spaCy/spacy/tokenizer.pyx

# cython: embedsignature=True
# cython: profile=True
# coding: utf8
from __future__ import unicode_literals

from cython.operator cimport dereference as deref
from cython.operator cimport preincrement as preinc
from cymem.cymem cimport Pool
from preshed.maps cimport PreshMap
cimport cython

from collections import OrderedDict
import re

from .tokens.doc cimport Doc
from .strings cimport hash_string
from .compat import unescape_unicode, basestring_
from .attrs import intify_attrs
from .symbols import ORTH

from .errors import Errors, Warnings, deprecation_warning
from . import util


cdef class Tokenizer:
    """Segment text, and create Doc objects with the discovered segment
    boundaries.

    DOCS: https://spacy.io/api/tokenizer
    """
    def __init__(self, Vocab vocab, rules=None, prefix_search=None,
                 suffix_search=None, infix_finditer=None, token_match=None):
        """Create a `Tokenizer`, to create `Doc` objects given unicode text.

        vocab (Vocab): A storage container for lexical types.
        rules (dict): Exceptions and special-cases for the tokenizer.
        prefix_search (callable): A function matching the signature of
            `re.compile(string).search` to match prefixes.
        suffix_search (callable): A function matching the signature of
            `re.compile(string).search` to match suffixes.
        `infix_finditer` (callable): A function matching the signature of
            `re.compile(string).finditer` to find infixes.
        token_match (callable): A boolean function matching strings to be
            recognised as tokens.
        RETURNS (Tokenizer): The newly constructed object.

        EXAMPLE:
            >>> tokenizer = Tokenizer(nlp.vocab)
            >>> tokenizer = English().Defaults.create_tokenizer(nlp)

        DOCS: https://spacy.io/api/tokenizer#init
        """
        self.mem = Pool()
        self._cache = PreshMap()
        self._specials = PreshMap()
        self.token_match = token_match
        self.prefix_search = prefix_search
        self.suffix_search = suffix_search
        self.infix_finditer = infix_finditer
        self.vocab = vocab
        self._rules = {}
        self._load_special_tokenization(rules)

    property token_match:
        def __get__(self):
            return self._token_match

        def __set__(self, token_match):
            self._token_match = token_match
            self._flush_cache()

    property prefix_search:
        def __get__(self):
            return self._prefix_search

        def __set__(self, prefix_search):
            self._prefix_search = prefix_search
            self._flush_cache()

    property suffix_search:
        def __get__(self):
            return self._suffix_search

        def __set__(self, suffix_search):
            self._suffix_search = suffix_search
            self._flush_cache()

    property infix_finditer:
        def __get__(self):
            return self._infix_finditer

        def __set__(self, infix_finditer):
            self._infix_finditer = infix_finditer
            self._flush_cache()

    property rules:
        def __get__(self):
            return self._rules

        def __set__(self, rules):
            self._rules = {}
            self._reset_cache([key for key in self._cache])
            self._reset_specials()
            self._cache = PreshMap()
            self._specials = PreshMap()
            self._load_special_tokenization(rules)

    def __reduce__(self):
        args = (self.vocab,
                self._rules,
                self.prefix_search,
                self.suffix_search,
                self.infix_finditer,
                self.token_match)
        return (self.__class__, args, None, None)

    cpdef Doc tokens_from_list(self, list strings):
        deprecation_warning(Warnings.W002)
        return Doc(self.vocab, words=strings)

    @cython.boundscheck(False)
    def __call__(self, unicode string):
        """Tokenize a string.

        string (unicode): The string to tokenize.
        RETURNS (Doc): A container for linguistic annotations.

        DOCS: https://spacy.io/api/tokenizer#call
        """
        if len(string) >= (2 ** 30):
            raise ValueError(Errors.E025.format(length=len(string)))
        cdef int length = len(string)
        cdef Doc doc = Doc(self.vocab)
        if length == 0:
            return doc
        cdef int i = 0
        cdef int start = 0
        cdef bint cache_hit
        cdef bint in_ws = string[0].isspace()
        cdef unicode span
        # The task here is much like string.split, but not quite
        # We find spans of whitespace and non-space characters, and ignore
        # spans that are exactly ' '. So, our sequences will all be separated
        # by either ' ' or nothing.
        for uc in string:
            if uc.isspace() != in_ws:
                if start < i:
                    # When we want to make this fast, get the data buffer once
                    # with PyUnicode_AS_DATA, and then maintain a start_byte
                    # and end_byte, so we can call hash64 directly. That way
                    # we don't have to create the slice when we hit the cache.
                    span = string[start:i]
                    key = hash_string(span)
                    cache_hit = self._try_cache(key, doc)
                    if not cache_hit:
                        self._tokenize(doc, span, key)
                if uc == ' ':
                    doc.c[doc.length - 1].spacy = True
                    start = i + 1
                else:
                    start = i
                in_ws = not in_ws
            i += 1
        if start < i:
            span = string[start:]
            key = hash_string(span)
            cache_hit = self._try_cache(key, doc)
            if not cache_hit:
                self._tokenize(doc, span, key)
            doc.c[doc.length - 1].spacy = string[-1] == " " and not in_ws
        return doc

    def pipe(self, texts, batch_size=1000, n_threads=-1):
        """Tokenize a stream of texts.

        texts: A sequence of unicode texts.
        batch_size (int): Number of texts to accumulate in an internal buffer.
        Defaults to 1000.
        YIELDS (Doc): A sequence of Doc objects, in order.

        DOCS: https://spacy.io/api/tokenizer#pipe
        """
        if n_threads != -1:
            deprecation_warning(Warnings.W016)
        for text in texts:
            yield self(text)

    def _flush_cache(self):
        self._reset_cache([key for key in self._cache if not key in self._specials])

    def _reset_cache(self, keys):
        for k in keys:
            del self._cache[k]
            if not k in self._specials:
                cached = <_Cached*>self._cache.get(k)
                if cached is not NULL:
                    self.mem.free(cached)

    def _reset_specials(self):
        for k in self._specials:
            cached = <_Cached*>self._specials.get(k)
            del self._specials[k]
            if cached is not NULL:
                self.mem.free(cached)

    cdef int _try_cache(self, hash_t key, Doc tokens) except -1:
        cached = <_Cached*>self._cache.get(key)
        if cached == NULL:
            return False
        cdef int i
        if cached.is_lex:
            for i in range(cached.length):
                tokens.push_back(cached.data.lexemes[i], False)
        else:
            for i in range(cached.length):
                tokens.push_back(&cached.data.tokens[i], False)
        return True

    cdef int _tokenize(self, Doc tokens, unicode span, hash_t orig_key) except -1:
        cdef vector[LexemeC*] prefixes
        cdef vector[LexemeC*] suffixes
        cdef int orig_size
        cdef int has_special = 0
        orig_size = tokens.length
        span = self._split_affixes(tokens.mem, span, &prefixes, &suffixes,
                                   &has_special)
        self._attach_tokens(tokens, span, &prefixes, &suffixes)
        self._save_cached(&tokens.c[orig_size], orig_key, has_special,
                          tokens.length - orig_size)

    cdef unicode _split_affixes(self, Pool mem, unicode string,
                                vector[const LexemeC*] *prefixes,
                                vector[const LexemeC*] *suffixes,
                                int* has_special):
        cdef size_t i
        cdef unicode prefix
        cdef unicode suffix
        cdef unicode minus_pre
        cdef unicode minus_suf
        cdef size_t last_size = 0
        while string and len(string) != last_size:
            if self._specials.get(hash_string(string)) != NULL:
                has_special[0] = 1
                break
            last_size = len(string)
            pre_len = self.find_prefix(string)
            if pre_len != 0:
                prefix = string[:pre_len]
                minus_pre = string[pre_len:]
                # Check whether we've hit a special-case
                if minus_pre and self._specials.get(hash_string(minus_pre)) != NULL:
                    string = minus_pre
                    prefixes.push_back(self.vocab.get(mem, prefix))
                    has_special[0] = 1
                    break
            suf_len = self.find_suffix(string)
            if suf_len != 0:
                suffix = string[-suf_len:]
                minus_suf = string[:-suf_len]
                # Check whether we've hit a special-case
                if minus_suf and (self._specials.get(hash_string(minus_suf)) != NULL):
                    string = minus_suf
                    suffixes.push_back(self.vocab.get(mem, suffix))
                    has_special[0] = 1
                    break
            if pre_len and suf_len and (pre_len + suf_len) <= len(string):
                string = string[pre_len:-suf_len]
                prefixes.push_back(self.vocab.get(mem, prefix))
                suffixes.push_back(self.vocab.get(mem, suffix))
            elif pre_len:
                string = minus_pre
                prefixes.push_back(self.vocab.get(mem, prefix))
            elif suf_len:
                string = minus_suf
                suffixes.push_back(self.vocab.get(mem, suffix))
            if string and (self._specials.get(hash_string(string)) != NULL):
                has_special[0] = 1
                break
        return string

    cdef int _attach_tokens(self, Doc tokens, unicode string,
                            vector[const LexemeC*] *prefixes,
                            vector[const LexemeC*] *suffixes) except -1:
        cdef bint cache_hit
        cdef int split, end
        cdef const LexemeC* const* lexemes
        cdef const LexemeC* lexeme
        cdef unicode span
        cdef int i
        if prefixes.size():
            for i in range(prefixes.size()):
                tokens.push_back(prefixes[0][i], False)
        if string:
            cache_hit = self._try_cache(hash_string(string), tokens)
            if cache_hit:
                pass
            elif self.token_match and self.token_match(string):
                # We're always saying 'no' to spaces here -- the caller will
                # fix up the outermost one, with reference to the original.
                # See Issue #859
                tokens.push_back(self.vocab.get(tokens.mem, string), False)
            else:
                matches = self.find_infix(string)
                if not matches:
                    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
        for i in range(n):
            if self.vocab._by_orth.get(tokens[i].lex.orth) == NULL:
                return 0
        # See #1250
        if has_special:
            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, unicode string):
        """Find internal split points of the string, such as hyphens.

        string (unicode): 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, unicode string):
        """Find the length of a prefix that should be segmented from the
        string, or None if no prefix rules match.

        string (unicode): 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, unicode string):
        """Find the length of a suffix that should be segmented from the
        string, or None if no suffix rules match.

        string (unicode): 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_tokenization(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 add_special_case(self, unicode string, substrings):
        """Add a special-case tokenization rule.

        string (unicode): 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
        """
        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)
        stale_cached = <_Cached*>self._cache.get(key)
        self._flush_cache()
        self._specials.set(key, cached)
        self._cache.set(key, cached)
        if stale_special is not NULL:
            self.mem.free(stale_special)
        if stale_special != stale_cached and stale_cached is not NULL:
            self.mem.free(stale_cached)
        self._rules[string] = substrings

    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 (unicode): 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
        suffix_search = self.suffix_search
        infix_finditer = self.infix_finditer
        token_match = self.token_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:
                while prefix_search(substring) or suffix_search(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 = ''
                        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 substring in special_cases:
                    tokens.extend(("SPECIAL-" + str(i + 1), self.vocab.strings[e[ORTH]]) for i, e in enumerate(special_cases[substring]))
                    substring = ''
                elif token_match(substring):
                    tokens.append(("TOKEN_MATCH", substring))
                    substring = ''
                elif list(infix_finditer(substring)):
                    infixes = infix_finditer(substring)
                    offset = 0
                    for match in infixes:
                        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))
        return tokens

    def to_disk(self, path, **kwargs):
        """Save the current state to a directory.

        path (unicode or 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, **kwargs):
        """Loads state from a directory. Modifies the object in place and
        returns it.

        path (unicode or 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, **kwargs)
        return self

    def to_bytes(self, exclude=tuple(), **kwargs):
        """Serialize the current state to a binary string.

        exclude (list): String names of serialization fields to exclude.
        RETURNS (bytes): The serialized form of the `Tokenizer` object.

        DOCS: https://spacy.io/api/tokenizer#to_bytes
        """
        serializers = OrderedDict((
            ("vocab", lambda: self.vocab.to_bytes()),
            ("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)),
            ("exceptions", lambda: OrderedDict(sorted(self._rules.items())))
        ))
        exclude = util.get_serialization_exclude(serializers, exclude, kwargs)
        return util.to_bytes(serializers, exclude)

    def from_bytes(self, bytes_data, exclude=tuple(), **kwargs):
        """Load state from a binary string.

        bytes_data (bytes): The data to load from.
        exclude (list): String names of serialization fields to exclude.
        RETURNS (Tokenizer): The `Tokenizer` object.

        DOCS: https://spacy.io/api/tokenizer#from_bytes
        """
        data = OrderedDict()
        deserializers = OrderedDict((
            ("vocab", lambda b: self.vocab.from_bytes(b)),
            ("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)),
            ("exceptions", lambda b: data.setdefault("rules", b))
        ))
        exclude = util.get_serialization_exclude(deserializers, exclude, kwargs)
        msg = util.from_bytes(bytes_data, deserializers, exclude)
        for key in ["prefix_search", "suffix_search", "infix_finditer"]:
            if key in data:
                data[key] = unescape_unicode(data[key])
        if "prefix_search" in data and isinstance(data["prefix_search"], basestring_):
            self.prefix_search = re.compile(data["prefix_search"]).search
        if "suffix_search" in data and isinstance(data["suffix_search"], basestring_):
            self.suffix_search = re.compile(data["suffix_search"]).search
        if "infix_finditer" in data and isinstance(data["infix_finditer"], basestring_):
            self.infix_finditer = re.compile(data["infix_finditer"]).finditer
        if "token_match" in data and isinstance(data["token_match"], basestring_):
            self.token_match = re.compile(data["token_match"]).match
        if "rules" in data and isinstance(data["rules"], dict):
            # make sure to hard reset the cache to remove data from the default exceptions
            self._rules = {}
            self._reset_cache([key for key in self._cache])
            self._reset_specials()
            self._cache = PreshMap()
            self._specials = PreshMap()
            self._load_special_tokenization(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