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added col 'emebed_type' in csv results

This commit is contained in:
andrea 2019-11-30 19:22:48 +01:00
parent f2083bf22a
commit fedc83f84e
2 changed files with 4 additions and 4 deletions
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2
src/FPEC_andrea.py
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@ -122,6 +122,6 @@ if __name__ == '__main__':
macrof1, microf1, macrok, microk = l_eval[lang]
metrics.append([macrof1, microf1, macrok, microk])
print('Lang %s: macro-F1=%.3f micro-F1=%.3f' % (lang, macrof1, microf1))
results.add_row(result_id, 'PolyEmbed_andrea', 'svm', _config_id, op.optimc, op.dataset.split('/')[-1],
results.add_row(result_id, 'PolyEmbed_andrea', 'svm', _config_id, config['we_type'], op.optimc, op.dataset.split('/')[-1],
'not_binary', 'not_ablation', classifier.time, lang, macrof1, microf1, macrok, microk, '')
print('Averages: MF1, mF1, MK, mK', np.mean(np.array(metrics), axis=0))

6
src/util/results.py
View File

@ -5,7 +5,7 @@ import numpy as np
class PolylingualClassificationResults:
def __init__(self, file, autoflush=True, verbose=False):
self.file = file
self.columns = ['id', 'method', 'learner', 'embed', 'optimp', 'dataset', 'binary', 'languages', 'time', 'lang', 'macrof1', 'microf1', 'macrok', 'microk', 'notes']
self.columns = ['id', 'method', 'learner', 'embed', 'embed_type', 'optimp', 'dataset', 'binary', 'languages', 'time', 'lang', 'macrof1', 'microf1', 'macrok', 'microk', 'notes']
self.autoflush = autoflush
self.verbose = verbose
if os.path.exists(file):
@ -20,8 +20,8 @@ class PolylingualClassificationResults:
def already_calculated(self, id):
return (self.df['id'] == id).any()
def add_row(self, id, method, learner, embed, optimp, dataset, binary, ablation_lang, time, lang, macrof1, microf1, macrok=np.nan, microk=np.nan, notes=''):
s = pd.Series([id, method, learner, embed, optimp, dataset, binary, ablation_lang, time, lang, macrof1, microf1, macrok, microk, notes], index=self.columns)
def add_row(self, id, method, learner, embed, embed_type, optimp, dataset, binary, ablation_lang, time, lang, macrof1, microf1, macrok=np.nan, microk=np.nan, notes=''):
s = pd.Series([id, method, learner, embed, embed_type, optimp, dataset, binary, ablation_lang, time, lang, macrof1, microf1, macrok, microk, notes], index=self.columns)
self.df = self.df.append(s, ignore_index=True)
if self.autoflush: self.flush()
self.tell(s.to_string())