def test_debug_not_found(self): '''Debug Errors''' print '-TEST-:', self.test_extractFromPage.__doc__.strip() pub_candidates = [] pub_candidates.append(Publication(-1, 2000, 'Formalizzazione e Ottimizzazione di Transazioni di modifica in CLP(AD)', "pubkey", -1, "authors", -5)) #---------------------------------------------------- pub_candidates = [] pub_candidates.append(Publication(-1, 2000, 'On the Use of Spreading Activation Methods in Automatic Information Retrieval', "pubkey", -1, "authors", -5)) pub_candidates.append(Publication(-1, 2000, 'Chairman\'s Message', "pubkey", -1, "authors", -5)) pub_candidates.append(Publication(-1, 2000, 'Introduction to Modern Information Retrieval', "pubkey", -1, "authors", -5)) pub_candidates.append(Publication(-1, 2000, 'Publications', "pubkey", -1, "authors", -5)) pub_candidates.append(Publication(-1, 2000, 'Die RISC-CISC Debatte', "pubkey", -1, "authors", -5)) pub_candidates.append(Publication(-1, 2000, 'Kryptologie', "pubkey", -1, "authors", -5)) pub_candidates.append(Publication(-1, 2000, 'Approximative Public-Key-Kryptosysteme', "pubkey", -1, "authors", -5)) pub_candidates.append(Publication(-1, 2000, 'Integritat in IT-Systemen', "pubkey", -1, "authors", -5)) pub_candidates.append(Publication(-1, 2000, 'Vollstandige Reduktionssysteme', "pubkey", -1, "authors", -5)) pub_candidates.append(Publication(-1, 2000, 'Approximative Public-Key-Kryptosysteme', "pubkey", -1, "authors", -5)) matcher = PubMatcher.getInstance() extractor = Extractor.getInstance() query, used_pubs = Extractor.pinMaxQuery(pub_candidates) print '%s pub, query: %s' % (len(used_pubs), query) all_models = extractor.getNodesByPubs(used_pubs) (pubs_found, pubs_notfound) = PubMatcher.getInstance().matchPub(used_pubs, all_models) for pub in pubs_found: print 'pubs found' , pub print '-' * 100 for pub in pubs_notfound: print 'not found' , pub print '- test done -'
def integrate_test_pubs(self, pub_candidates):
"""
For Debug Errors
"""
print "- INTEGRATE TEST -:", self.integrate_test_pubs.__doc__.strip()
extractor = Extractor.getInstance()
matcher = PubMatcher.getInstance()
# print queries
query, used_pubs = Extractor.pinMaxQuery(pub_candidates)
print "Test %s pub, query: \n\t%s" % (len(used_pubs), query)
url = self.settings.urltemplate_by_pubs % URLCleaner.encodeUrlForDownload(query)
# url = URLCleaner.encodeUrlForDownload(url)
print "\t", url
# do
all_models = extractor.getNodesByPubs(used_pubs)
(pubs_found, pubs_notfound) = matcher.matchPub(used_pubs, all_models, debug_output=True)
# print out
print "-" * 100
for pub in pubs_found:
print "[%s] %s" % (pub.ncitation, pub)
print "-" * 100
for pub in pubs_notfound:
print "[%s] %s" % ("-", pub)
print "-" * 100
print "- test done -"
def update(self):
pubs = self.pubdao.getPublicationByPerson(self.person.id,
self.generation)
if pubs is not None and len(pubs) == 0:
self.store.markPersonFinished(self.person)
print "[*] Mark Person as Finished '%s'." % self.person
return
self.show(pubs)
all_models = Extractor.getInstance().getNodesByPersonName(
self.person.names)
print 'all models:'
for model in all_models:
print model
raw_input()
if all_models is not None:
print "=" * 100
(pubs_found, pubs_notfound) = PubMatcher.getInstance().matchPub(
pubs, all_models)
if pubs_found is None or pubs_notfound is None:
print "[ERROR][-/-] person '%s', pubs_found is None or pubs_notfound is None, return"\
% self.person
return
print "{+A}[%s+%s=%s] Download by page, [found + not_found = total], person '%s'." % (
len(pubs_found), len(pubs_notfound),
len(pubs_found) + len(pubs_notfound), self.person)
else:
pubs_notfound = pubs
print 'pubs found :'
self.show(pubs_found)
print 'done'
def integrate_test_pubs(self, pub_candidates):
'''
For Debug Errors
'''
print '- INTEGRATE TEST -:', self.integrate_test_pubs.__doc__.strip()
extractor = Extractor.getInstance()
matcher = PubMatcher.getInstance()
# print queries
query, used_pubs = Extractor.pinMaxQuery(pub_candidates)
print 'Test %s pub, query: \n\t%s' % (len(used_pubs), query)
url = self.settings.urltemplate_by_pubs % URLCleaner.encodeUrlForDownload(
query)
# url = URLCleaner.encodeUrlForDownload(url)
print "\t", url
# do
all_models = extractor.getNodesByPubs(used_pubs)
(pubs_found, pubs_notfound) = matcher.matchPub(used_pubs,
all_models,
debug_output=True)
# print out
print '-' * 100
for pub in pubs_found:
print '[%s] %s' % (pub.ncitation, pub)
print '-' * 100
for pub in pubs_notfound:
print '[%s] %s' % ('-', pub)
print '-' * 100
print '- test done -'
def update(self):
pubs = self.pubdao.getPublicationByPerson(self.person.id, self.generation)
if pubs is not None and len(pubs) == 0:
self.store.markPersonFinished(self.person)
print "[*] Mark Person as Finished '%s'." % self.person
return
self.show(pubs)
all_models = Extractor.getInstance().getNodesByPersonName(self.person.names)
print 'all models:'
for model in all_models:
print model
raw_input()
if all_models is not None:
print "=" * 100
(pubs_found, pubs_notfound) = PubMatcher.getInstance().matchPub(pubs, all_models)
if pubs_found is None or pubs_notfound is None:
print "[ERROR][-/-] person '%s', pubs_found is None or pubs_notfound is None, return"\
% self.person
return
print "{+A}[%s+%s=%s] Download by page, [found + not_found = total], person '%s'." % (
len(pubs_found), len(pubs_notfound), len(pubs_found) + len(pubs_notfound), self.person
)
else:
pubs_notfound = pubs
print 'pubs found :'
self.show(pubs_found)
print 'done'
def test_debug_not_found(self):
'''Debug Errors'''
print '-TEST-:', self.test_extractFromPage.__doc__.strip()
pub_candidates = []
pub_candidates.append(
Publication(
-1, 2000,
'Formalizzazione e Ottimizzazione di Transazioni di modifica in CLP(AD)',
"pubkey", -1, "authors", -5))
#----------------------------------------------------
pub_candidates = []
pub_candidates.append(
Publication(
-1, 2000,
'On the Use of Spreading Activation Methods in Automatic Information Retrieval',
"pubkey", -1, "authors", -5))
pub_candidates.append(
Publication(-1, 2000, 'Chairman\'s Message', "pubkey", -1,
"authors", -5))
pub_candidates.append(
Publication(-1, 2000,
'Introduction to Modern Information Retrieval',
"pubkey", -1, "authors", -5))
pub_candidates.append(
Publication(-1, 2000, 'Publications', "pubkey", -1, "authors", -5))
pub_candidates.append(
Publication(-1, 2000, 'Die RISC-CISC Debatte', "pubkey", -1,
"authors", -5))
pub_candidates.append(
Publication(-1, 2000, 'Kryptologie', "pubkey", -1, "authors", -5))
pub_candidates.append(
Publication(-1, 2000, 'Approximative Public-Key-Kryptosysteme',
"pubkey", -1, "authors", -5))
pub_candidates.append(
Publication(-1, 2000, 'Integritat in IT-Systemen', "pubkey", -1,
"authors", -5))
pub_candidates.append(
Publication(-1, 2000, 'Vollstandige Reduktionssysteme', "pubkey",
-1, "authors", -5))
pub_candidates.append(
Publication(-1, 2000, 'Approximative Public-Key-Kryptosysteme',
"pubkey", -1, "authors", -5))
matcher = PubMatcher.getInstance()
extractor = Extractor.getInstance()
query, used_pubs = Extractor.pinMaxQuery(pub_candidates)
print '%s pub, query: %s' % (len(used_pubs), query)
all_models = extractor.getNodesByPubs(used_pubs)
(pubs_found, pubs_notfound) = PubMatcher.getInstance().matchPub(
used_pubs, all_models)
for pub in pubs_found:
print 'pubs found', pub
print '-' * 100
for pub in pubs_notfound:
print 'not found', pub
print '- test done -'
def debug_pubs(self):
'''Debug get by pub'''
print '-TEST-:', self.debug_pubs.__doc__.strip()
#----------------------------------------------------
pub_candidates = []
# group 1
# pub_candidates.append(Publication(-1, 2000, 'Some Reflections on Proof Transformations', "pubkey", -1, "Peter B. Andrews", -5))
# pub_candidates.append(Publication(-1, 2000, 'Theorem Proving via General Mappings', "pubkey", -1, "Peter B. Andrews", -5))
# pub_candidates.append(Publication(-1, 2000, 'Connections and Higher-Order Logic', "pubkey", -1, "Peter B. Andrews", -5))
# pub_candidates.append(Publication(-1, 2000, 'The TPS Theorem Proving System', "pubkey", -1, "Peter B. Andrews,Sunil Issar,Dan Nesmith,Frank Pfenning", -5))
# group 2
# pub_candidates.append(Publication(-1, 2000, 'Linearizable concurrent objects', "pubkey", -1, "MP Herlihy, JM Wing", -5))
# pub_candidates.append(Publication(-1, 2000, 'Protein structure prediction using a combination of sequence homology and global energy minimization I. Global energy minimization of surface loops', "pubkey", -1, "MJ Dudek, HA Scheraga", -5))
# group 3
# pub_candidates.append(Publication(-1, 2000, 'Implementation of Prolog databases and database operation builtins in the WAM-Plus model', "pubkey", -1, "Z Chenxi, C Yungui, L Bo", -5))
# group 4
pub_candidates.append(Publication(-1, 2000, 'Procedural Semantics for Fuzzy Disjunctive Programs on Residuated Lattices', "pubkey", -1, "Dusan Guller", -5))
extractor = Extractor.getInstance()
query, used_pubs = Extractor.pinMaxQuery(pub_candidates)
print '%s pub, query: %s' % (len(used_pubs), query)
#
# Get WEB PAGE
#
use_web = True # ***************
if use_web:
all_models = extractor.getNodesByPubs(used_pubs)
else:
f = file('debug_pubs.txt', 'r')
html = f.read()
models = self.extractor.extract_from_source(html)
all_models = self.extractor._Extractor__merge_into_extractedmap(None, models)
print '\n- all_models ----------------------'
if all_models is not None:
for key, models in all_models.items():
print key
for model in models:
print "\t", model
else:
print 'all_models is None'
print '- all_models end ----------------------\n'
(pubs_found, pubs_notfound) = PubMatcher.getInstance().matchPub(used_pubs, all_models)
for pub in pubs_found:
print 'pubs found' , pub
print '-' * 100
for pub in pubs_notfound:
print 'not found' , pub
print '- test done -'
def process_person(self):
''' real logic of process person '''
# all pubs need to update citation number.
# totalPubCount = self.pubdao.getPersonPubCount(self.person.id)
pubs = self.pubdao.getPublicationByPerson(self.person.id,
self.extractor.generation)
if pubs is not None and len(pubs) == 0:
self.store.markPersonFinished(self.person)
print "[*] Mark Person as Finished '%s'." % self.person
return
print "$Ex/get:> person '%s' has %d papers to crawl" % (
self.person.names, len(pubs))
# by crawlByPerson, a lot of publication maybe found and update.
pubs_found = None
pubs_notfound = None
if len(pubs) > 4:
all_models = Extractor.getInstance().getNodesByPersonName(
self.person.names)
if all_models is not None:
print "=" * 100
(pubs_found,
pubs_notfound) = PubMatcher.getInstance().matchPub(
pubs, all_models)
if pubs_found is None or pubs_notfound is None:
print "[ERROR][-/-] person '%s', pubs_found is None or pubs_notfound is None, return"\
% self.person
return
print "{+A}[%s+%s=%s] Download by page, [found + not_found = total], person '%s'." % (
len(pubs_found), len(pubs_notfound),
len(pubs_found) + len(pubs_notfound), self.person)
else:
pubs_notfound = pubs
else:
pubs_found = []
pubs_notfound = pubs
if pubs_found is not None:
for pub in pubs_found:
self.store.putToPubdbcache(pub)
print "{-A}[%4s] %s" % (pub.ncitation, pub)
if pubs_notfound is not None:
for pub in pubs_notfound:
self.store.putToPubCache(self.person, pub)
def process_person(self):
''' real logic of process person '''
# all pubs need to update citation number.
# totalPubCount = self.pubdao.getPersonPubCount(self.person.id)
pubs = self.pubdao.getPublicationByPerson(self.person.id, self.extractor.generation)
if pubs is not None and len(pubs) == 0:
self.store.markPersonFinished(self.person)
print "[*] Mark Person as Finished '%s'." % self.person
return
print "$Ex/get:> person '%s' has %d papers to crawl" % (self.person.names, len(pubs))
# by crawlByPerson, a lot of publication maybe found and update.
pubs_found = None
pubs_notfound = None
if len(pubs) > 4:
all_models = Extractor.getInstance().getNodesByPersonName(self.person.names)
if all_models is not None:
print "=" * 100
(pubs_found, pubs_notfound) = PubMatcher.getInstance().matchPub(pubs, all_models)
if pubs_found is None or pubs_notfound is None:
print "[ERROR][-/-] person '%s', pubs_found is None or pubs_notfound is None, return"\
% self.person
return
print "{+A}[%s+%s=%s] Download by page, [found + not_found = total], person '%s'." % (
len(pubs_found), len(pubs_notfound), len(pubs_found) + len(pubs_notfound), self.person
)
else:
pubs_notfound = pubs
else:
pubs_found = []
pubs_notfound = pubs
if pubs_found is not None:
for pub in pubs_found:
self.store.putToPubdbcache(pub)
print "{-A}[%4s] %s" % (pub.ncitation, pub)
if pubs_notfound is not None:
for pub in pubs_notfound:
self.store.putToPubCache(self.person, pub)
def runOriginal(self):
while self.extractor.running and not self.ask_to_stop:
self.mark()
self.extractor.wait_for_pause() # wait if paused
# url, url_without_author, pubs_in_url = store.getFromPubQueue() # get url and pubs
query, used_pubs = self.store.getFromPubQueue() # get url and pubs
if used_pubs is None or len(used_pubs) == 0:
print "[ERROR][t_pub_process:%s] Queue is Empty.(%s,%s)" % (
self.name, query, used_pubs)
time.sleep(10)
continue
self.extractor.wait_for_pause() # wait again
with self.extractor.busy_semaphore_lock:
self.extractor.busy_semaphore += 1
self.extractor.busy_pub_semaphore += 1
pubs_found = None
pubs_notfound = None
try:
all_models = Extractor.getInstance().getNodesByPubs(used_pubs)
if all_models is not None:
(pubs_found,
pubs_notfound) = PubMatcher.getInstance().matchPub(
used_pubs, all_models)
if pubs_found is None or pubs_notfound is None:
print '[ERROR][-/-] some pubs, pubs_found is None or pubs_notfound is None, return'
return
print "{+P}[%s/%s] [found/notfound] pub, query[%s]." % (
len(pubs_found), len(pubs_notfound), query)
else:
pubs_notfound = used_pubs
except Exception, e:
ExceptionHelper.print_exec(e)
print '-------------------------------------------------------'
print 'query:', query
print 'all_models', all_models
print 'used_pubs', used_pubs
print '-------------------------------------------------------'
return
finally:
def runOriginal(self):
while self.extractor.running and not self.ask_to_stop:
self.mark()
self.extractor.wait_for_pause() # wait if paused
# url, url_without_author, pubs_in_url = store.getFromPubQueue() # get url and pubs
query, used_pubs = self.store.getFromPubQueue() # get url and pubs
if used_pubs is None or len(used_pubs) == 0:
print "[ERROR][t_pub_process:%s] Queue is Empty.(%s,%s)" % (self.name, query, used_pubs)
time.sleep(10)
continue
self.extractor.wait_for_pause() # wait again
with self.extractor.busy_semaphore_lock:
self.extractor.busy_semaphore += 1
self.extractor.busy_pub_semaphore += 1
pubs_found = None
pubs_notfound = None
try:
all_models = Extractor.getInstance().getNodesByPubs(used_pubs)
if all_models is not None:
(pubs_found, pubs_notfound) = PubMatcher.getInstance().matchPub(used_pubs, all_models)
if pubs_found is None or pubs_notfound is None:
print '[ERROR][-/-] some pubs, pubs_found is None or pubs_notfound is None, return'
return
print "{+P}[%s/%s] [found/notfound] pub, query[%s]." % (len(pubs_found), len(pubs_notfound), query)
else:
pubs_notfound = used_pubs
except Exception, e:
ExceptionHelper.print_exec(e)
print '-------------------------------------------------------'
print 'query:', query
print 'all_models', all_models
print 'used_pubs', used_pubs
print '-------------------------------------------------------'
return
finally:
def __init__(self): self.extractor = Extractor.getInstance() self.matcher = PubMatcher.getInstance() self.pubdao = PublicationDao()
def debug_pubs(self):
'''Debug get by pub'''
print '-TEST-:', self.debug_pubs.__doc__.strip()
#----------------------------------------------------
pub_candidates = []
# group 1
# pub_candidates.append(Publication(-1, 2000, 'Some Reflections on Proof Transformations', "pubkey", -1, "Peter B. Andrews", -5))
# pub_candidates.append(Publication(-1, 2000, 'Theorem Proving via General Mappings', "pubkey", -1, "Peter B. Andrews", -5))
# pub_candidates.append(Publication(-1, 2000, 'Connections and Higher-Order Logic', "pubkey", -1, "Peter B. Andrews", -5))
# pub_candidates.append(Publication(-1, 2000, 'The TPS Theorem Proving System', "pubkey", -1, "Peter B. Andrews,Sunil Issar,Dan Nesmith,Frank Pfenning", -5))
# group 2
# pub_candidates.append(Publication(-1, 2000, 'Linearizable concurrent objects', "pubkey", -1, "MP Herlihy, JM Wing", -5))
# pub_candidates.append(Publication(-1, 2000, 'Protein structure prediction using a combination of sequence homology and global energy minimization I. Global energy minimization of surface loops', "pubkey", -1, "MJ Dudek, HA Scheraga", -5))
# group 3
# pub_candidates.append(Publication(-1, 2000, 'Implementation of Prolog databases and database operation builtins in the WAM-Plus model', "pubkey", -1, "Z Chenxi, C Yungui, L Bo", -5))
# group 4
pub_candidates.append(
Publication(
-1, 2000,
'Procedural Semantics for Fuzzy Disjunctive Programs on Residuated Lattices',
"pubkey", -1, "Dusan Guller", -5))
extractor = Extractor.getInstance()
query, used_pubs = Extractor.pinMaxQuery(pub_candidates)
print '%s pub, query: %s' % (len(used_pubs), query)
#
# Get WEB PAGE
#
use_web = True # ***************
if use_web:
all_models = extractor.getNodesByPubs(used_pubs)
else:
f = file('debug_pubs.txt', 'r')
html = f.read()
models = self.extractor.extract_from_source(html)
all_models = self.extractor._Extractor__merge_into_extractedmap(
None, models)
print '\n- all_models ----------------------'
if all_models is not None:
for key, models in all_models.items():
print key
for model in models:
print "\t", model
else:
print 'all_models is None'
print '- all_models end ----------------------\n'
(pubs_found, pubs_notfound) = PubMatcher.getInstance().matchPub(
used_pubs, all_models)
for pub in pubs_found:
print 'pubs found', pub
print '-' * 100
for pub in pubs_notfound:
print 'not found', pub
print '- test done -'
def __init__(self):
self.extractor = Extractor.getInstance()
self.matcher = PubMatcher.getInstance()
self.pubdao = PublicationDao()