mirror of https://github.com/explosion/spaCy.git
483 lines
18 KiB
Cython
483 lines
18 KiB
Cython
# coding: utf8
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from __future__ import unicode_literals
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from collections import defaultdict
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cimport numpy as np
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import numpy
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import numpy.linalg
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from libc.math cimport sqrt
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from .doc cimport token_by_start, token_by_end, get_token_attr
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from ..structs cimport TokenC, LexemeC
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from ..typedefs cimport flags_t, attr_t, hash_t
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from ..attrs cimport attr_id_t
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from ..parts_of_speech cimport univ_pos_t
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from ..util import normalize_slice
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from ..attrs cimport IS_PUNCT, IS_SPACE
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from ..lexeme cimport Lexeme
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from ..compat import is_config
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from .. import about
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cdef class Span:
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"""A slice from a Doc object."""
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def __cinit__(self, Doc doc, int start, int end, attr_t label=0, vector=None,
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vector_norm=None):
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"""Create a `Span` object from the slice `doc[start : end]`.
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doc (Doc): The parent document.
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start (int): The index of the first token of the span.
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end (int): The index of the first token after the span.
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label (uint64): A label to attach to the Span, e.g. for named entities.
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vector (ndarray[ndim=1, dtype='float32']): A meaning representation of the span.
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RETURNS (Span): The newly constructed object.
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"""
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if not (0 <= start <= end <= len(doc)):
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raise IndexError
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self.doc = doc
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self.start = start
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self.start_char = self.doc[start].idx if start < self.doc.length else 0
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self.end = end
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if end >= 1:
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self.end_char = self.doc[end - 1].idx + len(self.doc[end - 1])
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else:
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self.end_char = 0
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assert label in doc.vocab.strings, label
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self.label = label
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self._vector = vector
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self._vector_norm = vector_norm
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def __richcmp__(self, Span other, int op):
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# Eq
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if op == 0:
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return self.start_char < other.start_char
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elif op == 1:
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return self.start_char <= other.start_char
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elif op == 2:
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return self.start_char == other.start_char and self.end_char == other.end_char
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elif op == 3:
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return self.start_char != other.start_char or self.end_char != other.end_char
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elif op == 4:
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return self.start_char > other.start_char
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elif op == 5:
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return self.start_char >= other.start_char
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def __hash__(self):
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return hash((self.doc, self.label, self.start_char, self.end_char))
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def __len__(self):
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"""Get the number of tokens in the span.
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RETURNS (int): The number of tokens in the span.
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"""
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self._recalculate_indices()
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if self.end < self.start:
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return 0
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return self.end - self.start
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def __repr__(self):
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if is_config(python3=True):
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return self.text
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return self.text.encode('utf-8')
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def __getitem__(self, object i):
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"""Get a `Token` or a `Span` object
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i (int or tuple): The index of the token within the span, or slice of
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the span to get.
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RETURNS (Token or Span): The token at `span[i]`.
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EXAMPLE:
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>>> span[0]
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>>> span[1:3]
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"""
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self._recalculate_indices()
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if isinstance(i, slice):
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start, end = normalize_slice(len(self), i.start, i.stop, i.step)
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return Span(self.doc, start + self.start, end + self.start)
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else:
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if i < 0:
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return self.doc[self.end + i]
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else:
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return self.doc[self.start + i]
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def __iter__(self):
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"""Iterate over `Token` objects.
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YIELDS (Token): A `Token` object.
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"""
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self._recalculate_indices()
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for i in range(self.start, self.end):
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yield self.doc[i]
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def merge(self, *args, **attributes):
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"""Retokenize the document, such that the span is merged into a single
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token.
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**attributes: Attributes to assign to the merged token. By default,
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attributes are inherited from the syntactic root token of the span.
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RETURNS (Token): The newly merged token.
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"""
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return self.doc.merge(self.start_char, self.end_char, *args, **attributes)
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def similarity(self, other):
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"""Make a semantic similarity estimate. The default estimate is cosine
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similarity using an average of word vectors.
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other (object): The object to compare with. By default, accepts `Doc`,
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`Span`, `Token` and `Lexeme` objects.
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RETURNS (float): A scalar similarity score. Higher is more similar.
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"""
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if 'similarity' in self.doc.user_span_hooks:
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self.doc.user_span_hooks['similarity'](self, other)
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if self.vector_norm == 0.0 or other.vector_norm == 0.0:
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return 0.0
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return numpy.dot(self.vector, other.vector) / (self.vector_norm * other.vector_norm)
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cpdef np.ndarray to_array(self, object py_attr_ids):
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"""Given a list of M attribute IDs, export the tokens to a numpy
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`ndarray` of shape `(N, M)`, where `N` is the length of the document.
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The values will be 32-bit integers.
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attr_ids (list[int]): A list of attribute ID ints.
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RETURNS (numpy.ndarray[long, ndim=2]): A feature matrix, with one row
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per word, and one column per attribute indicated in the input
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`attr_ids`.
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"""
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cdef int i, j
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cdef attr_id_t feature
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cdef np.ndarray[attr_t, ndim=2] output
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# Make an array from the attributes --- otherwise our inner loop is Python
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# dict iteration.
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cdef np.ndarray[attr_t, ndim=1] attr_ids = numpy.asarray(py_attr_ids, dtype=numpy.uint64)
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output = numpy.ndarray(shape=(self.length, len(attr_ids)), dtype=numpy.uint64)
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for i in range(self.start, self.end):
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for j, feature in enumerate(attr_ids):
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output[i, j] = get_token_attr(&self.doc.c[i], feature)
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return output
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cpdef int _recalculate_indices(self) except -1:
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if self.end > self.doc.length \
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or self.doc.c[self.start].idx != self.start_char \
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or (self.doc.c[self.end-1].idx + self.doc.c[self.end-1].lex.length) != self.end_char:
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start = token_by_start(self.doc.c, self.doc.length, self.start_char)
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if self.start == -1:
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raise IndexError("Error calculating span: Can't find start")
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end = token_by_end(self.doc.c, self.doc.length, self.end_char)
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if end == -1:
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raise IndexError("Error calculating span: Can't find end")
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self.start = start
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self.end = end + 1
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property sent:
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"""The sentence span that this span is a part of.
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RETURNS (Span): The sentence span that the span is a part of.
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"""
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def __get__(self):
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if 'sent' in self.doc.user_span_hooks:
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return self.doc.user_span_hooks['sent'](self)
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# This should raise if we're not parsed.
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self.doc.sents
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cdef int n = 0
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root = &self.doc.c[self.start]
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while root.head != 0:
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root += root.head
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n += 1
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if n >= self.doc.length:
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raise RuntimeError
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return self.doc[root.l_edge : root.r_edge + 1]
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property has_vector:
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"""A boolean value indicating whether a word vector is associated with
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the object.
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RETURNS (bool): Whether a word vector is associated with the object.
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"""
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def __get__(self):
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if 'has_vector' in self.doc.user_span_hooks:
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return self.doc.user_span_hooks['has_vector'](self)
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return any(token.has_vector for token in self)
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property vector:
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"""A real-valued meaning representation. Defaults to an average of the
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token vectors.
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RETURNS (numpy.ndarray[ndim=1, dtype='float32']): A 1D numpy array
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representing the span's semantics.
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"""
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def __get__(self):
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if 'vector' in self.doc.user_span_hooks:
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return self.doc.user_span_hooks['vector'](self)
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if self._vector is None:
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self._vector = sum(t.vector for t in self) / len(self)
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return self._vector
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property vector_norm:
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"""The L2 norm of the document's vector representation.
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RETURNS (float): The L2 norm of the vector representation.
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"""
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def __get__(self):
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if 'vector_norm' in self.doc.user_span_hooks:
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return self.doc.user_span_hooks['vector'](self)
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cdef float value
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cdef double norm = 0
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if self._vector_norm is None:
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norm = 0
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for value in self.vector:
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norm += value * value
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self._vector_norm = sqrt(norm) if norm != 0 else 0
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return self._vector_norm
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property sentiment:
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# TODO: docstring
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def __get__(self):
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if 'sentiment' in self.doc.user_span_hooks:
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return self.doc.user_span_hooks['sentiment'](self)
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else:
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return sum([token.sentiment for token in self]) / len(self)
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property text:
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"""A unicode representation of the span text.
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RETURNS (unicode): The original verbatim text of the span.
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"""
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def __get__(self):
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text = self.text_with_ws
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if self[-1].whitespace_:
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text = text[:-1]
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return text
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property text_with_ws:
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"""The text content of the span with a trailing whitespace character if
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the last token has one.
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RETURNS (unicode): The text content of the span (with trailing whitespace).
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"""
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def __get__(self):
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return u''.join([t.text_with_ws for t in self])
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property noun_chunks:
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"""Yields base noun-phrase `Span` objects, if the document has been
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syntactically parsed. A base noun phrase, or "NP chunk", is a noun
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phrase that does not permit other NPs to be nested within it – so no
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NP-level coordination, no prepositional phrases, and no relative clauses.
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YIELDS (Span): Base noun-phrase `Span` objects
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"""
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def __get__(self):
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if not self.doc.is_parsed:
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raise ValueError(
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"noun_chunks requires the dependency parse, which "
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"requires data to be installed. For more info, see the "
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"documentation: \n%s\n" % about.__docs_models__)
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# Accumulate the result before beginning to iterate over it. This prevents
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# the tokenisation from being changed out from under us during the iteration.
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# The tricky thing here is that Span accepts its tokenisation changing,
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# so it's okay once we have the Span objects. See Issue #375
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spans = []
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cdef attr_t label
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for start, end, label in self.doc.noun_chunks_iterator(self):
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spans.append(Span(self, start, end, label=label))
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for span in spans:
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yield span
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property root:
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"""The token within the span that's highest in the parse tree.
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If there's a tie, the earliest is prefered.
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RETURNS (Token): The root token.
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EXAMPLE: The root token has the shortest path to the root of the sentence
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(or is the root itself). If multiple words are equally high in the
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tree, the first word is taken. For example:
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>>> toks = nlp(u'I like New York in Autumn.')
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Let's name the indices – easier than writing `toks[4]` etc.
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>>> i, like, new, york, in_, autumn, dot = range(len(toks))
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The head of 'new' is 'York', and the head of "York" is "like"
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>>> toks[new].head.text
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'York'
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>>> toks[york].head.text
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'like'
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Create a span for "New York". Its root is "York".
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>>> new_york = toks[new:york+1]
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>>> new_york.root.text
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'York'
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Here's a more complicated case, raised by issue #214:
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>>> toks = nlp(u'to, north and south carolina')
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>>> to, north, and_, south, carolina = toks
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>>> south.head.text, carolina.head.text
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('north', 'to')
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Here "south" is a child of "north", which is a child of "carolina".
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Carolina is the root of the span:
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>>> south_carolina = toks[-2:]
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>>> south_carolina.root.text
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'carolina'
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"""
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def __get__(self):
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self._recalculate_indices()
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if 'root' in self.doc.user_span_hooks:
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return self.doc.user_span_hooks['root'](self)
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# This should probably be called 'head', and the other one called
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# 'gov'. But we went with 'head' elsehwhere, and now we're stuck =/
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cdef int i
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# First, we scan through the Span, and check whether there's a word
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# with head==0, i.e. a sentence root. If so, we can return it. The
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# longer the span, the more likely it contains a sentence root, and
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# in this case we return in linear time.
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for i in range(self.start, self.end):
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if self.doc.c[i].head == 0:
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return self.doc[i]
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# If we don't have a sentence root, we do something that's not so
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# algorithmically clever, but I think should be quite fast, especially
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# for short spans.
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# For each word, we count the path length, and arg min this measure.
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# We could use better tree logic to save steps here...But I think this
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# should be okay.
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cdef int current_best = self.doc.length
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cdef int root = -1
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for i in range(self.start, self.end):
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if self.start <= (i+self.doc.c[i].head) < self.end:
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continue
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words_to_root = _count_words_to_root(&self.doc.c[i], self.doc.length)
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if words_to_root < current_best:
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current_best = words_to_root
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root = i
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if root == -1:
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return self.doc[self.start]
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else:
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return self.doc[root]
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property lefts:
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""" Tokens that are to the left of the span, whose head is within the
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`Span`.
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YIELDS (Token):A left-child of a token of the span.
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"""
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def __get__(self):
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for token in reversed(self): # Reverse, so we get the tokens in order
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for left in token.lefts:
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if left.i < self.start:
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yield left
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property rights:
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"""Tokens that are to the right of the Span, whose head is within the
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`Span`.
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YIELDS (Token): A right-child of a token of the span.
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"""
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def __get__(self):
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for token in self:
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for right in token.rights:
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if right.i >= self.end:
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yield right
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property subtree:
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"""Tokens that descend from tokens in the span, but fall outside it.
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YIELDS (Token): A descendant of a token within the span.
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"""
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def __get__(self):
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for word in self.lefts:
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yield from word.subtree
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yield from self
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for word in self.rights:
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yield from word.subtree
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property ent_id:
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"""An (integer) entity ID. Usually assigned by patterns in the `Matcher`.
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RETURNS (uint64): The entity ID.
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"""
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def __get__(self):
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return self.root.ent_id
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def __set__(self, hash_t key):
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# TODO
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raise NotImplementedError(
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"Can't yet set ent_id from Span. Vote for this feature on the issue "
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"tracker: http://github.com/explosion/spaCy/issues")
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property ent_id_:
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"""A (string) entity ID. Usually assigned by patterns in the `Matcher`.
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RETURNS (unicode): The entity ID.
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"""
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def __get__(self):
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return self.root.ent_id_
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def __set__(self, hash_t key):
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# TODO
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raise NotImplementedError(
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"Can't yet set ent_id_ from Span. Vote for this feature on the issue "
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"tracker: http://github.com/explosion/spaCy/issues")
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property orth_:
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# TODO: docstring
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def __get__(self):
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return ''.join([t.string for t in self]).strip()
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property lemma_:
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"""The span's lemma.
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RETURNS (unicode): The span's lemma.
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"""
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def __get__(self):
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return ' '.join([t.lemma_ for t in self]).strip()
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property upper_:
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# TODO: docstring
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def __get__(self):
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return ''.join([t.string.upper() for t in self]).strip()
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property lower_:
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# TODO: docstring
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def __get__(self):
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return ''.join([t.string.lower() for t in self]).strip()
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property string:
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# TODO: docstring
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def __get__(self):
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return ''.join([t.string for t in self])
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property label_:
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"""The span's label.
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RETURNS (unicode): The span's label.
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"""
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def __get__(self):
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return self.doc.vocab.strings[self.label]
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cdef int _count_words_to_root(const TokenC* token, int sent_length) except -1:
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# Don't allow spaces to be the root, if there are
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# better candidates
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if Lexeme.c_check_flag(token.lex, IS_SPACE) and token.l_kids == 0 and token.r_kids == 0:
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return sent_length-1
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if Lexeme.c_check_flag(token.lex, IS_PUNCT) and token.l_kids == 0 and token.r_kids == 0:
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return sent_length-1
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cdef int n = 0
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while token.head != 0:
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token += token.head
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n += 1
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if n >= sent_length:
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raise RuntimeError(
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"Array bounds exceeded while searching for root word. This likely "
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"means the parse tree is in an invalid state. Please report this "
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"issue here: http://github.com/explosion/spaCy/issues")
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return n
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