mira/bertalign
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

Bertalign

Browse Source 
Bertalign
This commit is contained in:
nlpfun
2021-05-17 23:33:49 +08:00
parent 0a92061119
commit 025bc2afe4
15 changed files with 451188 additions and 0 deletions
Download Patch File Download Diff File Expand all files Collapse all files

202
bin/vecalign/LICENSE Normal file
View File

@@ -0,0 +1,202 @@
Apache License
Version 2.0, January 2004
http://www.apache.org/licenses/
TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
1. Definitions.
"License" shall mean the terms and conditions for use, reproduction,
and distribution as defined by Sections 1 through 9 of this document.
"Licensor" shall mean the copyright owner or entity authorized by
the copyright owner that is granting the License.
"Legal Entity" shall mean the union of the acting entity and all
other entities that control, are controlled by, or are under common
control with that entity. For the purposes of this definition,
"control" means (i) the power, direct or indirect, to cause the
direction or management of such entity, whether by contract or
otherwise, or (ii) ownership of fifty percent (50%) or more of the
outstanding shares, or (iii) beneficial ownership of such entity.
"You" (or "Your") shall mean an individual or Legal Entity
exercising permissions granted by this License.
"Source" form shall mean the preferred form for making modifications,
including but not limited to software source code, documentation
source, and configuration files.
"Object" form shall mean any form resulting from mechanical
transformation or translation of a Source form, including but
not limited to compiled object code, generated documentation,
and conversions to other media types.
"Work" shall mean the work of authorship, whether in Source or
Object form, made available under the License, as indicated by a
copyright notice that is included in or attached to the work
(an example is provided in the Appendix below).
"Derivative Works" shall mean any work, whether in Source or Object
form, that is based on (or derived from) the Work and for which the
editorial revisions, annotations, elaborations, or other modifications
represent, as a whole, an original work of authorship. For the purposes
of this License, Derivative Works shall not include works that remain
separable from, or merely link (or bind by name) to the interfaces of,
the Work and Derivative Works thereof.
"Contribution" shall mean any work of authorship, including
the original version of the Work and any modifications or additions
to that Work or Derivative Works thereof, that is intentionally
submitted to Licensor for inclusion in the Work by the copyright owner
or by an individual or Legal Entity authorized to submit on behalf of
the copyright owner. For the purposes of this definition, "submitted"
means any form of electronic, verbal, or written communication sent
to the Licensor or its representatives, including but not limited to
communication on electronic mailing lists, source code control systems,
and issue tracking systems that are managed by, or on behalf of, the
Licensor for the purpose of discussing and improving the Work, but
excluding communication that is conspicuously marked or otherwise
designated in writing by the copyright owner as "Not a Contribution."
"Contributor" shall mean Licensor and any individual or Legal Entity
on behalf of whom a Contribution has been received by Licensor and
subsequently incorporated within the Work.
2. Grant of Copyright License. Subject to the terms and conditions of
this License, each Contributor hereby grants to You a perpetual,
worldwide, non-exclusive, no-charge, royalty-free, irrevocable
copyright license to reproduce, prepare Derivative Works of,
publicly display, publicly perform, sublicense, and distribute the
Work and such Derivative Works in Source or Object form.
3. Grant of Patent License. Subject to the terms and conditions of
this License, each Contributor hereby grants to You a perpetual,
worldwide, non-exclusive, no-charge, royalty-free, irrevocable
(except as stated in this section) patent license to make, have made,
use, offer to sell, sell, import, and otherwise transfer the Work,
where such license applies only to those patent claims licensable
by such Contributor that are necessarily infringed by their
Contribution(s) alone or by combination of their Contribution(s)
with the Work to which such Contribution(s) was submitted. If You
institute patent litigation against any entity (including a
cross-claim or counterclaim in a lawsuit) alleging that the Work
or a Contribution incorporated within the Work constitutes direct
or contributory patent infringement, then any patent licenses
granted to You under this License for that Work shall terminate
as of the date such litigation is filed.
4. Redistribution. You may reproduce and distribute copies of the
Work or Derivative Works thereof in any medium, with or without
modifications, and in Source or Object form, provided that You
meet the following conditions:
(a) You must give any other recipients of the Work or
Derivative Works a copy of this License; and
(b) You must cause any modified files to carry prominent notices
stating that You changed the files; and
(c) You must retain, in the Source form of any Derivative Works
that You distribute, all copyright, patent, trademark, and
attribution notices from the Source form of the Work,
excluding those notices that do not pertain to any part of
the Derivative Works; and
(d) If the Work includes a "NOTICE" text file as part of its
distribution, then any Derivative Works that You distribute must
include a readable copy of the attribution notices contained
within such NOTICE file, excluding those notices that do not
pertain to any part of the Derivative Works, in at least one
of the following places: within a NOTICE text file distributed
as part of the Derivative Works; within the Source form or
documentation, if provided along with the Derivative Works; or,
within a display generated by the Derivative Works, if and
wherever such third-party notices normally appear. The contents
of the NOTICE file are for informational purposes only and
do not modify the License. You may add Your own attribution
notices within Derivative Works that You distribute, alongside
or as an addendum to the NOTICE text from the Work, provided
that such additional attribution notices cannot be construed
as modifying the License.
You may add Your own copyright statement to Your modifications and
may provide additional or different license terms and conditions
for use, reproduction, or distribution of Your modifications, or
for any such Derivative Works as a whole, provided Your use,
reproduction, and distribution of the Work otherwise complies with
the conditions stated in this License.
5. Submission of Contributions. Unless You explicitly state otherwise,
any Contribution intentionally submitted for inclusion in the Work
by You to the Licensor shall be under the terms and conditions of
this License, without any additional terms or conditions.
Notwithstanding the above, nothing herein shall supersede or modify
the terms of any separate license agreement you may have executed
with Licensor regarding such Contributions.
6. Trademarks. This License does not grant permission to use the trade
names, trademarks, service marks, or product names of the Licensor,
except as required for reasonable and customary use in describing the
origin of the Work and reproducing the content of the NOTICE file.
7. Disclaimer of Warranty. Unless required by applicable law or
agreed to in writing, Licensor provides the Work (and each
Contributor provides its Contributions) on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or
implied, including, without limitation, any warranties or conditions
of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A
PARTICULAR PURPOSE. You are solely responsible for determining the
appropriateness of using or redistributing the Work and assume any
risks associated with Your exercise of permissions under this License.
8. Limitation of Liability. In no event and under no legal theory,
whether in tort (including negligence), contract, or otherwise,
unless required by applicable law (such as deliberate and grossly
negligent acts) or agreed to in writing, shall any Contributor be
liable to You for damages, including any direct, indirect, special,
incidental, or consequential damages of any character arising as a
result of this License or out of the use or inability to use the
Work (including but not limited to damages for loss of goodwill,
work stoppage, computer failure or malfunction, or any and all
other commercial damages or losses), even if such Contributor
has been advised of the possibility of such damages.
9. Accepting Warranty or Additional Liability. While redistributing
the Work or Derivative Works thereof, You may choose to offer,
and charge a fee for, acceptance of support, warranty, indemnity,
or other liability obligations and/or rights consistent with this
License. However, in accepting such obligations, You may act only
on Your own behalf and on Your sole responsibility, not on behalf
of any other Contributor, and only if You agree to indemnify,
defend, and hold each Contributor harmless for any liability
incurred by, or claims asserted against, such Contributor by reason
of your accepting any such warranty or additional liability.
END OF TERMS AND CONDITIONS
APPENDIX: How to apply the Apache License to your work.
To apply the Apache License to your work, attach the following
boilerplate notice, with the fields enclosed by brackets "[]"
replaced with your own identifying information. (Don't include
the brackets!) The text should be enclosed in the appropriate
comment syntax for the file format. We also recommend that a
file or class name and description of purpose be included on the
same "printed page" as the copyright notice for easier
identification within third-party archives.
Copyright [yyyy] [name of copyright owner]
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.

158
bin/vecalign/README.md Normal file
View File

@@ -0,0 +1,158 @@
# Vecalign
Vecalign is an accurate sentence alignment algorithm which is fast even for very long documents.
In conjunction with [LASER](https://github.com/facebookresearch/LASER), Vecalign
works in about 100 languages (i.e. 100^2 language pairs),
without the need for a machine translation system or lexicon.
Vecalign uses similarity of multilingual sentence embeddings to judge the similarity of sentences.
![multilingual_sentence_embedding image](media/multilingual_sentence_embedding.png)
[image based on [this Facebook AI post](https://engineering.fb.com/ai-research/laser-multilingual-sentence-embeddings/)]
Vecalign uses an approximation to Dynamic Programming based on
[Fast Dynamic Time Warping](https://content.iospress.com/articles/intelligent-data-analysis/ida00303)
which is linear in time and space with respect to the number of sentences being aligned.
![dynamic_programing_approximation visualization](media/dynamic_programing_approximation.gif)
### License
Copyright 2019 Brian Thompson
Vecalign is released under the [Apache License, Version 2.0](LICENSE).
For convenience, the dev and test datasets from Bleualign are provided. Bleualign is Copyright 2010 Rico Sennrich and is released under the [GNU General Public License Version 2](bleualign_data/LICENSE)
### Build Vecalign
You will need python 3.6+ with numpy and cython. You can build an environment using conda as follows:
```
# Use latest conda
conda update conda -y
# Create conda environment
conda create --force -y --name vecalign python=3.7
# Activate new environment
source `conda info --base`/etc/profile.d/conda.sh # See: https://github.com/conda/conda/issues/7980
conda activate vecalign
# Install required packages
conda install -y -c anaconda cython
conda install -y -c anaconda numpy
```
Note that Vecalign contains cython code, but there is no need to build it manually as it is compiled automatically by [pyximport](https://github.com/cython/cython/tree/master/pyximport).
### Run Vecalign (using provided embeddings)
```
./vecalign.py --alignment_max_size 8 --src bleualign_data/dev.de --tgt bleualign_data/dev.fr \
--src_embed bleualign_data/overlaps.de bleualign_data/overlaps.de.emb \
--tgt_embed bleualign_data/overlaps.fr bleualign_data/overlaps.fr.emb
```
Alignments are written to stdout:
```
[0]:[0]:0.156006
[1]:[1]:0.160997
[2]:[2]:0.217155
[3]:[3]:0.361439
[4]:[4]:0.346332
[5]:[5]:0.211873
[6]:[6, 7, 8]:0.507506
[7]:[9]:0.252747
[8, 9]:[10, 11, 12]:0.139594
[10, 11]:[13]:0.273751
[12]:[14]:0.165397
[13]:[15, 16, 17]:0.436312
[14]:[18, 19, 20, 21]:0.734142
[]:[22]:0.000000
[]:[23]:0.000000
[]:[24]:0.000000
[]:[25]:0.000000
[15]:[26, 27, 28]:0.840094
...
```
The first two entries are the source and target sentence indexes for each alignment, respectively.
The third entry in each line is the sentence alignment cost computed by Vecalign.
Note that this cost includes normalization but does *not* include the penalties terms for containing more than one sentence.
Note that the alignment cost is set to zero for insertions/deletions.
Also note that the results may vary slightly due to randomness in the normalization.
To score against a gold alignment, use the "-g" flag.
Flags "-s", "-t", and "-g" can accept multiple arguments. This is primarily useful for scoring, as the output alignments will all be concatenated together in stdout. For example, to align and score the bleualign test set:
```
./vecalign.py --alignment_max_size 8 --src bleualign_data/test*.de --tgt bleualign_data/test*.fr \
--gold bleualign_data/test*.defr \
--src_embed bleualign_data/overlaps.de bleualign_data/overlaps.de.emb \
--tgt_embed bleualign_data/overlaps.fr bleualign_data/overlaps.fr.emb > /dev/null
```
Which should give you results that approximately match the Vecalign paper:
```
---------------------------------
| | Strict | Lax |
| Precision | 0.899 | 0.985 |
| Recall | 0.904 | 0.987 |
| F1 | 0.902 | 0.986 |
---------------------------------
```
Note: Run `./vecalign.py -h` for full sentence alignment usage and options.
For stand-alone scoring against a gold reference, see [score.py](score.py)
### Embed your own documents
The Vecalign repository contains overlap and embedding files for the Bluealign dev/test files.
This section shows how those files were made, as an example for running on new data.
Vecalign requires not only embeddings of sentences in each document,
but also embeddings of *concatenations* of consecutive sentences.
The embeddings of multiple, consecutive sentences are needed to consider 1-many, many-1, and many-many alignments.
To create a file containing all the sentence combinations in the dev and test files from Bleualign:
```
./overlap.py -i bleualign_data/dev.fr bleualign_data/test*.fr -o bleualign_data/overlaps.fr -n 10
./overlap.py -i bleualign_data/dev.de bleualign_data/test*.de -o bleualign_data/overlaps.de -n 10
```
Note: Run `./overlap.py -h` to see full set of embedding options.
`bleualign_data/overlaps.fr` and `bleualign_data/overlaps.de` are text files containing one or more sentences per line.
These files must then be embedded using a multilingual sentence embedder.
We recommend the [Language-Agnostic SEntence Representations (LASER)](https://github.com/facebookresearch/LASER)
toolkit from Facebook, as it has strong performance and comes with a pretrained model which works well in about 100 languages.
However, Vecalign should also work with other embedding methods as well. Embeddings should be provided as a binary file containing float32 values.
The following assumes LASER is installed and the LASER environmental variable has been set.
To embed the Bleualign files using LASER:
```
$LASER/tasks/embed/embed.sh bleualign_data/overlaps.fr fr bleualign_data/overlaps.fr.emb
$LASER/tasks/embed/embed.sh bleualign_data/overlaps.de de bleualign_data/overlaps.de.emb
```
Note that LASER will not overwrite an embedding file if it exsts, so you may need to run first `rm bleualign_data/overlaps.fr.emb bleualign_data/overlaps.de.emb`.
### Publications
If you use Vecalign, please cite our [paper](https://www.aclweb.org/anthology/D19-1136.pdf):
```
@inproceedings{thompson-koehn-2019-vecalign,
title = "{V}ecalign: Improved Sentence Alignment in Linear Time and Space",
author = "Thompson, Brian and Koehn, Philipp",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
month = nov,
year = "2019",
address = "Hong Kong, China",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/D19-1136",
doi = "10.18653/v1/D19-1136",
pages = "1342--1348",
}
```

0
bin/vecalign/__init__.py Normal file
View File

411
bin/vecalign/dp_core.pyx Normal file
View File

@@ -0,0 +1,411 @@
# cython: language_level=3
"""
Copyright 2019 Brian Thompson
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
https://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
"""
import numpy as np
cimport numpy as np
cimport cython
def make_x_y_offsets(alignment_types):
# alignment types for which we will precompute costs
# deletion/insertion is added later
for x, y in alignment_types:
assert (x > 0)
assert (y > 0)
x_offsets = np.array([x for x, y in alignment_types], dtype=np.int32) # MUST **NOT** INCLUDE (0,1), (1,0)
y_offsets = np.array([y for x, y in alignment_types], dtype=np.int32) # MUST **NOT** INCLUDE (0,1), (1,0)
return x_offsets, y_offsets
def make_dense_costs(np.ndarray[float, ndim=3] vecs0, # itput
np.ndarray[float, ndim=3] vecs1, # input
np.ndarray[float, ndim=2] norm0, # input
np.ndarray[float, ndim=2] norm1, # input
int offset0 = 0, # index into vecs0/norms0
int offset1 = 0, # index into vecs1/norms1
):
"""
Make a full N*M feature matrix. By default, makes 1-1 alignments,
can build others by specifying offset0, offset1 to index into
vecs0, norms0 and vecs1, norms1 respectivly.
"""
assert vecs0.shape[0] > offset0
assert vecs1.shape[0] > offset1
assert norm0.shape[0] > offset0
assert norm1.shape[0] > offset1
cdef int size0 = np.shape(vecs0)[1]
assert norm0.shape[1] == size0
cdef int size1 = np.shape(vecs1)[1]
assert norm1.shape[1] == size1
cdef int vecsize = np.shape(vecs0)[2]
assert vecs1.shape[2] == vecsize
cdef int xi, yi
cdef float sumx
cdef np.ndarray[float, ndim=2] costs = np.empty((size0, size1), dtype=np.float32)
for xi in range(size0):
for yi in range(size1):
sumx = 0.0
for jj in range(vecsize):
sumx += vecs0[offset0, xi, jj] * vecs1[offset1, yi, jj]
costs[xi, yi] = 2.0 * (1.0 - sumx) / (1e-6 + norm0[offset0, xi] + norm1[offset1, yi])
# normalize by alignment type
costs[xi, yi] = costs[xi, yi] * (offset0 + 1) * (offset1 + 1)
return costs
def dense_dp(np.ndarray[float, ndim=2] alignment_cost, float pen):
"""
Compute cost matrix (csum) and backpointers (bp)
from full 2-D 1-1 alignment costs matrix (alignment_cost)
"""
size0 = alignment_cost.shape[0]
size1 = alignment_cost.shape[1]
# csum and traceback matrix are both on nodes
# so they are +1 in each dimension compared to the jump costs matrix
# For anything being used in accumulation, use float64
cdef np.ndarray[double, ndim=2] csum = np.empty((size0 + 1, size1 + 1), dtype=np.float64)
cdef np.ndarray[int, ndim=2] bp = np.empty((size0 + 1, size1 + 1), dtype=np.int32)
# bp and csum are nodes,
# while alignment_cost is the cost of going between the nodes
# Size of nodes should be one larger than alignment costs
b0, b1 = np.shape(bp)
c0, c1 = np.shape(csum)
j0, j1 = np.shape(alignment_cost)
assert (b0 == c0 == j0 + 1)
assert (b1 == c1 == j1 + 1)
cdef int cmax = np.shape(csum)[1]
cdef int rmax = np.shape(csum)[0]
cdef int c, r
cdef double cost0, cost1, cost2
# initialize the all c-direction deletion path
for c in range(cmax):
csum[0, c] = c * pen
bp[0, c] = 1
# initialize the all r-direction deletion path
for r in range(rmax):
csum[r, 0] = r * pen
bp[r, 0] = 2
# Initial cost is 0.0
csum[0, 0] = 0.0 # noop
bp[0, 0] = 4 # should not matter
# Calculate the rest recursively
for c in range(1, cmax):
for r in range(1, rmax):
# alignment_cost indexes are off by 1 wrt
# csum/bp, since csum/bp are nodes
cost0 = csum[r - 1, c - 1] + alignment_cost[r - 1, c - 1]
cost1 = csum[r, c - 1] + pen
cost2 = csum[r - 1, c] + pen
csum[r, c] = cost0
bp[r, c] = 0
if cost1 < csum[r, c]:
csum[r, c] = cost1
bp[r, c] = 1
if cost2 < csum[r, c]:
csum[r, c] = cost2
bp[r, c] = 2
return csum, bp
def score_path(np.ndarray[int, ndim=1] xx,
np.ndarray[int, ndim=1] yy,
np.ndarray[float, ndim=1] norm1,
np.ndarray[float, ndim=1] norm2,
np.ndarray[float, ndim=2] vecs1,
np.ndarray[float, ndim=2] vecs2,
np.ndarray[float, ndim=1] out):
cdef int xi, yi, ii, jj
cdef float outx
cdef int lenxy = xx.shape[0]
cdef int vecsize = vecs1.shape[1]
for ii in range(lenxy):
xi = xx[ii]
yi = yy[ii]
outx = 0.0
for jj in range(vecsize):
outx += vecs1[xi, jj] * vecs2[yi, jj]
out[ii] = 2.0 * (1.0 - outx) / (norm1[xi] + norm2[yi])
# Bounds checking and wraparound slow things down by about 2x
# Division by 0 checking has minimal speed impact
@cython.boundscheck(False) # turn off bounds-checking for entire function
@cython.wraparound(False) # turn off negative index wrapping for entire function
@cython.cdivision(True) # use c-style division (no division-by-zero check)
def make_sparse_costs(np.ndarray[float, ndim=3] vecs0, # intput: num aligns X num sents X dim
np.ndarray[float, ndim=3] vecs1, # input
np.ndarray[float, ndim=2] norms0, # intput: num aligns X num sents
np.ndarray[float, ndim=2] norms1, # input
x_y_path,
alignment_types,
int width_over2):
"""
Make features for DP, *for lines running across approximate path*, *for each alignment type*
x_offsets, y_offsets should not include (0,1), (1,0)
Basically, we take the feature matrix, rotate it 45 degress,
and compute a "wavy" matrix for the features.
It's like the diagonal but it moves around to hopefully always include the true path.
"""
cdef np.ndarray[int, ndim=2] x_y_path_ = np.array(x_y_path).astype(np.int32)
assert (vecs0.shape[0] == norms0.shape[0])
assert (vecs1.shape[0] == norms1.shape[0])
assert (vecs0.shape[1] == norms0.shape[1])
assert (vecs1.shape[1] == norms1.shape[1])
# check how many overlaps vectors were passed in
num_overlaps_in_vecs0 = vecs0.shape[0]
num_overlaps_in_vecs1 = vecs1.shape[0]
# check how many overlaps were requested
# edge case: alignment_types could be empty
# In that case, we should just return insertions/deletions
# and max_x_overlap == max_y_overlap == 0
max_x_overlap = max([0] + [x for x, y in alignment_types]) # add [0] in case alignment_types is empty
max_y_overlap = max([0] + [y for x, y in alignment_types]) # add [0] in case alignment_types is empty
# note: alignment types are specified 1-based, but vectors are stored 0-based
if max_x_overlap > num_overlaps_in_vecs0:
raise Exception('%d x overlaps requrested (via alignment_types), but vecs0 only has %d' % (
max_x_overlap, num_overlaps_in_vecs0))
if max_y_overlap > num_overlaps_in_vecs1:
raise Exception('%d y overlaps requrested (via alignment_types), but vecs1 only has %d' % (
max_y_overlap, num_overlaps_in_vecs1))
# number of sentences in each document
cdef int xsize = vecs0.shape[1]
cdef int ysize = vecs1.shape[1]
# vector diminsions should match
assert (vecs0.shape[2] == vecs1.shape[2])
cdef np.ndarray[int, ndim=1] x_offsets, y_offsets
x_offsets, y_offsets = make_x_y_offsets(alignment_types)
# reserve outputs
a_len = x_y_path_.shape[0]
b_len = 2 * width_over2
cdef np.ndarray[float, ndim=3] a_b_feats = np.empty((len(alignment_types), a_len, b_len), dtype=np.float32)
cdef np.ndarray[int, ndim=1] b_offset = np.empty(a_len).astype(np.int32)
cdef int x, y, aa, bb, xx, yy, a_idx, b_idx, bb2, x_offset, y_offset, ii_align, x_offset_idx, y_offset_idx
cdef int vecsize = vecs0.shape[2]
cdef int num_alignments = x_offsets.shape[0]
cdef float sumx, feat
cdef float inf = np.inf
for ii in range(x_y_path_.shape[0]):
x = x_y_path_[ii, 0]
y = x_y_path_[ii, 1]
# convert xy to ab cords
aa = x + y
bb = y
a_idx = aa
b_offset[aa] = bb - width_over2
for b_idx, bb2 in enumerate(range(bb - width_over2, bb + width_over2)):
# convert ab to xy cords
xx = aa - bb2
yy = bb2
for ii_align in range(num_alignments):
x_offset = x_offsets[ii_align]
x_offset_idx = x_offset - 1 # overlaps start at 1, vectors stored 0-based
y_offset = y_offsets[ii_align]
y_offset_idx = y_offset - 1
if 0 <= xx < xsize and 0 <= yy < ysize:
sumx = 0.0
for jj in range(vecsize):
sumx += vecs0[x_offset_idx, xx, jj] * vecs1[y_offset_idx, yy, jj]
feat = 2.0 * x_offset * y_offset * (1.0 - sumx) / (
1e-6 + norms0[x_offset_idx, xx] + norms1[y_offset_idx, yy])
else:
feat = inf
a_b_feats[ii_align, a_idx, b_idx] = feat
return a_b_feats, b_offset
def sparse_dp(np.ndarray[float, ndim=3] a_b_costs,
np.ndarray[int, ndim=1] b_offset_in,
alignment_types,
double del_penalty,
int x_in_size,
int y_in_size):
"""
Do DP along a path, using features saved off along path.
x_offsets, y_offsets should not include (0,1), (1,0)
xsize, ysize refer to the costs a_b_csum, but in x/y space
As in the simpler full-DP case,
we compute cumulative costs and backpointers on notes,
and there are COSTS associated with moving between them.
This means the size of the notes +1,+1 larger (in x,y) than the COSTS.
So the size of a_b_csum, a_b_xp, a_b_yp are all one larger in x and y compared to the costs
In order to save memory (and time, vs a sparse matrix with hashes to look up values), let:
a = x + y
b = x - y
b_offsets tells us how far from the left edge the features are computed for.
basically it's like we are computing along the diagonal,
but we shift the diagonal around based on our belief
about where the alignments are.
b_offsets is used for both costs AND csum, backpointers, so it needs to be
+2 longer (it is in the a-direction) than the costs (in the a direction)
"""
cdef np.ndarray[int, ndim=1] x_offsets, y_offsets
x_offsets, y_offsets = make_x_y_offsets(alignment_types)
# make x/y offsets, including (0,1), (1,), i.e. including deletion and insertion
x_offsets = np.concatenate([x_offsets, np.array([0, 1], dtype=np.int32)])
y_offsets = np.concatenate([y_offsets, np.array([1, 0], dtype=np.int32)])
cdef int a_in_size = a_b_costs.shape[1]
cdef int b_in_size = a_b_costs.shape[2]
cdef int a_out_size = a_in_size + 2
cdef int b_out_size = b_in_size
cdef int x_out_size = x_in_size + 1
cdef int y_out_size = y_in_size + 1
# costs are the costs of going between nodes.
# in x,y for the nodes, we basically add a buffer
# at x=0 and y=0, and shift the cost by (x=+1,y=+1)
# In a,b space, this means adding two points (for the buffer)
# at the beginning, and shifting by (a=+0,b=+1) since
# a=x+y and b=y
# for the first two points, we can simply replicate the
# original b_offset, since it should be -width_over2
# i.e. b_offset_in[0] == -width_over2
extra_two_points = np.array([b_offset_in[0], b_offset_in[0]], dtype=np.int32)
cdef np.ndarray[int, ndim=1] b_offset_out = np.concatenate([extra_two_points, b_offset_in + 1])
# outputs
# For anything being used in accumulation, use float64
cdef np.ndarray[double, ndim=2] a_b_csum = np.zeros((a_in_size + 2, b_in_size),
dtype=np.float64) + np.inf # error cumulative sum
cdef np.ndarray[int, ndim=2] a_b_xp = np.zeros((a_in_size + 2, b_in_size), dtype=np.int32) - 2 # backpointer for x
cdef np.ndarray[int, ndim=2] a_b_yp = np.zeros((a_in_size + 2, b_in_size), dtype=np.int32) - 2 # backpointer for y
cdef int num_alignments = x_offsets.shape[0]
cdef double inf = np.inf
cdef int xx_out, yy_out, ii_align, x_offset, y_offset
cdef int aa_in_cost, bb_in_cost, aa_out, bb_out, aa_out_prev, bb_out_prev, xx_in_cost, yy_in_cost, xx_out_prev, yy_out_prev
cdef double alignment_cost, total_cost, prev_cost
# increasing in a is the same as going along diagonals in x/y, so DP order works
# (and any ordering is fine in b - nothing depends on values adjacent on diagonal in x/y)
for aa_out in range(a_in_size + 2):
for bb_out in range(b_in_size):
#xx_out, yy_out = ab2xy_w_offset(aa_out, bb_out, b_offset_out)
yy_out = bb_out + b_offset_out[aa_out]
xx_out = aa_out - yy_out
# edge case: all deletions in y-direction
if xx_out == 0 and 0 <= yy_out < y_out_size:
a_b_csum[aa_out, bb_out] = del_penalty * yy_out
a_b_xp[aa_out, bb_out] = 0
a_b_yp[aa_out, bb_out] = 1
# edge case: all deletions in x-direction
elif yy_out == 0 and 0 <= xx_out < x_out_size:
a_b_csum[aa_out, bb_out] = del_penalty * xx_out
a_b_xp[aa_out, bb_out] = 1
a_b_yp[aa_out, bb_out] = 0
else:
# initialize output to inf
a_b_csum[aa_out, bb_out] = inf
a_b_xp[aa_out, bb_out] = -42
a_b_yp[aa_out, bb_out] = -42
for ii_align in range(num_alignments):
x_offset = x_offsets[ii_align]
y_offset = y_offsets[ii_align]
# coords of location of alignment cost, in input x/y space
xx_in_cost = xx_out - 1 # features were front padded,
yy_in_cost = yy_out - 1 # so offset is always 1
# the coords of location of previous cumsum cost, in input x/y space
xx_out_prev = xx_out - x_offset
yy_out_prev = yy_out - y_offset
if 0 <= xx_in_cost < x_in_size and 0 <= yy_in_cost < y_in_size and 0 <= xx_out_prev < x_out_size and 0 <= yy_out_prev < y_out_size:
# convert x,y to a,b
aa_in_cost = xx_in_cost + yy_in_cost
bb_in_cost = yy_in_cost - b_offset_in[aa_in_cost]
aa_out_prev = xx_out_prev + yy_out_prev
bb_out_prev = yy_out_prev - b_offset_out[aa_out_prev]
if 0 <= aa_in_cost < a_in_size and 0 <= bb_in_cost < b_in_size and 0 <= aa_out_prev < a_out_size and 0 <= bb_out_prev < b_out_size:
if x_offset == 0 or y_offset == 0:
alignment_cost = del_penalty
else:
alignment_cost = a_b_costs[ii_align, aa_in_cost, bb_in_cost]
prev_cost = a_b_csum[aa_out_prev, bb_out_prev]
total_cost = prev_cost + alignment_cost
if total_cost < a_b_csum[aa_out, bb_out]:
a_b_csum[aa_out, bb_out] = total_cost
a_b_xp[aa_out, bb_out] = x_offset
a_b_yp[aa_out, bb_out] = y_offset
return a_b_csum, a_b_xp, a_b_yp, b_offset_out

665
bin/vecalign/dp_utils.py Normal file
View File

@@ -0,0 +1,665 @@
"""
Copyright 2019 Brian Thompson
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
https://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
"""
import logging
import sys
from ast import literal_eval
from collections import OrderedDict
from math import ceil
from time import time
import numpy as np
import pyximport
pyximport.install(setup_args={'include_dirs':np.get_include()}, inplace=True, reload_support=True)
from dp_core import make_dense_costs, score_path, sparse_dp, make_sparse_costs, dense_dp
logger = logging.getLogger('vecalign') # set up in vecalign.py
def preprocess_line(line):
line = line.strip()
if len(line) == 0:
line = 'BLANK_LINE'
return line
def yield_overlaps(lines, num_overlaps):
lines = [preprocess_line(line) for line in lines]
for overlap in range(1, num_overlaps + 1):
for out_line in layer(lines, overlap):
# check must be here so all outputs are unique
out_line2 = out_line[:10000] # limit line so dont encode arbitrarily long sentences
yield out_line2
def read_in_embeddings(text_file, embed_file):
"""
Given a text file with candidate sentences and a corresponing embedding file,
make a maping from candidate sentence to embedding index,
and a numpy array of the embeddings
"""
sent2line = dict()
with open(text_file, 'rt', encoding="utf-8") as fin:
for ii, line in enumerate(fin):
if line.strip() in sent2line:
raise Exception('got multiple embeddings for the same line')
sent2line[line.strip()] = ii
line_embeddings = np.fromfile(embed_file, dtype=np.float32, count=-1)
if line_embeddings.size == 0:
raise Exception('Got empty embedding file')
laser_embedding_size = line_embeddings.size // len(sent2line) # currently hardcoded to 1024
if laser_embedding_size != 1024:
logger.warning('expected an embedding size of 1024, got %s', laser_embedding_size)
logger.info('laser_embedding_size determined to be %d', laser_embedding_size)
line_embeddings.resize(line_embeddings.shape[0] // laser_embedding_size, laser_embedding_size)
return sent2line, line_embeddings
def make_doc_embedding(sent2line, line_embeddings, lines, num_overlaps):
"""
lines: sentences in input document to embed
sent2line, line_embeddings: precomputed embeddings for lines (and overlaps of lines)
"""
lines = [preprocess_line(line) for line in lines]
vecsize = line_embeddings.shape[1]
vecs0 = np.empty((num_overlaps, len(lines), vecsize), dtype=np.float32)
for ii, overlap in enumerate(range(1, num_overlaps + 1)):
for jj, out_line in enumerate(layer(lines, overlap)):
try:
line_id = sent2line[out_line]
except KeyError:
logger.warning('Failed to find overlap=%d line "%s". Will use random vector.', overlap, out_line)
line_id = None
if line_id is not None:
vec = line_embeddings[line_id]
else:
vec = np.random.random(vecsize) - 0.5
vec = vec / np.linalg.norm(vec)
vecs0[ii, jj, :] = vec
return vecs0
def make_norm1(vecs0):
"""
make vectors norm==1 so that cosine distance can be computed via dot product
"""
for ii in range(vecs0.shape[0]):
for jj in range(vecs0.shape[1]):
norm = np.sqrt(np.square(vecs0[ii, jj, :]).sum())
vecs0[ii, jj, :] = vecs0[ii, jj, :] / (norm + 1e-5)
def layer(lines, num_overlaps, comb=' '):
"""
make front-padded overlapping sentences
"""
if num_overlaps < 1:
raise Exception('num_overlaps must be >= 1')
out = ['PAD', ] * min(num_overlaps - 1, len(lines))
for ii in range(len(lines) - num_overlaps + 1):
out.append(comb.join(lines[ii:ii + num_overlaps]))
return out
def read_alignments(fin):
alignments = []
with open(fin, 'rt', encoding="utf-8") as infile:
for line in infile:
fields = [x.strip() for x in line.split(':') if len(x.strip())]
if len(fields) < 2:
raise Exception('Got line "%s", which does not have at least two ":" separated fields' % line.strip())
try:
src = literal_eval(fields[0])
tgt = literal_eval(fields[1])
except:
raise Exception('Failed to parse line "%s"' % line.strip())
alignments.append((src, tgt))
# I know bluealign files have a few entries entries missing,
# but I don't fix them in order to be consistent previous reported scores
return alignments
def print_alignments(alignments, scores=None, file=sys.stdout):
if scores is not None:
for (x, y), s in zip(alignments, scores):
print('%s:%s:%.6f' % (x, y, s), file=file)
else:
for x, y in alignments:
print('%s:%s' % (x, y), file=file)
class DeletionKnob(object):
"""
A good deletion penalty is dependent on normalization, and probably language, domain, etc, etc
I want a way to control deletion penalty that generalizes well...
Sampling costs and use percentile seems to work fairly well.
"""
def __init__(self, samp, res_min, res_max):
self.res_min = res_min
self.res_max = res_max
if self.res_min >= self.res_max:
logger.warning('res_max <= res_min, increasing it')
self.res_max = self.res_min + 1e-4
num_bins = 1000
num_pts = 30
self.hist, self.bin_edges = np.histogram(samp, bins=num_bins,
range=[self.res_min, self.res_max],
density=True)
dx = self.bin_edges[1] - self.bin_edges[0]
self.cdf = np.cumsum(self.hist) * dx
interp_points = [(0, self.res_min), ]
for knob_val in np.linspace(0, 1, num_pts - 1)[1:-1]:
cdf_idx = np.searchsorted(self.cdf, knob_val)
cdf_val = self.res_min + cdf_idx / float(num_bins) * (self.res_max - self.res_min)
interp_points.append((knob_val, cdf_val))
interp_points.append((1, self.res_max))
self.x, self.y = zip(*interp_points)
def percentile_frac_to_del_penalty(self, knob_val):
del_pen = np.interp([knob_val], self.x, self.y)[0]
return del_pen
def make_alignment_types(max_alignment_size):
# return list of all (n,m) where n+m <= this
alignment_types = []
for x in range(1, max_alignment_size):
for y in range(1, max_alignment_size):
if x + y <= max_alignment_size:
alignment_types.append((x, y))
return alignment_types
def ab2xy_w_offset(aa, bb_idx, bb_offset):
bb_from_side = bb_idx + bb_offset[aa]
xx = aa - bb_from_side
yy = bb_from_side
return (xx, yy)
def xy2ab_w_offset(xx, yy, bb_offset):
aa = xx + yy
bb_from_side = yy
bb = bb_from_side - bb_offset[aa]
return aa, bb
def process_scores(scores, alignments):
# floating point sometimes gives negative numbers, which is a little unnerving ...
scores = np.clip(scores, a_min=0, a_max=None)
for ii, (x_algn, y_algn) in enumerate(alignments):
# deletion penalty is pretty arbitrary, just report 0
if len(x_algn) == 0 or len(y_algn) == 0:
scores[ii] = 0.0
# report sores un-normalized by alignment sizes
# (still normalized with random vectors, though)
else:
scores[ii] = scores[ii] / len(x_algn) / len(y_algn)
return scores
def sparse_traceback(a_b_csum, a_b_xp, a_b_yp, b_offset, xsize, ysize):
alignments = []
xx = xsize
yy = ysize
cum_costs = []
while True:
aa, bb = xy2ab_w_offset(xx, yy, b_offset)
cum_costs.append(a_b_csum[aa, bb])
xp = a_b_xp[aa, bb]
yp = a_b_yp[aa, bb]
if xx == yy == 0:
break
if xx < 0 or yy < 0:
raise Exception('traceback bug')
x_side = list(range(xx - xp, xx))
y_side = list(range(yy - yp, yy))
alignments.append((x_side, y_side))
xx = xx - xp
yy = yy - yp
alignments.reverse()
cum_costs.reverse()
costs = np.array(cum_costs[1:]) - np.array(cum_costs[:-1])
# "costs" are scaled by x_alignment_size * y_alignment_size
# and the cost of a deletion is del_penalty
# "scores": 0 for deletion/insertion,
# and cosine distance, *not* scaled
# by len(x_alignment)*len(y_alignment)
scores = process_scores(scores=costs, alignments=alignments)
return alignments, scores
def dense_traceback(x_y_tb):
xsize, ysize = x_y_tb.shape
xx = xsize - 1
yy = ysize - 1
alignments = []
while True:
if xx == yy == 0:
break
bp = x_y_tb[xx, yy]
if bp == 0:
xp, yp = 1, 1
alignments.append(([xx - 1], [yy - 1]))
elif bp == 1:
xp, yp = 0, 1
alignments.append(([], [yy - 1]))
elif bp == 2:
xp, yp = 1, 0
alignments.append(([xx - 1], []))
else:
raise Exception('got unknown value')
xx = xx - xp
yy = yy - yp
alignments.reverse()
return alignments
def append_slant(path, xwidth, ywidth):
"""
Append quantized approximation to a straight line
from current x,y to a point at (x+xwidth, y+ywidth)
"""
NN = xwidth + ywidth
xstart, ystart = path[-1]
for ii in range(1, NN + 1):
x = xstart + round(xwidth * ii / NN)
y = ystart + round(ywidth * ii / NN)
# In the case of ties we want them to round differently,
# so explicitly make sure we take a step of 1, not 0 or 2
lastx, lasty = path[-1]
delta = x + y - lastx - lasty
if delta == 1:
path.append((x, y))
elif delta == 2:
path.append((x - 1, y))
elif delta == 0:
path.append((x + 1, y))
def alignment_to_search_path(algn):
"""
Given an alignment, make searchpath.
Searchpath must step exactly one position in x XOR y at each time step.
In the case of a block of deletions, the order found by DP is not meaningful.
To make things consistent and to improve the probability of recovering
from search errors, we search an approximately straight line
through a block of deletions. We do the same through a many-many
alignment, even though we currently don't refine a many-many alignment...
"""
path = [(0, 0), ]
xdel, ydel = 0, 0
ydel = 0
for x, y in algn:
if len(x) and len(y):
append_slant(path, xdel, ydel)
xdel, ydel = 0, 0
append_slant(path, len(x), len(y))
elif len(x):
xdel += len(x)
elif len(y):
ydel += len(y)
append_slant(path, xdel, ydel)
return path
def extend_alignments(course_alignments, size0, size1):
"""
extend alignments to include new endpoints size0, size1
if alignments are larger than size0/size1, raise exception
"""
# could be a string of deletions or insertions at end, so cannot just grab last one
xmax = 0 # maximum x value in course_alignments
ymax = 0 # maximum y value in course_alignments
for x, y in course_alignments:
for xval in x:
xmax = max(xmax, xval)
for yval in y:
ymax = max(ymax, yval)
if xmax > size0 or ymax > size1:
raise Exception('asked to extend alignments but already bigger than requested')
# do not duplicate xmax/ymax, do include size0/size1
extra_x = list(range(xmax + 1, size0 + 1))
extra_y = list(range(ymax + 1, size1 + 1))
logger.debug('extending alignments in x by %d and y by %d', len(extra_x), len(extra_y))
if len(extra_x) == 0:
for yval in extra_y:
course_alignments.append(([], [yval]))
elif len(extra_y) == 0:
for xval in extra_x:
course_alignments.append(([xval], []))
else:
course_alignments.append((extra_x, extra_y))
def upsample_alignment(algn):
def upsample_one_alignment(xx):
return list(range(min(xx) * 2, (max(xx) + 1) * 2))
new_algn = []
for xx, yy in algn:
if len(xx) == 0:
for yyy in upsample_one_alignment(yy):
new_algn.append(([], [yyy]))
elif len(yy) == 0:
for xxx in upsample_one_alignment(xx):
new_algn.append(([xxx], []))
else:
new_algn.append((upsample_one_alignment(xx), upsample_one_alignment(yy)))
return new_algn
def make_del_knob(e_laser,
f_laser,
e_laser_norms,
f_laser_norms,
sample_size):
e_size = e_laser.shape[0]
f_size = f_laser.shape[0]
if e_size > 0 and f_size > 0 and sample_size > 0:
if e_size * f_size < sample_size:
# dont sample, just compute full matrix
sample_size = e_size * f_size
x_idxs = np.zeros(sample_size, dtype=np.int32)
y_idxs = np.zeros(sample_size, dtype=np.int32)
c = 0
for ii in range(e_size):
for jj in range(f_size):
x_idxs[c] = ii
y_idxs[c] = jj
c += 1
else:
# get random samples
x_idxs = np.random.choice(range(e_size), size=sample_size, replace=True).astype(np.int32)
y_idxs = np.random.choice(range(f_size), size=sample_size, replace=True).astype(np.int32)
# output
random_scores = np.empty(sample_size, dtype=np.float32)
score_path(x_idxs, y_idxs,
e_laser_norms, f_laser_norms,
e_laser, f_laser,
random_scores, )
min_score = 0
max_score = max(random_scores) # could bump this up... but its probably fine
else:
# Not much we can do here...
random_scores = np.array([0.0, 0.5, 1.0]) # ???
min_score = 0
max_score = 1 # ????
del_knob = DeletionKnob(random_scores, min_score, max_score)
return del_knob
def compute_norms(vecs0, vecs1, num_samples, overlaps_to_use=None):
# overlaps_to_use = 10 # 10 matches before
overlaps1, size1, dim = vecs1.shape
overlaps0, size0, dim0 = vecs0.shape
assert (dim == dim0)
if overlaps_to_use is not None:
if overlaps_to_use > overlaps1:
raise Exception('Cannot use more overlaps than provided. You may want to re-run make_verlaps.py with a larger -n value')
else:
overlaps_to_use = overlaps1
samps_per_overlap = ceil(num_samples / overlaps_to_use)
if size1 and samps_per_overlap:
# sample other size (from all overlaps) to compre to this side
vecs1_rand_sample = np.empty((samps_per_overlap * overlaps_to_use, dim), dtype=np.float32)
for overlap_ii in range(overlaps_to_use):
idxs = np.random.choice(range(size1), size=samps_per_overlap, replace=True)
random_vecs = vecs1[overlap_ii, idxs, :]
vecs1_rand_sample[overlap_ii * samps_per_overlap:(overlap_ii + 1) * samps_per_overlap, :] = random_vecs
norms0 = np.empty((overlaps0, size0), dtype=np.float32)
for overlap_ii in range(overlaps0):
e_laser = vecs0[overlap_ii, :, :]
sim = np.matmul(e_laser, vecs1_rand_sample.T)
norms0[overlap_ii, :] = 1.0 - sim.mean(axis=1)
else: # no samples, no normalization
norms0 = np.ones((overlaps0, size0)).astype(np.float32)
return norms0
def downsample_vectors(vecs1):
a, b, c = vecs1.shape
half = np.empty((a, b // 2, c), dtype=np.float32)
for ii in range(a):
# average consecutive vectors
for jj in range(0, b - b % 2, 2):
v1 = vecs1[ii, jj, :]
v2 = vecs1[ii, jj + 1, :]
half[ii, jj // 2, :] = v1 + v2
# compute mean for all vectors
mean = np.mean(half[ii, :, :], axis=0)
for jj in range(0, b - b % 2, 2):
# remove mean
half[ii, jj // 2, :] = half[ii, jj // 2, :] - mean
# make vectors norm==1 so dot product is cosine distance
make_norm1(half)
return half
def vecalign(vecs0,
vecs1,
final_alignment_types,
del_percentile_frac,
width_over2,
max_size_full_dp,
costs_sample_size,
num_samps_for_norm,
norms0=None,
norms1=None):
if width_over2 < 3:
logger.warning('width_over2 was set to %d, which does not make sense. increasing to 3.', width_over2)
width_over2 = 3
# make sure input embeddings are norm==1
make_norm1(vecs0)
make_norm1(vecs1)
# save off runtime stats for summary
runtimes = OrderedDict()
# Determine stack depth
s0, s1 = vecs0.shape[1], vecs1.shape[1]
max_depth = 0
while s0 * s1 > max_size_full_dp ** 2:
max_depth += 1
s0 = s0 // 2
s1 = s1 // 2
# init recursion stack
# depth is 0-based (full size is 0, 1 is half, 2 is quarter, etc)
stack = {0: {'v0': vecs0, 'v1': vecs1}}
# downsample sentence vectors
t0 = time()
for depth in range(1, max_depth + 1):
stack[depth] = {'v0': downsample_vectors(stack[depth - 1]['v0']),
'v1': downsample_vectors(stack[depth - 1]['v1'])}
runtimes['Downsample embeddings'] = time() - t0
# compute norms for all depths, add sizes, add alignment types
t0 = time()
for depth in stack:
stack[depth]['size0'] = stack[depth]['v0'].shape[1]
stack[depth]['size1'] = stack[depth]['v1'].shape[1]
stack[depth]['alignment_types'] = final_alignment_types if depth == 0 else [(1, 1)]
if depth == 0 and norms0 is not None:
if norms0.shape != vecs0.shape[:2]:
print('norms0.shape:', norms0.shape)
print('vecs0.shape[:2]:', vecs0.shape[:2])
raise Exception('norms0 wrong shape')
stack[depth]['n0'] = norms0
else:
stack[depth]['n0'] = compute_norms(stack[depth]['v0'], stack[depth]['v1'], num_samps_for_norm)
if depth == 0 and norms1 is not None:
if norms1.shape != vecs1.shape[:2]:
print('norms1.shape:', norms1.shape)
print('vecs1.shape[:2]:', vecs1.shape[:2])
raise Exception('norms1 wrong shape')
stack[depth]['n1'] = norms1
else:
stack[depth]['n1'] = compute_norms(stack[depth]['v1'], stack[depth]['v0'], num_samps_for_norm)
runtimes['Normalize embeddings'] = time() - t0
# Compute deletion penalty for all depths
t0 = time()
for depth in stack:
stack[depth]['del_knob'] = make_del_knob(e_laser=stack[depth]['v0'][0, :, :],
f_laser=stack[depth]['v1'][0, :, :],
e_laser_norms=stack[depth]['n0'][0, :],
f_laser_norms=stack[depth]['n1'][0, :],
sample_size=costs_sample_size)
stack[depth]['del_penalty'] = stack[depth]['del_knob'].percentile_frac_to_del_penalty(del_percentile_frac)
logger.debug('del_penalty at depth %d: %f', depth, stack[depth]['del_penalty'])
runtimes['Compute deletion penalties'] = time() - t0
tt = time() - t0
logger.debug('%d x %d full DP make features: %.6fs (%.3e per dot product)',
stack[max_depth]['size0'], stack[max_depth]['size1'], tt,
tt / (stack[max_depth]['size0'] + 1e-6) / (stack[max_depth]['size1'] + 1e-6))
# full DP at maximum recursion depth
t0 = time()
stack[max_depth]['costs_1to1'] = make_dense_costs(stack[max_depth]['v0'],
stack[max_depth]['v1'],
stack[max_depth]['n0'],
stack[max_depth]['n1'])
runtimes['Full DP make features'] = time() - t0
t0 = time()
_, stack[max_depth]['x_y_tb'] = dense_dp(stack[max_depth]['costs_1to1'], stack[max_depth]['del_penalty'])
stack[max_depth]['alignments'] = dense_traceback(stack[max_depth]['x_y_tb'])
runtimes['Full DP'] = time() - t0
# upsample the path up to the top resolution
compute_costs_times = []
dp_times = []
upsample_depths = [0, ] if max_depth == 0 else list(reversed(range(0, max_depth)))
for depth in upsample_depths:
if max_depth > 0: # upsample previoius alignment to current resolution
course_alignments = upsample_alignment(stack[depth + 1]['alignments'])
# features may have been truncated when downsampleing, so alignment may need extended
extend_alignments(course_alignments, stack[depth]['size0'], stack[depth]['size1']) # in-place
else: # We did a full size 1-1 search, so search same size with more alignment types
course_alignments = stack[0]['alignments']
# convert couse alignments to a searchpath
stack[depth]['searchpath'] = alignment_to_search_path(course_alignments)
# compute ccosts for sparse DP
t0 = time()
stack[depth]['a_b_costs'], stack[depth]['b_offset'] = make_sparse_costs(stack[depth]['v0'], stack[depth]['v1'],
stack[depth]['n0'], stack[depth]['n1'],
stack[depth]['searchpath'],
stack[depth]['alignment_types'],
width_over2)
tt = time() - t0
num_dot_products = len(stack[depth]['b_offset']) * len(stack[depth]['alignment_types']) * width_over2 * 2
logger.debug('%d x %d sparse DP (%d alignment types, %d window) make features: %.6fs (%.3e per dot product)',
stack[max_depth]['size0'], stack[max_depth]['size1'],
len(stack[depth]['alignment_types']), width_over2 * 2,
tt, tt / (num_dot_products + 1e6))
compute_costs_times.append(time() - t0)
t0 = time()
# perform sparse DP
stack[depth]['a_b_csum'], stack[depth]['a_b_xp'], stack[depth]['a_b_yp'], \
stack[depth]['new_b_offset'] = sparse_dp(stack[depth]['a_b_costs'], stack[depth]['b_offset'],
stack[depth]['alignment_types'], stack[depth]['del_penalty'],
stack[depth]['size0'], stack[depth]['size1'])
# performace traceback to get alignments and alignment scores
# for debugging, avoid overwriting stack[depth]['alignments']
akey = 'final_alignments' if depth == 0 else 'alignments'
stack[depth][akey], stack[depth]['alignment_scores'] = sparse_traceback(stack[depth]['a_b_csum'],
stack[depth]['a_b_xp'],
stack[depth]['a_b_yp'],
stack[depth]['new_b_offset'],
stack[depth]['size0'],
stack[depth]['size1'])
dp_times.append(time() - t0)
runtimes['Upsample DP compute costs'] = sum(compute_costs_times[:-1])
runtimes['Upsample DP'] = sum(dp_times[:-1])
runtimes['Final DP compute costs'] = compute_costs_times[-1]
runtimes['Final DP'] = dp_times[-1]
# log time stats
max_key_str_len = max([len(key) for key in runtimes])
for key in runtimes:
if runtimes[key] > 5e-5:
logger.info(key + ' took ' + '.' * (max_key_str_len + 5 - len(key)) + ('%.4fs' % runtimes[key]).rjust(7))
return stack

61
bin/vecalign/overlap.py Normal file
View File

@@ -0,0 +1,61 @@
#!/usr/bin/env python3
"""
Copyright 2019 Brian Thompson
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
https://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
"""
import argparse
from dp_utils import yield_overlaps
def go(output_file, input_files, num_overlaps):
output = set()
for fin in input_files:
lines = open(fin, 'rt', encoding="utf-8").readlines()
for out_line in yield_overlaps(lines, num_overlaps):
output.add(out_line)
# for reproducibility
output = list(output)
output.sort()
with open(output_file, 'wt', encoding="utf-8") as fout:
for line in output:
fout.write(line + '\n')
def _main():
parser = argparse.ArgumentParser('Create text file containing overlapping sentences.',
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument('-i', '--inputs', type=str, nargs='+',
help='input text file(s).')
parser.add_argument('-o', '--output', type=str,
help='output text file containing overlapping sentneces')
parser.add_argument('-n', '--num_overlaps', type=int, default=4,
help='Maximum number of allowed overlaps.')
args = parser.parse_args()
go(output_file=args.output,
num_overlaps=args.num_overlaps,
input_files=args.inputs)
if __name__ == '__main__':
_main()

170
bin/vecalign/score.py Normal file
View File

@@ -0,0 +1,170 @@
#!/usr/bin/env python3
"""
Copyright 2019 Brian Thompson
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
https://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
"""
import argparse
import sys
from collections import defaultdict
import numpy as np
from dp_utils import read_alignments
"""
Faster implementation of lax and strict precision and recall, based on
https://www.aclweb.org/anthology/W11-4624/.
"""
def _precision(goldalign, testalign):
"""
Computes tpstrict, fpstrict, tplax, fplax for gold/test alignments
"""
tpstrict = 0 # true positive strict counter
tplax = 0 # true positive lax counter
fpstrict = 0 # false positive strict counter
fplax = 0 # false positive lax counter
# convert to sets, remove alignments empty on both sides
testalign = set([(tuple(x), tuple(y)) for x, y in testalign if len(x) or len(y)])
goldalign = set([(tuple(x), tuple(y)) for x, y in goldalign if len(x) or len(y)])
# mappings from source test sentence idxs to
# target gold sentence idxs for which the source test sentence
# was found in corresponding source gold alignment
src_id_to_gold_tgt_ids = defaultdict(set)
for gold_src, gold_tgt in goldalign:
for gold_src_id in gold_src:
for gold_tgt_id in gold_tgt:
src_id_to_gold_tgt_ids[gold_src_id].add(gold_tgt_id)
for (test_src, test_target) in testalign:
if (test_src, test_target) == ((), ()):
continue
if (test_src, test_target) in goldalign:
# strict match
tpstrict += 1
tplax += 1
else:
# For anything with partial gold/test overlap on the source,
# see if there is also partial overlap on the gold/test target
# If so, its a lax match
target_ids = set()
for src_test_id in test_src:
for tgt_id in src_id_to_gold_tgt_ids[src_test_id]:
target_ids.add(tgt_id)
if set(test_target).intersection(target_ids):
fpstrict += 1
tplax += 1
else:
fpstrict += 1
fplax += 1
return np.array([tpstrict, fpstrict, tplax, fplax], dtype=np.int32)
def score_multiple(gold_list, test_list, value_for_div_by_0=0.0):
# accumulate counts for all gold/test files
pcounts = np.array([0, 0, 0, 0], dtype=np.int32)
rcounts = np.array([0, 0, 0, 0], dtype=np.int32)
for goldalign, testalign in zip(gold_list, test_list):
pcounts += _precision(goldalign=goldalign, testalign=testalign)
# recall is precision with no insertion/deletion and swap args
test_no_del = [(x, y) for x, y in testalign if len(x) and len(y)]
gold_no_del = [(x, y) for x, y in goldalign if len(x) and len(y)]
rcounts += _precision(goldalign=test_no_del, testalign=gold_no_del)
# Compute results
# pcounts: tpstrict,fnstrict,tplax,fnlax
# rcounts: tpstrict,fpstrict,tplax,fplax
if pcounts[0] + pcounts[1] == 0:
pstrict = value_for_div_by_0
else:
pstrict = pcounts[0] / float(pcounts[0] + pcounts[1])
if pcounts[2] + pcounts[3] == 0:
plax = value_for_div_by_0
else:
plax = pcounts[2] / float(pcounts[2] + pcounts[3])
if rcounts[0] + rcounts[1] == 0:
rstrict = value_for_div_by_0
else:
rstrict = rcounts[0] / float(rcounts[0] + rcounts[1])
if rcounts[2] + rcounts[3] == 0:
rlax = value_for_div_by_0
else:
rlax = rcounts[2] / float(rcounts[2] + rcounts[3])
if (pstrict + rstrict) == 0:
fstrict = value_for_div_by_0
else:
fstrict = 2 * (pstrict * rstrict) / (pstrict + rstrict)
if (plax + rlax) == 0:
flax = value_for_div_by_0
else:
flax = 2 * (plax * rlax) / (plax + rlax)
result = dict(recall_strict=rstrict,
recall_lax=rlax,
precision_strict=pstrict,
precision_lax=plax,
f1_strict=fstrict,
f1_lax=flax)
return result
def log_final_scores(res):
print(' ---------------------------------', file=sys.stderr)
print('| | Strict | Lax |', file=sys.stderr)
print('| Precision | {precision_strict:.3f} | {precision_lax:.3f} |'.format(**res), file=sys.stderr)
print('| Recall | {recall_strict:.3f} | {recall_lax:.3f} |'.format(**res), file=sys.stderr)
print('| F1 | {f1_strict:.3f} | {f1_lax:.3f} |'.format(**res), file=sys.stderr)
print(' ---------------------------------', file=sys.stderr)
def main():
parser = argparse.ArgumentParser(
'Compute strict/lax precision and recall for one or more pairs of gold/test alignments',
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument('-t', '--test', type=str, nargs='+', required=True,
help='one or more test alignment files')
parser.add_argument('-g', '--gold', type=str, nargs='+', required=True,
help='one or more gold alignment files')
args = parser.parse_args()
if len(args.test) != len(args.gold):
raise Exception('number of gold/test files must be the same')
gold_list = [read_alignments(x) for x in args.gold]
test_list = [read_alignments(x) for x in args.test]
res = score_multiple(gold_list=gold_list, test_list=test_list)
log_final_scores(res)
if __name__ == '__main__':
main()

165
bin/vecalign/vecalign.py Normal file
View File

@@ -0,0 +1,165 @@
#!/usr/bin/env python3
"""
Copyright 2019 Brian Thompson
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
https://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
"""
import argparse
import logging
import pickle
from math import ceil
from random import seed as seed
import numpy as np
logger = logging.getLogger('vecalign')
logger.setLevel(logging.WARNING)
logFormatter = logging.Formatter("%(asctime)s %(levelname)-5.5s %(message)s")
consoleHandler = logging.StreamHandler()
consoleHandler.setFormatter(logFormatter)
logger.addHandler(consoleHandler)
from dp_utils import make_alignment_types, print_alignments, read_alignments, \
read_in_embeddings, make_doc_embedding, vecalign
from score import score_multiple, log_final_scores
def _main():
# make runs consistent
seed(42)
np.random.seed(42)
parser = argparse.ArgumentParser('Sentence alignment using sentence embeddings and FastDTW',
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
#parser.add_argument('-s', '--src', type=str, nargs='+', required=True,
# help='preprocessed source file to align')
#parser.add_argument('-t', '--tgt', type=str, nargs='+', required=True,
# help='preprocessed target file to align')
parser.add_argument('--job', type=str, required=True, help='Job file for alignment task.')
parser.add_argument('-g', '--gold_alignment', type=str, nargs='+', required=False,
help='preprocessed target file to align')
parser.add_argument('--src_embed', type=str, nargs=2, required=True,
help='Source embeddings. Requires two arguments: first is a text file, sencond is a binary embeddings file. ')
parser.add_argument('--tgt_embed', type=str, nargs=2, required=True,
help='Target embeddings. Requires two arguments: first is a text file, sencond is a binary embeddings file. ')
parser.add_argument('-a', '--alignment_max_size', type=int, default=5,
help='Searches for alignments up to size N-M, where N+M <= this value. Note that the the embeddings must support the requested number of overlaps')
parser.add_argument('-d', '--del_percentile_frac', type=float, default=0.2,
help='Deletion penalty is set to this percentile (as a fraction) of the cost matrix distribution. Should be between 0 and 1.')
parser.add_argument('-v', '--verbose', help='sets consle to logging.DEBUG instead of logging.WARN',
action='store_true')
parser.add_argument('--max_size_full_dp', type=int, default=300,
help='Maximum size N for which is is acceptable to run full N^2 dynamic programming.')
parser.add_argument('--costs_sample_size', type=int, default=20000,
help='Sample size to estimate costs distribution, used to set deletion penalty in conjunction with deletion_percentile.')
parser.add_argument('--num_samps_for_norm', type=int, default=100,
help='Number of samples used for normalizing embeddings')
parser.add_argument('--search_buffer_size', type=int, default=5,
help='Width (one side) of search buffer. Larger values makes search more likely to recover from errors but increases runtime.')
parser.add_argument('--debug_save_stack', type=str,
help='Write stack to pickle file for debug purposes')
args = parser.parse_args()
#if len(args.src) != len(args.tgt):
# raise Exception('number of source files must match number of target files')
#if args.gold_alignment is not None:
# if len(args.gold_alignment) != len(args.src):
# raise Exception('number of gold alignment files, if provided, must match number of source and target files')
if args.verbose:
import logging
logger.setLevel(logging.INFO)
if args.alignment_max_size < 2:
logger.warning('Alignment_max_size < 2. Increasing to 2 so that 1-1 alignments will be considered')
args.alignment_max_size = 2
src_sent2line, src_line_embeddings = read_in_embeddings(args.src_embed[0], args.src_embed[1])
tgt_sent2line, tgt_line_embeddings = read_in_embeddings(args.tgt_embed[0], args.tgt_embed[1])
width_over2 = ceil(args.alignment_max_size / 2.0) + args.search_buffer_size
test_alignments = []
stack_list = []
# read in alignment jobs
job = read_job(args.job)
#for src_file, tgt_file in zip(args.src, args.tgt):
for rec in job:
#logger.info('Aligning src="%s" to tgt="%s"', src_file, tgt_file)
src_file, tgt_file, align_file = rec.split("\t")
print("Aligning {} to {}".format(src_file, tgt_file))
src_lines = open(src_file, 'rt', encoding="utf-8").readlines()
vecs0 = make_doc_embedding(src_sent2line, src_line_embeddings, src_lines, args.alignment_max_size)
tgt_lines = open(tgt_file, 'rt', encoding="utf-8").readlines()
vecs1 = make_doc_embedding(tgt_sent2line, tgt_line_embeddings, tgt_lines, args.alignment_max_size)
final_alignment_types = make_alignment_types(args.alignment_max_size)
logger.debug('Considering alignment types %s', final_alignment_types)
stack = vecalign(vecs0=vecs0,
vecs1=vecs1,
final_alignment_types=final_alignment_types,
del_percentile_frac=args.del_percentile_frac,
width_over2=width_over2,
max_size_full_dp=args.max_size_full_dp,
costs_sample_size=args.costs_sample_size,
num_samps_for_norm=args.num_samps_for_norm)
# write final alignments to stdout
#print_alignments(stack[0]['final_alignments'], stack[0]['alignment_scores'])
out_f = open(align_file, 'w', encoding="utf-8")
#print_alignments(stack[0]['final_alignments'], stack[0]['alignment_scores'],file=out_f)
print_alignments(stack[0]['final_alignments'],file=out_f)
#test_alignments.append(stack[0]['final_alignments'])
#stack_list.append(stack)
#if args.gold_alignment is not None:
# gold_list = [read_alignments(x) for x in args.gold_alignment]
# res = score_multiple(gold_list=gold_list, test_list=test_alignments)
# log_final_scores(res)
#if args.debug_save_stack:
# pickle.dump(stack_list, open(args.debug_save_stack, 'wb'))
def read_job(file):
job = []
with open(file, 'r', encoding="utf-8") as f:
for line in f:
if not line.startswith("#"):
job.append(line.strip())
return job
if __name__ == '__main__':
_main()