spaCy/spacy/tokens.pyx

# cython: embedsignature=True

from preshed.maps cimport PreshMap
from preshed.counter cimport PreshCounter

from .vocab cimport EMPTY_LEXEME
from .typedefs cimport attr_id_t, attr_t
from .typedefs cimport LEMMA
from .typedefs cimport ID, ORTH, NORM, LOWER, SHAPE, PREFIX, SUFFIX, LENGTH, CLUSTER
from .typedefs cimport POS, LEMMA
from .parts_of_speech import UNIV_POS_NAMES

from unidecode import unidecode

cimport numpy
import numpy

cimport cython


DEF PADDING = 5


cdef int bounds_check(int i, int length, int padding) except -1:
    if (i + padding) < 0:
        raise IndexError
    if (i - padding) >= length:
        raise IndexError


cdef attr_t get_token_attr(const TokenC* token, attr_id_t feat_name) nogil:
    if feat_name == LEMMA:
        return token.lemma
    elif feat_name == POS:
        return token.pos
    else:
        return get_lex_attr(token.lex, feat_name)


cdef attr_t get_lex_attr(const LexemeC* lex, attr_id_t feat_name) nogil:
    if feat_name < (sizeof(flags_t) * 8):
        return check_flag(lex, feat_name)
    elif feat_name == ID:
        return lex.id
    elif feat_name == ORTH:
        return lex.orth
    elif feat_name == LOWER:
        return lex.lower
    elif feat_name == NORM:
        return lex.norm
    elif feat_name == SHAPE:
        return lex.shape
    elif feat_name == PREFIX:
        return lex.prefix
    elif feat_name == SUFFIX:
        return lex.suffix
    elif feat_name == LENGTH:
        return lex.length
    elif feat_name == CLUSTER:
        return lex.cluster
    else:
        return 0


cdef class Tokens:
    """
    Container class for annotated text.  Constructed via English.__call__ or
    Tokenizer.__call__.
    """
    def __cinit__(self, Vocab vocab, unicode string):
        self.vocab = vocab
        self._string = string
        string_length = len(string)
        if string_length >= 3:
            size = int(string_length / 3.0)
        else:
            size = 5
        self.mem = Pool()
        # Guarantee self.lex[i-x], for any i >= 0 and x < padding is in bounds
        # However, we need to remember the true starting places, so that we can
        # realloc.
        data_start = <TokenC*>self.mem.alloc(size + (PADDING*2), sizeof(TokenC))
        cdef int i
        for i in range(size + (PADDING*2)):
            data_start[i].lex = &EMPTY_LEXEME
        self.data = data_start + PADDING
        self.max_length = size
        self.length = 0
        self.is_tagged = False
        self.is_parsed = False
        self._tag_strings = [] # These will be set by the POS tagger and parser
        self._dep_strings = [] # The strings are arbitrary and model-specific.

    def sentences(self):
        cdef int i
        sentences = []
        cdef Tokens sent = Tokens(self.vocab, self._string[self.data[0].idx:])
        cdef attr_t period = self.vocab.strings['.']
        cdef attr_t question = self.vocab.strings['?']
        cdef attr_t exclamation = self.vocab.strings['!']
        spans = []
        start = None
        for i in range(self.length):
            if start is None:
                start = i
            if self.data[i].lex.orth == period or self.data[i].lex.orth == exclamation or \
              self.data[i].lex.orth == question:
                spans.append((start, i+1))
                start = None
        if start is not None:
            spans.append((start, self.length))
        return spans

    def __getitem__(self, i):
        """Retrieve a token.
        
        Returns:
            token (Token):
        """
        if i < 0:
            i = self.length - i
        bounds_check(i, self.length, PADDING)
        return Token(self, i)

    def __iter__(self):
        """Iterate over the tokens.

        Yields:
            token (Token):
        """
        for i in range(self.length):
            yield self[i]

    def __len__(self):
        return self.length

    def __unicode__(self):
        cdef const TokenC* last = &self.data[self.length - 1]
        return self._string[:last.idx + last.lex.length]

    def __str__(self):
        return unidecode(unicode(self))

    cdef int push_back(self, int idx, LexemeOrToken lex_or_tok) except -1:
        if self.length == self.max_length:
            self._realloc(self.length * 2)
        cdef TokenC* t = &self.data[self.length]
        if LexemeOrToken is TokenC_ptr:
            t[0] = lex_or_tok[0]
        else:
            t.lex = lex_or_tok
        t.idx = idx
        self.length += 1
        return idx + t.lex.length

    @cython.boundscheck(False)
    cpdef long[:,:] to_array(self, object py_attr_ids):
        """Given a list of M attribute IDs, export the tokens to a numpy ndarray
        of shape N*M, where N is the length of the sentence.

        Arguments:
            attr_ids (list[int]): A list of attribute ID ints.

        Returns:
            feat_array (numpy.ndarray[long, ndim=2]):
              A feature matrix, with one row per word, and one column per attribute
              indicated in the input attr_ids.
        """
        cdef int i, j
        cdef attr_id_t feature
        cdef numpy.ndarray[long, ndim=2] output
        # Make an array from the attributes --- otherwise our inner loop is Python
        # dict iteration.
        cdef numpy.ndarray[long, ndim=1] attr_ids = numpy.asarray(py_attr_ids)
        output = numpy.ndarray(shape=(self.length, len(attr_ids)), dtype=numpy.int)
        for i in range(self.length):
            for j, feature in enumerate(attr_ids):
                output[i, j] = get_token_attr(&self.data[i], feature)
        return output

    def count_by(self, attr_id_t attr_id, exclude=None):
        """Produce a dict of {attribute (int): count (ints)} frequencies, keyed
        by the values of the given attribute ID.

          >>> from spacy.en import English, attrs
          >>> nlp = English()
          >>> tokens = nlp(u'apple apple orange banana')
          >>> tokens.count_by(attrs.ORTH)
          {12800L: 1, 11880L: 2, 7561L: 1}
          >>> tokens.to_array([attrs.ORTH])
          array([[11880],
                 [11880],
                 [ 7561],
                 [12800]])
        """
        cdef int i
        cdef attr_t attr
        cdef size_t count

        cdef PreshCounter counts = PreshCounter(2 ** 8)
        for i in range(self.length):
            if exclude is not None and exclude(self[i]):
                continue
            attr = get_token_attr(&self.data[i], attr_id)
            counts.inc(attr, 1)
        return dict(counts)

    def _realloc(self, new_size):
        self.max_length = new_size
        n = new_size + (PADDING * 2)
        # What we're storing is a "padded" array. We've jumped forward PADDING
        # places, and are storing the pointer to that. This way, we can access
        # words out-of-bounds, and get out-of-bounds markers.
        # Now that we want to realloc, we need the address of the true start,
        # so we jump the pointer back PADDING places.
        cdef TokenC* data_start = self.data - PADDING
        data_start = <TokenC*>self.mem.realloc(data_start, n * sizeof(TokenC))
        self.data = data_start + PADDING
        cdef int i
        for i in range(self.length, self.max_length + PADDING):
            self.data[i].lex = &EMPTY_LEXEME


@cython.freelist(64)
cdef class Token:
    """An individual token."""
    def __cinit__(self, Tokens tokens, int i):
        self._seq = tokens
        self.i = i
        cdef const TokenC* t = &tokens.data[i]
        self.idx = t.idx
        self.cluster = t.lex.cluster
        self.length = t.lex.length
        self.orth = t.lex.orth
        self.lower = t.lex.lower
        self.norm = t.lex.norm
        self.shape = t.lex.shape
        self.prefix = t.lex.prefix
        self.suffix = t.lex.suffix
        self.prob = t.lex.prob
        self.sentiment = t.lex.sentiment
        self.flags = t.lex.flags
        self.lemma = t.lemma
        self.pos = t.pos
        self.tag = t.tag
        self.dep = t.dep
        self.repvec = numpy.asarray(<float[:300,]> t.lex.repvec)
        cdef int next_idx = (t+1).idx
        if next_idx <= self.idx:
            next_idx = self.idx + self.length
        self.string = tokens._string[self.idx:next_idx]

    def __len__(self):
        return self._seq.data[self.i].lex.length

    def nbor(self, int i=1):
        return Token(self._seq, self.i + i)

    def child(self, int i=1):
        if not self._seq.is_parsed:
            msg = _parse_unset_error
            raise AttributeError(msg)

        cdef const TokenC* t = &self._seq.data[self.i]
        if i == 0:
            return self
        elif i >= 1:
            if t.r_kids == 0:
                return None
            else:
                return Token(self._seq, _nth_significant_bit(t.r_kids, i))
        else:
            if t.l_kids == 0:
                return None
            else:
                return Token(self._seq, _nth_significant_bit(t.l_kids, i))
        
    property head:
        """The token predicted by the parser to be the head of the current token."""
        def __get__(self):
            if not self._seq.is_parsed:
                msg = _parse_unset_error
                raise AttributeError(msg)
            cdef const TokenC* t = &self._seq.data[self.i]
            return Token(self._seq, self.i + t.head)

    property whitespace_:
        def __get__(self):
            return self.string[self.length:]

    property orth_:
        def __get__(self):
            return self._seq.vocab.strings[self.orth]

    property lower_:
        def __get__(self):
            return self._seq.vocab.strings[self.lower]

    property norm_:
        def __get__(self):
            return self._seq.vocab.strings[self.norm]

    property shape_:
        def __get__(self):
            return self._seq.vocab.strings[self.shape]

    property prefix_:
        def __get__(self):
            return self._seq.vocab.strings[self.prefix]

    property suffix_:
        def __get__(self):
            return self._seq.vocab.strings[self.suffix]

    property lemma_:
        def __get__(self):
            cdef const TokenC* t = &self._seq.data[self.i]
            if t.lemma == 0:
                return self.string
            cdef unicode py_ustr = self._seq.vocab.strings[t.lemma]
            return py_ustr

    property pos_:
        def __get__(self):
            id_to_string = {id_: string for string, id_ in UNIV_POS_NAMES.items()}
            return id_to_string[self.pos]

    property tag_:
        def __get__(self):
            return self._seq._tag_strings[self.tag]

    property dep_:
        def __get__(self):
            return self._seq._dep_strings[self.dep]


cdef inline uint32_t _nth_significant_bit(uint32_t bits, int n) nogil:
    cdef int i
    for i in range(32):
        if bits & (1 << i):
            n -= 1
            if n < 1:
                return i
    return 0


_parse_unset_error = """Text has not been parsed, so cannot access head, child or sibling.

Check that the parser data is installed.
Check that the parse=True argument was set in the call to English.__call__
"""