def test_cluster_additional_points_concave(self):
"""
Test with the concave hull
"""
hull = {
"C1":
np.array([[0.0, 0.0], [0.3, 0.5], [0.0, 1.0], [1.0, 1.0],
[0.7, 0.5], [1.0, 0.0]])
}
p = [
[-1.0, -1], # point that is totally out polygon
[0.2, 0.5], # point outside the polygon, but close
[0.8, 0.5], # point outside the polygon, but close
[0.5, 0.5], # point in
[0.5, 0.7], # point in
[0.5, 0.3], # point in
[0.2, 0.9], # point in
[0.2, 0.1], # point in
]
points = Table(
Domain([ContinuousVariable("x"),
ContinuousVariable("y")]), p)
clusters = cluster_additional_points(points, hull)
expected = [np.nan] * 3 + [0.0] * 5
np.testing.assert_array_equal(clusters.X.flatten(), expected)
def compute_secondary_clusters(embedding: Table, result: Result, state: TaskState):
if not result.clusters.groups or not embedding:
result.clusters.secondary_table = None
else:
state.set_status("Finding secondary clusters...")
hulls = {k: v[2] for k, v in result.clusters.groups.items()}
clusters = result.clusters.table
domain = clusters and clusters.domain["Clusters"]
table = cluster_additional_points(embedding, hulls, domain)
result.clusters.secondary_table = table
state.set_partial_result(("secondary_clusters", result))
def test_cluster_additional_points_more_clusters(self):
"""
Test with more concave hulls
"""
points_table = Table(
Domain([ContinuousVariable("x"),
ContinuousVariable("y")]), self.points)
clusters = cluster_additional_points(points_table, self.hull)
clusters = list(
map(clusters.domain.attributes[0].repr_val, clusters.X[:, 0]))
expected = ["?"] * 3 + ["C1"] * 5 + ["C2"] * 2 + ["?"] * 2
np.testing.assert_array_equal(clusters, expected)
def test_cluster_additional_points_with_existing_domain(self):
"""
Test with the concave hull
"""
points = Table(
Domain([ContinuousVariable("x"),
ContinuousVariable("y")]), self.points)
attr = DiscreteVariable("Clusters", values=["C2", "C1"])
clusters = cluster_additional_points(points,
self.hull,
cluster_attribute=attr)
cluster_map = list(
map(clusters.domain.attributes[0].repr_val, clusters.X[:, 0]))
expected = ["?"] * 3 + ["C1"] * 5 + ["C2"] * 2 + ["?"] * 2
np.testing.assert_array_equal(cluster_map, expected)
self.assertEqual(attr, clusters.domain.attributes[0])
self.assertListEqual(attr.values, clusters.domain.attributes[0].values)
def test_cluster_additional_points_simple(self):
"""
Test with the simple convex hull
"""
hull = {
"C1": np.array([[0.0, 0.0], [0.0, 1.0], [1.0, 1.0], [1.0, 0.0]])
}
x, y = np.meshgrid(np.arange(-1.0, 2.0, 0.2), np.arange(-1, 2.0, 0.2))
points = Table(
Domain([ContinuousVariable("x"),
ContinuousVariable("y")]),
np.concatenate((x.reshape(-1, 1), y.reshape(-1, 1)), axis=1),
)
clusters = cluster_additional_points(points, hull)
expected = [
0.0 if 0 < x < 1 and 0 < y < 1 else np.nan for x, y in points.X
]
np.testing.assert_array_equal(clusters.X.flatten(), expected)