def dist_mean_cosine(self, src_emb, tgt_emb): """ Mean-cosine model selection criterion. """ # get normalized embeddings 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 ['csls_knn_10']: dico_max_size = 10000 _params = self.params 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() print( "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 return mean_cosine
def build_dictionary(self): """ Build a dictionary from aligned embeddings. """ src_emb = self.mapping(self.src_emb.weight).data tgt_emb = self.tgt_emb.weight.data self.dico = build_dictionary(src_emb, tgt_emb, self.params)
def build_dictionary(self): """ Build a dictionary from aligned 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) self.dico = build_dictionary(src_emb, tgt_emb, self.params)
def dist_mean_cosine(self, to_log): """ Mean-cosine model selection criterion. """ # get normalized embeddings src_emb = self.src_mapping(self.src_emb.weight, self.src_adj).data tgt_emb = self.tgt_mapping(self.tgt_emb.weight, self.tgt_adj).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_max_rank = 0 _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
def export(self, src_dico, tgt_dico, emb_en, emb_it, seed, export_emb=False): params = _get_eval_params(self.params) eval = Evaluator(params, emb_en, emb_it, torch.cuda.is_available()) # Export adversarial dictionaries optim_X_AE = AE(params).cuda() optim_Y_AE = AE(params).cuda() print('Loading pre-trained models...') optim_X_AE.load_state_dict( torch.load(self.tune_dir + '/best/seed_{}_dico_{}_best_X.t7'.format( seed, params.dico_build))) optim_Y_AE.load_state_dict( torch.load(self.tune_dir + '/best/seed_{}_dico_{}_best_Y.t7'.format( seed, params.dico_build))) X_Z = optim_X_AE.encode(Variable(emb_en)).data Y_Z = optim_Y_AE.encode(Variable(emb_it)).data mstart_time = timer() for method in ['nn', 'csls_knn_10']: results = get_word_translation_accuracy(params.src_lang, src_dico[1], X_Z, params.tgt_lang, tgt_dico[1], emb_it, method=method, dico_eval=self.eval_file, device=params.cuda_device) acc1 = results[0][1] results = get_word_translation_accuracy(params.tgt_lang, tgt_dico[1], Y_Z, params.src_lang, src_dico[1], emb_en, method=method, dico_eval=self.eval_file2, device=params.cuda_device) acc2 = results[0][1] # csls = 0 print('{} takes {:.2f}s'.format(method, timer() - mstart_time)) print('Method:{} score:{:.4f}-{:.4f}'.format(method, acc1, acc2)) f_csls = eval.dist_mean_cosine(X_Z, emb_it) b_csls = eval.dist_mean_cosine(Y_Z, emb_en) csls = (f_csls + b_csls) / 2.0 print("Seed:{},ACC:{:.4f}-{:.4f},CSLS_FB:{:.6f}".format( seed, acc1, acc2, csls)) #''' print('Building dictionaries...') params.dico_build = "S2T&T2S" params.dico_method = "csls_knn_10" X_Z = X_Z / X_Z.norm(2, 1, keepdim=True).expand_as(X_Z) emb_it = emb_it / emb_it.norm(2, 1, keepdim=True).expand_as(emb_it) f_dico_induce = build_dictionary(X_Z, emb_it, params) f_dico_induce = f_dico_induce.cpu().numpy() Y_Z = Y_Z / Y_Z.norm(2, 1, keepdim=True).expand_as(Y_Z) emb_en = emb_en / emb_en.norm(2, 1, keepdim=True).expand_as(emb_en) b_dico_induce = build_dictionary(Y_Z, emb_en, params) b_dico_induce = b_dico_induce.cpu().numpy() f_dico_set = set([(a, b) for a, b in f_dico_induce]) b_dico_set = set([(b, a) for a, b in b_dico_induce]) intersect = list(f_dico_set & b_dico_set) union = list(f_dico_set | b_dico_set) with io.open( self.tune_dir + '/export/{}-{}.dict'.format(params.src_lang, params.tgt_lang), 'w', encoding='utf-8', newline='\n') as f: for item in f_dico_induce: f.write('{} {}\n'.format(src_dico[0][item[0]], tgt_dico[0][item[1]])) with io.open( self.tune_dir + '/export/{}-{}.dict'.format(params.tgt_lang, params.src_lang), 'w', encoding='utf-8', newline='\n') as f: for item in b_dico_induce: f.write('{} {}\n'.format(tgt_dico[0][item[0]], src_dico[0][item[1]])) with io.open(self.tune_dir + '/export/{}-{}.intersect'.format( params.src_lang, params.tgt_lang), 'w', encoding='utf-8', newline='\n') as f: for item in intersect: f.write('{} {}\n'.format(src_dico[0][item[0]], tgt_dico[0][item[1]])) with io.open(self.tune_dir + '/export/{}-{}.intersect'.format( params.tgt_lang, params.src_lang), 'w', encoding='utf-8', newline='\n') as f: for item in intersect: f.write('{} {}\n'.format(tgt_dico[0][item[1]], src_dico[0][item[0]])) with io.open( self.tune_dir + '/export/{}-{}.union'.format(params.src_lang, params.tgt_lang), 'w', encoding='utf-8', newline='\n') as f: for item in union: f.write('{} {}\n'.format(src_dico[0][item[0]], tgt_dico[0][item[1]])) with io.open( self.tune_dir + '/export/{}-{}.union'.format(params.tgt_lang, params.src_lang), 'w', encoding='utf-8', newline='\n') as f: for item in union: f.write('{} {}\n'.format(tgt_dico[0][item[1]], src_dico[0][item[0]])) if export_emb: print('Exporting {}-{}.{}'.format(params.src_lang, params.tgt_lang, params.src_lang)) loader.export_embeddings( src_dico[0], X_Z, path=self.tune_dir + '/export/{}-{}.{}'.format( params.src_lang, params.tgt_lang, params.src_lang), eformat='txt') print('Exporting {}-{}.{}'.format(params.src_lang, params.tgt_lang, params.tgt_lang)) loader.export_embeddings( tgt_dico[0], emb_it, path=self.tune_dir + '/export/{}-{}.{}'.format( params.src_lang, params.tgt_lang, params.tgt_lang), eformat='txt') print('Exporting {}-{}.{}'.format(params.tgt_lang, params.src_lang, params.tgt_lang)) loader.export_embeddings( tgt_dico[0], Y_Z, path=self.tune_dir + '/export/{}-{}.{}'.format( params.tgt_lang, params.src_lang, params.tgt_lang), eformat='txt') print('Exporting {}-{}.{}'.format(params.tgt_lang, params.src_lang, params.src_lang)) loader.export_embeddings( src_dico[0], emb_en, path=self.tune_dir + '/export/{}-{}.{}'.format( params.tgt_lang, params.src_lang, params.src_lang), eformat='txt')
def export_dict(self, src_dico, tgt_dico, emb_en, emb_it, seed): params = self.params # Export adversarial dictionaries optim_X_AE = VAE(params).cuda() optim_Y_AE = VAE(params).cuda() print('Loading pre-trained models...') optim_X_AE.load_state_dict( torch.load(self.tune_dir + '/best/seed_{}_best_X.t7'.format(seed))) optim_Y_AE.load_state_dict( torch.load(self.tune_dir + '/best/seed_{}_best_Y.t7'.format(seed))) X_Z = optim_X_AE.encode(Variable(emb_en)).data Y_Z = optim_Y_AE.encode(Variable(emb_it)).data mstart_time = timer() for method in [params.eval_method]: results = get_word_translation_accuracy(params.src_lang, src_dico[1], X_Z, params.tgt_lang, tgt_dico[1], emb_it, method=method, dico_eval='default') acc1 = results[0][1] for method in [params.eval_method]: results = get_word_translation_accuracy(params.tgt_lang, tgt_dico[1], Y_Z, params.src_lang, src_dico[1], emb_en, method=method, dico_eval='default') acc2 = results[0][1] # csls = 0 print('{} takes {:.2f}s'.format(method, timer() - mstart_time)) print('Method:{} score:{:.4f}-{:.4f}'.format(method, acc1, acc2)) print('Building dictionaries...') params.dico_build = "S2T&T2S" params.dico_method = "csls_knn_10" X_Z = X_Z / X_Z.norm(2, 1, keepdim=True).expand_as(X_Z) emb_it = emb_it / emb_it.norm(2, 1, keepdim=True).expand_as(emb_it) f_dico_induce = build_dictionary(X_Z, emb_it, params) f_dico_induce = f_dico_induce.cpu().numpy() Y_Z = Y_Z / Y_Z.norm(2, 1, keepdim=True).expand_as(Y_Z) emb_en = emb_en / emb_en.norm(2, 1, keepdim=True).expand_as(emb_en) b_dico_induce = build_dictionary(Y_Z, emb_en, params) b_dico_induce = b_dico_induce.cpu().numpy() f_dico_set = set([(a, b) for a, b in f_dico_induce]) b_dico_set = set([(b, a) for a, b in b_dico_induce]) intersect = list(f_dico_set & b_dico_set) union = list(f_dico_set | b_dico_set) with io.open( self.tune_dir + '/best/{}-{}.dict'.format(params.src_lang, params.tgt_lang), 'w', encoding='utf-8', newline='\n') as f: for item in f_dico_induce: f.write('{} {}\n'.format(src_dico[0][item[0]], tgt_dico[0][item[1]])) with io.open( self.tune_dir + '/best/{}-{}.dict'.format(params.tgt_lang, params.src_lang), 'w', encoding='utf-8', newline='\n') as f: for item in b_dico_induce: f.write('{} {}\n'.format(tgt_dico[0][item[0]], src_dico[0][item[1]])) with io.open(self.tune_dir + '/best/{}-{}.intersect'.format( params.src_lang, params.tgt_lang), 'w', encoding='utf-8', newline='\n') as f: for item in intersect: f.write('{} {}\n'.format(src_dico[0][item[0]], tgt_dico[0][item[1]])) with io.open(self.tune_dir + '/best/{}-{}.intersect'.format( params.tgt_lang, params.src_lang), 'w', encoding='utf-8', newline='\n') as f: for item in intersect: f.write('{} {}\n'.format(tgt_dico[0][item[1]], src_dico[0][item[0]])) with io.open( self.tune_dir + '/best/{}-{}.union'.format(params.src_lang, params.tgt_lang), 'w', encoding='utf-8', newline='\n') as f: for item in union: f.write('{} {}\n'.format(src_dico[0][item[0]], tgt_dico[0][item[1]])) with io.open( self.tune_dir + '/best/{}-{}.union'.format(params.tgt_lang, params.src_lang), 'w', encoding='utf-8', newline='\n') as f: for item in union: f.write('{} {}\n'.format(tgt_dico[0][item[1]], src_dico[0][item[0]]))