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predict_gender_mapper.py
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# _*_ coding: utf-8 _*_
import sys
import numpy
from scipy import sparse
sys.path.append("demo.zip")
from config_code.config_general import PTYPE, DEBUG, PLATFORM, config_default_value
from config_code.config_general import config_params_algorithm, config_params_onetimecount
from config_code.config_load_data import get_app_data, get_train_model
from config_code.config_dm_gender import config_gender_tags, config_gender_string
from predict.predict_gender import GenderPredict
pkg_dict, app_dict = get_app_data(("demo.zip/config_data/" if not DEBUG else "config_data/") + "app_segment.txt", "gender", PLATFORM)
gender_predict = GenderPredict(config_gender_tags, app_dict)
def get_train_set():
"""
获取训练集
"""
for line in sys.stdin:
# 处理行数据=====================================================================================================
frags = line.strip().split("\t")
if len(frags) < 3:
continue
# 获取用户数据
# user_id, user_id_md5 = frags[0].strip(), frags[1].strip()
user_pkg_list = set([pkgname.strip() for pkgname in frags[2].strip().split(",")])
user_pkg_index_list = [pkg_dict[pkgname] for pkgname in user_pkg_list if pkgname in pkg_dict]
# 跳过无用数据，train和predict有所不同
if (len(user_pkg_index_list) < 5) or (len(user_pkg_index_list) > 50):
continue
# 处理行数据=====================================================================================================
# 生成训练数据
result = gender_predict.check_user(user_pkg_index_list)
if result["max_num"] > 5 and result["rate"] >= 0.75:
print("\t".join([result["gender"], ",".join([str(i) for i in user_pkg_index_list]),
";".join([app_dict[pkg_index][0] for pkg_index in user_pkg_index_list]) if DEBUG else ""]))
return
def predict_gender():
"""
预测性别
"""
# 获取字典文件和模型变量
train_pkgindex_dict, model = get_train_model("gender_train_%s.txt" % PLATFORM, "gender", config_params_algorithm)
# 变量
train_ids_list = []
train_ids_md5_list = []
train_pkg_list = []
train_x_i = []
train_x_j = []
line_num = 0
# 开始map过程
for line in sys.stdin:
# 处理行数据=====================================================================================================
frags = line.strip().split("\t")
if len(frags) < 3:
continue
# 获取用户数据
user_id, user_id_md5 = frags[0].strip(), frags[1].strip()
user_pkg_list = set([pkgname.strip() for pkgname in frags[2].strip().split(",")])
user_pkg_index_list = [pkg_dict[pkgname] for pkgname in user_pkg_list if pkgname in pkg_dict]
# 跳过无用数据，train和predict有所不同
if len(user_pkg_index_list) == 0:
print("\t".join([user_id, user_id_md5, "gender", config_default_value, "0.0",
";".join([app_dict[pkg_index][0] for pkg_index in user_pkg_index_list]) if DEBUG else ""]))
continue
# 处理行数据=====================================================================================================
result = gender_predict.check_user(user_pkg_index_list)
if result["rate"] >= 0.75:
print("\t".join([user_id, user_id_md5, "gender", result["gender"], str(result["rate"]),
";".join([app_dict[pkg_index][0] for pkg_index in user_pkg_index_list]) if DEBUG else ""]))
else:
# 赋值
for pkg_index in [train_pkgindex_dict[i] for i in user_pkg_index_list if i in train_pkgindex_dict]:
train_x_i.append(line_num)
train_x_j.append(pkg_index)
train_ids_list.append(user_id)
train_ids_md5_list.append(user_id_md5)
train_pkg_list.append(user_pkg_index_list)
line_num += 1
# 统一预测
if line_num == config_params_onetimecount:
shape = (line_num, len(train_pkgindex_dict))
test_data = sparse.coo_matrix((numpy.ones(len(train_x_i)), (train_x_i, train_x_j)), shape=shape)
result_list = model.predict_proba(test_data)
for i in range(len(train_ids_list)):
user_id = train_ids_list[i]
user_id_md5 = train_ids_md5_list[i]
user_pkgs = train_pkg_list[i]
result = result_list[i]
print("\t".join([user_id, user_id_md5, "gender",
config_gender_string[0] if result[0] > 0.5 else (
config_gender_string[1] if result[1] > 0.5 else config_default_value),
str(result[0]) if result[0] > 0.5 else (
str(result[1]) if result[1] > 0.5 else "0.0"),
";".join([app_dict[pkg_index][0] for pkg_index in user_pkgs]) if DEBUG else ""]))
# 清空数据，进入下一轮缓存
train_ids_list = []
train_ids_md5_list = []
train_pkg_list = []
train_x_i = []
train_x_j = []
line_num = 0
# 最后的数据
if line_num > 0:
shape = (line_num, len(train_pkgindex_dict))
test_data = sparse.coo_matrix((numpy.ones(len(train_x_i)), (train_x_i, train_x_j)), shape=shape)
result_list = model.predict_proba(test_data)
for i in range(len(train_ids_list)):
user_id = train_ids_list[i]
user_id_md5 = train_ids_md5_list[i]
user_pkgs = train_pkg_list[i]
result = result_list[i]
print("\t".join([user_id, user_id_md5, "gender",
config_gender_string[0] if result[0] > 0.5 else (
config_gender_string[1] if result[1] > 0.5 else config_default_value),
str(result[0]) if result[0] > 0.5 else (
str(result[1]) if result[1] > 0.5 else "0.0"),
";".join([app_dict[pkg_index][0] for pkg_index in user_pkgs]) if DEBUG else ""]))
# 结束
return
if __name__ == '__main__':
if PTYPE == "train":
get_train_set()
elif PTYPE == "predict":
predict_gender()
else:
exit()