def test_weighted_graph_label(self):
g = nx.Graph()
edges = [(1, 2), (2, 3), (3, 4), (4, 1)]
g.add_edges_from(edges)
g[1][2]["w"] = 1
g[2][3]["w"] = 2
g[3][4]["w"] = 3
g[4][1]["w"] = 4
g = StellarGraph(g, edge_weight_label="w")
nodes = list(g.nodes())
n = 1
length = 1
seed = None
p = 1.0
q = 1.0
biasedrw = BiasedRandomWalk(g)
assert (len(
biasedrw.run(nodes=nodes,
n=n,
p=p,
q=q,
length=length,
seed=seed,
weighted=True)) == 4)
g = nx.Graph()
edges = [(1, 2), (2, 3), (3, 4), (4, 1)]
g.add_edges_from(edges)
g[1][2]["wt"] = 1
g[2][3]["wt"] = 2
g[3][4]["wt"] = 3
g[4][1]["wt"] = 4
# Deliberately use wrong name for edge weight!?
g = StellarGraph(g, edge_weight_label="w")
nodes = list(g.nodes())
n = 1
length = 1
seed = None
p = 1.0
q = 1.0
biasedrw = BiasedRandomWalk(g)
with pytest.raises(ValueError):
biasedrw.run(nodes=nodes,
n=n,
p=p,
q=q,
length=length,
seed=seed,
weighted=True)
def test_identity_unweighted_weighted_1_walks(self):
# graph with all edge weights = 1
g = nx.Graph()
edges = [(1, 2, 1), (2, 3, 1), (3, 4, 1), (4, 1, 1)]
g.add_weighted_edges_from(edges)
g = StellarGraph(g)
nodes = g.nodes()
n = 4
length = 4
seed = 42
p = 1.0
q = 1.0
biasedrw = BiasedRandomWalk(g)
assert biasedrw.run(nodes=nodes,
n=n,
p=p,
q=q,
length=length,
seed=seed,
weighted=True) == biasedrw.run(nodes=nodes,
n=n,
p=p,
q=q,
length=length,
seed=seed,
weighted=False)
def test_UnsupervisedSampler_parameter(self):
g = create_test_graph()
# rw = UniformRandomWalk(StellarGraph(g))
# if no graph is provided
with pytest.raises(ValueError):
UnsupervisedSampler(G=None)
# graph has to be a Stellargraph object
with pytest.raises(ValueError):
UnsupervisedSampler(G=g)
g = StellarGraph(g)
"""
# only Uniform random walk is supported at the moment
with pytest.raises(TypeError):
UnsupervisedSampler(G=g, walker="any random walker")
# if no walker is provided, default to Uniform Random Walk
sampler = UnsupervisedSampler(G=g)
assert isinstance(sampler.walker, UniformRandomWalk)
"""
# walk must have length strictly greater than 1
with pytest.raises(ValueError):
UnsupervisedSampler(G=g, length=1)
# at least 1 walk from each root node
with pytest.raises(ValueError):
UnsupervisedSampler(G=g, number_of_walks=0)
# nodes nodes parameter should be an iterable of node IDs
with pytest.raises(ValueError):
UnsupervisedSampler(G=g, nodes=1)
# if no root nodes are provided for sampling defaulting to using all nodes as root nodes
sampler = UnsupervisedSampler(G=g, nodes=None)
assert sampler.nodes == list(g.nodes())
# if the seed value is provided check
# that the random choices is reproducable
sampler = UnsupervisedSampler(G=g, seed=1)
assert sampler.random.choices(range(100), k=10) == [
13,
84,
76,
25,
49,
44,
65,
78,
9,
2,
]
def test_weighted_walks(self):
# all positive walks
g = nx.Graph()
edges = [(1, 2, 1), (2, 3, 2), (3, 4, 3), (4, 1, 4)]
g.add_weighted_edges_from(edges)
g = StellarGraph(g)
nodes = list(g.nodes())
n = 1
length = 1
seed = None
p = 1.0
q = 1.0
biasedrw = BiasedRandomWalk(g)
assert (len(
biasedrw.run(nodes=nodes,
n=n,
p=p,
q=q,
length=length,
seed=seed,
weighted=True)) == 4)
# negative edge
g = nx.Graph()
edges = [(1, 2, 1), (2, 3, -2), (3, 4, 3), (4, 1, 4)]
g.add_weighted_edges_from(edges)
g = StellarGraph(g)
biasedrw = BiasedRandomWalk(g)
with pytest.raises(ValueError):
biasedrw.run(nodes=nodes,
n=n,
p=p,
q=q,
length=length,
seed=seed,
weighted=True)
# edge with weight infinity
g = nx.Graph()
edges = [(1, 2, 1), (2, 3, np.inf), (3, 4, 3), (4, 1, 4)]
g.add_weighted_edges_from(edges)
g = StellarGraph(g)
biasedrw = BiasedRandomWalk(g)
with pytest.raises(ValueError):
biasedrw.run(nodes=nodes,
n=n,
p=p,
q=q,
length=length,
seed=seed,
weighted=True)
# missing edges
g = nx.Graph()
edges = [(1, 2, 1), (2, 3, None), (3, 4, 3), (4, 1, 4)]
g.add_weighted_edges_from(edges)
g = StellarGraph(g)
biasedrw = BiasedRandomWalk(g)
with pytest.raises(ValueError):
biasedrw.run(nodes=nodes,
n=n,
p=p,
q=q,
length=length,
seed=seed,
weighted=True)
# edges with NaN
g = nx.Graph()
edges = [(1, 2, 1), (2, 3, np.NaN), (3, 4, 3), (4, 1, 4)]
g.add_weighted_edges_from(edges)
g = StellarGraph(g)
biasedrw = BiasedRandomWalk(g)
with pytest.raises(ValueError):
biasedrw.run(nodes=nodes,
n=n,
p=p,
q=q,
length=length,
seed=seed,
weighted=True)
