mirror of
https://github.com/explosion/spaCy.git
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492 lines
19 KiB
Cython
492 lines
19 KiB
Cython
# coding: utf8
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from __future__ import unicode_literals
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import numpy
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import dill
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from collections import OrderedDict
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from thinc.neural.util import get_array_module
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from .lexeme cimport EMPTY_LEXEME
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from .lexeme cimport Lexeme
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from .strings cimport hash_string
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from .typedefs cimport attr_t
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from .tokens.token cimport Token
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from .attrs cimport PROB, LANG, ORTH, TAG
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from .structs cimport SerializedLexemeC
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from .compat import copy_reg, basestring_
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from .lemmatizer import Lemmatizer
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from .attrs import intify_attrs
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from .vectors import Vectors
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from ._ml import link_vectors_to_models
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from . import util
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cdef class Vocab:
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"""A look-up table that allows you to access `Lexeme` objects. The `Vocab`
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instance also provides access to the `StringStore`, and owns underlying
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C-data that is shared between `Doc` objects.
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"""
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def __init__(self, lex_attr_getters=None, tag_map=None, lemmatizer=None,
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strings=tuple(), oov_prob=-20., **deprecated_kwargs):
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"""Create the vocabulary.
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lex_attr_getters (dict): A dictionary mapping attribute IDs to
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functions to compute them. Defaults to `None`.
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tag_map (dict): Dictionary mapping fine-grained tags to coarse-grained
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parts-of-speech, and optionally morphological attributes.
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lemmatizer (object): A lemmatizer. Defaults to `None`.
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strings (StringStore): StringStore that maps strings to integers, and
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vice versa.
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RETURNS (Vocab): The newly constructed object.
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"""
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lex_attr_getters = lex_attr_getters if lex_attr_getters is not None else {}
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tag_map = tag_map if tag_map is not None else {}
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if lemmatizer in (None, True, False):
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lemmatizer = Lemmatizer({}, {}, {})
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self.cfg = {'oov_prob': oov_prob}
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self.mem = Pool()
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self._by_hash = PreshMap()
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self._by_orth = PreshMap()
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self.strings = StringStore()
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self.length = 0
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if strings:
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for string in strings:
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_ = self[string]
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self.lex_attr_getters = lex_attr_getters
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self.morphology = Morphology(self.strings, tag_map, lemmatizer)
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self.vectors = Vectors(self.strings, width=0)
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property lang:
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def __get__(self):
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langfunc = None
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if self.lex_attr_getters:
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langfunc = self.lex_attr_getters.get(LANG, None)
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return langfunc('_') if langfunc else ''
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def __len__(self):
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"""The current number of lexemes stored.
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RETURNS (int): The current number of lexemes stored.
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"""
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return self.length
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def add_flag(self, flag_getter, int flag_id=-1):
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"""Set a new boolean flag to words in the vocabulary.
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The flag_getter function will be called over the words currently in the
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vocab, and then applied to new words as they occur. You'll then be able
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to access the flag value on each token using token.check_flag(flag_id).
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See also: `Lexeme.set_flag`, `Lexeme.check_flag`, `Token.set_flag`,
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`Token.check_flag`.
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flag_getter (callable): A function `f(unicode) -> bool`, to get the
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flag value.
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flag_id (int): An integer between 1 and 63 (inclusive), specifying
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the bit at which the flag will be stored. If -1, the lowest
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available bit will be chosen.
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RETURNS (int): The integer ID by which the flag value can be checked.
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EXAMPLE:
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>>> my_product_getter = lambda text: text in ['spaCy', 'dislaCy']
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>>> MY_PRODUCT = nlp.vocab.add_flag(my_product_getter)
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>>> doc = nlp(u'I like spaCy')
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>>> assert doc[2].check_flag(MY_PRODUCT) == True
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"""
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if flag_id == -1:
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for bit in range(1, 64):
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if bit not in self.lex_attr_getters:
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flag_id = bit
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break
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else:
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raise ValueError(
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"Cannot find empty bit for new lexical flag. All bits "
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"between 0 and 63 are occupied. You can replace one by "
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"specifying the flag_id explicitly, e.g. "
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"`nlp.vocab.add_flag(your_func, flag_id=IS_ALPHA`.")
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elif flag_id >= 64 or flag_id < 1:
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raise ValueError(
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"Invalid value for flag_id: %d. Flag IDs must be between "
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"1 and 63 (inclusive)" % flag_id)
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for lex in self:
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lex.set_flag(flag_id, flag_getter(lex.orth_))
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self.lex_attr_getters[flag_id] = flag_getter
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return flag_id
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cdef const LexemeC* get(self, Pool mem, unicode string) except NULL:
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"""Get a pointer to a `LexemeC` from the lexicon, creating a new
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`Lexeme` if necessary using memory acquired from the given pool. If the
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pool is the lexicon's own memory, the lexeme is saved in the lexicon.
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"""
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if string == u'':
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return &EMPTY_LEXEME
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cdef LexemeC* lex
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cdef hash_t key = hash_string(string)
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lex = <LexemeC*>self._by_hash.get(key)
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cdef size_t addr
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if lex != NULL:
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if lex.orth != self.strings[string]:
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raise LookupError.mismatched_strings(
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lex.orth, self.strings[string], string)
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return lex
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else:
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return self._new_lexeme(mem, string)
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cdef const LexemeC* get_by_orth(self, Pool mem, attr_t orth) except NULL:
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"""Get a pointer to a `LexemeC` from the lexicon, creating a new
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`Lexeme` if necessary using memory acquired from the given pool. If the
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pool is the lexicon's own memory, the lexeme is saved in the lexicon.
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"""
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if orth == 0:
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return &EMPTY_LEXEME
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cdef LexemeC* lex
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lex = <LexemeC*>self._by_orth.get(orth)
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if lex != NULL:
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return lex
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else:
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return self._new_lexeme(mem, self.strings[orth])
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cdef const LexemeC* _new_lexeme(self, Pool mem, unicode string) except NULL:
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cdef hash_t key
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if len(string) < 3 or self.length < 10000:
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mem = self.mem
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cdef bint is_oov = mem is not self.mem
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lex = <LexemeC*>mem.alloc(sizeof(LexemeC), 1)
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lex.orth = self.strings.add(string)
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lex.length = len(string)
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lex.id = self.length
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if self.lex_attr_getters is not None:
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for attr, func in self.lex_attr_getters.items():
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value = func(string)
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if isinstance(value, unicode):
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value = self.strings.add(value)
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if attr == PROB:
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lex.prob = value
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elif value is not None:
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Lexeme.set_struct_attr(lex, attr, value)
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if is_oov:
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lex.id = 0
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else:
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key = hash_string(string)
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self._add_lex_to_vocab(key, lex)
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assert lex != NULL, string
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return lex
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cdef int _add_lex_to_vocab(self, hash_t key, const LexemeC* lex) except -1:
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self._by_hash.set(key, <void*>lex)
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self._by_orth.set(lex.orth, <void*>lex)
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self.length += 1
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def __contains__(self, unicode string):
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"""Check whether the string has an entry in the vocabulary.
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string (unicode): The ID string.
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RETURNS (bool) Whether the string has an entry in the vocabulary.
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"""
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key = hash_string(string)
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lex = self._by_hash.get(key)
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return lex is not NULL
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def __iter__(self):
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"""Iterate over the lexemes in the vocabulary.
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YIELDS (Lexeme): An entry in the vocabulary.
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"""
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cdef attr_t orth
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cdef size_t addr
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for orth, addr in self._by_orth.items():
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yield Lexeme(self, orth)
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def __getitem__(self, id_or_string):
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"""Retrieve a lexeme, given an int ID or a unicode string. If a
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previously unseen unicode string is given, a new lexeme is created and
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stored.
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id_or_string (int or unicode): The integer ID of a word, or its unicode
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string. If `int >= Lexicon.size`, `IndexError` is raised. If
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`id_or_string` is neither an int nor a unicode string, `ValueError`
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is raised.
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RETURNS (Lexeme): The lexeme indicated by the given ID.
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EXAMPLE:
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>>> apple = nlp.vocab.strings['apple']
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>>> assert nlp.vocab[apple] == nlp.vocab[u'apple']
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"""
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cdef attr_t orth
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if type(id_or_string) == unicode:
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orth = self.strings.add(id_or_string)
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else:
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orth = id_or_string
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return Lexeme(self, orth)
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cdef const TokenC* make_fused_token(self, substrings) except NULL:
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cdef int i
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tokens = <TokenC*>self.mem.alloc(len(substrings) + 1, sizeof(TokenC))
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for i, props in enumerate(substrings):
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props = intify_attrs(props, strings_map=self.strings,
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_do_deprecated=True)
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token = &tokens[i]
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# Set the special tokens up to have arbitrary attributes
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lex = <LexemeC*>self.get_by_orth(self.mem, props[ORTH])
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token.lex = lex
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if TAG in props:
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self.morphology.assign_tag(token, props[TAG])
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for attr_id, value in props.items():
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Token.set_struct_attr(token, attr_id, value)
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Lexeme.set_struct_attr(lex, attr_id, value)
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return tokens
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@property
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def vectors_length(self):
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return self.vectors.data.shape[1]
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def clear_vectors(self, width=None):
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"""Drop the current vector table. Because all vectors must be the same
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width, you have to call this to change the size of the vectors.
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"""
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if width is None:
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width = self.vectors.data.shape[1]
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self.vectors = Vectors(self.strings, width=width)
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def prune_vectors(self, nr_row, batch_size=1024):
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"""Reduce the current vector table to `nr_row` unique entries. Words
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mapped to the discarded vectors will be remapped to the closest vector
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among those remaining.
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For example, suppose the original table had vectors for the words:
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['sat', 'cat', 'feline', 'reclined']. If we prune the vector table to,
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two rows, we would discard the vectors for 'feline' and 'reclined'.
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These words would then be remapped to the closest remaining vector
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-- so "feline" would have the same vector as "cat", and "reclined"
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would have the same vector as "sat".
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The similarities are judged by cosine. The original vectors may
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be large, so the cosines are calculated in minibatches, to reduce
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memory usage.
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nr_row (int): The number of rows to keep in the vector table.
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batch_size (int): Batch of vectors for calculating the similarities.
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Larger batch sizes might be faster, while temporarily requiring
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more memory.
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RETURNS (dict): A dictionary keyed by removed words mapped to
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`(string, score)` tuples, where `string` is the entry the removed
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word was mapped to, and `score` the similarity score between the
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two words.
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"""
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xp = get_array_module(self.vectors.data)
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# Work in batches, to avoid memory problems.
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keep = self.vectors.data[:nr_row]
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toss = self.vectors.data[nr_row:]
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# Normalize the vectors, so cosine similarity is just dot product.
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# Note we can't modify the ones we're keeping in-place...
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keep = keep / (xp.linalg.norm(keep)+1e-8)
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keep = xp.ascontiguousarray(keep.T)
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neighbours = xp.zeros((toss.shape[0],), dtype='i')
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for i in range(0, toss.shape[0], batch_size):
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batch = toss[i : i+batch_size]
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batch /= xp.linalg.norm(batch)+1e-8
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neighbours[i:i+batch_size] = xp.dot(batch, keep).argmax(axis=1)
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for lex in self:
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# If we're losing the vector for this word, map it to the nearest
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# vector we're keeping.
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if lex.rank >= nr_row:
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lex.rank = neighbours[lex.rank-nr_row]
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self.vectors.add(lex.orth, row=lex.rank)
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# Make copy, to encourage the original table to be garbage collected.
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self.vectors.data = xp.ascontiguousarray(self.vectors.data[:nr_row])
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# TODO: return new mapping
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def get_vector(self, orth):
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"""Retrieve a vector for a word in the vocabulary. Words can be looked
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up by string or int ID. If no vectors data is loaded, ValueError is
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raised.
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RETURNS (numpy.ndarray): A word vector. Size
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and shape determined by the `vocab.vectors` instance. Usually, a
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numpy ndarray of shape (300,) and dtype float32.
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"""
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if isinstance(orth, basestring_):
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orth = self.strings.add(orth)
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if orth in self.vectors.key2row:
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return self.vectors[orth]
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else:
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return numpy.zeros((self.vectors_length,), dtype='f')
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def set_vector(self, orth, vector):
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"""Set a vector for a word in the vocabulary. Words can be referenced
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by string or int ID.
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"""
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if not isinstance(orth, basestring_):
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orth = self.strings[orth]
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self.vectors.add(orth, vector=vector)
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def has_vector(self, orth):
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"""Check whether a word has a vector. Returns False if no vectors have
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been loaded. Words can be looked up by string or int ID."""
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if isinstance(orth, basestring_):
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orth = self.strings.add(orth)
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return orth in self.vectors
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def to_disk(self, path, **exclude):
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"""Save the current state to a directory.
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path (unicode or Path): A path to a directory, which will be created if
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it doesn't exist. Paths may be either strings or Path-like objects.
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"""
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path = util.ensure_path(path)
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if not path.exists():
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path.mkdir()
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self.strings.to_disk(path / 'strings.json')
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with (path / 'lexemes.bin').open('wb') as file_:
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file_.write(self.lexemes_to_bytes())
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if self.vectors is not None:
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self.vectors.to_disk(path)
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def from_disk(self, path, **exclude):
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"""Loads state from a directory. Modifies the object in place and
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returns it.
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path (unicode or Path): A path to a directory. Paths may be either
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strings or `Path`-like objects.
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RETURNS (Vocab): The modified `Vocab` object.
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"""
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path = util.ensure_path(path)
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self.strings.from_disk(path / 'strings.json')
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with (path / 'lexemes.bin').open('rb') as file_:
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self.lexemes_from_bytes(file_.read())
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if self.vectors is not None:
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self.vectors.from_disk(path, exclude='strings.json')
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link_vectors_to_models(self)
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return self
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def to_bytes(self, **exclude):
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"""Serialize the current state to a binary string.
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**exclude: Named attributes to prevent from being serialized.
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RETURNS (bytes): The serialized form of the `Vocab` object.
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"""
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def deserialize_vectors():
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if self.vectors is None:
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return None
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else:
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return self.vectors.to_bytes()
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getters = OrderedDict((
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('strings', lambda: self.strings.to_bytes()),
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('lexemes', lambda: self.lexemes_to_bytes()),
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('vectors', deserialize_vectors)
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))
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return util.to_bytes(getters, exclude)
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def from_bytes(self, bytes_data, **exclude):
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"""Load state from a binary string.
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bytes_data (bytes): The data to load from.
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**exclude: Named attributes to prevent from being loaded.
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RETURNS (Vocab): The `Vocab` object.
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"""
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def serialize_vectors(b):
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if self.vectors is None:
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return None
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else:
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return self.vectors.from_bytes(b)
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setters = OrderedDict((
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('strings', lambda b: self.strings.from_bytes(b)),
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('lexemes', lambda b: self.lexemes_from_bytes(b)),
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('vectors', lambda b: serialize_vectors(b))
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))
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util.from_bytes(bytes_data, setters, exclude)
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return self
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def lexemes_to_bytes(self):
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cdef hash_t key
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cdef size_t addr
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cdef LexemeC* lexeme = NULL
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cdef SerializedLexemeC lex_data
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cdef int size = 0
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for key, addr in self._by_hash.items():
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if addr == 0:
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continue
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size += sizeof(lex_data.data)
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byte_string = b'\0' * size
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byte_ptr = <unsigned char*>byte_string
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cdef int j
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cdef int i = 0
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for key, addr in self._by_hash.items():
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if addr == 0:
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continue
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lexeme = <LexemeC*>addr
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lex_data = Lexeme.c_to_bytes(lexeme)
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for j in range(sizeof(lex_data.data)):
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byte_ptr[i] = lex_data.data[j]
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i += 1
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return byte_string
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def lexemes_from_bytes(self, bytes bytes_data):
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"""Load the binary vocabulary data from the given string."""
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cdef LexemeC* lexeme
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cdef hash_t key
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cdef unicode py_str
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cdef int i = 0
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cdef int j = 0
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cdef SerializedLexemeC lex_data
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chunk_size = sizeof(lex_data.data)
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cdef void* ptr
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cdef unsigned char* bytes_ptr = bytes_data
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for i in range(0, len(bytes_data), chunk_size):
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lexeme = <LexemeC*>self.mem.alloc(1, sizeof(LexemeC))
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for j in range(sizeof(lex_data.data)):
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lex_data.data[j] = bytes_ptr[i+j]
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Lexeme.c_from_bytes(lexeme, lex_data)
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ptr = self.strings._map.get(lexeme.orth)
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if ptr == NULL:
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continue
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py_str = self.strings[lexeme.orth]
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assert self.strings[py_str] == lexeme.orth, (py_str, lexeme.orth)
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key = hash_string(py_str)
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self._by_hash.set(key, lexeme)
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self._by_orth.set(lexeme.orth, lexeme)
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self.length += 1
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def pickle_vocab(vocab):
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sstore = vocab.strings
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morph = vocab.morphology
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length = vocab.length
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data_dir = vocab.data_dir
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lex_attr_getters = dill.dumps(vocab.lex_attr_getters)
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lexemes_data = vocab.lexemes_to_bytes()
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return (unpickle_vocab,
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(sstore, morph, data_dir, lex_attr_getters, lexemes_data, length))
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def unpickle_vocab(sstore, morphology, data_dir,
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lex_attr_getters, bytes lexemes_data, int length):
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cdef Vocab vocab = Vocab()
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vocab.length = length
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vocab.strings = sstore
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vocab.morphology = morphology
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vocab.data_dir = data_dir
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vocab.lex_attr_getters = dill.loads(lex_attr_getters)
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vocab.lexemes_from_bytes(lexemes_data)
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vocab.length = length
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|
link_vectors_to_models(vocab)
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return vocab
|
|
|
|
|
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copy_reg.pickle(Vocab, pickle_vocab, unpickle_vocab)
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|
|
|
|
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class LookupError(Exception):
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|
@classmethod
|
|
def mismatched_strings(cls, id_, id_string, original_string):
|
|
return cls(
|
|
"Error fetching a Lexeme from the Vocab. When looking up a "
|
|
"string, the lexeme returned had an orth ID that did not match "
|
|
"the query string. This means that the cached lexeme structs are "
|
|
"mismatched to the string encoding table. The mismatched:\n"
|
|
"Query string: {}\n"
|
|
"Orth cached: {}\n"
|
|
"Orth ID: {}".format(repr(original_string), repr(id_string), id_))
|