def dist_mean_cosine()

in src/evaluation/evaluator.py [0:0]

• 26 lines of code
• 4 McCabe index (conditional complexity)

    def dist_mean_cosine(self, to_log):
        """
        Mean-cosine model selection criterion.
        """
        # get normalized embeddings
        src_emb = self.mapping(self.src_emb.weight).data
        tgt_emb = self.tgt_emb.weight.data
        src_emb = src_emb / src_emb.norm(2, 1, keepdim=True).expand_as(src_emb)
        tgt_emb = tgt_emb / tgt_emb.norm(2, 1, keepdim=True).expand_as(tgt_emb)

        # build dictionary
        for dico_method in ['nn', 'csls_knn_10']:
            dico_build = 'S2T'
            dico_max_size = 10000
            # temp params / dictionary generation
            _params = deepcopy(self.params)
            _params.dico_method = dico_method
            _params.dico_build = dico_build
            _params.dico_threshold = 0
            _params.dico_max_rank = 10000
            _params.dico_min_size = 0
            _params.dico_max_size = dico_max_size
            s2t_candidates = get_candidates(src_emb, tgt_emb, _params)
            t2s_candidates = get_candidates(tgt_emb, src_emb, _params)
            dico = build_dictionary(src_emb, tgt_emb, _params, s2t_candidates, t2s_candidates)
            # mean cosine
            if dico is None:
                mean_cosine = -1e9
            else:
                mean_cosine = (src_emb[dico[:dico_max_size, 0]] * tgt_emb[dico[:dico_max_size, 1]]).sum(1).mean()
            mean_cosine = mean_cosine.item() if isinstance(mean_cosine, torch_tensor) else mean_cosine
            logger.info("Mean cosine (%s method, %s build, %i max size): %.5f"
                        % (dico_method, _params.dico_build, dico_max_size, mean_cosine))
            to_log['mean_cosine-%s-%s-%i' % (dico_method, _params.dico_build, dico_max_size)] = mean_cosine