def rank(self, scores, docs):
self.all_rankings, self.all_inverted_rankings = rnk.data_split_rank_and_invert(
scores, docs)
return self.all_rankings, self.all_inverted_rankings
def rank(self, scores, docs):
# get rankings and inverted rankings from scores (from the predefined function)
self.all_rankings, self.all_inverted_rankings = rnk.data_split_rank_and_invert(
scores, docs)
return self.all_rankings, self.all_inverted_rankings
print('Number of features: %d' % data.num_features)
print('Number of queries in training set: %d' % data.train.num_queries())
print('Number of documents in training set: %d' % data.train.num_docs())
print('Number of queries in validation set: %d' % data.validation.num_queries())
print('Number of documents in validation set: %d' % data.validation.num_docs())
print('Number of queries in test set: %d' % data.test.num_queries())
print('Number of documents in test set: %d' % data.test.num_docs())
# initialize a random model
random_model = np.random.uniform(size=data.num_features)
# one score for every document (1d vector in ordering of the dataset)
all_scores = np.dot(data.train.feature_matrix, random_model)
# rank every query for all scores (1d vector ordered by query ordering in dataset)
all_rankings, all_inverted_rankings = rnk.data_split_rank_and_invert(all_scores, data.train)
qid = 1
s_i, e_i = data.train.query_range(qid)
# to rank only a single query use rank_and_invert
query_ranking, query_inverted_ranking = rnk.rank_and_invert(all_scores[s_i:e_i])
assert np.all(np.equal(query_ranking, all_rankings[s_i:e_i]))
assert np.all(np.equal(query_inverted_ranking, all_inverted_rankings[s_i:e_i]))
print('-------')
print('Looking at query with id: %d' % qid)
print('Number of documents in query %d: %d' % (qid, data.train.query_size(qid)))
print('Scores for query %d: %s' % (qid, all_scores[s_i:e_i]))
print('Ranking for query %d: %s' % (qid, all_rankings[s_i:e_i]))