def test_random_DAG(N=1):
np.random.seed(12345)
i = 0
while i < N:
p = np.random.uniform(0.25, 1)
n_v = np.random.randint(5, 50)
G = random_DAG(n_v, p)
G_nx = to_networkx(G)
assert nx.is_directed_acyclic_graph(G_nx)
print("PASSED")
i += 1
def test_topological_ordering(N=1):
np.random.seed(12345)
i = 0
while i < N:
p = np.random.uniform(0.25, 1)
n_v = np.random.randint(5, 10)
G = random_DAG(n_v, p)
G_nx = to_networkx(G)
if nx.is_directed_acyclic_graph(G_nx):
topo_order = G.topological_ordering()
# test topological order
seen_it = set()
for n_i in topo_order:
seen_it.add(n_i)
assert any([c_i in seen_it
for c_i in G.get_neighbors(n_i)]) == False
print("PASSED")
i += 1
def plot_ucb1_gaussian_shortest_path():
"""
Plot the UCB1 policy on a graph shortest path problem each edge weight
drawn from an independent univariate Gaussian
"""
np.random.seed(12345)
ep_length = 1
n_duplicates = 5
n_episodes = 5000
p = np.random.rand()
n_vertices = np.random.randint(5, 15)
Gaussian = namedtuple("Gaussian", ["mean", "variance", "EV", "sample"])
# create randomly-weighted edges
print("Building graph")
E = []
G = random_DAG(n_vertices, p)
V = G.vertices
for e in G.edges:
mean, var = np.random.uniform(0, 1), np.random.uniform(0, 1)
w = lambda: np.random.normal(mean, var) # noqa: E731
rv = Gaussian(mean, var, mean, w)
E.append(Edge(e.fr, e.to, rv))
G = DiGraph(V, E)
while not G.path_exists(V[0], V[-1]):
print("Skipping")
idx = np.random.randint(0, len(V))
V[idx], V[-1] = V[-1], V[idx]
mab = ShortestPathBandit(G, V[0], V[-1])
policy = UCB1(C=1, ev_prior=0.5)
policy = BanditTrainer().train(policy, mab, ep_length, n_episodes,
n_duplicates)