def __init__(self): start_time = time.time() model_parameter_ins = model_parameter() data_name = model_parameter_ins.flags.FLAGS.data_name self.FLAGS = model_parameter_ins.get_parameter(data_name).FLAGS log_ins = create_log(data_name=data_name, model_name=self.FLAGS.model_name, lr=self.FLAGS.learning_rate) self.logger = log_ins.logger self.logger.info("hello world the experiment begin") self.logger.info("the model parameter is : " + str(self.FLAGS.flag_values_dict())) prepare_data_ins = DataLoader(self.FLAGS) self.logger.info("start loading dataset!") self.train_set, self.test_set = prepare_data_ins.load_train_test() print('test event len: %d' % (len(self.test_set))) self.logger.info("dataset loaded!") self.logger.info("DataHandle Process cost time: %.2fs" % (time.time() - start_time)) start_time = time.time() self.emb = history_embedding(is_training=self.FLAGS.is_training, type_num=self.FLAGS.type_num, max_seq_len=self.FLAGS.max_seq_len, sims_len=self.FLAGS.sims_len, FLAGS=self.FLAGS) self.logger.info('get train test data process cost: %.2fs' % (time.time() - start_time))
ndcg_value_sum = ndcg_value_sum + ndcg_value break print("P Pop the recall rate is: " + str(topk) + " " + str(recall_count / length)) print("P Pop the ndcg value is: " + str(topk) + " " + str(ndcg_value_sum / length)) print('error:' + str(error)) return if __name__ == "__main__": start_time = time.time() model_parameter_ins = model_parameter() experiment_name = model_parameter_ins.flags.FLAGS.experiment_name FLAGS = model_parameter_ins.get_parameter(experiment_name).FLAGS FLAGS.type = sys.argv[1] log_ins = create_log(type=FLAGS.type, experiment_type=FLAGS.experiment_type, version=FLAGS.version) logger = log_ins.logger logger.info("hello world the experiment begin") # logger.info("The model parameter is :" + str(self.FLAGS._parse_flags())) if FLAGS.type == "yoochoose": get_origin_data_ins = Get_yoochoose_data(FLAGS=FLAGS)
def __init__(self): start_time = time.time() model_parameter_ins = model_parameter() experiment_name = model_parameter_ins.flags.FLAGS.experiment_name self.FLAGS = model_parameter_ins.get_parameter(experiment_name).FLAGS log_ins = create_log(type=self.FLAGS.type, experiment_type=self.FLAGS.experiment_type, version=self.FLAGS.version) self.logger = log_ins.logger self.logger.info("hello world the experiment begin") # logger.info("The model parameter is :" + str(self.FLAGS._parse_flags())) #init data and embeding get_origin_data_ins = Get_origin_data(FLAGS=self.FLAGS) if self.FLAGS.experiment_type == "dib" \ or self.FLAGS.experiment_type == "no_emb" \ or self.FLAGS.experiment_type == "slirec" \ or self.FLAGS.experiment_type == "lstur" \ or self.FLAGS.experiment_type == "sasrec" \ or self.FLAGS.experiment_type == "grurec" \ or self.FLAGS.experiment_type == "bert" \ or self.FLAGS.experiment_type == "dmpn" \ or self.FLAGS.experiment_type == "atrank"\ or self.FLAGS.experiment_type == "dmpn2"\ or self.FLAGS.experiment_type == "dmpn3"\ or self.FLAGS.experiment_type == "dmpn4"\ or self.FLAGS.experiment_type == "dfm": prepare_data_behavior_ins = prepare_data_behavior(self.FLAGS, get_origin_data_ins.origin_data) elif self.FLAGS.experiment_type == "bpr": prepare_data_behavior_ins = prepare_data_bpr(self.FLAGS, get_origin_data_ins.origin_data) self.logger.info('DataHandle Process.\tCost time: %.2fs' % (time.time() - start_time)) start_time = time.time() #embedding if self.FLAGS.experiment_type == "no_emb": config_file = "config/no_embedding__dic.csv" self.emb = No_embedding(self.FLAGS.is_training, config_file) elif self.FLAGS.experiment_type == "bpr": self.emb = Bprmf_embedding(self.FLAGS.is_training,self.FLAGS.embedding_config_file, prepare_data_behavior_ins.user_count, prepare_data_behavior_ins.item_count) else: self.emb = Lstur_embedding(self.FLAGS.is_training, self.FLAGS.embedding_config_file, prepare_data_behavior_ins.user_count, prepare_data_behavior_ins.item_count, prepare_data_behavior_ins.category_count, self.FLAGS.max_len) self.train_set, self.test_set = prepare_data_behavior_ins.get_train_test() self.logger.info('Get Train Test Data Process.\tCost time: %.2fs' % (time.time() - start_time)) # self.item_category_dic = prepare_data_behavior_ins.item_category_dic self.global_step = 0 self.one_epoch_step = 0 self.now_epoch = 0
def __init__(self): start_time = time.time() model_parameter_ins = model_parameter() experiment_name = model_parameter_ins.flags.FLAGS.experiment_name self.FLAGS = model_parameter_ins.get_parameter(experiment_name).FLAGS log_ins = create_log(type=self.FLAGS.type, experiment_type=self.FLAGS.experiment_type, version=self.FLAGS.version) self.logger = log_ins.logger self.logger.info("hello world the experiment begin") # logger.info("The model parameter is :" + str(self.FLAGS._parse_flags())) if self.FLAGS.type == "yoochoose": get_origin_data_ins = Get_yoochoose_data(FLAGS=self.FLAGS) get_origin_data_ins.getDataStatistics() elif self.FLAGS.type == "movielen": get_origin_data_ins = Get_movie_data(FLAGS=self.FLAGS) get_origin_data_ins.getDataStatistics() if self.FLAGS.type == "tmall": get_origin_data_ins = Get_tmall_data(FLAGS=self.FLAGS) elif self.FLAGS.type == "movie_tv": get_origin_data_ins = Get_amazon_data_movie_tv(FLAGS=self.FLAGS) get_origin_data_ins.getDataStatistics() elif self.FLAGS.type == "elec": get_origin_data_ins = Get_amazon_data_elec(FLAGS=self.FLAGS) get_origin_data_ins.getDataStatistics() elif self.FLAGS.type == "music": get_origin_data_ins = Get_amazon_data_music(FLAGS=self.FLAGS) get_origin_data_ins.getDataStatistics() elif self.FLAGS.type == 'taobaoapp': get_origin_data_ins = Get_taobaoapp_data(FLAGS=self.FLAGS) get_origin_data_ins.getDataStatistics() elif self.FLAGS.type == "beauty": get_origin_data_ins = Get_amazon_data_beauty(FLAGS=self.FLAGS) get_origin_data_ins.getDataStatistics() elif self.FLAGS.type == "brightkite": get_origin_data_ins = Get_BrightKite_data(FLAGS=self.FLAGS) get_origin_data_ins.getDataStatistics() elif self.FLAGS.type == "order": get_origin_data_ins = Get_Order_data(FLAGS=self.FLAGS) get_origin_data_ins.getDataStatistics() #get_train_test_ins = Get_train_test(FLAGS=self.FLAGS,origin_data=get_origin_data_ins.origin_data) prepare_data_behavior_ins = prepare_data_base( self.FLAGS, get_origin_data_ins.origin_data) self.train_set, self.test_set = prepare_data_behavior_ins.get_train_test( ) #fetch part of test_data #if len(self.train_set) > 2000000: #self.test_set = random.sample(self.train_set,2000000) #self.test_set = self.test_set.sample(3500) self.logger.info('DataHandle Process.\tCost time: %.2fs' % (time.time() - start_time)) start_time = time.time() self.emb = Behavior_embedding_time_aware_attention( is_training=self.FLAGS.is_training, user_count=prepare_data_behavior_ins.user_count, item_count=prepare_data_behavior_ins.item_count, category_count=prepare_data_behavior_ins.category_count, max_length_seq=self.FLAGS.length_of_user_history) self.logger.info('Get Train Test Data Process.\tCost time: %.2fs' % (time.time() - start_time)) self.item_category_dic = prepare_data_behavior_ins.item_category_dic self.global_step = 0 self.one_epoch_step = 0 self.now_epoch = 0
def __init__(self): start_time = time.time() model_parameter_ins = model_parameter() experiment_name = model_parameter_ins.flags.FLAGS.experiment_name self.FLAGS = model_parameter_ins.get_parameter(experiment_name).FLAGS
def __init__(self): start_time = time.time() model_parameter_ins = model_parameter() data_name = model_parameter_ins.flags.FLAGS.data_name self.FLAGS = model_parameter_ins.get_parameter(data_name).FLAGS