def prepare_params(args):
if args.t == None:
args.t = args.max_time
monitoring_marks = utils.generate_monitoring_marks(args.t, args.max_time)
try:
data_graph = nx.read_gpickle(args.d)
except:
data_graph = nx.read_gml(args.d)
pattern = nx.read_gml(args.p)
if (not (os.path.exists(args.o))):
os.makedirs(args.o)
# DETERMINING ROOT NODE
if args.root_node_name == None or args.root_node_id == None:
####hops make this faster
hist = analyzer.get_sorted_labels_by_occurence_frequency_in_graph_hops(
data_graph)
###
#max degree node in pattern (hoPS)
max_degree_nodes = None #analyzer.get_maximum_degree_nodes(pattern)
root_node, root_node_predicate_name = utils.choose_root_node(
pattern, None, hist, max_degree_nodes)
else:
root_node = args.root_node_id
root_node_predicate_name = args.root_node_name
# get images of root node in the data graph
root_nodes = [
x for x in data_graph.nodes()
if data_graph.node[x]['predicate'] == root_node_predicate_name
]
OBdecomp = OBDsearch.get_heuristic4_OBD(pattern, startNode=root_node)
if OBdecomp == None:
no_obd_decomp = True
OBdecomp = OBDsearch.get_flatList(pattern, startNode=root_node)
Plist = [item for sublist in OBdecomp for item in sublist]
return data_graph, pattern, OBdecomp, root_node, root_node_predicate_name, args.t, args.max_time, monitoring_marks, root_nodes, Plist
default=None,
help='name of a root node')
args = parser.parse_args()
#Preparing the inputs
data_graph, pattern, OBdecomp, root_node, root_node_predicate_name, interval, max_time, monitoring_marks, root_nodes, Plist = prepare_inputs.prepare_params(
args)
output_folder = 'fk_AD_results'
output_path = os.path.join(args.o, output_folder)
if not os.path.exists(output_path):
os.makedirs(output_path)
if OBdecomp != None:
OBdecomp_false = [[item] for sublist in OBdecomp for item in sublist]
else:
OBdecomp_false = OBDsearch.get_flatList(pattern, startNode=root_node)
#Main procedure
monitoring_reports = {}
all_furer_times = []
fudicts = []
average_time = 0
start = time.time()
m, estimates = get_nr_embedding(data_graph, pattern, OBdecomp_false,
root_node_predicate_name, monitoring_marks)
end = time.time()
average_time += end - start
fdictionaries_Furer = globals_sampling.globalist_furer
times_Furer = globals_sampling.globaltimes_furer[1:]