Apply black formatter.

Author: Eleven1Liu

libmultilabel/nn/attentionxml.py

@@ -428,7 +428,7 @@ def test(self, dataset):
             save_k_predictions=self.save_k_predictions,
             metrics=self.metrics,
         )
-        
+
         word_dict_path = os.path.join(os.path.dirname(self.get_best_model_path(level=1)), self.WORD_DICT_NAME)
         if os.path.exists(word_dict_path):
             with open(word_dict_path, "rb") as f:
@@ -494,9 +494,11 @@ def reformat_text(self, dataset):
         # Convert words to numbers according to their indices in word_dict. Then pad each instance to a certain length.
         encoded_text = list(
             map(
-                lambda text: torch.tensor([self.word_dict.get(word, self.word_dict[UNK]) for word in text], dtype=torch.int64)
-                if text
-                else torch.tensor([self.word_dict[UNK]], dtype=torch.int64),
+                lambda text: (
+                    torch.tensor([self.word_dict.get(word, self.word_dict[UNK]) for word in text], dtype=torch.int64)
+                    if text
+                    else torch.tensor([self.word_dict[UNK]], dtype=torch.int64)
+                ),
                 [instance["text"][: self.max_seq_length] for instance in dataset],
             )
         )

libmultilabel/nn/model.py

@@ -187,14 +187,7 @@ class Model(MultiLabelModel):
         log_path (str): Path to a directory holding the log files and models.
     """
 
-    def __init__(
-        self,
-        classes,
-        network,
-        loss_function="binary_cross_entropy_with_logits",
-        log_path=None,
-        **kwargs
-    ):
+    def __init__(self, classes, network, loss_function="binary_cross_entropy_with_logits", log_path=None, **kwargs):
         super().__init__(num_classes=len(classes), log_path=log_path, **kwargs)
         self.save_hyperparameters(
             ignore=["log_path"]

torch_trainer.py

@@ -25,6 +25,7 @@ class TorchTrainer:
         save_checkpoints (bool, optional): Whether to save the last and the best checkpoint or not.
             Defaults to True.
     """
+
     WORD_DICT_NAME = "word_dict.pickle"
 
     def __init__(
@@ -87,7 +88,7 @@ def __init__(
                         normalize_embed=config.normalize_embed,
                         embed_cache_dir=config.embed_cache_dir,
                     )
-                    with open(word_dict_path, "wb") as f:                    
+                    with open(word_dict_path, "wb") as f:
                         pickle.dump(self.word_dict, f)
 
                 if not self.classes:
@@ -108,9 +109,11 @@ def __init__(
                         f"Add {self.config.val_metric} to `monitor_metrics`."
                     )
                     self.config.monitor_metrics += [self.config.val_metric]
-            self.trainer = PLTTrainer(self.config, classes=self.classes, embed_vecs=self.embed_vecs, word_dict=self.word_dict)
+            self.trainer = PLTTrainer(
+                self.config, classes=self.classes, embed_vecs=self.embed_vecs, word_dict=self.word_dict
+            )
             return
-        
+
         self._setup_model(log_path=self.log_path, checkpoint_path=config.checkpoint_path)
         self.trainer = init_trainer(
             checkpoint_dir=self.checkpoint_dir,
@@ -144,7 +147,7 @@ def _setup_model(
         """
         if "checkpoint_path" in self.config and self.config.checkpoint_path is not None:
             checkpoint_path = self.config.checkpoint_path
-        
+
         if checkpoint_path is not None:
             logging.info(f"Loading model from `{checkpoint_path}` with the previously saved hyper-parameter...")
             self.model = Model.load_from_checkpoint(checkpoint_path, log_path=log_path)