mirror of https://github.com/explosion/spaCy.git
Fix issue 2396 (#3089)
* 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
Matthew Honnibal
parent
76e3e695af
commit
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# spaCy contributor agreement
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This spaCy Contributor Agreement (**"SCA"**) is based on the
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[Oracle Contributor Agreement](http://www.oracle.com/technetwork/oca-405177.pdf).
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The SCA applies to any contribution that you make to any product or project
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managed by us (the **"project"**), and sets out the intellectual property rights
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you grant to us in the contributed materials. The term **"us"** shall mean
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[ExplosionAI UG (haftungsbeschränkt)](https://explosion.ai/legal). The term
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**"you"** shall mean the person or entity identified below.
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If you agree to be bound by these terms, fill in the information requested
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below and include the filled-in version with your first pull request, under the
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folder [`.github/contributors/`](/.github/contributors/). The name of the file
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should be your GitHub username, with the extension `.md`. For example, the user
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example_user would create the file `.github/contributors/example_user.md`.
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Read this agreement carefully before signing. These terms and conditions
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constitute a binding legal agreement.
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## Contributor Agreement
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1. The term "contribution" or "contributed materials" means any source code,
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object code, patch, tool, sample, graphic, specification, manual,
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documentation, or any other material posted or submitted by you to the project.
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2. With respect to any worldwide copyrights, or copyright applications and
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registrations, in your contribution:
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* you hereby assign to us joint ownership, and to the extent that such
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assignment is or becomes invalid, ineffective or unenforceable, you hereby
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grant to us a perpetual, irrevocable, non-exclusive, worldwide, no-charge,
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royalty-free, unrestricted license to exercise all rights under those
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rights to third parties through multiple levels of sublicensees or other
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* you agree that each of us can do all things in relation to your
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contribution as if each of us were the sole owners, and if one of us makes
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a derivative work of your contribution, the one who makes the derivative
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work (or has it made will be the sole owner of that derivative work;
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* you agree that you will not assert any moral rights in your contribution
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* you agree that we may register a copyright in your contribution and
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exercise all ownership rights associated with it; and
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* you agree that neither of us has any duty to consult with, obtain the
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consent of, pay or render an accounting to the other for any use or
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distribution of your contribution.
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3. With respect to any patents you own, or that you can license without payment
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* at our option, to sublicense these same rights to third parties through
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4. Except as set out above, you keep all right, title, and interest in your
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on the date you first submitted a contribution to us, even if your submission
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5. You covenant, represent, warrant and agree that:
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* Each contribution that you submit is and shall be an original work of
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authorship and you can legally grant the rights set out in this SCA;
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* to the best of your knowledge, each contribution will not violate any
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third party's copyrights, trademarks, patents, or other intellectual
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property rights; and
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* each contribution shall be in compliance with U.S. export control laws and
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other applicable export and import laws. You agree to notify us if you
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representations inaccurate in any respect. We may publicly disclose your
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participation in the project, including the fact that you have signed the SCA.
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6. This SCA is governed by the laws of the State of California and applicable
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U.S. Federal law. Any choice of law rules will not apply.
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7. Please place an “x” on one of the applicable statement below. Please do NOT
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mark both statements:
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* [x] I am signing on behalf of myself as an individual and no other person
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or entity, including my employer, has or will have rights with respect to my
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contributions.
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* [ ] I am signing on behalf of my employer or a legal entity and I have the
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actual authority to contractually bind that entity.
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## Contributor Details
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| Field | Entry |
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|------------------------------- | -------------------- |
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| Name | Álvaro Abella |
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| Company name (if applicable) | IOMED |
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| Title or role (if applicable) | CSO |
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| Date | 21/12/2018 |
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| GitHub username | alvaroabascar |
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| Website (optional) | |
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@ -0,0 +1,27 @@
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# coding: utf-8
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from __future__ import unicode_literals
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from ..util import get_doc
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import pytest
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import numpy
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@pytest.mark.parametrize('sentence,matrix', [
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(
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'She created a test for spacy',
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numpy.array([
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[0, 1, 1, 1, 1, 1],
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[1, 1, 1, 1, 1, 1],
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[1, 1, 2, 3, 3, 3],
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[1, 1, 3, 3, 3, 3],
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[1, 1, 3, 3, 4, 4],
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[1, 1, 3, 3, 4, 5]], dtype=numpy.int32)
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)
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])
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def test_issue2396(EN, sentence, matrix):
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doc = EN(sentence)
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span = doc[:]
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assert (doc.get_lca_matrix() == matrix).all()
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assert (span.get_lca_matrix() == matrix).all()
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@ -30,6 +30,9 @@ cdef int token_by_end(const TokenC* tokens, int length, int end_char) except -2
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cdef int set_children_from_heads(TokenC* tokens, int length) except -1
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cdef int [:,:] _get_lca_matrix(Doc, int start, int end)
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cdef class Doc:
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cdef readonly Pool mem
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cdef readonly Vocab vocab
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@ -1,3 +1,4 @@
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# coding: utf8
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# cython: infer_types=True
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# cython: bounds_check=False
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@ -715,48 +716,14 @@ cdef class Doc:
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return self
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def get_lca_matrix(self):
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"""Calculates the lowest common ancestor matrix for a given `Doc`.
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Returns LCA matrix containing the integer index of the ancestor, or -1
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if no common ancestor is found (ex if span excludes a necessary
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ancestor). Apologies about the recursion, but the impact on
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performance is negligible given the natural limitations on the depth
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of a typical human sentence.
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"""Calculates a matrix of Lowest Common Ancestors (LCA) for a given
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`Doc`, where LCA[i, j] is the index of the lowest common ancestor among
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token i and j.
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RETURNS (np.array[ndim=2, dtype=numpy.int32]): LCA matrix with shape
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(n, n), where n = len(self).
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"""
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# Efficiency notes:
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# We can easily improve the performance here by iterating in Cython.
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# To loop over the tokens in Cython, the easiest way is:
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# for token in doc.c[:doc.c.length]:
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# head = token + token.head
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# Both token and head will be TokenC* here. The token.head attribute
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# is an integer offset.
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def __pairwise_lca(token_j, token_k, lca_matrix):
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if lca_matrix[token_j.i][token_k.i] != -2:
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return lca_matrix[token_j.i][token_k.i]
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elif token_j == token_k:
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lca_index = token_j.i
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elif token_k.head == token_j:
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lca_index = token_j.i
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elif token_j.head == token_k:
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lca_index = token_k.i
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elif (token_j.head == token_j) and (token_k.head == token_k):
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lca_index = -1
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else:
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lca_index = __pairwise_lca(token_j.head, token_k.head,
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lca_matrix)
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lca_matrix[token_j.i][token_k.i] = lca_index
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lca_matrix[token_k.i][token_j.i] = lca_index
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return lca_index
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lca_matrix = numpy.empty((len(self), len(self)), dtype=numpy.int32)
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lca_matrix.fill(-2)
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for j in range(len(self)):
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token_j = self[j]
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for k in range(j, len(self)):
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token_k = self[k]
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lca_matrix[j][k] = __pairwise_lca(token_j, token_k, lca_matrix)
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lca_matrix[k][j] = lca_matrix[j][k]
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return lca_matrix
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return numpy.asarray(_get_lca_matrix(self, 0, len(self)))
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def to_disk(self, path, **exclude):
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"""Save the current state to a directory.
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@ -1060,6 +1027,73 @@ cdef int set_children_from_heads(TokenC* tokens, int length) except -1:
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tokens[tokens[i].l_edge].sent_start = True
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cdef int _get_tokens_lca(Token token_j, Token token_k):
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"""Given two tokens, returns the index of the lowest common ancestor
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(LCA) among the two. If they have no common ancestor, -1 is returned.
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token_j (Token): a token.
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token_k (Token): another token.
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RETURNS (int): index of lowest common ancestor, or -1 if the tokens
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have no common ancestor.
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"""
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if token_j == token_k:
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return token_j.i
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elif token_j.head == token_k:
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return token_k.i
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elif token_k.head == token_j:
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return token_j.i
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token_j_ancestors = set(token_j.ancestors)
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if token_k in token_j_ancestors:
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return token_k.i
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for token_k_ancestor in token_k.ancestors:
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if token_k_ancestor == token_j:
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return token_j.i
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if token_k_ancestor in token_j_ancestors:
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return token_k_ancestor.i
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return -1
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cdef int [:,:] _get_lca_matrix(Doc doc, int start, int end):
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"""Given a doc and a start and end position defining a set of contiguous
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tokens within it, returns a matrix of Lowest Common Ancestors (LCA), where
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LCA[i, j] is the index of the lowest common ancestor among token i and j.
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If the tokens have no common ancestor within the specified span,
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LCA[i, j] will be -1.
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doc (Doc): The index of the token, or the slice of the document
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start (int): First token to be included in the LCA matrix.
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end (int): Position of next to last token included in the LCA matrix.
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RETURNS (int [:, :]): memoryview of numpy.array[ndim=2, dtype=numpy.int32],
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with shape (n, n), where n = len(doc).
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"""
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cdef int [:,:] lca_matrix
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n_tokens= end - start
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lca_matrix = numpy.empty((n_tokens, n_tokens), dtype=numpy.int32)
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for j in range(start, end):
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token_j = doc[j]
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# the common ancestor of token and itself is itself:
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lca_matrix[j, j] = j
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for k in range(j + 1, end):
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lca = _get_tokens_lca(token_j, doc[k])
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# if lca is outside of span, we set it to -1
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if not start <= lca < end:
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lca_matrix[j, k] = -1
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lca_matrix[k, j] = -1
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else:
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lca_matrix[j, k] = lca
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lca_matrix[k, j] = lca
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return lca_matrix
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def pickle_doc(doc):
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bytes_data = doc.to_bytes(vocab=False, user_data=False)
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hooks_and_data = (doc.user_data, doc.user_hooks, doc.user_span_hooks,
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@ -7,7 +7,8 @@ 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 .doc cimport token_by_start, token_by_end, get_token_attr, _get_lca_matrix
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from .token cimport TokenC
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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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@ -183,6 +184,17 @@ cdef class Span:
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return self.doc.merge(self.start_char, self.end_char, *args,
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**attributes)
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def get_lca_matrix(self):
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"""Calculates a matrix of Lowest Common Ancestors (LCA) for a given
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`Span`, where LCA[i, j] is the index of the lowest common ancestor among
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the tokens span[i] and span[j]. If they have no common ancestor within
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the span, LCA[i, j] will be -1.
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RETURNS (np.array[ndim=2, dtype=numpy.int32]): LCA matrix with shape
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(n, n), where n = len(self).
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"""
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return numpy.asarray(_get_lca_matrix(self.doc, self.start, self.end))
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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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@ -209,47 +221,6 @@ cdef class Span:
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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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def get_lca_matrix(self):
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"""Calculates the lowest common ancestor matrix for a given `Span`.
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Returns LCA matrix containing the integer index of the ancestor, or -1
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if no common ancestor is found (ex if span excludes a necessary
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ancestor). Apologies about the recursion, but the impact on
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performance is negligible given the natural limitations on the depth
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of a typical human sentence.
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"""
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def __pairwise_lca(token_j, token_k, lca_matrix, margins):
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offset = margins[0]
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token_k_head = token_k.head if token_k.head.i in range(*margins) else token_k
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token_j_head = token_j.head if token_j.head.i in range(*margins) else token_j
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token_j_i = token_j.i - offset
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token_k_i = token_k.i - offset
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if lca_matrix[token_j_i][token_k_i] != -2:
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return lca_matrix[token_j_i][token_k_i]
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elif token_j == token_k:
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lca_index = token_j_i
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elif token_k_head == token_j:
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lca_index = token_j_i
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elif token_j_head == token_k:
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lca_index = token_k_i
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elif (token_j_head == token_j) and (token_k_head == token_k):
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lca_index = -1
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else:
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lca_index = __pairwise_lca(token_j_head, token_k_head, lca_matrix, margins)
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lca_matrix[token_j_i][token_k_i] = lca_index
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lca_matrix[token_k_i][token_j_i] = lca_index
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return lca_index
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lca_matrix = numpy.empty((len(self), len(self)), dtype=numpy.int32)
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lca_matrix.fill(-2)
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margins = [self.start, self.end]
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for j in range(len(self)):
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token_j = self[j]
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for k in range(len(self)):
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token_k = self[k]
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lca_matrix[j][k] = __pairwise_lca(token_j, token_k, lca_matrix, margins)
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lca_matrix[k][j] = lca_matrix[j][k]
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return lca_matrix
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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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