def train(self, df, model_path=''):
'''
隐语义模型训练
:param df: 格式包含该三列 --》 userid,iteamid,rating
:param model_path:模型持久化地址，默认为空，不执行持久化
:return: 训练好的模型
'''
# 数据类型转换为 surprise 需要的格式
data = Dataset.load_from_df(df, self.reader)
trainset = data.build_full_trainset()
algo_lfm = SVDpp()
algo_lfm.fit(trainset)
if model_path: surprise.dump.dump(model_path, algo=algo_lfm, verbose=1)
return algo_lfm
def run_svd(data, params, svdpp=False):
'''Returns trained SVD model based on matrix factorization'''
if svdpp:
alg = SVDpp(n_factors=utils.get_param(params, 'n_factors'),
n_epochs=utils.get_param(params, 'n_epochs'),
lr_all=utils.get_param(params, 'learning_rate'),
reg_all=utils.get_param(params, 'reg'),
verbose=True)
else:
alg = SVD(biased=utils.get_param(params, 'biased'),
n_factors=utils.get_param(params, 'n_factors'),
n_epochs=utils.get_param(params, 'n_epochs'),
lr_all=utils.get_param(params, 'learning_rate'),
reg_all=utils.get_param(params, 'reg'),
verbose=True)
alg.fit(data)
return alg