1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
|
import argparse
import csv
import random
import numpy as np
from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer
from sklearn.preprocessing import normalize
from Wasserstein_Distance import (WassersteinMatcher, WassersteinRetriever,
load_embeddings, process_corpus)
def main(args):
np.seterr(divide="ignore") # POT has issues with divide by zero errors
source_lang = args.source_lang
target_lang = args.target_lang
source_vectors_filename = args.source_vector
target_vectors_filename = args.target_vector
vectors_source = load_embeddings(source_vectors_filename)
vectors_target = load_embeddings(target_vectors_filename)
source_defs_filename = args.source_defs
target_defs_filename = args.target_defs
batch = args.batch
input_mode = args.mode
input_paradigm = args.paradigm
run_method = list()
run_paradigm = list()
if input_paradigm == "all":
run_paradigm.extend(("matching", "retrieval"))
else:
run_paradigm.append(input_paradigm)
if input_mode == "all":
run_method.extend(["wmd", "snk"])
else:
run_method.append(input_mode)
defs_source = [
line.rstrip("\n") for line in open(source_defs_filename, encoding="utf8")
]
defs_target = [
line.rstrip("\n") for line in open(target_defs_filename, encoding="utf8")
]
clean_src_corpus, clean_src_vectors, src_keys = process_corpus(
set(vectors_source.keys()), defs_source, vectors_source, source_lang
)
clean_target_corpus, clean_target_vectors, target_keys = process_corpus(
set(vectors_target.keys()), defs_target, vectors_target, target_lang
)
take = args.instances
common_keys = set(src_keys).intersection(set(target_keys))
take = min(len(common_keys), take) # you can't sample more than length
experiment_keys = random.sample(common_keys, take)
instances = len(experiment_keys)
clean_src_corpus = list(clean_src_corpus[experiment_keys])
clean_target_corpus = list(clean_target_corpus[experiment_keys])
if not batch:
print(
f"{source_lang} - {target_lang} "
+ f" document sizes: {len(clean_src_corpus)}, {len(clean_target_corpus)}"
)
del vectors_source, vectors_target, defs_source, defs_target
vec = CountVectorizer().fit(clean_src_corpus + clean_target_corpus)
common = [
word
for word in vec.get_feature_names()
if word in clean_src_vectors or word in clean_target_vectors
]
W_common = []
for w in common:
if w in clean_src_vectors:
W_common.append(np.array(clean_src_vectors[w]))
else:
W_common.append(np.array(clean_target_vectors[w]))
if not batch:
print(f"{source_lang} - {target_lang}: the vocabulary size is {len(W_common)}")
W_common = np.array(W_common)
W_common = normalize(W_common)
vect = TfidfVectorizer(vocabulary=common, dtype=np.double, norm=None)
vect.fit(clean_src_corpus + clean_target_corpus)
X_train_idf = vect.transform(clean_src_corpus)
X_test_idf = vect.transform(clean_target_corpus)
for paradigm in run_paradigm:
WassersteinDriver = None
if paradigm == "matching":
WassersteinDriver = WassersteinMatcher
else:
WassersteinDriver = WassersteinRetriever
for metric in run_method:
if not batch:
print(f"{metric}: {source_lang} - {target_lang}")
clf = WassersteinDriver(
W_embed=W_common, n_neighbors=5, n_jobs=14, sinkhorn=(metric == "snk")
)
clf.fit(X_train_idf[:instances], np.ones(instances))
p_at_one, percentage = clf.align(
X_test_idf[:instances], n_neighbors=instances
)
if not batch:
print(f"P @ 1: {p_at_one}\ninstances: {instances}\n{percentage}%")
else:
fields = [
f"{source_lang}",
f"{target_lang}",
f"{instances}",
f"{p_at_one}",
f"{percentage}",
]
with open(f"{metric}_{paradigm}_results.csv", "a") as f:
writer = csv.writer(f)
writer.writerow(fields)
if __name__ == "__main__":
parser = argparse.ArgumentParser(
description="align dictionaries using wmd and wasserstein distance"
)
parser.add_argument("source_lang", help="source language short name")
parser.add_argument("target_lang", help="target language short name")
parser.add_argument("source_vector", help="path of the source vector")
parser.add_argument("target_vector", help="path of the target vector")
parser.add_argument("source_defs", help="path of the source definitions")
parser.add_argument("target_defs", help="path of the target definitions")
parser.add_argument(
"-b",
"--batch",
action="store_true",
help="running in batch (store results in csv) or"
+ "running a single instance (output the results)",
)
parser.add_argument(
"mode",
choices=["all", "wmd", "snk"],
default="all",
help="which methods to run",
)
parser.add_argument(
"paradigm",
choices=["all", "retrieval", "matching"],
default="all",
help="which paradigms to align with",
)
parser.add_argument(
"-n",
"--instances",
help="number of instances in each language to retrieve",
default=1000,
type=int,
)
args = parser.parse_args()
main(args)
|
