Pushing change for lazy logging

medcat/meta_cat.py

@@ -269,8 +269,7 @@ def train_raw(self, data_loaded: Dict, save_dir_path: Optional[str] = None, data
         # Make sure the config number of classes is the same as the one found in the data
         if len(category_value2id) != self.config.model['nclasses']:
             logger.warning(
-                "The number of classes set in the config is not the same as the one found in the data: {} vs {}".format(
-                    self.config.model['nclasses'], len(category_value2id)))
+                "The number of classes set in the config is not the same as the one found in the data: %d vs %d" % (self.config.model['nclasses'], len(category_value2id)))
             logger.warning("Auto-setting the nclasses value in config and rebuilding the model.")
             self.config.model['nclasses'] = len(category_value2id)
 

medcat/utils/meta_cat/data_utils.py

@@ -194,7 +194,7 @@ def encode_category_values(data: Dict, existing_category_value2id: Optional[Dict
         for k in keys_ls:
             category_value2id_[k] = len(category_value2id_)
 
-        logger.warning("Labels found with 0 data; updates made\nFinal label encoding mapping:", category_value2id_)
+        logger.warning("Labels found with 0 data; updates made\nFinal label encoding mapping: %s" %category_value2id_)
         category_value2id = category_value2id_
 
     for c in category_values:
@@ -211,7 +211,7 @@ def encode_category_values(data: Dict, existing_category_value2id: Optional[Dict
         if data[i][2] in category_value2id.values():
             label_data_[data[i][2]] = label_data_[data[i][2]] + 1
 
-    logger.info(f"Original label_data: {label_data_}")
+    logger.info("Original label_data: %s" %label_data_)
     # Undersampling data
     if category_undersample is None or category_undersample == '':
         min_label = min(label_data_.values())
@@ -234,7 +234,7 @@ def encode_category_values(data: Dict, existing_category_value2id: Optional[Dict
     for i in range(len(data_undersampled)):
         if data_undersampled[i][2] in category_value2id.values():
             label_data[data_undersampled[i][2]] = label_data[data_undersampled[i][2]] + 1
-    logger.info(f"Updated label_data: {label_data}")
+    logger.info("Updated label_data: %s" %label_data)
 
     return data, data_undersampled, category_value2id
 

medcat/utils/meta_cat/ml_utils.py

@@ -201,7 +201,7 @@ def train_model(model: nn.Module, data: List, config: ConfigMetaCAT, save_dir_pa
             y_ = [x[2] for x in train_data]
             class_weights = compute_class_weight(class_weight="balanced", classes=np.unique(y_), y=y_)
             config.train['class_weights'] = class_weights.tolist()
-            logger.info(f"Class weights computed: {class_weights}")
+            logger.info(f"Class weights computed: %s" %class_weights)
 
             class_weights = torch.FloatTensor(class_weights).to(device)
             if config.train['loss_funct'] == 'cross_entropy':
@@ -259,7 +259,7 @@ def initialize_model(classifier, data_, batch_size_, lr_, epochs=4):
 
         # Total number of training steps
         total_steps = int((len(data_) / batch_size_) * epochs)
-        logger.info('Total steps for optimizer: {}'.format(total_steps))
+        logger.info('Total steps for optimizer: %d' %total_steps)
 
         # Set up the learning rate scheduler
         scheduler_ = get_linear_schedule_with_warmup(optimizer_,

medcat/utils/meta_cat/models.py

@@ -91,7 +91,7 @@ def __init__(self, config):
         super(BertForMetaAnnotation, self).__init__()
         _bertconfig = AutoConfig.from_pretrained(config.model.model_variant,num_hidden_layers=config.model['num_layers'])
         if config.model['input_size'] != _bertconfig.hidden_size:
-            logger.warning(f"\nInput size for {config.model.model_variant} model should be {_bertconfig.hidden_size}, provided input size is {config.model['input_size']} Input size changed to {_bertconfig.hidden_size}")
+            logger.warning("Input size for %s model should be %d, provided input size is %d. Input size changed to %d" %(config.model.model_variant,_bertconfig.hidden_size,config.model['input_size'],_bertconfig.hidden_size))
 
         bert = BertModel.from_pretrained(config.model.model_variant, config=_bertconfig)
         self.config = config