def get_one_user_recom():
user = '******'
alpha = 0.6
graph = read.get_graph_from_data('../data/ratings.txt')
iter_num = 100
recom_result = personal_rank(graph, user, alpha, iter_num, 100)
return recom_result
def get_one_user_by_mat():
"""
give one fix user by mat
"""
user = "******"
alpha = 0.8
graph = read.get_graph_from_data("../data/ratings.csv")
recom_result = personal_rank_matrix(graph, user, alpha, 100)
return recom_result
def get_user_recom_mat():
user = "******"
alpha = 0.8
graph = read.get_graph_from_data(
os.path.dirname(
os.path.dirname(os.path.dirname(os.path.dirname(__file__)))) +
"\\recommender\\data\\ml-latest-small\\ratings.csv")
recom_result = personal_rank_mat(graph, user, alpha, 100)
return recom_result
def get_one_user_recom():
"""
give one fix_user recom result
"""
user = "******"
alpha = 0.8
graph = read.get_graph_from_data("../data/ratings.csv")
iter_num = 100
recom_result = personal_rank(graph, user, alpha, iter_num, 100)
return recom_result
"""
def get_one_user_recom():
user = "******"
alpha = 0.7
graph = read.get_graph_from_data("../data/ratings.csv")
iter_num = 100
recom_result = personal_rank(graph, user, alpha, iter_num, 100)
# item_info = read.get_item_info("../data/movies.csv")
# for itemId in graph[user]:
# pure_itemId = itemId.split("_")[1]
# print(item_info[pure_itemId])
# print("-" * 100)
# for itemId in recom_result:
# pure_itemId = itemId.split("_")[1]
# print(item_info[pure_itemId])
# print(recom_result[itemId])
return recom_result
def get_one_user_recom():
"""
give fixed_user recom result
"""
user = "******"
alpha = 0.8
graph = read.get_graph_from_data("../data/ratings.dat")
iter_num = 100
recom_result = personal_rank(graph, user, alpha, iter_num, 100)
item_info = read.get_item_info("../data/movies.dat")
for itemid in graph[user]:
pure_itemid = itemid.split("_")[1]
print(item_info[pure_itemid])
print("----------")
for itemid in recom_result:
pure_itemid = itemid.split("_")[1]
print(item_info[pure_itemid], recom_result[itemid])
def get_one_user_recom():
"""
give one fix_user recom result
:return:
"""
user = "******"
alpha = 0.6
graph = read.get_graph_from_data("../data/rating.txt")
iter_num = 100
recom_result = personal_rank(graph, user, alpha, iter_num)
item_info = read.get_item_info("../data/movies.txt")
for itemid in graph[user]:
pure_itemid = itemid.split("_")[1]
print(item_info[pure_itemid])
print("result---")
for itemid in recom_result:
pure_itemid = itemid.split("_")[1]
print(item_info[pure_itemid])
print(recom_result[itemid])
return recom_result
def get_user_recom():
# user = "******"
# alpha = 0.6
# graph = read.get_graph_from_data(os.path.dirname(os.path.dirname(os.path.dirname(os.path.dirname(__file__)))) + "\\recommender\\data\\log.txt")
# print(personal_rank(graph, user, alpha, 10, 10))
user = "******"
alpha = 0.8
graph = read.get_graph_from_data(
os.path.dirname(
os.path.dirname(os.path.dirname(os.path.dirname(__file__)))) +
"\\recommender\\data\\ml-latest-small\\ratings.csv")
recom_result = personal_rank(graph, user, alpha, 100, 100)
# item_info = read.get_item_info(os.path.dirname(os.path.dirname(os.path.dirname(os.path.dirname(__file__)))) + "\\recommender\\data\\ml-latest-small\\movies.csv")
# for itemid, value in graph[user].items():
# print(item_info[itemid.split("_")[1]])
# print("-"*30)
# for itemid, value in recom_result.items():
# print(item_info[itemid.split("_")[1]])
# print(value)
return recom_result
def get_one_user_recom_matrix():
user = "******"
alpha = 0.7
graph = read.get_graph_from_data("../data/ratings.csv")
recom_result = personal_rank_matrix(graph, user, alpha, 100)
return recom_result
"""
get E-alpha*m_mat.T
Args:
m_mat:
vertex: total item and user point
alpha: the prob for random walking
Return:
a sparse
"""
total_len = len(vertex)
row = []
col = []
data = []
for index in range(total_len):
row.append(index)
col.append(index)
data.append(1)
row = np.array(row)
col = np.array(col)
data = np.array(data)
eye_t = coo_matrix((data, (row, col)), shape=(total_len, total_len))
return eye_t.tocsr() - alpha * m_mat.tocsr().transpose()
if __name__ == "__main__":
graph = read.get_graph_from_data("../data/log.txt")
m, vertex, address_dict = graph_to_m(graph)
# print(address_dict)
# print(m.todense())
# print(mat_all_point(m, vertex, 0.8))
第二步，根据M矩阵得到单位矩阵E 减去 α*M矩阵的转置
get E - alpha*m_mat.T
:param m_mat:
:param vertex: total user item nodes
:param alpha: the prob for random walk
:return: a sparse matrix
"""
total_len = len(vertex)
rows = []
cols = []
values = []
# 初始化单位矩阵E，这也是一个稀疏矩阵，只有对角线上的元素为1
for i in range(total_len):
rows.append(i)
cols.append(i)
values.append(1)
rows = np.array(rows)
cols = np.array(cols)
values = np.array(values)
E = coo_matrix((values, (rows, cols)), shape=(total_len, total_len))
# 使用csr格式，这种格式可以使得运算变得快一些
return E.tocsr() - alpha * m_mat.tocsr().transpose()
if __name__ == '__main__':
graph = read.get_graph_from_data("../data/log.csv")
m, vertex, address_dict = graph_to_matrix(graph)
# print(address_dict)
# print(m.todense())
print(maxtrix_all_nodes(m, vertex, alpha=0.8).todense())
def get_one_user_by_matrix():
user = '******'
alpha = 0.8
graph = read.get_graph_from_data('../data/ratings.txt')
recom_result = personal_rank_matrix(graph, user, alpha, 100)
return recom_result
'''
get E-alpha*m_mat.T,矩阵公司逆里边的部分
:param m_mat: 上面函数得出的m
:param vertex:所有顶点
:param alpha:
:return:
a sparse稀疏矩阵
'''
total_len = len(vertex)
row = []
col = []
data = []
for index in range(total_len):
row.append(index)
col.append(index)
data.append(1)
row = np.array(row)
col = np.array(col)
data = np.array(data)
eye_t = coo_matrix((data, (row, col)), shape=(total_len, total_len))
#print(eye_t.todense())
#sys.exit()
return eye_t.tocsr() - alpha * m_mat.tocsr().transpose()
if __name__ == '__main__':
graph = read.get_graph_from_data('../data/log.txt')
m, vertex, address_dict = graph_to_m(graph)
#print(mat_all_point(m,vertex,0.8))
mat_all_point(m, vertex, 0.8)
