def test_add_node_matrix_out_of_order():
graph = AdjacencyMatrix({})
gold_position = random.randint(10, 100)
graph._add_vertex = MagicMock(return_value=gold_position)
label = rand_str()
with pytest.raises(ValueError):
graph.add_vertex(label)
graph._add_vertex.assert_called_once_with(label)
def test_add_edge_greater_than_zero():
graph = AdjacencyMatrix(
[rand_str() for _ in range(random.randint(10, 100))])
source = target = 0
while source == target:
source = random.randint(0, graph.vertex_count - 1)
target = random.randint(0, graph.vertex_count - 1)
weight = (random.random() + 0.01) * -1
with pytest.raises(ValueError):
graph.add_edge(source, target, weight)
def test_add_node_matrix():
verts = [rand_str() for _ in range(random.randint(1, 10))]
graph = AdjacencyMatrix(verts)
graph._adjacency_matrix = np.random.rand(*graph._adjacency_matrix.shape)
gold_position = len(verts)
graph._add_vertex = MagicMock(return_value=gold_position)
gold_label = rand_str()
idx = graph.add_vertex(gold_label)
graph._add_vertex.assert_called_once_with(gold_label)
assert idx == gold_position
assert (graph.adjacency_matrix[:, idx] == 0).all()
assert (graph.adjacency_matrix[idx, :] == 0).all()
def test_add_edge_matrix():
graph = AdjacencyMatrix(
[rand_str() for _ in range(random.randint(10, 100))])
source = target = 0
while source == target:
source = random.randint(0, graph.vertex_count - 1)
target = random.randint(0, graph.vertex_count - 1)
weight = random.random()
source_req = random.choice([source, graph[source]])
target_req = random.choice([target, graph[target]])
graph.add_edge(source_req, target_req, weight)
assert graph.adjacency_matrix[source, target] == weight
def test_edge_count_matrix():
graph = AdjacencyMatrix({})
gold = random.randint(10, 100)
verts = random.randint(15, 30)
edges = np.zeros((verts, verts))
for _ in range(gold):
added = False
while not added:
src, tgt = np.random.randint(0, verts, size=(2, ))
if src == tgt:
continue
if edges[src, tgt] == 0:
edges[src, tgt] = 1
added = True
graph._adjacency_matrix = edges
assert graph.edge_count == gold
def test_text_rank_mining_massive_datasets():
""""This is testing with a worked example from here:
http://infolab.stanford.edu/~ullman/mmds/ch5.pdf
"""
g = AdjacencyMatrix(list("ABCD"))
g.add_edge("A", "B")
g.add_edge("A", "C")
g.add_edge("A", "D")
g.add_edge("B", "A")
g.add_edge("B", "D")
g.add_edge("C", "A")
g.add_edge("D", "B")
g.add_edge("D", "C")
gold = np.array([3 / 9, 2 / 9, 2 / 9, 2 / 9])
scores = [x[1] for x in text_rank(g, damping=1, convergence=0)]
np.testing.assert_allclose(scores, gold)
def random_graphs(p=None, min_vert=10, max_vert=100):
p = random.random() if p is None else p
verts = [str(i) for i in range(random.randint(min_vert, max_vert))]
graph = AdjacencyMatrix(verts)
graph2 = AdjacencyList(verts)
for src, tgt in combinations(verts, 2):
if src == tgt:
continue
if random.random() <= p:
graph.add_edge(src, tgt, 1)
graph2.add_edge(src, tgt, 1)
graph.add_edge(tgt, src, 1)
graph2.add_edge(tgt, src, 1)
return graph, graph2
def test_vertex_count_matrix():
graph = AdjacencyMatrix({})
verts = random.randint(15, 30)
for vert in range(verts):
graph.add_vertex(str(vert))
assert graph.vertex_count == verts