Allow pipeline components to set default scores and weights

spacy/default_config.cfg

@@ -34,8 +34,8 @@ seed = 0
 accumulate_gradient = 1
 use_pytorch_for_gpu_memory = false
 # Control how scores are printed and checkpoints are evaluated.
-scores = ["speed", "tag_acc", "dep_uas", "dep_las", "ents_f"]
-score_weights = {"tag_acc": 0.2, "dep_las": 0.4, "ents_f": 0.4}
+scores = ["token_acc", "speed"]
+score_weights = {}
 # These settings are invalid for the transformer models.
 init_tok2vec = null
 discard_oversize = false

spacy/language.py

@@ -21,7 +21,7 @@ from .pipe_analysis import analyze_pipes, analyze_all_pipes, validate_attrs
 from .gold import Example
 from .scorer import Scorer
 from .util import link_vectors_to_models, create_default_optimizer, registry
-from .util import SimpleFrozenDict
+from .util import SimpleFrozenDict, combine_score_weights
 from .lang.tokenizer_exceptions import URL_MATCH, BASE_EXCEPTIONS
 from .lang.punctuation import TOKENIZER_PREFIXES, TOKENIZER_SUFFIXES
 from .lang.punctuation import TOKENIZER_INFIXES
@@ -218,16 +218,24 @@ class Language:
     @property
     def config(self) -> Config:
         self._config.setdefault("nlp", {})
+        self._config.setdefault("training", {})
         self._config["nlp"]["lang"] = self.lang
         # We're storing the filled config for each pipeline component and so
         # we can populate the config again later
         pipeline = {}
+        scores = self._config["training"].get("scores", [])
+        score_weights = []
         for pipe_name in self.pipe_names:
             pipe_meta = self.get_pipe_meta(pipe_name)
             pipe_config = self.get_pipe_config(pipe_name)
             pipeline[pipe_name] = {"factory": pipe_meta.factory, **pipe_config}
+            scores.extend(pipe_meta.scores)
+            if pipe_meta.score_weights:
+                score_weights.append(pipe_meta.score_weights)
         self._config["nlp"]["pipeline"] = self.pipe_names
         self._config["components"] = pipeline
+        self._config["training"]["scores"] = list(scores)
+        self._config["training"]["score_weights"] = combine_score_weights(score_weights)
         if not srsly.is_json_serializable(self._config):
             raise ValueError(Errors.E961.format(config=self._config))
         return self._config
@@ -348,6 +356,8 @@ class Language:
         assigns: Iterable[str] = tuple(),
         requires: Iterable[str] = tuple(),
         retokenizes: bool = False,
+        scores: Iterable[str] = tuple(),
+        score_weights: Dict[str, float] = SimpleFrozenDict(),
         func: Optional[Callable] = None,
     ) -> Callable:
         """Register a new pipeline component factory. Can be used as a decorator
@@ -393,6 +403,8 @@ class Language:
                 default_config=default_config,
                 assigns=validate_attrs(assigns),
                 requires=validate_attrs(requires),
+                scores=scores,
+                score_weights=score_weights,
                 retokenizes=retokenizes,
             )
             cls.set_factory_meta(name, factory_meta)
@@ -417,6 +429,8 @@ class Language:
         assigns: Iterable[str] = tuple(),
         requires: Iterable[str] = tuple(),
         retokenizes: bool = False,
+        scores: Iterable[str] = tuple(),
+        score_weights: Dict[str, float] = SimpleFrozenDict(),
         func: Optional[Callable[[Doc], Doc]] = None,
     ) -> Callable:
         """Register a new pipeline component. Can be used for stateless function
@@ -450,6 +464,8 @@ class Language:
                 assigns=assigns,
                 requires=requires,
                 retokenizes=retokenizes,
+                scores=scores,
+                score_weights=score_weights,
                 func=factory_func,
             )
             return component_func
@@ -1484,6 +1500,8 @@ class FactoryMeta:
     assigns: Iterable[str] = tuple()
     requires: Iterable[str] = tuple()
     retokenizes: bool = False
+    scores: Iterable[str] = tuple()
+    score_weights: Dict[str, float] = None
 
 
 def _get_config_overrides(

spacy/pipeline/dep_parser.pyx

@@ -42,7 +42,9 @@ DEFAULT_PARSER_MODEL = Config().from_str(default_model_config)["model"]
         "learn_tokens": False,
         "min_action_freq": 30,
         "model": DEFAULT_PARSER_MODEL,
-    }
+    },
+    scores=["dep_uas", "dep_las", "sents_f"],
+    score_weights={"dep_uas": 0.5, "dep_las": 0.5, "sents_f": 0.0},
 )
 def make_parser(
     nlp: Language,

spacy/pipeline/ner.pyx

@@ -40,7 +40,9 @@ DEFAULT_NER_MODEL = Config().from_str(default_model_config)["model"]
         "learn_tokens": False,
         "min_action_freq": 30,
         "model": DEFAULT_NER_MODEL,
-    }
+    },
+    scores=["ents_f", "ents_r", "ents_p"],
+    score_weights={"ents_f": 1.0, "ents_r": 0.0, "ents_p": 0.0},
 )
 def make_ner(
     nlp: Language,

spacy/pipeline/senter.pyx

@@ -33,7 +33,9 @@ DEFAULT_SENTER_MODEL = Config().from_str(default_model_config)["model"]
 @Language.factory(
     "senter",
     assigns=["token.is_sent_start"],
-    default_config={"model": DEFAULT_SENTER_MODEL}
+    default_config={"model": DEFAULT_SENTER_MODEL},
+    scores=["sents_p", "sents_r", "sents_f"],
+    score_weights={"sents_p": 0.0, "sents_r": 0.0, "sents_f": 1.0},
 )
 def make_senter(nlp: Language, name: str, model: Model):
     return SentenceRecognizer(nlp.vocab, model, name)

spacy/pipeline/tagger.pyx

@@ -39,7 +39,9 @@ DEFAULT_TAGGER_MODEL = Config().from_str(default_model_config)["model"]
 @Language.factory(
     "tagger",
     assigns=["token.tag"],
-    default_config={"model": DEFAULT_TAGGER_MODEL, "set_morphology": False}
+    default_config={"model": DEFAULT_TAGGER_MODEL, "set_morphology": False},
+    scores=["tag_acc", "pos_acc"],
+    score_weights={"tag_acc": 0.5, "pos_acc": 0.5},
 )
 def make_tagger(nlp: Language, name: str, model: Model, set_morphology: bool):
     return Tagger(nlp.vocab, model, name, set_morphology=set_morphology)

spacy/tests/pipeline/test_pipe_factories.py

@@ -3,7 +3,7 @@ from spacy.language import Language
 from spacy.lang.en import English
 from spacy.lang.de import German
 from spacy.tokens import Doc
-from spacy.util import registry, SimpleFrozenDict
+from spacy.util import registry, SimpleFrozenDict, combine_score_weights
 from thinc.api import Model, Linear
 from thinc.config import ConfigValidationError
 from pydantic import StrictInt, StrictStr
@@ -328,3 +328,52 @@ def test_language_factories_invalid():
     assert len(nlp.factories)
     with pytest.raises(NotImplementedError):
         nlp.factories["foo"] = "bar"
+
+
+@pytest.mark.parametrize(
+    "weights,expected",
+    [
+        ([{"a": 1.0}, {"b": 1.0}, {"c": 1.0}], {"a": 0.33, "b": 0.33, "c": 0.33}),
+        ([{"a": 1.0}, {"b": 50}, {"c": 123}], {"a": 0.33, "b": 0.33, "c": 0.33}),
+        (
+            [{"a": 0.7, "b": 0.3}, {"c": 1.0}, {"d": 0.5, "e": 0.5}],
+            {"a": 0.23, "b": 0.1, "c": 0.33, "d": 0.17, "e": 0.17},
+        ),
+        (
+            [{"a": 100, "b": 400}, {"c": 0.5, "d": 0.5}],
+            {"a": 0.1, "b": 0.4, "c": 0.25, "d": 0.25},
+        ),
+    ],
+)
+def test_language_factories_combine_score_weights(weights, expected):
+    result = combine_score_weights(weights)
+    assert sum(result.values()) in (0.99, 1.0)
+    assert result == expected
+
+
+def test_language_factories_scores():
+    name = "test_language_factories_scores"
+    func = lambda doc: doc
+    scores1 = ["a1", "a2"]
+    weights1 = {"a1": 0.5, "a2": 0.5}
+    scores2 = ["b1", "b2", "b3"]
+    weights2 = {"b1": 0.2, "b2": 0.7, "b3": 0.1}
+    Language.component(
+        f"{name}1", scores=scores1, score_weights=weights1, func=func,
+    )
+    Language.component(
+        f"{name}2", scores=scores2, score_weights=weights2, func=func,
+    )
+    meta1 = Language.get_factory_meta(f"{name}1")
+    assert meta1.scores == scores1
+    assert meta1.score_weights == weights1
+    meta2 = Language.get_factory_meta(f"{name}2")
+    assert meta2.scores == scores2
+    assert meta2.score_weights == weights2
+    nlp = Language(config={"training": {"scores": ["speed"], "score_weights": {}}})
+    nlp.add_pipe(f"{name}1")
+    nlp.add_pipe(f"{name}2")
+    cfg = nlp.config["training"]
+    assert cfg["scores"] == ["speed", *scores1, *scores2]
+    expected_weights = {"a1": 0.25, "a2": 0.25, "b1": 0.1, "b2": 0.35, "b3": 0.05}
+    assert cfg["score_weights"] == expected_weights

spacy/util.py

@@ -1130,6 +1130,23 @@ def get_arg_names(func: Callable) -> List[str]:
     return list(set([*argspec.args, *argspec.kwonlyargs]))
 
 
+def combine_score_weights(weights: List[Dict[str, float]]) -> Dict[str, float]:
+    """Combine and normalize score weights defined by components, e.g.
+    {"ents_r": 0.2, "ents_p": 0.3, "ents_f": 0.5} and {"some_other_score": 1.0}.
+
+    weights (List[dict]): The weights defined by the components.
+    RETURNS (Dict[str, float]): The combined and normalized weights.
+    """
+    result = {}
+    for w_dict in weights:
+        # We need to account for weights that don't sum to 1.0 and normalize the
+        # score weights accordingly, then divide score by the number of components
+        total = sum([w for w in w_dict.values()])
+        for key, value in w_dict.items():
+            result[key] = round(value / total / len(weights), 2)
+    return result
+
+
 class DummyTokenizer:
     # add dummy methods for to_bytes, from_bytes, to_disk and from_disk to
     # allow serialization (see #1557)