Inherit from SpanCat instead of TrainablePipe

This commit changes the inheritance structure of Exclusive_Spancat,
now it's inheriting from SpanCategorizer than TrainablePipe. This
allows me to remove duplicate methods that are already present in
the parent function.

spacy/pipeline/spancat_exclusive.py

@@ -13,7 +13,7 @@ from ..tokens import Doc, Span, SpanGroup
 from ..training import Example, validate_examples
 from ..vocab import Vocab
 from .spancat import spancat_score, build_ngram_suggester
-from .trainable_pipe import TrainablePipe
+from .spancat import SpanCategorizer
 
 
 spancat_exclusive_default_config = """
@@ -71,7 +71,7 @@ def make_spancat(
     scorer: Optional[Callable],
     negative_weight: float = 1.0,
     allow_overlap: bool = True,
-) -> "SpanCategorizerExclusive":
+) -> "Exclusive_SpanCategorizer":
     """Create a SpanCategorizerExclusive component. The span categorizer consists of two
     parts: a suggester function that proposes candidate spans, and a labeller
     model that predicts a single label for each span.
@@ -94,7 +94,7 @@ def make_spancat(
     allow_overlap (bool): If True the data is assumed to
         contain overlapping spans.
     """
-    return SpanCategorizerExclusive(
+    return Exclusive_SpanCategorizer(
         nlp.vocab,
         suggester=suggester,
         model=model,
@@ -127,8 +127,8 @@ class Ranges:
         return False
 
 
-class SpanCategorizerExclusive(TrainablePipe):
+class Exclusive_SpanCategorizer(SpanCategorizer):
-    """Pipeline component to label spans of text.
+    """Pipeline component to label non-overlapping spans of text.
 
     DOCS: https://spacy.io/api/spancategorizerexclusive
     """
@@ -176,47 +176,6 @@ class SpanCategorizerExclusive(TrainablePipe):
         self.name = name
         self.scorer = scorer
 
-    @property
-    def key(self) -> str:
-        """Key of the doc.spans dict to save the spans under. During
-        initialization and training, the component will look for spans on the
-        reference document under the same key.
-        """
-        return str(self.cfg["spans_key"])
-
-    def add_label(self, label: str) -> int:
-        """Add a new label to the pipe.
-
-        label (str): The label to add.
-        RETURNS (int): 0 if label is already present, otherwise 1.
-
-        DOCS: https://spacy.io/api/spancategorizerexclusive#add_label
-        """
-        if not isinstance(label, str):
-            raise ValueError(Errors.E187)
-        if label in self.labels:
-            return 0
-        self._allow_extra_label()
-        self.cfg["labels"].append(label)  # type: ignore
-        self.vocab.strings.add(label)
-        return 1
-
-    @property
-    def labels(self) -> Tuple[str]:
-        """RETURNS (Tuple[str]): The labels currently added to the component.
-
-        DOCS: https://spacy.io/api/spancategorizerexclusive#labels
-        """
-        return tuple(self.cfg["labels"])  # type: ignore
-
-    @property
-    def label_data(self) -> List[str]:
-        """RETURNS (List[str]): Information about the component's labels.
-
-        DOCS: https://spacy.io/api/spancategorizerexclusive#label_data
-        """
-        return list(self.labels)
-
     @property
     def label_map(self) -> Dict[str, int]:
         """RETURNS (Dict[str, int]): The label map."""
@@ -232,37 +191,6 @@ class SpanCategorizerExclusive(TrainablePipe):
         """RETURNS (int): Number of labels including the negative label."""
         return len(self.label_data) + 1
 
-    def predict(self, docs: Iterable[Doc]):
-        """Apply the pipeline's model to a batch of docs, without modifying them.
-
-        docs (Iterable[Doc]): The documents to predict.
-        RETURNS: The models prediction for each document.
-
-        DOCS: https://spacy.io/api/spancategorizerexclusive#predict
-        """
-        indices = self.suggester(docs, ops=self.model.ops)
-        scores = self.model.predict((docs, indices))  # type: ignore
-        return indices, scores
-
-    def set_candidates(
-        self, docs: Iterable[Doc], *, candidates_key: str = "candidates"
-    ) -> None:
-        """Use the spancat suggester to add a list of span candidates to a
-        list of docs. Intended to be used for debugging purposes.
-
-        docs (Iterable[Doc]): The documents to modify.
-        candidates_key (str): Key of the Doc.spans dict to save the
-            candidate spans under.
-
-        DOCS: https://spacy.io/api/spancategorizerexclusive#set_candidates
-        """
-        suggester_output = self.suggester(docs, ops=self.model.ops)
-
-        for candidates, doc in zip(suggester_output, docs):  # type: ignore
-            doc.spans[candidates_key] = []
-            for index in candidates.dataXd:
-                doc.spans[candidates_key].append(doc[index[0] : index[1]])
-
     def set_annotations(self, docs: Iterable[Doc], indices_scores) -> None:
         """Modify a batch of Doc objects, using pre-computed scores.
 
@@ -286,47 +214,6 @@ class SpanCategorizerExclusive(TrainablePipe):
             )
             offset += indices.lengths[i]
 
-    def update(
-        self,
-        examples: Iterable[Example],
-        *,
-        drop: float = 0.0,
-        sgd: Optional[Optimizer] = None,
-        losses: Optional[Dict[str, float]] = None,
-    ) -> Dict[str, float]:
-        """Learn from a batch of documents and gold-standard information,
-        updating the pipe's model. Delegates to predict and get_loss.
-        examples (Iterable[Example]): A batch of Example objects.
-
-        drop (float): The dropout rate.
-        sgd (thinc.api.Optimizer): The optimizer.
-        losses (Dict[str, float]): Optional record of the loss during training.
-            Updated using the component name as the key.
-        RETURNS (Dict[str, float]): The updated losses dictionary.
-
-        DOCS: https://spacy.io/api/spancategorizerexclusive#update
-        """
-        if losses is None:
-            losses = {}
-        losses.setdefault(self.name, 0.0)
-        validate_examples(examples, "SpanCategorizer.update")
-        self._validate_categories(examples)
-        if not any(len(eg.predicted) if eg.predicted else 0 for eg in examples):
-            # Handle cases where there are no tokens in any docs.
-            return losses
-        docs = [eg.predicted for eg in examples]
-        spans = self.suggester(docs, ops=self.model.ops)
-        if spans.lengths.sum() == 0:
-            return losses
-        set_dropout_rate(self.model, drop)
-        scores, backprop_scores = self.model.begin_update((docs, spans))
-        loss, d_scores = self.get_loss(examples, (spans, scores))
-        backprop_scores(d_scores)  # type: ignore
-        if sgd is not None:
-            self.finish_update(sgd)
-        losses[self.name] += loss
-        return losses
-
     def get_loss(
         self, examples: Iterable[Example], spans_scores: Tuple[Ragged, Floats2d]
     ) -> Tuple[float, float]: