def Guess_User_Group_by_KMeans(db_dir, db_file_name):
"""
1. get distinct user ids
2. foreach user id, compute which group should it be
2.1 convert the data to orange data table
2.2 kmeans
3. save them into database
"""
sqlite = SQLDao.SQLiteDao(db_dir, db_file_name)
h1, uids = sqlite.get_distinct_user_id()
user_group_dict = {}
for uid in uids:
# retreive the user group info of a specific user
h2, uid_group_info = sqlite.get_group_info_by_user_id(uid[SQLDao.LABEL_USER_GROUP_INFO_USERID])
# convert the uid group info into the orange data table
features = []
features.append(Orange.feature.Continuous(SQLDao.LABEL_USER_GROUP_INFO_GROUPCOUNT))
domain = Orange.data.Domain(features, False)
domain.add_meta(
Orange.feature.Descriptor.new_meta_id(), Orange.feature.Continuous(SQLDao.LABEL_USER_GROUP_INFO_USERID)
)
domain.add_meta(
Orange.feature.Descriptor.new_meta_id(), Orange.feature.Continuous(SQLDao.LABEL_USER_GROUP_INFO_GROUPID)
)
datas = []
for i in uid_group_info:
data = Orange.data.Instance(domain, [i[SQLDao.LABEL_USER_GROUP_INFO_GROUPCOUNT]])
data[SQLDao.LABEL_USER_GROUP_INFO_USERID] = i[SQLDao.LABEL_USER_GROUP_INFO_USERID]
data[SQLDao.LABEL_USER_GROUP_INFO_GROUPID] = i[SQLDao.LABEL_USER_GROUP_INFO_GROUPID]
datas.append(data)
table = Orange.data.Table(domain, datas)
target_instances = []
if len(table) > 3:
km = Orange.clustering.kmeans.Clustering(data=table, distance=GroupCountDistance)
clusters = km.clusters
d = {}
for idx, c_label in enumerate(clusters):
if d.has_key(c_label):
d[c_label].append(table[idx])
else:
d[c_label] = [table[idx]]
if len(d) == 3:
# figure out which cluster represent the largest cluster
max_label = None
max_value = -1
for label, instances in d.items():
temp_list = [i[SQLDao.LABEL_USER_GROUP_INFO_GROUPCOUNT].value for i in instances]
if max(temp_list) > max_value:
max_value = max(temp_list)
max_label = label
pass
for instance in d[max_label]:
target_instances.append(instance)
else:
# just pick the group which has the largest group_count if it is large enough?
table.sort([SQLDao.LABEL_USER_GROUP_INFO_GROUPCOUNT])
if table[-1][SQLDao.LABEL_USER_GROUP_INFO_GROUPCOUNT].value > 20:
target_instances.append(table[-1])
# print 'processing %s'%uid[SQLDao.LABEL_USER_GROUP_INFO_USERID]
user_group_dict[uid[SQLDao.LABEL_USER_GROUP_INFO_USERID]] = target_instances
pass
print "finish cluster"
sqlite.save_user_group_clustered(user_group_dict)
def Guess_User_Group_by_KMeans(db_dir, db_file_name):
'''
1. get distinct user ids
2. foreach user id, compute which group should it be
2.1 convert the data to orange data table
2.2 kmeans
3. save them into database
'''
sqlite = SQLDao.SQLiteDao(db_dir, db_file_name)
h1, uids = sqlite.get_distinct_user_id()
user_group_dict = {}
for uid in uids:
# retreive the user group info of a specific user
h2, uid_group_info = sqlite.get_group_info_by_user_id(
uid[SQLDao.LABEL_USER_GROUP_INFO_USERID])
# convert the uid group info into the orange data table
features = []
features.append(
Orange.feature.Continuous(SQLDao.LABEL_USER_GROUP_INFO_GROUPCOUNT))
domain = Orange.data.Domain(features, False)
domain.add_meta(
Orange.feature.Descriptor.new_meta_id(),
Orange.feature.Continuous(SQLDao.LABEL_USER_GROUP_INFO_USERID))
domain.add_meta(
Orange.feature.Descriptor.new_meta_id(),
Orange.feature.Continuous(SQLDao.LABEL_USER_GROUP_INFO_GROUPID))
datas = []
for i in uid_group_info:
data = Orange.data.Instance(
domain, [i[SQLDao.LABEL_USER_GROUP_INFO_GROUPCOUNT]])
data[SQLDao.LABEL_USER_GROUP_INFO_USERID] = i[
SQLDao.LABEL_USER_GROUP_INFO_USERID]
data[SQLDao.LABEL_USER_GROUP_INFO_GROUPID] = i[
SQLDao.LABEL_USER_GROUP_INFO_GROUPID]
datas.append(data)
table = Orange.data.Table(domain, datas)
target_instances = []
if len(table) > 3:
km = Orange.clustering.kmeans.Clustering(
data=table, distance=GroupCountDistance)
clusters = km.clusters
d = {}
for idx, c_label in enumerate(clusters):
if d.has_key(c_label):
d[c_label].append(table[idx])
else:
d[c_label] = [table[idx]]
if len(d) == 3:
# figure out which cluster represent the largest cluster
max_label = None
max_value = -1
for label, instances in d.items():
temp_list = [
i[SQLDao.LABEL_USER_GROUP_INFO_GROUPCOUNT].value
for i in instances
]
if max(temp_list) > max_value:
max_value = max(temp_list)
max_label = label
pass
for instance in d[max_label]:
target_instances.append(instance)
else:
# just pick the group which has the largest group_count if it is large enough?
table.sort([SQLDao.LABEL_USER_GROUP_INFO_GROUPCOUNT])
if table[-1][SQLDao.LABEL_USER_GROUP_INFO_GROUPCOUNT].value > 20:
target_instances.append(table[-1])
# print 'processing %s'%uid[SQLDao.LABEL_USER_GROUP_INFO_USERID]
user_group_dict[uid[
SQLDao.LABEL_USER_GROUP_INFO_USERID]] = target_instances
pass
print 'finish cluster'
sqlite.save_user_group_clustered(user_group_dict)
