ipython_parallel.py

    @view.parallel(block=False)
    def cosine_similarity(rows):
        from sklearn.metrics import pairwise

        print('hi!')

        return [len(rows)]

        @functools.lru_cache(maxsize=None)
        def compute_tensor_dot(word, other_word=None):
            print('Product')
            space = __import__('__main__').space

            vector = space[word]
            other_vector = space[other_word] if other_word is not None else vector

            return numpy.tensordot(vector, other_vector, axes=0).flatten()

        similarities = []
        for verb, subject, object_, landmark in rows:
            print('Composing {} with {} and {}'.format(verb, subject, object_))
            sentence_verb = compute_tensor_dot(verb) * compute_tensor_dot(subject, object_)

            print('Composing {} with {} and {}'.format(landmark, subject, object_))
            sentence_landmark = compute_tensor_dot(landmark) * compute_tensor_dot(subject, object_)

            similarities.append(
                pairwise.cosine_similarity(sentence_verb, sentence_landmark)[0][0]
            )

        return similarities

    view['space'] = space
    ar = cosine_similarity(gs11_data[['verb', 'subject', 'object', 'landmark']].values)