diff --git a/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/快手用户活跃预测-checkpoint.ipynb b/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/快手用户活跃预测-checkpoint.ipynb index 90134b5..d8faec7 100644 --- a/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/快手用户活跃预测-checkpoint.ipynb +++ b/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/快手用户活跃预测-checkpoint.ipynb @@ -1,35 +1,36 @@ { "cells": [ { - "cell_type": "code", - "execution_count": 1, + "cell_type": "markdown", "metadata": {}, - "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" + "## 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": { + 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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", + " 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\n", + " n_features: 每天提取的特征数，如某个客户有30天数据，就有30天且每天有12列特征\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 = np.zeros([self.seq_length, n_feagures]) # 构建矩阵（初始化0）：持续天数 × 特征个数，后续新创建的往里填充\n", " self.array[0,0] = 1\n", " self.page_rank = np.zeros([self.seq_length])\n", " self.pointer = 1\n", @@ -536,21 +30645,21 @@ " pop,value = i.split(';')\n", " self.page_rank[int(pos)-self.register_day] = value\n", " \n", - " def get_array(self):\n", + " def get_array(self): # 返回递归的结果\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", + " active = self.array[:, :10].sum(axis=1) # 这里选了一部分特征做了个sum，即是发生登录、点赞、转发等，计为活动1次\n", + " for i in range(self.seq_length-7): # 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) # 未来七天内活跃过，那么标签就是1\n", " return self.label" ] }, { "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 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<__main__.user_seq at 0x1ee119374e0>,\n", + " 1167296: <__main__.user_seq at 0x1ee11937518>,\n", + " 1318762: <__main__.user_seq at 0x1ee11937550>,\n", + " 359954: <__main__.user_seq at 0x1ee11937588>,\n", + " 686230: <__main__.user_seq at 0x1ee119375c0>,\n", + " 480465: <__main__.user_seq at 0x1ee119375f8>,\n", + " ...}" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "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, @@ -640,10 +31769,10 @@ } ], "source": [ - "launch['launch'] = 1\n", + "launch['launch'] = 1 # 每个用户登录次数置为1，如果登录了多次则求和\n", "launch_table = launch.groupby(['user_id', 'launch_day'], \n", " as_index=False).agg({'launch':'sum'})\n", - "launch_table.head()" + "launch_table.head() # launch_day是时间、launch登录次数" ] }, { @@ -652,9 +31781,10 @@ "metadata": {}, "outputs": [], "source": [ - "def record_to_sequence(table):\n", + "def record_to_sequence(table): # 得到用户特征序列表\n", " table.columns=['user_id','day','value']\n", - " table.sort_values(by=['user_id','day'],inplace=True)\n", + " table.sort_values(by=['user_id','day'],inplace=True) # 排序每个客户的时间\n", + " # 拼接字符串，如上的launch_day是13，同行的launch是1，则变成13:1\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" @@ -736,7 +31866,14 @@ ], "source": [ "launch_table=record_to_sequence(launch_table)\n", - "launch_table.head()" + "launch_table.head() # 序列特征结果" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "将用户数据填充到对应的位置，如ID=16的用户会在其特征表的13，14，15...行进行填充" ] }, { @@ -745,8 +31882,15 @@ "metadata": {}, "outputs": [], "source": [ - "for index,row in launch_table.iterrows():\n", - " data[row[0]].put_feature(1,row[1])" + "for index,row in launch_table.iterrows(): # 根据登录信息对用户特指标进行填充\n", + " data[row[0]].put_feature(1,row[1]) # 在指定特征位置填充" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**创作视频信息**" ] }, { @@ -759,7 +31903,16 @@ "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])" + " data[row[0]].put_feature(2,row[1]) # 第一个位置是注册信息，所以我们从2开始填充" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**用户使用时行为特征，例如点赞、转发等**\n", + "\n", + "分别对不同行为进行统计，构建6种不同行为特征" ] }, { @@ -769,13 +31922,20 @@ "outputs": [], "source": [ "for i in range(6):\n", - " act=activity[activity.act_type==i].copy()\n", + " act=activity[activity.act_type==i].copy() # act_type不同数字对应着不同的行为\n", " act=act.groupby(['user_id','act_day'],as_index=False).agg({'video_id':'count'})\n", " act = record_to_sequence(act)\n", " for index,row in act.iterrows():\n", " data[row[0]].put_feature(i+3,row[1])" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**产生行为的界面信息**" + ] + }, { "cell_type": "code", "execution_count": 21, @@ -790,6 +31950,13 @@ " data[row[0]].put_feature(i+9,row[1])" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**用户观看其他用户作品的信息**" + ] + }, { "cell_type": "code", "execution_count": 22, @@ -798,6 +31965,7 @@ "source": [ "watched=register.loc[:,['user_id']].copy()\n", "watched.columns=['author_id']\n", + "# 如果作者id和用户id不相等，则是观看其它用户作品的信息\n", "watched=pd.merge(watched,activity[activity.author_id!=activity.user_id],how='inner')\n", "watched=watched.groupby(['user_id','act_day'],as_index=False).agg({'video_id':'count'})\n", "watched=record_to_sequence(watched)\n", @@ -805,12 +31973,20 @@ " data[row[0]].put_feature(10,row[1])" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**用户观看自己作品的信息**" + ] + }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [], "source": [ + "# 如果作者id和用户id相等，则是观看自己作品的信息\n", "watched=pd.merge(watched,activity[activity.author_id==activity.user_id],how='inner')\n", "watched=watched.groupby(['user_id','act_day'],as_index=False).agg({'video_id':'count'})\n", "watched=record_to_sequence(watched)\n", @@ -818,6 +31994,26 @@ " data[row[0]].put_feature(11,row[1])" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "特征已构建完成，这里是用的RNN网络，所以构建矩阵，提取序列\n", + "\n", + "如果是用lgb或者xgb，则是DataFrame类型，做一些统计类型的特征" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 标签制作\n", + "\n", + "活跃用户定义为（自己定义的）：在未来7天内使用过APP\n", + "\n", + "从用户注册开始进行统计，对于每1天的数据展开，如果其7天后仍有行为产生，则标签为1" + ] + }, { "cell_type": "code", "execution_count": 24, @@ -2466,7 +33662,14 @@ } ], "source": [ - "label" + "label # 1表示注册7天后，有积极行为的；0则不积极" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "用户特征数据即为之前提取的data中各项特征，转换为ndarray即可" ] }, { @@ -17955,190 +49158,29 @@ "data" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 网络训练模块" + ] + }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ - "import pandas as pd\n", - "import numpy as np\n", - "from random import shuffle\n", - "\n", - "def f(table,name='prob'):\n", - " table=table.copy()\n", - " score=[]\n", - " for i in [0.40,0.41,0.42,0.43,0.44,0.45]:\n", - " table['pred']=1*(table[name]>i)\n", - " c=((table.pred==1)&(table.label==1)).sum()\n", - " p=c/table.pred.sum()\n", - " r=c/table.label.sum()\n", - " score.append(2*p*r/(p+r))\n", - " return score\n", - "\n", - "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\n", - "\n", - "class user_seq:\n", - " \n", - " def __init__(self,register_day,seq_length,n_features):\n", - " self.register_day=register_day\n", - " self.seq_length=seq_length\n", - " self.array=np.zeros([self.seq_length,n_features])\n", - " self.array[0,0]=1\n", - " self.page_rank=np.zeros([self.seq_length])\n", - " self.pointer=1\n", - " \n", - " def put_feature(self,feature_number,string):\n", - " for i in string.split(','):\n", - " pos,value=i.split(':')\n", - " self.array[int(pos)-self.register_day,feature_number]=1\n", - "\n", - " def put_PR(self,string):\n", - " for i in string.split(','):\n", - " pos,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", - " def get_label(self):\n", - " self.label=np.array([None]*self.seq_length)\n", - " active=self.array[:,:10].sum(axis=1)\n", - " for i in range(self.seq_length-7):\n", - " self.label[i]=1*(np.sum(active[i+1:i+8])>0)\n", - " return self.label\n", - " \n", - "\n", - "class DataGenerator:\n", - " \n", - " def __init__(self,register,launch,create,activity):\n", - " \n", - " register=register.copy()\n", - " launch=launch.copy()\n", - " create=create.copy()\n", - " activity=activity.copy()\n", - " \n", - " #user_queue\n", - " register['seq_length']=31-register['register_day']\n", - " self.user_queue={i:[] for i in range(1,31)}\n", - " for index,row in register.iterrows():\n", - " self.user_queue[row[-1]].append(row[0]) #row[-1]是seq_length,row[0]是user_id\n", - " \n", - " #初始化self.data\n", - " n_features=12 #row[0]是user_id,row[1]是register_day,row[-1]是seq_length\n", - " self.data={row[0]:user_seq(register_day=row[1],seq_length=row[-1],n_features=n_features) for index,row in register.iterrows()}\n", - " \n", - "\n", - " #提取launch_seq\n", - " launch['launch']=1\n", - " launch_table=launch.groupby(['user_id','launch_day'],as_index=False).agg({'launch':'sum'})\n", - " launch_table=record_to_sequence(launch_table)\n", - " for index,row in launch_table.iterrows():\n", - " self.data[row[0]].put_feature(1,row[1]) #row[0]是user_id,row[1]是string\n", - " \n", - " #提取create_seq\n", - " 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", - " self.data[row[0]].put_feature(2,row[1]) #row[0]是user_id,row[1]是string\n", - "\n", - " #提取act_seq\n", - " for i in range(6):\n", - " act=activity[activity.act_type==i].copy()\n", - " act=act.groupby(['user_id','act_day'],as_index=False).agg({'video_id':'count'})\n", - " act=record_to_sequence(act)\n", - " for index,row in act.iterrows():\n", - " self.data[row[0]].put_feature(i+3,row[1]) #row[0]是user_id,row[1]是string\n", - "\n", - " #提取page_seq\n", - " for i in range(1):\n", - " act=activity[activity.page==i].copy()\n", - " act=act.groupby(['user_id','act_day'],as_index=False).agg({'video_id':'count'})\n", - " act=record_to_sequence(act)\n", - " for index,row in act.iterrows():\n", - " self.data[row[0]].put_feature(i+9,row[1]) #row[0]是user_id,row[1]是string\n", - "\n", - " #提取watched\n", - " watched=register.loc[:,['user_id']].copy()\n", - " watched.columns=['author_id']\n", - " watched=pd.merge(watched,activity[activity.author_id!=activity.user_id],how='inner')\n", - " watched=watched.groupby(['author_id','act_day'],as_index=False).agg({'video_id':'count'})\n", - " watched=record_to_sequence(watched)\n", - " for index,row in watched.iterrows():\n", - " self.data[row[0]].put_feature(10,row[1]) #row[0]是user_id,row[1]是string\n", - "\n", - " #提取watched by self\n", - " watched=activity[activity.author_id==activity.user_id].copy()\n", - " watched=watched.groupby(['user_id','act_day'],as_index=False).agg({'video_id':'count'})\n", - " watched=record_to_sequence(watched)\n", - " for index,row in watched.iterrows():\n", - " self.data[row[0]].put_feature(11,row[1]) #row[0]是user_id,row[1]是string\n", - "\n", - " #提取label\n", - " self.label={user_id:user.get_label() for user_id,user in self.data.items()}\n", - " \n", - " #提取data\n", - " self.data={user_id:user.get_array() for user_id,user in self.data.items()}\n", - "\n", - "\n", - " #set sample strategy\n", - " self.local_random_list=[]\n", - " for i in range(15,31):\n", - " self.local_random_list+=[i]*(i-14)\n", - " \n", - " self.online_random_list=[]\n", - " for i in range(8,31):\n", - " self.online_random_list+=[i]*(i-7)\n", - "\n", - " self.local_train_list=list(range(15,31))\n", - " self.local_test_list=list(range(8,31))\n", - " self.online_train_list=list(range(8,31))\n", - " self.online_test_list=list(range(1,31))\n", - "\n", - " self.pointer={i:0 for i in range(1,31)}\n", - " \n", - " \n", - " def reset_pointer(self):\n", - " self.pointer={i:0 for i in range(1,31)}\n", - " \n", - " \n", - " def next_batch(self,batch_size=1000):\n", - "\n", - " seq_length=self.local_random_list[np.random.randint(len(self.local_random_list))]\n", - " batch_size=batch_size//(seq_length-14)+1\n", - "\n", - " if self.pointer[seq_length]+batch_size>len(self.user_queue[seq_length]):\n", - " self.pointer[seq_length]=0\n", - " shuffle(self.user_queue[seq_length])\n", - " #print('---------------------',seq_length,'shuffled ------------------------------')\n", - " start=self.pointer[seq_length]\n", - " user_list=self.user_queue[seq_length][start:start+batch_size]\n", - " self.pointer[seq_length]+=batch_size\n", - "\n", - " user_matrix=np.array(user_list)\n", - " data_matrix=np.array([self.data[i] for i in user_list])\n", - " label_matrix=np.array([self.label[i] for i in user_list])\n", - " \n", - " return seq_length,user_matrix,data_matrix,label_matrix\n", - " \n", - " \n", - " def get_set(self,usage='train'):\n", - " \n", - " if usage=='train':\n", - " test_list=self.local_train_list\n", - " else:\n", - " test_list=self.local_test_list\n", - " \n", - " user_list=[np.array(self.user_queue[seq_length]) for seq_length in test_list]\n", - " data_list=[np.array([self.data[user_id] for user_id in self.user_queue[seq_length]]) for seq_length in test_list]\n", - " label_list=[np.array([self.label[user_id] for user_id in self.user_queue[seq_length]]) for seq_length in test_list]\n", - " return test_list,user_list,data_list,label_list" + "# 导入封装好的包，上面方法的集合\n", + "from deep_tools import f\n", + "from deep_tools import DataGenerator" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "构建RNN网络模型" ] }, { @@ -18190,11 +49232,11 @@ " # 变量与输入\n", " lr = tf.placeholder(tf.float32, [], name='learning_rate') # 定义学习率\n", " \n", - " # 隐藏层到输出层的参数w, b w_shape(n_hu,1) b_shape(1) n_huWie隐藏单元的个数\n", + " # 隐藏层到输出层的参数W, b，n_hu隐藏单元的个数\n", " W_out = tf.get_variable('W_out', [n_hu, 1]) \n", " b_out = tf.get_variable('b_out', [1])\n", " \n", - " # x和y x_shape(batch_size, seq_length, n_features)\n", + " # x(batch_size, seq_length, n_features)\n", " x = tf.placeholder(tf.float32, [None, None, n_features])\n", " y = tf.placeholder(tf.float32, [None, None])\n", " \n", @@ -18205,14 +49247,11 @@ " # RNN 层\n", " cell = tf.nn.rnn_cell.GRUCell(n_hu) # n_hu表示每个GRUcell里面的单元个数\n", " initial_state = cell.zero_state(batch_size, dtype=tf.float32) # 指定初识状态，因为之前没有训练过\n", - " outputs, state = tf.nn.dynamic_rnn(cell, x, initial_state=initial_state) # 使用的动态Rnn\n", - " # outputs(batch_size, max_seq_length, n_hu) 这是所有时间步的输出\n", - " # state (batch_size, n_hu) 这是最后一个时间步的输出\n", - " # 具体：https://blog.csdn.net/u010960155/article/details/81707498\n", + " outputs, state = tf.nn.dynamic_rnn(cell, x, initial_state=initial_state) # 使用的动态RNN\n", " \n", " # 输出层\n", - " outputs = tf.reshape(outputs, [-1, n_hu]) # （batch_size*max_seq_length, n_hu）\n", - " logits = tf.matmul(outputs, W_out) + b_out # (batch_size*max_seq_length)\n", + " outputs = tf.reshape(outputs, [-1, n_hu])\n", + " logits = tf.matmul(outputs, W_out) + b_out\n", " logits = tf.reshape(logits, tf.stack([batch_size, seq_length]))" ] }, @@ -18223,7 +49262,7 @@ "outputs": [], "source": [ "# 选择部分预测结果与标签当做训练损失计算\n", - "logits_local_train = logits[:, :-14] # 这里-14或者是更小，因为本地训练，我们用前16天训练，16-23天测试。\n", + "logits_local_train = logits[:, :-14] # 这里-14或者是更小，可以看到上面的label后面几天都是None。\n", "label_local_train = y[:, :-14]" ] }, @@ -18252,18 +49291,17 @@ ], "source": [ "# 设置损失函数\n", - "\n", - "# 正则化项\n", - "regularizer = tf.contrib.layers.l2_regularizer(0.00001)\n", + "regularizer = tf.contrib.layers.l2_regularizer(0.00001) # 正则化项\n", "penalty = tf.contrib.layers.apply_regularization(regularizer, tf.trainable_variables())\n", "\n", + "# sigmoid_cross_entropy二分类任务，判断0或者1\n", "obj_local = tf.losses.sigmoid_cross_entropy(label_local_train, logits_local_train) + penalty\n", "optimizer = tf.train.AdamOptimizer(lr)\n", "set_local = optimizer.minimize(obj_local)\n", "\n", "# 选择部分预测结果与标签当做测试损失计算\n", - "logits_local_test = logits[:, -8] # 预测倒数第8天作为自己的测试标准\n", - "label_local_test = y[:, -8] # 这里也可以选择其他的天" + "logits_local_test = logits[:, -8] # 预测倒数第8天作为自己的测试标准，某一天，也可以选择其他的天\n", + "label_local_test = y[:, -8]" ] }, { @@ -18273,10 +49311,17 @@ "outputs": [], "source": [ "def train(n_obs=1000, step=1000, lr_feed=0.01):\n", - " date_seq = [31] + list(range(2, 16)) + [16] * 15\n", + " \"\"\"\n", + " n_obs迭代时选择的样本个数\n", + " step迭代次数\n", + " lr_feed学习率\n", + " \"\"\"\n", + " # 输入序列，2则是取序列长度为2的，选择2-16，因为16比较多，我们*15；人为的选择\n", + " date_seq = [31] + list(range(2, 16)) + [16] * 15 \n", " variables = [set_local, obj_local, label_local_train, logits_local_train]\n", " \n", " for i in range(step):\n", + " # next_batch 根据序列长度，先从user_queue字典里找到客户，再取指定部分\n", " length, id_list, data_x, data_y = data_generator.next_batch(n_obs)\n", " _, los, lab, log = sess.run(variables, \n", " feed_dict={x:data_x, y:data_y, lr:lr_feed})" @@ -18289,7 +49334,7 @@ "outputs": [], "source": [ "sess = tf.Session()\n", - "sess.run(tf.global_variables_initializer())" + "sess.run(tf.global_variables_initializer()) # 全局变量初始化" ] }, { @@ -18298,7 +49343,7 @@ "metadata": {}, "outputs": [], "source": [ - "train(n_obs=1000, step=2000, lr_feed=0.01)" + "train(n_obs=1000, step=2000, lr_feed=0.01) # step训练两千次" ] }, { @@ -18436,8 +49481,9 @@ ], "source": [ "def test():\n", - " n_NA = 14 # 本地训练， 我们是1-16天训练，16-23天预测，所以这个地方最大是30-14\n", + " n_NA = 14\n", " # 优化目标和数据\n", + " \n", " variables_1 = [obj_local, logits_local_train, label_local_train]\n", " variables_2 = [logits_local_test, label_local_test]\n", " \n", @@ -18447,7 +49493,9 @@ " # 训练损失\n", " for length, id_list, data_x, data_y in zip(*data_generator.get_set('train')):\n", " _obj, _logits_train, _label_train = sess.run(variables_1,\n", - " feed_dict={x:data_x, y:data_y, lr:0.001})\n", + " feed_dict={x:data_x,\n", + " y:data_y,\n", + " lr:0.001})\n", " obs_count += (length - n_NA) * len(id_list)\n", " cum_loss += _obj * (length - n_NA) * len(id_list)\n", " correct += np.sum((1 * (_logits_train>0) == _label_train))\n", @@ -18474,11 +49522,14 @@ ] }, { - "cell_type": "code", - "execution_count": null, + "cell_type": "markdown", "metadata": {}, - "outputs": [], - "source": [] + "source": [ + "#### 评估标准：\n", + "\n", + "\n", + "可以看到上面的test_score（F1 Score）很高，也是比赛中非常接近冠军的值" + ] } ], "metadata": { diff --git a/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/Untitled.ipynb b/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/Untitled.ipynb deleted file mode 100644 index 8d403c8..0000000 --- a/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/Untitled.ipynb +++ /dev/null @@ -1,3476 +0,0 @@ -{ - "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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" - ], - "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": 34, - "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": 35, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{744025: <__main__.user_seq at 0x1b78b7631d0>,\n", - " 1270299: <__main__.user_seq at 0x1b78b763f98>,\n", - " 571220: <__main__.user_seq at 0x1b78b763eb8>,\n", - 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" - ], - "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": 38, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "launch_table=record_to_sequence(launch_table)\n", - "launch_table.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "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": 40, - "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": 41, - "metadata": {}, - "outputs": [], - "source": [ - "for i in range(6):\n", - " act=activity[activity.act_type==i].copy()\n", - " act=act.groupby(['user_id','act_day'],as_index=False).agg({'video_id':'count'})\n", - " act = record_to_sequence(act)\n", - " for index,row in act.iterrows():\n", - " data[row[0]].put_feature(i+3,row[1])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "产生信息的界面信息" - ] - }, - { - "cell_type": "code", - "execution_count": 42, - "metadata": {}, - "outputs": [], - "source": [ - "for i in range(1):\n", - " act=activity[activity.page==i].copy()\n", - " act=act.groupby(['user_id','act_day'],as_index=False).agg({'video_id':'count'})\n", - " act = record_to_sequence(act)\n", - " for index,row in act.iterrows():\n", - " data[row[0]].put_feature(i+9,row[1])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "观测其它用户作品信息" - ] - }, - { - "cell_type": "code", - "execution_count": 43, - "metadata": {}, - "outputs": [], - "source": [ - "watched=register.loc[:,['user_id']].copy()\n", - "watched.columns=['author_id']\n", - "watched=pd.merge(watched,activity[activity.author_id!=activity.user_id],how='inner')\n", - "watched=watched.groupby(['user_id','act_day'],as_index=False).agg({'video_id':'count'})\n", - "watched=record_to_sequence(watched)\n", - "for index,row in watched.iterrows():\n", - " data[row[0]].put_feature(10,row[1])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "观看自己的作品信息" - ] - }, - { - "cell_type": "code", - "execution_count": 44, - "metadata": {}, - "outputs": [], - "source": [ - "watched=pd.merge(watched,activity[activity.author_id==activity.user_id],how='inner')\n", - "watched=watched.groupby(['user_id','act_day'],as_index=False).agg({'video_id':'count'})\n", - "watched=record_to_sequence(watched)\n", - 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" None], dtype=object),\n", - " 381644: array([1, 1, 0, 0, 0, 0, 0, 1, 1, 1, None, None, None, None, None, None,\n", - " None], dtype=object),\n", - " 1167296: array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, None, None, None, None, None, None,\n", - " None], dtype=object),\n", - " 1318762: array([1, 0, 0, 0, 0, 0, 0, 0, 0, 0, None, None, None, None, None, None,\n", - " None], dtype=object),\n", - " 359954: array([1, 0, 0, 0, 0, 0, 0, 0, 0, 0, None, None, None, None, None, None,\n", - " None], dtype=object),\n", - " 686230: array([1, 1, 1, 1, 1, 0, 0, 1, 1, None, None, None, None, None, None,\n", - " None], dtype=object),\n", - " 480465: array([1, 0, 0, 0, 0, 0, 0, 0, 0, None, None, None, None, None, None,\n", - " None], dtype=object),\n", - " ...}" - ] - }, - "execution_count": 46, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "label" - ] - }, - { - "cell_type": "code", - "execution_count": 47, - "metadata": {}, - "outputs": [], - "source": [ - "data = {user_id: user.get_array() for user_id, user in data.items()}" - ] - }, - { - "cell_type": "code", - "execution_count": 56, - "metadata": {}, - "outputs": [], - "source": [ - "import pandas as pd\n", - "import numpy as np\n", - "from random import shuffle\n", - "\n", - "def f(table,name='prob'):\n", - " table=table.copy()\n", - " score=[]\n", - " for i in [0.40,0.41,0.42,0.43,0.44,0.45]:\n", - " table['pred']=1*(table[name]>i)\n", - " c=((table.pred==1)&(table.label==1)).sum()\n", - " p=c/table.pred.sum()\n", - " r=c/table.label.sum()\n", - " score.append(2*p*r/(p+r))\n", - " return score\n", - "\n", - "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\n", - "\n", - "class user_seq:\n", - " \n", - " def __init__(self,register_day,seq_length,n_features):\n", - " self.register_day=register_day\n", - " self.seq_length=seq_length\n", - " self.array=np.zeros([self.seq_length,n_features])\n", - " self.array[0,0]=1\n", - " self.page_rank=np.zeros([self.seq_length])\n", - " self.pointer=1\n", - " \n", - " def put_feature(self,feature_number,string):\n", - " for i in string.split(','):\n", - " pos,value=i.split(':')\n", - " self.array[int(pos)-self.register_day,feature_number]=1\n", - "\n", - " def put_PR(self,string):\n", - " for i in string.split(','):\n", - " pos,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", - " def get_label(self):\n", - " self.label=np.array([None]*self.seq_length)\n", - " active=self.array[:,:10].sum(axis=1)\n", - " for i in range(self.seq_length-7):\n", - " self.label[i]=1*(np.sum(active[i+1:i+8])>0)\n", - " return self.label\n", - " \n", - "\n", - "class DataGenerator:\n", - " \n", - " def __init__(self,register,launch,create,activity):\n", - " \n", - " register=register.copy()\n", - " launch=launch.copy()\n", - " create=create.copy()\n", - " activity=activity.copy()\n", - " \n", - " #user_queue\n", - " register['seq_length']=31-register['register_day']\n", - " self.user_queue={i:[] for i in range(1,31)}\n", - " for index,row in register.iterrows():\n", - " self.user_queue[row[-1]].append(row[0]) #row[-1]是seq_length,row[0]是user_id\n", - " \n", - " #初始化self.data\n", - " n_features=12 #row[0]是user_id,row[1]是register_day,row[-1]是seq_length\n", - " self.data={row[0]:user_seq(register_day=row[1],seq_length=row[-1],n_features=n_features) for index,row in register.iterrows()}\n", - " \n", - "\n", - " #提取launch_seq\n", - " launch['launch']=1\n", - " launch_table=launch.groupby(['user_id','launch_day'],as_index=False).agg({'launch':'sum'})\n", - " launch_table=record_to_sequence(launch_table)\n", - " for index,row in launch_table.iterrows():\n", - " self.data[row[0]].put_feature(1,row[1]) #row[0]是user_id,row[1]是string\n", - " \n", - " #提取create_seq\n", - " 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", - " self.data[row[0]].put_feature(2,row[1]) #row[0]是user_id,row[1]是string\n", - "\n", - " #提取act_seq\n", - " for i in range(6):\n", - " act=activity[activity.act_type==i].copy()\n", - " act=act.groupby(['user_id','act_day'],as_index=False).agg({'video_id':'count'})\n", - " act=record_to_sequence(act)\n", - " for index,row in act.iterrows():\n", - " self.data[row[0]].put_feature(i+3,row[1]) #row[0]是user_id,row[1]是string\n", - "\n", - " #提取page_seq\n", - " for i in range(1):\n", - " act=activity[activity.page==i].copy()\n", - " act=act.groupby(['user_id','act_day'],as_index=False).agg({'video_id':'count'})\n", - " act=record_to_sequence(act)\n", - " for index,row in act.iterrows():\n", - " self.data[row[0]].put_feature(i+9,row[1]) #row[0]是user_id,row[1]是string\n", - "\n", - " #提取watched\n", - " watched=register.loc[:,['user_id']].copy()\n", - " watched.columns=['author_id']\n", - " watched=pd.merge(watched,activity[activity.author_id!=activity.user_id],how='inner')\n", - " watched=watched.groupby(['author_id','act_day'],as_index=False).agg({'video_id':'count'})\n", - " watched=record_to_sequence(watched)\n", - " for index,row in watched.iterrows():\n", - " self.data[row[0]].put_feature(10,row[1]) #row[0]是user_id,row[1]是string\n", - "\n", - " #提取watched by self\n", - " watched=activity[activity.author_id==activity.user_id].copy()\n", - " watched=watched.groupby(['user_id','act_day'],as_index=False).agg({'video_id':'count'})\n", - " watched=record_to_sequence(watched)\n", - " for index,row in watched.iterrows():\n", - " self.data[row[0]].put_feature(11,row[1]) #row[0]是user_id,row[1]是string\n", - "\n", - " #提取label\n", - " self.label={user_id:user.get_label() for user_id,user in self.data.items()}\n", - " \n", - " #提取data\n", - " self.data={user_id:user.get_array() for user_id,user in self.data.items()}\n", - "\n", - "\n", - " #set sample strategy\n", - " self.local_random_list=[]\n", - " for i in range(15,31):\n", - " self.local_random_list+=[i]*(i-14)\n", - " \n", - " self.online_random_list=[]\n", - " for i in range(8,31):\n", - " self.online_random_list+=[i]*(i-7)\n", - "\n", - " self.local_train_list=list(range(15,31))\n", - " self.local_test_list=list(range(8,31))\n", - " self.online_train_list=list(range(8,31))\n", - " self.online_test_list=list(range(1,31))\n", - "\n", - " self.pointer={i:0 for i in range(1,31)}\n", - " \n", - " \n", - " def reset_pointer(self):\n", - " self.pointer={i:0 for i in range(1,31)}\n", - " \n", - " \n", - " def next_batch(self,batch_size=1000):\n", - "\n", - " seq_length=self.local_random_list[np.random.randint(len(self.local_random_list))]\n", - " batch_size=batch_size//(seq_length-14)+1\n", - "\n", - " if self.pointer[seq_length]+batch_size>len(self.user_queue[seq_length]):\n", - " self.pointer[seq_length]=0\n", - " shuffle(self.user_queue[seq_length])\n", - " #print('---------------------',seq_length,'shuffled ------------------------------')\n", - " start=self.pointer[seq_length]\n", - " user_list=self.user_queue[seq_length][start:start+batch_size]\n", - " self.pointer[seq_length]+=batch_size\n", - "\n", - " user_matrix=np.array(user_list)\n", - " data_matrix=np.array([self.data[i] for i in user_list])\n", - " label_matrix=np.array([self.label[i] for i in user_list])\n", - " \n", - " return seq_length,user_matrix,data_matrix,label_matrix\n", - " \n", - " \n", - " def get_set(self,usage='train'):\n", - " \n", - " if usage=='train':\n", - " test_list=self.local_train_list\n", - " else:\n", - " test_list=self.local_test_list\n", - " \n", - " user_list=[np.array(self.user_queue[seq_length]) for seq_length in test_list]\n", - " data_list=[np.array([self.data[user_id] for user_id in self.user_queue[seq_length]]) for seq_length in test_list]\n", - " label_list=[np.array([self.label[user_id] for user_id in self.user_queue[seq_length]]) for seq_length in test_list]\n", - " return test_list,user_list,data_list,label_list" - ] - }, - { - "cell_type": "code", - "execution_count": 57, - "metadata": {}, - "outputs": [], - "source": [ - "# 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'])\n", - "\n", - "data_generator=DataGenerator(register,launch,create,activity)" - ] - }, - { - "cell_type": "code", - "execution_count": 48, - "metadata": {}, - "outputs": [], - "source": [ - "data_generator = data" - ] - }, - { - "cell_type": "code", - "execution_count": 49, - "metadata": {}, - "outputs": [ - { - "ename": "NameError", - "evalue": "name 'tf' is not defined", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[0mn_features\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m12\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2\u001b[0m \u001b[0mn_hu\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m8\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[1;32mwith\u001b[0m \u001b[0mtf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvariable_scope\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'train'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m \u001b[1;31m# tf.variable_scope用来指定变量的作用域\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 4\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 5\u001b[0m \u001b[1;31m# 变量与输入\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;31mNameError\u001b[0m: name 'tf' is not defined" - ] - } - ], - "source": [ - "n_features = 12\n", - "n_hu = 8\n", - "with tf.variable_scope('train'): # tf.variable_scope用来指定变量的作用域\n", - " \n", - " # 变量与输入\n", - " lr = tf.placeholder(tf.float32, [], name='learning_rate') # 定义学习率\n", - " \n", - " # 隐藏层到输出层的参数w, b w_shape(n_hu,1) b_shape(1) n_huWie隐藏单元的个数\n", - " W_out = tf.get_variable('W_out', [n_hu, 1]) \n", - " b_out = tf.get_variable('b_out', [1])\n", - " \n", - " # x和y x_shape(batch_size, seq_length, n_features)\n", - " x = tf.placeholder(tf.float32, [None, None, n_features])\n", - " y = tf.placeholder(tf.float32, [None, None])\n", - " \n", - " # batch_size和seq_length的大小\n", - " batch_size = tf.shape(x)[0]\n", - " seq_length = tf.shape(x)[1]\n", - " \n", - " # RNN 层\n", - " cell = tf.nn.rnn_cell.GRUCell(n_hu) # n_hu表示每个GRUcell里面的单元个数\n", - " initial_state = cell.zero_state(batch_size, dtype=tf.float32) # 指定初识状态，因为之前没有训练过\n", - " outputs, state = tf.nn.dynamic_rnn(cell, x, initial_state=initial_state) # 使用的动态Rnn\n", - " # outputs(batch_size, max_seq_length, n_hu) 这是所有时间步的输出\n", - " # state (batch_size, n_hu) 这是最后一个时间步的输出\n", - " # 具体：https://blog.csdn.net/u010960155/article/details/81707498\n", - " \n", - " # 输出层\n", - " outputs = tf.reshape(outputs, [-1, n_hu]) # （batch_size*max_seq_length, n_hu）\n", - " logits = tf.matmul(outputs, W_out) + b_out # (batch_size*max_seq_length)\n", - " logits = tf.reshape(logits, tf.stack([batch_size, seq_length]))" - ] - }, - { - "cell_type": "code", - "execution_count": 51, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.3" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/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/assets/20201129210029573.png b/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/assets/20201129210029573.png new file mode 100644 index 0000000..6914d29 Binary files /dev/null and b/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/assets/20201129210029573.png differ diff --git a/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/快手用户活跃预测.ipynb b/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/快手用户活跃预测.ipynb index 8867f7a..babc5bf 100644 --- a/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/快手用户活跃预测.ipynb +++ b/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/快手用户活跃预测.ipynb @@ -30645,15 +30645,15 @@ " pop,value = i.split(';')\n", " self.page_rank[int(pos)-self.register_day] = value\n", " \n", - " def get_array(self):\n", + " def get_array(self): # 返回递归的结果\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", + " active = self.array[:, :10].sum(axis=1) # 这里选了一部分特征做了个sum，即是发生登录、点赞、转发等，计为活动1次\n", + " for i in range(self.seq_length-7): # 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) # 未来七天内活跃过，那么标签就是1\n", " return self.label" ] }, @@ -31769,10 +31769,10 @@ } ], "source": [ - "launch['launch'] = 1\n", + "launch['launch'] = 1 # 每个用户登录次数置为1，如果登录了多次则求和\n", "launch_table = launch.groupby(['user_id', 'launch_day'], \n", " as_index=False).agg({'launch':'sum'})\n", - "launch_table.head()" + "launch_table.head() # launch_day是时间、launch登录次数" ] }, { @@ -31781,9 +31781,10 @@ "metadata": {}, "outputs": [], "source": [ - "def record_to_sequence(table):\n", + "def record_to_sequence(table): # 得到用户特征序列表\n", " table.columns=['user_id','day','value']\n", - " table.sort_values(by=['user_id','day'],inplace=True)\n", + " table.sort_values(by=['user_id','day'],inplace=True) # 排序每个客户的时间\n", + " # 拼接字符串，如上的launch_day是13，同行的launch是1，则变成13:1\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" @@ -31865,7 +31866,14 @@ ], "source": [ "launch_table=record_to_sequence(launch_table)\n", - "launch_table.head()" + "launch_table.head() # 序列特征结果" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "将用户数据填充到对应的位置，如ID=16的用户会在其特征表的13，14，15...行进行填充" ] }, { @@ -31874,8 +31882,15 @@ "metadata": {}, "outputs": [], "source": [ - "for index,row in launch_table.iterrows():\n", - " data[row[0]].put_feature(1,row[1])" + "for index,row in launch_table.iterrows(): # 根据登录信息对用户特指标进行填充\n", + " data[row[0]].put_feature(1,row[1]) # 在指定特征位置填充" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**创作视频信息**" ] }, { @@ -31888,7 +31903,16 @@ "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])" + " data[row[0]].put_feature(2,row[1]) # 第一个位置是注册信息，所以我们从2开始填充" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**用户使用时行为特征，例如点赞、转发等**\n", + "\n", + "分别对不同行为进行统计，构建6种不同行为特征" ] }, { @@ -31898,13 +31922,20 @@ "outputs": [], "source": [ "for i in range(6):\n", - " act=activity[activity.act_type==i].copy()\n", + " act=activity[activity.act_type==i].copy() # act_type不同数字对应着不同的行为\n", " act=act.groupby(['user_id','act_day'],as_index=False).agg({'video_id':'count'})\n", " act = record_to_sequence(act)\n", " for index,row in act.iterrows():\n", " data[row[0]].put_feature(i+3,row[1])" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**产生行为的界面信息**" + ] + }, { "cell_type": "code", "execution_count": 21, @@ -31919,6 +31950,13 @@ " data[row[0]].put_feature(i+9,row[1])" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**用户观看其他用户作品的信息**" + ] + }, { "cell_type": "code", "execution_count": 22, @@ -31927,6 +31965,7 @@ "source": [ "watched=register.loc[:,['user_id']].copy()\n", "watched.columns=['author_id']\n", + "# 如果作者id和用户id不相等，则是观看其它用户作品的信息\n", "watched=pd.merge(watched,activity[activity.author_id!=activity.user_id],how='inner')\n", "watched=watched.groupby(['user_id','act_day'],as_index=False).agg({'video_id':'count'})\n", "watched=record_to_sequence(watched)\n", @@ -31934,12 +31973,20 @@ " data[row[0]].put_feature(10,row[1])" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**用户观看自己作品的信息**" + ] + }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [], "source": [ + "# 如果作者id和用户id相等，则是观看自己作品的信息\n", "watched=pd.merge(watched,activity[activity.author_id==activity.user_id],how='inner')\n", "watched=watched.groupby(['user_id','act_day'],as_index=False).agg({'video_id':'count'})\n", "watched=record_to_sequence(watched)\n", @@ -31947,6 +31994,26 @@ " data[row[0]].put_feature(11,row[1])" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "特征已构建完成，这里是用的RNN网络，所以构建矩阵，提取序列\n", + "\n", + "如果是用lgb或者xgb，则是DataFrame类型，做一些统计类型的特征" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 标签制作\n", + "\n", + "活跃用户定义为（自己定义的）：在未来7天内使用过APP\n", + "\n", + "从用户注册开始进行统计，对于每1天的数据展开，如果其7天后仍有行为产生，则标签为1" + ] + }, { "cell_type": "code", "execution_count": 24, @@ -33595,7 +33662,14 @@ } ], "source": [ - "label" + "label # 1表示注册7天后，有积极行为的；0则不积极" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "用户特征数据即为之前提取的data中各项特征，转换为ndarray即可" ] }, { @@ -49084,17 +49158,31 @@ "data" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 网络训练模块" + ] + }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ - "# 导入封装好的包\n", + "# 导入封装好的包，上面方法的集合\n", "from deep_tools import f\n", "from deep_tools import DataGenerator" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "构建RNN网络模型" + ] + }, { "cell_type": "code", "execution_count": 3, @@ -49144,11 +49232,11 @@ " # 变量与输入\n", " lr = tf.placeholder(tf.float32, [], name='learning_rate') # 定义学习率\n", " \n", - " # 隐藏层到输出层的参数w, b w_shape(n_hu,1) b_shape(1) n_huWie隐藏单元的个数\n", + " # 隐藏层到输出层的参数W, b，n_hu隐藏单元的个数\n", " W_out = tf.get_variable('W_out', [n_hu, 1]) \n", " b_out = tf.get_variable('b_out', [1])\n", " \n", - " # x和y x_shape(batch_size, seq_length, n_features)\n", + " # x(batch_size, seq_length, n_features)\n", " x = tf.placeholder(tf.float32, [None, None, n_features])\n", " y = tf.placeholder(tf.float32, [None, None])\n", " \n", @@ -49159,14 +49247,11 @@ " # RNN 层\n", " cell = tf.nn.rnn_cell.GRUCell(n_hu) # n_hu表示每个GRUcell里面的单元个数\n", " initial_state = cell.zero_state(batch_size, dtype=tf.float32) # 指定初识状态，因为之前没有训练过\n", - " outputs, state = tf.nn.dynamic_rnn(cell, x, initial_state=initial_state) # 使用的动态Rnn\n", - " # outputs(batch_size, max_seq_length, n_hu) 这是所有时间步的输出\n", - " # state (batch_size, n_hu) 这是最后一个时间步的输出\n", - " # 具体：https://blog.csdn.net/u010960155/article/details/81707498\n", + " outputs, state = tf.nn.dynamic_rnn(cell, x, initial_state=initial_state) # 使用的动态RNN\n", " \n", " # 输出层\n", - " outputs = tf.reshape(outputs, [-1, n_hu]) # （batch_size*max_seq_length, n_hu）\n", - " logits = tf.matmul(outputs, W_out) + b_out # (batch_size*max_seq_length)\n", + " outputs = tf.reshape(outputs, [-1, n_hu])\n", + " logits = tf.matmul(outputs, W_out) + b_out\n", " logits = tf.reshape(logits, tf.stack([batch_size, seq_length]))" ] }, @@ -49177,7 +49262,7 @@ "outputs": [], "source": [ "# 选择部分预测结果与标签当做训练损失计算\n", - "logits_local_train = logits[:, :-14] # 这里-14或者是更小，因为本地训练，我们用前16天训练，16-23天测试。\n", + "logits_local_train = logits[:, :-14] # 这里-14或者是更小，可以看到上面的label后面几天都是None。\n", "label_local_train = y[:, :-14]" ] }, @@ -49206,18 +49291,17 @@ ], "source": [ "# 设置损失函数\n", - "\n", - "# 正则化项\n", - "regularizer = tf.contrib.layers.l2_regularizer(0.00001)\n", + "regularizer = tf.contrib.layers.l2_regularizer(0.00001) # 正则化项\n", "penalty = tf.contrib.layers.apply_regularization(regularizer, tf.trainable_variables())\n", "\n", + "# sigmoid_cross_entropy二分类任务，判断0或者1\n", "obj_local = tf.losses.sigmoid_cross_entropy(label_local_train, logits_local_train) + penalty\n", "optimizer = tf.train.AdamOptimizer(lr)\n", "set_local = optimizer.minimize(obj_local)\n", "\n", "# 选择部分预测结果与标签当做测试损失计算\n", - "logits_local_test = logits[:, -8] # 预测倒数第8天作为自己的测试标准\n", - "label_local_test = y[:, -8] # 这里也可以选择其他的天" + "logits_local_test = logits[:, -8] # 预测倒数第8天作为自己的测试标准，某一天，也可以选择其他的天\n", + "label_local_test = y[:, -8]" ] }, { @@ -49227,10 +49311,17 @@ "outputs": [], "source": [ "def train(n_obs=1000, step=1000, lr_feed=0.01):\n", - " date_seq = [31] + list(range(2, 16)) + [16] * 15\n", + " \"\"\"\n", + " n_obs迭代时选择的样本个数\n", + " step迭代次数\n", + " lr_feed学习率\n", + " \"\"\"\n", + " # 输入序列，2则是取序列长度为2的，选择2-16，因为16比较多，我们*15；人为的选择\n", + " date_seq = [31] + list(range(2, 16)) + [16] * 15 \n", " variables = [set_local, obj_local, label_local_train, logits_local_train]\n", " \n", " for i in range(step):\n", + " # next_batch 根据序列长度，先从user_queue字典里找到客户，再取指定部分\n", " length, id_list, data_x, data_y = data_generator.next_batch(n_obs)\n", " _, los, lab, log = sess.run(variables, \n", " feed_dict={x:data_x, y:data_y, lr:lr_feed})" @@ -49243,7 +49334,7 @@ "outputs": [], "source": [ "sess = tf.Session()\n", - "sess.run(tf.global_variables_initializer())" + "sess.run(tf.global_variables_initializer()) # 全局变量初始化" ] }, { @@ -49252,7 +49343,7 @@ "metadata": {}, "outputs": [], "source": [ - "train(n_obs=1000, step=2000, lr_feed=0.01)" + "train(n_obs=1000, step=2000, lr_feed=0.01) # step训练两千次" ] }, { @@ -49390,8 +49481,9 @@ ], "source": [ "def test():\n", - " n_NA = 14 # 本地训练， 我们是1-16天训练，16-23天预测，所以这个地方最大是30-14\n", + " n_NA = 14\n", " # 优化目标和数据\n", + " \n", " variables_1 = [obj_local, logits_local_train, label_local_train]\n", " variables_2 = [logits_local_test, label_local_test]\n", " \n", @@ -49401,7 +49493,9 @@ " # 训练损失\n", " for length, id_list, data_x, data_y in zip(*data_generator.get_set('train')):\n", " _obj, _logits_train, _label_train = sess.run(variables_1,\n", - " feed_dict={x:data_x, y:data_y, lr:0.001})\n", + " feed_dict={x:data_x,\n", + " y:data_y,\n", + " lr:0.001})\n", " obs_count += (length - n_NA) * len(id_list)\n", " cum_loss += _obj * (length - n_NA) * len(id_list)\n", " correct += np.sum((1 * (_logits_train>0) == _label_train))\n", @@ -49427,6 +49521,24 @@ "test()" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 评估标准：\n", + "\n", + "\n", + "可以看到上面的test_score（F1 Score）很高，也是比赛中非常接近冠军的值" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 总结：\n", + "构建数据之前，考虑清楚模型的选取，如果是选择RNN网络，构建的数据应该是个序列，t × f，t是时间步长，f是时间步长对应的特征，如数据中用户的注册数据就是t，只需要把f构建好就可以训练了。" + ] + }, { "cell_type": "code", "execution_count": null,