def ground_truth(en_sent, fr_sent):
"""
Function that extracts the ground truth for a pair of sentences in english and french
:param en_sent: The the sentence in english
:param fr_sent: The sentence in french
:return:
"""
# keys = set(fr_sent)
# score matrix
score = np.empty([len(en_sent), len(fr_sent)], dtype=np.float32)
# label
truth = np.zeros([len(en_sent), len(fr_sent)], dtype=np.float32)
# we find the ground truth. We randomize access to break ties randomly
for j in range(len(en_sent)):
for k in range(len(fr_sent)):
score[j, k] = FastVector.cosine_similarity(en_dict[en_sent[j]],
fr_dict[fr_sent[k]])
# we find the ground truth. We randomize access to break ties randomly
for j in range(len(en_sent)):
argmax = int(score[j].argmax())
truth[j, argmax] = 1.
return truth.reshape(-1)
U, s, V = np.linalg.svd(product)
# return orthogonal transformation which aligns source language to the target
return np.matmul(U, V)
# Now we load the French and Russian word vectors, and evaluate the similarity of "chat" and "кот":
# In[2]:
fr_dictionary = FastVector(vector_file='zh_vec.txt')
ru_dictionary = FastVector(vector_file='en_vec.txt')
fr_vector = fr_dictionary["chat"]
ru_vector = ru_dictionary["кот"]
print(FastVector.cosine_similarity(fr_vector, ru_vector))
# "chat" and "кот" both mean "cat", so they should be highly similar; clearly the two word vector spaces are not yet aligned. To align them, we need a bilingual dictionary of French and Russian translation pairs. As it happens, this is a great opportunity to show you something truly amazing...
#
# Many words appear in the vocabularies of more than one language; words like "alberto", "london" and "presse". These words usually mean similar things in each language. Therefore we can form a bilingual dictionary, by simply extracting every word that appears in both the French and Russian vocabularies.
# In[3]:
ru_words = set(ru_dictionary.word2id.keys())
fr_words = set(fr_dictionary.word2id.keys())
overlap = list(ru_words & fr_words)
bilingual_dictionary = [(entry, entry) for entry in overlap]
# Let's align the French vectors to the Russian vectors, using only this "free" dictionary that we acquired without any bilingual expert knowledge.
# In[ ]:
product = np.matmul(source_matrix.transpose(), target_matrix)
U, s, V = np.linalg.svd(product)
# return orthogonal transformation which aligns source language to the target
return np.matmul(U, V)
# copy embedding files from https://fasttext.cc/docs/en/crawl-vectors.html#models
en_dictionary = FastVector(vector_file='cc.en.300.vec')
zh_dictionary = FastVector(vector_file='cc.zh.300.vec')
en_vector = en_dictionary["love"]
zh_vector = zh_dictionary["爱"]
# going to print 0.0004326613965749648
print(FastVector.cosine_similarity(en_vector, zh_vector))
zh_words = set(zh_dictionary.word2id.keys())
en_words = set(en_dictionary.word2id.keys())
overlap = list(zh_words & en_words)
bilingual_dictionary = [(entry, entry) for entry in overlap]
# form the training matrices
source_matrix, target_matrix = make_training_matrices(en_dictionary,
zh_dictionary,
bilingual_dictionary)
# learn and apply the transformation
transform = learn_transformation(source_matrix, target_matrix)
en_dictionary.apply_transform(transform)
except ValueError:
continue
if it_w not in it_dictionary:
print(it_w + " - " + fr_w + " - it word not found")
if (lang == skip_lang):
continue
if fr_w not in fr_dictionary:
print(it_w + " - " + fr_w + " - " + lang + " word not found")
if (lang == skip_lang):
continue
# Cosine between words
try:
cosine = FastVector.cosine_similarity(it_dictionary[it_w],
fr_dictionary[fr_w])
except KeyError:
cosine = 1
# Synonyms list
synonyms_list = []
if it_w in synonyms_dict:
synonyms_list = synonyms_dict[it_w]
if len(synonyms_list) < 1:
synonyms_list.append(it_w)
# Array containing all cosines from synonyms
synonyms_cosine_list = []
for s in synonyms_list:
if s not in it_dictionary:
def test_word(en_dictionary, other_dictionary, SRC_WORD, TGT_WORD):
print "Testing WORD[%s->%s]" % (SRC_WORD, TGT_WORD)
en_vector = en_dictionary[SRC_WORD]
other_vector = other_dictionary[TGT_WORD]
print(FastVector.cosine_similarity(en_vector, other_vector))
args = parse_args()
print('loading vectors')
en_dictionary = FastVector(vector_file=args.en_embedding)
fr_dictionary = FastVector(vector_file=args.fr_embedding)
#print('transforming vectors')
#fr_dictionary.apply_transform('alignment_matrices/fr.txt')
#print('CCA...')
#en_fr = read_dictionary(args.embedding_path+'en_fr.txt')
#en_dictionary.embed, fr_dictionary.embed = cca(en_dictionary, fr_dictionary, en_fr, dim=250)
print(
"Hello score:",
FastVector.cosine_similarity(en_dictionary["hello"],
fr_dictionary["bonjour"]))
print('processing data')
en_train_file = args.source_path + 'en_train.tsv'
en_test_file = args.source_path + 'en_test.tsv'
fr_train_file = args.source_path + 'fr_train.tsv'
fr_test_file = args.source_path + 'fr_test.tsv'
print('english train')
en_train_df = read_dataset(en_train_file)
en_train_y, en_train_x, en_vectorizor = process_dataset(
en_train_df, en_dictionary, None)
n_classes = len(set(en_train_y))
label_encoder = dict(zip(list(set(en_train_y)), np.arange(n_classes)))
en_train_y = np.array([label_encoder[i] for i in en_train_y])
