def recommend(users, time_slice, train_percent, top_n, use_type):
axis_poi, data_map, predicts, recommends = init_data2(users, train_percent, time_slice)
print "predicts: ", predicts
print "recommends: ", recommends
print "data_map: ", data_map
poi_dimension = len(axis_poi)
tensor = trans2(data_map, poi_dimension, users, time_slice)
print "tensor: ", tensor
# sparse(np.array(tensor))
threshold = 0.8
U, S, D = HOSVD(np.array(tensor), threshold)
# new_T, T, Z, Un, Sn, Vn = hosvd(tensor)
# new_T2, Z2, Un2, Sn2, Vn2 = hosvd2(tensor)
print "the mode-1 unfold of core tensor:"
print unfold(S, 1)
print "The n-mode singular values:"
print D
A = reconstruct(S, U)
print "reconstruct tensor: ", A
print frobenius_norm(tensor-A)
# sparse(A)
#
# print tensor[0][0][6]
# print A[0][0][6]
total = 0
available = 0
sum_precision = 0
sum_recall = 0
sum_f1_score = 0
for user in users:
data = data_map[user]
# print "data: ", data
for slot in range(0, time_slice):
check_list = data[slot]
data = A[users.index(user)][slot]
sort_data = []
for item in range(0, len(data)):
meta_data = (item, data[item])
sort_data.append(meta_data)
sort_data.sort(key=lambda x: x[1], reverse=True)
result_predict = []
result_recommend = []
for item in range(0, len(sort_data)):
if (sort_data[item][0] in set(data_map[user][slot])) and (len(result_predict) < top_n):
result_predict.append(sort_data[item][0])
else:
if len(result_recommend) < top_n:
result_recommend.append(sort_data[item][0])
# 1. 正确率 = 提取出的正确信息条数 / 提取出的信息条数
# 2. 召回率 = 提取出的正确信息条数 / 样本中的信息条数
# 两者取值在0和1之间，数值越接近1，查准率或查全率就越高。
# 3. F值 = 正确率 * 召回率 * 2 / (正确率 + 召回率) （F 值即为正确率和召回率的调和平均值）
check_predict = predicts[user][slot]
check_recommend = recommends[user][slot]
count_predict = 0
count_recommend = 0
for item in result_predict:
if item in check_predict:
count_predict += 1
for item in result_recommend:
if item in check_recommend:
count_recommend += 1
total += 1
if use_type == "recommendation":
if len(result_recommend) == 0:
print "用户"+str(user)+"在时间"+str(slot)+"的f1_score（推荐）: 没有生成推荐数据,无法完成推荐"
else:
precision = count_recommend / len(result_recommend)
if len(check_recommend) == 0:
print "用户"+str(user)+"在时间"+str(slot)+"的f1_score（推荐）: 校验推荐数据缺失,无法有效计算f1值"
else:
available += 1
recall = count_recommend / len(check_recommend)
if precision + recall == 0:
f1_score = 0
print "用户"+str(user)+"在时间"+str(slot)+"的f1_score（推荐）: "+str(f1_score)
else:
f1_score = (2 * precision * recall) / (precision + recall)
print "用户"+str(user)+"在时间"+str(slot)+"的f1_score（推荐）: "+str(f1_score)+",准确率为"+\
str(precision)+",召回率为"+str(recall)
sum_precision += precision
sum_recall += recall
sum_f1_score += f1_score
else:
if len(result_predict) == 0:
print "用户"+str(user)+"在时间"+str(slot)+"的f1_score（预测）: 没有生成预测数据,无法完成预测"
else:
precision = count_predict / len(result_predict)
if len(check_predict) == 0:
print "用户"+str(user)+"在时间"+str(slot)+"的f1_score（预测）: 校验预测数据缺失,无法有效计算f1值"
else:
available += 1
recall = count_predict / len(check_predict)
if precision + recall == 0:
f1_score = 0
print "用户"+str(user)+"在时间"+str(slot)+"的f1_score（预测）: "+str(f1_score)
else:
f1_score = (2 * precision * recall) / (precision + recall)
print "用户"+str(user)+"在时间"+str(slot)+"的f1_score（预测）: "+str(f1_score)+",准确率为"+\
str(precision)+",召回率为"+str(recall)
sum_precision += precision
sum_recall += recall
sum_f1_score += f1_score
return sum_precision / total, sum_recall / total, sum_f1_score / total, available / total
print item[0], item[1], item[2]
sum += item[2]
print "POIRank值的和： ", sum
prefs = {}
axis_prefs, prefs_axis, datas = init_poi_type_data(tuple(range(0, 182)))
print "axis_prefs: ", axis_prefs
for key in axis_prefs.keys():
print key, " ", axis_prefs[key]
print "datas: ", datas
for key in datas.keys():
data = datas[key]
# print "data_length: ", len(data)
if len(data) > 1:
tensor = trans2(data, len(axis_prefs), 2)
result = param(tensor)
T = pykov.Chain(result)
res = T.steady()
print "user" + str(key) + ": ", vec(res, len(axis_prefs),
len(pois_axis2))
prefs[key] = vec(res, len(axis_prefs), len(pois_axis2))
# 综合推荐
user = 1
rank_result2 = []
for item in rank_result:
res = item[2] * prefs[user][prefs_axis[name2type[item[1]]]]
rank_result2.append((item[1], res))
def recommend(users, time_slice, train_percent, top_n, use_type):
axis_poi, data_map, predicts, recommends = init_data2(users, train_percent, time_slice)
print "predicts: ", predicts
print "recommends: ", recommends
print "data_map: ", data_map
poi_dimension = len(axis_poi)
tensor = trans2(data_map, poi_dimension, users, time_slice)
print "tensor: ", tensor
# sparse(np.array(tensor))
threshold = 0.8
U, S, D = HOSVD(np.array(tensor), threshold)
# new_T, T, Z, Un, Sn, Vn = hosvd(tensor)
# new_T2, Z2, Un2, Sn2, Vn2 = hosvd2(tensor)
print "the mode-1 unfold of core tensor:"
print unfold(S, 1)
print "The n-mode singular values:"
print D
A = reconstruct(S, U)
print "reconstruct tensor: ", A
print frobenius_norm(tensor-A)
# sparse(A)
#
# print tensor[0][0][6]
# print A[0][0][6]
total = 0
available = 0
sum_precision = 0
sum_recall = 0
sum_f1_score = 0
for user in users:
data = data_map[user]
# print "data: ", data
for slot in range(0, time_slice):
check_list = data[slot]
data = A[users.index(user)][slot]
sort_data = []
for item in range(0, len(data)):
meta_data = (item, data[item])
sort_data.append(meta_data)
sort_data.sort(key=lambda x: x[1], reverse=True)
result_predict = []
result_recommend = []
for item in range(0, len(sort_data)):
if (sort_data[item][0] in set(data_map[user][slot])) and (len(result_predict) < top_n):
result_predict.append(sort_data[item][0])
else:
if len(result_recommend) < top_n:
result_recommend.append(sort_data[item][0])
# 1. 正确率 = 提取出的正确信息条数 / 提取出的信息条数
# 2. 召回率 = 提取出的正确信息条数 / 样本中的信息条数
# 两者取值在0和1之间，数值越接近1，查准率或查全率就越高。
# 3. F值 = 正确率 * 召回率 * 2 / (正确率 + 召回率) （F 值即为正确率和召回率的调和平均值）
check_predict = predicts[user][slot]
check_recommend = recommends[user][slot]
count_predict = 0
count_recommend = 0
for item in result_predict:
if item in check_predict:
count_predict += 1
for item in result_recommend:
if item in check_recommend:
count_recommend += 1
total += 1
if use_type == "recommendation":
if len(result_recommend) == 0:
print "用户"+str(user)+"在时间"+str(slot)+"的f1_score（推荐）: 没有生成推荐数据,无法完成推荐"
else:
precision = count_recommend / len(result_recommend)
if len(check_recommend) == 0:
print "用户"+str(user)+"在时间"+str(slot)+"的f1_score（推荐）: 校验推荐数据缺失,无法有效计算f1值"
else:
available += 1
recall = count_recommend / len(check_recommend)
if precision + recall == 0:
f1_score = 0
print "用户"+str(user)+"在时间"+str(slot)+"的f1_score（推荐）: "+str(f1_score)
else:
f1_score = (2 * precision * recall) / (precision + recall)
print "用户"+str(user)+"在时间"+str(slot)+"的f1_score（推荐）: "+str(f1_score)+",准确率为"+\
str(precision)+",召回率为"+str(recall)
sum_precision += precision
sum_recall += recall
sum_f1_score += f1_score
else:
if len(result_predict) == 0:
print "用户"+str(user)+"在时间"+str(slot)+"的f1_score（预测）: 没有生成预测数据,无法完成预测"
else:
precision = count_predict / len(result_predict)
if len(check_predict) == 0:
print "用户"+str(user)+"在时间"+str(slot)+"的f1_score（预测）: 校验预测数据缺失,无法有效计算f1值"
else:
available += 1
recall = count_predict / len(check_predict)
if precision + recall == 0:
f1_score = 0
print "用户"+str(user)+"在时间"+str(slot)+"的f1_score（预测）: "+str(f1_score)
else:
f1_score = (2 * precision * recall) / (precision + recall)
print "用户"+str(user)+"在时间"+str(slot)+"的f1_score（预测）: "+str(f1_score)+",准确率为"+\
str(precision)+",召回率为"+str(recall)
sum_precision += precision
sum_recall += recall
sum_f1_score += f1_score
return sum_precision / total, sum_recall / total, sum_f1_score / total, available / total
print item[0], item[1], item[2]
sum += item[2]
print "POIRank值的和： ", sum
prefs = {}
axis_prefs, prefs_axis, datas = init_poi_type_data(tuple(range(0, 182)))
print "axis_prefs: ", axis_prefs
for key in axis_prefs.keys():
print key, " ", axis_prefs[key]
print "datas: ", datas
for key in datas.keys():
data = datas[key]
# print "data_length: ", len(data)
if len(data) > 1:
tensor = trans2(data, len(axis_prefs), 2)
result = param(tensor)
T = pykov.Chain(result)
res = T.steady()
print "user"+str(key)+": ", vec(res, len(axis_prefs), len(pois_axis2))
prefs[key] = vec(res, len(axis_prefs), len(pois_axis2))
# 综合推荐
user = 1
rank_result2 = []
for item in rank_result:
res = item[2] * prefs[user][prefs_axis[name2type[item[1]]]]
rank_result2.append((item[1], res))
