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Bertalign and evaluation scripts

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nlpfun
2021-11-28 13:58:26 +08:00
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# 2021/11/27
# bfsujason@163.com
"""
Usage:
python bin/gale_align.py \
-m data/mac/test/meta_data.tsv \
-s data/mac/test/zh \
-t data/mac/test/en \
-o data/mac/test/auto
"""
import os
import time
import math
import shutil
import argparse
import numba as nb
import numpy as np
def main():
# user-defined parameters
parser = argparse.ArgumentParser(description='Sentence alignment using Gale-Church Algrorithm')
parser.add_argument('-s', '--src', type=str, required=True, help='Source directory.')
parser.add_argument('-t', '--tgt', type=str, required=True, help='Target directory.')
parser.add_argument('-o', '--out', type=str, required=True, help='Output directory.')
parser.add_argument('-m', '--meta', type=str, required=True, help='Metadata file.')
args = parser.parse_args()
make_dir(args.out)
# fixed parameters to determine the window size for alignment
min_win_size = 10
max_win_size = 600
win_per_100 = 8
# alignment types
align_types = np.array(
[
[0,1],
[1,0],
[1,1],
[1,2],
[2,1],
[2,2],
], dtype=np.int)
# prior probability
priors = np.array([0, 0.0099, 0.89, 0.089, 0.011])
# mean and variance
c = 1
s2 = 6.8
# perform gale-church align
jobs = create_jobs(args.meta, args.src, args.tgt, args.out)
for rec in jobs:
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()
tgt_lines = open(tgt_file, 'rt', encoding="utf-8").readlines()
src_len = calculate_txt_len(src_lines)
tgt_len = calculate_txt_len(tgt_lines)
m = src_len.shape[0] - 1
n = tgt_len.shape[0] - 1
# find search path
w, search_path = find_search_path(m, n, min_win_size, max_win_size, win_per_100)
cost, back = align(src_len, tgt_len, w, search_path, align_types, priors, c, s2)
alignments = back_track(m, n, back, search_path, align_types)
# save alignments
save_alignments(alignments, align_file)
def save_alignments(alignments, file):
with open(file, 'wt', encoding='utf-8') as f:
for id in alignments:
f.write("{}:{}\n".format(id[0], id[1]))
def back_track(i, j, b, search_path, a_types):
alignment = []
while ( i !=0 and j != 0 ):
j_offset = j - search_path[i][0]
a = b[i][j_offset]
s = a_types[a][0]
t = a_types[a][1]
src_range = [i - offset - 1 for offset in range(s)][::-1]
tgt_range = [j - offset - 1 for offset in range(t)][::-1]
alignment.append((src_range, tgt_range))
i = i-s
j = j-t
return alignment[::-1]
@nb.jit(nopython=True, fastmath=True, cache=True)
def align(src_len, tgt_len, w, search_path, align_types, priors, c, s2):
#initialize cost and backpointer matrix
m = src_len.shape[0] - 1
cost = np.zeros((m + 1, 2 * w + 1))
back = np.zeros((m + 1, 2 * w + 1), dtype=nb.int64)
cost[0][0] = 0
back[0][0] = -1
for i in range(m + 1):
i_start = search_path[i][0]
i_end = search_path[i][1]
for j in range(i_start, i_end + 1):
if i + j == 0:
continue
best_score = np.inf
best_a = -1
for a in range(align_types.shape[0]):
a_1 = align_types[a][0]
a_2 = align_types[a][1]
prev_i = i - a_1
prev_j = j - a_2
if prev_i < 0 or prev_j < 0 : # no previous cell
continue
prev_i_start = search_path[prev_i][0]
prev_i_end = search_path[prev_i][1]
if prev_j < prev_i_start or prev_j > prev_i_end: # out of bound of cost matrix
continue
prev_j_offset = prev_j - prev_i_start
score = cost[prev_i][prev_j_offset] - math.log(priors[a_1 + a_2]) + \
get_score(src_len[i] - src_len[i - a_1], tgt_len[j] - tgt_len[j - a_2], c, s2)
if score < best_score:
best_score = score
best_a = a
j_offset = j - i_start
cost[i][j_offset] = best_score
back[i][j_offset] = best_a
return cost, back
@nb.jit(nopython=True, fastmath=True, cache=True)
def get_score(len_s, len_t, c, s2):
mean = (len_s + len_t / c) / 2
z = (len_t - len_s * c) / math.sqrt(mean * s2)
pd = 2 * (1 - norm_cdf(abs(z)))
if pd > 0:
return -math.log(pd)
return 25
@nb.jit(nopython=True, fastmath=True, cache=True)
def find_search_path(src_len, tgt_len, min_win_size, max_win_size, win_per_100):
yx_ratio = tgt_len / src_len
win_size_1 = int(yx_ratio * tgt_len * win_per_100 / 100)
win_size_2 = int(abs(tgt_len - src_len) * 3/4)
w_1 = min(max(min_win_size, max(win_size_1, win_size_2)), max_win_size)
w_2 = int(max(src_len, tgt_len) * 0.06)
w = max(w_1, w_2)
search_path = np.zeros((src_len + 1, 2), dtype=nb.int64)
for i in range(0, src_len + 1):
center = int(yx_ratio * i)
w_start = max(0, center - w)
w_end = min(center + w, tgt_len)
search_path[i] = [w_start, w_end]
return w, search_path
@nb.jit(nopython=True, fastmath=True, cache=True)
def norm_cdf(z):
t = 1/float(1+0.2316419*z) # t = 1/(1+pz) , z=0.2316419
p_norm = 1 - 0.3989423*math.exp(-z*z/2) * ((0.319381530 * t)+ \
(-0.356563782 * t)+ \
(1.781477937 * t) + \
(-1.821255978* t) + \
(1.330274429 * t))
return p_norm
def calculate_txt_len(lines):
txt_len = []
txt_len.append(0)
for i, line in enumerate(lines):
# UTF-8 byte length
txt_len.append(txt_len[i] + len(line.strip().encode("utf-8")))
return np.array(txt_len)
def create_jobs(meta, src, tgt, out):
jobs = []
fns = get_fns(meta)
for file in fns:
src_path = os.path.abspath(os.path.join(src, file))
tgt_path = os.path.abspath(os.path.join(tgt, file))
out_path = os.path.abspath(os.path.join(out, file + '.align'))
jobs.append('\t'.join([src_path, tgt_path, out_path]))
return jobs
def get_fns(meta):
fns = []
with open(meta, 'rt', encoding='utf-8') as f:
next(f) # skip header
for line in f:
recs = line.strip().split('\t')
fns.append(recs[0])
return fns
def make_dir(path):
if os.path.isdir(path):
shutil.rmtree(path)
os.makedirs(path, exist_ok=True)
if __name__ == '__main__':
t_0 = time.time()
main()
print("It takes {:.3f} seconds to align all the sentences.".format(time.time() - t_0))