def main(prefix):
result = re.load_result(prefix)
result.import_dict(ml_glpp_parameters(result))
re.save_result(prefix, result)
return result
def load_and_sort(configs):
crs = [(c, load_result(c).result) for c in configs]
def key(cr):
if is_failed(cr[1]):
return -float("inf")
else:
return error_reduction(cr[1])
return sorted(crs, key=key, reverse=True)
def main(prefix):
result = re.load_result(prefix)
try:
an = lnp_result_info(result)
except:
print " (not a LNP simulation)"
an = information_analysis(result.intensity)
setattr(result, "info", an)
re.save_result(prefix, result)
return result
def default():
print("File contents:")
with gzip.open(resultpath(config.default_config), mode="rt") as f:
for line in f:
print(line, end="")
print_barline()
r = load_result(config.default_config).result
print("Error reduction:", error_reduction(r))
print("Prior correctness:", prior_correctness(r))
print("Correctness:", correctness(r))
def complete():
crs = []
for p in Path("data/results").iterdir():
cr = load_result(str(p))
crs.append((cr.config, cr.result))
def key(cr):
if is_failed(cr[1]):
return -float("inf")
else:
return error_reduction(cr[1])
crs = sorted(crs, key=key, reverse=True)
return crs
def exp10():
# Query visualization.
dataset = 'aids10k'
model = 'beam80'
k = 5
info_dict = {
'draw_node_size': 10,
'draw_node_label_enable': True,
'draw_node_label_font_size': 8,
'draw_node_color_map': {
'C': 'red',
'O': 'blue',
'N': 'green'
},
'draw_edge_label_enable': True,
'draw_edge_label_font_size': 6,
'each_graph_text_list': [],
'each_graph_font_size': 10,
'plot_dpi': 200,
'plot_save_path': ''
}
r = load_result(dataset, model)
ged_mat = r.ged_mat()
time_mat = r.time_mat()
ids = r.ged_sort_id_mat()
m, n = ged_mat.shape
train_data = load_data(dataset, train=True)
test_data = load_data(dataset, train=False)
for i in range(m):
q = test_data.graphs[i]
gids = ids[i][:k]
gs = [train_data.graphs[j] for j in gids]
info_dict['each_graph_text_list'] = \
['query id: {}'.format(q.graph['gid'])] + \
[get_text_label(ged_mat, time_mat, i, j, \
train_data.graphs[j]) for j in gids]
info_dict['plot_save_path'] = \
get_root_path() + \
'/files/{}//query_vis/{}/query_vis_{}_{}_{}.png'.format( \
dataset, model, dataset, model, i)
vis(q, gs, info_dict)
def test_plot(): p = 'results/single_trial_test_R.pickle' assert(path.exists(p)) res = r.load_result(p) res.plot()
