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import argparse
import csv
import random
import numpy as np
from lapjv import lapjv
from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer
from sklearn.preprocessing import normalize
from Wasserstein_Distance import load_embeddings, process_corpus
def main(args):
run_paradigm = list()
if args.paradigm == "all":
run_paradigm.extend(("matching", "retrieval"))
else:
run_paradigm.append(args.paradigm)
source_lang = args.source_lang
target_lang = args.target_lang
batch = args.batch
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
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_source_corpus, clean_source_vectors, source_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(source_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_source_corpus = list(clean_source_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_source_corpus)}, {len(clean_target_corpus)}"
)
del vectors_source, vectors_target, defs_source, defs_target
vocab_counter = CountVectorizer().fit(clean_source_corpus + clean_target_corpus)
common = [
w
for w in vocab_counter.get_feature_names()
if w in clean_source_vectors or w in clean_target_vectors
]
W_common = []
for w in common:
if w in clean_source_vectors:
W_common.append(np.array(clean_source_vectors[w]))
else:
W_common.append(np.array(clean_target_vectors[w]))
W_common = np.array(W_common)
W_common = normalize(W_common) # default is l2
vect_tfidf = TfidfVectorizer(vocabulary=common, dtype=np.double, norm="l2")
vect_tfidf.fit(clean_source_corpus + clean_target_corpus)
X_idf_source = vect_tfidf.transform(clean_source_corpus)
X_idf_target = vect_tfidf.transform(clean_target_corpus)
X_idf_source_array = X_idf_source.toarray()
X_idf_target_array = X_idf_target.toarray()
S_emb_source = np.matmul(X_idf_source_array, W_common)
S_emb_target = np.matmul(X_idf_target_array, W_common)
S_emb_target_transpose = np.transpose(S_emb_target)
cost_matrix = np.matmul(S_emb_source, S_emb_target_transpose)
for paradigm in run_paradigm:
if paradigm == "matching":
matching_cost_matrix = cost_matrix * -1000
row_ind, col_ind, a = lapjv(matching_cost_matrix, verbose=False)
result = zip(row_ind, col_ind)
hit_at_one = len([x for x, y in result if x == y])
p_at_one = hit_at_one / instances
percentage = hit_at_one / instances * 100
if not batch:
print(f"{paradigm} - semb on {source_lang} - {target_lang}")
print(f"P @ 1: {p_at_one}")
print(f"{percentage}% {instances} definitions")
if batch:
fields = [
f"{source_lang}",
f"{target_lang}",
f"{instances}",
f"{hit_at_one}",
f"{percentage}",
]
with open("semb_matcing_results.csv", "a") as f:
writer = csv.writer(f)
writer.writerow(fields)
if paradigm == "retrieval":
hit_at_one = len(
[x for x, y in enumerate(cost_matrix.argmax(axis=1)) if x == y]
)
percentage = hit_at_one / instances * 100
if not batch:
print(f"{hit_at_one} definitions have retrieved correctly")
if batch:
fields = [
f"{source_lang}",
f"{target_lang}",
f"{instances}",
f"{hit_at_one}",
f"{percentage}",
]
with open("semb_retrieval_results.csv", "a") as f:
writer = csv.writer(f)
writer.writerow(fields)
if __name__ == "__main__":
parser = argparse.ArgumentParser(
description="align dictionaries using sentence embedding representation"
)
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(
"-n",
"--instances",
help="number of instances in each language to use",
default=1000,
type=int,
)
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(
"paradigm",
choices=["all", "retrieval", "matching"],
default="all",
help="which paradigms to align with",
)
args = parser.parse_args()
main(args)
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