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"import numpy as np\n", - "import tensorflow as tf # 深度学习框架 pip install tensorflow\n", - "import datetime" + "## 任务目标与数据分析\n", + "### 预测用户未来一段时间的活跃度\n", + "二分类问题" ] }, { - "cell_type": "markdown", + "cell_type": "code", + "execution_count": 6, "metadata": {}, + "outputs": [], "source": [ - "## 预测用户未来一段时间的活跃度\n", - "二分类问题" + "import pandas as pd\n", + "import numpy as np\n", + "import tensorflow as tf\n", + "import datetime" ] }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "\"\"\"数据集链接：https://pan.baidu.com/s/1HRR5qKCYlcdgxLja-_Kv0w 提取码：ep7q \"\"\"\n", "# 注册数据\n", "register = pd.read_csv('user_register_log.txt', sep='\\t', names=['user_id', 'register_day', 'register_type', 'device_type'])\n", - "# 用户登录数据，30天\n", + "# 用户登录数据，一共30天时间长度，不是每个人的30天，是某个时间的30天\n", "launch = pd.read_csv('app_launch_log.txt', sep='\\t', names=['user_id', 'launch_day'])\n", "# 用户创建数据\n", "create = pd.read_csv('video_create_log.txt', sep='\\t', names=['user_id', 'create_day'])\n", @@ -134,6 +135,14 @@ "register.head() # user_id用户id、register_day注册时间、register_type来源渠道、device_type设备信息" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "1.注册日志\n", + "" + ] + }, { "cell_type": "code", "execution_count": 4, @@ -212,6 +221,14 @@ "launch.head() # launch_day启动的天，如下的330986在4、9、11、12都登录了" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "2.APP启动日志\n", + "" + ] + }, { "cell_type": "code", "execution_count": 5, @@ -290,6 +307,14 @@ "create.head() # create_day创建视频日期" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "3.视频创建日志表\n", + "" + ] + }, { "cell_type": "code", "execution_count": 6, @@ -392,9 +417,41 @@ "activity.head() # act_day行为日期、page发生行为的地方、video_id视频ID、author_id作者id、act_type用户行为" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "4.用户行为日志表\n", + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 整体模型框架\n", + "这里使用神经网络RNN来完成" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "用户进来的时间，可能是第一天，可能是第二天等，当用户进来后，比如第7天，用户有了数据，从第7天开始第8天第9天用户的数据纬度都是一致的，也就是用户进来的那天当做用户自己的t1，第二天是t2以此类推到tn，用来预测该客户未来的某一天的可能性，比如用第一天(t1)数据用来预测第七天数据(t1+7)。\n", + "\n", + "目前是没有标签的，需要自己打标签，比如预测第7天，就给每个客户的ti+7天打上标签，用来预测，0表示没登录，1表示登录" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 构建用户特征" + ] + }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -479,7 +536,7 @@ "4 745554 1 2 0 30" ] }, - "execution_count": 11, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -492,20 +549,30072 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 9, "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", + "\"\"\"\n", + "根据前面的记录天数，创建一个以时间为key的字典\n", + "如果用户只有20天数据，则后面10天为None，如果取10，而用户超过了10，那么只取前10\n", + "\"\"\"\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", + "for index, row in register.iterrows(): # 这个iterrows是对DataFrame进行遍历，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": 13, + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{1: [355948,\n", + " 1141243,\n", + " 885314,\n", + " 473037,\n", + " 1161976,\n", + " 1105249,\n", + " 1034711,\n", + " 444663,\n", + " 629789,\n", + " 684292,\n", + " 1220188,\n", + " 38479,\n", + " 1166260,\n", + " 739798,\n", + " 1023596,\n", + " 731282,\n", + " 759110,\n", + " 327708,\n", + " 1102438,\n", + " 466836,\n", + " 496107,\n", + " 1222898,\n", + " 166312,\n", + " 236360,\n", + " 473209,\n", + " 91356,\n", + " 393059,\n", + " 439146,\n", + " 956525,\n", + " 49020,\n", + " 1306827,\n", + " 1198470,\n", + 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np.zeros([self.seq_length])\n", " self.pointer = 1\n", @@ -550,7 +30659,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ @@ -559,6 +30668,1026 @@ "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": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{744025: <__main__.user_seq at 0x1ee11bfc748>,\n", + " 1270299: <__main__.user_seq at 0x1ee11bfc630>,\n", + " 571220: <__main__.user_seq at 0x1ee11bfc940>,\n", + " 1308501: <__main__.user_seq at 0x1ee11bfc6d8>,\n", + " 745554: <__main__.user_seq at 0x1ee11bfc550>,\n", + " 1031012: <__main__.user_seq at 0x1ee11bfca58>,\n", + " 913297: <__main__.user_seq at 0x1ee11bfcfd0>,\n", + " 266500: <__main__.user_seq at 0x1ee11bfcc18>,\n", + " 475120: <__main__.user_seq at 0x1ee11bfcef0>,\n", + " 547944: <__main__.user_seq at 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"execute_result" + } + ], + "source": [ + "data # 得到每个用户特征列表，目前里面都是0" + ] + }, { "cell_type": "code", "execution_count": 15,