Fix initialize and update tests

sense2vec/component.py

@@ -1,6 +1,7 @@
-from typing import Tuple, Union, List, Dict
+from typing import Tuple, Union, List, Dict, Callable, Iterable, Optional
 from spacy.language import Language
 from spacy.tokens import Doc, Token, Span
+from spacy.training import Example
 from spacy.vocab import Vocab
 from spacy.util import SimpleFrozenDict
 from pathlib import Path
@@ -215,6 +216,24 @@ def s2v_other_senses(self, obj: Union[Token, Span]) -> List[str]:
         key = self.s2v_key(obj)
         return obj.doc._._s2v.get_other_senses(key)
 
+    def initialize(
+        self,
+        get_examples: Callable[[], Iterable[Example]],
+        *,
+        nlp: Optional[Language] = None,
+        data_path: Optional[str] = None
+    ):
+        """Initialize the component and load in data. Can be used to add the
+        component with vectors to a pipeline before training.
+
+        get_examples (Callable[[], Iterable[Example]]): Function that
+            returns a representative sample of gold-standard Example objects.
+        nlp (Language): The current nlp object the component is part of.
+        data_path (Optional[str]): Optional path to sense2vec model.
+        """
+        if data_path is not None:
+            self.from_disk(data_path)
+
     def to_bytes(self) -> bytes:
         """Serialize the component to a bytestring.
 

sense2vec/sense2vec.py

@@ -1,7 +1,5 @@
-from typing import Tuple, List, Union, Sequence, Dict, Callable, Any, Iterable
-from typing import Optional
+from typing import Tuple, List, Union, Sequence, Dict, Callable, Any
 from pathlib import Path
-from spacy.language import Language
 from spacy.vectors import Vectors
 from spacy.strings import StringStore
 from spacy.util import SimpleFrozenDict
@@ -297,24 +295,6 @@ def to_bytes(self, exclude: Sequence[str] = tuple()) -> bytes:
             data["cache"] = self.cache
         return srsly.msgpack_dumps(data)
 
-    def initialize(
-        self,
-        get_examples: Callable[[], Iterable],
-        *,
-        nlp: Optional[Language] = None,
-        data_path: Optional[str] = None
-    ):
-        """Initialize the component and load in data. Can be used to add the
-        component with vectors to a pipeline before training.
-
-        get_examples (Callable[[], Iterable[Example]]): Function that
-            returns a representative sample of gold-standard Example objects.
-        nlp (Language): The current nlp object the component is part of.
-        data_path (Optional[str]): Optional path to sense2vec model.
-        """
-        if data_path is not None:
-            self.from_disk(data_path)
-
     def from_bytes(self, bytes_data: bytes, exclude: Sequence[str] = tuple()):
         """Load a Sense2Vec object from a bytestring.
 

sense2vec/tests/test_component.py

@@ -1,8 +1,10 @@
 import pytest
 import numpy
+import spacy
 from spacy.vocab import Vocab
 from spacy.tokens import Doc, Span
 from sense2vec import Sense2VecComponent
+from pathlib import Path
 
 
 @pytest.fixture
@@ -103,3 +105,33 @@ def test_component_to_from_bytes(doc):
     assert doc[0]._.in_s2v is False
     new_doc = new_s2v(doc)
     assert new_doc[0]._.in_s2v is True
+
+
+def test_component_initialize():
+    data_path = Path(__file__).parent / "data"
+    # With from_disk
+    nlp = spacy.blank("en")
+    s2v = nlp.add_pipe("sense2vec")
+    if Doc.has_extension("s2v_phrases"):
+        s2v.first_run = False  # don't set up extensions again
+    s2v.from_disk(data_path)
+    doc = Doc(nlp.vocab, words=["beekeepers"], pos=["NOUN"])
+    s2v(doc)
+    assert doc[0]._.s2v_key == "beekeepers|NOUN"
+    most_similar = [item for item, score in doc[0]._.s2v_most_similar(2)]
+    assert most_similar[0] == ("honey bees", "NOUN")
+    assert most_similar[1] == ("Beekeepers", "NOUN")
+
+    # With initialize
+    nlp = spacy.blank("en")
+    s2v = nlp.add_pipe("sense2vec")
+    s2v.first_run = False  # don't set up extensions again
+    init_cfg = {"sense2vec": {"data_path": str(data_path)}}
+    nlp.config["initialize"]["components"] = init_cfg
+    nlp.initialize()
+    doc = Doc(nlp.vocab, words=["beekeepers"], pos=["NOUN"])
+    s2v(doc)
+    assert doc[0]._.s2v_key == "beekeepers|NOUN"
+    most_similar = [item for item, score in doc[0]._.s2v_most_similar(2)]
+    assert most_similar[0] == ("honey bees", "NOUN")
+    assert most_similar[1] == ("Beekeepers", "NOUN")

setup.cfg

@@ -36,7 +36,7 @@ install_requires =
 
 [options.entry_points]
 spacy_factories =
-    sense2vec = sense2vec:make_sense2vec
+    sense2vec = sense2vec:component.make_sense2vec
 prodigy_recipes =
     sense2vec.teach = sense2vec:prodigy_recipes.teach
     sens2vec.to-patterns = sense2vec:prodigy_recipes.to_patterns