'payload': payload, 'upload_time': the_time } time.sleep(0.01) shuffledQids = training_queries.keys()[:] random.shuffle(shuffledQids) while True: counter = 0 time.sleep(0.01) for qid in shuffledQids: counter += 1 #print qid, counter, 'of', qlen q = training_queries[qid] c = user_model.get_clicks(uploads[qid]['ranked_lerot_list'], q.get_labels(), query=q, ranker_list=uploads[qid]['payload'], upload_time=uploads[qid]['upload_time']) if c is not None: break if c is not None: print c break s = learner.update_solution(c) eval = evaluation.evaluate_all(s, test_queries) print eval with open('evaluation', 'a') as f: f.write(str(eval) + '\n') if runid == 1000: break
import sys, random import include import lerot.retrieval_system, lerot.environment, lerot.evaluation, lerot.query #learner = lerot.retrieval_system.ListwiseLearningSystem(46, '-w random -c comparison.TeamDraft -r ranker.DeterministicRankingFunction -s 3 -d 0.1 -a 0.01') learner = lerot.retrieval_system.PairwiseLearningSystem(46, "--init_weights random --epsilon 0.0 --eta 0.001 --ranker " "ranker.DeterministicRankingFunction --ranker_tie first") user_model = lerot.environment.CascadeUserModel('--p_click 0:0.0,1:.5,2:1.0 --p_stop 0:0.0,1:0.0,2:0.0') evaluation = lerot.evaluation.NdcgEval() training_queries = lerot.query.load_queries(sys.argv[1], 46) test_queries = lerot.query.load_queries(sys.argv[2], 46) while True: q = training_queries[random.choice(training_queries.keys())] l = learner.get_ranked_list(q) c = user_model.get_clicks(l, q.get_labels()) s = learner.update_solution(c) print evaluation.evaluate_all(s, test_queries)
shuffledQids = training_queries.keys()[:] random.shuffle(shuffledQids) #shuffle query ids list while True: for qid in shuffledQids: q = training_queries[qid] c = user_model.get_clicks(uploads[qid]['current_l'], q.get_labels()) if isinstance(c, np.ndarray): break if isinstance(c, np.ndarray): break q = training_queries[qid] learner.get_ranked_list(q) #, firstTime learner.current_l = uploads[qid]['current_l'] s = learner.update_solution(c) #update learner using clicks eval = evaluation.evaluate_all(learner.ranker, test_queries) out_path = os.path.abspath( os.path.join(os.path.dirname(__file__), os.pardir) ) + '/output_data/pairwise_local_evaluation_data/fold' + str( fold) + '_' + (str(repetition)) evalString += str(eval) evalString += '\n' print eval if runid >= 100 and runid % 100 == 0: with open(out_path, 'a') as f: f.write(str(evalString)) evalString = '' with open( os.path.abspath( os.path.join(os.path.dirname(__file__), os.pardir)) + '/output_data/pairwise_local_evaluation_data/' +
#print qid, counter, 'of', qlen #['__feature_vectors__', '__docids__', '__labels__', '__qid__', '__comments__'] q = training_queries[qid] l = learner.get_ranked_list(q) payload, the_time = user_model.upload_run(q, l, runid) uploads[qid] = {'ranked_lerot_list':l, 'payload':payload, 'upload_time':the_time} time.sleep(0.01) shuffledQids = training_queries.keys()[:] random.shuffle(shuffledQids) while True: counter = 0 time.sleep(0.01) for qid in shuffledQids: counter += 1 #print qid, counter, 'of', qlen q = training_queries[qid] c = user_model.get_clicks(uploads[qid]['ranked_lerot_list'], q.get_labels(), query=q, ranker_list=uploads[qid]['payload'], upload_time=uploads[qid]['upload_time']) if c is not None: break if c is not None: print c break s = learner.update_solution(c) eval = evaluation.evaluate_all(s, test_queries) print eval with open('evaluation', 'a') as f: f.write(str(eval)+'\n') if runid == 1000: break
'current_query': q } shuffledQids = training_queries.keys()[:] random.shuffle(shuffledQids)#shuffle query ids list while True: for qid in shuffledQids: q = training_queries[qid] c = user_model.get_clicks(uploads[qid]['current_l'], q.get_labels()) if isinstance(c, np.ndarray):break if isinstance(c, np.ndarray): break q = training_queries[qid] learner.get_ranked_list(q)#, firstTime learner.current_l = uploads[qid]['current_l'] s = learner.update_solution(c)#update learner using clicks eval = evaluation.evaluate_all(learner.ranker, test_queries) out_path = os.path.abspath(os.path.join(os.path.dirname(__file__), os.pardir))+'/output_data/pairwise_local_evaluation_data/fold'+str(fold)+'_'+(str(repetition)) evalString += str(eval) evalString += '\n' print eval if runid >= 100 and runid % 100 == 0: with open(out_path, 'a') as f: f.write(str(evalString)) evalString = '' with open(os.path.abspath(os.path.join(os.path.dirname(__file__), os.pardir))+'/output_data/pairwise_local_evaluation_data/'+'learnerPickle.c', 'wb') as fp: pickle.dump(learner, fp) if runid == 1000: break