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gFun
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2 changed files with 5 additions and 13 deletions
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8
main.py
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@ -49,16 +49,16 @@ def main(args):
if args.bert_embedder: if args.bert_embedder:
bertEmbedder = BertGen(multilingualIndex, batch_size=args.batch_bert, nepochs=args.nepochs_bert, bertEmbedder = BertGen(multilingualIndex, batch_size=args.batch_bert, nepochs=args.nepochs_bert,
patience=args.patience_bert, gpus=args.gpus, n_jobs=args.n_jobs) patience=args.patience_bert, gpus=args.gpus, n_jobs=args.n_jobs)
bertEmbedder.transform(lX)
embedder_list.append(bertEmbedder) embedder_list.append(bertEmbedder)
# Init DocEmbedderList (i.e., first-tier learners or view generators) and metaclassifier # Init DocEmbedderList (i.e., first-tier learners or view generators) and metaclassifier
docEmbedders = DocEmbedderList(embedder_list=embedder_list, probabilistic=True) docEmbedders = DocEmbedderList(embedder_list=embedder_list, probabilistic=True)
meta = MetaClassifier(meta_learner=get_learner(calibrate=False, kernel='rbf'), 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 # Init Funnelling Architecture
gfun = Funnelling(first_tier=docEmbedders, meta_classifier=meta, n_jobs=args.n_jobs) gfun = Funnelling(first_tier=docEmbedders, meta_classifier=meta)
# Training --------------------------------------- # Training ---------------------------------------
print('\n[Training Generalized Funnelling]') print('\n[Training Generalized Funnelling]')
@ -71,7 +71,7 @@ def main(args):
print('\n[Testing Generalized Funnelling]') print('\n[Testing Generalized Funnelling]')
time_te = time.time() time_te = time.time()
ly_ = gfun.predict(lXte) ly_ = gfun.predict(lXte)
l_eval = evaluate(ly_true=lyte, ly_pred=ly_, n_jobs=args.n_jobs) l_eval = evaluate(ly_true=lyte, ly_pred=ly_)
time_te = round(time.time() - time_te, 3) time_te = round(time.time() - time_te, 3)
print(f'Testing completed in {time_te} seconds!') print(f'Testing completed in {time_te} seconds!')

10
src/view_generators.py
View File

@ -18,7 +18,6 @@ This module contains the view generators that take care of computing the view sp
from abc import ABC, abstractmethod from abc import ABC, abstractmethod
# from time import time # from time import time
import torch
from pytorch_lightning import Trainer from pytorch_lightning import Trainer
from pytorch_lightning.loggers import TensorBoardLogger from pytorch_lightning.loggers import TensorBoardLogger
from pytorch_lightning.callbacks.early_stopping import EarlyStopping from pytorch_lightning.callbacks.early_stopping import EarlyStopping
@ -242,10 +241,6 @@ class RecurrentGen(ViewGen):
self.logger = TensorBoardLogger(save_dir='../tb_logs', name='rnn', default_hp_metric=False) self.logger = TensorBoardLogger(save_dir='../tb_logs', name='rnn', default_hp_metric=False)
self.early_stop_callback = EarlyStopping(monitor='val-macroF1', min_delta=0.00, self.early_stop_callback = EarlyStopping(monitor='val-macroF1', min_delta=0.00,
patience=self.patience, verbose=False, mode='max') patience=self.patience, verbose=False, mode='max')
# modifying EarlyStopping global var in order to compute >= with respect to the best score
self.early_stop_callback.mode_dict['max'] = torch.ge
self.lr_monitor = LearningRateMonitor(logging_interval='epoch') self.lr_monitor = LearningRateMonitor(logging_interval='epoch')
def _init_model(self): def _init_model(self):
@ -353,9 +348,6 @@ class BertGen(ViewGen):
self.early_stop_callback = EarlyStopping(monitor='val-macroF1', min_delta=0.00, self.early_stop_callback = EarlyStopping(monitor='val-macroF1', min_delta=0.00,
patience=self.patience, verbose=False, mode='max') patience=self.patience, verbose=False, mode='max')
# modifying EarlyStopping global var in order to compute >= with respect to the best score
self.early_stop_callback.mode_dict['max'] = torch.ge
def _init_model(self): def _init_model(self):
output_size = self.multilingualIndex.get_target_dim() output_size = self.multilingualIndex.get_target_dim()
return BertModel(output_size=output_size, stored_path=self.stored_path, gpus=self.gpus) return BertModel(output_size=output_size, stored_path=self.stored_path, gpus=self.gpus)
@ -369,7 +361,7 @@ class BertGen(ViewGen):
:param ly: dict {lang: target vectors} :param ly: dict {lang: target vectors}
:return: self. :return: self.
""" """
print('# Fitting BertGen (B)...') print('# Fitting BertGen (M)...')
create_if_not_exist(self.logger.save_dir) create_if_not_exist(self.logger.save_dir)
self.multilingualIndex.train_val_split(val_prop=0.2, max_val=2000, seed=1) 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) bertDataModule = BertDataModule(self.multilingualIndex, batchsize=self.batch_size, max_len=512)