# cython: profile=True from __future__ import unicode_literals from libc.stdint cimport uint32_t from libc.stdint cimport uint64_t from libc.math cimport exp as c_exp from libcpp.queue cimport priority_queue from libcpp.pair cimport pair from ..structs cimport UniStr from ..strings cimport slice_unicode from cymem.cymem cimport Address, Pool from preshed.maps cimport PreshMap from preshed.counter cimport PreshCounter from ..attrs cimport ORTH, ID, SPACY, TAG, HEAD, DEP, ENT_IOB, ENT_TYPE from ..tokens.doc cimport Doc from ..vocab cimport Vocab from ..structs cimport LexemeC from ..typedefs cimport attr_t from .bits cimport BitArray from .huffman cimport HuffmanCodec from os import path import numpy from .. import util cimport cython # Format # - Total number of bytes in message (32 bit int) --- handled outside this # - Number of words (32 bit int) # - Words, terminating in an EOL symbol, huffman coded ~12 bits per word # - Spaces 1 bit per word # - Attributes: # POS tag # Head offset # Dep label # Entity IOB # Entity tag cdef class _BinaryCodec: def encode(self, attr_t[:] msg, BitArray bits): cdef int i for i in range(len(msg)): bits.append(msg[i]) def decode(self, BitArray bits, attr_t[:] msg): cdef int i = 0 for bit in bits: msg[i] = bit i += 1 if i == len(msg): break def _gen_orths(Vocab vocab): cdef attr_t orth cdef size_t addr for orth, addr in vocab._by_orth.items(): lex = addr yield orth, c_exp(lex.prob) def _gen_chars(Vocab vocab): cdef attr_t orth cdef size_t addr char_weights = {b' ': 0.0} cdef unicode string cdef unicode char for orth, addr in vocab._by_orth.items(): lex = addr string = vocab.strings[lex.orth] for char in string: char_weights.setdefault(char, 0.0) char_weights[char] += c_exp(lex.prob) char_weights[u' '] += c_exp(lex.prob) return char_weights.items() cdef class Packer: def __init__(self, Vocab vocab, attr_freqs): self.vocab = vocab self.orth_codec = HuffmanCodec(_gen_orths(vocab)) self.char_codec = HuffmanCodec(_gen_chars(vocab)) codecs = [] attrs = [] for attr, freqs in sorted(attr_freqs): if attr in (ORTH, ID, SPACY): continue codecs.append(HuffmanCodec(freqs)) attrs.append(attr) self._codecs = tuple(codecs) self.attrs = tuple(attrs) @classmethod def from_dir(cls, Vocab vocab, data_dir): return cls(vocab, util.read_encoding_freqs(data_dir)) def pack(self, Doc doc): cdef BitArray bits = BitArray() cdef uint32_t length = len(doc.string) bits.extend(length, 32) self._char_encode(doc, bits) array = doc.to_array(self.attrs) for i, codec in enumerate(self._codecs): codec.encode(array[:, i], bits) return bits def unpack(self, BitArray bits): bits.seek(0) cdef uint32_t length = bits.read32() doc = self._char_decode(bits, length) array = numpy.zeros(shape=(len(doc), len(self._codecs)), dtype=numpy.int32) for i, codec in enumerate(self._codecs): codec.decode(bits, array[:, i]) doc.from_array(self.attrs, array) return doc def _char_encode(self, Doc doc, BitArray bits): cdef unicode string = doc.string self.char_codec.encode(string, bits) for token in doc: for i in range(len(token)-1): bits.append(False) bits.append(True) if token.whitespace_: bits.append(False) def _char_decode(self, BitArray bits, n): chars = [u''] * n self.char_codec.decode(bits, chars) cdef unicode string = u''.join(chars) cdef Doc tokens = Doc(self.vocab) cdef int i cdef int start = 0 cdef bint is_spacy cdef UniStr span cdef int length = len(string) iter_bits = iter(bits) for i in range(length): is_end_token = iter_bits.next() if is_end_token: slice_unicode(&span, string, start, i+1) lex = self.vocab.get(tokens.mem, &span) is_spacy = (i+1) < length and string[i+1] == u' ' tokens.push_back(lex, is_spacy) start = i + 1 + is_spacy return tokens