AiLearning-Theory-Applying/机器学习竞赛实战_优胜解决方案/快手短视频用户活跃度分析/.ipynb_checkpoints/Untitled-checkpoint.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "# import tensorflow as tf  # 深度学习框架 pip install tensorflow\n",
    "import datetime\n",
    "# from deep_tools import f  # conda install -c bioconda deeptools，pip方法不奏效\n",
    "# from deep_tools import DataCenerator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"读取数据集\"\"\"\n",
    "register = pd.read_csv('user_register_log.txt', sep='\\t', names=['user_id', 'register_day', 'register_type', 'device_type'])\n",
    "launch = pd.read_csv('app_launch_log.txt', sep='\\t', names=['user_id', 'launch_day'])\n",
    "create = pd.read_csv('video_create_log.txt', sep='\\t', names=['user_id', 'create_day'])\n",
    "activity = pd.read_csv('user_activity_log.txt', sep='\\t', names=['user_id', 'act_day', 'page', 'video_id', 'author_id', 'act_type'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>register_day</th>\n",
       "      <th>register_type</th>\n",
       "      <th>device_type</th>\n",
       "      <th>seq_length</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>744025</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>283</td>\n",
       "      <td>30</td>\n",
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       "      <td>1270299</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>259</td>\n",
       "      <td>30</td>\n",
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       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>30</td>\n",
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       "      <td>23</td>\n",
       "      <td>30</td>\n",
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       "      <td>745554</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
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      "text/plain": [
       "   user_id  register_day  register_type  device_type  seq_length\n",
       "0   744025             1              1          283          30\n",
       "1  1270299             1              1          259          30\n",
       "2   571220             1              1            2          30\n",
       "3  1308501             1              0           23          30\n",
       "4   745554             1              2            0          30"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"计算序列长度： 持续时间 = 数据总时间 - 注册时间\"\"\"\n",
    "register['seq_length'] = 31 - register['register_day']\n",
    "register.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"根据前面的记录天数，创建一个字典，来存储不同记录天数的用户到底有哪些 \"\"\"\n",
    "user_queue = {i: []  for i in range(1,31)}\n",
    "\n",
    "for index, row in register.iterrows():  # 这个iterrows是对DataFrame进行行遍历，是在数据框中的行进行迭代的一个生成器，它返回每行的索引及一个包含行本身的对象。\n",
    "    user_queue[row[-1]].append(row[0]) # row[-1]是seq_length, row[0]是user_id"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"定义一个user_seq类\"\"\"\n",
    "class user_seq:\n",
    "    \n",
    "    def __init__(self, register_day, seq_length, n_feagures):\n",
    "        \"\"\"\n",
    "            register_day: 用户第几天进行的登录\n",
    "            seq_length: 用户序列的长度，就是记录了几天登录信息， 行数\n",
    "            n_features: 每天提出的特征个数， 列数\n",
    "        \"\"\"\n",
    "        self.register_day = register_day\n",
    "        self.seq_length = seq_length\n",
    "        self.array = np.zeros([self.seq_length, n_feagures])   # 这就是上面那个用户对应的矩阵形式，初始化位0\n",
    "        self.array[0,0] = 1\n",
    "        self.page_rank = np.zeros([self.seq_length])\n",
    "        self.pointer = 1\n",
    "    \n",
    "    # 提取特征填入特征矩阵\n",
    "    def put_feature(self, feature_number, string):\n",
    "        for i in string.split(','):\n",
    "            pos, value = i.split(':')     # 注册后的第几天进行了登录，1为指示符\n",
    "            self.array[int(pos)-self.register_day, feature_number] = 1   # 从注册后开始记录\n",
    "    \n",
    "    def put_PR(self,string):\n",
    "        for i in string.split(','):\n",
    "            pop,value = i.split(';')\n",
    "            self.page_rank[int(pos)-self.register_day] = value\n",
    "    \n",
    "    def get_array(self):\n",
    "        return self.array\n",
    "    \n",
    "    # 得到标签  如果一个用户在未来七天活跃了，那么标记为1\n",
    "    def get_label(self):\n",
    "        self.label = np.array([None] * self.seq_length)    # 一个seq_length长度的数组\n",
    "        active = self.array[:, :10].sum(axis=1)          # 这里选了一部分特征做了个sum，意思是不管是转发，登录，啥的，只要做了就算一次活动\n",
    "        for i in range(self.seq_length-7):      # 这地方得控制一下，如果一个用户15-30的数据，那么我们标签最多只能到23天，因为30天之后的数据我们没有\n",
    "            self.label[i] = 1 * (np.sum(active[i+1:i+8]) > 0)    # 这里对于当前的i，如果未来七天内活跃过，那么标签就是1\n",
    "        return self.label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"创建用户的记录矩阵\"\"\"\n",
    "n_features = 12\n",
    "data = {row[0]:user_seq(register_day=row[1], seq_length=row[-1],n_feagures=n_features) for index, row in register.iterrows()}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
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       " ...}"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>16</td>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>16</td>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user_id  launch_day  launch\n",
       "0       16          13       1\n",
       "1       16          14       1\n",
       "2       16          15       1\n",
       "3       16          18       1\n",
       "4       16          19       1"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "launch['launch'] = 1\n",
    "launch_table = launch.groupby(['user_id', 'launch_day'], \n",
    "                              as_index=False).agg({'launch':'sum'})\n",
    "launch_table.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "def record_to_sequence(table):\n",
    "    table.columns=['user_id','day','value']\n",
    "    table.sort_values(by=['user_id','day'],inplace=True)\n",
    "    table['string']=table.day.map(str)+':'+table.value.map(str)\n",
    "    table=table.groupby(['user_id'],as_index=False).agg({'string':lambda x:','.join(x)})\n",
    "    return table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>string</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>16</td>\n",
       "      <td>13:1,14:1,15:1,18:1,19:1,20:1,21:1,22:1,23:1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>30</td>\n",
       "      <td>24:1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>98</td>\n",
       "      <td>16:1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>105</td>\n",
       "      <td>12:1,14:1,15:1,16:1,17:1,18:1,19:1,20:1,21:1,2...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>176</td>\n",
       "      <td>27:1,28:1,29:1,30:1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user_id                                             string\n",
       "0       16       13:1,14:1,15:1,18:1,19:1,20:1,21:1,22:1,23:1\n",
       "1       30                                               24:1\n",
       "2       98                                               16:1\n",
       "3      105  12:1,14:1,15:1,16:1,17:1,18:1,19:1,20:1,21:1,2...\n",
       "4      176                                27:1,28:1,29:1,30:1"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "launch_table=record_to_sequence(launch_table)\n",
    "launch_table.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "for index,row in launch_table.iterrows():\n",
    "    data[row[0]].put_feature(1,row[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "创作视频信息"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "create['create']=1\n",
    "create_table = create.groupby(['user_id','create_day'],as_index=False).agg({'create':'sum'})\n",
    "create_table = record_to_sequence(create_table)\n",
    "for index,row in create_table.iterrows():\n",
    "    data[row[0]].put_feature(2,row[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "用户行为数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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