def bl_IsRec(a_dataset):
model_name = 'IsRec' # ''
epoch_nums = [20] # 15,100,1000
neighbor_size = 15
topTopicNums = [3] # [3,4,5,6]
train_data, test_data = get_train_test_data(a_dataset.train_data,
a_dataset.test_data)
for epoch_num in epoch_nums:
for topTopicNum in topTopicNums:
HINRec_model = HINRec(model_name=model_name,
epoch_num=epoch_num,
neighbor_size=neighbor_size,
topTopicNum=topTopicNum)
if os.path.exists(HINRec_model.weight_path):
print('have trained,return!')
else:
HINRec_model.train(test_data)
# HINRec_model.test_model(test_data)
HINRec_model.save_model()
evalute_by_epoch(HINRec_model,
HINRec_model,
HINRec_model.model_name,
test_data,
evaluate_by_slt_apiNum=True
) # ,if_save_recommend_result=True)
def bl_IsRec_best(a_dataset):
model_name = 'IsRec_best' # 'IsRec' 'IsRec_best_modified'
epoch_num = 20
neighbor_size = 15
topTopicNum = 3
cluster_mode = 'LDA'
cluster_mode_topic_nums = [50] # 10,25,75,,100,125,150
train_data, test_data = get_train_test_data(a_dataset.train_data,
a_dataset.test_data)
for cluster_mode_topic_num in cluster_mode_topic_nums:
HINRec_model = HINRec(model_name=model_name,
semantic_mode='TF_IDF',
epoch_num=epoch_num,
neighbor_size=neighbor_size,
topTopicNum=topTopicNum,
cluster_mode=cluster_mode,
cluster_mode_topic_num=cluster_mode_topic_num)
if os.path.exists(HINRec_model.weight_path):
print('have trained,return!')
else:
HINRec_model.train(test_data)
HINRec_model.save_model()
evalute_by_epoch(HINRec_model,
HINRec_model,
HINRec_model.model_name,
test_data,
evaluate_by_slt_apiNum=True) # )
def bl_PasRec():
model_name = 'PasRec_2path' # 'PasRec_2path'
epoch_num = 60 # 之前是40 40比20差点
neighbor_size = 15
topTopicNum = 3
args = data_repository.get_args()
train_data, test_data = data_repository.get_ds(
).train_data, data_repository.get_ds().test_data
HINRec_model = HINRec(args,
model_name=model_name,
epoch_num=epoch_num,
neighbor_size=neighbor_size,
topTopicNum=topTopicNum)
if os.path.exists(HINRec_model.weight_path):
print('have trained,return!')
else:
# 这里是每隔20epoch测试一下，所以train中输入test_data
HINRec_model.train(test_data)
HINRec_model.save_model()
evalute_by_epoch(
HINRec_model,
HINRec_model,
HINRec_model.model_name,
test_data,
evaluate_by_slt_apiNum=False) # ,if_save_recommend_result=True)