def compute_minhash(lst):
m1 = MinHash(num_perm=128)
for d in set(lst):
m1.update(d.encode('utf8'))
return m1
def create_minhash(data):
minhash = MinHash(HASH_SIZE, seed=12)
for qgram in data:
minhash.update(qgram.encode('utf-8'))
return minhash
f = open("../data/" + dataset + ".graph")
ptr = f.readline().strip("\n").strip(" ").split(" ")
idx = f.readline().strip("\n").strip(" ").split(' ')
if len(idx) != nume:
print("error idx", len(idx))
exit()
if len(ptr) != numv + 1:
print("error ptr", len(ptr))
exit()
t0 = time.time()
lsh = MinHashLSH(threshold=lsh_thres, num_perm=per)
allver = []
lists = [[] for i in range(numv)]
for i in range(numv):
m = MinHash(num_perm=per)
for iter in range((int)(ptr[i]), (int)(ptr[i + 1])):
m.update(str(idx[iter]).encode('utf-8'))
lists[i].append(idx[iter])
lsh.insert(str(i), m)
allver.append(m)
#res = lsh.query(allver[0])
#print(res)
t1 = time.time()
print("init LSH", t1 - t0)
def jd(l1, l2):
if len(l1) == 0 or len(l2) == 0:
return 0
s1 = set(l1)
from datasketch import MinHash
k_sig = 1024
minhash = MinHash(num_perm=k_sig)
minhash2 = MinHash(num_perm=k_sig)
minhash3 = MinHash(num_perm=k_sig)
minhash.update(
"This is a good algorithm that can perform wild ranges of services".encode(
'utf-8'))
minhash2.update(
"The computing algorithm for this is very cool and works really well".
encode('utf-8'))
minhash3.update(
"The computing algorithm for this is very cool and works really wellx".
encode('utf-8'))
# print(minhash.hashvalues)
# print(minhash2.hashvalues)
# print(minhash3.hashvalues)
print(minhash2.jaccard(minhash3))
# print(minhash2.permutations)
# print(minhash3.permutations)
def get_minhash(item_str):
temp = MinHash()
for d in item_str:
temp.update(d.encode('utf8'))
return temp
from datasketch import MinHash
data1 = ['minhash', 'is', 'a', 'probabilistic', 'data', 'structure', 'for',
'estimating', 'the', 'similarity', 'between', 'datasets']
data2 = ['minhash', 'is', 'a', 'probability', 'data', 'structure', 'for',
'estimating', 'the', 'similarity', 'between', 'documents']
m1, m2 = MinHash(), MinHash()
for d in data1:
m1.update(d.encode('utf8'))
for d in data2:
m2.update(d.encode('utf8'))
print("Estimated Jaccard for data1 and data2 is", m1.jaccard(m2))
s1 = set(data1)
s2 = set(data2)
actual_jaccard = float(len(s1.intersection(s2)))/float(len(s1.union(s2)))
print("Actual Jaccard for data1 and data2 is", actual_jaccard)
print("Estimated Jaccard for data1 and data2 is", m1.jaccard(m1))
print(MinHash(hashvalues=m1.hashvalues).jaccard(m1))
def test_bytesize(self):
m1 = MinHash(4, 1, hashfunc=fake_hash_func)
lm1 = LeanMinHash(m1)
self.assertTrue(lm1.bytesize() == (4 * 4) + 4 + 8)
def getHashSig(tagsListOfPep):
minHash = MinHash(num_perm=NUM_PERMUTATION)
for tag in tagsListOfPep:
minHash.update(tag.encode('utf-8'))
return minHash.digest()
def min_hash(self,
num_perm=64,
seed=42,
use_components=None,
type_option=None,
n_char=None,
n_word=None,
npz=None,
isrequest=False):
"""
Minhash function.
Parameters
--------
num_perm: int
Number of permutations.
seed: int
For random permutations.
use_components: str, optional: ['name', 'addr'] or ['name'] or ['addr'].
Components to use.
type_option: str, optional: ['char', 'word'] or ['char'] or ['word'].
Components to use.
n_char: list of int
sizes of char grams.
n_word: list of int
sizes of word grams.
isrequest: bool
If this LpuList created for base, we have False.
npz: bool
Indicator or using npz files.
"""
if npz:
self.options = type_option
self.num_perm = num_perm
n = n_char if type_option == 'char' else n_word
self.features['not_weighed_{}_{}_{}minhash'.format(use_components[0], type_option[0], n[0])] = \
np.load(npz)['min_hash']
return self
use_components = use_components or ['name']
type_option = type_option or ['char']
n_char = n_char or [3]
n_word = n_word or [1]
if 'char' not in type_option and 'word' not in type_option:
assert False, "Проверьте значение параметра type_option."
if 'name' not in use_components and 'addr' not in use_components:
assert False, "Проверьте значение параметра use_components."
self.options = type_option
self.num_perm = num_perm
for i in use_components:
for j in type_option:
n_list = n_char if j == 'char' else n_word
for n in n_list:
help_list = []
for idx, name in enumerate(
self.features['{}_{}_{}grams'.format(i, j, n)]):
minhash = MinHash(num_perm, seed=seed)
for ngram in name:
minhash.update(ngram.encode('utf8'))
lean_minhash = LeanMinHash(minhash)
help_list.append(lean_minhash)
self.features['not_weighed_{}_{}_{}minhash'.format(
i, j, n)] = np.array(help_list)
file_path = 'data/min_hash_dadata/{}_{}_{}_not_weighed_minhash.npz'.format(
i, j, n)
if not isrequest:
np.savez_compressed(file_path,
min_hash=np.array(help_list))
return self
# m1 = MinHash(num_perm=128)
# m2 = MinHash(num_perm=128)
# m3 = MinHash(num_perm=128)
# for d in set1:
# m1.update(d.encode('utf8'))
# for d in set2:
# m2.update(d.encode('utf8'))
# for d in set3:
# m3.update(d.encode('utf8'))
# Create LSH index
# lsh = MinHashLSH(threshold=0.2, num_perm=128, params = (2,3))
k_sig = 1024
m1 = MinHash(num_perm=k_sig)
m2 = MinHash(num_perm=k_sig)
m3 = MinHash(num_perm=k_sig)
m4 = MinHash(num_perm=k_sig)
m5 = MinHash(num_perm=k_sig)
for d in set1:
m1.update(d.encode('utf8'))
for d in set2:
m2.update(d.encode('utf8'))
for d in set3:
m3.update(d.encode('utf8'))
for d in set4:
m4.update(d.encode('utf8'))
for d in set5:
m5.update(d.encode('utf8'))
if __name__ == '__main__':
# data1 = 'VGFGEEWEDAAWCN'
data1 = ['TAG', 'VGF', 'GTB', 'EEW']
# data1 = ['TAG', 'RBT', 'WCDS']
data2 = 'TAGDSAFDVGFGTEEWEQWWRFRSDAAWCDSNBH'
data3 = 'TAGSSAFDDBFDTWEEWTDWWRFRSCASWCDSQBH'
data2 = [data2[i:i + 3] for i in range(len(data2) - 2)]
data3 = [data3[i:i + 3] for i in range(len(data3) - 2)]
# for i in range(len(data2)-2):
# print(data2[i:i+3])
m1, m2 = MinHash(), MinHash()
# for d in data1:
d = 'AVB'
m1.update(d.encode('utf8'))
# for d in data2:
d = 'BVA'
m2.update(d.encode('utf8'))
print("Estimated Jaccard for data1 and data2 is", m2.jaccard(m1))
# sumSimi = 0
# m2 = MinHash()
# m2.update(data2.encode('utf8'))
# for d in data1:
# print(d)
# m1 = MinHash()
# m1.update(d.encode('utf8'))
def main():
path = '../../../Desktop' # Directory in local computer
"""Shows basic usage of the Drive v3 API.
Prints the names and ids of the first 10 files the user has access to.
"""
creds = None
# The file token.pickle stores the user's access and refresh tokens, and is
# created automatically when the authorization flow completes for the first
# time.
if os.path.exists('token.pickle'):
with open('token.pickle', 'rb') as token:
creds = pickle.load(token)
# If there are no (valid) credentials available, let the user log in.
if not creds or not creds.valid:
if creds and creds.expired and creds.refresh_token:
creds.refresh(Request())
else:
flow = InstalledAppFlow.from_client_secrets_file(
'credentials.json', SCOPES)
creds = flow.run_local_server()
# Save the credentials for the next run
with open('token.pickle', 'wb') as token:
pickle.dump(creds, token)
service = build('drive', 'v3', credentials=creds)
# Call the Drive v3 API
results = service.files().list(
pageSize=1000, fields="nextPageToken, files(parents, name, id, modifiedTime, mimeType, md5Checksum)").execute()
items = results.get('files', [])
currentTimesinceEpoc = time.time()
currentday = time.strftime('%d', time.localtime(currentTimesinceEpoc))
currentmonth = time.strftime('%m', time.localtime(currentTimesinceEpoc))
currentyear = time.strftime('%Y', time.localtime(currentTimesinceEpoc))
currentdate = dt.datetime(int(currentyear),int(currentmonth), int(currentday),0,0,0)
folderModifTimes = {}
if not items:
print('No files found.')
else:
'''location heirarchy for Drive files'''
for item in items:
modifyear, modifmonth, modifday = item['modifiedTime'].split('-')
modifday = modifday.split('T')[0]
modifdate = dt.datetime(int(modifyear),int(modifmonth), int(modifday),0,0,0)
modificationTimesinceEpoc = str(currentTimesinceEpoc - (currentdate-modifdate).total_seconds())
item ['modificationTimesinceEpoc'] = modificationTimesinceEpoc
if item.get('parents') != None and len(item.get('parents')) >1: #more tham 1 parent not handled
print("many parents")
if item.get('parents') == None:
if item['mimeType'] == 'application/vnd.google-apps.folder':
parentname = 'Drive'
parentid = 'drive'
else:
parentname = 'Parent-less Files'
parentid = 'parentlessfiles'
else:
parentname = (service.files().get(fileId=item.get('parents')[0]).execute())['name']
parentid = item.get('parents')[0]
if folderModifTimes.get(parentid) == None or float(folderModifTimes[parentid]) < float(modificationTimesinceEpoc):
folderModifTimes[parentid] = modificationTimesinceEpoc
item['parentid'] = parentid
item['parentname'] = parentname
new_items = []
print('location' + '^' + 'dir' + 'root' + '^' + 'Root' + '^' + str(currentTimesinceEpoc) + '^' + 'dir'+os.path.abspath(path)+ '^' + 'Desktop' + '^' + str(currentTimesinceEpoc))
print('location' + '^' + 'dir' + 'root' + '^' + 'Root' + '^' + str(currentTimesinceEpoc) + '^' + 'dir'+'https://drive.google.com/open?id=' +'drive'+ '^' + 'Drive' + '^' + str(currentTimesinceEpoc))
print('location' + '^' + 'dir' + 'https://drive.google.com/open?id=' +'drive' + '^' + 'Drive' + '^' + str(currentTimesinceEpoc) + '^' + 'dir'+'https://drive.google.com/open?id=' +'parentlessfiles'+ '^' + 'Parent-less Files' + '^' + folderModifTimes['parentlessfiles'])
for item in items:
if item['mimeType'] == 'application/vnd.google-apps.folder': #if a folder
if folderModifTimes.get(item['id']) == None:
print('location' + '^' + 'dir' + 'https://drive.google.com/open?id=' +item['parentid'] + '^' + item['parentname'] + '^' + folderModifTimes[item['parentid']] + '^' + 'dir'+'https://drive.google.com/open?id=' +item['id']+ '^' + item['name'] + '^' + item ['modificationTimesinceEpoc'])
else:
print('location' + '^' + 'dir' + 'https://drive.google.com/open?id=' +item['parentid'] + '^' + item['parentname'] + '^' + folderModifTimes[item['parentid']] + '^' + 'dir'+'https://drive.google.com/open?id=' +item['id']+ '^' + item['name'] + '^' + folderModifTimes[item['id']])
else: #if not a folder
new_items.append(item) #further links would only be between files
print('location' + '^' + 'dir' +'https://drive.google.com/open?id=' + item['parentid'] + '^' + item['parentname'] + '^' + folderModifTimes[item['parentid']] + '^' +'https://drive.google.com/open?id=' +item['id']+ '^' + item['name'] + '^' + item ['modificationTimesinceEpoc'])
items = new_items
for r, d, f in os.walk(path):
f = [file for file in f if not (file[0] == '.' or file[0] == '_')]
d[:] = [dr for dr in d if not (dr[0] == '.' or dr[0] == '_')]
for file in f:
filepath = os.path.join(r, file)
filepath2 = filepath.split('/')
filename = filepath2[-1]
filemtime = str(os.path.getmtime(os.path.abspath(filepath)))
mime = magic.Magic(mime=True)
mimeType = mime.from_file(os.path.abspath(filepath))
md5Checksum = hs.fileChecksum(os.path.abspath(filepath), "md5")
item = {'id': os.path.abspath(filepath),'name' :filename, 'modificationTimesinceEpoc': filemtime, 'mimeType': mimeType, 'md5Checksum': md5Checksum }
items.append(item)
with open('email_metadata', 'r') as f:
attachments = f.read().split('\n')
for attachment in attachments:
if len(attachment.split("^"))==1:
break
attachment_id, attachment_name, attachment_mimeType, attachment_md5Checksum, attachment_mtime = attachment.split("^")
item = {'id': attachment_id+'~'+attachment_name,'name' : attachment_name, 'modificationTimesinceEpoc': attachment_mtime, 'mimeType': attachment_mimeType, 'md5Checksum': attachment_md5Checksum }
items.append(item)
''' same hash'''
for f1 in items:
for f2 in items:
if f1['id']!=f2['id'] and f1.get('md5Checksum') != None and f2.get('md5Checksum') != None and f1.get('md5Checksum') == f2.get('md5Checksum'):
f1mtime = f1['modificationTimesinceEpoc']
f2mtime = f2['modificationTimesinceEpoc']
print('content' + '^'+ f1['id'] + '^' + f1['name'] + '^' + f1mtime + '^'+ f2['id'] + '^' + f2['name'] + '^' + f2mtime)
'''content similarity'''
text_files = []
'''scanning the directory'''
for f in items:
filepath = f['id']
file_type = f['mimeType']
major_minor = file_type.split('/')
if major_minor[0] == 'text' or file_type == 'application/vnd.google-apps.document':
text_files.append(f)
documents =[]
os.mkdir('DriveTextFiles') #To temporarily store drive text files
for f in text_files:
try:
documents.append((open(f['id']).read(),f))
except FileNotFoundError: # downloading drive text files
try :
file_id = f['id']
if f['mimeType'] == 'application/vnd.google-apps.document':
request = service.files().export_media(fileId=file_id, mimeType='text/plain')
else:
request = service.files().get_media(fileId=file_id)
fh = io.FileIO(os.path.join('DriveTextFiles',f['id']), 'wb')
downloader = googleapiclient.http.MediaIoBaseDownload(fh, request)
done = False
while done is False:
status, done = downloader.next_chunk()
documents.append((open(os.path.join('DriveTextFiles',f['id'])).read(),f))
except (googleapiclient.errors.HttpError, FileNotFoundError):
pass
lsh = MinHashLSH(threshold=0.3, num_perm=128)
for f in documents:
setdoc = set(f[0].split())
m = MinHash(num_perm=128)
for d in setdoc:
m.update(d.encode('utf8'))
lsh.insert(f[1]['id'] + '^' +f[1]['name']+ '^' +f[1]['modificationTimesinceEpoc'], m)
results = []
for doc in documents:
setdoc = set(doc[0].split())
m = MinHash(num_perm=128)
for d in setdoc:
m.update(d.encode('utf8'))
result = lsh.query(m)
results.append((doc[1]['id'] + '^' +doc[1]['name']+ '^' +doc[1]['modificationTimesinceEpoc'],result))
'''forming links between files with similar content'''
for result in results:
f2mtime = result[0].split('^')[2]
for r in result[1]:
if r!=result[0]:
f1mtime = r.split('^')[2]
print('content' + '^'+ r.split('^')[0] + '^' + r.split('^')[1] + '^' + f1mtime +'^'+ result[0].split('^')[0] + '^' + result[0].split('^')[1] + '^' + f2mtime)
shutil.rmtree('DriveTextFiles')
'''name similarity'''
for f1 in items:
for f2 in items:
distance1 = textdistance.jaro.distance(f1['name'],f2['name'])
distance2 = textdistance.levenshtein.distance(f1['name'],f2['name'])
if ((distance1<=0.30 and f1['id']!=f2['id'] and distance2<0.75*min(len(f1['name']),len(f2['name']),8)) or distance1<=0.15 or distance2<=0.25*min(len(f1['name']),len(f2['name']),8)) and f1['id']!=f2['id']:
f1mtime = f1['modificationTimesinceEpoc']
f2mtime = f2['modificationTimesinceEpoc']
print('name' + '^'+ f1['id'] + '^' + f1['name'] + '^' + f1mtime +'^' + f2['id'] + '^' + f2['name'] + '^' + f2mtime)
'''time similarity'''
file_threshhold = 10
filetimes = []
i = 0
for item in items:
filetimes.append([float(item['modificationTimesinceEpoc'])])
i = i+1
kmeans = KMeans(n_clusters=int(i/file_threshhold) +1, random_state=0).fit(filetimes)
labels = kmeans.labels_
for j in range(int(i/file_threshhold)+1) : #iterating through all clusters
idx = []
for i in range(0, len(labels)) :
if labels[i] == j :
idx.append(i)
filesj = [items[i] for i in idx] #all the files in a cluster
#forming similar time links
for f1 in filesj:
for f2 in filesj:
if f1['id']!=f2['id'] :
f1mtime = f1['modificationTimesinceEpoc']
f2mtime = f2['modificationTimesinceEpoc']
print('time' + '^'+ f1['id'] + '^' + f1['name'] + '^' + f1mtime + '^'+ f2['id'] + '^' + f2['name'] + '^' + f2mtime)
import database as mydb
from datasketch import MinHashLSHForest, MinHash, MinHashLSH
import pdb
from Translator import translate
from Article import Article
# Create MinHash objects
lsh = MinHashLSH(threshold=0.1,
num_perm=128,
storage_config={
"type": "redis",
"basename": b"docs_tech",
"redis": {
"host": "localhost",
"port": 6379
}
})
lsh = MinHashLSH(threshold=0.2, num_perm=128)
articles_en = mydb.execute_query("SELECT id, keyword, title FROM english")
keywords_en = [
Article(id=item[0], keyword=item[1], content=item[2])
for item in articles_en
]
for item in keywords_en:
minhash = MinHash(num_perm=128)
list_keyword = item.keyword.split(",")
for k in list_keyword:
minhash.update(k.encode("utf-8"))
lsh.insert(str(item.id), minhash)
# forest.add(str(item.id), minhash)
# forest.index()
def compare(first_signature_input, second_signature_input):
first_minhash = MinHash(hashvalues=first_signature_input)
second_minhash = MinHash(hashvalues=second_signature_input)
return first_minhash.jaccard(second_minhash)
for file in f:
filepath = os.path.join(r, file)
mime = magic.Magic(mime=True)
file_type = mime.from_file(filepath)
major_minor = file_type.split('/')
if major_minor[0] == 'text' :
text_files.append(os.path.abspath(filepath))
documents = [(open(f).read(),f) for f in text_files]
lsh = MinHashLSH(threshold=0.4, num_perm=128)
for f in documents:
setdoc = set(f[0].split())
m = MinHash(num_perm=128)
for d in setdoc:
m.update(d.encode('utf8'))
lsh.insert(f[1], m)
results = []
for doc in documents:
setdoc = set(doc[0].split())
m = MinHash(num_perm=128)
for d in setdoc:
m.update(d.encode('utf8'))
result = lsh.query(m)
results.append(result)
results = np.array(results)
def minHash(self, code_tokens):
minHash = MinHash(num_perm=self.num_perm)
for d in code_tokens: # TODO modify this for n-grams
minHash.update("".join(d).encode('utf-8'))
return minHash
def compare(signature1, signature2):
mh1, mh2 = MinHash(hashvalues=signature1), MinHash(hashvalues=signature2)
return mh1.jaccard(mh2)
def generate_minhash(self):
self.minhash = MinHash(num_perm=400)
for shingle in self.get_n_best_shingles(n=400):
self.minhash.update(shingle.encode('utf8'))
def test_is_empty(self):
m = MinHash()
lm = LeanMinHash(m)
self.assertTrue(lm.is_empty())
def minhash_IDs(IDs):
m = MinHash()
for ID in IDs:
m.update(ID.encode('utf-8'))
return m
def hash(s):
s.lower().translate(str.maketrans('', '', string.punctuation))
mh = MinHash(num_perm=128)
for d in [s[i:i + N] for i in range(len(s) - N + 1)]:
mh.update(d.encode('utf8'))
return mh
def run(codebase_path, updates_file_path, commits):
print("Creating Clone Index from HEAD~" + str(commits + 1))
result = subprocess.run([
'git', '-C',
str(codebase_path), 'log', '-' + str(commits + 1), '--no-merges',
'--pretty=format:"%h"'
],
stdout=subprocess.PIPE)
result_commits = result.stdout.decode('utf-8')
result_commits = result_commits.replace('"', '')
result_commits = result_commits.split('\n')
# checkout to the current commit
subprocess.run([
'git', '-C',
str(codebase_path), 'checkout', result_commits[len(result_commits) - 1]
],
stdout=subprocess.DEVNULL,
stderr=subprocess.STDOUT)
# start the timer
index_cr_start = timer()
codebase = CodebaseReader(codebase_path)
lines_per_files = codebase.get_lines_per_file()
# Create LSH index
lsh_index = MinHashLSH(threshold=config.THRESHOLD,
num_perm=config.PERMUTATIONS)
for file in lines_per_files:
min_hash = MinHash(num_perm=config.PERMUTATIONS)
for line in lines_per_files[file]:
min_hash.update(line.encode('utf8'))
lsh_index.insert(file, min_hash)
index_cr_end = timer()
index_cr_diff = round(index_cr_end - index_cr_start, 5)
incremental_step_time = 0
commits_processed = 0 # we use this instead of len(data['commits']) bcs there might me commits that only affect
# excluded (e.g. test) files and in that case the specific commit does not get processed
try:
with open(updates_file_path) as f:
data = json.load(f)
commits = data['commits']
for commit in commits:
creates_lst = []
updates_lst = []
deletes_lst = []
renames_lst = []
print('========> Running Analysis for codebase @commit: ',
commit['id'], "<========")
# checkout to the current commit
subprocess.run([
'git', '-C',
str(codebase_path), 'checkout', commit['id']
],
stdout=subprocess.DEVNULL,
stderr=subprocess.STDOUT)
is_processed = False
for change in commit['changes']:
change_type = change['type']
if change_type in ['A', 'M', 'D']:
affected_filename = change['filename']
file_path = Path(affected_filename)
# skip directories not read when creating the initial index & skip invalid files
if is_in_exlcuded_dir(
file_path) or is_in_excluded_format(file_path):
continue
is_processed = True # if I get here then there is at least 1 change in that commit that is processed
file_path = codebase_path / file_path
print('-> Parsing change [', change_type,
'] for file [', file_path, ']')
if change_type == 'A':
creates_lst.append(str(file_path))
elif change_type == 'M':
updates_lst.append(str(file_path))
elif change_type == 'D':
deletes_lst.append(str(file_path))
else:
affected_filenames = change['filename']
from_filename = Path(affected_filenames[0])
to_filename = Path(affected_filenames[1])
# skip directories not read when creating the initial index & skip invalid files
if is_in_exlcuded_dir(from_filename) or is_in_excluded_format(from_filename) or \
is_in_exlcuded_dir(to_filename) or is_in_excluded_format(to_filename):
continue
is_processed = True # if I get here then there is at least 1 change in that commit that is processed
from_filename = codebase_path / from_filename
to_filename = codebase_path / to_filename
print('-> Parsing change [', change_type,
'] for renamed/moved file [', from_filename, ']',
'to [', to_filename, ']')
renames_lst.append(
(str(from_filename), str(to_filename)))
if is_processed:
changes_handler = ChangesHandler(lsh_index, codebase,
deletes_lst, updates_lst,
creates_lst, renames_lst)
# start incremental step timer
start = timer()
# handle commit changes
changes_handler.handle_changes()
# end incremental step timer
end = timer()
time_diff = round(end - start, 5)
print("Detection/Index update time: " + str(time_diff) +
" seconds")
commits_processed += 1
incremental_step_time += time_diff
else:
print("Commit " + commit['id'] +
" was skipped because all files were excluded")
# checkout back to HEAD
subprocess.run(
['git', '-C',
str(codebase_path), 'checkout', '-'],
stdout=subprocess.DEVNULL,
stderr=subprocess.STDOUT)
print(
"============================================================")
print("Total LOCs: ", codebase.get__initial_codebase_lines())
print("Total Index creation time: ", index_cr_diff, " seconds")
print("Total commits: ", len(commits))
print("Total commits processed: ", commits_processed)
if commits_processed > 0:
print(
"Average Incremental Step Time: ",
round(incremental_step_time / float(commits_processed), 5),
" seconds")
else:
print("0 commits out of ", len(commits),
" were processed. Something went terribly wrong!")
print(
"============================================================")
f.close()
except IOError:
traceback.print_exc()
print("File \"" + str(updates_file_path) + "\" not found.")
def query(self, v, n):
m = MinHash(num_perm=self._n_perm)
for e in v:
m.update(str(e).encode('utf8'))
return map(int, self._index.query(m, n))
def get_minhash_signature(shingle_set):
mh = MinHash()
for el in shingle_set:
mh.update(el.encode('utf8'))
return mh.hashvalues
def _main():
if len(sys.argv) != 5:
usage()
folder = sys.argv[1]
label_fn = sys.argv[2]
k = int(sys.argv[3])
outFn = sys.argv[4]
# Get sample labels
labels = get_labels(folder,label_fn)
# Randomly choose samples from labels with at least 10 samples in them
samples = dict()
for c in labels:
if len(labels[c]) < k:
continue
for s in random.sample(labels[c],k):
samples[s] = c
stats = dict()
history = dict()
# Iterate over samples and calculate their similarities
for s1 in samples:
c1 = samples[s1]
if s1 not in history:
history[s1] = set()
if c1 not in stats:
stats[c1] = dict()
stats[c1]['jaccard'] = dict()
stats[c1]['lsh'] = dict()
stats[c1]['hamming'] = dict()
for s2 in samples:
# Don't duplicate similarity measurements
if s1 == s2:
continue
if s2 in history:
if s1 in history[s2]:
continue
c2 = samples[s2]
if c2 not in stats:
stats[c2] = dict()
stats[c2]['jaccard'] = dict()
stats[c2]['lsh'] = dict()
stats[c2]['hamming'] = dict()
if c2 not in stats[c1]['jaccard']:
stats[c1]['jaccard'][c2] = list()
stats[c1]['lsh'][c2] = Counter()
stats[c1]['hamming'][c2] = list()
if c1 not in stats[c2]['jaccard']:
stats[c2]['jaccard'][c1] = list()
stats[c2]['lsh'][c1] = Counter()
stats[c2]['hamming'][c1] = list()
# Note that we've compared these samples now
history[s1].add(s2)
# Read API sequences
lseq1 = readFile(folder,s1)
lseq2 = readFile(folder,s2)
seq1 = set(lseq1)
seq2 = set(lseq2)
# https://ekzhu.github.io/datasketch/lsh.html
# Compare these two samples
m1 = MinHash(num_perm=128)
m2 = MinHash(num_perm=128)
for d in seq1:
m1.update(d.encode('utf8'))
for d in seq2:
m2.update(d.encode('utf8'))
# Calculate LSH similarity
lsh = MinHashLSH(threshold=0.7, num_perm=128)
lsh.insert(samples[s1],m1)
result = lsh.query(m2)
if len(result) == 1:
rl = True
else:
rl = False
# Calculate Jaccard similarity
rj = float(len(seq1.intersection(seq2)))/float(len(seq1.union(seq2)))
# Pad smallest sequence
if len(lseq1) < len(lseq2):
diff = len(lseq2) - len(lseq1)
lseq1.extend([0]*diff)
elif len(lseq1) > len(lseq2):
diff = len(lseq1) - len(lseq2)
lseq2.extend([0]*diff)
# Calculate Hamming distance
rh = sum(s1 != s2 for s1,s2 in zip(lseq1,lseq2))
# Keep track of similarities
stats[c1]['jaccard'][c2].append(rj)
stats[c1]['lsh'][c2][rl] += 1
stats[c1]['hamming'][c2].append(rh)
stats[c2]['jaccard'][c1].append(rj)
stats[c2]['lsh'][c1][rl] += 1
stats[c2]['hamming'][c1].append(rh)
# Print status
sys.stdout.write('{0} {4} {1} {5}: Jaccard similarity: {2} | > 0.7 LSH similarity: {3} | Hamming distance: {6}\n'.format(samples[s1],samples[s2],rj,rl,s1,s2,rh))
# Print summary stats
with open(outFn,'w') as fw:
fw.write('class:\n')
fw.write(' class jaccard_average LSH_ similar_counts hamming_average\n')
fw.write('\n')
for c in stats:
fw.write('{0}:\n'.format(c))
for c2 in stats[c]['jaccard']:
add = float(sum(stats[c]['jaccard'][c2]))
total = float(len(stats[c]['jaccard'][c2]))
add2 = float(sum(stats[c]['hamming'][c2]))
total2 = float(len(stats[c]['hamming'][c2]))
fw.write(' {0} {1} {2} {3}\n'.format(c2, add/total, stats[c]['lsh'][c2], add2/total2))
def get_mh(values, permutations=512):
mh = MinHash(num_perm=permutations)
for el in values:
mh.update(str(el).encode('utf8'))
return mh
from datasketch import MinHash, MinHashLSH
data1 = ['这个', '程序', '代码', '太乱', '那个', '代码', '规范']
data2 = ['这个', '程序', '代码', '不', '规范', '那个', '更', '规范']
data3 = ['这个', '程序', '代码', '不', '规范', '那个', '规范', '些']
# 创建MinHash对象
m1 = MinHash()
m2 = MinHash()
m3 = MinHash()
for d in data1:
m1.update(d.encode('utf8'))
for d in data2:
m2.update(d.encode('utf8'))
for d in data3:
m3.update(d.encode('utf8'))
# 创建LSH
lsh = MinHashLSH(threshold=0.5, num_perm=128)
lsh.insert("m2", m2)
lsh.insert("m3", m3)
result = lsh.query(m1)
print("近似的邻居（Jaccard相似度>0.5）", result)
def make_min_hash(self,words):
min_hash = MinHash(self.__num_permutation)
for word in words:
min_hash.update(word.encode('utf8'))
return min_hash
data1 = [
'minhash', 'is', 'a', 'probabilistic', 'data', 'structure', 'for',
'estimating', 'the', 'similarity', 'between', 'datasets'
]
data2 = [
'minhash', 'is', 'a', 'probability', 'data', 'structure', 'for',
'estimating', 'the', 'similarity', 'between', 'documents'
]
data3 = [
'minhash', 'is', 'probability', 'data', 'structure', 'for', 'estimating',
'the', 'similarity', 'between', 'documents'
]
# Create MinHash objects
m1 = MinHash(num_perm=128)
m2 = MinHash(num_perm=128)
m3 = MinHash(num_perm=128)
for d in data1:
m1.update(d.encode('utf8'))
for d in data2:
m2.update(d.encode('utf8'))
for d in data3:
m3.update(d.encode('utf8'))
# Create a MinHash LSH Forest with the same num_perm parameter
forest = MinHashLSHForest(num_perm=128)
# Add m2 and m3 into the index
forest.add("m2", m2)
forest.add("m3", m3)
artist_shingle = defaultdict(list)
corpus = processLyrics(corpus)
for artist,lyrics in corpus.items():
tokens = clean_text(lyrics)
artist_shingle[artist].append(tokens)
from datasketch import MinHashLSHForest, MinHash
from sklearn.metrics import jaccard_similarity_score
g = []
listlsh = []
lsh = MinHashLSHForest(num_perm=128)
for artist,sets in artist_shingle.items():
a = MinHash(num_perm=128)
for d in sets[0]:
a.update(d.encode('utf8'))
listlsh.append(a)
lsh.add(artist,a)
lsh.index()
tester = {}
with open('tester.json') as file:
tester = json.loads(file.read().encode('latin-1'))
numcorrect_1 =0
numcorrect_5 = 0
numcorrect_10 = 0
total = 0
for artist,songlist in tester.items():
for song in songlist:
