1 files changed, 0 insertions, 153 deletions

diff --git a/sentence_emb_matching.py b/sentence_emb_matching.py
deleted file mode 100644
index 38812d7..0000000
--- a/sentence_emb_matching.py
+++ /dev/null
@@ -1,153 +0,0 @@
-import argparse
-
-parser = argparse.ArgumentParser(description='run matching using sentence embeddings and cosine similarity')
-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 retrieve', default=2000, type=int)
-
-args = parser.parse_args()
-
-source_lang = args.source_lang
-target_lang = args.target_lang
-
-def load_embeddings(path, dimension = 300):
-    """
-    Loads the embeddings from a word2vec formatted file.
-    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
-
-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')]
-
-import numpy as np
-from mosestokenizer import *
-
-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.
-    '''
-    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)
-                clean_vectors[word] = 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
-
-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,
-        )
-
-import random
-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])
-
-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
-
-from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer
-
-vocab_counter = CountVectorizer().fit(clean_src_corpus + clean_target_corpus)
-common = [w for w in vocab_counter.get_feature_names() if w in clean_src_vectors or w 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]))
-
-print(f'{source_lang} - {target_lang}: the vocabulary size is {len(W_common)}')
-
-from sklearn.preprocessing import normalize
-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_src_corpus + clean_target_corpus)
-X_idf_source = vect_tfidf.transform(clean_src_corpus)
-X_idf_target = vect_tfidf.transform(clean_target_corpus)
-
-print(f'Matrices are {X_idf_source.shape} and {W_common.shape}')
-print(f'The dimensions are {X_idf_source.ndim} and {W_common.ndim}')
-
-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)
-
-from lapjv import lapjv
-cost_matrix = cost_matrix * -1000
-row_ind, col_ind, a = lapjv(cost_matrix, verbose=False)
-
-result = zip(row_ind, col_ind)
-hit_one = len([x for x,y in result if x == y])
-print(f'{hit_one} definitions have been mapped correctly, shape of cost matrix: {str(cost_matrix.shape)}')
-
-import csv
-percentage = hit_one / instances * 100
-fields = [f'{source_lang}', f'{target_lang}', f'{instances}', f'{hit_one}', f'{percentage}']
-
-with open('semb_matcing.csv', 'a') as f:
-    writer = csv.writer(f)
-    writer.writerow(fields)
-