Merge pull request #10 from a-belov/patch-3

Update predictor.py (compatibility with Python 3.11)

rnnmorph/predictor.py

@@ -17,14 +17,29 @@
 from rnnmorph.settings import MODELS_PATHS
 
 
+def pymorphy2_hotfix():
+    # src: https://github.com/pymorphy2/pymorphy2/issues/160#issuecomment-1486657176
+    from inspect import getfullargspec
+    from pymorphy2.units.base import BaseAnalyzerUnit
+
+    def _get_param_names_fixed(klass):
+        if klass.__init__ is object.__init__:
+            return []
+        args = getfullargspec(klass.__init__).args
+        return sorted(args[1:])
+
+    setattr(BaseAnalyzerUnit, '_get_param_names', _get_param_names_fixed)
+
+
 class Predictor:
     """
     Интерфейс POS-теггера.
     """
+
     def predict(self, words: List[str], include_all_forms: bool) -> List[WordFormOut]:
         """
         Предсказать теги для одного предложения.
-        
+
         :param words: массив слов (знаки препинания - отдельные токены).
         :param include_all_forms: флаг, включающий все варианты разбора.
         :return: массив форм с леммами, тегами и оригинальными словами.
@@ -35,7 +50,7 @@ def predict_sentences(self, sentences: List[List[str]], batch_size: int,
                           include_all_forms: bool) -> List[List[WordFormOut]]:
         """
         Предсказать теги для массива предложений. В сетку как batch загружается.
-        
+
         :param sentences: массив предложений.
         :param batch_size: размер батча.
         :param include_all_forms: флаг, включающий все варианты разбора.
@@ -48,15 +63,16 @@ class RNNMorphPredictor(Predictor):
     """
     POS-теггер на освное RNN.
     """
+
     def __init__(self,
                  language="ru",
-                 eval_model_config_path: str=None,
-                 eval_model_weights_path: str=None,
-                 gram_dict_input: str=None,
-                 gram_dict_output: str=None,
-                 word_vocabulary: str=None,
-                 char_set_path: str=None,
-                 build_config: str=None):
+                 eval_model_config_path: str = None,
+                 eval_model_weights_path: str = None,
+                 gram_dict_input: str = None,
+                 gram_dict_output: str = None,
+                 word_vocabulary: str = None,
+                 char_set_path: str = None,
+                 build_config: str = None):
         if eval_model_config_path is None:
             eval_model_config_path = MODELS_PATHS[language]["eval_model_config"]
         if eval_model_weights_path is None:
@@ -74,7 +90,11 @@ def __init__(self,
 
         self.language = language
         self.converter = converters.converter('opencorpora-int', 'ud14') if language == "ru" else None
-        self.morph = MorphAnalyzer() if language == "ru" else None
+        if language == "ru":
+            pymorphy2_hotfix()
+            self.morph = MorphAnalyzer()
+        else:
+            None
         if self.language == "en":
             nltk.download("wordnet")
             nltk.download('averaged_perceptron_tagger')
@@ -87,12 +107,12 @@ def __init__(self,
         self.model.prepare(gram_dict_input, gram_dict_output, word_vocabulary, char_set_path)
         self.model.load_eval(self.build_config, eval_model_config_path, eval_model_weights_path)
 
-    def predict(self, words: List[str], include_all_forms: bool=False) -> List[WordFormOut]:
+    def predict(self, words: List[str], include_all_forms: bool = False) -> List[WordFormOut]:
         words_probabilities = self.model.predict_probabilities([words], 1, self.build_config)[0]
         return self.__get_sentence_forms(words, words_probabilities, include_all_forms)
 
-    def predict_sentences(self, sentences: List[List[str]], batch_size: int=64,
-                          include_all_forms: bool=False) -> List[List[WordFormOut]]:
+    def predict_sentences(self, sentences: List[List[str]], batch_size: int = 64,
+                          include_all_forms: bool = False) -> List[List[WordFormOut]]:
         sentences_probabilities = self.model.predict_probabilities(sentences, batch_size, self.build_config)
         answers = []
         for words, words_probabilities in zip(sentences, sentences_probabilities):
@@ -103,7 +123,7 @@ def __get_sentence_forms(self, words: List[str], words_probabilities: List[List[
                              include_all_forms: bool) -> List[WordFormOut]:
         """
         Получить теги и формы.
-        
+
         :param words: слова.
         :param words_probabilities: вероятности тегов слов.
         :param include_all_forms: флаг, включающий все варианты разбора.
@@ -153,10 +173,10 @@ def __compose_out_form(self, word: str, probabilities: List[float],
         return result_form
 
     def __get_lemma(self, word: str, pos_tag: str, gram: str, word_forms=None,
-                    enable_normalization: bool=True):
+                    enable_normalization: bool = True):
         """
         Получить лемму.
-        
+
         :param word: слово.
         :param pos_tag: часть речи.
         :param gram: граммаическое значение.
@@ -201,7 +221,7 @@ def __get_lemma(self, word: str, pos_tag: str, gram: str, word_forms=None,
     def __normalize_for_gikrya(form):
         """
         Поучение леммы по правилам, максимально близким к тем, которые в корпусе ГИКРЯ.
-        
+
         :param form: форма из pymorphy2.
         :return: леммма.
         """