From d275fd7d0d811bfacbfa6febbf62ab653c1ec98e Mon Sep 17 00:00:00 2001
From: Benoit Favre <benoit.favre@lis-lab.fr>
Date: Fri, 29 May 2020 21:56:29 +0200
Subject: [PATCH] fix prediction and add f-score loss
---
data.py | 2 +-
model.py | 7 ++++---
predict.py | 3 +--
3 files changed, 6 insertions(+), 6 deletions(-)
diff --git a/data.py b/data.py
index 8958f60..6891e11 100644
--- a/data.py
+++ b/data.py
@@ -27,7 +27,7 @@ def to_int(tokenizer, label_vocab, hparams, dataset):
for article in dataset:
text = ' | '.join([''.join(article[feature]) for feature in hparams.selected_features])
int_texts.append(bert_text_to_ids(tokenizer, text, hparams.max_len))
- int_labels.append([1 if label in 'topics' in article and article['topics'] else 0 for label in sorted_labels])
+ int_labels.append([1 if 'topics' in article and label in article['topics'] else 0 for label in sorted_labels])
return int_texts, int_labels
diff --git a/model.py b/model.py
index 7cc9e3a..a60163b 100644
--- a/model.py
+++ b/model.py
@@ -12,7 +12,7 @@ from transformers import AutoModel
import data
# based on https://www.kaggle.com/rejpalcz/best-loss-function-for-f1-score-metric
-def f1_score_binary(y_pred, y_true, epsilon=1e-7):
+def binary_f1_score_with_logits(y_pred, y_true, epsilon=1e-7):
y_pred = torch.sigmoid(y_pred)
y_true = y_true.float()
@@ -25,7 +25,8 @@ def f1_score_binary(y_pred, y_true, epsilon=1e-7):
recall = tp / (tp + fn + epsilon)
f1 = 2 * (precision * recall) / (precision + recall + epsilon)
- f1 = f1.clamp(min=epsilon, max=1 - epsilon)
+ #f1 = f1.clamp(min=epsilon, max=1 - epsilon)
+ f1 = torch.where(torch.isnan(f1), torch.zeros_like(f1), f1)
return 1 - f1.mean()
@@ -46,7 +47,7 @@ class Model(LightningModule):
if self.hparams.loss == 'bce':
self.loss_function = F.binary_cross_entropy_with_logits
elif self.hparams.loss == 'f1':
- self.loss_function = f1_score_binary
+ self.loss_function = binary_f1_score_with_logits
else:
raise ValueError('invalid loss "%s"' % self.hparams.loss)
diff --git a/predict.py b/predict.py
index f71641a..67fc6f4 100644
--- a/predict.py
+++ b/predict.py
@@ -34,14 +34,13 @@ def main(hparams):
#y = y.to(device)
with torch.no_grad():
y_scores = model(x)
- y_pred = y_scores > 0
predictions.extend(y_scores.cpu().tolist())
return predictions
predictions = generate_predictions(model, test_loader)
for i, article in enumerate(dataset):
article['topic-scores'] = {label: score for label, score in zip(sorted_labels, predictions[i])}
- article['topic-pred'] = [label for label, score in zip(sorted_labels, predictions[i]) if score > 0]
+ article['topic-pred'] = [label for label, score in zip(sorted_labels, predictions[i]) if score >= 0]
print(json.dumps(dataset, indent=2))
--
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