Add experiment scripts

1 files changed, 153 insertions, 0 deletions

diff --git a/sentence_emb_matching.py b/sentence_emb_matching.py
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+++ b/sentence_emb_matching.py
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+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)
+