Remove prior_prob from supported properties in Candidate. Introduce KnowledgeBase.supports_prior_probs().

spacy/errors.py

@@ -209,7 +209,10 @@ class Warnings(metaclass=ErrorsWithCodes):
             "`enabled` ({enabled}). Be aware that this might affect other components in your pipeline.")
     W124 = ("{host}:{port} is already in use, using the nearest available port {serve_port} as an alternative.")
 
+    # v4 warning strings
     W400 = ("`use_upper=False` is ignored, the upper layer is always enabled")
+    W401 = ("`incl_prior is True`, but the selected knowledge base type {kb_type} doesn't support prior probability "
+            "lookups.")
 
 
 class Errors(metaclass=ErrorsWithCodes):

spacy/kb/candidate.pyx

@@ -41,11 +41,6 @@ cdef class Candidate:
         """RETURNS (vector[float]): Entity vector."""
         raise NotImplementedError
 
-    @property
-    def prior_prob(self) -> float:
-        """RETURNS (List[float]): Entity vector."""
-        raise NotImplementedError
-
 
 cdef class InMemoryCandidate(Candidate):
     """Candidate for InMemoryLookupKB."""
@@ -89,6 +84,7 @@ cdef class InMemoryCandidate(Candidate):
 
     @property
     def prior_prob(self) -> float:
+        """RETURNS (float): Prior probability that this mention resolves to this entity."""
         return self._prior_prob
 
     @property
@@ -101,4 +97,5 @@ cdef class InMemoryCandidate(Candidate):
 
     @property
     def entity_freq(self) -> float:
+        """RETURNS (float): Entity frequency in KB corpus."""
         return self._entity_freq

spacy/kb/kb.pyx

@@ -106,3 +106,10 @@ cdef class KnowledgeBase:
         raise NotImplementedError(
             Errors.E1045.format(parent="KnowledgeBase", method="from_disk", name=self.__name__)
         )
+
+    @property
+    def supports_prior_probs(self) -> bool:
+        """RETURNS (bool): Whether this KB type supports looking up prior probabilities for entity mentions."""
+        raise NotImplementedError(
+            Errors.E1045.format(parent="KnowledgeBase", method="supports_prior_probs", name=self.__name__)
+        )

spacy/kb/kb_in_memory.pyx

@@ -283,6 +283,9 @@ cdef class InMemoryLookupKB(KnowledgeBase):
 
         return 0.0
 
+    def supports_prior_probs(self) -> bool:
+        return True
+
     def to_bytes(self, **kwargs):
         """Serialize the current state to a binary string.
         """

spacy/pipeline/entity_linker.py

@@ -1,5 +1,5 @@
-from typing import Optional, Iterable, Callable, Dict, Sequence, Union, List, Any
+import warnings
-from typing import cast
+from typing import Optional, Iterable, Callable, Dict, Sequence, Union, List, Any, cast
 from numpy import dtype
 from thinc.types import Floats1d, Floats2d, Ints1d, Ragged
 from pathlib import Path
@@ -10,14 +10,13 @@ from thinc.api import CosineDistance, Model, Optimizer, Config
 from thinc.api import set_dropout_rate
 
 from ..kb import KnowledgeBase, Candidate
-from ..ml import empty_kb
 from ..tokens import Doc, Span
 from .pipe import deserialize_config
 from .trainable_pipe import TrainablePipe
 from ..language import Language
 from ..vocab import Vocab
 from ..training import Example, validate_examples, validate_get_examples
-from ..errors import Errors
+from ..errors import Errors, Warnings
 from ..util import SimpleFrozenList, registry
 from .. import util
 from ..scorer import Scorer
@@ -240,6 +239,8 @@ class EntityLinker(TrainablePipe):
 
         if candidates_batch_size < 1:
             raise ValueError(Errors.E1044)
+        if self.incl_prior and not self.kb.supports_prior_probs:
+            warnings.warn(Warnings.W401)
 
     def set_kb(self, kb_loader: Callable[[Vocab], KnowledgeBase]):
         """Define the KB of this pipe by providing a function that will
@@ -532,8 +533,9 @@ class EntityLinker(TrainablePipe):
                         else:
                             random.shuffle(candidates)
                             # set all prior probabilities to 0 if incl_prior=False
-                            prior_probs = xp.asarray([c.prior_prob for c in candidates])
+                            if self.incl_prior and self.kb.supports_prior_probs:
-                            if not self.incl_prior:
+                                prior_probs = xp.asarray([c.prior_prob for c in candidates])  # type: ignore
+                            else:
                                 prior_probs = xp.asarray([0.0 for _ in candidates])
                             scores = prior_probs
                             # add in similarity from the context