spaCy/spacy/tokens/_serialize.py
2018-09-28 15:23:14 +02:00

118 lines
4.1 KiB
Python

from __future__ import unicode_literals
import numpy
import msgpack
import gzip
from thinc.neural.ops import NumpyOps
from ..compat import copy_reg
from ..tokens import Doc
from ..attrs import SPACY, ORTH
class Binder(object):
'''Serialize analyses from a collection of doc objects.'''
def __init__(self, attrs=None):
'''Create a Binder 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 []
self.attrs = list(attrs)
# Ensure ORTH is always attrs[0]
if ORTH in self.attrs:
self.attrs.pop(ORTH)
if SPACY in self.attrs:
self.attrs.pop(SPACY)
self.attrs.insert(0, ORTH)
self.tokens = []
self.spaces = []
self.strings = set()
def add(self, doc):
'''Add a doc's annotations to the binder 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)
def get_docs(self, vocab):
'''Recover Doc objects from the annotations, using the given vocab.'''
attrs = self.attrs
for string in self.strings:
vocab[string]
orth_col = self.attrs.index(ORTH)
for tokens, spaces in zip(self.tokens, self.spaces):
words = [vocab.strings[orth] for orth in tokens[:, orth_col]]
doc = Doc(vocab, words=words, spaces=spaces)
doc = doc.from_array(self.attrs, tokens)
yield doc
def merge(self, other):
'''Extend the annotations of this binder 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)
def to_bytes(self):
'''Serialize the binder'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)
}
return gzip.compress(msgpack.dumps(msg))
def from_bytes(self, string):
'''Deserialize the binder's annotations from a byte string.'''
msg = 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)
for tokens in self.tokens:
assert len(tokens.shape) == 2, tokens.shape
return self
def merge_bytes(binder_strings):
'''Concatenate multiple serialized binders into one byte string.'''
output = None
for byte_string in binder_strings:
binder = Binder().from_bytes(byte_string)
if output is None:
output = binder
else:
output.merge(binder)
return output.to_bytes()
def pickle_binder(binder):
return (unpickle_binder, (binder.to_bytes(),))
def unpickle_binder(byte_string):
return Binder().from_bytes(byte_string)
copy_reg.pickle(Binder, pickle_binder, unpickle_binder)