def plot(self,
c,
X,
probas,
interception_func=None,
setting_kwargs={},
uninfected_small=False,
lighten_obs=True,
lighten_prediction=False,
highlight_missing_infection=False,
color_mapper=None,
**kwargs):
setting = heatmap_plot_setting(self.g,
c,
X,
probas,
color_mapper=color_mapper,
**setting_kwargs)
if uninfected_small:
uninfected = set(np.arange(len(c))) - set(infected_nodes(c))
# make terminals larger
setting['node_size_info'][tuple(
X)] = setting['node_size_info'][tuple(X)] * 1.5
# make uninfected smaller
setting['node_size_info'][tuple(
uninfected)] = setting['node_size_info']['default'] / 1.5
if lighten_obs:
setting['node_color_info'][X] = 0
if lighten_prediction:
depth = setting['node_color_info']
source = cascade_source(c)
depth[depth == 1] = 0.5
depth[source] = 1
if highlight_missing_infection:
missing = set(infected_nodes(c)) - set(X) - set(
(probas == 1).nonzero()[0])
if interception_func is not None:
interception_func(setting)
visualize(self.g, self.pos, **setting, **kwargs)
def default_plot_setting(g,
c,
X,
size_multiplier=1.0,
edge_width_multiplier=1.0,
deemphasize_hidden_infs=False):
source = cascade_source(c)
inf_nodes = infected_nodes(c)
hidden_infs = set(inf_nodes) - set(X)
node_color_info = OrderedDict()
node_color_info[tuple(X)] = COLOR_BLUE
if not deemphasize_hidden_infs:
# print(COLOR_DARK_RED)
node_color_info[tuple(hidden_infs)] = COLOR_YELLOW
node_color_info[(source, )] = COLOR_GREEN
node_color_info['default'] = COLOR_WHITE
node_shape_info = OrderedDict()
node_shape_info[tuple(X)] = SHAPE_SQUARE
node_shape_info['default'] = SHAPE_CIRCLE
node_shape_info[(source, )] = SHAPE_PENTAGON
node_size_info = OrderedDict()
node_size_info[tuple(X)] = 15 * size_multiplier
node_size_info[(source, )] = 20 * size_multiplier
if not deemphasize_hidden_infs:
node_size_info[tuple(hidden_infs)] = 12.5 * size_multiplier
node_size_info['default'] = 6 * size_multiplier
node_text_info = {'default': ''}
edge_color_info = {'default': 'white'}
edge_pen_width_info = {'default': 2.0 * edge_width_multiplier}
return {
'node_color_info': node_color_info,
'node_shape_info': node_shape_info,
'node_size_info': node_size_info,
'edge_color_info': edge_color_info,
'edge_pen_width_info': edge_pen_width_info,
'node_text_info': node_text_info
}
from cascade_generator import observe_cascade
from helpers import cascade_source
from glob import glob
graph = 'grqc'
from_obs = 0.1
model = 'si'
from_dir = 'cascade-weighted/{}-m{}-s0.02-o{}/'.format(graph, model, from_obs)
target_obs_list = [0.2, 0.3, 0.4, 0.5]
for target_obs in target_obs_list:
print('target_obs', target_obs)
to_dir = 'cascade-weighted/{}-m{}-s0.02-o{}/'.format(
graph, model, target_obs)
if not os.path.exists(to_dir):
os.makedirs(to_dir)
for input_path in glob(from_dir + '*'):
# print('input_path', input_path)
id_ = os.path.basename(input_path)
_, c = pkl.load(open(input_path, 'rb'))[:2]
s = cascade_source(c)
obs = observe_cascade(c, s, target_obs, 'uniform')
output_path = to_dir + id_
# print('output_path', output_path)
pkl.dump((obs, c, None), open(output_path, 'wb'))