'彻底按照item推荐进行修改，即修改MHCN，sampler'

data/retweet_dataset.py

@@ -4,20 +4,20 @@
 import os, math, tqdm, time
 import re, jieba
 from datetime import datetime
+from collections import defaultdict
 
 class Dataset(object):
  def __init__(self, data, staff_df):
  self.data = self.sort_by_time(data)
  self.staff_df = staff_df # 作为构建社交关系的补充数据源
  self.user = {}
+ self.examiner = defaultdict(dict)
  self.title = {}
  self.id2user = dict()
  self.id2title = dict()
  self.interaction_set = set()
  self.initialize(self.data)
- # self.flatten_record(self.data, self.records_save_path)
- # self.dataset_split()
- # self.get_interaction_dataset(self.interaction_save_path)
+
 
  def initialize(self, data):
  print('Initializing data......')
@@ -46,14 +46,29 @@ def initialize(self, data):
 
  # 下一环节处理人id、名称提取
  if isinstance(next_step_worker, str):
- worker_list = next_step_worker.split(',')
+ temp = next_step_worker.split(',')
+ self.data.at[idx, '下一环节处理人'] = temp
+ worker_list = self.data.loc[idx, '下一环节处理人']
  elif math.isnan(next_step_worker) or next_step_worker == '':
- worker_list = ['null_token']
+ self.data.at[idx, '下一环节处理人'] = ["null_token"]
+ worker_list = self.data.loc[idx, '下一环节处理人']
  for man in worker_list:
  if man.strip() not in self.user:
  self.user[man] = len(self.user)
  self.id2user[self.user[man]] = man
 
+ for idx in tqdm.tqdm(range(self.data.shape[0])):
+ examiner = self.data.loc[idx, ][3]
+ # 提取审批者，曾经派发过的员工{examiner1:{worker1 : 1, worker2 : 1}, examiner2 : {worker3:1}}
+ if examiner not in self.examiner:
+ related_workers = self.data[self.data['审批人UID'] == examiner]['下一环节处理人'].to_list()
+ worker_list = set()
+ for rw in related_workers:
+ worker_list = worker_list.union(set(rw))
+
+ for man in worker_list:
+ self.examiner[self.user[examiner]][self.user[man]] = 1
+
  def sort_by_time(self, records_data_df):
  # ！！按照审批时间排序，之前的数据做训练集、验证集，近的数据做测试集
  time_col = records_data_df["审批时间"].to_list()
@@ -67,26 +82,26 @@ def sort_by_time(self, records_data_df):
  def flatten_record(self, records_save_path):
  '''
  将处理记录转化成一个审批人对应一个下以环节处理人的形式，1 to N ---> 1 to 1
- 生成retweet_records.txt文档,每一行包括（用户id、公文id、公文标题、rating）
+ 生成retweet_records.txt文档,每一行包括（审批人id、公文id、公文标题、处理人id、rating）
  :param records_save_path
- :return 处理后的dataframe
+ :return 处理记录txt文档
  '''
  print(self.data.info())
  print('Preprocessing records.....')
  self.records_save_path = records_save_path
 
- with open(self.records_save_path, 'w', encoding='utf-8') as fp1:
+ with open(self.records_save_path, 'w', encoding='utf-8') as fp:
  for idx in tqdm.tqdm(range(self.data.shape[0])):
- next_step_worker, one_title, examiner = self.data.loc[idx, ][0], self.data.loc[idx, ][1], self.data.loc[idx, ][3]
- fp1.write(str(self.user[examiner]) + ' ' + str(self.title[one_title]) + ' ' + one_title + ' 1\n') # （用户id、公文id、公文标题、rating）
+ next_step_worker, one_title, examiner = self.data.loc[idx, ][0], self.data.loc[idx, ][1], \
+ self.data.loc[idx, ][3]
+
+ # 找出审批者曾经派发过文件的所有员工，不在worker list的标签为0，在的标签为1
+ for worker in self.examiner[self.user[examiner]].keys():
+ if self.id2user[worker] in next_step_worker: # 此时worker list已经是
+ fp.write(str(self.user[examiner]) + ' ' + str(self.title[one_title]) + ' ' + one_title + ' '+ str(worker) + ' 1\n') # （审批人id、公文id、公文标题、处理人id、rating）
+ else:
+ fp.write(str(self.user[examiner]) + ' ' + str(self.title[one_title]) + ' ' + one_title + ' '+ str(worker) + ' 0\n') # （审批人id、公文id、公文标题、处理人id、rating）
 
- # 下一环节处理人id、名称提取
- if isinstance(next_step_worker, str):
- worker_list = next_step_worker.split(',')
- elif math.isnan(next_step_worker) or next_step_worker == '':
- worker_list = ['null_token']
- for man in worker_list:
- fp1.write(str(self.user[man]) + ' ' + str(self.title[one_title]) + ' ' + one_title + ' 1\n')
 
  def get_interaction_dataset(self, interaction_save_path, records_save_path, train_frac = 0.8):
  # return: 每一行包括(user1, user2) 代表两人是好友
@@ -240,23 +255,23 @@ def main():
  # 加载初始文档
  data = pd.read_excel('../dataset/retweet_prediction/retweet_records.xlsx')
  staff = pd.read_excel('../dataset/retweet_prediction/staff.xlsx')
- records_save_path = '../dataset/retweet_prediction/retweet_records.txt'
+ records_save_path = '../dataset/retweet_prediction/retweets.txt'
  interaction_save_path = '../dataset/retweet_prediction/trust.txt'
  train_save_path = "../dataset/retweet_prediction/train.txt"
  ds = Dataset(data, staff)
 
  # 一对多 转化为 一对一
  flattened = True
  if not flattened:
- ds.flatten_record(records_save_path, interaction_save_path )
+ ds.flatten_record(records_save_path)
 
  # split the dataset
- split = True
+ split = False
  if flattened and not split:
  ds.dataset_split(records_save_path)
 
  # 生成关系网
- get_interaction = False
+ get_interaction = True
  if not get_interaction and split and flattened:
  ds.get_interaction_dataset(interaction_save_path, train_save_path)