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A_info_dict.py
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# -*- coding:UTF-8 -*-
"""
从JData_User.csv,JData_Product.csv,JData_Comment.csv三个文件获取用户/商品特征字典
"""
import csv
import numpy as np
import pandas as pd
'''遍历字典num项'''
def ergodic_dict(dict, num=5):
index = 0
for key in dict:
print key, dict[key]
index += 1
if index > num:
break
print 'length:', len(dict)
print ' '
'''遍历集合num项'''
def ergodic_set(st, num=5):
index = 0
for key in st:
print key
index += 1
if index > num:
break
print 'length:', len(st)
print ' '
'''获取用户特征字典{uID：[age, sex, rank]}
age:0,1,2,3,4,5,6
sex:0,1,2,3
rank:1,2,3,4,5
'''
def usr_feature():
r_file = file('JData/JData_User.csv', 'r')
reader = csv.reader(r_file)
feature_dict = {}
for line in reader:
if reader.line_num == 1:
continue
if line[4] == 'NULL':
reg = 0
if line[4] != 'NULL':
reg = int(line[4].split('-')[0])-2000
if line[1].decode('gbk').encode('utf8') == '-1':
feature_dict[line[0]] = [line[1], line[2], line[3], reg]
elif line[1].decode('gbk').encode('utf8') == '56岁以上':
feature_dict[line[0]] = ['56', line[2], line[3], reg]
elif line[1].decode('gbk').encode('utf8') == '46-55岁':
feature_dict[line[0]] = ['46-55', line[2], line[3], reg]
elif line[1].decode('gbk').encode('utf8') == '36-45岁':
feature_dict[line[0]] = ['36-45', line[2], line[3], reg]
elif line[1].decode('gbk').encode('utf8') == '26-35岁':
feature_dict[line[0]] = ['26-35', line[2], line[3], reg]
elif line[1].decode('gbk').encode('utf8') == '16-25岁':
feature_dict[line[0]] = ['16-25', line[2], line[3], reg]
else:
feature_dict[line[0]] = ['15', line[2], line[3], reg]
r_file.close()
age_dict = {'-1': [1,0,0,0,0,0,0], '15': [0,1,0,0,0,0,0], '16-25': [0,0,1,0,0,0,0],
'26-35': [0,0,0,1,0,0,0], '36-45': [0,0,0,0,1,0,0], '46-55': [0,0,0,0,0,1,0],
'56': [0,0,0,0,0,0,1]}
sex_dict = {'0': [1,0,0,0], '1': [0,1,0,0], '2': [0,0,1,0], 'NULL': [0,0,0,1]}
rank_dict = {'1': [1, 0, 0, 0, 0], '2': [0, 1, 0, 0, 0], '3': [0, 0, 1, 0, 0], '4': [0, 0, 0, 1, 0], '5': [0, 0, 0, 0, 1]}
transfer_feature = {}
for key in feature_dict:
#print original_feature[key]
transfer_feature[key] = age_dict[feature_dict[key][0]] + sex_dict[feature_dict[key][1]] + rank_dict[feature_dict[key][2]] + [feature_dict[key][3]]
return transfer_feature
'''获取商品特征字典{pID:{a1,a2,a3,cate,brand}}
a1:[-1,1,2,3]
a2:[-1,1,2]
a3:[-1,1,2]
'''
def product_feature():
r_file = file('JData/JData_Product.csv', 'r')
reader = csv.reader(r_file)
feature_dict = {}
a1_dict = {'-1': [1,0,0,0], '1': [0,1,0,0], '2': [0,0,1,0], '3': [0,0,0,1]}
a2_dict = {'-1': [1,0,0], '1': [0,1,0], '2': [0,0,1]}
a3_dict = {'-1': [1,0,0], '1': [0,1,0], '2': [0,0,1]}
for line in reader:
if reader.line_num == 1:
continue
feature_dict[line[0]] = a1_dict[line[1]] + a2_dict[line[2]] + a3_dict[line[3]] + [line[4]] + [line[5]]
return feature_dict
def product_brand():
I_brand_dict = dict()
r_file = file('JData/JData_Product.csv', 'r')
reader = csv.reader(r_file)
for line in reader:
if reader.line_num == 1:
continue
I_brand_dict[line[0]] = line[5]
brand_set = np.sort(np.array(list(set(I_brand_dict.values())), int))
transfer_dict = dict()
for item in brand_set:
length = len(str(bin(item))[2:])
trans = '0' * (8-length) + str(bin(item))[2:]
transfer_dict[item] = np.array(list(trans), int).tolist()
return transfer_dict
'''按时间节点拆分评论行为'''
def write_comment_file():
comm = pd.read_csv('JData/JData_Comment.csv', sep=',')
name_lst = comm['dt'].unique()
for item in name_lst:
mon = int(item.split('-')[1])
day = int(item.split('-')[2])
comm[comm['dt'] == item].to_csv('Comment/'+str(mon)+'_'+str(day)+'.csv', sep=',')
'''获取商品评论特征字典{pID:[评论数，有无差评]}'''
def Comment_feature():
r_file = file('JData/JData_Comment.csv', 'r')
reader = csv.reader(r_file)
comment_num = dict()
has_bad_comment = dict()
bad_comment_rate = dict()
time_set = set()
for line in reader:
if reader.line_num == 1:
continue
time_set.add(line[0])
if line[1] not in comment_num:
comment_num[line[1]] = list()
comment_num[line[1]].append(line[2])
if line[1] not in has_bad_comment:
has_bad_comment[line[1]] = list()
has_bad_comment[line[1]].append(line[3])
if line[1] not in bad_comment_rate:
bad_comment_rate[line[1]] = list()
bad_comment_rate[line[1]].append(line[4])
#print time_set
return comment_num, has_bad_comment, bad_comment_rate
'''商品评论变化趋势特征字典，返回：
商品评论数trend字典：{商品ID：[11个时间节点的评论数]}，范围:[0,1,2,3,4]
商品差评率trend字典：{商品ID：[11个时间节点的差评率]}，范围：[0.0,1.0]区间内浮点数
'''
def comment_trend():
comm_num_trend_dict = {}
bad_comm_trend_dict = {}
file_name_dict = {0: '2_1', 1: '2_8', 2: '2_15', 3: '2_22', 4: '2_29', 5: '3_7', 6: '3_14', 7: '3_21', 8: '3_28', 9: '4_4', 10: '4_11', 11: '4_15'}
for key in file_name_dict:
r_file = file('Comment/' + file_name_dict[key] + '.csv', 'r')
reader = csv.reader(r_file)
for line in reader:
if reader.line_num == 1:
continue
if line[2] not in comm_num_trend_dict:
comm_num_trend_dict[line[2]] = [-1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1]
comm_num_trend_dict[line[2]][key] = line[3]
if line[2] not in bad_comm_trend_dict:
bad_comm_trend_dict[line[2]] = [-1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0]
bad_comm_trend_dict[line[2]][key] = line[5]
return comm_num_trend_dict, bad_comm_trend_dict
'''统计用户注册时间：{2016年以前：[1,0,0,0,0], 2016年1月：[0,1,0,0,0], 2月：[0,0,1,0,0], 3月：[0,0,0,1,0], 4月：[0,0,0,0,1]}'''
def signup_time_dict():
r_file = file('JData/JData_User.csv', 'r')
reader = csv.reader(r_file)
feature_dict = {}
for line in reader:
if reader.line_num == 1:
continue
year = line[-1].split('-')[0]
# print year
if year != '2016':
feature_dict[line[0]] = [1, 0, 0, 0, 0, 0]
else:
month = line[-1].split('-')[1]
if int(month) == 1:
feature_dict[line[0]] = [0, 1, 0, 0, 0, 0]
elif int(month) == 2:
feature_dict[line[0]] = [0, 0, 1, 0, 0, 0]
elif int(month) == 3:
feature_dict[line[0]] = [0, 0, 0, 1, 0, 0]
elif int(month) == 4:
feature_dict[line[0]] = [0, 0, 0, 0, 1, 0]
else:
feature_dict[line[0]] = [0, 0, 0, 0, 0, 1]
return feature_dict
if __name__ == '__main__':
print 'get feature dict!'
# comm_feat = Comment_feature()
# ergodic_dict(comm_feat, num=5)
#signup_feat = signup_time_dict()
#ergodic_dict(signup_feat, num=200)
#print set(np.array(product_feature().values())[:, 4])
#product_brand()