def get_n_idf_ranked_list(self, doc_ids):
# scan posting list to get all relevant terms
rel_terms = []
rel_df = []
for term_id in self.posting_list:
for doc_id in doc_ids:
if doc_id in [doc[0] for doc in self.posting_list[term_id]]:
if term_id in rel_terms:
rel_df[len(rel_df) - 1 ] += 1
else:
rel_terms.append(term_id)
rel_df.append(1)
n_idf_ranking = []
for idx, term in enumerate(rel_terms):
n_idf_ranking.append([rel_df[idx] * qp.calculate_idf(self.get_df_by_term_id(term), self.get_collection_size()), term])
n_idf_ranking.sort(reverse=True)
return n_idf_ranking
def get_f_idf_ranked_list(self, doc_ids):
# scan posting list to get all relevant terms
rel_terms = []
rel_tf = []
for term_id in self.posting_list:
for doc_id in doc_ids:
abridged_list = [doc[0] for doc in self.posting_list[term_id]]
if doc_id in abridged_list:
if term_id in rel_terms:
rel_tf[len(rel_tf) - 1 ] += self.posting_list[term_id][abridged_list.index(doc_id)][1]
else:
rel_terms.append(term_id)
rel_tf.append(self.posting_list[term_id][abridged_list.index(doc_id)][1])
f_idf_ranking = []
for idx, term in enumerate(rel_terms):
f_idf_ranking.append([rel_tf[idx] * qp.calculate_idf(self.get_df_by_term_id(term), self.get_collection_size()), term])
f_idf_ranking.sort(reverse=True)
return f_idf_ranking
