def process_file(data_file):
curr_time = get_time()
# Parse Into Network
d = XMLParser(data_file, get_time()).parse_to_dict()
net = NetworkParser(d)
# Graph Analysis
output("Analyzing File " + data_file)
na = NetworkAnalysis(net.G, os.path.basename(data_file), output_path)
na.outputBasicStats()
na.outputNodesAndEdges()
# na.nodeRemoval()
basic = na.d3dump(public_out_path, str(curr_time))
# Run Decentralized Search
if decentralized_search_settings["run_decentralized_search"]:
hiearchyG = net.G.copy()
category_hierarchy = CategoryBasedHierarchicalModel(
hiearchyG,
similarity_matrix_type=category_hierarchical_model_settings[
"similarity_matrix_type"],
max_branching_factor_root=category_hierarchical_model_settings[
"max_branching_factor_root"])
category_hierarchy.build_hierarchical_model()
decentralized_search_model = HierarchicalDecentralizedSearch(
hiearchyG,
category_hierarchy.hierarchy,
na,
detailed_print=decentralized_search_settings["detailed_print"],
hierarchy_nodes_only=decentralized_search_settings[
"hierarchy_nodes_only"],
apply_weighted_score=decentralized_search_settings[
"apply_weighted_score"],
)
n_found, n_missing, av_path_len, av_unique_nodes, path_lengths_deciles = decentralized_search_model.run_decentralized_search(
1000, decentralized_search_settings["widen_search"],
decentralized_search_settings["plots"])
basic.update({
"decentralized_num_paths_found":
n_found,
"decentralized_num_paths_missing":
n_missing,
"decentralized_average_decentralized_path_length":
av_path_len,
"decentralized_average_num_unique_nodes":
av_unique_nodes,
"hierarchy_num_nodes":
(len(category_hierarchy.hierarchy.nodes()) -
len(category_hierarchy.ranked_categories)),
"hierarchy_num_cat_nodes":
len(category_hierarchy.ranked_categories),
"hierarchy_num_levels":
category_hierarchy.num_hierarchy_levels
})
basic["hierarchy_ratio_cat_nodes"] = basic[
"hierarchy_num_cat_nodes"] / basic["hierarchy_num_nodes"]
path_lengths_deciles_dict = {}
for i in range(len(path_lengths_deciles)):
path_lengths_deciles_dict["path_length_" + str(
(i + 1) * 10) + "_percentile"] = path_lengths_deciles[i]
basic.update(path_lengths_deciles_dict)
random_search_model = RandomSearch(net.G, na)
n_found, n_missing, av_path_len, av_unique_nodes = random_search_model.run_search(
1000, decentralized_search_settings["widen_search"],
decentralized_search_settings["plots"])
basic.update({
"random_num_paths_found": n_found,
"random_num_paths_missing": n_missing,
"random_average_decentralized_path_length": av_path_len,
"random_average_num_unique_nodes": av_unique_nodes
})
if generate_data:
na.write_permanent_data_json(
public_data, basic) # write out decentralized results
# na.generateDrawing()
output("Completed Analyzing: " + data_file)
def time_process(data_file):
curr_time = dt.datetime.now()
# run loop
fobj = XMLParser(data_file, curr_time)
lim = fobj.find_oldest_time()
while curr_time > lim:
curr_time -= TIME_INCR
print('running time analysis for ' + str(curr_time))
fobj.update_time(curr_time)
d = fobj.parse_to_dict()
if d:
net = NetworkParser(d)
output("Analyzing File " + data_file + ' at time ' +
str(curr_time))
na = NetworkAnalysis(net.G, os.path.basename(data_file),
output_path, curr_time)
basic = na.d3dump(public_out_path, str(curr_time))
# Run Decentralized Search
try:
if decentralized_search_settings[
"run_decentralized_search"]:
hiearchyG = net.G.copy()
category_hierarchy = CategoryBasedHierarchicalModel(
hiearchyG,
similarity_matrix_type=
category_hierarchical_model_settings[
"similarity_matrix_type"],
max_branching_factor_root=
category_hierarchical_model_settings[
"max_branching_factor_root"])
category_hierarchy.build_hierarchical_model()
decentralized_search_model = HierarchicalDecentralizedSearch(
hiearchyG,
category_hierarchy.hierarchy,
na,
detailed_print=decentralized_search_settings[
"detailed_print"],
hierarchy_nodes_only=decentralized_search_settings[
"hierarchy_nodes_only"],
apply_weighted_score=decentralized_search_settings[
"apply_weighted_score"],
)
n_found, n_missing, av_path_len, av_unique_nodes, path_lengths_deciles = decentralized_search_model.run_decentralized_search(
1000,
decentralized_search_settings["widen_search"],
decentralized_search_settings["plots"])
basic.update({
"decentralized_num_paths_found":
n_found,
"decentralized_num_paths_missing":
n_missing,
"decentralized_average_decentralized_path_length":
av_path_len,
"decentralized_average_num_unique_nodes":
av_unique_nodes,
"hierarchy_num_nodes":
(len(category_hierarchy.hierarchy.nodes()) -
len(category_hierarchy.ranked_categories)),
"hierarchy_num_levels":
category_hierarchy.num_hierarchy_levels
})
path_lengths_deciles_dict = {}
for i in range(len(path_lengths_deciles)):
path_lengths_deciles_dict["path_length_" + str((i + 1) * 10) + "_percentile"] = \
path_lengths_deciles[i]
basic.update(path_lengths_deciles_dict)
random_search_model = RandomSearch(net.G, na)
n_found, n_missing, av_path_len, av_unique_nodes = random_search_model.run_search(
1000,
decentralized_search_settings["widen_search"],
decentralized_search_settings["plots"])
basic.update({
"random_num_paths_found":
n_found,
"random_num_paths_missing":
n_missing,
"random_average_decentralized_path_length":
av_path_len,
"random_average_num_unique_nodes":
av_unique_nodes
})
except:
pass
if generate_data: # write out decentralized results
na.write_permanent_data_json(public_data, basic,
str(curr_time.date()))
output("Completed Analyzing: " + data_file)
