():
data = None
t = Topology()
with pytest.raises(Exception):
t.fit_transform(data)