mirror of
https://github.com/explosion/spaCy.git
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431 lines
17 KiB
Cython
431 lines
17 KiB
Cython
# cython: embedsignature=True
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# coding: utf8
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from __future__ import unicode_literals
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import ujson
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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 cymem.cymem cimport Pool
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from preshed.maps cimport PreshMap
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from .strings cimport hash_string
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cimport cython
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from . import util
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from .tokens.doc cimport Doc
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cdef class Tokenizer:
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"""
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Segment text, and create Doc objects with the discovered segment boundaries.
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"""
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@classmethod
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def load(cls, path, Vocab vocab, rules=None, prefix_search=None, suffix_search=None,
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infix_finditer=None, token_match=None):
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"""
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Load a Tokenizer, reading unsupplied components from the path.
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Arguments:
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path (Path):
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The path to load from.
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vocab (Vocab):
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A storage container for lexical types.
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rules (dict):
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Exceptions and special-cases for the tokenizer.
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token_match:
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A boolean function matching strings that becomes tokens.
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prefix_search:
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Signature of re.compile(string).search
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suffix_search:
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Signature of re.compile(string).search
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infix_finditer:
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Signature of re.compile(string).finditer
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Returns Tokenizer
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"""
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path = util.ensure_path(path)
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if rules is None:
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with (path / 'tokenizer' / 'specials.json').open('r', encoding='utf8') as file_:
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rules = ujson.load(file_)
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if prefix_search in (None, True):
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with (path / 'tokenizer' / 'prefix.txt').open() as file_:
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entries = file_.read().split('\n')
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prefix_search = util.compile_prefix_regex(entries).search
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if suffix_search in (None, True):
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with (path / 'tokenizer' / 'suffix.txt').open() as file_:
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entries = file_.read().split('\n')
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suffix_search = util.compile_suffix_regex(entries).search
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if infix_finditer in (None, True):
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with (path / 'tokenizer' / 'infix.txt').open() as file_:
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entries = file_.read().split('\n')
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infix_finditer = util.compile_infix_regex(entries).finditer
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return cls(vocab, rules, prefix_search, suffix_search, infix_finditer, token_match)
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def __init__(self, Vocab vocab, rules, prefix_search, suffix_search, infix_finditer, token_match=None):
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"""
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Create a Tokenizer, to create Doc objects given unicode text.
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Arguments:
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vocab (Vocab):
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A storage container for lexical types.
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rules (dict):
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Exceptions and special-cases for the tokenizer.
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prefix_search:
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A function matching the signature of re.compile(string).search
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to match prefixes.
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suffix_search:
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A function matching the signature of re.compile(string).search
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to match suffixes.
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infix_finditer:
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A function matching the signature of re.compile(string).finditer
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to find infixes.
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token_match:
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A boolean function matching strings that becomes tokens.
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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.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._rules = {}
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for chunk, substrings in sorted(rules.items()):
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self.add_special_case(chunk, substrings)
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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_re,
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self._suffix_re,
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self._infix_re,
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self.token_match)
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return (self.__class__, args, None, None)
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cpdef Doc tokens_from_list(self, list strings):
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return Doc(self.vocab, words=strings)
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#raise NotImplementedError(
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# "Method deprecated in 1.0.\n"
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# "Old: tokenizer.tokens_from_list(strings)\n"
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# "New: Doc(tokenizer.vocab, words=strings)")
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@cython.boundscheck(False)
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def __call__(self, unicode string):
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"""
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Tokenize a string.
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Arguments:
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string (unicode): The string to tokenize.
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Returns:
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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(
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"String is too long: %d characters. Max is 2**30." % len(string)
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)
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cdef int length = len(string)
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cdef Doc tokens = Doc(self.vocab)
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if length == 0:
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return tokens
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cdef int i = 0
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cdef int start = 0
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cdef bint cache_hit
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cdef bint in_ws = string[0].isspace()
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cdef unicode 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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cache_hit = self._try_cache(key, tokens)
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if not cache_hit:
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self._tokenize(tokens, span, key)
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if uc == ' ':
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tokens.c[tokens.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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cache_hit = self._try_cache(key, tokens)
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if not cache_hit:
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self._tokenize(tokens, span, key)
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tokens.c[tokens.length - 1].spacy = string[-1] == ' ' and not in_ws
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return tokens
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def pipe(self, texts, batch_size=1000, n_threads=2):
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"""
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Tokenize a stream of texts.
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Arguments:
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texts: A sequence of unicode texts.
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batch_size (int):
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The number of texts to accumulate in an internal buffer.
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n_threads (int):
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The number of threads to use, if the implementation supports
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multi-threading. The default tokenizer is single-threaded.
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Yields:
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Doc A sequence of Doc objects, in order.
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"""
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for text in texts:
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yield self(text)
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cdef int _try_cache(self, hash_t key, Doc tokens) except -1:
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cached = <_Cached*>self._cache.get(key)
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if cached == NULL:
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# See 'flush_cache' below for hand-wringing about
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# how to handle this.
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cached = <_Cached*>self._specials.get(key)
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if cached == NULL:
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return False
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else:
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self._cache.set(key, cached)
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cdef int i
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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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return True
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cdef int _tokenize(self, Doc tokens, unicode span, hash_t orig_key) 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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special_case = <const _Cached*>self._specials.get(orig_key)
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if special_case is not NULL:
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for i in range(special_case.length):
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tokens.push_back(&special_case.data.tokens[i], False)
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self._cache.set(orig_key, <void*>special_case)
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else:
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span = self._split_affixes(tokens.mem, span, &prefixes, &suffixes)
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self._attach_tokens(tokens, span, &prefixes, &suffixes)
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self._save_cached(&tokens.c[orig_size], orig_key, tokens.length - orig_size)
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cdef unicode _split_affixes(self, Pool mem, unicode string,
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vector[const LexemeC*] *prefixes,
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vector[const LexemeC*] *suffixes):
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cdef size_t i
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cdef unicode prefix
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cdef unicode suffix
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cdef unicode minus_pre
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cdef unicode 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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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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# Check whether we've hit a special-case
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if minus_pre 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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if self.token_match and self.token_match(string):
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break
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suf_len = self.find_suffix(string)
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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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# Check whether we've hit a special-case
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if minus_suf 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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if string and (self._specials.get(hash_string(string)) != NULL):
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break
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return string
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cdef int _attach_tokens(self, Doc tokens, unicode string,
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vector[const LexemeC*] *prefixes,
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vector[const LexemeC*] *suffixes) except -1:
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cdef bint cache_hit
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cdef int split, end
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cdef const LexemeC* const* lexemes
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cdef const LexemeC* lexeme
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cdef unicode 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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cache_hit = self._try_cache(hash_string(string), tokens)
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if cache_hit:
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pass
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elif self.token_match and self.token_match(string):
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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)
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else:
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# let's say we have dyn-o-mite-dave
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# the regex finds the start and end positions of the hyphens
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start = 0
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for match in matches:
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infix_start = match.start()
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infix_end = match.end()
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if infix_start == start:
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continue
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span = string[start:infix_start]
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tokens.push_back(self.vocab.get(tokens.mem, span), False)
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if infix_start != infix_end:
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# If infix_start != infix_end, it means the infix
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# token is non-empty. Empty infix tokens are useful
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# for tokenization in some languages (see
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# https://github.com/explosion/spaCy/issues/768)
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infix_span = string[infix_start:infix_end]
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tokens.push_back(self.vocab.get(tokens.mem, infix_span), False)
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start = infix_end
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span = string[start:]
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if span:
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tokens.push_back(self.vocab.get(tokens.mem, span), False)
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cdef vector[const LexemeC*].reverse_iterator it = suffixes.rbegin()
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while it != suffixes.rend():
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lexeme = deref(it)
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preinc(it)
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tokens.push_back(lexeme, False)
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cdef int _save_cached(self, const TokenC* tokens, hash_t key, int n) except -1:
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cdef int i
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for i in range(n):
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if tokens[i].lex.id == 0:
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return 0
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cached = <_Cached*>self.mem.alloc(1, sizeof(_Cached))
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cached.length = n
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cached.is_lex = True
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lexemes = <const LexemeC**>self.mem.alloc(n, sizeof(LexemeC**))
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for i in range(n):
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lexemes[i] = tokens[i].lex
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cached.data.lexemes = <const LexemeC* const*>lexemes
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self._cache.set(key, cached)
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def find_infix(self, unicode string):
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"""
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Find internal split points of the string, such as hyphens.
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string (unicode): The string to segment.
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Returns List[re.MatchObject]
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A list of objects that have .start() and .end() methods, denoting the
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placement of internal segment separators, e.g. hyphens.
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"""
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if self.infix_finditer is None:
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return 0
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return list(self.infix_finditer(string))
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def find_prefix(self, unicode string):
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"""
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Find the length of a prefix that should be segmented from the string,
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or None if no prefix rules match.
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Arguments:
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string (unicode): The string to segment.
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Returns (int or None): The length of the prefix if present, otherwise None.
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"""
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if self.prefix_search is None:
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return 0
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match = self.prefix_search(string)
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return (match.end() - match.start()) if match is not None else 0
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def find_suffix(self, unicode string):
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"""
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Find the length of a suffix that should be segmented from the string,
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or None if no suffix rules match.
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Arguments:
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string (unicode): The string to segment.
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Returns (int or None): The length of the suffix if present, otherwise None.
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"""
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if self.suffix_search is None:
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return 0
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match = self.suffix_search(string)
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return (match.end() - match.start()) if match is not None else 0
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def _load_special_tokenization(self, special_cases):
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"""
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Add special-case tokenization rules.
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"""
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for chunk, substrings in sorted(special_cases.items()):
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self.add_special_case(chunk, substrings)
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def add_special_case(self, unicode string, substrings):
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"""
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Add a special-case tokenization rule.
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Arguments:
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string (unicode): The string to specially tokenize.
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token_attrs:
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A sequence of dicts, where each dict describes a token and its
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attributes. The ORTH fields of the attributes must exactly match
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the string when they are concatenated.
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Returns None
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"""
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substrings = list(substrings)
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cached = <_Cached*>self.mem.alloc(1, sizeof(_Cached))
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cached.length = len(substrings)
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cached.is_lex = False
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cached.data.tokens = self.vocab.make_fused_token(substrings)
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key = hash_string(string)
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self._specials.set(key, cached)
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self._rules[string] = substrings
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# After changing the tokenization rules, the previous tokenization
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# may be stale.
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self.flush_cache()
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def flush_cache(self):
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'''Flush the tokenizer's cache. May not free memory immediately.
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This is called automatically after `add_special_case`, but if you
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write to the prefix or suffix functions, you'll have to call this
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yourself. You may also need to flush the tokenizer cache after
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changing the lex_attr_getter functions.
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'''
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cdef hash_t key
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for key in self._cache.keys():
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special_case = self._specials.get(key)
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# Don't free data shared with special-case rules
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if special_case is not NULL:
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continue
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cached = <_Cached*>self._cache.get(key)
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if cached is not NULL:
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self.mem.free(cached)
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self._cache = PreshMap(1000)
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# We could here readd the data from specials --- but if we loop over
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# a bunch of special-cases, we'll get a quadratic behaviour. The extra
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# lookup isn't so bad? Tough to tell.
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