def do_continue(self, line): ''' Will continue the training of the currently selected model ''' if not self.has_model_selected(): print 'please select or create a model' return False m = ModelManager(self.prompt[:-1]) state = m.load_current_state(add_hidden=True) model = m.load_currently_selected_model() train.train2(m, state, model)
#evaluation.evaluate_lshf(m, context_and_answer_relevance) exit() from ann import lsh_forest from ann.candidate_selection import * m = ModelManager('ubuntu_vhred_vanilla') #from ann.lsh_forest import save_linked_utterance_embeddings #save_linked_utterance_embeddings(m) #lsh_forest.train_lsh_forest(m, corpus_percentage=0.05) ann = lsh_forest.load_lshf(m) utt_embs = lsh_forest.load_utterance_embeddings(m) encoder = m.load_currently_selected_model() embs = encode('how do i update all packages ? __eou__', encoder) d_emb = embs[0][0][0] distances, labels, embeddings = ann.kneighbors(d_emb, 10) translator = get_label_translator(m, as_text=True) labels = [(label[0], label[1] + 1) for label in labels] search_context = { 'distances': distances, 'labels': labels, 'candidate_dialogue_embeddings': embeddings,