def run_one_query(query, num, models, ai=True, noise=False, rank=RankingAI.rank):
papers = get_papers_from_pubmed(query, num)
# papers = list(PubMedFetcher(query, num_of_documents=10).papers.values())
PaperProcessor.add_journal_if(papers)
if ai:
rank(papers, query)
else:
RankingAI.passthrough(papers)
# CSVHandler.write_list_to_csv("test.csv", papers, additional_fields=["ReferenceRank"])
if noise:
scaling_factors = [1, 0.1, 0.01, 0.001]
else:
scaling_factors = [1]
results = [[] for _ in range(len(models))]
times = [0 for _ in range(len(models))]
for idx, model in enumerate(models):
t = time.time()
eva = NewEvaluation(papers, query, model=model, scaling_factors=scaling_factors, noise=noise)
time_passed = time.time() - t
times[idx] = time_passed
results[idx].extend(eva.result)
with open('tmp/{}{}.csv'.format(query, len(papers)), 'w', newline='') as csvfile:
fieldnames = ['Model No.', 'Description', 'Result']
writer = csv.writer(csvfile)
writer.writerow(fieldnames)
for idx, result in enumerate(results):
row = [re.search(r'(\d+)$', models[idx].__name__).group(0), models[idx].__doc__] + result
writer.writerow(row)
import os, sys
sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
import pubmed
from papers_csv_handler import CSVHandler
from paper_processor import PaperProcessor
import logging
from IPython import embed
logging.getLogger().setLevel(logging.DEBUG)
papers = list(pubmed.PubMedFetcher("h**o sapiens", num_of_documents=2116).papers.values())
PaperProcessor.add_journal_if(papers)
CSVHandler.write_list_to_csv("test.csv", papers)
def evaluate(self, query, scaling_factor):
m = hashlib.md5(str.encode(query+str(self.num_of_papers)))
digest = m.hexdigest()
filename = "tmp/"+digest
if os.path.exists(filename):
with open(filename, 'rb') as f:
pubmed = pickle.load(f)
else:
pubmed = PaperProcessor(query, num_of_documents=self.num_of_papers, postprocessing=False)
pubmed.add_missing_info()
with open(filename, 'wb') as f:
pickle.dump(pubmed, f)
# pubmed.add_missing_info()
# c = 0
# for value in pubmed.papers.values():
# if value['Journal_IF'] == 0:
# c += 1
#
# print(c)
papers = np.array(list(pubmed.papers.values()))
logging.info("Got {} papers for query \"{}\", scaling_factor={}".format(len(papers), query, scaling_factor))
all_time_result = 0
times = 1
for i in range(1, times+1):
overall_diff = 0
kf = KFold(len(papers), 5, shuffle=True)
for train, test in kf:
train_set = papers[train]
test_set = papers[test]
for train_sample in train_set:
score = len(papers) - train_sample["Ranking"] + 1
# Gaussian noise
# Standard Variation: Score/5
score = 1 if score == 0 else abs(score)
noise = np.random.normal(0, score / 5)
score *= scaling_factor
if self.noise:
score += noise
train_sample["Score"] = score
# for test_sample in test_set:
# test_sample["Score"] = np.random.rand()
self.model(train_set, test_set, query)
test_set = list(test_set)
test_size = len(test_set)
test_set.sort(key = lambda x: x["Ranking"])
for idx, test_sample in enumerate(test_set):
test_sample["Correct_Ranking"] = idx
total_diff = 0
test_set.sort(key = lambda x: x["Score"])
test_set.reverse()
# logging.info("Test size: {}".format(test_size))
for idx, test_sample in enumerate(test_set):
total_diff += abs(test_sample["Correct_Ranking"] - idx) / test_size
ave_diff = total_diff / test_size
overall_diff += ave_diff / 5
all_time_result += overall_diff / times
# logging.info("{}th trial, Average Difference: {}".format(i, overall_diff))
# logging.info("For all trials, Average Difference: {}".format(all_time_result))
return all_time_result, len(papers)
