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import argparse
import numpy as np
from mosestokenizer import *
import nltk
import random
from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer
from sklearn.preprocessing import normalize
from Wass_Matcher import Wasserstein_Matcher
def load_embeddings(path, dimension=300):
"""
Loads the embeddings from a word2vec formatted file.
word2vec format is one line per word and it's associated embedding
(dimension x floating numbers) separated by spaces
The first line may or may not include the word count and dimension
"""
vectors = {}
with open(path, mode='r', encoding='utf8') as fp:
first_line = fp.readline().rstrip('\n')
if first_line.count(' ') == 1:
# includes the "word_count dimension" information
(word_count, dimension) = map(int, first_line.split())
else:
# assume the file only contains vectors
fp.seek(0)
for line in fp:
elems = line.split()
vectors[" ".join(elems[:-dimension])] = " ".join(elems[-dimension:])
return vectors
def clean_corpus_using_embeddings_vocabulary(
embeddings_dictionary,
corpus,
vectors,
language,
):
'''
Cleans corpus using the dictionary of embeddings.
Any word without an associated embedding in the dictionary is ignored.
Adds '__target-language' and '__source-language' at the end of the words according to their language.
'''
clean_corpus, clean_vectors, keys = [], {}, []
words_we_want = set(embeddings_dictionary)
tokenize = MosesTokenizer(language)
for key, doc in enumerate(corpus):
clean_doc = []
words = tokenize(doc)
for word in words:
if word in words_we_want:
clean_doc.append(word + '__%s' % language)
clean_vectors[word + '__%s' % language] = np.array(vectors[word].split()).astype(np.float)
if len(clean_doc) > 3 and len(clean_doc) < 25:
keys.append(key)
clean_corpus.append(' '.join(clean_doc))
tokenize.close()
return np.array(clean_corpus), clean_vectors, keys
def mrr_precision_at_k(golden, preds, k_list=[1,]):
"""
Calculates Mean Reciprocal Error and Hits@1 == Precision@1
"""
my_score = 0
precision_at = np.zeros(len(k_list))
for key, elem in enumerate(golden):
if elem in preds[key]:
location = np.where(preds[key]==elem)[0][0]
my_score += 1/(1+ location)
for k_index, k_value in enumerate(k_list):
if location < k_value:
precision_at[k_index] += 1
return my_score/len(golden), (precision_at/len(golden))[0]
def main(args):
numpy.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
mode = args.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 = clean_corpus_using_embeddings_vocabulary(
set(vectors_source.keys()),
defs_source,
vectors_source,
source_lang,
)
clean_target_corpus, clean_target_vectors, target_keys = clean_corpus_using_embeddings_vocabulary(
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} : 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)
vect_tf = CountVectorizer(vocabulary=common, dtype=np.double)
vect_tf.fit(clean_src_corpus + clean_target_corpus)
X_train_tf = vect_tf.transform(clean_src_corpus)
X_test_tf = vect_tf.transform(clean_target_corpus)
if (mode == 'wmd' or mode == 'all'):
if (not batch):
print(f'WMD - tfidf: {source_lang} - {target_lang}')
clf = Wasserstein_Matcher(W_embed=W_common, n_neighbors=5, n_jobs=14)
clf.fit(X_train_idf[:instances], np.ones(instances))
row_ind, col_ind, a = clf.kneighbors(X_test_idf[:instances], n_neighbors=instances)
result = zip(row_ind, col_ind)
hit_one = len([x for x,y in result if x == y])
percentage = hit_one / instances * 100
if (not batch):
print(f'{hit_one} definitions have been mapped correctly, {percentage}%')
if (batch):
import csv
fields = [f'{source_lang}', f'{target_lang}', f'{instances}', f'{hit_one}', f'{percentage}']
with open('wmd_matching_results.csv', 'a') as f:
writer = csv.writer(f)
writer.writerow(fields)
if (mode == 'snk' or mode == 'all'):
if (not batch):
print(f'Sinkhorn - tfidf: {source_lang} - {target_lang}')
clf = Wasserstein_Matcher(W_embed=W_common, n_neighbors=5, n_jobs=14, sinkhorn=True)
clf.fit(X_train_idf[:instances], np.ones(instances))
row_ind, col_ind, a = clf.kneighbors(X_test_idf[:instances], n_neighbors=instances)
result = zip(row_ind, col_ind)
hit_one = len([x for x,y in result if x == y])
percentage = hit_one / instances * 100
if (not batch):
print(f'{hit_one} definitions have been mapped correctly, {percentage}%')
if (batch):
fields = [f'{source_lang}', f'{target_lang}', f'{instances}', f'{hit_one}', f'{percentage}']
with open('sinkhorn_matching_result.csv', 'a') as f:
writer = csv.writer(f)
writer.writerow(fields)
if __name__ == "__main__":
parser = argparse.ArgumentParser(description='matching 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('-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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