def matching(text, command):
list_ = [] # list can be road or bus stop belong to the command
if command == 'road':
path_road = 'D:/Project/Transportation_SMU-NEC_collaboration/Data'
name_road = 'road_abbrevation_all.csv'
list_ = load_file(path_road, name_road)
elif command == 'busstop':
path_stop = 'D:/Project/Transportation_SMU-NEC_collaboration/Data'
name_busstop = 'bus_stop_crf.csv' # delete the header of file bus stop
list_ = load_file(path_stop, name_busstop)
list_element = []
for index in range(0, len(list_)):
ele = list_[index].lower()
split_road = ele.split(';')
for road in split_road:
if pattern_match(road, text) is True:
list_element.append(road)
break
list_token_element = []
for element in list_element:
split_ = element.split()
for value in split_:
list_token_element.append(value)
return list_token_element
def extract_road_busstop_expression(list_line, list_dict):
y_label = []
y_reg = []
list_svc = []
cnt = 1
list_write = []
for line in list_line:
split_line = line.split('\t')
index = split_line[0]
label = split_line[1].strip()
y_label.append(label)
svc = split_line[2].strip()
list_svc.append(svc)
text = split_line[3].strip().lower() # this is a text for road or bus stop
# print index, label, svc
list_road_match = []
for index in range(0, len(list_dict)):
road = list_road[index]
split_road = road.split(';')
for token in split_road:
if pattern_match(token.lower(), text) is True:
split_token = token.split()
for value in split_token:
if value not in list_road_match:
list_road_match.append(value.lower())
break
flag = 'FALSE'
if svc in list_road_match:
flag = 'TRUE'
y_reg.append(flag)
else:
flag = 'FALSE'
y_reg.append(flag)
print '-- finished this line -- %i' % cnt + '\t' + flag
list_write.append('-- finished this line -- %i' % cnt + '\t' + flag)
cnt += 1
break
# for value in y_reg:
# print value
# for i in range(0, len(y_reg)):
# if y_label[i] != y_reg[i]:
# print list_svc[i]
write_file('d:/', 'busstop', list_write)
print metrics.accuracy_score(y_label, y_reg)
print metrics.classification_report(y_label, y_reg)
print metrics.confusion_matrix(y_label, y_reg)
def match_road(string, list_road):
list_index = []
for index in range(0, len(list_road)):
road = list_road[index]
split_road = road.split(';')
for token in split_road:
if pattern_match(token, string) is True:
list_index.append(index)
break
return list_index
