def gen_inverse_documen_fre():
print "calc inverse document frequency---start"
weibo_list_train = []
weibo_list_test = []
status_map_train = read_status() # train
for uid in status_map_train:
content_concated = " ".join(
[one_con_li[4] for one_con_li in status_map_train[uid]])
weibo_list_train.append(content_concated)
weibo_map_test = read_status(file_name='test/test_status.txt')
for uid in weibo_map_test:
content_concated = " ".join(
[one_con_li[4] for one_con_li in weibo_map_test[uid]])
weibo_list_test.append(content_concated)
all_weibo = []
all_weibo.extend(weibo_list_train)
all_weibo.extend(weibo_list_test)
N = len(weibo_list_test) + len(weibo_list_train)
global inverse_doc_fre
for one_weibo in all_weibo:
for one_word in set(one_weibo.split(' ')):
inverse_doc_fre[one_word] = 1.0 + inverse_doc_fre.get(
one_word, 0.0)
for one_word in inverse_doc_fre:
inverse_doc_fre[one_word] = math.log(
float(N) / inverse_doc_fre[one_word])
print "calc inverse document frequency---end"
def gen_test_predict_result():
print "test data predict -- start"
test_status = read_status(file_name='test/test_status.txt')
for uid in test_status:
weibo_list = test_status[uid]
true_loc_list = []
for one_weibo in weibo_list:
one_weibo_content = one_weibo[4]
occur_loc_list = process_one_weibo_return_loclist_original(
one_weibo_content)
if len(occur_loc_list) != 0:
for one_loc in occur_loc_list:
one_weibo_vec = train_vecdic_to_vec(
gen_vector_for_one_weibo(one_weibo_content))
if predit_oneloc_with_oneweibo(one_loc, one_weibo_vec):
true_loc_list.append(tran_loc_to_request(one_loc))
if len(true_loc_list) != 0:
test_predict_result[uid] = pick_location_this(true_loc_list)
print "uid : ", uid, ' predict loc: ', test_predict_result[uid]
print "test data predict -- end"
pass
def train_and_predict_age():
target_lable, feature_list = feature_construct_for_train(
read_lable(), read_links(), read_status())
feature_map = feature_construct_for_teat(
read_links(file_name='test/test_links.txt'),
read_status(file_name='test/test_status.txt'))
from sklearn.ensemble import RandomForestClassifier
clf = RandomForestClassifier(n_estimators=10)
clf = clf.fit(feature_list, target_lable)
pre_result = {}
for uid in feature_map:
pre_result[uid] = clf.predict(feature_map[uid])[0]
return pre_result
def gen_test_predict_result():
print "test data predict -- start"
test_status = read_status(file_name='test/test_status.txt')
for uid in test_status:
weibo_list = test_status[uid]
true_loc_dic = {}
for one_weibo in weibo_list:
one_weibo_content = one_weibo[4]
occur_loc_list = process_one_weibo_return_loclist_original(one_weibo_content)
if len(occur_loc_list) != 0:
one_weibo_vec = normalize(np.array(train_vecdic_to_vec(gen_vector_for_one_weibo(one_weibo_content))))
for one_loc in occur_loc_list:
true_loc_dic[one_loc] = 0.5
if one_loc in loc_map_twotypes_data:
# true_loc_dic[one_loc] = max(true_loc_dic.get(one_loc, 0.0) , ((one_weibo_vec * loc_map_twotypes_data[one_loc][1]).sum() + 1.0) / ((one_weibo_vec * loc_map_twotypes_data[one_loc][1]).sum() + 1.0 + (one_weibo_vec * loc_map_twotypes_data[one_loc][0]).sum()))
true_loc_dic[one_loc] = true_loc_dic.get(one_loc, 0.0) + (one_weibo_vec * loc_map_twotypes_data[one_loc][1]).sum() - (one_weibo_vec * loc_map_twotypes_data[one_loc][0]).sum()
# print true_loc_dic[one_loc],(one_weibo_vec * loc_map_twotypes_data[one_loc][1]).sum(),(one_weibo_vec * loc_map_twotypes_data[one_loc][0]).sum()
else:
true_loc_dic[one_loc] = 0.8
# if len(true_loc_list) != 0:
# test_predict_result[uid] = pick_location_this(true_loc_list)
# true_loc_for_pick = {}
# for one_loc in true_loc_dic:
# true_loc_for_pick[tran_loc_to_request(one_loc)] = true_loc_for_pick.get(tran_loc_to_request(one_loc),0.0) + true_loc_dic[one_loc]
requested_true_loc = {}
for loc in true_loc_dic:
if tran_loc_to_request(loc) not in requested_true_loc:
requested_true_loc[tran_loc_to_request(loc)] = 0.0
requested_true_loc[tran_loc_to_request(loc)] += true_loc_dic[loc]
if len(requested_true_loc) !=0:
max_val = -1.0;
max_loc = ""
for one_loc in requested_true_loc:
if requested_true_loc[one_loc] > max_val:
max_val = requested_true_loc[one_loc]
max_loc = one_loc
test_predict_result[uid] = max_loc
# print "uid : ", uid, ' predict loc: ', test_predict_result[uid]
print "test data predict -- end"
pass
def for_train():
status_map_train = read_status() # train
lable_train = read_lable_original_for_loc()
print "-- construct data for train -- start"
for uid in lable_train:
weibo_list = status_map_train[uid]
loc = lable_train[uid]['loc']
for one_weibo in weibo_list:
weibo_content = one_weibo[4]
occur_loc_list = process_one_weibo_return_loclist_original(weibo_content)
if len(occur_loc_list) != 0:
this_vec = gen_vector_for_one_weibo(weibo_content)
for occur_loc in occur_loc_list:
# 初始化 大容器
if occur_loc not in loc_map_twotypes_data:
loc_map_twotypes_data[occur_loc] = {}
loc_map_twotypes_data[occur_loc][0] = np.array([0.0 for i in range(len(dictionry))])
loc_map_twotypes_data[occur_loc][10] = 1.0
loc_map_twotypes_data[occur_loc][1] = np.array([0.0 for i in range(len(dictionry))])
loc_map_twotypes_data[occur_loc][11] = 1.0
for i in this_vec:
if occur_loc == loc:
loc_map_twotypes_data[occur_loc][1][i] += this_vec[i]
loc_map_twotypes_data[occur_loc][11] += 1.0
else:
loc_map_twotypes_data[occur_loc][0][i] += this_vec[i]
loc_map_twotypes_data[occur_loc][10] += 1.0
for one_loc in loc_map_twotypes_data:
loc_map_twotypes_data[one_loc][0] = loc_map_twotypes_data[one_loc][0]/loc_map_twotypes_data[occur_loc][10]
loc_map_twotypes_data[one_loc][1] = loc_map_twotypes_data[one_loc][1]/loc_map_twotypes_data[occur_loc][11]
print loc_map_twotypes_data[one_loc][0].sum()
print "-- construct data for train -- end"
from tutils_about_weibo import tranfiorm_age
from utils_about_weibo_3 import read_lable, read_links, read_status
def get_unique_source(weibo_map):
unique_source = {}
for uid in weibo_map:
for one_weibo in weibo_map[uid]:
source = one_weibo[2]
if source not in unique_source:
unique_source[source] = 1
return unique_source.keys()
unique_source_list = get_unique_source(read_status())
def feature_construct_for_train(label_map, links_map, weibo_map):
target_lable = []
feature_list = []
for uid in label_map:
target_lable.append(tranfiorm_age(int(label_map[uid]['age'])))
this_feature = []
# 第一个特征，有多少个粉丝
this_feature.append(len(links_map.get(uid, [1])))
def gen_test_predict_result_use_PINGLV():
read_wordmap()
status_map_train = read_status() # train
lable_train = read_lable_original_for_loc()
print "-- construct data for train -- start"
for uid in lable_train:
weibo_list = status_map_train[uid]
loc = lable_train[uid]['loc']
for one_weibo in weibo_list:
weibo_content = one_weibo[4]
occur_loc_list = process_one_weibo_return_loclist_original(
weibo_content)
if len(occur_loc_list) != 0:
for occur_loc in occur_loc_list:
# 初始化 大容器
if occur_loc not in loc_map_twotypes_data:
loc_map_twotypes_data[occur_loc] = {}
loc_map_twotypes_data[occur_loc][0] = 0.0
loc_map_twotypes_data[occur_loc][1] = 1.0
# print this_vec
if occur_loc == loc:
loc_map_twotypes_data[occur_loc][1] += 1.0
else:
loc_map_twotypes_data[occur_loc][0] += 1.0
print "-- construct data for train -- end"
print "test data predict -- start"
test_status = read_status(file_name='test/test_status.txt')
for uid in test_status:
weibo_list = test_status[uid]
true_loc_dic = {}
oriloc_list = []
for one_weibo in weibo_list:
one_weibo_content = one_weibo[4]
occur_loc_list = process_one_weibo_return_loclist_original(
one_weibo_content)
if len(occur_loc_list) != 0:
for one_loc in occur_loc_list:
trust_val = 1.0
if one_loc in loc_map_twotypes_data:
trust_val = loc_map_twotypes_data[one_loc][1] / (
loc_map_twotypes_data[one_loc][0] +
loc_map_twotypes_data[one_loc][1])
oriloc_list.append(one_loc + "-" + str(trust_val))
request_loc = tran_loc_to_request(one_loc)
true_loc_dic[request_loc] = true_loc_dic.get(
request_loc, 0.0) + trust_val
if len(true_loc_dic) != 0:
max_val = -1.0
max_loc = ""
for one_loc in true_loc_dic:
if true_loc_dic[one_loc] > max_val:
max_val = true_loc_dic[one_loc]
max_loc = one_loc
test_predict_result[uid] = max_loc
one_p_line = ""
for i in true_loc_dic:
one_p_line += " " + str(i) + ":" + str(true_loc_dic[i])
print "ori_loc :", ' '.join(oriloc_list)
print "transform_loc :", one_p_line[1:]
print "uid : ", uid, ' predict loc: ', test_predict_result.get(
uid, '---')
print "test data predict -- end"
pass
def for_train():
status_map_train = read_status() # train
lable_train = read_lable_original_for_loc()
print "-- construct data for train -- start"
for uid in lable_train:
weibo_list = status_map_train[uid]
loc = lable_train[uid]['loc']
for one_weibo in weibo_list:
weibo_content = one_weibo[4]
occur_loc_list = process_one_weibo_return_loclist_original(
weibo_content)
if len(occur_loc_list) != 0:
this_vec = gen_vector_for_one_weibo(weibo_content)
for occur_loc in occur_loc_list:
# 初始化 大容器
if occur_loc not in loc_map_twotypes_data:
loc_map_twotypes_data[occur_loc] = {}
loc_map_twotypes_data[occur_loc][0] = []
loc_map_twotypes_data[occur_loc][1] = []
loc_map_twotypes_clf[occur_loc] = {}
# print this_vec
if occur_loc == loc:
loc_map_twotypes_data[occur_loc][1].append(this_vec)
else:
loc_map_twotypes_data[occur_loc][0].append(this_vec)
print "-- construct data for train -- end"
print "--print data size -- start"
for one_loc in loc_map_twotypes_data:
print 'location :', one_loc, " true size: ", len(
loc_map_twotypes_data[one_loc][1]), " false size: ", len(
loc_map_twotypes_data[one_loc][0])
print "--print data size -- end"
print "-- train the clf model -- start"
for index, one_loc in enumerate(loc_map_twotypes_clf.keys()):
print 'location :', one_loc, " true size: ", len(
loc_map_twotypes_data[one_loc][1]), " false size: ", len(
loc_map_twotypes_data[one_loc]
[0]), " index: ", index, " all: ", len(
loc_map_twotypes_clf.keys())
lable_list = []
data_list = []
max_size = max([
len(loc_map_twotypes_data[one_loc][1]),
len(loc_map_twotypes_data[one_loc][0])
])
loc_map_twotypes_data[one_loc][1].extend(
select_x_this(loc_map_twotypes_data[one_loc][1],
max_size - len(loc_map_twotypes_data[one_loc][1])))
loc_map_twotypes_data[one_loc][0].extend(
select_x_this(loc_map_twotypes_data[one_loc][0],
max_size - len(loc_map_twotypes_data[one_loc][0])))
lable_list = [
0.0 for i in range(len(loc_map_twotypes_data[one_loc][0]))
]
lable_list.extend(
[1.0 for i in range(len(loc_map_twotypes_data[one_loc][1]))])
data_list = loc_map_twotypes_data[one_loc][0]
data_list.extend(loc_map_twotypes_data[one_loc][1])
X = []
for one_dic_vec in data_list:
X.append(train_vecdic_to_vec(one_dic_vec))
# print data_list
# print 'location :',one_loc,"one loc train data size: ",len(data_list)," lable size: ",len(lable_list)
# from sklearn.ensemble import RandomForestClassifier
# clf = RandomForestClassifier(n_estimators=5, n_jobs=-1)
# clf = clf.fit(X, lable_list)
clf = svm.LinearSVC()
clf.fit(X, lable_list)
# pre_label = clf.predict(p_X)
loc_map_twotypes_clf[one_loc] = clf
print "-- train the clf model -- end"
pass
# for word in before_79_map_word_count_source.keys():
# before_79_map_word_count_source[word] = float(before_79_map_word_count_source[word]) / count____before_79_map_word_weibo_num
# for word in in_80_to_89__map_word_count_source.keys():
# in_80_to_89__map_word_count_source[word] = float(in_80_to_89__map_word_count_source[word]) / count____in_80_to_89__map_word_weibo_num
# for word in past_90__map_word_count_source.keys():
# past_90__map_word_count_source[word] = float(past_90__map_word_count_source[word]) / count____past_90__map_word_weibo_num
#
# for word in before_79_map_word_count_content.keys():
# before_79_map_word_count_content[word] = float(before_79_map_word_count_content[word]) / count____before_79_map_word_weibo_num
# for word in in_80_to_89__map_word_count_content.keys():
# in_80_to_89__map_word_count_content[word] = float(in_80_to_89__map_word_count_content[word]) / count____in_80_to_89__map_word_weibo_num
# for word in past_90__map_word_count_content.keys():
# past_90__map_word_count_content[word] = float(past_90__map_word_count_content[word]) / count____past_90__map_word_weibo_num
# print len(before_79_map_word_count_source)
# print len(in_80_to_89__map_word_count_source)
# print len(past_90__map_word_count_source)
#
# print len(before_79_map_word_count_content)
# print len(in_80_to_89__map_word_count_content)
# print len(past_90__map_word_count_content)
return before_79_map_word_count_source, in_80_to_89__map_word_count_source, past_90__map_word_count_source, before_79_map_word_count_content, in_80_to_89__map_word_count_content, past_90__map_word_count_content
pass
if __name__ == '__main__':
# read_train_weibo_status(read_lable(), read_status())
read_train_weibo_status_for_age(read_lable(), read_status())
pass
def gen_model_predict_result():
print "1,construct dataset"
status_map_train = read_status() # train
status_map_test = read_status('test/test_status.txt') # train
train_lable_map = read_lable()
uid_list_train = []
age_list_train = []
sex_list_train = []
loc_list_train = []
weibo_list_train = []
uid_list_test = []
weibo_list_test = []
others_feature_train = []
label_map_train = train_lable_map
links_map_train = read_links()
weibo_map_train = status_map_train
for uid in status_map_train:
content_concated = " ".join(
[one_con_li[4] for one_con_li in status_map_train[uid]])
age = tranfiorm_age(train_lable_map[uid]['age'])
sex = train_lable_map[uid]['sex']
loc = train_lable_map[uid]['loc']
uid_list_train.append(uid)
age_list_train.append(age)
sex_list_train.append(sex)
loc_list_train.append(loc)
weibo_list_train.append(content_concated)
this_feature = []
# 第一个特征，有多少个粉丝
this_feature.append(len(links_map_train.get(uid, [1])))
# 第2，微博数
weibo_num = len(weibo_map_train.get(uid, [1]))
this_feature.append(weibo_num)
# 微博重复数
unique_source = {}
unique_weibo = {}
source_num = {}
for one_weibo in weibo_map_train[uid]:
source = one_weibo[2]
content = one_weibo[4]
if source not in unique_source:
unique_source[source] = 1
if content not in unique_weibo:
unique_weibo[content] = 1
source_num[source] = source_num.get(source, 0.0) + 1.0
this_feature.append(weibo_num / float(len(unique_source)))
this_feature.append(weibo_num / float(len(unique_weibo)))
this_list_for_source = []
for one_source in unique_source_list:
this_list_for_source.append(source_num[one_source] if one_source in
source_num else 0.0)
this_feature.extend(this_list_for_source)
others_feature_train.append(this_feature)
links_map_test = read_links(file_name='test/test_links.txt')
weibo_map_test = read_status(file_name='test/test_status.txt')
others_feature_test = []
for uid in status_map_test:
content_concated = " ".join(
[one_con_li[4] for one_con_li in status_map_test[uid]])
uid_list_test.append(uid)
weibo_list_test.append(content_concated)
this_feature = []
# 第一个特征，有多少个粉丝
this_feature.append(len(links_map_test.get(uid, [1])))
# 第2，微博数
weibo_num = len(weibo_map_test.get(uid, [1]))
this_feature.append(weibo_num)
# 微博重复数
unique_source = {}
unique_weibo = {}
source_num = {}
for one_weibo in weibo_map_test[uid]:
source = one_weibo[2]
content = one_weibo[4]
if source not in unique_source:
unique_source[source] = 1
if content not in unique_weibo:
unique_weibo[content] = 1
source_num[source] = source_num.get(source, 0.0) + 1.0
this_feature.append(weibo_num / float(len(unique_source)))
this_feature.append(weibo_num / float(len(unique_weibo)))
this_list_for_source = []
for one_source in unique_source_list:
this_list_for_source.append(source_num[one_source] if one_source in
source_num else 0.0)
this_feature.extend(this_list_for_source)
others_feature_test.append(this_feature)
# store_to_file(weibo_list_train, weibo_list_test)
# one_hot
all_features_list = gen_feature_list(weibo_list_train, weibo_list_test)
# 添加原始feature
print "添加原始feature start"
for index, da in enumerate(others_feature_train):
all_features_list[index].extend(da)
for index, da in enumerate(others_feature_test):
all_features_list[index + len(others_feature_train)].extend(da)
X = all_features_list[:len(weibo_list_train)]
p_X = all_features_list[len(weibo_list_train):]
print "添加原始feature end"
print "2,one hot and lda done, start training and predict"
return train_and_predict(X, age_list_train, p_X,
uid_list_test), train_and_predict(
X, sex_list_train, p_X,
uid_list_test), train_and_predict(
X, loc_list_train, p_X, uid_list_test)
