mirror of
https://github.com/explosion/spaCy.git
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d093d6343b
* rename Pipe to TrainablePipe * split functionality between Pipe and TrainablePipe * remove unnecessary methods from certain components * cleanup * hasattr(component, "pipe") should be sufficient again * remove serialization and vocab/cfg from Pipe * unify _ensure_examples and validate_examples * small fixes * hasattr checks for self.cfg and self.vocab * make is_resizable and is_trainable properties * serialize strings.json instead of vocab * fix KB IO + tests * fix typos * more typos * _added_strings as a set * few more tests specifically for _added_strings field * bump to 3.0.0a36
106 lines
3.8 KiB
Cython
106 lines
3.8 KiB
Cython
# cython: infer_types=True, profile=True
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import warnings
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from typing import Optional, Tuple, Iterable, Iterator, Callable, Union, Dict
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import srsly
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from ..tokens.doc cimport Doc
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from ..training import Example
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from ..errors import Errors, Warnings
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from ..language import Language
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cdef class Pipe:
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"""This class is a base class and not instantiated directly. It provides
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an interface for pipeline components to implement.
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Trainable pipeline components like the EntityRecognizer or TextCategorizer
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should inherit from the subclass 'TrainablePipe'.
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DOCS: https://nightly.spacy.io/api/pipe
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"""
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@classmethod
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def __init_subclass__(cls, **kwargs):
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"""Raise a warning if an inheriting class implements 'begin_training'
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(from v2) instead of the new 'initialize' method (from v3)"""
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if hasattr(cls, "begin_training"):
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warnings.warn(Warnings.W088.format(name=cls.__name__))
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def __call__(self, Doc doc) -> Doc:
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"""Apply the pipe to one document. The document is modified in place,
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and returned. This usually happens under the hood when the nlp object
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is called on a text and all components are applied to the Doc.
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docs (Doc): The Doc to process.
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RETURNS (Doc): The processed Doc.
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DOCS: https://nightly.spacy.io/api/pipe#call
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"""
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raise NotImplementedError(Errors.E931.format(parent="Pipe", method="__call__", name=self.name))
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def pipe(self, stream: Iterable[Doc], *, batch_size: int=128) -> Iterator[Doc]:
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"""Apply the pipe to a stream of documents. This usually happens under
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the hood when the nlp object is called on a text and all components are
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applied to the Doc.
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stream (Iterable[Doc]): A stream of documents.
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batch_size (int): The number of documents to buffer.
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YIELDS (Doc): Processed documents in order.
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DOCS: https://nightly.spacy.io/api/pipe#pipe
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"""
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for doc in stream:
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doc = self(doc)
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yield doc
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def initialize(self, get_examples: Callable[[], Iterable[Example]], *, nlp: Language=None):
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"""Initialize the pipe. For non-trainable components, this method
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is optional. For trainable components, which should inherit
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from the subclass TrainablePipe, the provided data examples
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should be used to ensure that the internal model is initialized
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properly and all input/output dimensions throughout the network are
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inferred.
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get_examples (Callable[[], Iterable[Example]]): Function that
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returns a representative sample of gold-standard Example objects.
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nlp (Language): The current nlp object the component is part of.
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DOCS: https://nightly.spacy.io/api/pipe#initialize
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"""
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pass
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def score(self, examples: Iterable[Example], **kwargs) -> Dict[str, Union[float, Dict[str, float]]]:
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"""Score a batch of examples.
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examples (Iterable[Example]): The examples to score.
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RETURNS (Dict[str, Any]): The scores.
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DOCS: https://nightly.spacy.io/api/pipe#score
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"""
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return {}
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@property
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def is_trainable(self) -> bool:
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return False
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@property
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def labels(self) -> Optional[Tuple[str]]:
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return tuple()
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@property
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def label_data(self):
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"""Optional JSON-serializable data that would be sufficient to recreate
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the label set if provided to the `pipe.initialize()` method.
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"""
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return None
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def _require_labels(self) -> None:
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"""Raise an error if this component has no labels defined."""
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if not self.labels or list(self.labels) == [""]:
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raise ValueError(Errors.E143.format(name=self.name))
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def deserialize_config(path):
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if path.exists():
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return srsly.read_json(path)
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else:
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return {}
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