def build_index(directory, dictionary_file, postings_file):
files = os.listdir(directory)
dictionary = Dictionary(dictionary_file)
postings = Postings(postings_file)
stemmer = nltk.stem.porter.PorterStemmer()
last = ''
for doc_id in files:
tf_list = {}
line_number = 1
offset = 0
# Use linecache to get line
line = linecache.getline(os.path.join(directory, doc_id), line_number)
while line != '':
# tokenize lines into sentences
sentences = nltk.sent_tokenize(line)
for sentence in sentences:
# tokenize sentence
tokens = nltk.word_tokenize(sentence)
for token in tokens:
# apply stemming and case folding
stemmed_token = stemmer.stem(token).lower()
# if term already exists in dictionary, we find row number
if dictionary.has_term(stemmed_token):
offset = dictionary.get_offset(stemmed_token)
# If postings for that term already has doc id,
# then increment tf,
# Else increment df and add the doc id
if postings.has_doc_id(doc_id, offset):
postings.increment_tf(doc_id, offset)
else:
dictionary.increment_df(stemmed_token)
postings.add_doc_id(doc_id, offset)
# else, we add it to dictionary and postings
else:
offset = postings.add_new_term()
postings.add_doc_id(doc_id, offset)
dictionary.add_new_term(stemmed_token, offset)
#Keep track of tf values of all terms in doc
if stemmed_token in tf_list:
tf_list[stemmed_token] += 1
else:
tf_list[stemmed_token] = 1
line_number += 1
line = linecache.getline(os.path.join(directory, doc_id), line_number)
# Store doc length
dictionary.add_doc_length(doc_id, tf_list.values())
# save data
postings.save(dictionary)
dictionary.save()
