mirror of
https://github.com/explosion/spaCy.git
synced 2024-12-27 10:26:35 +03:00
311 lines
12 KiB
Python
311 lines
12 KiB
Python
import zlib
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from pathlib import Path
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from typing import Dict, Iterable, Iterator, List, Optional, Set, Union
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import numpy
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import srsly
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from numpy import ndarray
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from thinc.api import NumpyOps
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from ..attrs import IDS, ORTH, SPACY, intify_attr
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from ..compat import copy_reg
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from ..errors import Errors
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from ..util import SimpleFrozenList, ensure_path
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from ..vocab import Vocab
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from .doc import Doc
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from .span_groups import SpanGroups
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# fmt: off
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ALL_ATTRS = ("ORTH", "NORM", "TAG", "HEAD", "DEP", "ENT_IOB", "ENT_TYPE", "ENT_KB_ID", "ENT_ID", "LEMMA", "MORPH", "POS", "SENT_START")
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# fmt: on
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class DocBin:
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"""Pack Doc objects for binary serialization.
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The DocBin class lets you efficiently serialize the information from a
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collection of Doc objects. You can control which information is serialized
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by passing a list of attribute IDs, and optionally also specify whether the
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user data is serialized. The DocBin is faster and produces smaller data
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sizes than pickle, and allows you to deserialize without executing arbitrary
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Python code.
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The serialization format is gzipped msgpack, where the msgpack object has
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the following structure:
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{
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"attrs": List[uint64], # e.g. [TAG, HEAD, ENT_IOB, ENT_TYPE]
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"tokens": bytes, # Serialized numpy uint64 array with the token data
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"spans": List[Dict[str, bytes]], # SpanGroups data for each doc
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"spaces": bytes, # Serialized numpy boolean array with spaces data
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"lengths": bytes, # Serialized numpy int32 array with the doc lengths
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"strings": List[str] # List of unique strings in the token data
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"version": str, # DocBin version number
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}
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Strings for the words, tags, labels etc are represented by 64-bit hashes in
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the token data, and every string that occurs at least once is passed via the
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strings object. This means the storage is more efficient if you pack more
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documents together, because you have less duplication in the strings.
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A notable downside to this format is that you can't easily extract just one
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document from the DocBin.
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"""
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def __init__(
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self,
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attrs: Iterable[str] = ALL_ATTRS,
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store_user_data: bool = False,
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docs: Iterable[Doc] = SimpleFrozenList(),
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) -> None:
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"""Create a DocBin object to hold serialized annotations.
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attrs (Iterable[str]): List of attributes to serialize. 'orth' and
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'spacy' are always serialized, so they're not required.
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store_user_data (bool): Whether to write the `Doc.user_data` to bytes/file.
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docs (Iterable[Doc]): Docs to add.
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DOCS: https://spacy.io/api/docbin#init
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"""
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int_attrs = [intify_attr(attr) for attr in attrs]
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if None in int_attrs:
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non_valid = [attr for attr in attrs if intify_attr(attr) is None]
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raise KeyError(
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Errors.E983.format(dict="attrs", key=non_valid, keys=IDS.keys())
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) from None
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attrs = sorted(int_attrs)
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self.version = "0.1"
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self.attrs = [attr for attr in attrs if attr != ORTH and attr != SPACY]
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self.attrs.insert(0, ORTH) # Ensure ORTH is always attrs[0]
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self.tokens: List[ndarray] = []
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self.spaces: List[ndarray] = []
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self.cats: List[Dict] = []
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self.span_groups: List[bytes] = []
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self.user_data: List[Optional[bytes]] = []
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self.flags: List[Dict] = []
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self.strings: Set[str] = set()
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self.store_user_data = store_user_data
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for doc in docs:
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self.add(doc)
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def __len__(self) -> int:
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"""RETURNS: The number of Doc objects added to the DocBin."""
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return len(self.tokens)
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def add(self, doc: Doc) -> None:
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"""Add a Doc's annotations to the DocBin for serialization.
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doc (Doc): The Doc object to add.
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DOCS: https://spacy.io/api/docbin#add
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"""
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array = doc.to_array(self.attrs)
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if len(array.shape) == 1:
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array = array.reshape((array.shape[0], 1))
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self.tokens.append(array)
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spaces = doc.to_array(SPACY)
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assert array.shape[0] == spaces.shape[0] # this should never happen
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spaces = spaces.reshape((spaces.shape[0], 1))
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self.spaces.append(numpy.asarray(spaces, dtype=bool))
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self.flags.append({"has_unknown_spaces": doc.has_unknown_spaces})
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for token in doc:
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self.strings.add(token.text)
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self.strings.add(token.tag_)
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self.strings.add(token.lemma_)
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self.strings.add(token.norm_)
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self.strings.add(str(token.morph))
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self.strings.add(token.dep_)
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self.strings.add(token.ent_type_)
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self.strings.add(token.ent_kb_id_)
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self.strings.add(token.ent_id_)
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self.cats.append(doc.cats)
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if self.store_user_data:
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self.user_data.append(srsly.msgpack_dumps(doc.user_data))
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self.span_groups.append(doc.spans.to_bytes())
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for key, group in doc.spans.items():
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for span in group:
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self.strings.add(span.label_)
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if span.kb_id in span.doc.vocab.strings:
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self.strings.add(span.kb_id_)
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if span.id in span.doc.vocab.strings:
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self.strings.add(span.id_)
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def get_docs(self, vocab: Vocab) -> Iterator[Doc]:
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"""Recover Doc objects from the annotations, using the given vocab.
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Note that the user data of each doc will be read (if available) and returned,
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regardless of the setting of 'self.store_user_data'.
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vocab (Vocab): The shared vocab.
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YIELDS (Doc): The Doc objects.
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DOCS: https://spacy.io/api/docbin#get_docs
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"""
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for string in self.strings:
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vocab[string]
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orth_col = self.attrs.index(ORTH)
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for i in range(len(self.tokens)):
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flags = self.flags[i]
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tokens = self.tokens[i]
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spaces: Optional[ndarray] = self.spaces[i]
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if flags.get("has_unknown_spaces"):
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spaces = None
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doc = Doc(vocab, words=tokens[:, orth_col], spaces=spaces) # type: ignore
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doc = doc.from_array(self.attrs, tokens) # type: ignore
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doc.cats = self.cats[i]
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# backwards-compatibility: may be b'' or serialized empty list
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if self.span_groups[i] and self.span_groups[i] != SpanGroups._EMPTY_BYTES:
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doc.spans.from_bytes(self.span_groups[i])
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else:
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doc.spans.clear()
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if i < len(self.user_data) and self.user_data[i] is not None:
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user_data = srsly.msgpack_loads(self.user_data[i], use_list=False)
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doc.user_data.update(user_data)
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yield doc
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def merge(self, other: "DocBin") -> None:
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"""Extend the annotations of this DocBin with the annotations from
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another. Will raise an error if the pre-defined attrs of the two
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DocBins don't match, or if they differ in whether or not to store
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user data.
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other (DocBin): The DocBin to merge into the current bin.
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DOCS: https://spacy.io/api/docbin#merge
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"""
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if self.attrs != other.attrs:
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raise ValueError(
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Errors.E166.format(param="attrs", current=self.attrs, other=other.attrs)
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)
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if self.store_user_data != other.store_user_data:
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raise ValueError(
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Errors.E166.format(
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param="store_user_data",
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current=self.store_user_data,
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other=other.store_user_data,
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)
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)
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self.tokens.extend(other.tokens)
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self.spaces.extend(other.spaces)
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self.strings.update(other.strings)
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self.cats.extend(other.cats)
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self.span_groups.extend(other.span_groups)
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self.flags.extend(other.flags)
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self.user_data.extend(other.user_data)
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def to_bytes(self) -> bytes:
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"""Serialize the DocBin's annotations to a bytestring.
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RETURNS (bytes): The serialized DocBin.
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DOCS: https://spacy.io/api/docbin#to_bytes
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"""
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for tokens in self.tokens:
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assert len(tokens.shape) == 2, tokens.shape # this should never happen
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lengths = [len(tokens) for tokens in self.tokens]
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tokens = numpy.vstack(self.tokens) if self.tokens else numpy.asarray([])
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spaces = numpy.vstack(self.spaces) if self.spaces else numpy.asarray([])
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msg = {
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"version": self.version,
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"attrs": self.attrs,
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"tokens": tokens.tobytes("C"),
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"spaces": spaces.tobytes("C"),
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"lengths": numpy.asarray(lengths, dtype="int32").tobytes("C"),
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"strings": list(sorted(self.strings)),
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"cats": self.cats,
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"flags": self.flags,
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"span_groups": self.span_groups,
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}
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if self.store_user_data:
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msg["user_data"] = self.user_data
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return zlib.compress(srsly.msgpack_dumps(msg))
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def from_bytes(self, bytes_data: bytes) -> "DocBin":
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"""Deserialize the DocBin's annotations from a bytestring.
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bytes_data (bytes): The data to load from.
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RETURNS (DocBin): The loaded DocBin.
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DOCS: https://spacy.io/api/docbin#from_bytes
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"""
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try:
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msg = srsly.msgpack_loads(zlib.decompress(bytes_data))
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except zlib.error:
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raise ValueError(Errors.E1014)
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self.attrs = msg["attrs"]
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self.strings = set(msg["strings"])
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lengths = numpy.frombuffer(msg["lengths"], dtype="int32")
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flat_spaces = numpy.frombuffer(msg["spaces"], dtype=bool)
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flat_tokens = numpy.frombuffer(msg["tokens"], dtype="uint64")
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shape = (flat_tokens.size // len(self.attrs), len(self.attrs))
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flat_tokens = flat_tokens.reshape(shape)
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flat_spaces = flat_spaces.reshape((flat_spaces.size, 1))
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self.tokens = NumpyOps().unflatten(flat_tokens, lengths)
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self.spaces = NumpyOps().unflatten(flat_spaces, lengths)
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self.cats = msg["cats"]
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self.span_groups = msg.get("span_groups", [b"" for _ in lengths])
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self.flags = msg.get("flags", [{} for _ in lengths])
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if "user_data" in msg:
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self.user_data = list(msg["user_data"])
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else:
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self.user_data = [None] * len(self)
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for tokens in self.tokens:
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assert len(tokens.shape) == 2, tokens.shape # this should never happen
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return self
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def to_disk(self, path: Union[str, Path]) -> None:
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"""Save the DocBin to a file (typically called .spacy).
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path (str / Path): The file path.
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DOCS: https://spacy.io/api/docbin#to_disk
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"""
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path = ensure_path(path)
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with path.open("wb") as file_:
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try:
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file_.write(self.to_bytes())
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except ValueError:
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raise ValueError(Errors.E870)
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def from_disk(self, path: Union[str, Path]) -> "DocBin":
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"""Load the DocBin from a file (typically called .spacy).
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path (str / Path): The file path.
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RETURNS (DocBin): The loaded DocBin.
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DOCS: https://spacy.io/api/docbin#to_disk
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"""
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path = ensure_path(path)
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with path.open("rb") as file_:
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self.from_bytes(file_.read())
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return self
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def merge_bins(bins):
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merged = None
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for byte_string in bins:
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if byte_string is not None:
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doc_bin = DocBin(store_user_data=True).from_bytes(byte_string)
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if merged is None:
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merged = doc_bin
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else:
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merged.merge(doc_bin)
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if merged is not None:
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return merged.to_bytes()
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else:
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return b""
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def pickle_bin(doc_bin):
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return (unpickle_bin, (doc_bin.to_bytes(),))
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def unpickle_bin(byte_string):
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return DocBin().from_bytes(byte_string)
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copy_reg.pickle(DocBin, pickle_bin, unpickle_bin)
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# Compatibility, as we had named it this previously.
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Binder = DocBin
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__all__ = ["DocBin"]
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