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Implemented custom micro and macro F1 in pl (cpu and gpu)

This commit is contained in:
andrea 2021-01-20 12:13:51 +01:00
parent 8dbe48ff7a
commit 91666bd263
2 changed files with 24 additions and 8 deletions
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4
refactor/main.py
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@ -15,7 +15,7 @@ def main(args):
_DATASET = '/home/moreo/CLESA/rcv2/rcv1-2_doclist_trByLang1000_teByLang1000_processed_run0.pickle' _DATASET = '/home/moreo/CLESA/rcv2/rcv1-2_doclist_trByLang1000_teByLang1000_processed_run0.pickle'
EMBEDDINGS_PATH = '/home/andreapdr/gfun/embeddings' EMBEDDINGS_PATH = '/home/andreapdr/gfun/embeddings'
data = MultilingualDataset.load(_DATASET) data = MultilingualDataset.load(_DATASET)
data.set_view(languages=['it'], categories=[0, 1]) # data.set_view(languages=['it'], categories=[0, 1])
lX, ly = data.training() lX, ly = data.training()
lXte, lyte = data.test() lXte, lyte = data.test()
@ -28,7 +28,7 @@ def main(args):
# gFun = VanillaFunGen(base_learner=get_learner(calibrate=True), n_jobs=N_JOBS) # gFun = VanillaFunGen(base_learner=get_learner(calibrate=True), n_jobs=N_JOBS)
# gFun = MuseGen(muse_dir='/home/andreapdr/funneling_pdr/embeddings', n_jobs=N_JOBS) # gFun = MuseGen(muse_dir='/home/andreapdr/funneling_pdr/embeddings', n_jobs=N_JOBS)
# gFun = WordClassGen(n_jobs=N_JOBS) # gFun = WordClassGen(n_jobs=N_JOBS)
gFun = RecurrentGen(multilingualIndex, pretrained_embeddings=lMuse, wce=False, batch_size=5, nepochs=100, gFun = RecurrentGen(multilingualIndex, pretrained_embeddings=lMuse, wce=False, batch_size=256, nepochs=50,
gpus=args.gpus, n_jobs=N_JOBS) gpus=args.gpus, n_jobs=N_JOBS)
# gFun = BertGen(multilingualIndex, gpus=args.gpus, batch_size=128, n_jobs=N_JOBS) # gFun = BertGen(multilingualIndex, gpus=args.gpus, batch_size=128, n_jobs=N_JOBS)

28
refactor/models/pl_gru.py
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@ -29,10 +29,11 @@ class RecurrentModel(pl.LightningModule):
self.drop_embedding_range = drop_embedding_range self.drop_embedding_range = drop_embedding_range
self.drop_embedding_prop = drop_embedding_prop self.drop_embedding_prop = drop_embedding_prop
self.loss = torch.nn.BCEWithLogitsLoss() self.loss = torch.nn.BCEWithLogitsLoss()
self.microf1 = F1(num_classes=output_size, multilabel=True, average='micro') # self.microf1 = F1(num_classes=output_size, multilabel=True, average='micro')
self.macrof1 = F1(num_classes=output_size, multilabel=True, average='macro') # self.macrof1 = F1(num_classes=output_size, multilabel=True, average='macro')
self.accuracy = Accuracy() self.accuracy = Accuracy()
self.customMetrics = CustomF1(num_classes=output_size, device=self.gpus) self.customMicroF1 = CustomF1(num_classes=output_size, average='micro', device=self.gpus)
self.customMacroF1 = CustomF1(num_classes=output_size, average='macro', device=self.gpus)
self.lPretrained_embeddings = nn.ModuleDict() self.lPretrained_embeddings = nn.ModuleDict()
self.lLearnable_embeddings = nn.ModuleDict() self.lLearnable_embeddings = nn.ModuleDict()
@ -103,10 +104,12 @@ class RecurrentModel(pl.LightningModule):
# Squashing logits through Sigmoid in order to get confidence score # Squashing logits through Sigmoid in order to get confidence score
predictions = torch.sigmoid(logits) > 0.5 predictions = torch.sigmoid(logits) > 0.5
accuracy = self.accuracy(predictions, ly) accuracy = self.accuracy(predictions, ly)
custom = self.customMetrics(predictions, ly) microF1 = self.customMicroF1(predictions, ly)
macroF1 = self.customMacroF1(predictions, ly)
self.log('train-loss', loss, on_step=True, on_epoch=True, prog_bar=False, logger=True) self.log('train-loss', loss, on_step=True, on_epoch=True, prog_bar=False, logger=True)
self.log('train-accuracy', accuracy, on_step=True, on_epoch=True, prog_bar=False, logger=True) self.log('train-accuracy', accuracy, on_step=True, on_epoch=True, prog_bar=False, logger=True)
self.log('custom', custom, on_step=True, on_epoch=True, prog_bar=True, logger=True) self.log('microF1', microF1, on_step=False, on_epoch=True, prog_bar=True, logger=True)
self.log('macroF1', macroF1, on_step=False, on_epoch=True, prog_bar=True, logger=True)
return {'loss': loss} return {'loss': loss}
def validation_step(self, val_batch, batch_idx): def validation_step(self, val_batch, batch_idx):
@ -212,4 +215,17 @@ class CustomF1(Metric):
return (num / den).to(self.device) return (num / den).to(self.device)
return torch.FloatTensor([1.]).to(self.device) return torch.FloatTensor([1.]).to(self.device)
if self.average == 'macro': if self.average == 'macro':
raise NotImplementedError class_specific = []
for i in range(self.num_classes):
class_tp = self.true_positive[i]
# class_tn = self.true_negative[i]
class_fp = self.false_positive[i]
class_fn = self.false_negative[i]
num = 2.0 * class_tp
den = 2.0 * class_tp + class_fp + class_fn
if den > 0:
class_specific.append(num / den)
else:
class_specific.append(1.)
average = torch.sum(torch.Tensor(class_specific))/self.num_classes
return average.to(self.device)