mirror of
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134 lines
3.9 KiB
Cython
134 lines
3.9 KiB
Cython
from libc.stdint cimport int32_t, uint64_t
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import numpy
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from collections import OrderedDict
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import msgpack
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import msgpack_numpy
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msgpack_numpy.patch()
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from cymem.cymem cimport Pool
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cimport numpy as np
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from libcpp.vector cimport vector
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from .typedefs cimport attr_t
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from .strings cimport StringStore
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from . import util
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from ._cfile cimport CFile
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MAX_VEC_SIZE = 10000
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cdef class Vectors:
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'''Store, save and load word vectors.'''
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cdef public object data
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cdef readonly StringStore strings
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cdef public object key2row
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cdef public object keys
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def __init__(self, strings, data_or_width):
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self.strings = StringStore()
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if isinstance(data_or_width, int):
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self.data = data = numpy.zeros((len(strings), data_or_width),
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dtype='f')
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else:
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data = data_or_width
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self.data = data
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self.key2row = {}
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self.keys = np.ndarray((self.data.shape[0],), dtype='uint64')
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for i, string in enumerate(strings):
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key = self.strings.add(string)
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self.key2row[key] = i
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self.keys[i] = key
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def __reduce__(self):
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return (Vectors, (self.strings, self.data))
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def __getitem__(self, key):
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if isinstance(key, basestring):
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key = self.strings[key]
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i = self.key2row[key]
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if i is None:
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raise KeyError(key)
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else:
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return self.data[i]
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def __setitem__(self, key, vector):
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if isinstance(key, basestring):
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key = self.strings.add(key)
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i = self.key2row[key]
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self.data[i] = vector
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def __iter__(self):
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yield from self.data
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def __len__(self):
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return len(self.strings)
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def items(self):
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for i, string in enumerate(self.strings):
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yield string, self.data[i]
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@property
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def shape(self):
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return self.data.shape
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def most_similar(self, key):
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raise NotImplementedError
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def to_disk(self, path, **exclude):
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serializers = OrderedDict((
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('vectors', lambda p: numpy.save(p.open('wb'), self.data)),
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('strings.json', self.strings.to_disk),
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('keys', lambda p: numpy.save(p.open('wb'), self.keys)),
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))
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return util.to_disk(path, serializers, exclude)
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def from_disk(self, path, **exclude):
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def load_keys(path):
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self.keys = numpy.load(path)
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for i, key in enumerate(self.keys):
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self.keys[i] = key
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self.key2row[key] = i
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def load_vectors(path):
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self.data = numpy.load(path)
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serializers = OrderedDict((
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('keys', load_keys),
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('vectors', load_vectors),
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('strings.json', self.strings.from_disk),
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))
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util.from_disk(path, serializers, exclude)
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return self
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def to_bytes(self, **exclude):
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def serialize_weights():
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if hasattr(self.data, 'to_bytes'):
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return self.data.to_bytes()
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else:
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return msgpack.dumps(self.data)
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serializers = OrderedDict((
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('keys', lambda: msgpack.dumps(self.keys)),
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('strings', lambda: self.strings.to_bytes()),
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('vectors', serialize_weights)
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))
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return util.to_bytes(serializers, exclude)
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def from_bytes(self, data, **exclude):
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def deserialize_weights(b):
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if hasattr(self.data, 'from_bytes'):
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self.data.from_bytes()
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else:
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self.data = msgpack.loads(b)
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def load_keys(keys):
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for i, key in enumerate(keys):
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self.keys[i] = key
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self.key2row[key] = i
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deserializers = OrderedDict((
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('keys', lambda b: load_keys(msgpack.loads(b))),
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('strings', lambda b: self.strings.from_bytes(b)),
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('vectors', deserialize_weights)
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))
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util.from_bytes(deserializers, exclude)
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return self
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