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gFun
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implemented zero-shot experiment code for VanillaFunGen and WordClassGen

This commit is contained in:
andrea 2021-02-04 12:44:36 +01:00
parent 8968570d82
commit f3fafd0f00
3 changed files with 8 additions and 8 deletions
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4
main.py
View File

@ -62,7 +62,7 @@ def main(args):
# Init DocEmbedderList (i.e., first-tier learners or view generators) and metaclassifier
docEmbedders = DocEmbedderList(embedder_list=embedder_list, probabilistic=True)
meta = MetaClassifier(meta_learner=get_learner(calibrate=False, kernel='rbf'),
meta_parameters=get_params(optimc=args.optimc))
meta_parameters=get_params(optimc=args.optimc), n_jobs=args.n_jobs)
# Init Funnelling Architecture
gfun = Funnelling(first_tier=docEmbedders, meta_classifier=meta)
@ -80,7 +80,7 @@ def main(args):
if args.zero_shot:
gfun.set_zero_shot(val=False)
ly_ = gfun.predict(lXte)
l_eval = evaluate(ly_true=lyte, ly_pred=ly_)
l_eval = evaluate(ly_true=lyte, ly_pred=ly_, n_jobs=args.n_jobs)
time_te = round(time.time() - time_te, 3)
print(f'Testing completed in {time_te} seconds!')

2
src/funnelling.py
View File

@ -23,7 +23,7 @@ class DocEmbedderList:
if isinstance(embedder, VanillaFunGen):
_tmp.append(embedder)
else:
_tmp.append(FeatureSet2Posteriors(embedder))
_tmp.append(FeatureSet2Posteriors(embedder, n_jobs=embedder.n_jobs))
self.embedders = _tmp
def fit(self, lX, ly):

10
src/view_generators.py
View File

@ -78,7 +78,7 @@ class VanillaFunGen(ViewGen):
self.train_langs = train_langs
def fit(self, lX, ly):
print('# Fitting VanillaFunGen (X)...')
print('\n# Fitting VanillaFunGen (X)...')
if self.zero_shot:
print(f'# Zero-shot setting! Training langs will be set to: {sorted(self.train_langs)}')
self.langs = sorted(self.train_langs)
@ -150,7 +150,7 @@ class MuseGen(ViewGen):
:param ly: dict {lang: target vectors}
:return: self.
"""
print('# Fitting MuseGen (M)...')
print('\n# Fitting MuseGen (M)...')
if self.zero_shot:
print(f'# Zero-shot setting! Training langs will be set to: {sorted(self.train_langs)}')
self.vectorizer.fit(lX)
@ -226,7 +226,7 @@ class WordClassGen(ViewGen):
:param ly: dict {lang: target vectors}
:return: self.
"""
print('# Fitting WordClassGen (W)...')
print('\n# Fitting WordClassGen (W)...')
if self.zero_shot:
print(f'# Zero-shot setting! Training langs will be set to: {sorted(self.train_langs)}')
self.langs = sorted(self.train_langs)
@ -359,7 +359,7 @@ class RecurrentGen(ViewGen):
:param ly: dict {lang: target vectors}
:return: self.
"""
print('# Fitting RecurrentGen (G)...')
print('\n# Fitting RecurrentGen (G)...')
create_if_not_exist(self.logger.save_dir)
recurrentDataModule = RecurrentDataModule(self.multilingualIndex, batchsize=self.batch_size, n_jobs=self.n_jobs,
zero_shot=self.zero_shot, zscl_langs=self.train_langs)
@ -468,7 +468,7 @@ class BertGen(ViewGen):
:param ly: dict {lang: target vectors}
:return: self.
"""
print('# Fitting BertGen (M)...')
print('\n# Fitting BertGen (M)...')
create_if_not_exist(self.logger.save_dir)
self.multilingualIndex.train_val_split(val_prop=0.2, max_val=2000, seed=1)
bertDataModule = BertDataModule(self.multilingualIndex, batchsize=self.batch_size, max_len=512,