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fixed pl early stop --> patience was consumed if actual_monitor == best_monitor. Set policy to greater or equal.

This commit is contained in:
andrea 2021-02-11 18:37:34 +01:00
parent a1c4247e17
commit a4f74dcf41
2 changed files with 9 additions and 1 deletions
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2
main.py
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@ -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_) l_eval = evaluate(ly_true=lyte, ly_pred=ly_, n_jobs=args.n_jobs)
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!')

8
src/view_generators.py
View File

@ -18,6 +18,7 @@ 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
@ -241,6 +242,10 @@ 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):
@ -348,6 +353,9 @@ 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)