# cython: profile=True from libc.string cimport memcpy import numpy import srsly from thinc.api import get_array_module, get_current_ops import functools from .lexeme cimport EMPTY_LEXEME, OOV_RANK from .lexeme cimport Lexeme from .typedefs cimport attr_t from .tokens.token cimport Token from .attrs cimport LANG, ORTH from .compat import copy_reg from .errors import Errors from .attrs import intify_attrs, NORM, IS_STOP from .vectors import Vectors, Mode as VectorsMode from .util import registry from .lookups import Lookups from . import util from .lang.norm_exceptions import BASE_NORMS from .lang.lex_attrs import LEX_ATTRS, is_stop, get_lang def create_vocab(lang, defaults, vectors_name=None): # If the spacy-lookups-data package is installed, we pre-populate the lookups # with lexeme data, if available lex_attrs = {**LEX_ATTRS, **defaults.lex_attr_getters} # This is messy, but it's the minimal working fix to Issue #639. lex_attrs[IS_STOP] = functools.partial(is_stop, stops=defaults.stop_words) # Ensure that getter can be pickled lex_attrs[LANG] = functools.partial(get_lang, lang=lang) lex_attrs[NORM] = util.add_lookups( lex_attrs.get(NORM, LEX_ATTRS[NORM]), BASE_NORMS, ) return Vocab( lex_attr_getters=lex_attrs, writing_system=defaults.writing_system, get_noun_chunks=defaults.syntax_iterators.get("noun_chunks"), vectors_name=vectors_name, ) cdef class Vocab: """A look-up table that allows you to access `Lexeme` objects. The `Vocab` instance also provides access to the `StringStore`, and owns underlying C-data that is shared between `Doc` objects. DOCS: https://spacy.io/api/vocab """ def __init__(self, lex_attr_getters=None, strings=tuple(), lookups=None, oov_prob=-20., vectors_name=None, writing_system={}, get_noun_chunks=None, **deprecated_kwargs): """Create the vocabulary. lex_attr_getters (dict): A dictionary mapping attribute IDs to functions to compute them. Defaults to `None`. strings (StringStore): StringStore that maps strings to integers, and vice versa. lookups (Lookups): Container for large lookup tables and dictionaries. oov_prob (float): Default OOV probability. vectors_name (str): Optional name to identify the vectors table. get_noun_chunks (Optional[Callable[[Union[Doc, Span], Iterator[Tuple[int, int, int]]]]]): A function that yields base noun phrases used for Doc.noun_chunks. """ lex_attr_getters = lex_attr_getters if lex_attr_getters is not None else {} if lookups in (None, True, False): lookups = Lookups() self.cfg = {'oov_prob': oov_prob} self.mem = Pool() self._by_orth = PreshMap() self.strings = StringStore() self.length = 0 if strings: for string in strings: _ = self[string] self.lex_attr_getters = lex_attr_getters self.morphology = Morphology(self.strings) self.vectors = Vectors(strings=self.strings, name=vectors_name) self.lookups = lookups self.writing_system = writing_system self.get_noun_chunks = get_noun_chunks property vectors: def __get__(self): return self._vectors def __set__(self, vectors): for s in vectors.strings: self.strings.add(s) self._vectors = vectors self._vectors.strings = self.strings @property def lang(self): langfunc = None if self.lex_attr_getters: langfunc = self.lex_attr_getters.get(LANG, None) return langfunc("_") if langfunc else "" def __len__(self): """The current number of lexemes stored. RETURNS (int): The current number of lexemes stored. """ return self.length def add_flag(self, flag_getter, int flag_id=-1): """Set a new boolean flag to words in the vocabulary. The flag_getter function will be called over the words currently in the vocab, and then applied to new words as they occur. You'll then be able to access the flag value on each token using token.check_flag(flag_id). See also: `Lexeme.set_flag`, `Lexeme.check_flag`, `Token.set_flag`, `Token.check_flag`. flag_getter (callable): A function `f(str) -> bool`, to get the flag value. flag_id (int): An integer between 1 and 63 (inclusive), specifying the bit at which the flag will be stored. If -1, the lowest available bit will be chosen. RETURNS (int): The integer ID by which the flag value can be checked. DOCS: https://spacy.io/api/vocab#add_flag """ if flag_id == -1: for bit in range(1, 64): if bit not in self.lex_attr_getters: flag_id = bit break else: raise ValueError(Errors.E062) elif flag_id >= 64 or flag_id < 1: raise ValueError(Errors.E063.format(value=flag_id)) for lex in self: lex.set_flag(flag_id, flag_getter(lex.orth_)) self.lex_attr_getters[flag_id] = flag_getter return flag_id cdef const LexemeC* get(self, Pool mem, str string) except NULL: """Get a pointer to a `LexemeC` from the lexicon, creating a new `Lexeme` if necessary using memory acquired from the given pool. If the pool is the lexicon's own memory, the lexeme is saved in the lexicon. """ if string == "": return &EMPTY_LEXEME cdef LexemeC* lex cdef hash_t key = self.strings[string] lex = self._by_orth.get(key) cdef size_t addr if lex != NULL: assert lex.orth in self.strings if lex.orth != key: raise KeyError(Errors.E064.format(string=lex.orth, orth=key, orth_id=string)) return lex else: return self._new_lexeme(mem, string) cdef const LexemeC* get_by_orth(self, Pool mem, attr_t orth) except NULL: """Get a pointer to a `LexemeC` from the lexicon, creating a new `Lexeme` if necessary using memory acquired from the given pool. If the pool is the lexicon's own memory, the lexeme is saved in the lexicon. """ if orth == 0: return &EMPTY_LEXEME cdef LexemeC* lex lex = self._by_orth.get(orth) if lex != NULL: return lex else: return self._new_lexeme(mem, self.strings[orth]) cdef const LexemeC* _new_lexeme(self, Pool mem, str string) except NULL: # I think this heuristic is bad, and the Vocab should always # own the lexemes. It avoids weird bugs this way, as it's how the thing # was originally supposed to work. The best solution to the growing # memory use is to periodically reset the vocab, which is an action # that should be up to the user to do (so we don't need to keep track # of the doc ownership). # TODO: Change the C API so that the mem isn't passed in here. mem = self.mem #if len(string) < 3 or self.length < 10000: # mem = self.mem cdef bint is_oov = mem is not self.mem lex = mem.alloc(1, sizeof(LexemeC)) lex.orth = self.strings.add(string) lex.length = len(string) if self.vectors is not None: lex.id = self.vectors.key2row.get(lex.orth, OOV_RANK) else: lex.id = OOV_RANK if self.lex_attr_getters is not None: for attr, func in self.lex_attr_getters.items(): value = func(string) if isinstance(value, str): value = self.strings.add(value) if value is not None: Lexeme.set_struct_attr(lex, attr, value) if not is_oov: self._add_lex_to_vocab(lex.orth, lex) if lex == NULL: raise ValueError(Errors.E085.format(string=string)) return lex cdef int _add_lex_to_vocab(self, hash_t key, const LexemeC* lex) except -1: self._by_orth.set(lex.orth, lex) self.length += 1 def __contains__(self, key): """Check whether the string or int key has an entry in the vocabulary. string (str): The ID string. RETURNS (bool) Whether the string has an entry in the vocabulary. DOCS: https://spacy.io/api/vocab#contains """ cdef hash_t int_key if isinstance(key, bytes): int_key = self.strings[key.decode("utf8")] elif isinstance(key, str): int_key = self.strings[key] else: int_key = key lex = self._by_orth.get(int_key) return lex is not NULL def __iter__(self): """Iterate over the lexemes in the vocabulary. YIELDS (Lexeme): An entry in the vocabulary. DOCS: https://spacy.io/api/vocab#iter """ cdef attr_t key cdef size_t addr for key, addr in self._by_orth.items(): lex = Lexeme(self, key) yield lex def __getitem__(self, id_or_string): """Retrieve a lexeme, given an int ID or a unicode string. If a previously unseen unicode string is given, a new lexeme is created and stored. id_or_string (int or str): The integer ID of a word, or its unicode string. If `int >= Lexicon.size`, `IndexError` is raised. If `id_or_string` is neither an int nor a unicode string, `ValueError` is raised. RETURNS (Lexeme): The lexeme indicated by the given ID. EXAMPLE: >>> apple = nlp.vocab.strings["apple"] >>> assert nlp.vocab[apple] == nlp.vocab[u"apple"] DOCS: https://spacy.io/api/vocab#getitem """ cdef attr_t orth if isinstance(id_or_string, str): orth = self.strings.add(id_or_string) else: orth = id_or_string return Lexeme(self, orth) cdef const TokenC* make_fused_token(self, substrings) except NULL: cdef int i tokens = self.mem.alloc(len(substrings) + 1, sizeof(TokenC)) for i, props in enumerate(substrings): props = intify_attrs(props, strings_map=self.strings) token = &tokens[i] # Set the special tokens up to have arbitrary attributes lex = self.get_by_orth(self.mem, props[ORTH]) token.lex = lex for attr_id, value in props.items(): Token.set_struct_attr(token, attr_id, value) # NORM is the only one that overlaps between the two # (which is maybe not great?) if attr_id != NORM: Lexeme.set_struct_attr(lex, attr_id, value) return tokens @property def vectors_length(self): return self.vectors.shape[1] def reset_vectors(self, *, width=None, shape=None): """Drop the current vector table. Because all vectors must be the same width, you have to call this to change the size of the vectors. """ if width is not None and shape is not None: raise ValueError(Errors.E065.format(width=width, shape=shape)) elif shape is not None: self.vectors = Vectors(strings=self.strings, shape=shape) else: width = width if width is not None else self.vectors.shape[1] self.vectors = Vectors(strings=self.strings, shape=(self.vectors.shape[0], width)) def deduplicate_vectors(self): if self.vectors.mode != VectorsMode.default: raise ValueError(Errors.E858.format( mode=self.vectors.mode, alternative="" )) ops = get_current_ops() xp = get_array_module(self.vectors.data) filled = xp.asarray( sorted(list({row for row in self.vectors.key2row.values()})) ) # deduplicate data and remap keys data = numpy.unique(ops.to_numpy(self.vectors.data[filled]), axis=0) data = ops.asarray(data) if data.shape == self.vectors.data.shape: # nothing to deduplicate return row_by_bytes = {row.tobytes(): i for i, row in enumerate(data)} key2row = { key: row_by_bytes[self.vectors.data[row].tobytes()] for key, row in self.vectors.key2row.items() } # replace vectors with deduplicated version self.vectors = Vectors(strings=self.strings, data=data, name=self.vectors.name) for key, row in key2row.items(): self.vectors.add(key, row=row) def prune_vectors(self, nr_row, batch_size=1024): """Reduce the current vector table to `nr_row` unique entries. Words mapped to the discarded vectors will be remapped to the closest vector among those remaining. For example, suppose the original table had vectors for the words: ['sat', 'cat', 'feline', 'reclined']. If we prune the vector table to two rows, we would discard the vectors for 'feline' and 'reclined'. These words would then be remapped to the closest remaining vector -- so "feline" would have the same vector as "cat", and "reclined" would have the same vector as "sat". The similarities are judged by cosine. The original vectors may be large, so the cosines are calculated in minibatches, to reduce memory usage. nr_row (int): The number of rows to keep in the vector table. batch_size (int): Batch of vectors for calculating the similarities. Larger batch sizes might be faster, while temporarily requiring more memory. RETURNS (dict): A dictionary keyed by removed words mapped to `(string, score)` tuples, where `string` is the entry the removed word was mapped to, and `score` the similarity score between the two words. DOCS: https://spacy.io/api/vocab#prune_vectors """ if self.vectors.mode != VectorsMode.default: raise ValueError(Errors.E858.format( mode=self.vectors.mode, alternative="" )) ops = get_current_ops() xp = get_array_module(self.vectors.data) # Make sure all vectors are in the vocab for orth in self.vectors: self[orth] # Make prob negative so it sorts by rank ascending # (key2row contains the rank) priority = [(-lex.prob, self.vectors.key2row[lex.orth], lex.orth) for lex in self if lex.orth in self.vectors.key2row] priority.sort() indices = xp.asarray([i for (prob, i, key) in priority], dtype="uint64") keys = xp.asarray([key for (prob, i, key) in priority], dtype="uint64") keep = xp.ascontiguousarray(self.vectors.data[indices[:nr_row]]) toss = xp.ascontiguousarray(self.vectors.data[indices[nr_row:]]) self.vectors = Vectors(strings=self.strings, data=keep, keys=keys[:nr_row], name=self.vectors.name) syn_keys, syn_rows, scores = self.vectors.most_similar(toss, batch_size=batch_size) syn_keys = ops.to_numpy(syn_keys) remap = {} for i, key in enumerate(ops.to_numpy(keys[nr_row:])): self.vectors.add(key, row=syn_rows[i][0]) word = self.strings[key] synonym = self.strings[syn_keys[i][0]] score = scores[i][0] remap[word] = (synonym, score) return remap def get_vector(self, orth): """Retrieve a vector for a word in the vocabulary. Words can be looked up by string or int ID. If the current vectors do not contain an entry for the word, a 0-vector with the same number of dimensions as the current vectors is returned. orth (int / unicode): The hash value of a word, or its unicode string. RETURNS (numpy.ndarray or cupy.ndarray): A word vector. Size and shape determined by the `vocab.vectors` instance. Usually, a numpy ndarray of shape (300,) and dtype float32. DOCS: https://spacy.io/api/vocab#get_vector """ if isinstance(orth, str): orth = self.strings.add(orth) if self.has_vector(orth): return self.vectors[orth] xp = get_array_module(self.vectors.data) vectors = xp.zeros((self.vectors_length,), dtype="f") return vectors def set_vector(self, orth, vector): """Set a vector for a word in the vocabulary. Words can be referenced by string or int ID. orth (int / str): The word. vector (numpy.ndarray or cupy.nadarry[ndim=1, dtype='float32']): The vector to set. DOCS: https://spacy.io/api/vocab#set_vector """ if isinstance(orth, str): orth = self.strings.add(orth) if self.vectors.is_full and orth not in self.vectors: new_rows = max(100, int(self.vectors.shape[0]*1.3)) if self.vectors.shape[1] == 0: width = vector.size else: width = self.vectors.shape[1] self.vectors.resize((new_rows, width)) lex = self[orth] # Add word to vocab if necessary row = self.vectors.add(orth, vector=vector) if row >= 0: lex.rank = row def has_vector(self, orth): """Check whether a word has a vector. Returns False if no vectors have been loaded. Words can be looked up by string or int ID. orth (int / str): The word. RETURNS (bool): Whether the word has a vector. DOCS: https://spacy.io/api/vocab#has_vector """ if isinstance(orth, str): orth = self.strings.add(orth) return orth in self.vectors property lookups: def __get__(self): return self._lookups def __set__(self, lookups): self._lookups = lookups if lookups.has_table("lexeme_norm"): self.lex_attr_getters[NORM] = util.add_lookups( self.lex_attr_getters.get(NORM, LEX_ATTRS[NORM]), self.lookups.get_table("lexeme_norm"), ) def to_disk(self, path, *, exclude=tuple()): """Save the current state to a directory. path (str or Path): A path to a directory, which will be created if it doesn't exist. exclude (Iterable[str]): String names of serialization fields to exclude. DOCS: https://spacy.io/api/vocab#to_disk """ path = util.ensure_path(path) if not path.exists(): path.mkdir() setters = ["strings", "vectors"] if "strings" not in exclude: self.strings.to_disk(path / "strings.json") if "vectors" not in "exclude": self.vectors.to_disk(path, exclude=["strings"]) if "lookups" not in "exclude": self.lookups.to_disk(path) def from_disk(self, path, *, exclude=tuple()): """Loads state from a directory. Modifies the object in place and returns it. path (str or Path): A path to a directory. exclude (Iterable[str]): 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"] 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 "lookups" not in exclude: self.lookups.from_disk(path) 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()): """Serialize the current state to a binary string. exclude (Iterable[str]): 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(exclude=["strings"]) getters = { "strings": lambda: self.strings.to_bytes(), "vectors": deserialize_vectors, "lookups": lambda: self.lookups.to_bytes(), } return util.to_bytes(getters, exclude) def from_bytes(self, bytes_data, *, exclude=tuple()): """Load state from a binary string. bytes_data (bytes): The data to load from. exclude (Iterable[str]): 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, exclude=["strings"]) setters = { "strings": lambda b: self.strings.from_bytes(b), "vectors": lambda b: serialize_vectors(b), "lookups": lambda b: self.lookups.from_bytes(b), } 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() return self def _reset_cache(self, keys, strings): # I'm not sure this made sense. Disable it for now. raise NotImplementedError def pickle_vocab(vocab): sstore = vocab.strings vectors = vocab.vectors morph = vocab.morphology lex_attr_getters = srsly.pickle_dumps(vocab.lex_attr_getters) lookups = vocab.lookups get_noun_chunks = vocab.get_noun_chunks return (unpickle_vocab, (sstore, vectors, morph, lex_attr_getters, lookups, get_noun_chunks)) def unpickle_vocab(sstore, vectors, morphology, lex_attr_getters, lookups, get_noun_chunks): cdef Vocab vocab = Vocab() vocab.vectors = vectors vocab.strings = sstore vocab.morphology = morphology vocab.lex_attr_getters = srsly.pickle_loads(lex_attr_getters) vocab.lookups = lookups vocab.get_noun_chunks = get_noun_chunks return vocab copy_reg.pickle(Vocab, pickle_vocab, unpickle_vocab)