len(nonzero) *
my_percentage) # retain top 'my_percentage' % words as keywords
keywords['tfidf'].append([tuple[0] for tuple in nonzero[:numb_to_retain]])
if counter % round(len(vectors_list) / 5) == 0:
print(counter)
##########################
# performance comparison #
##########################
perf = dict(zip(method_names, [[], [], [], []]))
for idx, truth in enumerate(keywds_gold_standard):
for mn in method_names:
### fill the gap (append to the 'perf[mn]' list by using the 'accuracy_metrics' function) ###
# print(mn)
# print(keywords[mn][idx])
# print(truth)
# print('')
perf[mn].append(accuracy_metrics(keywords[mn][idx], truth))
lkgs = len(keywds_gold_standard)
# print macro-averaged results (averaged at the collection level)
for k, v in perf.items():
print('')
print(f' -> {k} performance: \n')
print('precision:', round(100 * sum([tuple[0] for tuple in v]) / lkgs, 2))
print('recall:', round(100 * sum([tuple[1] for tuple in v]) / lkgs, 2))
print('F-1 score:', round(100 * sum([tuple[2] for tuple in v]) / lkgs, 2))
# print progress
if counter % round(len(vectors_list) / 10) == 0:
print counter, 'vectors processed'
##########################
# performance evaluation #
##########################
perf_gow = []
perf_gow_w = []
perf_tfidf = []
perf_pr = []
for idx, truth in enumerate(keywords_gold_standard):
perf_gow.append(accuracy_metrics(keywords_gow[idx], truth))
perf_gow_w.append(accuracy_metrics(keywords_gow_w[idx], truth))
perf_tfidf.append(accuracy_metrics(keywords_tfidf[idx], truth))
perf_pr.append(accuracy_metrics(keywords_pr[idx], truth))
lkgs = len(keywords_gold_standard)
# macro-averaged results (averaged at the collection level)
results = {
'gow': perf_gow,
'gow_w': perf_gow_w,
'tfidf': perf_tfidf,
'pr': perf_pr
}
nonzero = [tuple for tuple in terms_weights if tuple[1] != 0]
nonzero = sorted(nonzero, key=operator.itemgetter(1),
reverse=True) # in decreasing order
numb_to_retain = int(
len(nonzero) *
my_percentage) # retain top 'my_percentage' % words as keywords
keywords['tfidf'].append([tuple[0] for tuple in nonzero[:numb_to_retain]])
if counter % round(len(vectors_list) / 5) == 0:
print(counter)
##########################
# performance comparison #
##########################
perf = dict(zip(method_names, [[], [], [], []]))
for idx, truth in enumerate(keywds_gold_standard):
for mn in method_names:
perf[mn].append(accuracy_metrics(keywords.get(mn)[idx], truth))
### fill the gap (append to the 'perf[mn]' list by using the 'accuracy_metrics' function) ###
lkgs = len(keywds_gold_standard)
# print macro-averaged results (averaged at the collection level)
for k, v in perf.items():
print(k + ' performance: \n')
print('precision:', round(100 * sum([tuple[0] for tuple in v]) / lkgs, 2))
print('recall:', round(100 * sum([tuple[1] for tuple in v]) / lkgs, 2))
print('F-1 score:', round(100 * sum([tuple[2] for tuple in v]) / lkgs, 2))
print('\n')
print(counter)
# In[10]:
##########################
# performance comparison #
##########################
perf_kc = []
perf_tfidf = []
perf_pr = []
for idx, truth in enumerate(keywords_gold_standard):
# use the 'accuracy_metrics' function
perf_kc.append(accuracy_metrics(keywords_kc[idx], truth))
perf_tfidf.append(accuracy_metrics(keywords_tfidf[idx], truth))
perf_pr.append(accuracy_metrics(keywords_pr[idx], truth))
lkgs = len(keywords_gold_standard)
# print macro-averaged results (averaged at the collection level)
results = {'k-core': perf_kc, 'tfidf': perf_tfidf, 'PageRank': perf_pr}
for name, result in results.items():
print(name + ' performance: \n')
print('precision:',
round(100 * sum([tuple[0] for tuple in result]) / lkgs, 2))
print('recall:', round(100 * sum([tuple[1] for tuple in result]) / lkgs,
2))
print('F-1 score:',
counter += 1
if counter % 100 == 0:
print counter, 'abstracts processed'
##########################
# performance evaluation #
##########################
perf_gow = []
perf_tfidf = []
perf_pr = []
perf_rand = []
for ind, truth in enumerate(keywords_gold_standard):
perf_gow.append(accuracy_metrics(keywords_gow[ind], truth))
perf_tfidf.append(accuracy_metrics(keywords_tfidf[ind], truth))
perf_pr.append(accuracy_metrics(keywords_pr[ind], truth))
perf_rand.append(accuracy_metrics(keywords_rand[ind], truth))
lkgs = len(keywords_gold_standard)
# macro-averaged results (averaged at the collection level)
results = {
'gow': perf_gow,
'tfidf': perf_tfidf,
'pr': perf_pr,
'rand': perf_rand
}