spaCy/spacy/tokens/_serialize.py

135 lines
4.8 KiB
Python

# coding: utf8
from __future__ import unicode_literals
import numpy
import gzip
import srsly
from thinc.neural.ops import NumpyOps
from ..compat import copy_reg
from ..tokens import Doc
from ..attrs import SPACY, ORTH
class DocBox(object):
"""Serialize analyses from a collection of doc objects."""
def __init__(self, attrs=None, store_user_data=False):
"""Create a DocBox object, to hold serialized annotations.
attrs (list): List of attributes to serialize. 'orth' and 'spacy' are
always serialized, so they're not required. Defaults to None.
"""
attrs = attrs or []
# Ensure ORTH is always attrs[0]
self.attrs = [attr for attr in attrs if attr != ORTH and attr != SPACY]
self.attrs.insert(0, ORTH)
self.tokens = []
self.spaces = []
self.user_data = []
self.strings = set()
self.store_user_data = store_user_data
def add(self, doc):
"""Add a doc's annotations to the DocBox for serialization."""
array = doc.to_array(self.attrs)
if len(array.shape) == 1:
array = array.reshape((array.shape[0], 1))
self.tokens.append(array)
spaces = doc.to_array(SPACY)
assert array.shape[0] == spaces.shape[0]
spaces = spaces.reshape((spaces.shape[0], 1))
self.spaces.append(numpy.asarray(spaces, dtype=bool))
self.strings.update(w.text for w in doc)
if self.store_user_data:
self.user_data.append(srsly.msgpack_dumps(doc.user_data))
def get_docs(self, vocab):
"""Recover Doc objects from the annotations, using the given vocab."""
for string in self.strings:
vocab[string]
orth_col = self.attrs.index(ORTH)
for i in range(len(self.tokens)):
tokens = self.tokens[i]
spaces = self.spaces[i]
words = [vocab.strings[orth] for orth in tokens[:, orth_col]]
doc = Doc(vocab, words=words, spaces=spaces)
doc = doc.from_array(self.attrs, tokens)
if self.store_user_data:
doc.user_data.update(srsly.msgpack_loads(self.user_data[i]))
yield doc
def merge(self, other):
"""Extend the annotations of this DocBox with the annotations from another."""
assert self.attrs == other.attrs
self.tokens.extend(other.tokens)
self.spaces.extend(other.spaces)
self.strings.update(other.strings)
if self.store_user_data:
self.user_data.extend(other.user_data)
def to_bytes(self):
"""Serialize the DocBox's annotations into a byte string."""
for tokens in self.tokens:
assert len(tokens.shape) == 2, tokens.shape
lengths = [len(tokens) for tokens in self.tokens]
msg = {
"attrs": self.attrs,
"tokens": numpy.vstack(self.tokens).tobytes("C"),
"spaces": numpy.vstack(self.spaces).tobytes("C"),
"lengths": numpy.asarray(lengths, dtype="int32").tobytes("C"),
"strings": list(self.strings),
}
if self.store_user_data:
msg["user_data"] = self.user_data
return gzip.compress(srsly.msgpack_dumps(msg))
def from_bytes(self, string):
"""Deserialize the DocBox's annotations from a byte string."""
msg = srsly.msgpack_loads(gzip.decompress(string))
self.attrs = msg["attrs"]
self.strings = set(msg["strings"])
lengths = numpy.fromstring(msg["lengths"], dtype="int32")
flat_spaces = numpy.fromstring(msg["spaces"], dtype=bool)
flat_tokens = numpy.fromstring(msg["tokens"], dtype="uint64")
shape = (flat_tokens.size // len(self.attrs), len(self.attrs))
flat_tokens = flat_tokens.reshape(shape)
flat_spaces = flat_spaces.reshape((flat_spaces.size, 1))
self.tokens = NumpyOps().unflatten(flat_tokens, lengths)
self.spaces = NumpyOps().unflatten(flat_spaces, lengths)
if self.store_user_data and "user_data" in msg:
self.user_data = list(msg["user_data"])
for tokens in self.tokens:
assert len(tokens.shape) == 2, tokens.shape
return self
def merge_boxes(boxes):
merged = None
for byte_string in boxes:
if byte_string is not None:
box = DocBox(store_user_data=True).from_bytes(byte_string)
if merged is None:
merged = box
else:
merged.merge(box)
if merged is not None:
return merged.to_bytes()
else:
return b""
def pickle_box(box):
return (unpickle_box, (box.to_bytes(),))
def unpickle_box(byte_string):
return DocBox().from_bytes(byte_string)
copy_reg.pickle(DocBox, pickle_box, unpickle_box)
# Compatibility, as we had named it this previously.
Binder = DocBox
__all__ = ["DocBox"]