Refactor Chinese initialization

spacy/errors.py

@@ -672,14 +672,22 @@ class Errors:
     E999 = ("Unable to merge the `Doc` objects because they do not all share "
             "the same `Vocab`.")
     E1000 = ("The Chinese word segmenter is pkuseg but no pkuseg model was "
-             "specified. Provide the name of a pretrained model or the path to "
-             "a model when initializing the pipeline:\n"
+             "loaded. Provide the name of a pretrained model or the path to "
+             "a model and initialize the pipeline:\n\n"
              'config = {\n'
-             '   "@tokenizers": "spacy.zh.ChineseTokenizer",\n'
-             '   "segmenter": "pkuseg",\n'
-             '   "pkuseg_model": "default", # or "/path/to/pkuseg_model" \n'
+             '    "nlp": {\n'
+             '        "tokenizer": {\n'
+             '            "@tokenizers": "spacy.zh.ChineseTokenizer",\n'
+             '            "segmenter": "pkuseg",\n'
+             '        }\n'
+             '    },\n'
+             '    "initialize": {"tokenizer": {\n'
+             '            "pkuseg_model": "default", # or /path/to/model\n'
+             '        }\n'
+             '    },\n'
              '}\n'
-             'nlp = Chinese.from_config({"nlp": {"tokenizer": config}})')
+             'nlp = Chinese.from_config(config)\n'
+             'nlp.initialize()')
     E1001 = ("Target token outside of matched span for match with tokens "
              "'{span}' and offset '{index}' matched by patterns '{patterns}'.")
     E1002 = ("Span index out of range.")

spacy/lang/zh/__init__.py

@@ -59,32 +59,13 @@ class ChineseTokenizer(DummyTokenizer):
         self,
         nlp: Language,
         segmenter: Segmenter = Segmenter.char,
-        pkuseg_model: Optional[str] = None,
-        pkuseg_user_dict: Optional[str] = None,
     ):
         self.vocab = nlp.vocab
         if isinstance(segmenter, Segmenter):
             segmenter = segmenter.value
         self.segmenter = segmenter
-        self.pkuseg_model = pkuseg_model
-        self.pkuseg_user_dict = pkuseg_user_dict
         self.pkuseg_seg = None
         self.jieba_seg = None
-        self.configure_segmenter(segmenter)
-
-    def initialize(
-        self,
-        get_examples: Callable[[], Iterable[Example]],
-        *,
-        nlp: Optional[Language],
-        pkuseg_model: Optional[str] = None,
-        pkuseg_user_dict: Optional[str] = None
-    ):
-        self.pkuseg_model = pkuseg_model
-        self.pkuseg_user_dict = pkuseg_user_dict
-        self.configure_segmenter(self.segmenter)
-
-    def configure_segmenter(self, segmenter: str):
         if segmenter not in Segmenter.values():
             warn_msg = Warnings.W103.format(
                 lang="Chinese",
@@ -94,12 +75,21 @@ class ChineseTokenizer(DummyTokenizer):
             )
             warnings.warn(warn_msg)
             self.segmenter = Segmenter.char
-        self.jieba_seg = try_jieba_import(self.segmenter)
-        self.pkuseg_seg = try_pkuseg_import(
-            self.segmenter,
-            pkuseg_model=self.pkuseg_model,
-            pkuseg_user_dict=self.pkuseg_user_dict,
-        )
+        if segmenter == Segmenter.jieba:
+            self.jieba_seg = try_jieba_import()
+
+    def initialize(
+        self,
+        get_examples: Callable[[], Iterable[Example]],
+        *,
+        nlp: Optional[Language],
+        pkuseg_model: Optional[str] = None,
+        pkuseg_user_dict: str = "default",
+    ):
+        if self.segmenter == Segmenter.pkuseg:
+            self.pkuseg_seg = try_pkuseg_import(
+                pkuseg_model=pkuseg_model, pkuseg_user_dict=pkuseg_user_dict,
+            )
 
     def __call__(self, text: str) -> Doc:
         if self.segmenter == Segmenter.jieba:
@@ -154,14 +144,10 @@ class ChineseTokenizer(DummyTokenizer):
     def _get_config(self) -> Dict[str, Any]:
         return {
             "segmenter": self.segmenter,
-            "pkuseg_model": self.pkuseg_model,
-            "pkuseg_user_dict": self.pkuseg_user_dict,
         }
 
     def _set_config(self, config: Dict[str, Any] = {}) -> None:
         self.segmenter = config.get("segmenter", Segmenter.char)
-        self.pkuseg_model = config.get("pkuseg_model", None)
-        self.pkuseg_user_dict = config.get("pkuseg_user_dict", "default")
 
     def to_bytes(self, **kwargs):
         pkuseg_features_b = b""
@@ -339,42 +325,33 @@ class Chinese(Language):
     Defaults = ChineseDefaults
 
 
-def try_jieba_import(segmenter: str) -> None:
+def try_jieba_import() -> None:
     try:
         import jieba
 
-        if segmenter == Segmenter.jieba:
-            # segment a short text to have jieba initialize its cache in advance
-            list(jieba.cut("作为", cut_all=False))
+        # segment a short text to have jieba initialize its cache in advance
+        list(jieba.cut("作为", cut_all=False))
 
         return jieba
     except ImportError:
-        if segmenter == Segmenter.jieba:
-            msg = (
-                "Jieba not installed. To use jieba, install it with `pip "
-                " install jieba` or from https://github.com/fxsjy/jieba"
-            )
-            raise ImportError(msg) from None
+        msg = (
+            "Jieba not installed. To use jieba, install it with `pip "
+            " install jieba` or from https://github.com/fxsjy/jieba"
+        )
+        raise ImportError(msg) from None
 
 
-def try_pkuseg_import(
-    segmenter: str, pkuseg_model: Optional[str], pkuseg_user_dict: str
-) -> None:
+def try_pkuseg_import(pkuseg_model: str, pkuseg_user_dict: str) -> None:
     try:
         import pkuseg
 
-        if pkuseg_model is None:
-            return None
-        else:
-            return pkuseg.pkuseg(pkuseg_model, pkuseg_user_dict)
+        return pkuseg.pkuseg(pkuseg_model, pkuseg_user_dict)
     except ImportError:
-        if segmenter == Segmenter.pkuseg:
-            msg = "pkuseg not installed. To use pkuseg, " + _PKUSEG_INSTALL_MSG
-            raise ImportError(msg) from None
+        msg = "pkuseg not installed. To use pkuseg, " + _PKUSEG_INSTALL_MSG
+        raise ImportError(msg) from None
     except FileNotFoundError:
-        if segmenter == Segmenter.pkuseg:
-            msg = "Unable to load pkuseg model from: " + pkuseg_model
-            raise FileNotFoundError(msg) from None
+        msg = "Unable to load pkuseg model from: " + pkuseg_model
+        raise FileNotFoundError(msg) from None
 
 
 def _get_pkuseg_trie_data(node, path=""):

spacy/tests/conftest.py

@@ -272,10 +272,14 @@ def zh_tokenizer_char():
 def zh_tokenizer_jieba():
     pytest.importorskip("jieba")
     config = {
-        "@tokenizers": "spacy.zh.ChineseTokenizer",
-        "segmenter": "jieba",
+        "nlp": {
+            "tokenizer": {
+                "@tokenizers": "spacy.zh.ChineseTokenizer",
+                "segmenter": "jieba",
+            }
+        }
     }
-    nlp = get_lang_class("zh").from_config({"nlp": {"tokenizer": config}})
+    nlp = get_lang_class("zh").from_config(config)
     return nlp.tokenizer
 
 
@@ -290,7 +294,10 @@ def zh_tokenizer_pkuseg():
                 "segmenter": "pkuseg",
             }
         },
-        "initialize": {"tokenizer": {"pkuseg_model": "default"}},
+        "initialize": {"tokenizer": {
+                "pkuseg_model": "default",
+            }
+        },
     }
     nlp = get_lang_class("zh").from_config(config)
     nlp.initialize()

spacy/tests/lang/zh/test_serialize.py

@@ -28,9 +28,17 @@ def test_zh_tokenizer_serialize_jieba(zh_tokenizer_jieba):
 @pytest.mark.slow
 def test_zh_tokenizer_serialize_pkuseg_with_processors(zh_tokenizer_pkuseg):
     config = {
-        "@tokenizers": "spacy.zh.ChineseTokenizer",
-        "segmenter": "pkuseg",
-        "pkuseg_model": "medicine",
+        "nlp": {
+            "tokenizer": {
+                "@tokenizers": "spacy.zh.ChineseTokenizer",
+                "segmenter": "pkuseg",
+            }
+        },
+        "initialize": {"tokenizer": {
+                "pkuseg_model": "medicine",
+            }
+        },
     }
-    nlp = Chinese.from_config({"nlp": {"tokenizer": config}})
+    nlp = Chinese.from_config(config)
+    nlp.initialize()
     zh_tokenizer_serialize(nlp.tokenizer)