def main(argv):
collectionFile = ''
tokenizerType = ''
queriesFile = ''
rankType = ''
start = []
end = []
try:
opts, args = getopt.getopt(argv, "hf:t:q:r:", ["collectionFile=", "tokenizerType=", "queriesFilePath=",
"rankType="])
except getopt.GetoptError:
print('main.py -f <collectionFile> -t <tokenizerType: 0 - Simple, 1 - Better> -q <queriesFilePath> '
'-r <rankType: 0 - TF-IDF, 1 - BM25>')
sys.exit()
if len(opts) != 4:
print('main.py -f <collectionFile> -t <tokenizerType: 0 - Simple, 1 - Better> -q <queriesFilePath> '
'-r <rankType: 0 - TF-IDF, 1 - BM25>')
sys.exit()
for opt, arg in opts:
if opt == '-h':
print('main.py -f <collectionFile> -t <tokenizerType: 0 - Simple, 1 - Better> -q <queriesFilePath> '
'-r <rankType: 0 - TF-IDF, 1 - BM25>')
sys.exit()
elif opt in ("-f", "--collectionFile"):
if not path.exists(arg):
print('Incorrect path to collection file.')
sys.exit()
collectionFile = arg
elif opt in ("-t", "--tokenizerType"):
if arg != '0' and arg != '1':
print('Incorrect tokenizer type. Simple tokenizer: 0, Better tokenizer: 1.')
sys.exit()
tokenizerType = arg
elif opt in ("-q", "--queriesFilePath"):
if not path.exists(arg):
print('Incorrect path to queries file.')
sys.exit()
queriesFile = arg
elif opt in ("-r", "--rankType"):
if arg != '0' and arg != '1':
print('Incorrect rank type. TF-IDF: 0, BM25: 1.')
sys.exit()
rankType = arg
# Indexer
(Indexer(collectionFile, tokenizerType)).writeIndexToFile('index')
f = open(queriesFile, 'r')
queries = f.read().splitlines()
f.close()
scores = []
if tokenizerType == '0': # simple
tokenizer = Tokenizer.SimpleTokenizer('')
else: # better
tokenizer = Tokenizer.BetterTokenizer('')
for query in queries:
# Query Operations
tokenizer.changeText(query)
queryTerms = tokenizer.getTerms()
# Searcher
documentsInfo, avgDocLen = Searcher.searchDocuments(queryTerms, 'index')
# Ranker
ranker = Ranker(documentsInfo, avgDocLen)
# Start time (latency purpose)
start.append(timer())
# If rankType = 0 (tf-idf)
if rankType == '0':
scores += [ranker.lnc_ltc()]
# If rankType = 1 (BM25)
else:
scores += [ranker.bm25(1.2, 0.75)]
# End time (latency purpose)
end.append(timer())
# Evaluation
Evaluation.getResults('./data/queries.relevance.txt', queries, scores, start, end)
def main(argv):
# ----------------------------------------- HANDLING PROGRAM INPUT -------------------------------------------------
collectionFile = ''
tokenizerType = ''
queriesFile = ''
rankType = ''
storePos = ''
proximity = ''
try:
opts, args = getopt.getopt(argv, "hf:t:q:r:p:b:", ["collectionFile=", "tokenizerType=", "queriesFilePath=",
"rankType=", "storePositions=", "proximityBoost="])
except getopt.GetoptError:
print('main.py -f <collectionFile> -t <tokenizerType: 0 - Simple, 1 - Better> -q <queriesFilePath> '
'-r <rankType: 0 - TF-IDF, 1 - BM25> -p <storePositions: 0 - No, 1 - Yes> '
'-b <proximityBoost: 0 - No, 1 - Yes>')
sys.exit()
if len(opts) != 6:
print('main.py -f <collectionFile> -t <tokenizerType: 0 - Simple, 1 - Better> -q <queriesFilePath> '
'-r <rankType: 0 - TF-IDF, 1 - BM25> -p <storePositions: 0 - No, 1 - Yes> '
'-b <proximityBoost: 0 - No, 1 - Yes>')
sys.exit()
for opt, arg in opts:
if opt == '-h':
print('main.py -f <collectionFile> -t <tokenizerType: 0 - Simple, 1 - Better> -q <queriesFilePath> '
'-r <rankType: 0 - TF-IDF, 1 - BM25> -p <storePositions: 0 - No, 1 - Yes> '
'-b <proximityBoost: 0 - No, 1 - Yes>')
sys.exit()
elif opt in ("-f", "--collectionFile"):
if not path.exists(arg):
print('Incorrect path to collection file.')
sys.exit()
collectionFile = arg
elif opt in ("-t", "--tokenizerType"):
if arg != '0' and arg != '1':
print('Incorrect tokenizer type. Simple tokenizer: 0, Better tokenizer: 1.')
sys.exit()
tokenizerType = arg
elif opt in ("-q", "--queriesFilePath"):
if not path.exists(arg):
print('Incorrect path to queries file.')
sys.exit()
queriesFile = arg
elif opt in ("-r", "--rankType"):
if arg != '0' and arg != '1':
print('Incorrect rank type. TF-IDF: 0, BM25: 1.')
sys.exit()
rankType = arg
elif opt in ("-p", "--storePositions"):
if arg != '0' and arg != '1':
print('\nIncorrect store positions choice. No: 0, Yes: 1.')
sys.exit()
storePos = arg
elif opt in ("-b", "--proximityBoost"):
if arg != '0' and arg != '1':
print('\nIncorrect proximity boost choice. No: 0, Yes: 1.')
sys.exit()
proximity = arg
# ----------------------------------------------- INDEXER ----------------------------------------------------------
indexer = Indexer(collectionFile, tokenizerType, True if storePos=='1' else False)
start = timeit.default_timer()
indexer.index()
stop = timeit.default_timer()
print('Indexing total time - {} tokenizer: {} min and {} seconds'.format("simple" if tokenizerType == "0" else "better", (stop - start)//60, (stop - start) % 60))
f = open(queriesFile, 'r')
queries = f.read().splitlines()
f.close()
scores = []
if tokenizerType == '0': # simple
tokenizer = Tokenizer.SimpleTokenizer('')
else: # better
tokenizer = Tokenizer.BetterTokenizer('')
start_queries = []
end_queries = []
time_searcher = 0
time_ranker = 0
for query in queries:
# --------------------------------------- QUERY OPERATIONS -----------------------------------------------------
tokenizer.changeText(query)
#queryTerms, queryTermsPositions = tokenizer.getTerms(withPositions=True if storePos == '1' else False)
queryTerms = tokenizer.getTerms(withPositions=False)
# ------------------------------------------- SEARCHER ---------------------------------------------------------
start = timeit.default_timer()
documentsInfo, avgDocLen = Searcher.searchDocuments(queryTerms, 'index', True if storePos == '1' else False)
stop = timeit.default_timer()
time_searcher = time_searcher + stop - start
# -------------------------------------------- RANKER ----------------------------------------------------------'
start = timeit.default_timer()
ranker = Ranker(documentsInfo, avgDocLen)
# Start time (latency purpose)
start_queries.append(timer())
# If rankType = 0 (tf-idf)
if rankType == '0':
# If proximity = 1 (Proximity Boost)
if proximity == '1':
scores += [ranker.proximity_boost(ranker.lnc_ltc(), queryTerms)]
else:
scores += [ranker.lnc_ltc()]
# If rankType = 1 (BM25)
else:
# If proximity = 1 (Proximity Boost)
if proximity == '1':
scores += [ranker.proximity_boost(ranker.bm25(1.2, 0.75), queryTerms)]
else:
scores += [ranker.bm25(1.2, 0.75)]
stop = timeit.default_timer()
time_ranker = time_ranker + stop - start
# End time (latency purpose)
end_queries.append(timer())
print('Searching time for all queries: {} min and {} seconds'.format(time_searcher // 60, time_searcher % 60))
print('Ranking time for all queries: {} min and {} seconds'.format(time_ranker // 60, time_ranker % 60))
# Evaluation
Evaluation.getResults('./data/queries.relevance.txt', queries, scores, start_queries, end_queries)