本文整理汇总了Python中recsys.algorithm.factorize.SVD.save_model方法的典型用法代码示例。如果您正苦于以下问题：Python SVD.save_model方法的具体用法？Python SVD.save_model怎么用？Python SVD.save_model使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类recsys.algorithm.factorize.SVD的用法示例。
在下文中一共展示了SVD.save_model方法的3个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: compute_SVD
# 需要导入模块: from recsys.algorithm.factorize import SVD [as 别名]
# 或者: from recsys.algorithm.factorize.SVD import save_model [as 别名]
def compute_SVD():
svd = SVD()
svd.set_data(load_data())
K=100
svd.compute(k=K, min_values=10, pre_normalize=None, mean_center=True, post_normalize=True, savefile=None)
svd.save_model(os.path.join(utils.get_add_dir(), 'ratings'))示例2: process_svd
# 需要导入模块: from recsys.algorithm.factorize import SVD [as 别名]
# 或者: from recsys.algorithm.factorize.SVD import save_model [as 别名]
def process_svd(preload):
if preload:
svd = SVD(filename='./data/svd-all') # Loading already computed SVD model
else:
print "Reading data..."
svdlibc = SVDLIBC('./data/behavior-ml-score.csv')
svdlibc.to_sparse_matrix(sep=',', format={'col':0, 'row':1, 'value':2, 'ids': str})
k=100
print "Computing SVD..."
svdlibc.compute(k)
svd = svdlibc.export()
svd.save_model('./data/svd-all', options={'k': k})
#svd.predict('TV268', 9, 1, 3)
return svd示例3: SVD
# 需要导入模块: from recsys.algorithm.factorize import SVD [as 别名]
# 或者: from recsys.algorithm.factorize.SVD import save_model [as 别名]
})
#Create SVD
K=100
svd = SVD()
svd.set_data(train)
svd.compute(k=K, min_values=None, pre_normalize=None, mean_center=True, post_normalize=True)
# save
# svd.set_data(None) # clear data before saving
# pickle.dump(svd, open('./model/svd.obj', 'w'))
svd.save_model('./model/svd.obj.zip',
{'k': K, 'min_values': 5,
'pre_normalize': None, 'mean_center': True, 'post_normalize': True})
# similarity between items x and y
print '-------- SIMILARITIES:'
for prodid1 in [0, 1, 3, 4]:
for prodid2 in [0, 1, 3, 4]:
print prodid1, prodid2, svd.similarity(prodid1, prodid2)
# similar to item x
# svd.similar(1)
#
# # predict ratings
# evaluate(svd, test, True)
#