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
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import numpy
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import msgpack
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import gzip
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from thinc.neural.ops import NumpyOps
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from ..compat import copy_reg
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from ..tokens import Doc
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from ..attrs import SPACY, ORTH
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class Binder(object):
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'''Serialize analyses from a collection of doc objects.'''
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def __init__(self, attrs=None):
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'''Create a Binder object, to hold serialized annotations.
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attrs (list):
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List of attributes to serialize. 'orth' and 'spacy' are always
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serialized, so they're not required. Defaults to None.
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'''
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attrs = attrs or []
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self.attrs = list(attrs)
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# Ensure ORTH is always attrs[0]
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if ORTH in self.attrs:
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self.attrs.pop(ORTH)
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if SPACY in self.attrs:
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self.attrs.pop(SPACY)
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self.attrs.insert(0, ORTH)
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self.tokens = []
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self.spaces = []
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self.strings = set()
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def add(self, doc):
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'''Add a doc's annotations to the binder for serialization.'''
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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]
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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.strings.update(w.text for w in doc)
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def get_docs(self, vocab):
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'''Recover Doc objects from the annotations, using the given vocab.'''
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attrs = self.attrs
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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 tokens, spaces in zip(self.tokens, self.spaces):
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words = [vocab.strings[orth] for orth in tokens[:, orth_col]]
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doc = Doc(vocab, words=words, spaces=spaces)
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doc = doc.from_array(self.attrs, tokens)
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yield doc
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def merge(self, other):
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'''Extend the annotations of this binder with the annotations from another.'''
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assert self.attrs == other.attrs
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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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def to_bytes(self):
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'''Serialize the binder's annotations into a byte string.'''
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for tokens in self.tokens:
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assert len(tokens.shape) == 2, tokens.shape
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lengths = [len(tokens) for tokens in self.tokens]
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msg = {
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'attrs': self.attrs,
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'tokens': numpy.vstack(self.tokens).tobytes('C'),
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'spaces': numpy.vstack(self.spaces).tobytes('C'),
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'lengths': numpy.asarray(lengths, dtype='int32').tobytes('C'),
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'strings': list(self.strings)
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}
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return gzip.compress(msgpack.dumps(msg))
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def from_bytes(self, string):
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'''Deserialize the binder's annotations from a byte string.'''
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msg = msgpack.loads(gzip.decompress(string))
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self.attrs = msg['attrs']
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self.strings = set(msg['strings'])
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lengths = numpy.fromstring(msg['lengths'], dtype='int32')
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flat_spaces = numpy.fromstring(msg['spaces'], dtype=bool)
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flat_tokens = numpy.fromstring(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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for tokens in self.tokens:
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assert len(tokens.shape) == 2, tokens.shape
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return self
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def merge_bytes(binder_strings):
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'''Concatenate multiple serialized binders into one byte string.'''
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output = None
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for byte_string in binder_strings:
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binder = Binder().from_bytes(byte_string)
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if output is None:
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output = binder
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else:
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output.merge(binder)
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return output.to_bytes()
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def pickle_binder(binder):
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return (unpickle_binder, (binder.to_bytes(),))
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def unpickle_binder(byte_string):
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return Binder().from_bytes(byte_string)
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copy_reg.pickle(Binder, pickle_binder, unpickle_binder)
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