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Fix issue 2396 (#3089)

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* Test on #2396: bug in Doc.get_lca_matrix()

* reimplementation of Doc.get_lca_matrix(), (closes #2396)

* reimplement Span.get_lca_matrix(), and call it from Doc.get_lca_matrix()

* tests Span.get_lca_matrix() as well as Doc.get_lca_matrix()

* implement _get_lca_matrix as a helper function in doc.pyx; call it from Doc.get_lca_matrix and Span.get_lca_matrix

* use memory view instead of np.ndarray in _get_lca_matrix (faster)

* fix bug when calling Span.get_lca_matrix; return lca matrix as np.array instead of memoryview

* cleaner conditional, add comment
This commit is contained in:
Álvaro Abella Bascarán 2018-12-29 18:02:26 +01:00 committed by Matthew Honnibal
parent 76e3e695af
commit 9bc4cc1352
5 changed files with 224 additions and 83 deletions
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106
.github/contributors/alvaroabascar.md vendored Normal file
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@ -0,0 +1,106 @@
# spaCy contributor agreement
This spaCy Contributor Agreement (**"SCA"**) is based on the
[Oracle Contributor Agreement](http://www.oracle.com/technetwork/oca-405177.pdf).
The SCA applies to any contribution that you make to any product or project
managed by us (the **"project"**), and sets out the intellectual property rights
you grant to us in the contributed materials. The term **"us"** shall mean
[ExplosionAI UG (haftungsbeschränkt)](https://explosion.ai/legal). The term
**"you"** shall mean the person or entity identified below.
If you agree to be bound by these terms, fill in the information requested
below and include the filled-in version with your first pull request, under the
folder [`.github/contributors/`](/.github/contributors/). The name of the file
should be your GitHub username, with the extension `.md`. For example, the user
example_user would create the file `.github/contributors/example_user.md`.
Read this agreement carefully before signing. These terms and conditions
constitute a binding legal agreement.
## Contributor Agreement
1. The term "contribution" or "contributed materials" means any source code,
object code, patch, tool, sample, graphic, specification, manual,
documentation, or any other material posted or submitted by you to the project.
2. With respect to any worldwide copyrights, or copyright applications and
registrations, in your contribution:
* you hereby assign to us joint ownership, and to the extent that such
assignment is or becomes invalid, ineffective or unenforceable, you hereby
grant to us a perpetual, irrevocable, non-exclusive, worldwide, no-charge,
royalty-free, unrestricted license to exercise all rights under those
copyrights. This includes, at our option, the right to sublicense these same
rights to third parties through multiple levels of sublicensees or other
licensing arrangements;
* you agree that each of us can do all things in relation to your
contribution as if each of us were the sole owners, and if one of us makes
a derivative work of your contribution, the one who makes the derivative
work (or has it made will be the sole owner of that derivative work;
* you agree that you will not assert any moral rights in your contribution
against us, our licensees or transferees;
* you agree that we may register a copyright in your contribution and
exercise all ownership rights associated with it; and
* you agree that neither of us has any duty to consult with, obtain the
consent of, pay or render an accounting to the other for any use or
distribution of your contribution.
3. With respect to any patents you own, or that you can license without payment
to any third party, you hereby grant to us a perpetual, irrevocable,
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* make, have made, use, sell, offer to sell, import, and otherwise transfer
your contribution in whole or in part, alone or in combination with or
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which your contribution was submitted, and
* at our option, to sublicense these same rights to third parties through
multiple levels of sublicensees or other licensing arrangements.
4. Except as set out above, you keep all right, title, and interest in your
contribution. The rights that you grant to us under these terms are effective
on the date you first submitted a contribution to us, even if your submission
took place before the date you sign these terms.
5. You covenant, represent, warrant and agree that:
* Each contribution that you submit is and shall be an original work of
authorship and you can legally grant the rights set out in this SCA;
* to the best of your knowledge, each contribution will not violate any
third party's copyrights, trademarks, patents, or other intellectual
property rights; and
* each contribution shall be in compliance with U.S. export control laws and
other applicable export and import laws. You agree to notify us if you
become aware of any circumstance which would make any of the foregoing
representations inaccurate in any respect. We may publicly disclose your
participation in the project, including the fact that you have signed the SCA.
6. This SCA is governed by the laws of the State of California and applicable
U.S. Federal law. Any choice of law rules will not apply.
7. Please place an “x” on one of the applicable statement below. Please do NOT
mark both statements:
* [x] I am signing on behalf of myself as an individual and no other person
or entity, including my employer, has or will have rights with respect to my
contributions.
* [ ] I am signing on behalf of my employer or a legal entity and I have the
actual authority to contractually bind that entity.
## Contributor Details
| Field | Entry |
|------------------------------- | -------------------- |
| Name | Álvaro Abella |
| Company name (if applicable) | IOMED |
| Title or role (if applicable) | CSO |
| Date | 21/12/2018 |
| GitHub username | alvaroabascar |
| Website (optional) | |

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spacy/tests/regression/test_issue2396.py Normal file
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# coding: utf-8
from __future__ import unicode_literals
from ..util import get_doc
import pytest
import numpy
@pytest.mark.parametrize('sentence,matrix', [
(
'She created a test for spacy',
numpy.array([
[0, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1],
[1, 1, 2, 3, 3, 3],
[1, 1, 3, 3, 3, 3],
[1, 1, 3, 3, 4, 4],
[1, 1, 3, 3, 4, 5]], dtype=numpy.int32)
)
])
def test_issue2396(EN, sentence, matrix):
doc = EN(sentence)
span = doc[:]
assert (doc.get_lca_matrix() == matrix).all()
assert (span.get_lca_matrix() == matrix).all()

3
spacy/tokens/doc.pxd
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@ -30,6 +30,9 @@ cdef int token_by_end(const TokenC* tokens, int length, int end_char) except -2
cdef int set_children_from_heads(TokenC* tokens, int length) except -1
cdef int [:,:] _get_lca_matrix(Doc, int start, int end)
cdef class Doc:
cdef readonly Pool mem
cdef readonly Vocab vocab

116
spacy/tokens/doc.pyx
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@ -1,3 +1,4 @@
# coding: utf8
# cython: infer_types=True
# cython: bounds_check=False
@ -715,48 +716,14 @@ cdef class Doc:
return self
def get_lca_matrix(self):
"""Calculates the lowest common ancestor matrix for a given `Doc`.
Returns LCA matrix containing the integer index of the ancestor, or -1
if no common ancestor is found (ex if span excludes a necessary
ancestor). Apologies about the recursion, but the impact on
performance is negligible given the natural limitations on the depth
of a typical human sentence.
"""Calculates a matrix of Lowest Common Ancestors (LCA) for a given
`Doc`, where LCA[i, j] is the index of the lowest common ancestor among
token i and j.
RETURNS (np.array[ndim=2, dtype=numpy.int32]): LCA matrix with shape
(n, n), where n = len(self).
"""
# Efficiency notes:
# We can easily improve the performance here by iterating in Cython.
# To loop over the tokens in Cython, the easiest way is:
# for token in doc.c[:doc.c.length]:
# head = token + token.head
# Both token and head will be TokenC* here. The token.head attribute
# is an integer offset.
def __pairwise_lca(token_j, token_k, lca_matrix):
if lca_matrix[token_j.i][token_k.i] != -2:
return lca_matrix[token_j.i][token_k.i]
elif token_j == token_k:
lca_index = token_j.i
elif token_k.head == token_j:
lca_index = token_j.i
elif token_j.head == token_k:
lca_index = token_k.i
elif (token_j.head == token_j) and (token_k.head == token_k):
lca_index = -1
else:
lca_index = __pairwise_lca(token_j.head, token_k.head,
lca_matrix)
lca_matrix[token_j.i][token_k.i] = lca_index
lca_matrix[token_k.i][token_j.i] = lca_index
return lca_index
lca_matrix = numpy.empty((len(self), len(self)), dtype=numpy.int32)
lca_matrix.fill(-2)
for j in range(len(self)):
token_j = self[j]
for k in range(j, len(self)):
token_k = self[k]
lca_matrix[j][k] = __pairwise_lca(token_j, token_k, lca_matrix)
lca_matrix[k][j] = lca_matrix[j][k]
return lca_matrix
return numpy.asarray(_get_lca_matrix(self, 0, len(self)))
def to_disk(self, path, **exclude):
"""Save the current state to a directory.
@ -1060,6 +1027,73 @@ cdef int set_children_from_heads(TokenC* tokens, int length) except -1:
tokens[tokens[i].l_edge].sent_start = True
cdef int _get_tokens_lca(Token token_j, Token token_k):
"""Given two tokens, returns the index of the lowest common ancestor
(LCA) among the two. If they have no common ancestor, -1 is returned.
token_j (Token): a token.
token_k (Token): another token.
RETURNS (int): index of lowest common ancestor, or -1 if the tokens
have no common ancestor.
"""
if token_j == token_k:
return token_j.i
elif token_j.head == token_k:
return token_k.i
elif token_k.head == token_j:
return token_j.i
token_j_ancestors = set(token_j.ancestors)
if token_k in token_j_ancestors:
return token_k.i
for token_k_ancestor in token_k.ancestors:
if token_k_ancestor == token_j:
return token_j.i
if token_k_ancestor in token_j_ancestors:
return token_k_ancestor.i
return -1
cdef int [:,:] _get_lca_matrix(Doc doc, int start, int end):
"""Given a doc and a start and end position defining a set of contiguous
tokens within it, returns a matrix of Lowest Common Ancestors (LCA), where
LCA[i, j] is the index of the lowest common ancestor among token i and j.
If the tokens have no common ancestor within the specified span,
LCA[i, j] will be -1.
doc (Doc): The index of the token, or the slice of the document
start (int): First token to be included in the LCA matrix.
end (int): Position of next to last token included in the LCA matrix.
RETURNS (int [:, :]): memoryview of numpy.array[ndim=2, dtype=numpy.int32],
with shape (n, n), where n = len(doc).
"""
cdef int [:,:] lca_matrix
n_tokens= end - start
lca_matrix = numpy.empty((n_tokens, n_tokens), dtype=numpy.int32)
for j in range(start, end):
token_j = doc[j]
# the common ancestor of token and itself is itself:
lca_matrix[j, j] = j
for k in range(j + 1, end):
lca = _get_tokens_lca(token_j, doc[k])
# if lca is outside of span, we set it to -1
if not start <= lca < end:
lca_matrix[j, k] = -1
lca_matrix[k, j] = -1
else:
lca_matrix[j, k] = lca
lca_matrix[k, j] = lca
return lca_matrix
def pickle_doc(doc):
bytes_data = doc.to_bytes(vocab=False, user_data=False)
hooks_and_data = (doc.user_data, doc.user_hooks, doc.user_span_hooks,

55
spacy/tokens/span.pyx
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@ -7,7 +7,8 @@ import numpy
import numpy.linalg
from libc.math cimport sqrt
from .doc cimport token_by_start, token_by_end, get_token_attr
from .doc cimport token_by_start, token_by_end, get_token_attr, _get_lca_matrix
from .token cimport TokenC
from ..structs cimport TokenC, LexemeC
from ..typedefs cimport flags_t, attr_t, hash_t
from ..attrs cimport attr_id_t
@ -183,6 +184,17 @@ cdef class Span:
return self.doc.merge(self.start_char, self.end_char, *args,
**attributes)
def get_lca_matrix(self):
"""Calculates a matrix of Lowest Common Ancestors (LCA) for a given
`Span`, where LCA[i, j] is the index of the lowest common ancestor among
the tokens span[i] and span[j]. If they have no common ancestor within
the span, LCA[i, j] will be -1.
RETURNS (np.array[ndim=2, dtype=numpy.int32]): LCA matrix with shape
(n, n), where n = len(self).
"""
return numpy.asarray(_get_lca_matrix(self.doc, self.start, self.end))
def similarity(self, other):
"""Make a semantic similarity estimate. The default estimate is cosine
similarity using an average of word vectors.
@ -209,47 +221,6 @@ cdef class Span:
return 0.0
return numpy.dot(self.vector, other.vector) / (self.vector_norm * other.vector_norm)
def get_lca_matrix(self):
"""Calculates the lowest common ancestor matrix for a given `Span`.
Returns LCA matrix containing the integer index of the ancestor, or -1
if no common ancestor is found (ex if span excludes a necessary
ancestor). Apologies about the recursion, but the impact on
performance is negligible given the natural limitations on the depth
of a typical human sentence.
"""
def __pairwise_lca(token_j, token_k, lca_matrix, margins):
offset = margins[0]
token_k_head = token_k.head if token_k.head.i in range(*margins) else token_k
token_j_head = token_j.head if token_j.head.i in range(*margins) else token_j
token_j_i = token_j.i - offset
token_k_i = token_k.i - offset
if lca_matrix[token_j_i][token_k_i] != -2:
return lca_matrix[token_j_i][token_k_i]
elif token_j == token_k:
lca_index = token_j_i
elif token_k_head == token_j:
lca_index = token_j_i
elif token_j_head == token_k:
lca_index = token_k_i
elif (token_j_head == token_j) and (token_k_head == token_k):
lca_index = -1
else:
lca_index = __pairwise_lca(token_j_head, token_k_head, lca_matrix, margins)
lca_matrix[token_j_i][token_k_i] = lca_index
lca_matrix[token_k_i][token_j_i] = lca_index
return lca_index
lca_matrix = numpy.empty((len(self), len(self)), dtype=numpy.int32)
lca_matrix.fill(-2)
margins = [self.start, self.end]
for j in range(len(self)):
token_j = self[j]
for k in range(len(self)):
token_k = self[k]
lca_matrix[j][k] = __pairwise_lca(token_j, token_k, lca_matrix, margins)
lca_matrix[k][j] = lca_matrix[j][k]
return lca_matrix
cpdef np.ndarray 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 document.