def keep_qrels_topics(run, qrels):
"""
Returns a new run composed by only the entries of the input run belonging
to topics contained in the input qrels.
Important: Entries of the new run are shallow copies of entries of the input run.
:param run:
:type run: TrecRun
:param qrels:
:type qrels: QRels
:return: TrecRun
"""
new_run = {
topic_id: entries
for topic_id, entries in run.entries.items()
if topic_id in qrels.allJudgements
}
return pytrec_eval.TrecRun(new_run, run.name + '_only_qrels_topics')
def loadAll(runFilenames):
"""Load all runs in the list runFilenames and returns a list of TrecRun s."""
return [pytrec_eval.TrecRun(name) for name in runFilenames]
continue
sdm_doc_score_map = get_scores(sdm_docs[str(topic_number)])
cur_topic_subgraph = scores[str(topic_number)]
doc_count = 1
for doc, score in sorted(cur_topic_subgraph.items(), key=lambda item: item[1], reverse = True):
sdm_score = float(sdm_doc_score_map[doc])
try:
centrality_score = normalize(score, cur_topic_subgraph.values())
except:
centrality_score = 0
combined_score = lambda1*sdm_score + lambda2*centrality_score
temp_results.append(str(topic_number) + " Q0 " + doc + " " + str(doc_count) + " " + str(combined_score)+ " STANDARD")
doc_count+=1
with open("temp_file.test", "w") as outfile:
outfile.write("\n".join(temp_results))
run = pytrec_eval.TrecRun('temp_file.test')
qrels = pytrec_eval.QRels('qrels_file.test')
curr_result = pytrec_eval.evaluate(run, qrels, [pytrec_eval.ndcg])[0]
if curr_result > best_score:
if best_results is not None:
best_results.clear()
best_score = curr_result
best_results = list(temp_results)
print("Run completed with lambda1=" + str(lambda1) + ", lambda2=" + str(lambda2) + " and NDCG=" + str(curr_result) + ". Took: " + str(time() - start_load) + " s")
for result in best_results:
output.append(result)
with open("results_file.test", "w") as outfile:
outfile.write("\n".join(output))
print ("Results took %.2f seconds to run." %(time() - start_project))