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https://github.com/explosion/spaCy.git
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trainable_lemmatizer/entity_linker: add store_activations option
This commit is contained in:
Daniël de Kok
parent
8772b9ccc4
commit
1c9be0d8ab
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@ -7,7 +7,7 @@ import numpy as np
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import srsly
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from thinc.api import Config, Model, SequenceCategoricalCrossentropy
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from thinc.types import Floats2d, Ints1d, Ints2d
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from thinc.types import ArrayXd, Floats2d, Ints1d
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from ._edit_tree_internals.edit_trees import EditTrees
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from ._edit_tree_internals.schemas import validate_edit_tree
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@ -21,6 +21,9 @@ from ..vocab import Vocab
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from .. import util
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ActivationsT = Dict[str, Union[List[Floats2d], List[Ints1d]]]
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default_model_config = """
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[model]
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@architectures = "spacy.Tagger.v2"
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@ -49,6 +52,7 @@ DEFAULT_EDIT_TREE_LEMMATIZER_MODEL = Config().from_str(default_model_config)["mo
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"overwrite": False,
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"top_k": 1,
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"scorer": {"@scorers": "spacy.lemmatizer_scorer.v1"},
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"store_activations": False,
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},
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default_score_weights={"lemma_acc": 1.0},
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)
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@ -61,6 +65,7 @@ def make_edit_tree_lemmatizer(
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overwrite: bool,
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top_k: int,
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scorer: Optional[Callable],
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store_activations: Union[bool, List[str]],
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):
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"""Construct an EditTreeLemmatizer component."""
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return EditTreeLemmatizer(
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@ -72,6 +77,7 @@ def make_edit_tree_lemmatizer(
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overwrite=overwrite,
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top_k=top_k,
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scorer=scorer,
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store_activations=store_activations,
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)
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@ -91,6 +97,7 @@ class EditTreeLemmatizer(TrainablePipe):
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overwrite: bool = False,
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top_k: int = 1,
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scorer: Optional[Callable] = lemmatizer_score,
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store_activations=False,
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):
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"""
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Construct an edit tree lemmatizer.
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@ -116,6 +123,7 @@ class EditTreeLemmatizer(TrainablePipe):
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self.cfg: Dict[str, Any] = {"labels": []}
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self.scorer = scorer
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self.store_activations = store_activations # type: ignore
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def get_loss(
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self, examples: Iterable[Example], scores: List[Floats2d]
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@ -144,21 +152,24 @@ class EditTreeLemmatizer(TrainablePipe):
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return float(loss), d_scores
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def predict(self, docs: Iterable[Doc]) -> List[Ints2d]:
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def predict(self, docs: Iterable[Doc]) -> ActivationsT:
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n_docs = len(list(docs))
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if not any(len(doc) for doc in docs):
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# Handle cases where there are no tokens in any docs.
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n_labels = len(self.cfg["labels"])
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guesses: List[Ints2d] = [
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guesses: List[Ints1d] = [
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self.model.ops.alloc((0,), dtype="i") for doc in docs
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]
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scores: List[Floats2d] = [
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self.model.ops.alloc((0, n_labels), dtype="i") for doc in docs
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]
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assert len(guesses) == n_docs
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return guesses
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return {"probs": scores, "guesses": guesses}
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scores = self.model.predict(docs)
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assert len(scores) == n_docs
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guesses = self._scores2guesses(docs, scores)
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assert len(guesses) == n_docs
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return guesses
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return {"probs": scores, "guesses": guesses}
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def _scores2guesses(self, docs, scores):
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guesses = []
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@ -186,8 +197,12 @@ class EditTreeLemmatizer(TrainablePipe):
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return guesses
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def set_annotations(self, docs: Iterable[Doc], batch_tree_ids):
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def set_annotations(self, docs: Iterable[Doc], activations: ActivationsT):
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batch_tree_ids = activations["guesses"]
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for i, doc in enumerate(docs):
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doc.activations[self.name] = {}
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for activation in self.store_activations:
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doc.activations[self.name][activation] = activations[activation][i]
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doc_tree_ids = batch_tree_ids[i]
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if hasattr(doc_tree_ids, "get"):
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doc_tree_ids = doc_tree_ids.get()
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@ -377,3 +392,7 @@ class EditTreeLemmatizer(TrainablePipe):
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self.tree2label[tree_id] = len(self.cfg["labels"])
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self.cfg["labels"].append(tree_id)
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return self.tree2label[tree_id]
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@property
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def activations(self):
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return ["probs", "guesses"]
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@ -1,5 +1,7 @@
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from typing import Optional, Iterable, Callable, Dict, Union, List, Any
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from thinc.types import Floats2d
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from typing import cast
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from numpy import dtype
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from thinc.types import Floats2d, Ragged
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from pathlib import Path
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from itertools import islice
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import srsly
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@ -21,6 +23,9 @@ from ..util import SimpleFrozenList, registry
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from .. import util
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from ..scorer import Scorer
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ActivationsT = Dict[str, Union[List[Ragged], List[str]]]
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# See #9050
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BACKWARD_OVERWRITE = True
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@ -56,6 +61,7 @@ DEFAULT_NEL_MODEL = Config().from_str(default_model_config)["model"]
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"overwrite": True,
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"scorer": {"@scorers": "spacy.entity_linker_scorer.v1"},
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"use_gold_ents": True,
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"store_activations": False,
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},
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default_score_weights={
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"nel_micro_f": 1.0,
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@ -77,6 +83,7 @@ def make_entity_linker(
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overwrite: bool,
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scorer: Optional[Callable],
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use_gold_ents: bool,
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store_activations: Union[bool, List[str]],
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):
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"""Construct an EntityLinker component.
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@ -121,6 +128,7 @@ def make_entity_linker(
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overwrite=overwrite,
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scorer=scorer,
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use_gold_ents=use_gold_ents,
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store_activations=store_activations,
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)
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@ -156,6 +164,7 @@ class EntityLinker(TrainablePipe):
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overwrite: bool = BACKWARD_OVERWRITE,
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scorer: Optional[Callable] = entity_linker_score,
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use_gold_ents: bool,
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store_activations=False,
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) -> None:
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"""Initialize an entity linker.
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@ -192,6 +201,7 @@ class EntityLinker(TrainablePipe):
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self.kb = empty_kb(entity_vector_length)(self.vocab)
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self.scorer = scorer
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self.use_gold_ents = use_gold_ents
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self.store_activations = store_activations
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def set_kb(self, kb_loader: Callable[[Vocab], KnowledgeBase]):
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"""Define the KB of this pipe by providing a function that will
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@ -377,7 +387,7 @@ class EntityLinker(TrainablePipe):
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loss = loss / len(entity_encodings)
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return float(loss), out
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def predict(self, docs: Iterable[Doc]) -> List[str]:
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def predict(self, docs: Iterable[Doc]) -> ActivationsT:
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"""Apply the pipeline's model to a batch of docs, without modifying them.
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Returns the KB IDs for each entity in each doc, including NIL if there is
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no prediction.
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@ -390,13 +400,21 @@ class EntityLinker(TrainablePipe):
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self.validate_kb()
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entity_count = 0
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final_kb_ids: List[str] = []
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xp = self.model.ops.xp
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ops = self.model.ops
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xp = ops.xp
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docs_ents: List[Ragged] = []
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docs_scores: List[Ragged] = []
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if not docs:
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return final_kb_ids
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return {"kb_ids": final_kb_ids, "ents": docs_ents, "scores": docs_scores}
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if isinstance(docs, Doc):
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docs = [docs]
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for i, doc in enumerate(docs):
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for doc in docs:
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doc_ents = []
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doc_scores = []
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doc_scores_lens: List[int] = []
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if len(doc) == 0:
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doc_scores.append(Ragged(ops.alloc1f(0), ops.alloc1i(0)))
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doc_ents.append(Ragged(xp.zeros(0, dtype="uint64"), ops.alloc1i(0)))
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continue
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sentences = [s for s in doc.sents]
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# Looping through each entity (TODO: rewrite)
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@ -419,11 +437,17 @@ class EntityLinker(TrainablePipe):
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if ent.label_ in self.labels_discard:
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# ignoring this entity - setting to NIL
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final_kb_ids.append(self.NIL)
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self._add_activations(
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doc_scores, doc_scores_lens, doc_ents, [0.0], [0]
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)
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else:
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candidates = list(self.get_candidates(self.kb, ent))
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if not candidates:
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# no prediction possible for this entity - setting to NIL
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final_kb_ids.append(self.NIL)
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self._add_activations(
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doc_scores, doc_scores_lens, doc_ents, [0.0], [0]
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)
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elif len(candidates) == 1:
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# shortcut for efficiency reasons: take the 1 candidate
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# TODO: thresholding
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@ -456,30 +480,48 @@ class EntityLinker(TrainablePipe):
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raise ValueError(Errors.E161)
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scores = prior_probs + sims - (prior_probs * sims)
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# TODO: thresholding
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self._add_activations(
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doc_scores,
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doc_scores_lens,
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doc_ents,
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scores,
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[c.entity for c in candidates],
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)
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best_index = scores.argmax().item()
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best_candidate = candidates[best_index]
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final_kb_ids.append(best_candidate.entity_)
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self._add_doc_activations(
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docs_scores, docs_ents, doc_scores, doc_scores_lens, doc_ents
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)
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if not (len(final_kb_ids) == entity_count):
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err = Errors.E147.format(
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method="predict", msg="result variables not of equal length"
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)
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raise RuntimeError(err)
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return final_kb_ids
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return {"kb_ids": final_kb_ids, "ents": docs_ents, "scores": docs_scores}
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def set_annotations(self, docs: Iterable[Doc], kb_ids: List[str]) -> None:
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def set_annotations(self, docs: Iterable[Doc], activations: ActivationsT) -> None:
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"""Modify a batch of documents, using pre-computed scores.
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docs (Iterable[Doc]): The documents to modify.
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kb_ids (List[str]): The IDs to set, produced by EntityLinker.predict.
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activations (List[str]): The activations used for setting annotations, produced
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by EntityLinker.predict.
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DOCS: https://spacy.io/api/entitylinker#set_annotations
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"""
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kb_ids = cast(List[str], activations["kb_ids"])
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count_ents = len([ent for doc in docs for ent in doc.ents])
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if count_ents != len(kb_ids):
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raise ValueError(Errors.E148.format(ents=count_ents, ids=len(kb_ids)))
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i = 0
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overwrite = self.cfg["overwrite"]
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for doc in docs:
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for j, doc in enumerate(docs):
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doc.activations[self.name] = {}
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for activation in self.store_activations:
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# We only copy activations that are Ragged.
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doc.activations[self.name][activation] = cast(
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Ragged, activations[activation][j]
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)
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for ent in doc.ents:
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kb_id = kb_ids[i]
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i += 1
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@ -578,3 +620,30 @@ class EntityLinker(TrainablePipe):
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def add_label(self, label):
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raise NotImplementedError
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@property
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def activations(self):
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return ["ents", "scores"]
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def _add_doc_activations(
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self, docs_scores, docs_ents, doc_scores, doc_scores_lens, doc_ents
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):
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if len(self.store_activations) == 0:
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return
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ops = self.model.ops
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docs_scores.append(
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Ragged(ops.flatten(doc_scores), ops.asarray1i(doc_scores_lens))
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)
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docs_ents.append(
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Ragged(
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ops.flatten(doc_ents, dtype="uint64"), ops.asarray1i(doc_scores_lens)
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)
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)
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def _add_activations(self, doc_scores, doc_scores_lens, doc_ents, scores, ents):
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if len(self.store_activations) == 0:
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return
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ops = self.model.ops
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doc_scores.append(ops.asarray1f(scores))
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doc_scores_lens.append(doc_scores[-1].shape[0])
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doc_ents.append(ops.xp.array(ents, dtype="uint64"))
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@ -1,3 +1,4 @@
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from typing import cast
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import pickle
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import pytest
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from hypothesis import given
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@ -6,6 +7,7 @@ from spacy import util
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from spacy.lang.en import English
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from spacy.language import Language
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from spacy.pipeline._edit_tree_internals.edit_trees import EditTrees
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from spacy.pipeline.trainable_pipe import TrainablePipe
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from spacy.training import Example
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from spacy.strings import StringStore
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from spacy.util import make_tempdir
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@ -278,3 +280,28 @@ def test_empty_strings():
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no_change = trees.add("xyz", "xyz")
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empty = trees.add("", "")
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assert no_change == empty
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def test_store_activations():
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nlp = English()
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lemmatizer = cast(TrainablePipe, nlp.add_pipe("trainable_lemmatizer"))
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lemmatizer.min_tree_freq = 1
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train_examples = []
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for t in TRAIN_DATA:
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train_examples.append(Example.from_dict(nlp.make_doc(t[0]), t[1]))
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nlp.initialize(get_examples=lambda: train_examples)
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nO = lemmatizer.model.get_dim("nO")
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doc = nlp("This is a test.")
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assert len(list(doc.activations["trainable_lemmatizer"].keys())) == 0
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lemmatizer.store_activations = True
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doc = nlp("This is a test.")
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assert list(doc.activations["trainable_lemmatizer"].keys()) == ["probs", "guesses"]
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assert doc.activations["trainable_lemmatizer"]["probs"].shape == (5, nO)
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assert doc.activations["trainable_lemmatizer"]["guesses"].shape == (5,)
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lemmatizer.store_activations = ["probs"]
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doc = nlp("This is a test.")
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assert list(doc.activations["trainable_lemmatizer"].keys()) == ["probs"]
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assert doc.activations["trainable_lemmatizer"]["probs"].shape == (5, nO)
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@ -1,7 +1,8 @@
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from typing import Callable, Iterable
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from typing import Callable, Iterable, cast
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import pytest
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from numpy.testing import assert_equal
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from thinc.types import Ragged
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from spacy import registry, util
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from spacy.attrs import ENT_KB_ID
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@ -9,7 +10,7 @@ from spacy.compat import pickle
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from spacy.kb import Candidate, KnowledgeBase, get_candidates
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from spacy.lang.en import English
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from spacy.ml import load_kb
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from spacy.pipeline import EntityLinker
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from spacy.pipeline import EntityLinker, TrainablePipe
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from spacy.pipeline.legacy import EntityLinker_v1
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from spacy.pipeline.tok2vec import DEFAULT_TOK2VEC_MODEL
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from spacy.scorer import Scorer
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@ -1115,3 +1116,79 @@ def test_tokenization_mismatch():
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nlp.add_pipe("sentencizer", first=True)
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results = nlp.evaluate(train_examples)
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def test_store_activations():
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nlp = English()
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vector_length = 3
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assert "Q2146908" not in nlp.vocab.strings
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# Convert the texts to docs to make sure we have doc.ents set for the training examples
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train_examples = []
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for text, annotation in TRAIN_DATA:
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doc = nlp(text)
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train_examples.append(Example.from_dict(doc, annotation))
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def create_kb(vocab):
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# create artificial KB - assign same prior weight to the two russ cochran's
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# Q2146908 (Russ Cochran): American golfer
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# Q7381115 (Russ Cochran): publisher
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mykb = KnowledgeBase(vocab, entity_vector_length=vector_length)
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mykb.add_entity(entity="Q2146908", freq=12, entity_vector=[6, -4, 3])
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mykb.add_entity(entity="Q7381115", freq=12, entity_vector=[9, 1, -7])
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mykb.add_alias(
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alias="Russ Cochran",
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entities=["Q2146908", "Q7381115"],
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probabilities=[0.5, 0.5],
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)
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return mykb
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# Create the Entity Linker component and add it to the pipeline
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entity_linker = cast(TrainablePipe, nlp.add_pipe("entity_linker", last=True))
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assert isinstance(entity_linker, EntityLinker)
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entity_linker.set_kb(create_kb)
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assert "Q2146908" in entity_linker.vocab.strings
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assert "Q2146908" in entity_linker.kb.vocab.strings
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# initialize the NEL pipe
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optimizer = nlp.initialize(get_examples=lambda: train_examples)
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for i in range(2):
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losses = {}
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nlp.update(train_examples, sgd=optimizer, losses=losses)
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nO = entity_linker.model.get_dim("nO")
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nlp.add_pipe("sentencizer", first=True)
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patterns = [
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{"label": "PERSON", "pattern": [{"LOWER": "russ"}, {"LOWER": "cochran"}]},
|
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{"label": "ORG", "pattern": [{"LOWER": "ec"}, {"LOWER": "comics"}]},
|
||||
]
|
||||
ruler = nlp.add_pipe("entity_ruler", before="entity_linker")
|
||||
ruler.add_patterns(patterns)
|
||||
|
||||
doc = nlp("Russ Cochran was a publisher")
|
||||
assert len(doc.activations["entity_linker"].keys()) == 0
|
||||
|
||||
entity_linker.store_activations = True
|
||||
doc = nlp("Russ Cochran was a publisher")
|
||||
assert set(doc.activations["entity_linker"].keys()) == {"ents", "scores"}
|
||||
ents = doc.activations["entity_linker"]["ents"]
|
||||
assert isinstance(ents, Ragged)
|
||||
assert ents.data.shape == (2, 1)
|
||||
assert ents.data.dtype == "uint64"
|
||||
assert ents.lengths.shape == (1,)
|
||||
scores = doc.activations["entity_linker"]["scores"]
|
||||
assert isinstance(scores, Ragged)
|
||||
assert scores.data.shape == (2, 1)
|
||||
assert scores.data.dtype == "float32"
|
||||
assert scores.lengths.shape == (1,)
|
||||
|
||||
entity_linker.store_activations = ["scores"]
|
||||
doc = nlp("Russ Cochran was a publisher")
|
||||
assert set(doc.activations["entity_linker"].keys()) == {"scores"}
|
||||
scores = doc.activations["entity_linker"]["scores"]
|
||||
assert isinstance(scores, Ragged)
|
||||
assert scores.data.shape == (2, 1)
|
||||
assert scores.data.dtype == "float32"
|
||||
assert scores.lengths.shape == (1,)
|
||||
|
|
|
|||
|
|
@ -1,7 +1,7 @@
|
|||
from typing import Callable, Protocol, Iterable, Iterator, Optional
|
||||
from typing import Union, Tuple, List, Dict, Any, overload
|
||||
from cymem.cymem import Pool
|
||||
from thinc.types import ArrayXd, Floats1d, Floats2d, Ints2d
|
||||
from thinc.types import ArrayXd, Floats1d, Floats2d, Ints2d, Ragged
|
||||
from .span import Span
|
||||
from .token import Token
|
||||
from ._dict_proxies import SpanGroups
|
||||
|
|
@ -22,7 +22,7 @@ class Doc:
|
|||
max_length: int
|
||||
length: int
|
||||
sentiment: float
|
||||
activations: Dict[str, Dict[str, ArrayXd]]
|
||||
activations: Dict[str, Dict[str, Union[ArrayXd, Ragged]]]
|
||||
cats: Dict[str, float]
|
||||
user_hooks: Dict[str, Callable[..., Any]]
|
||||
user_token_hooks: Dict[str, Callable[..., Any]]
|
||||
|
|
|
|||
Loading…
Reference in New Issue
Block a user