def build_index(self, db_file, outfile):
db = sqlite3.connect(db_file)
self.index = index = defaultdict(lambda: defaultdict())
start_id = 0
while 1:
q = self.DB_ITER_QUERY.format(id=start_id, limit=self.ITER_STEP)
print q
res = db.execute(q)
res_rows = res.fetchall()
if len(res_rows) == 0: break
for doc_id, doc_body in res_rows:
start_id = doc_id = int(doc_id)
doc_body = doc_body.lower()
words = kwutils.tokenize(doc_body)
w_stemmed = kwutils.stem(words, self.p)
w_stopped = w_stemmed
w_stopped = kwutils.filter_stopwords(w_stemmed, self.sw)
for w in w_stopped:
w = w.strip()
if len(w) > 0:
index[w][doc_id] = 1
with open(outfile, 'w') as f_out:
f_out.write(json.dumps(index))
def build_index(self, db_file, outfile):
db = sqlite3.connect(db_file)
self.index = index = defaultdict(lambda:defaultdict())
start_id = 0
while 1:
q = self.DB_ITER_QUERY.format(id=start_id, limit=self.ITER_STEP)
print q
res = db.execute(q)
res_rows = res.fetchall()
if len(res_rows) == 0: break
for doc_id, doc_body in res_rows:
start_id = doc_id = int(doc_id)
doc_body = doc_body.lower()
words = kwutils.tokenize(doc_body)
w_stemmed = kwutils.stem(words, self.p)
w_stopped = w_stemmed
w_stopped = kwutils.filter_stopwords(w_stemmed, self.sw)
for w in w_stopped:
w = w.strip()
if len(w) > 0:
index[w][doc_id] = 1
with open(outfile, 'w') as f_out:
f_out.write(json.dumps(index))
def analyze(self, sentence):
words = kwutils.tokenize(sentence)
w_stemmed = kwutils.stem(words, self.p)
w_stopped = kwutils.filter_stopwords(w_stemmed, self.sw)
vector = set()
for word in w_stopped:
for doc_id in self.ndx.get(word, []):
vector.add(doc_id)
return vector
def analyze(self, sentence):
words = kwutils.tokenize(sentence)
w_stemmed = kwutils.stem(words, self.p)
w_stopped = kwutils.filter_stopwords(w_stemmed, self.sw)
vector = set()
for word in w_stopped:
for doc_id in self.ndx.get(word, []):
vector.add(doc_id)
return vector
def search(self, phrase):
phrase = phrase.lower()
words = kwutils.tokenize(phrase)
w_stemmed = kwutils.stem(words, self.p)
w_stopped = w_stemmed
w_stopped = kwutils.filter_stopwords(w_stemmed, self.sw)
rank = defaultdict(lambda: defaultdict(lambda: 0))
for word in w_stopped:
if len(word) == 0: continue
#q = self.DB_WORD_QUERY.format(word=word)
#print >>sys.stderr, q
#res = self.db.execute(q)
res = self.indexer.find(word)
for doc_id in res:
rank[doc_id][word] = 1
return self.rank_docs(rank, w_stopped)
def search(self, phrase):
phrase = phrase.lower()
words = kwutils.tokenize(phrase)
w_stemmed = kwutils.stem(words, self.p)
w_stopped = w_stemmed
w_stopped = kwutils.filter_stopwords(w_stemmed, self.sw)
rank = defaultdict(lambda: defaultdict(lambda: 0))
for word in w_stopped:
if len(word) == 0: continue
#q = self.DB_WORD_QUERY.format(word=word)
#print >>sys.stderr, q
#res = self.db.execute(q)
res = self.indexer.find(word)
for doc_id in res:
rank[doc_id][word] = 1
return self.rank_docs(rank, w_stopped)
def build(self, docpath, outfile):
p = PorterStemmer()
sw = stopwords.StopWords(self.stopword_file)
ndx = defaultdict(list)
for filename in os.listdir(docpath):
if not filename.endswith(".txt"): continue
doc_id = hash(filename.replace(".txt", ""))
with open(os.path.join(docpath, filename)) as f:
f_content = kwutils.normalize(f.read().lower())
words = kwutils.tokenize(f_content)
w_stemmed = kwutils.stem(words, p)
w_stopped = kwutils.filter_stopwords(w_stemmed, sw)
for word in w_stopped:
if len(word) > 0:
if not doc_id in ndx[word]:
ndx[word].append(doc_id)
with open(outfile, 'w') as f:
f.write(json.dumps(ndx))
