def test_full_observation_tree_closure(tree_and_cascade):
g = tree_and_cascade[0]
for p in np.arange(0.2, 1.0, 0.1):
infection_times, source, obs_nodes = gen_nontrivial_cascade(g, p, 1.0)
scores = tree_sizes_by_roots(g,
obs_nodes,
infection_times,
source,
method='closure')
assert get_rank_index(scores, source) == 0
def one_run(g, p, q, model, result_dir, i, verbose, debug):
infection_times, source, obs_nodes, true_tree = gen_nontrivial_cascade(
g, p, q, model=model,
return_tree=True, source_includable=True)
stime = time.time()
tree = get_tree(g, infection_times, source, obs_nodes, method,
verbose=verbose,
debug=debug)
# pickle cascade and pred_tree
true_edges = extract_edges(true_tree)
pred_edges = extract_edges(tree)
pkl.dump((infection_times, source, obs_nodes, true_edges, pred_edges),
open(result_dir + '/{}.pkl'.format(i), 'wb'))
return time.time() - stime
def cascades_on_grid():
cascades = []
g = load_graph('data/grid/2-6/graph.gt')
for model in MODELS:
for q in QS:
for i in range(K):
ret = gen_nontrivial_cascade(g,
P,
q,
model=model,
return_tree=True,
source_includable=True)
ret = (g, ) + ret + (model, q, i)
cascades.append(
ret) # g, infection_times, source, obs_nodes, true_tree
return cascades
def one_run(g, p, q, model, result_dir, i, verbose, debug):
infection_times, source, obs_nodes, true_tree = gen_nontrivial_cascade(
g, p, q, model=model, return_tree=True, source_includable=True)
stime = time.time()
tree = get_tree(g,
infection_times,
source,
obs_nodes,
method,
verbose=verbose,
debug=debug)
# pickle cascade and pred_tree
true_edges = extract_edges(true_tree)
pred_edges = extract_edges(tree)
pkl.dump((infection_times, source, obs_nodes, true_edges, pred_edges),
open(result_dir + '/{}.pkl'.format(i), 'wb'))
return time.time() - stime
def grid_and_cascade():
g = load_graph('data/grid/2-6/graph.gt')
c, s, o = gen_nontrivial_cascade(g, 0.8, 0.5)
return g, c, s, o
def tree_and_cascade():
g = load_graph('data/balanced-tree/2-6/graph.gt')
c, s, o = gen_nontrivial_cascade(g, 0.8, 0.5)
return g, c, s, o