def retrive_documents(self,query_id):
k1 = 1.2
k3 = 8.00
avg_dl = 122
b = 1 # from 0.25 to 2.00 increase 0.25
q = Query(query_id)
#q.set_concepts(self.QueryConceptExtraction(q.text))
self._expand_query(q)
return
print "Retrieving Documents for: ", q.text
Collection._load()
Collection._load_go()
Collection._load_tags()
Collection._load_indexes() #Loads documents into _documents with PMID and Index
score = dict()
N = Collection._count
Nt = dict()
for term in q.text:
Nt[term] = Collection._get_frequency(term)
counter = 0
for doc in Collection._documents:
summation = 0;
dl = doc.length * 1.00
for t in q.text:
tfn = doc.get_frequency(t)
QQ = ' '.join(q.text)
qtf = Document._term_frequency(QQ, t)
K = k1*((1-b)+b*(dl/avg_dl))
w = log((N-Nt[t]+0.5)/(Nt[t]+0.5),2)
if w<0:
#this makes the result a negative number
# if we break the result will be bigger than or equal to zero
break
p1 = (((k1+1)*tfn)/(K+tfn))
p2 = ((k3+1)*qtf/(k3+qtf))
p3 = w
summation += p1*p2*p3
score[doc.PMID] = summation
counter += 1
def Indexing(self):
'''
IR Indexing Operations
- Elimination of Stopwords
-
'''
DB._execute("DELETE from collection_index")
print "Indexing is started..."
tp = TextProcessor()
Collection._load()
Collection._load_tags() #loading document with PMID, tags and abstracts
for doc in Collection._documents:
index_list = []
for term in doc.abstract:
index_list.append(term)
if GlobalVariables.global_context_activated:
for term in doc.tag:
index_list.append(term)
index_list = tp.EliminateStopWords(index_list)
index_list = tp.Stem(index_list)
doc.set_index(index_list)
print "Indexing is Done!"
