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https://github.com/explosion/spaCy.git
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878 lines
36 KiB
Cython
878 lines
36 KiB
Cython
# cython: embedsignature=True, binding=True
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cimport cython
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from cymem.cymem cimport Pool
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from cython.operator cimport dereference as deref
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from cython.operator cimport preincrement as preinc
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from libc.string cimport memcpy, memset
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from libcpp.set cimport set as stdset
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from preshed.maps cimport PreshMap
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import re
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from .lexeme cimport EMPTY_LEXEME
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from .strings cimport hash_string
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from .tokens.doc cimport Doc
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from . import util
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from .attrs import intify_attrs
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from .errors import Errors
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from .scorer import Scorer
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from .symbols import NORM, ORTH
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from .tokens import Span
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from .training import validate_examples
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from .util import get_words_and_spaces
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cdef class Tokenizer:
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"""Segment text, and create Doc objects with the discovered segment
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boundaries.
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DOCS: https://spacy.io/api/tokenizer
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"""
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def __init__(self, Vocab vocab, rules=None, prefix_search=None,
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suffix_search=None, infix_finditer=None, token_match=None,
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url_match=None, faster_heuristics=True, max_cache_size=10000):
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"""Create a `Tokenizer`, to create `Doc` objects given unicode text.
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vocab (Vocab): A storage container for lexical types.
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rules (dict): Exceptions and special-cases for the tokenizer.
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prefix_search (callable): A function matching the signature of
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`re.compile(string).search` to match prefixes.
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suffix_search (callable): A function matching the signature of
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`re.compile(string).search` to match suffixes.
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infix_finditer (callable): A function matching the signature of
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`re.compile(string).finditer` to find infixes.
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token_match (callable): A function matching the signature of
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`re.compile(string).match`, for matching strings to be
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recognized as tokens.
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url_match (callable): A function matching the signature of
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`re.compile(string).match`, for matching strings to be
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recognized as urls.
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faster_heuristics (bool): Whether to restrict the final
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Matcher-based pass for rules to those containing affixes or space.
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Defaults to True.
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max_cache_size (int): Maximum number of tokenization chunks to cache.
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EXAMPLE:
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>>> tokenizer = Tokenizer(nlp.vocab)
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DOCS: https://spacy.io/api/tokenizer#init
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"""
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self.mem = Pool()
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self._cache = PreshMap()
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self._specials = PreshMap()
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self.token_match = token_match
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self.url_match = url_match
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self.prefix_search = prefix_search
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self.suffix_search = suffix_search
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self.infix_finditer = infix_finditer
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self.vocab = vocab
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self.faster_heuristics = faster_heuristics
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self._rules = {}
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self._special_matcher = PhraseMatcher(self.vocab)
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self._load_special_cases(rules)
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self.max_cache_size = max_cache_size
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@property
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def token_match(self):
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return self._token_match
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@token_match.setter
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def token_match(self, token_match):
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self._token_match = token_match
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self._reload_special_cases()
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@property
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def url_match(self):
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return self._url_match
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@url_match.setter
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def url_match(self, url_match):
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self._url_match = url_match
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self._reload_special_cases()
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@property
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def prefix_search(self):
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return self._prefix_search
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@prefix_search.setter
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def prefix_search(self, prefix_search):
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self._prefix_search = prefix_search
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self._reload_special_cases()
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@property
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def suffix_search(self):
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return self._suffix_search
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@suffix_search.setter
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def suffix_search(self, suffix_search):
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self._suffix_search = suffix_search
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self._reload_special_cases()
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@property
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def infix_finditer(self):
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return self._infix_finditer
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@infix_finditer.setter
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def infix_finditer(self, infix_finditer):
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self._infix_finditer = infix_finditer
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self._reload_special_cases()
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@property
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def rules(self):
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return self._rules
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@rules.setter
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def rules(self, rules):
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self._rules = {}
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self._flush_cache()
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self._flush_specials()
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self._cache = PreshMap()
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self._specials = PreshMap()
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self._load_special_cases(rules)
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@property
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def faster_heuristics(self):
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return bool(self._faster_heuristics)
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@faster_heuristics.setter
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def faster_heuristics(self, faster_heuristics):
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self._faster_heuristics = bool(faster_heuristics)
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self._reload_special_cases()
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def __reduce__(self):
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args = (self.vocab,
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self.rules,
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self.prefix_search,
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self.suffix_search,
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self.infix_finditer,
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self.token_match,
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self.url_match)
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return (self.__class__, args, None, None)
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def __call__(self, str string):
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"""Tokenize a string.
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string (str): The string to tokenize.
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RETURNS (Doc): A container for linguistic annotations.
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DOCS: https://spacy.io/api/tokenizer#call
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"""
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doc = self._tokenize_affixes(string, True)
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self._apply_special_cases(doc)
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return doc
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@cython.boundscheck(False)
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cdef Doc _tokenize_affixes(self, str string, bint with_special_cases):
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"""Tokenize according to affix and token_match settings.
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string (str): The string to tokenize.
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RETURNS (Doc): A container for linguistic annotations.
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"""
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if len(string) >= (2 ** 30):
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raise ValueError(Errors.E025.format(length=len(string)))
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cdef int length = len(string)
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cdef Doc doc = Doc(self.vocab)
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if length == 0:
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return doc
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cdef int i = 0
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cdef int start = 0
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cdef int has_special = 0
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cdef bint in_ws = string[0].isspace()
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cdef str span
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# The task here is much like string.split, but not quite
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# We find spans of whitespace and non-space characters, and ignore
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# spans that are exactly ' '. So, our sequences will all be separated
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# by either ' ' or nothing.
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for uc in string:
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if uc.isspace() != in_ws:
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if start < i:
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# When we want to make this fast, get the data buffer once
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# with PyUnicode_AS_DATA, and then maintain a start_byte
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# and end_byte, so we can call hash64 directly. That way
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# we don't have to create the slice when we hit the cache.
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span = string[start:i]
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key = hash_string(span)
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if not self._try_specials_and_cache(key, doc, &has_special, with_special_cases):
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self._tokenize(doc, span, key, &has_special, with_special_cases)
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if uc == ' ':
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doc.c[doc.length - 1].spacy = True
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start = i + 1
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else:
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start = i
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in_ws = not in_ws
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i += 1
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if start < i:
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span = string[start:]
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key = hash_string(span)
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if not self._try_specials_and_cache(key, doc, &has_special, with_special_cases):
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self._tokenize(doc, span, key, &has_special, with_special_cases)
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doc.c[doc.length - 1].spacy = string[-1] == " " and not in_ws
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return doc
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def pipe(self, texts, batch_size=1000):
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"""Tokenize a stream of texts.
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texts: A sequence of unicode texts.
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batch_size (int): Number of texts to accumulate in an internal buffer.
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Defaults to 1000.
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YIELDS (Doc): A sequence of Doc objects, in order.
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DOCS: https://spacy.io/api/tokenizer#pipe
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"""
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for text in texts:
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yield self(text)
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def _flush_cache(self):
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self._reset_cache([key for key in self._cache])
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def _reset_cache(self, keys):
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for k in keys:
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cached = <_Cached*>self._cache.get(k)
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del self._cache[k]
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if cached is not NULL:
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self.mem.free(cached)
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def _flush_specials(self):
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self._special_matcher = PhraseMatcher(self.vocab)
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for k in self._specials:
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cached = <_Cached*>self._specials.get(k)
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del self._specials[k]
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if cached is not NULL:
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self.mem.free(cached)
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cdef int _apply_special_cases(self, Doc doc) except -1:
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"""Retokenize doc according to special cases.
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doc (Doc): Document.
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"""
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cdef int i
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cdef int max_length = 0
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cdef bint modify_in_place
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cdef Pool mem = Pool()
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cdef vector[SpanC] c_matches
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cdef vector[SpanC] c_filtered
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cdef int offset
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cdef int modified_doc_length
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# Find matches for special cases
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self._special_matcher.find_matches(doc, 0, doc.length, &c_matches)
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# Skip processing if no matches
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if c_matches.size() == 0:
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return True
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self._filter_special_spans(c_matches, c_filtered, doc.length)
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# Put span info in span.start-indexed dict and calculate maximum
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# intermediate document size
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(span_data, max_length, modify_in_place) = self._prepare_special_spans(doc, c_filtered)
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# If modifications never increase doc length, can modify in place
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if modify_in_place:
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tokens = doc.c
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# Otherwise create a separate array to store modified tokens
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else:
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assert max_length > 0
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tokens = <TokenC*>mem.alloc(max_length, sizeof(TokenC))
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# Modify tokenization according to filtered special cases
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offset = self._retokenize_special_spans(doc, tokens, span_data)
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# Allocate more memory for doc if needed
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modified_doc_length = doc.length + offset
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while modified_doc_length >= doc.max_length:
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doc._realloc(doc.max_length * 2)
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# If not modified in place, copy tokens back to doc
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if not modify_in_place:
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memcpy(doc.c, tokens, max_length * sizeof(TokenC))
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for i in range(doc.length + offset, doc.length):
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memset(&doc.c[i], 0, sizeof(TokenC))
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doc.c[i].lex = &EMPTY_LEXEME
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doc.length = doc.length + offset
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return True
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cdef void _filter_special_spans(self, vector[SpanC] &original, vector[SpanC] &filtered, int doc_len) nogil:
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cdef int seen_i
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cdef SpanC span
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cdef stdset[int] seen_tokens
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stdsort(original.begin(), original.end(), len_start_cmp)
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cdef int orig_i = original.size() - 1
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while orig_i >= 0:
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span = original[orig_i]
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if not seen_tokens.count(span.start) and not seen_tokens.count(span.end - 1):
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filtered.push_back(span)
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for seen_i in range(span.start, span.end):
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seen_tokens.insert(seen_i)
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orig_i -= 1
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stdsort(filtered.begin(), filtered.end(), start_cmp)
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cdef object _prepare_special_spans(self, Doc doc, vector[SpanC] &filtered):
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spans = [doc[match.start:match.end] for match in filtered]
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cdef bint modify_in_place = True
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cdef int curr_length = doc.length
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cdef int max_length = 0
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cdef int span_length_diff = 0
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span_data = {}
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for span in spans:
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rule = self._rules.get(span.text, None)
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span_length_diff = 0
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if rule:
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span_length_diff = len(rule) - (span.end - span.start)
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if span_length_diff > 0:
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modify_in_place = False
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curr_length += span_length_diff
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if curr_length > max_length:
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max_length = curr_length
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span_data[span.start] = (span.text, span.start, span.end, span_length_diff)
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return (span_data, max_length, modify_in_place)
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cdef int _retokenize_special_spans(self, Doc doc, TokenC* tokens, object span_data):
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cdef int i = 0
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cdef int j = 0
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cdef int offset = 0
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cdef _Cached* cached
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cdef int idx_offset = 0
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cdef int orig_final_spacy
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cdef int orig_idx
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cdef int span_start
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cdef int span_end
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while i < doc.length:
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if i not in span_data:
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tokens[i + offset] = doc.c[i]
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i += 1
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else:
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span = span_data[i]
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span_start = span[1]
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span_end = span[2]
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cached = <_Cached*>self._specials.get(hash_string(span[0]))
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if cached == NULL:
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# Copy original tokens if no rule found
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for j in range(span_end - span_start):
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tokens[i + offset + j] = doc.c[i + j]
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i += span_end - span_start
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else:
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# Copy special case tokens into doc and adjust token and
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# character offsets
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idx_offset = 0
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orig_final_spacy = doc.c[span_end - 1].spacy
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orig_idx = doc.c[i].idx
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for j in range(cached.length):
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tokens[i + offset + j] = cached.data.tokens[j]
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tokens[i + offset + j].idx = orig_idx + idx_offset
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idx_offset += cached.data.tokens[j].lex.length
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if cached.data.tokens[j].spacy:
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idx_offset += 1
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tokens[i + offset + cached.length - 1].spacy = orig_final_spacy
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i += span_end - span_start
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offset += span[3]
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return offset
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cdef int _try_specials_and_cache(self, hash_t key, Doc tokens, int* has_special, bint with_special_cases) except -1:
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cdef bint specials_hit = 0
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cdef bint cache_hit = 0
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cdef int i
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if with_special_cases:
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cached = <_Cached*>self._specials.get(key)
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if cached == NULL:
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specials_hit = False
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else:
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for i in range(cached.length):
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tokens.push_back(&cached.data.tokens[i], False)
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has_special[0] = 1
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specials_hit = True
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if not specials_hit:
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cached = <_Cached*>self._cache.get(key)
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if cached == NULL:
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cache_hit = False
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else:
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if cached.is_lex:
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for i in range(cached.length):
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tokens.push_back(cached.data.lexemes[i], False)
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else:
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for i in range(cached.length):
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tokens.push_back(&cached.data.tokens[i], False)
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cache_hit = True
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if not specials_hit and not cache_hit:
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return False
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return True
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cdef int _tokenize(self, Doc tokens, str span, hash_t orig_key, int* has_special, bint with_special_cases) except -1:
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cdef vector[LexemeC*] prefixes
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cdef vector[LexemeC*] suffixes
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cdef int orig_size
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orig_size = tokens.length
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span = self._split_affixes(tokens.mem, span, &prefixes, &suffixes,
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has_special, with_special_cases)
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self._attach_tokens(tokens, span, &prefixes, &suffixes, has_special,
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with_special_cases)
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if len(self._cache) < self.max_cache_size:
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self._save_cached(&tokens.c[orig_size], orig_key, has_special,
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tokens.length - orig_size)
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cdef str _split_affixes(
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self,
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Pool mem,
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str string,
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vector[const LexemeC*] *prefixes,
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vector[const LexemeC*] *suffixes,
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int* has_special,
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bint with_special_cases
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):
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cdef str prefix
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cdef str suffix
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cdef str minus_pre
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cdef str minus_suf
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cdef size_t last_size = 0
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while string and len(string) != last_size:
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if self.token_match and self.token_match(string):
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break
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if with_special_cases and self._specials.get(hash_string(string)) != NULL:
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break
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last_size = len(string)
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pre_len = self.find_prefix(string)
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if pre_len != 0:
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prefix = string[:pre_len]
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minus_pre = string[pre_len:]
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if minus_pre and with_special_cases and self._specials.get(hash_string(minus_pre)) != NULL:
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string = minus_pre
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prefixes.push_back(self.vocab.get(mem, prefix))
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break
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suf_len = self.find_suffix(string[pre_len:])
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if suf_len != 0:
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suffix = string[-suf_len:]
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minus_suf = string[:-suf_len]
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if minus_suf and with_special_cases and self._specials.get(hash_string(minus_suf)) != NULL:
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string = minus_suf
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suffixes.push_back(self.vocab.get(mem, suffix))
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break
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if pre_len and suf_len and (pre_len + suf_len) <= len(string):
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string = string[pre_len:-suf_len]
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prefixes.push_back(self.vocab.get(mem, prefix))
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suffixes.push_back(self.vocab.get(mem, suffix))
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elif pre_len:
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string = minus_pre
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prefixes.push_back(self.vocab.get(mem, prefix))
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elif suf_len:
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string = minus_suf
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suffixes.push_back(self.vocab.get(mem, suffix))
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return string
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cdef int _attach_tokens(self, Doc tokens, str string,
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vector[const LexemeC*] *prefixes,
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vector[const LexemeC*] *suffixes,
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int* has_special,
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bint with_special_cases) except -1:
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cdef const LexemeC* lexeme
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cdef str span
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cdef int i
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if prefixes.size():
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for i in range(prefixes.size()):
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tokens.push_back(prefixes[0][i], False)
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if string:
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if self._try_specials_and_cache(hash_string(string), tokens, has_special, with_special_cases):
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pass
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elif (
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(self.token_match and self.token_match(string)) or
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(self.url_match and self.url_match(string))
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):
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# We're always saying 'no' to spaces here -- the caller will
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# fix up the outermost one, with reference to the original.
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# See Issue #859
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tokens.push_back(self.vocab.get(tokens.mem, string), False)
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else:
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matches = self.find_infix(string)
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if not matches:
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tokens.push_back(self.vocab.get(tokens.mem, string), False)
|
|
else:
|
|
# Let's say we have dyn-o-mite-dave - the regex finds the
|
|
# start and end positions of the hyphens
|
|
start = 0
|
|
start_before_infixes = start
|
|
for match in matches:
|
|
infix_start = match.start()
|
|
infix_end = match.end()
|
|
|
|
if infix_start == start_before_infixes:
|
|
continue
|
|
|
|
if infix_start != start:
|
|
span = string[start:infix_start]
|
|
tokens.push_back(self.vocab.get(tokens.mem, span), False)
|
|
|
|
if infix_start != infix_end:
|
|
# If infix_start != infix_end, it means the infix
|
|
# token is non-empty. Empty infix tokens are useful
|
|
# for tokenization in some languages (see
|
|
# https://github.com/explosion/spaCy/issues/768)
|
|
infix_span = string[infix_start:infix_end]
|
|
tokens.push_back(self.vocab.get(tokens.mem, infix_span), False)
|
|
start = infix_end
|
|
span = string[start:]
|
|
if span:
|
|
tokens.push_back(self.vocab.get(tokens.mem, span), False)
|
|
cdef vector[const LexemeC*].reverse_iterator it = suffixes.rbegin()
|
|
while it != suffixes.rend():
|
|
lexeme = deref(it)
|
|
preinc(it)
|
|
tokens.push_back(lexeme, False)
|
|
|
|
cdef int _save_cached(self, const TokenC* tokens, hash_t key,
|
|
int* has_special, int n) except -1:
|
|
cdef int i
|
|
if n <= 0:
|
|
# avoid mem alloc of zero length
|
|
return 0
|
|
if self.vocab.in_memory_zone:
|
|
return 0
|
|
# See #1250
|
|
if has_special[0]:
|
|
return 0
|
|
cached = <_Cached*>self.mem.alloc(1, sizeof(_Cached))
|
|
cached.length = n
|
|
cached.is_lex = True
|
|
lexemes = <const LexemeC**>self.mem.alloc(n, sizeof(LexemeC**))
|
|
for i in range(n):
|
|
lexemes[i] = tokens[i].lex
|
|
cached.data.lexemes = <const LexemeC* const*>lexemes
|
|
self._cache.set(key, cached)
|
|
|
|
def find_infix(self, str string):
|
|
"""Find internal split points of the string, such as hyphens.
|
|
|
|
string (str): The string to segment.
|
|
RETURNS (list): A list of `re.MatchObject` objects that have `.start()`
|
|
and `.end()` methods, denoting the placement of internal segment
|
|
separators, e.g. hyphens.
|
|
|
|
DOCS: https://spacy.io/api/tokenizer#find_infix
|
|
"""
|
|
if self.infix_finditer is None:
|
|
return 0
|
|
return list(self.infix_finditer(string))
|
|
|
|
def find_prefix(self, str string):
|
|
"""Find the length of a prefix that should be segmented from the
|
|
string, or None if no prefix rules match.
|
|
|
|
string (str): The string to segment.
|
|
RETURNS (int): The length of the prefix if present, otherwise `None`.
|
|
|
|
DOCS: https://spacy.io/api/tokenizer#find_prefix
|
|
"""
|
|
if self.prefix_search is None:
|
|
return 0
|
|
match = self.prefix_search(string)
|
|
return (match.end() - match.start()) if match is not None else 0
|
|
|
|
def find_suffix(self, str string):
|
|
"""Find the length of a suffix that should be segmented from the
|
|
string, or None if no suffix rules match.
|
|
|
|
string (str): The string to segment.
|
|
Returns (int): The length of the suffix if present, otherwise `None`.
|
|
|
|
DOCS: https://spacy.io/api/tokenizer#find_suffix
|
|
"""
|
|
if self.suffix_search is None:
|
|
return 0
|
|
match = self.suffix_search(string)
|
|
return (match.end() - match.start()) if match is not None else 0
|
|
|
|
def _load_special_cases(self, special_cases):
|
|
"""Add special-case tokenization rules."""
|
|
if special_cases is not None:
|
|
for chunk, substrings in sorted(special_cases.items()):
|
|
self.add_special_case(chunk, substrings)
|
|
|
|
def _validate_special_case(self, chunk, substrings):
|
|
"""Check whether the `ORTH` fields match the string. Check that
|
|
additional features beyond `ORTH` and `NORM` are not set by the
|
|
exception.
|
|
|
|
chunk (str): The string to specially tokenize.
|
|
substrings (iterable): A sequence of dicts, where each dict describes
|
|
a token and its attributes.
|
|
"""
|
|
attrs = [intify_attrs(spec, _do_deprecated=True) for spec in substrings]
|
|
orth = "".join([spec[ORTH] for spec in attrs])
|
|
if chunk != orth:
|
|
raise ValueError(Errors.E997.format(chunk=chunk, orth=orth, token_attrs=substrings))
|
|
for substring in attrs:
|
|
for attr in substring:
|
|
if attr not in (ORTH, NORM):
|
|
raise ValueError(Errors.E1005.format(attr=self.vocab.strings[attr], chunk=chunk))
|
|
|
|
def add_special_case(self, str string, substrings):
|
|
"""Add a special-case tokenization rule.
|
|
|
|
string (str): The string to specially tokenize.
|
|
substrings (iterable): A sequence of dicts, where each dict describes
|
|
a token and its attributes. The `ORTH` fields of the attributes
|
|
must exactly match the string when they are concatenated.
|
|
|
|
DOCS: https://spacy.io/api/tokenizer#add_special_case
|
|
"""
|
|
self._validate_special_case(string, substrings)
|
|
substrings = list(substrings)
|
|
cached = <_Cached*>self.mem.alloc(1, sizeof(_Cached))
|
|
cached.length = len(substrings)
|
|
cached.is_lex = False
|
|
cached.data.tokens = self.vocab.make_fused_token(substrings)
|
|
key = hash_string(string)
|
|
stale_special = <_Cached*>self._specials.get(key)
|
|
self._specials.set(key, cached)
|
|
if stale_special is not NULL:
|
|
self.mem.free(stale_special)
|
|
self._rules[string] = substrings
|
|
self._flush_cache()
|
|
if not self.faster_heuristics or self.find_prefix(string) or self.find_infix(string) or self.find_suffix(string) or " " in string:
|
|
self._special_matcher.add(string, None, self._tokenize_affixes(string, False))
|
|
|
|
def _reload_special_cases(self):
|
|
self._flush_cache()
|
|
self._flush_specials()
|
|
self._load_special_cases(self._rules)
|
|
|
|
def explain(self, text):
|
|
"""A debugging tokenizer that provides information about which
|
|
tokenizer rule or pattern was matched for each token. The tokens
|
|
produced are identical to `nlp.tokenizer()` except for whitespace
|
|
tokens.
|
|
|
|
string (str): The string to tokenize.
|
|
RETURNS (list): A list of (pattern_string, token_string) tuples
|
|
|
|
DOCS: https://spacy.io/api/tokenizer#explain
|
|
"""
|
|
prefix_search = self.prefix_search
|
|
if prefix_search is None:
|
|
prefix_search = re.compile("a^").search
|
|
suffix_search = self.suffix_search
|
|
if suffix_search is None:
|
|
suffix_search = re.compile("a^").search
|
|
infix_finditer = self.infix_finditer
|
|
if infix_finditer is None:
|
|
infix_finditer = re.compile("a^").finditer
|
|
token_match = self.token_match
|
|
if token_match is None:
|
|
token_match = re.compile("a^").match
|
|
url_match = self.url_match
|
|
if url_match is None:
|
|
url_match = re.compile("a^").match
|
|
special_cases = {}
|
|
for orth, special_tokens in self.rules.items():
|
|
special_cases[orth] = [intify_attrs(special_token, strings_map=self.vocab.strings, _do_deprecated=True) for special_token in special_tokens]
|
|
tokens = []
|
|
for substring in text.split():
|
|
suffixes = []
|
|
while substring:
|
|
if substring in special_cases:
|
|
tokens.extend(("SPECIAL-" + str(i + 1), self.vocab.strings[e[ORTH]]) for i, e in enumerate(special_cases[substring]))
|
|
substring = ''
|
|
continue
|
|
while prefix_search(substring) or suffix_search(substring):
|
|
if token_match(substring):
|
|
tokens.append(("TOKEN_MATCH", substring))
|
|
substring = ''
|
|
break
|
|
if substring in special_cases:
|
|
tokens.extend(("SPECIAL-" + str(i + 1), self.vocab.strings[e[ORTH]]) for i, e in enumerate(special_cases[substring]))
|
|
substring = ''
|
|
break
|
|
if prefix_search(substring):
|
|
split = prefix_search(substring).end()
|
|
# break if pattern matches the empty string
|
|
if split == 0:
|
|
break
|
|
tokens.append(("PREFIX", substring[:split]))
|
|
substring = substring[split:]
|
|
if substring in special_cases:
|
|
continue
|
|
if suffix_search(substring):
|
|
split = suffix_search(substring).start()
|
|
# break if pattern matches the empty string
|
|
if split == len(substring):
|
|
break
|
|
suffixes.append(("SUFFIX", substring[split:]))
|
|
substring = substring[:split]
|
|
if len(substring) == 0:
|
|
continue
|
|
if token_match(substring):
|
|
tokens.append(("TOKEN_MATCH", substring))
|
|
substring = ''
|
|
elif url_match(substring):
|
|
tokens.append(("URL_MATCH", substring))
|
|
substring = ''
|
|
elif substring in special_cases:
|
|
tokens.extend((f"SPECIAL-{i + 1}", self.vocab.strings[e[ORTH]]) for i, e in enumerate(special_cases[substring]))
|
|
substring = ''
|
|
elif list(infix_finditer(substring)):
|
|
infixes = infix_finditer(substring)
|
|
offset = 0
|
|
for match in infixes:
|
|
if offset == 0 and match.start() == 0:
|
|
continue
|
|
if substring[offset : match.start()]:
|
|
tokens.append(("TOKEN", substring[offset : match.start()]))
|
|
if substring[match.start() : match.end()]:
|
|
tokens.append(("INFIX", substring[match.start() : match.end()]))
|
|
offset = match.end()
|
|
if substring[offset:]:
|
|
tokens.append(("TOKEN", substring[offset:]))
|
|
substring = ''
|
|
elif substring:
|
|
tokens.append(("TOKEN", substring))
|
|
substring = ''
|
|
tokens.extend(reversed(suffixes))
|
|
# Find matches for special cases handled by special matcher
|
|
words, spaces = get_words_and_spaces([t[1] for t in tokens], text)
|
|
t_words = []
|
|
t_spaces = []
|
|
for word, space in zip(words, spaces):
|
|
if not word.isspace():
|
|
t_words.append(word)
|
|
t_spaces.append(space)
|
|
doc = Doc(self.vocab, words=t_words, spaces=t_spaces)
|
|
matches = self._special_matcher(doc)
|
|
spans = [Span(doc, s, e, label=m_id) for m_id, s, e in matches]
|
|
spans = util.filter_spans(spans)
|
|
# Replace matched tokens with their exceptions
|
|
i = 0
|
|
final_tokens = []
|
|
spans_by_start = {s.start: s for s in spans}
|
|
while i < len(tokens):
|
|
if i in spans_by_start:
|
|
span = spans_by_start[i]
|
|
exc = [d[ORTH] for d in special_cases[span.label_]]
|
|
# The phrase matcher can overmatch for tokens separated by
|
|
# spaces in the text but not in the underlying rule, so skip
|
|
# cases where the texts aren't identical
|
|
if span.text != "".join([self.vocab.strings[orth] for orth in exc]):
|
|
final_tokens.append(tokens[i])
|
|
i += 1
|
|
else:
|
|
for j, orth in enumerate(exc):
|
|
final_tokens.append((f"SPECIAL-{j + 1}", self.vocab.strings[orth]))
|
|
i += len(span)
|
|
else:
|
|
final_tokens.append(tokens[i])
|
|
i += 1
|
|
return final_tokens
|
|
|
|
def score(self, examples, **kwargs):
|
|
validate_examples(examples, "Tokenizer.score")
|
|
return Scorer.score_tokenization(examples)
|
|
|
|
def to_disk(self, path, **kwargs):
|
|
"""Save the current state to a directory.
|
|
|
|
path (str / Path): A path to a directory, which will be created if
|
|
it doesn't exist.
|
|
exclude (list): String names of serialization fields to exclude.
|
|
|
|
DOCS: https://spacy.io/api/tokenizer#to_disk
|
|
"""
|
|
path = util.ensure_path(path)
|
|
with path.open("wb") as file_:
|
|
file_.write(self.to_bytes(**kwargs))
|
|
|
|
def from_disk(self, path, *, exclude=tuple()):
|
|
"""Loads state from a directory. Modifies the object in place and
|
|
returns it.
|
|
|
|
path (str / Path): A path to a directory.
|
|
exclude (list): String names of serialization fields to exclude.
|
|
RETURNS (Tokenizer): The modified `Tokenizer` object.
|
|
|
|
DOCS: https://spacy.io/api/tokenizer#from_disk
|
|
"""
|
|
path = util.ensure_path(path)
|
|
with path.open("rb") as file_:
|
|
bytes_data = file_.read()
|
|
self.from_bytes(bytes_data, exclude=exclude)
|
|
return self
|
|
|
|
def to_bytes(self, *, exclude=tuple()):
|
|
"""Serialize the current state to a binary string.
|
|
|
|
exclude (list): String names of serialization fields to exclude.
|
|
RETURNS (bytes): The serialized form of the `Tokenizer` object.
|
|
|
|
DOCS: https://spacy.io/api/tokenizer#to_bytes
|
|
"""
|
|
serializers = {
|
|
"vocab": lambda: self.vocab.to_bytes(exclude=exclude),
|
|
"prefix_search": lambda: _get_regex_pattern(self.prefix_search),
|
|
"suffix_search": lambda: _get_regex_pattern(self.suffix_search),
|
|
"infix_finditer": lambda: _get_regex_pattern(self.infix_finditer),
|
|
"token_match": lambda: _get_regex_pattern(self.token_match),
|
|
"url_match": lambda: _get_regex_pattern(self.url_match),
|
|
"exceptions": lambda: dict(sorted(self._rules.items())),
|
|
"faster_heuristics": lambda: self.faster_heuristics,
|
|
}
|
|
return util.to_bytes(serializers, exclude)
|
|
|
|
def from_bytes(self, bytes_data, *, exclude=tuple()):
|
|
"""Load state from a binary string.
|
|
|
|
bytes_data (bytes): The data to load from.
|
|
exclude (list): String names of serialization fields to exclude.
|
|
RETURNS (Tokenizer): The `Tokenizer` object.
|
|
|
|
DOCS: https://spacy.io/api/tokenizer#from_bytes
|
|
"""
|
|
data = {}
|
|
deserializers = {
|
|
"vocab": lambda b: self.vocab.from_bytes(b, exclude=exclude),
|
|
"prefix_search": lambda b: data.setdefault("prefix_search", b),
|
|
"suffix_search": lambda b: data.setdefault("suffix_search", b),
|
|
"infix_finditer": lambda b: data.setdefault("infix_finditer", b),
|
|
"token_match": lambda b: data.setdefault("token_match", b),
|
|
"url_match": lambda b: data.setdefault("url_match", b),
|
|
"exceptions": lambda b: data.setdefault("rules", b),
|
|
"faster_heuristics": lambda b: data.setdefault("faster_heuristics", b),
|
|
}
|
|
# reset all properties and flush all caches (through rules),
|
|
# reset rules first so that _reload_special_cases is trivial/fast as
|
|
# the other properties are reset
|
|
self.rules = {}
|
|
self.prefix_search = None
|
|
self.suffix_search = None
|
|
self.infix_finditer = None
|
|
self.token_match = None
|
|
self.url_match = None
|
|
util.from_bytes(bytes_data, deserializers, exclude)
|
|
if "prefix_search" in data and isinstance(data["prefix_search"], str):
|
|
self.prefix_search = re.compile(data["prefix_search"]).search
|
|
if "suffix_search" in data and isinstance(data["suffix_search"], str):
|
|
self.suffix_search = re.compile(data["suffix_search"]).search
|
|
if "infix_finditer" in data and isinstance(data["infix_finditer"], str):
|
|
self.infix_finditer = re.compile(data["infix_finditer"]).finditer
|
|
if "token_match" in data and isinstance(data["token_match"], str):
|
|
self.token_match = re.compile(data["token_match"]).match
|
|
if "url_match" in data and isinstance(data["url_match"], str):
|
|
self.url_match = re.compile(data["url_match"]).match
|
|
if "faster_heuristics" in data:
|
|
self.faster_heuristics = data["faster_heuristics"]
|
|
# always load rules last so that all other settings are set before the
|
|
# internal tokenization for the phrase matcher
|
|
if "rules" in data and isinstance(data["rules"], dict):
|
|
self.rules = data["rules"]
|
|
return self
|
|
|
|
|
|
def _get_regex_pattern(regex):
|
|
"""Get a pattern string for a regex, or None if the pattern is None."""
|
|
return None if regex is None else regex.__self__.pattern
|
|
|
|
|
|
cdef extern from "<algorithm>" namespace "std" nogil:
|
|
void stdsort "sort"(vector[SpanC].iterator,
|
|
vector[SpanC].iterator,
|
|
bint (*)(SpanC, SpanC))
|
|
|
|
|
|
cdef bint len_start_cmp(SpanC a, SpanC b) nogil:
|
|
if a.end - a.start == b.end - b.start:
|
|
return b.start < a.start
|
|
return a.end - a.start < b.end - b.start
|
|
|
|
|
|
cdef bint start_cmp(SpanC a, SpanC b) nogil:
|
|
return a.start < b.start
|