mirror of https://github.com/explosion/spaCy.git
118 lines
4.1 KiB
Python
118 lines
4.1 KiB
Python
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 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."""
|
|
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(srsly.msgpack_dumps(msg))
|
|
|
|
def from_bytes(self, string):
|
|
"""Deserialize the binder'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)
|
|
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)
|