spaCy/spacy/pipeline/spancat.py

458 lines
17 KiB
Python

import numpy
from typing import List, Dict, Callable, Tuple, Optional, Iterable, Any, cast
from thinc.api import Config, Model, get_current_ops, set_dropout_rate, Ops
from thinc.api import Optimizer
from thinc.types import Ragged, Ints2d, Floats2d, Ints1d
from ..compat import Protocol, runtime_checkable
from ..scorer import Scorer
from ..language import Language
from .trainable_pipe import TrainablePipe
from ..tokens import Doc, SpanGroup, Span
from ..vocab import Vocab
from ..training import Example, validate_examples
from ..errors import Errors
from ..util import registry
spancat_default_config = """
[model]
@architectures = "spacy.SpanCategorizer.v1"
scorer = {"@layers": "spacy.LinearLogistic.v1"}
[model.reducer]
@layers = spacy.mean_max_reducer.v1
hidden_size = 128
[model.tok2vec]
@architectures = "spacy.Tok2Vec.v1"
[model.tok2vec.embed]
@architectures = "spacy.MultiHashEmbed.v1"
width = 96
rows = [5000, 2000, 1000, 1000]
attrs = ["ORTH", "PREFIX", "SUFFIX", "SHAPE"]
include_static_vectors = false
[model.tok2vec.encode]
@architectures = "spacy.MaxoutWindowEncoder.v1"
width = ${model.tok2vec.embed.width}
window_size = 1
maxout_pieces = 3
depth = 4
"""
DEFAULT_SPANCAT_MODEL = Config().from_str(spancat_default_config)["model"]
@runtime_checkable
class Suggester(Protocol):
def __call__(self, docs: Iterable[Doc], *, ops: Optional[Ops] = None) -> Ragged:
...
@registry.misc("spacy.ngram_suggester.v1")
def build_ngram_suggester(sizes: List[int]) -> Suggester:
"""Suggest all spans of the given lengths. Spans are returned as a ragged
array of integers. The array has two columns, indicating the start and end
position."""
def ngram_suggester(docs: Iterable[Doc], *, ops: Optional[Ops] = None) -> Ragged:
if ops is None:
ops = get_current_ops()
spans = []
lengths = []
for doc in docs:
starts = ops.xp.arange(len(doc), dtype="i")
starts = starts.reshape((-1, 1))
length = 0
for size in sizes:
if size <= len(doc):
starts_size = starts[: len(doc) - (size - 1)]
spans.append(ops.xp.hstack((starts_size, starts_size + size)))
length += spans[-1].shape[0]
if spans:
assert spans[-1].ndim == 2, spans[-1].shape
lengths.append(length)
lengths_array = cast(Ints1d, ops.asarray(lengths, dtype="i"))
if len(spans) > 0:
output = Ragged(ops.xp.vstack(spans), lengths_array)
else:
output = Ragged(ops.xp.zeros((0, 0), dtype="i"), lengths_array)
assert output.dataXd.ndim == 2
return output
return ngram_suggester
@registry.misc("spacy.ngram_range_suggester.v1")
def build_ngram_range_suggester(min_size: int, max_size: int) -> Suggester:
"""Suggest all spans of the given lengths between a given min and max value - both inclusive.
Spans are returned as a ragged array of integers. The array has two columns,
indicating the start and end position."""
sizes = list(range(min_size, max_size + 1))
return build_ngram_suggester(sizes)
@Language.factory(
"spancat",
assigns=["doc.spans"],
default_config={
"threshold": 0.5,
"spans_key": "sc",
"max_positive": None,
"model": DEFAULT_SPANCAT_MODEL,
"suggester": {"@misc": "spacy.ngram_suggester.v1", "sizes": [1, 2, 3]},
"scorer": {"@scorers": "spacy.spancat_scorer.v1"},
},
default_score_weights={"spans_sc_f": 1.0, "spans_sc_p": 0.0, "spans_sc_r": 0.0},
)
def make_spancat(
nlp: Language,
name: str,
suggester: Suggester,
model: Model[Tuple[List[Doc], Ragged], Floats2d],
spans_key: str,
scorer: Optional[Callable],
threshold: float,
max_positive: Optional[int],
) -> "SpanCategorizer":
"""Create a SpanCategorizer component. The span categorizer consists of two
parts: a suggester function that proposes candidate spans, and a labeller
model that predicts one or more labels for each span.
suggester (Callable[[Iterable[Doc], Optional[Ops]], Ragged]): A function that suggests spans.
Spans are returned as a ragged array with two integer columns, for the
start and end positions.
model (Model[Tuple[List[Doc], Ragged], Floats2d]): A model instance that
is given a list of documents and (start, end) indices representing
candidate span offsets. The model predicts a probability for each category
for each span.
spans_key (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.
threshold (float): Minimum probability to consider a prediction positive.
Spans with a positive prediction will be saved on the Doc. Defaults to
0.5.
max_positive (Optional[int]): Maximum number of labels to consider positive
per span. Defaults to None, indicating no limit.
"""
return SpanCategorizer(
nlp.vocab,
suggester=suggester,
model=model,
spans_key=spans_key,
threshold=threshold,
max_positive=max_positive,
name=name,
scorer=scorer,
)
def spancat_score(examples: Iterable[Example], **kwargs) -> Dict[str, Any]:
kwargs = dict(kwargs)
attr_prefix = "spans_"
key = kwargs["spans_key"]
kwargs.setdefault("attr", f"{attr_prefix}{key}")
kwargs.setdefault("allow_overlap", True)
kwargs.setdefault(
"getter", lambda doc, key: doc.spans.get(key[len(attr_prefix) :], [])
)
kwargs.setdefault("has_annotation", lambda doc: key in doc.spans)
return Scorer.score_spans(examples, **kwargs)
@registry.scorers("spacy.spancat_scorer.v1")
def make_spancat_scorer():
return spancat_score
class SpanCategorizer(TrainablePipe):
"""Pipeline component to label spans of text.
DOCS: https://spacy.io/api/spancategorizer
"""
def __init__(
self,
vocab: Vocab,
model: Model[Tuple[List[Doc], Ragged], Floats2d],
suggester: Suggester,
name: str = "spancat",
*,
spans_key: str = "spans",
threshold: float = 0.5,
max_positive: Optional[int] = None,
scorer: Optional[Callable] = spancat_score,
) -> None:
"""Initialize the span categorizer.
vocab (Vocab): The shared vocabulary.
model (thinc.api.Model): The Thinc Model powering the pipeline component.
name (str): The component instance name, used to add entries to the
losses during training.
spans_key (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. Defaults to
`"spans"`.
threshold (float): Minimum probability to consider a prediction
positive. Spans with a positive prediction will be saved on the Doc.
Defaults to 0.5.
max_positive (Optional[int]): Maximum number of labels to consider
positive per span. Defaults to None, indicating no limit.
scorer (Optional[Callable]): The scoring method. Defaults to
Scorer.score_spans for the Doc.spans[spans_key] with overlapping
spans allowed.
DOCS: https://spacy.io/api/spancategorizer#init
"""
self.cfg = {
"labels": [],
"spans_key": spans_key,
"threshold": threshold,
"max_positive": max_positive,
}
self.vocab = vocab
self.suggester = suggester
self.model = model
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/spancategorizer#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/spancategorizer#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/spancategorizer#label_data
"""
return list(self.labels)
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/spancategorizer#predict
"""
indices = self.suggester(docs, ops=self.model.ops)
scores = self.model.predict((docs, indices)) # type: ignore
return indices, scores
def set_annotations(self, docs: Iterable[Doc], indices_scores) -> None:
"""Modify a batch of Doc objects, using pre-computed scores.
docs (Iterable[Doc]): The documents to modify.
scores: The scores to set, produced by SpanCategorizer.predict.
DOCS: https://spacy.io/api/spancategorizer#set_annotations
"""
labels = self.labels
indices, scores = indices_scores
offset = 0
for i, doc in enumerate(docs):
indices_i = indices[i].dataXd
doc.spans[self.key] = self._make_span_group(
doc, indices_i, scores[offset : offset + indices.lengths[i]], labels # type: ignore[arg-type]
)
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/spancategorizer#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]:
"""Find the loss and gradient of loss for the batch of documents and
their predicted scores.
examples (Iterable[Examples]): The batch of examples.
spans_scores: Scores representing the model's predictions.
RETURNS (Tuple[float, float]): The loss and the gradient.
DOCS: https://spacy.io/api/spancategorizer#get_loss
"""
spans, scores = spans_scores
spans = Ragged(
self.model.ops.to_numpy(spans.data), self.model.ops.to_numpy(spans.lengths)
)
label_map = {label: i for i, label in enumerate(self.labels)}
target = numpy.zeros(scores.shape, dtype=scores.dtype)
offset = 0
for i, eg in enumerate(examples):
# Map (start, end) offset of spans to the row in the d_scores array,
# so that we can adjust the gradient for predictions that were
# in the gold standard.
spans_index = {}
spans_i = spans[i].dataXd
for j in range(spans.lengths[i]):
start = int(spans_i[j, 0]) # type: ignore
end = int(spans_i[j, 1]) # type: ignore
spans_index[(start, end)] = offset + j
for gold_span in self._get_aligned_spans(eg):
key = (gold_span.start, gold_span.end)
if key in spans_index:
row = spans_index[key]
k = label_map[gold_span.label_]
target[row, k] = 1.0
# The target is a flat array for all docs. Track the position
# we're at within the flat array.
offset += spans.lengths[i]
target = self.model.ops.asarray(target, dtype="f") # type: ignore
# The target will have the values 0 (for untrue predictions) or 1
# (for true predictions).
# The scores should be in the range [0, 1].
# If the prediction is 0.9 and it's true, the gradient
# will be -0.1 (0.9 - 1.0).
# If the prediction is 0.9 and it's false, the gradient will be
# 0.9 (0.9 - 0.0)
d_scores = scores - target
loss = float((d_scores ** 2).sum())
return loss, d_scores
def initialize(
self,
get_examples: Callable[[], Iterable[Example]],
*,
nlp: Optional[Language] = None,
labels: Optional[List[str]] = None,
) -> None:
"""Initialize the pipe for training, using a representative set
of data examples.
get_examples (Callable[[], Iterable[Example]]): Function that
returns a representative sample of gold-standard Example objects.
nlp (Optional[Language]): The current nlp object the component is part of.
labels (Optional[List[str]]): The labels to add to the component, typically generated by the
`init labels` command. If no labels are provided, the get_examples
callback is used to extract the labels from the data.
DOCS: https://spacy.io/api/spancategorizer#initialize
"""
subbatch: List[Example] = []
if labels is not None:
for label in labels:
self.add_label(label)
for eg in get_examples():
if labels is None:
for span in eg.reference.spans.get(self.key, []):
self.add_label(span.label_)
if len(subbatch) < 10:
subbatch.append(eg)
self._require_labels()
if subbatch:
docs = [eg.x for eg in subbatch]
spans = build_ngram_suggester(sizes=[1])(docs)
Y = self.model.ops.alloc2f(spans.dataXd.shape[0], len(self.labels))
self.model.initialize(X=(docs, spans), Y=Y)
else:
self.model.initialize()
def _validate_categories(self, examples: Iterable[Example]):
# TODO
pass
def _get_aligned_spans(self, eg: Example):
return eg.get_aligned_spans_y2x(
eg.reference.spans.get(self.key, []), allow_overlap=True
)
def _make_span_group(
self, doc: Doc, indices: Ints2d, scores: Floats2d, labels: List[str]
) -> SpanGroup:
spans = SpanGroup(doc, name=self.key)
max_positive = self.cfg["max_positive"]
threshold = self.cfg["threshold"]
keeps = scores >= threshold
ranked = (scores * -1).argsort() # type: ignore
if max_positive is not None:
assert isinstance(max_positive, int)
span_filter = ranked[:, max_positive:]
for i, row in enumerate(span_filter):
keeps[i, row] = False
spans.attrs["scores"] = scores[keeps].flatten()
indices = self.model.ops.to_numpy(indices)
keeps = self.model.ops.to_numpy(keeps)
for i in range(indices.shape[0]):
start = indices[i, 0]
end = indices[i, 1]
for j, keep in enumerate(keeps[i]):
if keep:
spans.append(Span(doc, start, end, label=labels[j]))
return spans