more uniform function naming

delft/utilities/Embeddings.py

@@ -87,7 +87,7 @@ def __init__(self, name,
         if use_ELMo:
             self.make_ELMo()
             self.embed_size = ELMo_embed_size + self.embed_size
-            description = self._get_description('elmo-'+self.lang)
+            description = self.get_description('elmo-'+self.lang)
             self.env_ELMo = None
             if description and description["cache-training"] and self.use_cache:
                 self.embedding_ELMo_cache = os.path.join(description["path-cache"], "cache")
@@ -106,7 +106,7 @@ def __init__(self, name,
             #self.session.run(tf.global_variables_initializer())
             self.make_BERT()
             self.embed_size = BERT_embed_size + self.embed_size
-            description = self._get_description('bert-base-'+self.lang)
+            description = self.get_description('bert-base-'+self.lang)
             self.env_BERT = None
             if description and description["cache-training"] and self.use_cache:
                 self.embedding_BERT_cache = os.path.join(description["path-cache"], "cache")
@@ -131,7 +131,7 @@ def make_embeddings_simple_in_memory(self, name="fasttext-crawl"):
         nbWords = 0
         print('loading embeddings...')
         begin = True
-        description = self._get_description(name)
+        description = self.get_description(name)
         if description is not None:
             embeddings_path = description["path"]
             self.lang = description["lang"]
@@ -168,7 +168,7 @@ def make_embeddings_simple_in_memory(self, name="fasttext-crawl"):
 
     def make_embeddings_lmdb(self, name="fasttext-crawl"):
         print('\nCompiling embeddings... (this is done only one time per embeddings at first usage)')
-        description = self._get_description(name)
+        description = self.get_description(name)
 
         if description is None:
             print('\nNo description found in embeddings registry for embeddings', name)
@@ -195,7 +195,7 @@ def load_embeddings_from_file(self, embeddings_path):
         nb_lines = 0
 
         # read number of lines first
-        embedding_file = _open_embedding_file(embeddings_path)
+        embedding_file = open_embedding_file(embeddings_path)
         if embedding_file is None:
             print("Error: could not open embeddings file", embeddings_path)
             return
@@ -204,7 +204,7 @@ def load_embeddings_from_file(self, embeddings_path):
             nb_lines += 1
         embedding_file.close()
 
-        embedding_file = _open_embedding_file(embeddings_path)
+        embedding_file = open_embedding_file(embeddings_path)
         #with open(embeddings_path, encoding='utf8') as f:
         for line in tqdm(embedding_file, total=nb_lines):
             line = line.decode()
@@ -266,7 +266,7 @@ def clean_downloads(self):
                 print('Failed to delete %s. Reason: %s' % (file_path, e))
 
     def make_embeddings_simple(self, name="fasttext-crawl"):
-        description = self._get_description(name)
+        description = self.get_description(name)
         if description is not None:
             self.extension = description["format"]
 
@@ -294,7 +294,7 @@ def make_embeddings_simple(self, name="fasttext-crawl"):
             envFilePath = os.path.join(self.embedding_lmdb_path, name)
             load_db = True
             if os.path.isdir(envFilePath):
-                description = self._get_description(name)
+                description = self.get_description(name)
                 if description is not None:
                     self.lang = description["lang"]
 
@@ -333,7 +333,7 @@ def make_embeddings_simple(self, name="fasttext-crawl"):
     def make_ELMo(self):
         # Location of pretrained BiLM for the specified language
         # TBD check if ELMo language resources are present
-        description = self._get_description('elmo-'+self.lang)
+        description = self.get_description('elmo-'+self.lang)
         if description is not None:
             self.lang = description["lang"]
             vocab_file = description["path-vocab"]
@@ -358,7 +358,7 @@ def make_ELMo(self):
 
     def make_BERT(self):
         # Location of BERT model
-        description = self._get_description('bert-base-'+self.lang)
+        description = self.get_description('bert-base-'+self.lang)
         if description is not None:
             self.lang = description["lang"]
             config_file = description["path-config"]
@@ -570,7 +570,7 @@ def get_sentence_vector_with_BERT(self, token_list):
         return concatenated_squeezed_result
 
 
-    def _get_description(self, name):
+    def get_description(self, name):
         for emb in self.registry["embeddings"]:
             if emb["name"] == name:
                 return emb
@@ -824,7 +824,7 @@ def _serialize_pickle(a):
 def _deserialize_pickle(serialized):
     return pickle.loads(serialized)
 
-def _open_embedding_file(embeddings_path):
+def open_embedding_file(embeddings_path):
     # embeddings can be uncompressed or compressed with gzip or zip
     if embeddings_path.endswith(".gz"):
         embedding_file = gzip.open(embeddings_path, mode="rt")