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Implemented funnelling architecture

This commit is contained in:
andrea 2021-01-25 18:25:08 +01:00
parent 94866e5ad8
commit a5af2134bf
2 changed files with 10 additions and 9 deletions
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9
refactor/main.py
View File

@ -9,7 +9,7 @@ from time import time
def main(args):
OPTIMC = True # TODO
OPTIMC = False # TODO
N_JOBS = 8
print('Running refactored...')
@ -20,6 +20,7 @@ def main(args):
EMBEDDINGS_PATH = '/home/andreapdr/gfun/embeddings'
data = MultilingualDataset.load(_DATASET)
data.set_view(languages=['it', 'fr'])
data.show_dimensions()
lX, ly = data.training()
lXte, lyte = data.test()
@ -53,8 +54,8 @@ def main(args):
# Init DocEmbedderList
docEmbedders = DocEmbedderList(embedder_list=embedder_list, probabilistic=True)
meta_parameters = None if not OPTIMC else [{'C': [1, 1e3, 1e2, 1e1, 1e-1]}]
meta = MetaClassifier(meta_learner=get_learner(calibrate=False, kernel='rbf', C=meta_parameters),
meta_parameters=get_params(optimc=True))
meta = MetaClassifier(meta_learner=get_learner(calibrate=False, kernel='rbf'),
meta_parameters=get_params(optimc=OPTIMC))
# Init Funnelling Architecture
gfun = Funnelling(first_tier=docEmbedders, meta_classifier=meta)
@ -71,7 +72,7 @@ def main(args):
print('\n[Testing Generalized Funnelling]')
time_te = time()
ly_ = gfun.predict(lXte)
l_eval = evaluate(ly_true=ly, ly_pred=ly_)
l_eval = evaluate(ly_true=lyte, ly_pred=ly_)
time_te = round(time() - time_te, 3)
print(f'Testing completed in {time_te} seconds!')

10
refactor/view_generators.py
View File

@ -55,7 +55,7 @@ class VanillaFunGen(ViewGen):
self.vectorizer = TfidfVectorizerMultilingual(sublinear_tf=True, use_idf=True)
def fit(self, lX, lY):
print('# Fitting VanillaFunGen...')
print('# Fitting VanillaFunGen (X)...')
lX = self.vectorizer.fit_transform(lX)
self.doc_projector.fit(lX, lY)
return self
@ -85,7 +85,7 @@ class MuseGen(ViewGen):
self.vectorizer = TfidfVectorizerMultilingual(sublinear_tf=True, use_idf=True)
def fit(self, lX, ly):
print('# Fitting MuseGen...')
print('# Fitting MuseGen (M)...')
self.vectorizer.fit(lX)
self.langs = sorted(lX.keys())
self.lMuse = MuseLoader(langs=self.langs, cache=self.muse_dir)
@ -120,7 +120,7 @@ class WordClassGen(ViewGen):
self.vectorizer = TfidfVectorizerMultilingual(sublinear_tf=True, use_idf=True)
def fit(self, lX, ly):
print('# Fitting WordClassGen...')
print('# Fitting WordClassGen (W)...')
lX = self.vectorizer.fit_transform(lX)
self.langs = sorted(lX.keys())
wce = Parallel(n_jobs=self.n_jobs)(
@ -207,7 +207,7 @@ class RecurrentGen(ViewGen):
:param ly:
:return:
"""
print('# Fitting RecurrentGen...')
print('# Fitting RecurrentGen (G)...')
recurrentDataModule = RecurrentDataModule(self.multilingualIndex, batchsize=self.batch_size)
trainer = Trainer(gradient_clip_val=1e-1, gpus=self.gpus, logger=self.logger, max_epochs=self.nepochs,
checkpoint_callback=False)
@ -260,7 +260,7 @@ class BertGen(ViewGen):
return BertModel(output_size=output_size, stored_path=self.stored_path, gpus=self.gpus)
def fit(self, lX, ly):
print('# Fitting BertGen...')
print('# Fitting BertGen (M)...')
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)
trainer = Trainer(gradient_clip_val=1e-1, max_epochs=self.nepochs, gpus=self.gpus,