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fixed funnelling transform function. Now it averages lZparts accoring to the actual number of embedders used for a given language (e.g., 'da' -> -x -m -w -b ->'da' will be averaged by 4, 'en' -> -m, -b -> 'en' will be averaged by 2)

This commit is contained in:
andrea 2021-02-12 16:15:38 +01:00
parent 4cbef64e28
commit 0fdb39532c
2 changed files with 23 additions and 12 deletions
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13
main.py
View File

@ -25,13 +25,12 @@ def main(args):
lX, ly = data.training()
lXte, lyte = data.test()
# # TODO: debug settings
# print(f'\n[Running on DEBUG mode - samples per language are reduced to 50 max!]\n')
# lX = {k: v[:50] for k, v in lX.items()}
# ly = {k: v[:50] for k, v in ly.items()}
# lXte = {k: v[:50] for k, v in lXte.items()}
# lyte = {k: v[:50] for k, v in lyte.items()}
# TODO: debug settings
# print(f'\n[Running on DEBUG mode - samples per language are reduced to 5 max!]\n')
# lX = {k: v[:5] for k, v in lX.items()}
# ly = {k: v[:5] for k, v in ly.items()}
# lXte = {k: v[:5] for k, v in lXte.items()}
# lyte = {k: v[:5] for k, v in lyte.items()}
# Init multilingualIndex - mandatory when deploying Neural View Generators...
if args.gru_embedder or args.bert_embedder:

22
src/funnelling.py
View File

@ -43,8 +43,8 @@ class DocEmbedderList:
:param lX:
:return: common latent space (averaged).
"""
langs = sorted(lX.keys())
lZparts = {lang: None for lang in langs}
self.langs = sorted(lX.keys())
lZparts = {lang: None for lang in self.langs}
for embedder in self.embedders:
lZ = embedder.transform(lX)
@ -57,12 +57,24 @@ class DocEmbedderList:
n_embedders = len(self.embedders)
# Zero shot experiments: removing k:v if v is None (i.e, it is a lang that will be used in zero shot setting)
lZparts = {k: v for k, v in lZparts.items() if v is not None}
return {lang: lZparts[lang]/n_embedders for lang in sorted(lZparts.keys())} # Averaging feature spaces
lang_number_embedders = self.get_number_embedders_zeroshot()
return {lang: lZparts[lang]/lang_number_embedders[lang] for lang in sorted(lZparts.keys())} # Averaging feature spaces
def fit_transform(self, lX, ly):
return self.fit(lX, ly).transform(lX)
def get_number_embedders_zeroshot(self):
lang_number_embedders = {lang: len(self.embedders) for lang in self.langs}
for lang in self.langs:
for embedder in self.embedders:
if isinstance(embedder, VanillaFunGen):
if lang not in embedder.train_langs:
lang_number_embedders[lang] = 2 # todo: number of view gen is hard-codede
else:
if lang not in embedder.embedder.train_langs:
lang_number_embedders[lang] = 2 # todo: number of view gen is hard-codede
return lang_number_embedders
class FeatureSet2Posteriors:
"""
@ -80,7 +92,7 @@ class FeatureSet2Posteriors:
self.l2 = l2
self.n_jobs = n_jobs
self.prob_classifier = MetaClassifier(
SVC(kernel='rbf', gamma='auto', probability=True, cache_size=1000, random_state=1), n_jobs=n_jobs)
SVC(kernel='rbf', gamma='auto', probability=True, cache_size=1000, random_state=1), n_jobs=self.n_jobs)
def fit(self, lX, ly):
lZ = self.embedder.fit_transform(lX, ly)