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rank_trainingset.py
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rank_trainingset.py
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import math
import re
from Helper.common import *
class DatasetGenerator:
# Remove the project containing fewer than 6 method invocations
def __init__(self, k, input_path, Dataset_path, Splitdata_path, check_dir, half_or_all, num_for_test):
self.k = k
self.Dataset_path = Dataset_path
self.check_mk_dir(Dataset_path)
self.splitdata_path = Splitdata_path
self.check_mk_dir(Splitdata_path)
self.data = input_path
self.half_or_all = half_or_all
self.num_for_test = num_for_test
self.check_dir = check_dir
def start(self):
self.SplitDatat()
def SplitDatat(self):
files = getFileList(self.data, ".csv")
all_files = []
for file in files:
filename = os.path.split(file)[-1][:-4]
if os.path.exists(os.path.join(self.check_dir, filename + ".txt")):
all_files.append(file)
all = len(all_files)
print(str(all) + " files in total.")
n = int(math.ceil(all * 1.0 / self.k))
list_of_groups = [all_files[i: i + n] for i in range(0, all, n)]
for i in range(self.k):
self.recordGroups(list_of_groups)
Dir_name = self.Dataset_path + "dataset_" + str(i) + "/"
self.check_mk_dir(Dir_name)
Training_file = Dir_name + "TrainingSet.txt"
Test_dir = Dir_name + "TestSet/"
self.check_mk_dir(Test_dir)
GT_dir = Dir_name + "GroundTruth/"
self.check_mk_dir(GT_dir)
sub_files = list_of_groups[i]
for file in sub_files:
if self.half_or_all == "all":
self.split_test_GT(file, Test_dir, GT_dir)
else:
self.split_test_GT_half(file, Test_dir, GT_dir)
for file in all_files:
if file in sub_files:
continue
self.cp_to_trainingset(file, Training_file)
def split_test_GT(self, file, Test_dir, GT_dir):
filename = os.path.split(file)[-1]
last_line_number = row_count(file)
if last_line_number > 10000:
return
with open(file, "r") as fr:
reader = csv.reader(fr)
# filter less than 6
headings = next(reader)
print("[+] Line num: " + str(last_line_number))
if last_line_number < 7:
return
test_file = os.path.join(Test_dir, filename)
GT_file = os.path.join(GT_dir, filename)
fw = open(test_file, "w")
writer = csv.writer(fw)
writer.writerow(headings)
for row in reader:
if last_line_number == reader.line_num:
print("[+] Processing the last line...")
string = row[1].strip('\"[] ')
pattern = r'(<.*?>)'
mi = re.findall(pattern, string)
if self.num_for_test == "1":
stop_index = 1
elif self.num_for_test == "4":
stop_index = 4
for_test = mi[:stop_index]
for_GT = mi[stop_index:]
with open(GT_file, "w") as fwg:
writerg = csv.writer(fwg)
writerg.writerow(headings)
writerg.writerow([row[0], for_GT])
else:
writer.writerow(row)
writer.writerow([row[0], for_test])
fw.close()
def split_test_GT_half(self, file, Test_dir, GT_dir):
filename = os.path.split(file)[-1]
last_line_number = row_count(file)
if last_line_number > 10000:
return
with open(file, "r") as fr:
reader = csv.reader(fr)
# filter less than 6
headings = next(reader)
print("[+] Line num: " + str(last_line_number))
if last_line_number < 7:
return
test_file = os.path.join(Test_dir, filename)
GT_file = os.path.join(GT_dir, filename)
fw = open(test_file, "w")
writer = csv.writer(fw)
writer.writerow(headings)
if last_line_number % 2 == 0:
half_line = (last_line_number / 2) + 1
else:
half_line = (last_line_number + 1) / 2 + 1
for row in reader:
if reader.line_num < half_line:
writer.writerow(row)
elif reader.line_num == half_line:
print("[+] Processing the half line...")
string = row[1].strip('\"[] ')
pattern = r'(<.*?>)'
mi = re.findall(pattern, string)
if self.num_for_test == "1":
stop_index = 1
elif self.num_for_test == "4":
stop_index = 4
for_test = mi[:stop_index]
for_GT = mi[stop_index:]
with open(GT_file, "w") as fwg:
writerg = csv.writer(fwg)
writerg.writerow(headings)
writerg.writerow([row[0], for_GT])
break
writer.writerow([row[0], for_test])
fw.close()
def recordGroups(self, list_of_groups):
for i in range(self.k):
record_file = os.path.join(self.splitdata_path, str(i) + ".txt")
with open(record_file, "w") as fw:
for item in list_of_groups[i]:
fw.write(item + "\n")
def check_mk_dir(self, path):
if not os.path.exists(path):
os.mkdir(path)
def cp_to_trainingset(self, file, Training_file):
# filter less than 6
line_count = row_count(file)
if line_count < 6 or line_count > 10000:
return
filename = os.path.split(file)[-1][:-4]
with open(Training_file, "a+") as fw:
fw.write(filename + "\n")
def sortedDictValues1(adict):
keys1 = sorted(adict.keys())
return [[key, adict[key]] for key in keys1]
def rank(original, new):
check_and_mk_dir(new)
files = getFileList(original, ".csv")
for file in files:
file_map = {}
filename = os.path.split(file)[-1][:-4]
file_new = os.path.join(new, filename + ".csv")
if os.path.exists(file_new):
continue
with open(file, "r") as fr:
reader = csv.reader(fr)
headings = next(reader)
fw = open(file_new, "w")
writer = csv.writer(fw)
writer.writerow(headings)
for line in reader:
line_new = []
string = line[1].strip('\"[] ')
pattern = r'(<.*?>)'
mi = re.findall(pattern, string)
file_map[line[0]] = mi
lst = sortedDictValues1(file_map)
for item in lst:
writer.writerow(item)
fw.close()
if __name__ == '__main__':
all_or_half = ["all", "half"]
left_num = ["1", "4"]
check_dir = "/home/username/APIRecommendation/Description_presolved/"
Input_path = "/home/username/APIRecommendation/Presolved_filtered/"
New_Input_path = "/home/username/APIRecommendation/Presolved_ranked/"
rank(Input_path, New_Input_path)
Dataset_path = "datasets_ranked_half_4/"
Splitdata_path = Dataset_path + "splitdata/"
datasetGenerator = DatasetGenerator(10, New_Input_path, Dataset_path, Splitdata_path, check_dir, "half", "4")
datasetGenerator.start()