class BM25Score(summaryrank.Feature):
""" BM25 score for the sentence """
def __init__(self, args):
super(BM25Score, self).__init__(args)
self.k1 = args.bm25_k1
self.b = args.bm25_b
self.avgdl = args.bm25_avgdl
self._freq_stats = GalagoIndex(args.index, 'postings.krovetz') if args.index else None
@classmethod
def init_parser(cls, parser, group):
# be warned, using the secret API
if not parser._get_option_tuples('--index'):
group.add_argument('--index', metavar='PATH',
help='the background Galago index')
group.add_argument('--bm25-k1', type=float, metavar='NUM',
help='parameter k1 (default: %(default)s)')
group.add_argument('--bm25-b', type=float, metavar='NUM',
help='parameter b (default: %(default)s)')
group.add_argument('--bm25-avgdl', type=float, metavar='NUM',
help='parameter avgdl (default: %(default)s)')
group.set_defaults(bm25_k1=1.2, bm25_b=0.75, bm25_avgdl=25)
@classmethod
def check_parser_args(cls, parser, args):
pass
def check(self, model):
assert model.contains(['topics_stem', 'sentences_stem'])
if not self._freq_stats:
assert model.contains(['freq_stats'])
def compute(self, model):
result = []
if not self._freq_stats:
self._freq_stats = GalagoIndexDump.load(model.get_path('freq_stats'))
N = self._freq_stats.num_docs()
topics_stem = model.load_topics('topics_stem')
queries = dict((m['qid'], text.split()) for text, m in topics_stem)
for text, m in model.load_sentences('sentences_stem'):
stems = text.split()
sentence_tf = collections.Counter(stems)
sentence_len = len(stems)
score = float(0)
for query_stem in queries[m['qid']]:
df = self._freq_stats.df(query_stem)
comp1 = math.log(float(N - df + 0.5) / (df + 0.5))
comp2 = float(sentence_tf[query_stem] * (self.k1 + 1))
comp3 = sentence_tf[query_stem] + \
self.k1 * (1 - self.b + float(self.b * sentence_len) / self.avgdl)
score += comp1 * comp2 / comp3
result.append(score)
return result
