mirror of
https://github.com/explosion/spaCy.git
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584 lines
23 KiB
Cython
584 lines
23 KiB
Cython
# cython: profile=True
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from libc.string cimport memcpy
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import srsly
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from thinc.api import get_array_module
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from .lexeme cimport EMPTY_LEXEME, OOV_RANK
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from .lexeme cimport Lexeme
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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 LANG, ORTH, TAG, POS
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from .compat import copy_reg
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from .errors import Errors
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from .lemmatizer import Lemmatizer
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from .attrs import intify_attrs, NORM
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from .vectors import Vectors
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from .util import link_vectors_to_models
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from .lookups import Lookups
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from . import util
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from .lang.norm_exceptions import BASE_NORMS
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from .lang.lex_attrs import LEX_ATTRS
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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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DOCS: https://spacy.io/api/vocab
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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(), lookups=None, lookups_extra=None,
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oov_prob=-20., vectors_name=None, **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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lookups (Lookups): Container for large lookup tables and dictionaries.
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lookups_extra (Lookups): Container for optional lookup tables and dictionaries.
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name (unicode): Optional name to identify the vectors table.
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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 lookups in (None, True, False):
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lookups = Lookups()
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if "lexeme_norm" not in lookups:
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lookups.add_table("lexeme_norm")
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if lemmatizer in (None, True, False):
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lemmatizer = Lemmatizer(lookups)
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if lookups_extra in (None, True, False):
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lookups_extra = Lookups()
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self.cfg = {'oov_prob': oov_prob}
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self.mem = Pool()
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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(name=vectors_name)
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self.lookups = lookups
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self.lookups_extra = lookups_extra
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@property
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def lang(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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property writing_system:
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"""A dict with information about the language's writing system. To get
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the data, we use the vocab.lang property to fetch the Language class.
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If the Language class is not loaded, an empty dict is returned.
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"""
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def __get__(self):
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if not util.lang_class_is_loaded(self.lang):
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return {}
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lang_class = util.get_lang_class(self.lang)
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return dict(lang_class.Defaults.writing_system)
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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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DOCS: https://spacy.io/api/vocab#add_flag
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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(Errors.E062)
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elif flag_id >= 64 or flag_id < 1:
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raise ValueError(Errors.E063.format(value=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 == "":
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return &EMPTY_LEXEME
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cdef LexemeC* lex
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cdef hash_t key = self.strings[string]
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lex = <LexemeC*>self._by_orth.get(key)
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cdef size_t addr
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if lex != NULL:
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assert lex.orth in self.strings
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if lex.orth != key:
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raise KeyError(Errors.E064.format(string=lex.orth,
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orth=key, orth_id=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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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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if self.vectors is not None:
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lex.id = self.vectors.key2row.get(lex.orth, OOV_RANK)
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else:
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lex.id = OOV_RANK
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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 value is not None:
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Lexeme.set_struct_attr(lex, attr, value)
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if not is_oov:
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self._add_lex_to_vocab(lex.orth, lex)
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if lex == NULL:
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raise ValueError(Errors.E085.format(string=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_orth.set(lex.orth, <void*>lex)
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self.length += 1
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def __contains__(self, key):
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"""Check whether the string or int key 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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DOCS: https://spacy.io/api/vocab#contains
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"""
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cdef hash_t int_key
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if isinstance(key, bytes):
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int_key = self.strings[key.decode("utf8")]
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elif isinstance(key, unicode):
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int_key = self.strings[key]
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else:
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int_key = key
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lex = self._by_orth.get(int_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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DOCS: https://spacy.io/api/vocab#iter
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"""
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cdef attr_t key
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cdef size_t addr
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for key, addr in self._by_orth.items():
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lex = Lexeme(self, key)
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yield lex
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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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DOCS: https://spacy.io/api/vocab#getitem
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"""
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cdef attr_t orth
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if isinstance(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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elif POS in props:
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# Don't allow POS to be set without TAG -- this causes problems,
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# see #1773
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props.pop(POS)
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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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# NORM is the only one that overlaps between the two
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# (which is maybe not great?)
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if attr_id != NORM:
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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 reset_vectors(self, *, width=None, shape=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 not None and shape is not None:
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raise ValueError(Errors.E065.format(width=width, shape=shape))
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elif shape is not None:
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self.vectors = Vectors(shape=shape)
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else:
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width = width if width is not None else self.vectors.data.shape[1]
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self.vectors = Vectors(shape=(self.vectors.shape[0], 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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DOCS: https://spacy.io/api/vocab#prune_vectors
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"""
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xp = get_array_module(self.vectors.data)
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# Make prob negative so it sorts by rank ascending
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# (key2row contains the rank)
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priority = [(-lex.prob, self.vectors.key2row[lex.orth], lex.orth)
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for lex in self if lex.orth in self.vectors.key2row]
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priority.sort()
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indices = xp.asarray([i for (prob, i, key) in priority], dtype="uint64")
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keys = xp.asarray([key for (prob, i, key) in priority], dtype="uint64")
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keep = xp.ascontiguousarray(self.vectors.data[indices[:nr_row]])
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toss = xp.ascontiguousarray(self.vectors.data[indices[nr_row:]])
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self.vectors = Vectors(data=keep, keys=keys[:nr_row], name=self.vectors.name)
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syn_keys, syn_rows, scores = self.vectors.most_similar(toss, batch_size=batch_size)
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remap = {}
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for i, key in enumerate(keys[nr_row:]):
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self.vectors.add(key, row=syn_rows[i][0])
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word = self.strings[key]
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synonym = self.strings[syn_keys[i][0]]
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score = scores[i][0]
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remap[word] = (synonym, score)
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link_vectors_to_models(self)
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return remap
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def get_vector(self, orth, minn=None, maxn=None):
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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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If `minn` is defined, then the resulting vector uses Fasttext's
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subword features by average over ngrams of `orth`.
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orth (int / unicode): The hash value of a word, or its unicode string.
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minn (int): Minimum n-gram length used for Fasttext's ngram computation.
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Defaults to the length of `orth`.
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maxn (int): Maximum n-gram length used for Fasttext's ngram computation.
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Defaults to the length of `orth`.
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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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DOCS: https://spacy.io/api/vocab#get_vector
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"""
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if isinstance(orth, str):
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orth = self.strings.add(orth)
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word = self[orth].orth_
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if orth in self.vectors.key2row:
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return self.vectors[orth]
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# Assign default ngram limits to minn and maxn which is the length of the word.
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if minn is None:
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minn = len(word)
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if maxn is None:
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maxn = len(word)
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xp = get_array_module(self.vectors.data)
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vectors = xp.zeros((self.vectors_length,), dtype="f")
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# Fasttext's ngram computation taken from
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# https://github.com/facebookresearch/fastText
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ngrams_size = 0;
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for i in range(len(word)):
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ngram = ""
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if (word[i] and 0xC0) == 0x80:
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continue
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n = 1
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j = i
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while (j < len(word) and n <= maxn):
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if n > maxn:
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break
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ngram += word[j]
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j = j + 1
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while (j < len(word) and (word[j] and 0xC0) == 0x80):
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ngram += word[j]
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j = j + 1
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if (n >= minn and not (n == 1 and (i == 0 or j == len(word)))):
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if self.strings[ngram] in self.vectors.key2row:
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vectors = xp.add(self.vectors[self.strings[ngram]], vectors)
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ngrams_size += 1
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n = n + 1
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if ngrams_size > 0:
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vectors = vectors * (1.0/ngrams_size)
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return vectors
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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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orth (int / unicode): The word.
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vector (numpy.ndarray[ndim=1, dtype='float32']): The vector to set.
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DOCS: https://spacy.io/api/vocab#set_vector
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"""
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if isinstance(orth, str):
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orth = self.strings.add(orth)
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if self.vectors.is_full and orth not in self.vectors:
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new_rows = max(100, int(self.vectors.shape[0]*1.3))
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if self.vectors.shape[1] == 0:
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width = vector.size
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else:
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width = self.vectors.shape[1]
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self.vectors.resize((new_rows, width))
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lex = self[orth] # Add word to vocab if necessary
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row = self.vectors.add(orth, vector=vector)
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lex.rank = row
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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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orth (int / unicode): The word.
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RETURNS (bool): Whether the word has a vector.
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DOCS: https://spacy.io/api/vocab#has_vector
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"""
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if isinstance(orth, str):
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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=tuple(), **kwargs):
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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.
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exclude (list): String names of serialization fields to exclude.
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DOCS: https://spacy.io/api/vocab#to_disk
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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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setters = ["strings", "vectors"]
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exclude = util.get_serialization_exclude(setters, exclude, kwargs)
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if "strings" not in exclude:
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self.strings.to_disk(path / "strings.json")
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if "vectors" not in "exclude" and self.vectors is not None:
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self.vectors.to_disk(path)
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if "lookups" not in "exclude" and self.lookups is not None:
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self.lookups.to_disk(path)
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if "lookups_extra" not in "exclude" and self.lookups_extra is not None:
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self.lookups_extra.to_disk(path, filename="lookups_extra.bin")
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def from_disk(self, path, exclude=tuple(), **kwargs):
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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.
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|
exclude (list): String names of serialization fields to exclude.
|
|
RETURNS (Vocab): The modified `Vocab` object.
|
|
|
|
DOCS: https://spacy.io/api/vocab#to_disk
|
|
"""
|
|
path = util.ensure_path(path)
|
|
getters = ["strings", "vectors"]
|
|
exclude = util.get_serialization_exclude(getters, exclude, kwargs)
|
|
if "strings" not in exclude:
|
|
self.strings.from_disk(path / "strings.json") # TODO: add exclude?
|
|
if "vectors" not in exclude:
|
|
if self.vectors is not None:
|
|
self.vectors.from_disk(path, exclude=["strings"])
|
|
if self.vectors.name is not None:
|
|
link_vectors_to_models(self)
|
|
if "lookups" not in exclude:
|
|
self.lookups.from_disk(path)
|
|
if "lookups_extra" not in exclude:
|
|
self.lookups_extra.from_disk(path, filename="lookups_extra.bin")
|
|
if "lexeme_norm" in self.lookups:
|
|
self.lex_attr_getters[NORM] = util.add_lookups(
|
|
self.lex_attr_getters.get(NORM, LEX_ATTRS[NORM]), self.lookups.get_table("lexeme_norm")
|
|
)
|
|
self.length = 0
|
|
self._by_orth = PreshMap()
|
|
return self
|
|
|
|
def to_bytes(self, exclude=tuple(), **kwargs):
|
|
"""Serialize the current state to a binary string.
|
|
|
|
exclude (list): String names of serialization fields to exclude.
|
|
RETURNS (bytes): The serialized form of the `Vocab` object.
|
|
|
|
DOCS: https://spacy.io/api/vocab#to_bytes
|
|
"""
|
|
def deserialize_vectors():
|
|
if self.vectors is None:
|
|
return None
|
|
else:
|
|
return self.vectors.to_bytes()
|
|
|
|
getters = {
|
|
"strings": lambda: self.strings.to_bytes(),
|
|
"vectors": deserialize_vectors,
|
|
"lookups": lambda: self.lookups.to_bytes(),
|
|
"lookups_extra": lambda: self.lookups_extra.to_bytes()
|
|
}
|
|
exclude = util.get_serialization_exclude(getters, exclude, kwargs)
|
|
return util.to_bytes(getters, exclude)
|
|
|
|
def from_bytes(self, bytes_data, exclude=tuple(), **kwargs):
|
|
"""Load state from a binary string.
|
|
|
|
bytes_data (bytes): The data to load from.
|
|
exclude (list): String names of serialization fields to exclude.
|
|
RETURNS (Vocab): The `Vocab` object.
|
|
|
|
DOCS: https://spacy.io/api/vocab#from_bytes
|
|
"""
|
|
def serialize_vectors(b):
|
|
if self.vectors is None:
|
|
return None
|
|
else:
|
|
return self.vectors.from_bytes(b)
|
|
|
|
setters = {
|
|
"strings": lambda b: self.strings.from_bytes(b),
|
|
"lexemes": lambda b: self.lexemes_from_bytes(b),
|
|
"vectors": lambda b: serialize_vectors(b),
|
|
"lookups": lambda b: self.lookups.from_bytes(b),
|
|
"lookups_extra": lambda b: self.lookups_extra.from_bytes(b)
|
|
}
|
|
exclude = util.get_serialization_exclude(setters, exclude, kwargs)
|
|
util.from_bytes(bytes_data, setters, exclude)
|
|
if "lexeme_norm" in self.lookups:
|
|
self.lex_attr_getters[NORM] = util.add_lookups(
|
|
self.lex_attr_getters.get(NORM, LEX_ATTRS[NORM]), self.lookups.get_table("lexeme_norm")
|
|
)
|
|
self.length = 0
|
|
self._by_orth = PreshMap()
|
|
if self.vectors.name is not None:
|
|
link_vectors_to_models(self)
|
|
return self
|
|
|
|
def _reset_cache(self, keys, strings):
|
|
# I'm not sure this made sense. Disable it for now.
|
|
raise NotImplementedError
|
|
|
|
|
|
def load_extra_lookups(self, table_name):
|
|
if table_name not in self.lookups_extra:
|
|
if self.lang + "_extra" in util.registry.lookups:
|
|
tables = util.registry.lookups.get(self.lang + "_extra")
|
|
for name, filename in tables.items():
|
|
if table_name == name:
|
|
data = util.load_language_data(filename)
|
|
self.lookups_extra.add_table(name, data)
|
|
if table_name not in self.lookups_extra:
|
|
self.lookups_extra.add_table(table_name)
|
|
return self.lookups_extra.get_table(table_name)
|
|
|
|
|
|
def pickle_vocab(vocab):
|
|
sstore = vocab.strings
|
|
vectors = vocab.vectors
|
|
morph = vocab.morphology
|
|
data_dir = vocab.data_dir
|
|
lex_attr_getters = srsly.pickle_dumps(vocab.lex_attr_getters)
|
|
lookups = vocab.lookups
|
|
lookups_extra = vocab.lookups_extra
|
|
return (unpickle_vocab,
|
|
(sstore, vectors, morph, data_dir, lex_attr_getters, lookups, lookups_extra))
|
|
|
|
|
|
def unpickle_vocab(sstore, vectors, morphology, data_dir,
|
|
lex_attr_getters, lookups, lookups_extra):
|
|
cdef Vocab vocab = Vocab()
|
|
vocab.vectors = vectors
|
|
vocab.strings = sstore
|
|
vocab.morphology = morphology
|
|
vocab.data_dir = data_dir
|
|
vocab.lex_attr_getters = srsly.pickle_loads(lex_attr_getters)
|
|
vocab.lookups = lookups
|
|
vocab.lookups_extra = lookups_extra
|
|
return vocab
|
|
|
|
|
|
copy_reg.pickle(Vocab, pickle_vocab, unpickle_vocab)
|