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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])
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}\n"
+ f" document sizes: {len(clean_src_corpus)}, {len(clean_target_corpus)}\n"
+ f" vocabulary size: {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"{paradigm} - {metric} on {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}\n{percentage}% {instances} definitions\n")
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)
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