diff --git a/机器学习竞赛实战_优胜解决方案/京东用户购买意向预测/3-特征工程 - 副本.ipynb b/机器学习竞赛实战_优胜解决方案/京东用户购买意向预测/3-特征工程 - 副本.ipynb
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--- a/机器学习竞赛实战_优胜解决方案/京东用户购买意向预测/3-特征工程 - 副本.ipynb	
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-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 3-特征工程"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "用户基本特征：\n",
-    "* 获取基本的用户特征，基于用户本身属性多为类别特征的特点，对age,sex,usr_lv_cd进行独热编码操作，对于用户注册时间暂时不处理，\n",
-    "\n",
-    "商品基本特征：\n",
-    "* 根据商品文件获取基本的特征\n",
-    "* 针对属性a1,a2,a3进行独热编码\n",
-    "* 商品类别和品牌直接作为特征，不同的品牌的影响力不同，购买力也不同\n",
-    "\n",
-    "评论特征：\n",
-    "* 分时间段\n",
-    "* 对评论数进行独热编码: 0表示无评论，1表示有1条评论，2表示有2-10条评论，3表示有11-50条评论，4表示大于50条评论，对0~4 进行独热编码\n",
-    "\n",
-    "行为特征：\n",
-    "* 分时间段，一般是最近做的对未来的影响越明显\n",
-    "* 对行为类别进行独热编码：对1~6进行独热编码\n",
-    "* 分别按照用户-类别行为分组和用户-类别-商品行为分组统计，然后计算\n",
-    "* 用户对同类别下其他商品的行为计数\n",
-    "* 不同时间累积的行为计数（3,5,7,10,15,21,30）\n",
-    "\n",
-    "累积用户特征：\n",
-    "* 分时间段\n",
-    "* 用户不同行为的\n",
-    "* 购买转化率\n",
-    "* 均值，同上有不同时间的均值，3天、5天.....\n",
-    "\n",
-    "用户近期行为特征：\n",
-    "* 在上面针对用户进行累积特征提取的基础上，分别提取用户近一个月、近三天的特征，然后提取一个月内用户除去最近三天的行为占据一个月的行为的比重\n",
-    "\n",
-    "用户对同类别下各种商品的行为:\n",
-    "* 用户对各个类别的各项行为操作统计\n",
-    "* 用户对各个类别操作行为统计占对所有类别操作行为统计的比重\n",
-    "\n",
-    "累积商品特征:\n",
-    "\n",
-    "* 分时间段\n",
-    "* 针对商品的不同行为的\n",
-    "* 购买转化率\n",
-    "* 均值\n",
-    "\n",
-    "类别特征：\n",
-    "* 分时间段下各个商品类别的\n",
-    "* 购买转化率\n",
-    "* 均值"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import time\n",
-    "from datetime import datetime\n",
-    "from datetime import timedelta\n",
-    "import pandas as pd\n",
-    "import pickle\n",
-    "import os\n",
-    "import math\n",
-    "import numpy as np"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "<class 'pandas.core.frame.DataFrame'>\n",
-      "RangeIndex: 11485424 entries, 0 to 11485423\n",
-      "Data columns (total 7 columns):\n",
-      "user_id     float32\n",
-      "sku_id      float32\n",
-      "time        object\n",
-      "model_id    float32\n",
-      "type        float32\n",
-      "cate        float32\n",
-      "brand       float32\n",
-      "dtypes: float32(6), object(1)\n",
-      "memory usage: 350.5+ MB\n"
-     ]
-    }
-   ],
-   "source": [
-    "#float32 降低内存消耗\n",
-    "test = pd.read_csv('data/JData_Action_201602.csv')\n",
-    "test[['user_id','sku_id','model_id','type','cate','brand']] = test[['user_id','sku_id','model_id','type','cate','brand']].astype('float32')\n",
-    "test.dtypes\n",
-    "test.info()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "<class 'pandas.core.frame.DataFrame'>\n",
-      "RangeIndex: 11485424 entries, 0 to 11485423\n",
-      "Data columns (total 7 columns):\n",
-      "user_id     int64\n",
-      "sku_id      int64\n",
-      "time        object\n",
-      "model_id    float64\n",
-      "type        int64\n",
-      "cate        int64\n",
-      "brand       int64\n",
-      "dtypes: float64(1), int64(5), object(1)\n",
-      "memory usage: 613.4+ MB\n"
-     ]
-    }
-   ],
-   "source": [
-    "# 不转换float32\n",
-    "test = pd.read_csv('data/JData_Action_201602.csv')\n",
-    "# test[['user_id','sku_id','model_id','type','cate','brand']] = test[['user_id','sku_id','model_id','type','cate','brand']].astype('float32')\n",
-    "test.dtypes\n",
-    "test.info()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "可以明显看到`int64`使用的memory usage: 613.4+ MB，比`float32`的多了近一倍的内存使用"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# 数据路径\n",
-    "action_1_path = r'data/JData_Action_201602.csv'\n",
-    "action_2_path = r'data/JData_Action_201603.csv'\n",
-    "action_3_path = r'data/JData_Action_201604.csv'\n",
-    " \n",
-    "comment_path = r'data/JData_Comment.csv'\n",
-    "product_path = r'data/JData_Product.csv'\n",
-    "user_path = r'data/JData_User.csv'\n",
-    " \n",
-    "comment_date = [\n",
-    "    \"2016-02-01\", \"2016-02-08\", \"2016-02-15\", \"2016-02-22\", \"2016-02-29\",\n",
-    "    \"2016-03-07\", \"2016-03-14\", \"2016-03-21\", \"2016-03-28\", \"2016-04-04\",\n",
-    "    \"2016-04-11\", \"2016-04-15\"\n",
-    "]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# 基本方法\n",
-    "def get_actions_0():\n",
-    "    action = pd.read_csv(action_0_path)\n",
-    "    return action\n",
-    " \n",
-    "def get_actions_1():\n",
-    "    action = pd.read_csv(action_1_path)\n",
-    "    action[['user_id','sku_id','model_id','type','cate','brand']] = action[['user_id','sku_id','model_id','type','cate','brand']].astype('float32')\n",
-    "    return action\n",
-    "\n",
-    "def get_actions_2():\n",
-    "    action = pd.read_csv(action_2_path)\n",
-    "    action[['user_id','sku_id','model_id','type','cate','brand']] = action[['user_id','sku_id','model_id','type','cate','brand']].astype('float32')\n",
-    " \n",
-    "    return action\n",
-    "def get_actions_3():\n",
-    "    action = pd.read_csv(action_3_path)\n",
-    "    action[['user_id','sku_id','model_id','type','cate','brand']] = action[['user_id','sku_id','model_id','type','cate','brand']].astype('float32')\n",
-    " \n",
-    "    return action\n",
-    " \n",
-    "#如果电脑性能好就不用分块\n",
-    "def get_actions_10():\n",
-    "    \n",
-    "    reader = pd.read_csv(action_1_path, iterator=True)\n",
-    "    reader[['user_id','sku_id','model_id','type','cate','brand']] = reader[['user_id','sku_id','model_id','type','cate','brand']].astype('float32')\n",
-    "    chunks = []\n",
-    "    loop = True\n",
-    "    while loop:\n",
-    "        try:\n",
-    "            chunk = reader.get_chunk(50000)\n",
-    "            chunks.append(chunk)\n",
-    "        except StopIteration:\n",
-    "            loop = False\n",
-    "            print(\"Iteration is stopped\")\n",
-    "    action = pd.concat(chunks, ignore_index=True)\n",
-    "            \n",
-    "    return action\n",
-    "def get_actions_20():\n",
-    "    \n",
-    "    reader = pd.read_csv(action_2_path, iterator=True)\n",
-    "    reader[['user_id','sku_id','model_id','type','cate','brand']] = reader[['user_id','sku_id','model_id','type','cate','brand']].astype('float32')\n",
-    "    chunks = []\n",
-    "    loop = True\n",
-    "    while loop:\n",
-    "        try:\n",
-    "            chunk = reader.get_chunk(50000)\n",
-    "            chunks.append(chunk)\n",
-    "        except StopIteration:\n",
-    "            loop = False\n",
-    "            print(\"Iteration is stopped\")\n",
-    "    action = pd.concat(chunks, ignore_index=True)\n",
-    "            \n",
-    "    return action\n",
-    "def get_actions_30():\n",
-    "    \n",
-    "    reader = pd.read_csv(action_3_path, iterator=True)\n",
-    "    reader[['user_id','sku_id','model_id','type','cate','brand']] = reader[['user_id','sku_id','model_id','type','cate','brand']].astype('float32')\n",
-    "    chunks = []\n",
-    "    loop = True\n",
-    "    while loop:\n",
-    "        try:\n",
-    "            chunk = reader.get_chunk(50000)\n",
-    "            chunks.append(chunk)\n",
-    "        except StopIteration:\n",
-    "            loop = False\n",
-    "            print(\"Iteration is stopped\")\n",
-    "    action = pd.concat(chunks, ignore_index=True)\n",
-    "            \n",
-    "    return action\n",
-    "\n",
-    "# 读取并拼接所有行为记录文件\n",
-    "def get_all_action():\n",
-    "    action_1 = get_actions_1()\n",
-    "    action_2 = get_actions_2()\n",
-    "    action_3 = get_actions_3()\n",
-    "    actions = pd.concat([action_1, action_2, action_3]) # type: pd.DataFrame\n",
-    " \n",
-    "    return actions\n",
-    " \n",
-    "# 获取某个时间段的行为记录\n",
-    "def get_actions(start_date, end_date, all_actions):\n",
-    "    \"\"\"\n",
-    "    :param start_date:\n",
-    "    :param end_date:\n",
-    "    :return: actions: pd.Dataframe\n",
-    "    \"\"\"\n",
-    "    actions = all_actions[(all_actions.time >= start_date) & (all_actions.time < end_date)].copy()\n",
-    "    return actions"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 用户特征\n",
-    "### 用户基本特征\n",
-    "获取基本的用户特征，基于用户本身属性多为类别特征的特点，对age,sex,usr_lv_cd进行独热编码操作，对于用户注册时间暂时不处理"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sklearn import preprocessing\n",
-    " \n",
-    "def get_basic_user_feat():\n",
-    "    # 针对年龄的中文字符问题处理，首先是读入的时候编码，删除空值，然后将其数值化，最后独热编码，此外对于sex也进行了数值类型转换\n",
-    "    user = pd.read_csv(user_path, encoding='gbk')\n",
-    "    # axis=0/1=包含缺失值的行/列，how=any/all=有则删除指定行或者列/必须全部才删除，inplace=是否在原表上修改\n",
-    "    user.dropna(axis=0, how='any',inplace=True)\n",
-    "    user['sex'] = user['sex'].astype(int)    \n",
-    "    user['age'] = user['age'].astype(int)\n",
-    "    le = preprocessing.LabelEncoder()\n",
-    "    age_df = le.fit_transform(user['age'])  # 将标签值标准化成0,1,2,3...\n",
-    " \n",
-    "    age_df = pd.get_dummies(age_df, prefix='age')  # 将标准化值变成onehot编码0/1\n",
-    "# 如原数据age[0,1,2,3]变成：\n",
-    "#      age_0[1,0,0,0]\n",
-    "#      age_1[0,1,0,0]\n",
-    "#      age_2[0,0,1,0]\n",
-    "#      age_3[0,0,0,1], 有值的位置为1，没有为0，且只有0/1两种情况，列名+列值=新列名\n",
-    "    sex_df = pd.get_dummies(user['sex'], prefix='sex')\n",
-    "    user_lv_df = pd.get_dummies(user['user_lv_cd'], prefix='user_lv_cd')\n",
-    "    user = pd.concat([user['user_id'], age_df, sex_df, user_lv_df], axis=1)  # 合并\n",
-    "    return user"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "user_id        False\n",
-       "age             True\n",
-       "sex             True\n",
-       "user_lv_cd     False\n",
-       "user_reg_tm     True\n",
-       "dtype: bool"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "user = pd.read_csv(user_path, encoding='gbk')\n",
-    "user.isnull().any()  # 判断是否有空值，True为有"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "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>age</th>\n",
-       "      <th>sex</th>\n",
-       "      <th>user_lv_cd</th>\n",
-       "      <th>user_reg_tm</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>34072</th>\n",
-       "      <td>234073</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>34072</th>\n",
-       "      <td>234073</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>34072</th>\n",
-       "      <td>234073</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>38905</th>\n",
-       "      <td>238906</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>38905</th>\n",
-       "      <td>238906</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>38905</th>\n",
-       "      <td>238906</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>67704</th>\n",
-       "      <td>267705</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>67704</th>\n",
-       "      <td>267705</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>67704</th>\n",
-       "      <td>267705</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "       user_id  age  sex  user_lv_cd user_reg_tm\n",
-       "34072   234073  NaN  NaN           1         NaN\n",
-       "34072   234073  NaN  NaN           1         NaN\n",
-       "34072   234073  NaN  NaN           1         NaN\n",
-       "38905   238906  NaN  NaN           1         NaN\n",
-       "38905   238906  NaN  NaN           1         NaN\n",
-       "38905   238906  NaN  NaN           1         NaN\n",
-       "67704   267705  NaN  NaN           1         NaN\n",
-       "67704   267705  NaN  NaN           1         NaN\n",
-       "67704   267705  NaN  NaN           1         NaN"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "user[user.isnull().values==True]  # 查看空值的部分"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "这些数据不仅没有年龄、性别、注册时间，数据只有9条，比较少，我们直接删除"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "user_id        False\n",
-       "age            False\n",
-       "sex            False\n",
-       "user_lv_cd     False\n",
-       "user_reg_tm    False\n",
-       "dtype: bool"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "user.dropna(axis=0, how='any',inplace=True)\n",
-    "user.isnull().any()  # 判断是否有空值，已经全部为False"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 商品特征\n",
-    "### 商品基本特征\n",
-    "根据商品文件获取基本的特征，针对属性a1,a2,a3进行独热编码，商品类别和品牌直接作为特征"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def get_basic_product_feat():\n",
-    "    product = pd.read_csv(product_path)\n",
-    "    attr1_df = pd.get_dummies(product[\"a1\"], prefix=\"a1\")\n",
-    "    attr2_df = pd.get_dummies(product[\"a2\"], prefix=\"a2\")\n",
-    "    attr3_df = pd.get_dummies(product[\"a3\"], prefix=\"a3\")\n",
-    "    product = pd.concat([product[['sku_id', 'cate', 'brand']], attr1_df, attr2_df, attr3_df], axis=1)\n",
-    "    return product"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 评论特征\n",
-    "* 分时间段\n",
-    "* 对评论数进行独热编码"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def get_comments_product_feat(end_date):\n",
-    "    comments = pd.read_csv(comment_path)\n",
-    "    comment_date_end = end_date\n",
-    "    comment_date_begin = comment_date[0]\n",
-    "    for date in reversed(comment_date):\n",
-    "        if date < comment_date_end:\n",
-    "            comment_date_begin = date\n",
-    "            break\n",
-    "    comments = comments[comments.dt==comment_date_begin]\n",
-    "    df = pd.get_dummies(comments['comment_num'], prefix='comment_num')\n",
-    "    # 为了防止某个时间段不具备评论数为0的情况（测试集出现过这种情况）\n",
-    "    for i in range(0, 5):\n",
-    "        if 'comment_num_' + str(i) not in df.columns:\n",
-    "            df['comment_num_' + str(i)] = 0\n",
-    "    df = df[['comment_num_0', 'comment_num_1', 'comment_num_2', 'comment_num_3', 'comment_num_4']]\n",
-    "    \n",
-    "    comments = pd.concat([comments, df], axis=1) # type: pd.DataFrame\n",
-    "        #del comments['dt']\n",
-    "        #del comments['comment_num']\n",
-    "    comments = comments[['sku_id', 'has_bad_comment', 'bad_comment_rate','comment_num_0', 'comment_num_1', \n",
-    "                         'comment_num_2', 'comment_num_3', 'comment_num_4']]\n",
-    "    return comments"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "train_start_date = '2016-02-01'\n",
-    "train_end_date = datetime.strptime(train_start_date, '%Y-%m-%d') + timedelta(days=3)\n",
-    "train_end_date = train_end_date.strftime('%Y-%m-%d')\n",
-    "day = 3\n",
-    " \n",
-    "start_date = datetime.strptime(train_end_date, '%Y-%m-%d') - timedelta(days=day)\n",
-    "start_date = start_date.strftime('%Y-%m-%d')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "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>sku_id</th>\n",
-       "      <th>has_bad_comment</th>\n",
-       "      <th>bad_comment_rate</th>\n",
-       "      <th>comment_num_0</th>\n",
-       "      <th>comment_num_1</th>\n",
-       "      <th>comment_num_2</th>\n",
-       "      <th>comment_num_3</th>\n",
-       "      <th>comment_num_4</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>1000</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0.0417</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>10000</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0.0000</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>100011</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0.0376</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>100018</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0.0000</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>100020</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0.0000</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   sku_id  has_bad_comment  bad_comment_rate  comment_num_0  comment_num_1  \\\n",
-       "0    1000                1            0.0417              0              0   \n",
-       "1   10000                0            0.0000              0              0   \n",
-       "2  100011                1            0.0376              0              0   \n",
-       "3  100018                0            0.0000              0              0   \n",
-       "4  100020                0            0.0000              0              0   \n",
-       "\n",
-       "   comment_num_2  comment_num_3  comment_num_4  \n",
-       "0              0              1              0  \n",
-       "1              1              0              0  \n",
-       "2              0              0              1  \n",
-       "3              0              1              0  \n",
-       "4              0              1              0  "
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "comments = pd.read_csv(comment_path)\n",
-    "comment_date_end = train_end_date\n",
-    "comment_date_begin = comment_date[0]\n",
-    "for date in reversed(comment_date):\n",
-    "     if date < comment_date_end:\n",
-    "        comment_date_begin = date\n",
-    "        break\n",
-    "comments = comments[comments.dt==comment_date_begin]\n",
-    "df = pd.get_dummies(comments['comment_num'], prefix='comment_num')\n",
-    "for i in range(0, 5):\n",
-    "    if 'comment_num_' + str(i) not in df.columns:\n",
-    "         df['comment_num_' + str(i)] = 0\n",
-    "df = df[['comment_num_0', 'comment_num_1', 'comment_num_2', 'comment_num_3', 'comment_num_4']]\n",
-    "    \n",
-    "comments = pd.concat([comments, df], axis=1) # type: pd.DataFrame\n",
-    "comments = comments[['sku_id', 'has_bad_comment', 'bad_comment_rate','comment_num_0', 'comment_num_1', \n",
-    "                        'comment_num_2', 'comment_num_3', 'comment_num_4']]\n",
-    "comments.head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "comment_num_0/1/2/3/4分别对应着，0无评论/1表示1条/2表示2-10条/3表示11-50条/4表示大于50条，bad_comment_rate差评率，has_bad_comment是否包含差评。"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 行为特征\n",
-    "* 分时间段\n",
-    "* 对行为类别进行独热编码\n",
-    "* 分别按照用户-类别行为分组和用户-类别-商品行为分组统计，然后计算\n",
-    "    * 用户对同类别下其他商品的行为计数\n",
-    "    * 针对用户对同类别下目标商品的行为计数与该时间段的行为均值作差"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def get_action_feat(start_date, end_date, all_actions, i):\n",
-    "    actions = get_actions(start_date, end_date, all_actions)\n",
-    "    actions = actions[['user_id', 'sku_id', 'cate','type']]\n",
-    "    # 不同时间累积的行为计数（3,5,7,10,15,21,30）\n",
-    "    df = pd.get_dummies(actions['type'], prefix='action_before_%s' %i)\n",
-    "    before_date = 'action_before_%s' %i\n",
-    "    actions = pd.concat([actions, df], axis=1)  # type: pd.DataFrame\n",
-    "    # 分组统计，用户-类别-商品,不同用户对不同类别下商品的行为计数\n",
-    "    actions = actions.groupby(['user_id', 'sku_id','cate'], as_index=False).sum()\n",
-    "    # 分组统计，用户-类别，不同用户对不同商品类别的行为计数\n",
-    "    user_cate = actions.groupby(['user_id','cate'], as_index=False).sum()\n",
-    "    del user_cate['sku_id']\n",
-    "    del user_cate['type']\n",
-    "    actions = pd.merge(actions, user_cate, how='left', on=['user_id','cate'])\n",
-    "    #本类别下其他商品点击量\n",
-    "    # 前述两种分组含有相同名称的不同行为的计数，系统会自动针对名称调整添加后缀,x,y，所以这里作差统计的是同一类别下其他商品的行为计数\n",
-    "    actions[before_date+'_1.0_y'] = actions[before_date+'_1.0_y'] - actions[before_date+'_1.0_x']\n",
-    "    actions[before_date+'_2.0_y'] = actions[before_date+'_2.0_y'] - actions[before_date+'_2.0_x']\n",
-    "    actions[before_date+'_3.0_y'] = actions[before_date+'_3.0_y'] - actions[before_date+'_3.0_x']\n",
-    "    actions[before_date+'_4.0_y'] = actions[before_date+'_4.0_y'] - actions[before_date+'_4.0_x']\n",
-    "    actions[before_date+'_5.0_y'] = actions[before_date+'_5.0_y'] - actions[before_date+'_5.0_x']\n",
-    "    actions[before_date+'_6.0_y'] = actions[before_date+'_6.0_y'] - actions[before_date+'_6.0_x']\n",
-    "    # 统计用户对不同类别下商品计数与该类别下商品行为计数均值（对时间）的差值\n",
-    "    actions[before_date+'minus_mean_1'] = actions[before_date+'_1.0_x'] - (actions[before_date+'_1.0_x']/i)\n",
-    "    actions[before_date+'minus_mean_2'] = actions[before_date+'_2.0_x'] - (actions[before_date+'_2.0_x']/i)\n",
-    "    actions[before_date+'minus_mean_3'] = actions[before_date+'_3.0_x'] - (actions[before_date+'_3.0_x']/i)\n",
-    "    actions[before_date+'minus_mean_4'] = actions[before_date+'_4.0_x'] - (actions[before_date+'_4.0_x']/i)\n",
-    "    actions[before_date+'minus_mean_5'] = actions[before_date+'_5.0_x'] - (actions[before_date+'_5.0_x']/i)\n",
-    "    actions[before_date+'minus_mean_6'] = actions[before_date+'_6.0_x'] - (actions[before_date+'_6.0_x']/i)\n",
-    "    del actions['type']\n",
-    " \n",
-    "    return actions"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 用户-行为\n",
-    "#### 累积用户特征\n",
-    "* 分时间段\n",
-    "* 用户不同行为的\n",
-    "    * 购买转化率\n",
-    "    * 均值"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def get_accumulate_user_feat(end_date, all_actions, day):\n",
-    "    start_date = datetime.strptime(end_date, '%Y-%m-%d') - timedelta(days=day)\n",
-    "    start_date = start_date.strftime('%Y-%m-%d')\n",
-    "    before_date = 'user_action_%s' % day\n",
-    "\n",
-    "    feature = [\n",
-    "        'user_id', before_date + '_1', before_date + '_2', before_date + '_3',\n",
-    "        before_date + '_4', before_date + '_5', before_date + '_6',\n",
-    "        before_date + '_1_ratio', before_date + '_2_ratio',\n",
-    "        before_date + '_3_ratio', before_date + '_5_ratio',\n",
-    "        before_date + '_6_ratio', before_date + '_1_mean',\n",
-    "        before_date + '_2_mean', before_date + '_3_mean',\n",
-    "        before_date + '_4_mean', before_date + '_5_mean',\n",
-    "        before_date + '_6_mean', before_date + '_1_std',\n",
-    "        before_date + '_2_std', before_date + '_3_std', before_date + '_4_std',\n",
-    "        before_date + '_5_std', before_date + '_6_std'\n",
-    "    ]\n",
-    "\n",
-    "    actions = get_actions(start_date, end_date, all_actions)\n",
-    "    df = pd.get_dummies(actions['type'], prefix=before_date)\n",
-    "\n",
-    "    actions['date'] = pd.to_datetime(actions['time']).apply(lambda x: x.date())\n",
-    "\n",
-    "    actions = pd.concat([actions[['user_id', 'date']], df], axis=1)\n",
-    "   \n",
-    "    # 分组统计，按用户分组，统计用户各项行为的转化率、均值\n",
-    "    actions = actions.groupby(['user_id'], as_index=False).sum()\n",
-    "\n",
-    "    actions[before_date + '_1_ratio'] =  np.log(1 + actions[before_date + '_4.0']) - np.log(1 + actions[before_date +'_1.0'])\n",
-    "    actions[before_date + '_2_ratio'] =  np.log(1 + actions[before_date + '_4.0']) - np.log(1 + actions[before_date +'_2.0'])\n",
-    "    actions[before_date + '_3_ratio'] =  np.log(1 + actions[before_date + '_4.0']) - np.log(1 + actions[before_date +'_3.0'])\n",
-    "    actions[before_date + '_5_ratio'] =  np.log(1 + actions[before_date + '_4.0']) - np.log(1 + actions[before_date +'_5.0'])\n",
-    "    actions[before_date + '_6_ratio'] =  np.log(1 + actions[before_date + '_4.0']) - np.log(1 + actions[before_date +'_6.0'])\n",
-    "    # 均值\n",
-    "    actions[before_date + '_1_mean'] = actions[before_date + '_1.0'] / day\n",
-    "    actions[before_date + '_2_mean'] = actions[before_date + '_2.0'] / day\n",
-    "    actions[before_date + '_3_mean'] = actions[before_date + '_3.0'] / day\n",
-    "    actions[before_date + '_4_mean'] = actions[before_date + '_4.0'] / day\n",
-    "    actions[before_date + '_5_mean'] = actions[before_date + '_5.0'] / day\n",
-    "    actions[before_date + '_6_mean'] = actions[before_date + '_6.0'] / day\n",
-    "    #actions = pd.merge(actions, actions_date, how='left', on='user_id')\n",
-    "    #actions = actions[feature]\n",
-    "    return actions"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 用户近期行为特征\n",
-    "在上面针对用户进行累积特征提取的基础上，分别提取用户近一个月、近三天的特征，然后提取一个月内用户除去最近三天的行为占据一个月的行为的比重"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def get_recent_user_feat(end_date, all_actions):\n",
-    "    actions_3 = get_accumulate_user_feat(end_date, all_actions, 3)\n",
-    "    actions_30 = get_accumulate_user_feat(end_date, all_actions, 30)\n",
-    "    actions = pd.merge(actions_3, actions_30, how ='left', on='user_id')\n",
-    "    del actions_3\n",
-    "    del actions_30\n",
-    "    \n",
-    "    actions['recent_action1'] =  np.log(1 + actions['user_action_30_1.0']-actions['user_action_3_1.0']) - np.log(1 + actions['user_action_30_1.0'])\n",
-    "    actions['recent_action2'] =  np.log(1 + actions['user_action_30_2.0']-actions['user_action_3_2.0']) - np.log(1 + actions['user_action_30_2.0'])\n",
-    "    actions['recent_action3'] =  np.log(1 + actions['user_action_30_3.0']-actions['user_action_3_3.0']) - np.log(1 + actions['user_action_30_3.0'])\n",
-    "    actions['recent_action4'] =  np.log(1 + actions['user_action_30_4.0']-actions['user_action_3_4.0']) - np.log(1 + actions['user_action_30_4.0'])\n",
-    "    actions['recent_action5'] =  np.log(1 + actions['user_action_30_5.0']-actions['user_action_3_5.0']) - np.log(1 + actions['user_action_30_5.0'])\n",
-    "    actions['recent_action6'] =  np.log(1 + actions['user_action_30_6.0']-actions['user_action_3_6.0']) - np.log(1 + actions['user_action_30_6.0'])\n",
-    "    \n",
-    "    return actions"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 用户对同类别下各种商品的行为\n",
-    "* 用户对各个类别的各项行为操作统计\n",
-    "* 用户对各个类别操作行为统计占对所有类别操作行为统计的比重"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#增加了用户对不同类别的交互特征\n",
-    "def get_user_cate_feature(start_date, end_date, all_actions):\n",
-    "    actions = get_actions(start_date, end_date, all_actions)\n",
-    "    actions = actions[['user_id', 'cate', 'type']]\n",
-    "    df = pd.get_dummies(actions['type'], prefix='type')\n",
-    "    actions = pd.concat([actions[['user_id', 'cate']], df], axis=1)\n",
-    "    actions = actions.groupby(['user_id', 'cate']).sum()\n",
-    "    actions = actions.unstack()\n",
-    "    actions.columns = actions.columns.swaplevel(0, 1)\n",
-    "    actions.columns = actions.columns.droplevel()\n",
-    "    actions.columns = [\n",
-    "        'cate_4_type1', 'cate_5_type1', 'cate_6_type1', 'cate_7_type1',\n",
-    "        'cate_8_type1', 'cate_9_type1', 'cate_10_type1', 'cate_11_type1',\n",
-    "        'cate_4_type2', 'cate_5_type2', 'cate_6_type2', 'cate_7_type2',\n",
-    "        'cate_8_type2', 'cate_9_type2', 'cate_10_type2', 'cate_11_type2',\n",
-    "        'cate_4_type3', 'cate_5_type3', 'cate_6_type3', 'cate_7_type3',\n",
-    "        'cate_8_type3', 'cate_9_type3', 'cate_10_type3', 'cate_11_type3',\n",
-    "        'cate_4_type4', 'cate_5_type4', 'cate_6_type4', 'cate_7_type4',\n",
-    "        'cate_8_type4', 'cate_9_type4', 'cate_10_type4', 'cate_11_type4',\n",
-    "        'cate_4_type5', 'cate_5_type5', 'cate_6_type5', 'cate_7_type5',\n",
-    "        'cate_8_type5', 'cate_9_type5', 'cate_10_type5', 'cate_11_type5',\n",
-    "        'cate_4_type6', 'cate_5_type6', 'cate_6_type6', 'cate_7_type6',\n",
-    "        'cate_8_type6', 'cate_9_type6', 'cate_10_type6', 'cate_11_type6'\n",
-    "    ]\n",
-    "    actions = actions.fillna(0)\n",
-    "    actions['cate_action_sum'] = actions.sum(axis=1)\n",
-    "    actions['cate8_percentage'] = (\n",
-    "        actions['cate_8_type1'] + actions['cate_8_type2'] +\n",
-    "        actions['cate_8_type3'] + actions['cate_8_type4'] +\n",
-    "        actions['cate_8_type5'] + actions['cate_8_type6']\n",
-    "    ) / actions['cate_action_sum']\n",
-    "    actions['cate4_percentage'] = (\n",
-    "        actions['cate_4_type1'] + actions['cate_4_type2'] +\n",
-    "        actions['cate_4_type3'] + actions['cate_4_type4'] +\n",
-    "        actions['cate_4_type5'] + actions['cate_4_type6']\n",
-    "    ) / actions['cate_action_sum']\n",
-    "    actions['cate5_percentage'] = (\n",
-    "        actions['cate_5_type1'] + actions['cate_5_type2'] +\n",
-    "        actions['cate_5_type3'] + actions['cate_5_type4'] +\n",
-    "        actions['cate_5_type5'] + actions['cate_5_type6']\n",
-    "    ) / actions['cate_action_sum']\n",
-    "    actions['cate6_percentage'] = (\n",
-    "        actions['cate_6_type1'] + actions['cate_6_type2'] +\n",
-    "        actions['cate_6_type3'] + actions['cate_6_type4'] +\n",
-    "        actions['cate_6_type5'] + actions['cate_6_type6']\n",
-    "    ) / actions['cate_action_sum']\n",
-    "    actions['cate7_percentage'] = (\n",
-    "        actions['cate_7_type1'] + actions['cate_7_type2'] +\n",
-    "        actions['cate_7_type3'] + actions['cate_7_type4'] +\n",
-    "        actions['cate_7_type5'] + actions['cate_7_type6']\n",
-    "    ) / actions['cate_action_sum']\n",
-    "    actions['cate9_percentage'] = (\n",
-    "        actions['cate_9_type1'] + actions['cate_9_type2'] +\n",
-    "        actions['cate_9_type3'] + actions['cate_9_type4'] +\n",
-    "        actions['cate_9_type5'] + actions['cate_9_type6']\n",
-    "    ) / actions['cate_action_sum']\n",
-    "    actions['cate10_percentage'] = (\n",
-    "        actions['cate_10_type1'] + actions['cate_10_type2'] +\n",
-    "        actions['cate_10_type3'] + actions['cate_10_type4'] +\n",
-    "        actions['cate_10_type5'] + actions['cate_10_type6']\n",
-    "    ) / actions['cate_action_sum']\n",
-    "    actions['cate11_percentage'] = (\n",
-    "        actions['cate_11_type1'] + actions['cate_11_type2'] +\n",
-    "        actions['cate_11_type3'] + actions['cate_11_type4'] +\n",
-    "        actions['cate_11_type5'] + actions['cate_11_type6']\n",
-    "    ) / actions['cate_action_sum']\n",
-    "\n",
-    "    actions['cate8_type1_percentage'] = np.log(\n",
-    "        1 + actions['cate_8_type1']) - np.log(\n",
-    "            1 + actions['cate_8_type1'] + actions['cate_4_type1'] +\n",
-    "            actions['cate_5_type1'] + actions['cate_6_type1'] +\n",
-    "            actions['cate_7_type1'] + actions['cate_9_type1'] +\n",
-    "            actions['cate_10_type1'] + actions['cate_11_type1'])\n",
-    "\n",
-    "    actions['cate8_type2_percentage'] = np.log(\n",
-    "        1 + actions['cate_8_type2']) - np.log(\n",
-    "            1 + actions['cate_8_type2'] + actions['cate_4_type2'] +\n",
-    "            actions['cate_5_type2'] + actions['cate_6_type2'] +\n",
-    "            actions['cate_7_type2'] + actions['cate_9_type2'] +\n",
-    "            actions['cate_10_type2'] + actions['cate_11_type2'])\n",
-    "    actions['cate8_type3_percentage'] = np.log(\n",
-    "        1 + actions['cate_8_type3']) - np.log(\n",
-    "            1 + actions['cate_8_type3'] + actions['cate_4_type3'] +\n",
-    "            actions['cate_5_type3'] + actions['cate_6_type3'] +\n",
-    "            actions['cate_7_type3'] + actions['cate_9_type3'] +\n",
-    "            actions['cate_10_type3'] + actions['cate_11_type3'])\n",
-    "    actions['cate8_type4_percentage'] = np.log(\n",
-    "        1 + actions['cate_8_type4']) - np.log(\n",
-    "            1 + actions['cate_8_type4'] + actions['cate_4_type4'] +\n",
-    "            actions['cate_5_type4'] + actions['cate_6_type4'] +\n",
-    "            actions['cate_7_type4'] + actions['cate_9_type4'] +\n",
-    "            actions['cate_10_type4'] + actions['cate_11_type4'])\n",
-    "    actions['cate8_type5_percentage'] = np.log(\n",
-    "        1 + actions['cate_8_type5']) - np.log(\n",
-    "            1 + actions['cate_8_type5'] + actions['cate_4_type5'] +\n",
-    "            actions['cate_5_type5'] + actions['cate_6_type5'] +\n",
-    "            actions['cate_7_type5'] + actions['cate_9_type5'] +\n",
-    "            actions['cate_10_type5'] + actions['cate_11_type5'])\n",
-    "    actions['cate8_type6_percentage'] = np.log(\n",
-    "        1 + actions['cate_8_type6']) - np.log(\n",
-    "            1 + actions['cate_8_type6'] + actions['cate_4_type6'] +\n",
-    "            actions['cate_5_type6'] + actions['cate_6_type6'] +\n",
-    "            actions['cate_7_type6'] + actions['cate_9_type6'] +\n",
-    "            actions['cate_10_type6'] + actions['cate_11_type6'])\n",
-    "    actions['user_id'] = actions.index\n",
-    "    actions = actions[[\n",
-    "        'user_id', 'cate8_percentage', 'cate4_percentage', 'cate5_percentage',\n",
-    "        'cate6_percentage', 'cate7_percentage', 'cate9_percentage',\n",
-    "        'cate10_percentage', 'cate11_percentage', 'cate8_type1_percentage',\n",
-    "        'cate8_type2_percentage', 'cate8_type3_percentage',\n",
-    "        'cate8_type4_percentage', 'cate8_type5_percentage',\n",
-    "        'cate8_type6_percentage'\n",
-    "    ]]\n",
-    "    return actions"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "2016-02-01\n",
-      "2016-02-04\n"
-     ]
-    }
-   ],
-   "source": [
-    "train_start_date = '2016-02-01'\n",
-    "train_end_date = datetime.strptime(train_start_date, '%Y-%m-%d') + timedelta(days=3)\n",
-    "train_end_date = train_end_date.strftime('%Y-%m-%d')\n",
-    "day = 3\n",
-    "\n",
-    "start_date = datetime.strptime(train_end_date, '%Y-%m-%d') - timedelta(days=day)\n",
-    "start_date = start_date.strftime('%Y-%m-%d')\n",
-    "\n",
-    "print (start_date)\n",
-    "print (train_end_date)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "all_actions = get_all_action()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
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-       "\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>cate</th>\n",
-       "      <th>type</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>29</th>\n",
-       "      <td>272629.0</td>\n",
-       "      <td>10.0</td>\n",
-       "      <td>1.0</td>\n",
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-       "    <tr>\n",
-       "      <th>30</th>\n",
-       "      <td>272629.0</td>\n",
-       "      <td>10.0</td>\n",
-       "      <td>1.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>31</th>\n",
-       "      <td>272629.0</td>\n",
-       "      <td>10.0</td>\n",
-       "      <td>6.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>32</th>\n",
-       "      <td>272629.0</td>\n",
-       "      <td>10.0</td>\n",
-       "      <td>1.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>33</th>\n",
-       "      <td>272629.0</td>\n",
-       "      <td>10.0</td>\n",
-       "      <td>6.0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "     user_id  cate  type\n",
-       "29  272629.0  10.0   1.0\n",
-       "30  272629.0  10.0   1.0\n",
-       "31  272629.0  10.0   6.0\n",
-       "32  272629.0  10.0   1.0\n",
-       "33  272629.0  10.0   6.0"
-      ]
-     },
-     "execution_count": 20,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "actions = get_actions(start_date, train_end_date, all_actions)\n",
-    "actions = actions[['user_id', 'cate', 'type']]\n",
-    "actions.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
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-       "\n",
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-       "\n",
-       "    .dataframe thead th {\n",
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-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
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-       "      <th>type_2.0</th>\n",
-       "      <th>type_3.0</th>\n",
-       "      <th>type_4.0</th>\n",
-       "      <th>type_5.0</th>\n",
-       "      <th>type_6.0</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>user_id</th>\n",
-       "      <th>cate</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th rowspan=\"4\" valign=\"top\">200002.0</th>\n",
-       "      <th>4.0</th>\n",
-       "      <td>16.0</td>\n",
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-       "    <tr>\n",
-       "      <th>5.0</th>\n",
-       "      <td>4.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>6.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7.0</th>\n",
-       "      <td>4.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>3.0</td>\n",
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-       "      <th>8.0</th>\n",
-       "      <td>4.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>12.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>200003.0</th>\n",
-       "      <th>4.0</th>\n",
-       "      <td>8.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>12.0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "               type_1.0  type_2.0  type_3.0  type_4.0  type_5.0  type_6.0\n",
-       "user_id  cate                                                            \n",
-       "200002.0 4.0       16.0       0.0       0.0       0.0       0.0      20.0\n",
-       "         5.0        4.0       0.0       0.0       0.0       0.0       6.0\n",
-       "         7.0        4.0       0.0       0.0       0.0       0.0       3.0\n",
-       "         8.0        4.0       0.0       0.0       0.0       0.0      12.0\n",
-       "200003.0 4.0        8.0       0.0       0.0       0.0       0.0      12.0"
-      ]
-     },
-     "execution_count": 21,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df = pd.get_dummies(actions['type'], prefix='type')\n",
-    "actions = pd.concat([actions[['user_id', 'cate']], df], axis=1)\n",
-    "actions = actions.groupby(['user_id', 'cate']).sum()\n",
-    "actions.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
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-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead tr th {\n",
-       "        text-align: left;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead tr:last-of-type th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
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-       "      <th colspan=\"8\" halign=\"left\">type_1.0</th>\n",
-       "      <th colspan=\"2\" halign=\"left\">type_2.0</th>\n",
-       "      <th>...</th>\n",
-       "      <th colspan=\"2\" halign=\"left\">type_5.0</th>\n",
-       "      <th colspan=\"8\" halign=\"left\">type_6.0</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>cate</th>\n",
-       "      <th>4.0</th>\n",
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-       "      <th>9.0</th>\n",
-       "      <th>10.0</th>\n",
-       "      <th>11.0</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>user_id</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
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-       "      <th></th>\n",
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-       "      <th></th>\n",
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-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>200002.0</th>\n",
-       "      <td>16.0</td>\n",
-       "      <td>4.0</td>\n",
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-       "      <td>4.0</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>20.0</td>\n",
-       "      <td>6.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>12.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>200003.0</th>\n",
-       "      <td>8.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>12.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>12.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>19.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>200008.0</th>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>8.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>20.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>200023.0</th>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>200030.0</th>\n",
-       "      <td>8.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>17.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>5 rows × 48 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "         type_1.0                                     type_2.0       ...  \\\n",
-       "cate         4.0  5.0  6.0  7.0   8.0  9.0  10.0 11.0     4.0  5.0   ...   \n",
-       "user_id                                                              ...   \n",
-       "200002.0     16.0  4.0  NaN  4.0   4.0  NaN  NaN  NaN      0.0  0.0  ...   \n",
-       "200003.0      8.0  NaN  NaN  NaN  12.0  NaN  NaN  NaN      0.0  NaN  ...   \n",
-       "200008.0      NaN  NaN  NaN  8.0   NaN  NaN  NaN  NaN      NaN  NaN  ...   \n",
-       "200023.0      NaN  NaN  NaN  NaN   1.0  NaN  NaN  NaN      NaN  NaN  ...   \n",
-       "200030.0      8.0  NaN  NaN  NaN   NaN  NaN  NaN  NaN      0.0  NaN  ...   \n",
-       "\n",
-       "         type_5.0      type_6.0                                       \n",
-       "cate         10.0 11.0     4.0  5.0  6.0   7.0   8.0  9.0  10.0 11.0  \n",
-       "user_id                                                               \n",
-       "200002.0      NaN  NaN     20.0  6.0  NaN   3.0  12.0  NaN  NaN  NaN  \n",
-       "200003.0      NaN  NaN     12.0  NaN  NaN   NaN  19.0  NaN  NaN  NaN  \n",
-       "200008.0      NaN  NaN      NaN  NaN  NaN  20.0   NaN  NaN  NaN  NaN  \n",
-       "200023.0      NaN  NaN      NaN  NaN  NaN   NaN   0.0  NaN  NaN  NaN  \n",
-       "200030.0      NaN  NaN     17.0  NaN  NaN   NaN   NaN  NaN  NaN  NaN  \n",
-       "\n",
-       "[5 rows x 48 columns]"
-      ]
-     },
-     "execution_count": 22,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "actions = actions.unstack()\n",
-    "actions.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "MultiIndex(levels=[['type_1.0', 'type_2.0', 'type_3.0', 'type_4.0', 'type_5.0', 'type_6.0'], [4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0]],\n",
-       "           codes=[[0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 4, 4, 4, 4, 4, 4, 4, 4, 5, 5, 5, 5, 5, 5, 5, 5], [0, 1, 2, 3, 4, 5, 6, 7, 0, 1, 2, 3, 4, 5, 6, 7, 0, 1, 2, 3, 4, 5, 6, 7, 0, 1, 2, 3, 4, 5, 6, 7, 0, 1, 2, 3, 4, 5, 6, 7, 0, 1, 2, 3, 4, 5, 6, 7]],\n",
-       "           names=[None, 'cate'])"
-      ]
-     },
-     "execution_count": 23,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "actions.columns"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "MultiIndex(levels=[[4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0], ['type_1.0', 'type_2.0', 'type_3.0', 'type_4.0', 'type_5.0', 'type_6.0']],\n",
-       "           codes=[[0, 1, 2, 3, 4, 5, 6, 7, 0, 1, 2, 3, 4, 5, 6, 7, 0, 1, 2, 3, 4, 5, 6, 7, 0, 1, 2, 3, 4, 5, 6, 7, 0, 1, 2, 3, 4, 5, 6, 7, 0, 1, 2, 3, 4, 5, 6, 7], [0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 4, 4, 4, 4, 4, 4, 4, 4, 5, 5, 5, 5, 5, 5, 5, 5]],\n",
-       "           names=['cate', None])"
-      ]
-     },
-     "execution_count": 24,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "actions.columns = actions.columns.swaplevel(0, 1)\n",
-    "actions.columns"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Index(['type_1.0', 'type_1.0', 'type_1.0', 'type_1.0', 'type_1.0', 'type_1.0',\n",
-       "       'type_1.0', 'type_1.0', 'type_2.0', 'type_2.0', 'type_2.0', 'type_2.0',\n",
-       "       'type_2.0', 'type_2.0', 'type_2.0', 'type_2.0', 'type_3.0', 'type_3.0',\n",
-       "       'type_3.0', 'type_3.0', 'type_3.0', 'type_3.0', 'type_3.0', 'type_3.0',\n",
-       "       'type_4.0', 'type_4.0', 'type_4.0', 'type_4.0', 'type_4.0', 'type_4.0',\n",
-       "       'type_4.0', 'type_4.0', 'type_5.0', 'type_5.0', 'type_5.0', 'type_5.0',\n",
-       "       'type_5.0', 'type_5.0', 'type_5.0', 'type_5.0', 'type_6.0', 'type_6.0',\n",
-       "       'type_6.0', 'type_6.0', 'type_6.0', 'type_6.0', 'type_6.0', 'type_6.0'],\n",
-       "      dtype='object')"
-      ]
-     },
-     "execution_count": 25,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "actions.columns = actions.columns.droplevel()\n",
-    "actions.columns"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Index(['cate_4_type1', 'cate_5_type1', 'cate_6_type1', 'cate_7_type1',\n",
-       "       'cate_8_type1', 'cate_9_type1', 'cate_10_type1', 'cate_11_type1',\n",
-       "       'cate_4_type2', 'cate_5_type2', 'cate_6_type2', 'cate_7_type2',\n",
-       "       'cate_8_type2', 'cate_9_type2', 'cate_10_type2', 'cate_11_type2',\n",
-       "       'cate_4_type3', 'cate_5_type3', 'cate_6_type3', 'cate_7_type3',\n",
-       "       'cate_8_type3', 'cate_9_type3', 'cate_10_type3', 'cate_11_type3',\n",
-       "       'cate_4_type4', 'cate_5_type4', 'cate_6_type4', 'cate_7_type4',\n",
-       "       'cate_8_type4', 'cate_9_type4', 'cate_10_type4', 'cate_11_type4',\n",
-       "       'cate_4_type5', 'cate_5_type5', 'cate_6_type5', 'cate_7_type5',\n",
-       "       'cate_8_type5', 'cate_9_type5', 'cate_10_type5', 'cate_11_type5',\n",
-       "       'cate_4_type6', 'cate_5_type6', 'cate_6_type6', 'cate_7_type6',\n",
-       "       'cate_8_type6', 'cate_9_type6', 'cate_10_type6', 'cate_11_type6'],\n",
-       "      dtype='object')"
-      ]
-     },
-     "execution_count": 26,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "actions.columns = [\n",
-    "        'cate_4_type1', 'cate_5_type1', 'cate_6_type1', 'cate_7_type1',\n",
-    "        'cate_8_type1', 'cate_9_type1', 'cate_10_type1', 'cate_11_type1',\n",
-    "        'cate_4_type2', 'cate_5_type2', 'cate_6_type2', 'cate_7_type2',\n",
-    "        'cate_8_type2', 'cate_9_type2', 'cate_10_type2', 'cate_11_type2',\n",
-    "        'cate_4_type3', 'cate_5_type3', 'cate_6_type3', 'cate_7_type3',\n",
-    "        'cate_8_type3', 'cate_9_type3', 'cate_10_type3', 'cate_11_type3',\n",
-    "        'cate_4_type4', 'cate_5_type4', 'cate_6_type4', 'cate_7_type4',\n",
-    "        'cate_8_type4', 'cate_9_type4', 'cate_10_type4', 'cate_11_type4',\n",
-    "        'cate_4_type5', 'cate_5_type5', 'cate_6_type5', 'cate_7_type5',\n",
-    "        'cate_8_type5', 'cate_9_type5', 'cate_10_type5', 'cate_11_type5',\n",
-    "        'cate_4_type6', 'cate_5_type6', 'cate_6_type6', 'cate_7_type6',\n",
-    "        'cate_8_type6', 'cate_9_type6', 'cate_10_type6', 'cate_11_type6'\n",
-    "    ]\n",
-    "actions.columns"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "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>cate_4_type1</th>\n",
-       "      <th>cate_5_type1</th>\n",
-       "      <th>cate_6_type1</th>\n",
-       "      <th>cate_7_type1</th>\n",
-       "      <th>cate_8_type1</th>\n",
-       "      <th>cate_9_type1</th>\n",
-       "      <th>cate_10_type1</th>\n",
-       "      <th>cate_11_type1</th>\n",
-       "      <th>cate_4_type2</th>\n",
-       "      <th>cate_5_type2</th>\n",
-       "      <th>...</th>\n",
-       "      <th>cate_11_type5</th>\n",
-       "      <th>cate_4_type6</th>\n",
-       "      <th>cate_5_type6</th>\n",
-       "      <th>cate_6_type6</th>\n",
-       "      <th>cate_7_type6</th>\n",
-       "      <th>cate_8_type6</th>\n",
-       "      <th>cate_9_type6</th>\n",
-       "      <th>cate_10_type6</th>\n",
-       "      <th>cate_11_type6</th>\n",
-       "      <th>cate_action_sum</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>user_id</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>200002.0</th>\n",
-       "      <td>16.0</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>20.0</td>\n",
-       "      <td>6.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>12.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>69.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>200003.0</th>\n",
-       "      <td>8.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>12.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>12.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>19.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>51.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>200008.0</th>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>8.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>20.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>28.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>200023.0</th>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>200030.0</th>\n",
-       "      <td>8.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>17.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>25.0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>5 rows × 49 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "          cate_4_type1  cate_5_type1  cate_6_type1  cate_7_type1  \\\n",
-       "user_id                                                            \n",
-       "200002.0          16.0           4.0           0.0           4.0   \n",
-       "200003.0           8.0           0.0           0.0           0.0   \n",
-       "200008.0           0.0           0.0           0.0           8.0   \n",
-       "200023.0           0.0           0.0           0.0           0.0   \n",
-       "200030.0           8.0           0.0           0.0           0.0   \n",
-       "\n",
-       "          cate_8_type1  cate_9_type1  cate_10_type1  cate_11_type1  \\\n",
-       "user_id                                                              \n",
-       "200002.0           4.0           0.0            0.0            0.0   \n",
-       "200003.0          12.0           0.0            0.0            0.0   \n",
-       "200008.0           0.0           0.0            0.0            0.0   \n",
-       "200023.0           1.0           0.0            0.0            0.0   \n",
-       "200030.0           0.0           0.0            0.0            0.0   \n",
-       "\n",
-       "          cate_4_type2  cate_5_type2  ...  cate_11_type5  cate_4_type6  \\\n",
-       "user_id                               ...                                \n",
-       "200002.0           0.0           0.0  ...            0.0          20.0   \n",
-       "200003.0           0.0           0.0  ...            0.0          12.0   \n",
-       "200008.0           0.0           0.0  ...            0.0           0.0   \n",
-       "200023.0           0.0           0.0  ...            0.0           0.0   \n",
-       "200030.0           0.0           0.0  ...            0.0          17.0   \n",
-       "\n",
-       "          cate_5_type6  cate_6_type6  cate_7_type6  cate_8_type6  \\\n",
-       "user_id                                                            \n",
-       "200002.0           6.0           0.0           3.0          12.0   \n",
-       "200003.0           0.0           0.0           0.0          19.0   \n",
-       "200008.0           0.0           0.0          20.0           0.0   \n",
-       "200023.0           0.0           0.0           0.0           0.0   \n",
-       "200030.0           0.0           0.0           0.0           0.0   \n",
-       "\n",
-       "          cate_9_type6  cate_10_type6  cate_11_type6  cate_action_sum  \n",
-       "user_id                                                                \n",
-       "200002.0           0.0            0.0            0.0             69.0  \n",
-       "200003.0           0.0            0.0            0.0             51.0  \n",
-       "200008.0           0.0            0.0            0.0             28.0  \n",
-       "200023.0           0.0            0.0            0.0              1.0  \n",
-       "200030.0           0.0            0.0            0.0             25.0  \n",
-       "\n",
-       "[5 rows x 49 columns]"
-      ]
-     },
-     "execution_count": 27,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "actions = actions.fillna(0)\n",
-    "actions['cate_action_sum'] = actions.sum(axis=1)\n",
-    "actions.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
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-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>cate_4_type1</th>\n",
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-       "      <th>cate_5_type2</th>\n",
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-       "      <th>cate_4_type6</th>\n",
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-       "      <th>cate_8_type6</th>\n",
-       "      <th>cate_9_type6</th>\n",
-       "      <th>cate_10_type6</th>\n",
-       "      <th>cate_11_type6</th>\n",
-       "      <th>cate_action_sum</th>\n",
-       "      <th>cate8_percentage</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>user_id</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
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-       "      <th></th>\n",
-       "      <th></th>\n",
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-       "      <th>200003.0</th>\n",
-       "      <td>8.0</td>\n",
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-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>12.0</td>\n",
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-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>12.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>19.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>51.0</td>\n",
-       "      <td>0.607843</td>\n",
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-       "    <tr>\n",
-       "      <th>200008.0</th>\n",
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-       "      <td>0.0</td>\n",
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-       "      <td>0.0</td>\n",
-       "      <td>20.0</td>\n",
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-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>28.0</td>\n",
-       "      <td>0.000000</td>\n",
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-       "    <tr>\n",
-       "      <th>200023.0</th>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
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-       "      <td>0.0</td>\n",
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-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
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-       "      <td>1.0</td>\n",
-       "      <td>1.000000</td>\n",
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-       "    <tr>\n",
-       "      <th>200030.0</th>\n",
-       "      <td>8.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>17.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>25.0</td>\n",
-       "      <td>0.000000</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>5 rows × 50 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "          cate_4_type1  cate_5_type1  cate_6_type1  cate_7_type1  \\\n",
-       "user_id                                                            \n",
-       "200002.0          16.0           4.0           0.0           4.0   \n",
-       "200003.0           8.0           0.0           0.0           0.0   \n",
-       "200008.0           0.0           0.0           0.0           8.0   \n",
-       "200023.0           0.0           0.0           0.0           0.0   \n",
-       "200030.0           8.0           0.0           0.0           0.0   \n",
-       "\n",
-       "          cate_8_type1  cate_9_type1  cate_10_type1  cate_11_type1  \\\n",
-       "user_id                                                              \n",
-       "200002.0           4.0           0.0            0.0            0.0   \n",
-       "200003.0          12.0           0.0            0.0            0.0   \n",
-       "200008.0           0.0           0.0            0.0            0.0   \n",
-       "200023.0           1.0           0.0            0.0            0.0   \n",
-       "200030.0           0.0           0.0            0.0            0.0   \n",
-       "\n",
-       "          cate_4_type2  cate_5_type2  ...  cate_4_type6  cate_5_type6  \\\n",
-       "user_id                               ...                               \n",
-       "200002.0           0.0           0.0  ...          20.0           6.0   \n",
-       "200003.0           0.0           0.0  ...          12.0           0.0   \n",
-       "200008.0           0.0           0.0  ...           0.0           0.0   \n",
-       "200023.0           0.0           0.0  ...           0.0           0.0   \n",
-       "200030.0           0.0           0.0  ...          17.0           0.0   \n",
-       "\n",
-       "          cate_6_type6  cate_7_type6  cate_8_type6  cate_9_type6  \\\n",
-       "user_id                                                            \n",
-       "200002.0           0.0           3.0          12.0           0.0   \n",
-       "200003.0           0.0           0.0          19.0           0.0   \n",
-       "200008.0           0.0          20.0           0.0           0.0   \n",
-       "200023.0           0.0           0.0           0.0           0.0   \n",
-       "200030.0           0.0           0.0           0.0           0.0   \n",
-       "\n",
-       "          cate_10_type6  cate_11_type6  cate_action_sum  cate8_percentage  \n",
-       "user_id                                                                    \n",
-       "200002.0            0.0            0.0             69.0          0.231884  \n",
-       "200003.0            0.0            0.0             51.0          0.607843  \n",
-       "200008.0            0.0            0.0             28.0          0.000000  \n",
-       "200023.0            0.0            0.0              1.0          1.000000  \n",
-       "200030.0            0.0            0.0             25.0          0.000000  \n",
-       "\n",
-       "[5 rows x 50 columns]"
-      ]
-     },
-     "execution_count": 28,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "actions['cate8_percentage'] = (\n",
-    "        actions['cate_8_type1'] + actions['cate_8_type2'] +\n",
-    "        actions['cate_8_type3'] + actions['cate_8_type4'] +\n",
-    "        actions['cate_8_type5'] + actions['cate_8_type6']\n",
-    "    ) / actions['cate_action_sum']\n",
-    "actions.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
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-       "      <th></th>\n",
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-       "      <th>cate_11_type1</th>\n",
-       "      <th>cate_4_type2</th>\n",
-       "      <th>cate_5_type2</th>\n",
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-       "      <th>cate_6_type6</th>\n",
-       "      <th>cate_7_type6</th>\n",
-       "      <th>cate_8_type6</th>\n",
-       "      <th>cate_9_type6</th>\n",
-       "      <th>cate_10_type6</th>\n",
-       "      <th>cate_11_type6</th>\n",
-       "      <th>cate_action_sum</th>\n",
-       "      <th>cate8_percentage</th>\n",
-       "      <th>cate8_type1_percentage</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>user_id</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
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-       "      <th>200002.0</th>\n",
-       "      <td>16.0</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>0.0</td>\n",
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-       "      <td>4.0</td>\n",
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-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
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-       "      <td>6.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>12.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>69.0</td>\n",
-       "      <td>0.231884</td>\n",
-       "      <td>-1.757858</td>\n",
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-       "    <tr>\n",
-       "      <th>200003.0</th>\n",
-       "      <td>8.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>12.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>19.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>51.0</td>\n",
-       "      <td>0.607843</td>\n",
-       "      <td>-0.479573</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>200008.0</th>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>8.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>20.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>28.0</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>-2.197225</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>200023.0</th>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>200030.0</th>\n",
-       "      <td>8.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>25.0</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>-2.197225</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>5 rows × 51 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "          cate_4_type1  cate_5_type1  cate_6_type1  cate_7_type1  \\\n",
-       "user_id                                                            \n",
-       "200002.0          16.0           4.0           0.0           4.0   \n",
-       "200003.0           8.0           0.0           0.0           0.0   \n",
-       "200008.0           0.0           0.0           0.0           8.0   \n",
-       "200023.0           0.0           0.0           0.0           0.0   \n",
-       "200030.0           8.0           0.0           0.0           0.0   \n",
-       "\n",
-       "          cate_8_type1  cate_9_type1  cate_10_type1  cate_11_type1  \\\n",
-       "user_id                                                              \n",
-       "200002.0           4.0           0.0            0.0            0.0   \n",
-       "200003.0          12.0           0.0            0.0            0.0   \n",
-       "200008.0           0.0           0.0            0.0            0.0   \n",
-       "200023.0           1.0           0.0            0.0            0.0   \n",
-       "200030.0           0.0           0.0            0.0            0.0   \n",
-       "\n",
-       "          cate_4_type2  cate_5_type2  ...  cate_5_type6  cate_6_type6  \\\n",
-       "user_id                               ...                               \n",
-       "200002.0           0.0           0.0  ...           6.0           0.0   \n",
-       "200003.0           0.0           0.0  ...           0.0           0.0   \n",
-       "200008.0           0.0           0.0  ...           0.0           0.0   \n",
-       "200023.0           0.0           0.0  ...           0.0           0.0   \n",
-       "200030.0           0.0           0.0  ...           0.0           0.0   \n",
-       "\n",
-       "          cate_7_type6  cate_8_type6  cate_9_type6  cate_10_type6  \\\n",
-       "user_id                                                             \n",
-       "200002.0           3.0          12.0           0.0            0.0   \n",
-       "200003.0           0.0          19.0           0.0            0.0   \n",
-       "200008.0          20.0           0.0           0.0            0.0   \n",
-       "200023.0           0.0           0.0           0.0            0.0   \n",
-       "200030.0           0.0           0.0           0.0            0.0   \n",
-       "\n",
-       "          cate_11_type6  cate_action_sum  cate8_percentage  \\\n",
-       "user_id                                                      \n",
-       "200002.0            0.0             69.0          0.231884   \n",
-       "200003.0            0.0             51.0          0.607843   \n",
-       "200008.0            0.0             28.0          0.000000   \n",
-       "200023.0            0.0              1.0          1.000000   \n",
-       "200030.0            0.0             25.0          0.000000   \n",
-       "\n",
-       "          cate8_type1_percentage  \n",
-       "user_id                           \n",
-       "200002.0               -1.757858  \n",
-       "200003.0               -0.479573  \n",
-       "200008.0               -2.197225  \n",
-       "200023.0                0.000000  \n",
-       "200030.0               -2.197225  \n",
-       "\n",
-       "[5 rows x 51 columns]"
-      ]
-     },
-     "execution_count": 29,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "actions['cate8_type1_percentage'] = np.log(\n",
-    "        1 + actions['cate_8_type1']) - np.log(\n",
-    "            1 + actions['cate_8_type1'] + actions['cate_4_type1'] +\n",
-    "            actions['cate_5_type1'] + actions['cate_6_type1'] +\n",
-    "            actions['cate_7_type1'] + actions['cate_9_type1'] +\n",
-    "            actions['cate_10_type1'] + actions['cate_11_type1'])\n",
-    "actions.head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 商品-行为\n",
-    "#### 累积商品特征\n",
-    "* 分时间段\n",
-    "* 针对商品的不同行为的\n",
-    "    * 购买转化率\n",
-    "    * 均值\n",
-    "    * 标准差"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def get_accumulate_product_feat(start_date, end_date, all_actions):\n",
-    "    feature = [\n",
-    "        'sku_id', 'product_action_1', 'product_action_2',\n",
-    "        'product_action_3', 'product_action_4',\n",
-    "        'product_action_5', 'product_action_6',\n",
-    "        'product_action_1_ratio', 'product_action_2_ratio',\n",
-    "        'product_action_3_ratio', 'product_action_5_ratio',\n",
-    "        'product_action_6_ratio', 'product_action_1_mean',\n",
-    "        'product_action_2_mean', 'product_action_3_mean',\n",
-    "        'product_action_4_mean', 'product_action_5_mean',\n",
-    "        'product_action_6_mean', 'product_action_1_std',\n",
-    "        'product_action_2_std', 'product_action_3_std', 'product_action_4_std',\n",
-    "        'product_action_5_std', 'product_action_6_std'\n",
-    "    ]\n",
-    "\n",
-    "    actions = get_actions(start_date, end_date, all_actions)\n",
-    "    df = pd.get_dummies(actions['type'], prefix='product_action')\n",
-    "    # 按照商品-日期分组，计算某个时间段该商品的各项行为的标准差\n",
-    "    actions['date'] = pd.to_datetime(actions['time']).apply(lambda x: x.date())\n",
-    "    actions = pd.concat([actions[['sku_id', 'date']], df], axis=1)\n",
-    "    actions = actions.groupby(['sku_id'], as_index=False).sum()\n",
-    "    days_interal = (datetime.strptime(end_date, '%Y-%m-%d') - datetime.strptime(start_date, '%Y-%m-%d')).days\n",
-    "    \n",
-    "    # 针对商品分组，计算购买转化率\n",
-    "    actions['product_action_1_ratio'] =  np.log(1 + actions['product_action_4.0']) - np.log(1 + actions['product_action_1.0'])\n",
-    "    actions['product_action_2_ratio'] =  np.log(1 + actions['product_action_4.0']) - np.log(1 + actions['product_action_2.0'])\n",
-    "    actions['product_action_3_ratio'] =  np.log(1 + actions['product_action_4.0']) - np.log(1 + actions['product_action_3.0'])\n",
-    "    actions['product_action_5_ratio'] =  np.log(1 + actions['product_action_4.0']) - np.log(1 + actions['product_action_5.0'])\n",
-    "    actions['product_action_6_ratio'] =  np.log(1 + actions['product_action_4.0']) - np.log(1 + actions['product_action_6.0'])\n",
-    "    \n",
-    "    # 计算各种行为的均值\n",
-    "    actions['product_action_1_mean'] = actions[\n",
-    "        'product_action_1.0'] / days_interal\n",
-    "    actions['product_action_2_mean'] = actions[\n",
-    "        'product_action_2.0'] / days_interal\n",
-    "    actions['product_action_3_mean'] = actions[\n",
-    "        'product_action_3.0'] / days_interal\n",
-    "    actions['product_action_4_mean'] = actions[\n",
-    "        'product_action_4.0'] / days_interal\n",
-    "    actions['product_action_5_mean'] = actions[\n",
-    "        'product_action_5.0'] / days_interal\n",
-    "    actions['product_action_6_mean'] = actions[\n",
-    "        'product_action_6.0'] / days_interal\n",
-    "    return actions"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 类别特征\n",
-    "#### 分时间段下各个商品类别的\n",
-    "* 购买转化率\n",
-    "    * 标准差\n",
-    "    * 均值"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def get_accumulate_cate_feat(start_date, end_date, all_actions):\n",
-    "    feature = ['cate','cate_action_1', 'cate_action_2', 'cate_action_3', 'cate_action_4', 'cate_action_5', \n",
-    "               'cate_action_6', 'cate_action_1_ratio', 'cate_action_2_ratio', \n",
-    "               'cate_action_3_ratio', 'cate_action_5_ratio', 'cate_action_6_ratio', 'cate_action_1_mean',\n",
-    "               'cate_action_2_mean', 'cate_action_3_mean', 'cate_action_4_mean', 'cate_action_5_mean',\n",
-    "               'cate_action_6_mean', 'cate_action_1_std', 'cate_action_2_std', 'cate_action_3_std',\n",
-    "               'cate_action_4_std', 'cate_action_5_std', 'cate_action_6_std']\n",
-    "    actions = get_actions(start_date, end_date, all_actions)\n",
-    "    actions['date'] = pd.to_datetime(actions['time']).apply(lambda x: x.date())\n",
-    "    df = pd.get_dummies(actions['type'], prefix='cate_action')\n",
-    "    actions = pd.concat([actions[['cate','date']], df], axis=1)\n",
-    "    \n",
-    "    # 按照类别分组，统计各个商品类别下行为的转化率\n",
-    "    actions = actions.groupby(['cate'], as_index=False).sum()\n",
-    "    days_interal = (datetime.strptime(end_date, '%Y-%m-%d')-datetime.strptime(start_date, '%Y-%m-%d')).days\n",
-    "    \n",
-    "    actions['cate_action_1_ratio'] =(np.log(1 + actions['cate_action_4.0']) - np.log(1 + actions['cate_action_1.0']))\n",
-    "    actions['cate_action_2_ratio'] =(np.log(1 + actions['cate_action_4.0']) - np.log(1 + actions['cate_action_2.0']))\n",
-    "    actions['cate_action_3_ratio'] =(np.log(1 + actions['cate_action_4.0']) - np.log(1 + actions['cate_action_3.0']))\n",
-    "    actions['cate_action_5_ratio'] =(np.log(1 + actions['cate_action_4.0']) - np.log(1 + actions['cate_action_5.0']))\n",
-    "    actions['cate_action_6_ratio'] =(np.log(1 + actions['cate_action_4.0']) - np.log(1 + actions['cate_action_6.0']))\n",
-    "    \n",
-    "    # 按照类别分组，统计各个商品类别下行为在一段时间的均值\n",
-    "    actions['cate_action_1_mean'] = actions['cate_action_1.0'] /  days_interal\n",
-    "    actions['cate_action_2_mean'] = actions['cate_action_2.0'] /  days_interal\n",
-    "    actions['cate_action_3_mean'] = actions['cate_action_3.0'] /  days_interal\n",
-    "    actions['cate_action_4_mean'] = actions['cate_action_4.0'] /  days_interal\n",
-    "    actions['cate_action_5_mean'] = actions['cate_action_5.0'] /  days_interal\n",
-    "    actions['cate_action_6_mean'] = actions['cate_action_6.0'] /  days_interal\n",
-    "    return actions"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 构造训练集/测试集\n",
-    "### 构造训练集/验证集\n",
-    "标签,采用滑动窗口的方式，构造训练集的时候针对产生购买的行为标记为1\n",
-    "整合特征"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def get_labels(start_date, end_date, all_actions):\n",
-    "    actions = get_actions(start_date, end_date, all_actions)\n",
-    "#     actions = actions[actions['type'] == 4]\n",
-    "    # 修改为预测购买了商品8的用户预测\n",
-    "    actions = actions[(actions['type'] == 4) & (actions['cate']==8)]\n",
-    "    \n",
-    "    actions = actions.groupby(['user_id', 'sku_id'], as_index=False).sum()\n",
-    "    actions['label'] = 1\n",
-    "    actions = actions[['user_id', 'sku_id', 'label']]\n",
-    "    return actions"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "get all actions!\n"
-     ]
-    }
-   ],
-   "source": [
-    "train_start_date = '2016-03-01'\n",
-    "train_actions = None\n",
-    "all_actions = get_all_action()\n",
-    "print (\"get all actions!\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "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>sku_id</th>\n",
-       "      <th>time</th>\n",
-       "      <th>model_id</th>\n",
-       "      <th>type</th>\n",
-       "      <th>cate</th>\n",
-       "      <th>brand</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>266079.0</td>\n",
-       "      <td>138778.0</td>\n",
-       "      <td>2016-01-31 23:59:02</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>8.0</td>\n",
-       "      <td>403.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>266079.0</td>\n",
-       "      <td>138778.0</td>\n",
-       "      <td>2016-01-31 23:59:03</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>6.0</td>\n",
-       "      <td>8.0</td>\n",
-       "      <td>403.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>200719.0</td>\n",
-       "      <td>61226.0</td>\n",
-       "      <td>2016-01-31 23:59:07</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>8.0</td>\n",
-       "      <td>30.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>200719.0</td>\n",
-       "      <td>61226.0</td>\n",
-       "      <td>2016-01-31 23:59:08</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>6.0</td>\n",
-       "      <td>8.0</td>\n",
-       "      <td>30.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>263587.0</td>\n",
-       "      <td>72348.0</td>\n",
-       "      <td>2016-01-31 23:59:08</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>5.0</td>\n",
-       "      <td>159.0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "    user_id    sku_id                 time  model_id  type  cate  brand\n",
-       "0  266079.0  138778.0  2016-01-31 23:59:02       NaN   1.0   8.0  403.0\n",
-       "1  266079.0  138778.0  2016-01-31 23:59:03       0.0   6.0   8.0  403.0\n",
-       "2  200719.0   61226.0  2016-01-31 23:59:07       NaN   1.0   8.0   30.0\n",
-       "3  200719.0   61226.0  2016-01-31 23:59:08       0.0   6.0   8.0   30.0\n",
-       "4  263587.0   72348.0  2016-01-31 23:59:08       NaN   1.0   5.0  159.0"
-      ]
-     },
-     "execution_count": 34,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "all_actions.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "<class 'pandas.core.frame.DataFrame'>\n",
-      "Int64Index: 50601736 entries, 0 to 13199933\n",
-      "Data columns (total 7 columns):\n",
-      "user_id     float32\n",
-      "sku_id      float32\n",
-      "time        object\n",
-      "model_id    float32\n",
-      "type        float32\n",
-      "cate        float32\n",
-      "brand       float32\n",
-      "dtypes: float32(6), object(1)\n",
-      "memory usage: 1.9+ GB\n"
-     ]
-    }
-   ],
-   "source": [
-    "all_actions.info()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(50601736, 7)"
-      ]
-     },
-     "execution_count": 36,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "all_actions.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "get_basic_user_feat finsihed\n"
-     ]
-    }
-   ],
-   "source": [
-    "user = get_basic_user_feat()\n",
-    "print ('get_basic_user_feat finsihed')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "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>age_0</th>\n",
-       "      <th>age_1</th>\n",
-       "      <th>age_2</th>\n",
-       "      <th>age_3</th>\n",
-       "      <th>age_4</th>\n",
-       "      <th>age_5</th>\n",
-       "      <th>age_6</th>\n",
-       "      <th>sex_0</th>\n",
-       "      <th>sex_1</th>\n",
-       "      <th>sex_2</th>\n",
-       "      <th>user_lv_cd_1</th>\n",
-       "      <th>user_lv_cd_2</th>\n",
-       "      <th>user_lv_cd_3</th>\n",
-       "      <th>user_lv_cd_4</th>\n",
-       "      <th>user_lv_cd_5</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>200001.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>200002.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>200003.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>200004.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>200005.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "    user_id  age_0  age_1  age_2  age_3  age_4  age_5  age_6  sex_0  sex_1  \\\n",
-       "0  200001.0    0.0    0.0    0.0    0.0    0.0    0.0    1.0    0.0    0.0   \n",
-       "1  200002.0    1.0    0.0    0.0    0.0    0.0    0.0    0.0    1.0    0.0   \n",
-       "2  200003.0    0.0    0.0    0.0    0.0    1.0    0.0    0.0    0.0    1.0   \n",
-       "3  200004.0    1.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
-       "4  200005.0    0.0    0.0    1.0    0.0    0.0    0.0    0.0    1.0    0.0   \n",
-       "\n",
-       "   sex_2  user_lv_cd_1  user_lv_cd_2  user_lv_cd_3  user_lv_cd_4  user_lv_cd_5  \n",
-       "0    1.0           0.0           0.0           0.0           0.0           1.0  \n",
-       "1    0.0           1.0           0.0           0.0           0.0           0.0  \n",
-       "2    0.0           0.0           0.0           0.0           1.0           0.0  \n",
-       "3    1.0           1.0           0.0           0.0           0.0           0.0  \n",
-       "4    0.0           0.0           0.0           0.0           1.0           0.0  "
-      ]
-     },
-     "execution_count": 38,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "user.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "get_basic_product_feat finsihed\n"
-     ]
-    }
-   ],
-   "source": [
-    "product = get_basic_product_feat()\n",
-    "print ('get_basic_product_feat finsihed')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 40,
-   "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>sku_id</th>\n",
-       "      <th>cate</th>\n",
-       "      <th>brand</th>\n",
-       "      <th>a1_-1</th>\n",
-       "      <th>a1_1</th>\n",
-       "      <th>a1_2</th>\n",
-       "      <th>a1_3</th>\n",
-       "      <th>a2_-1</th>\n",
-       "      <th>a2_1</th>\n",
-       "      <th>a2_2</th>\n",
-       "      <th>a3_-1</th>\n",
-       "      <th>a3_1</th>\n",
-       "      <th>a3_2</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>10</td>\n",
-       "      <td>8</td>\n",
-       "      <td>489</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>100002</td>\n",
-       "      <td>8</td>\n",
-       "      <td>489</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>100003</td>\n",
-       "      <td>8</td>\n",
-       "      <td>30</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>100006</td>\n",
-       "      <td>8</td>\n",
-       "      <td>545</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>10001</td>\n",
-       "      <td>8</td>\n",
-       "      <td>244</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   sku_id  cate  brand  a1_-1  a1_1  a1_2  a1_3  a2_-1  a2_1  a2_2  a3_-1  \\\n",
-       "0      10     8    489      0     0     0     1      0     1     0      0   \n",
-       "1  100002     8    489      0     0     0     1      0     0     1      0   \n",
-       "2  100003     8     30      0     1     0     0      1     0     0      1   \n",
-       "3  100006     8    545      0     1     0     0      0     0     1      0   \n",
-       "4   10001     8    244      1     0     0     0      0     1     0      0   \n",
-       "\n",
-       "   a3_1  a3_2  \n",
-       "0     1     0  \n",
-       "1     0     1  \n",
-       "2     0     0  \n",
-       "3     1     0  \n",
-       "4     0     1  "
-      ]
-     },
-     "execution_count": 40,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "product.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 41,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "datetime.datetime(2016, 3, 4, 0, 0)"
-      ]
-     },
-     "execution_count": 41,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "train_start_date = '2016-03-01'\n",
-    "train_end_date = datetime.strptime(train_start_date, '%Y-%m-%d') + timedelta(days=3)\n",
-    "train_end_date"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 42,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "2016-03-04\n"
-     ]
-    }
-   ],
-   "source": [
-    "train_end_date = train_end_date.strftime('%Y-%m-%d')\n",
-    "# 修正prod_acc,cate_acc的时间跨度\n",
-    "start_days = datetime.strptime(train_end_date, '%Y-%m-%d') - timedelta(days=30)\n",
-    "start_days = start_days.strftime('%Y-%m-%d')\n",
-    "print (train_end_date)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 43,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def make_actions(user, product, all_actions, train_start_date):\n",
-    "    train_end_date = datetime.strptime(train_start_date, '%Y-%m-%d') + timedelta(days=3)\n",
-    "    train_end_date = train_end_date.strftime('%Y-%m-%d')\n",
-    "    # 修正prod_acc,cate_acc的时间跨度\n",
-    "    start_days = datetime.strptime(train_end_date, '%Y-%m-%d') - timedelta(days=30)\n",
-    "    start_days = start_days.strftime('%Y-%m-%d')\n",
-    "    print (train_end_date)\n",
-    "    user_acc = get_recent_user_feat(train_end_date, all_actions)\n",
-    "    print ('get_recent_user_feat finsihed')\n",
-    "    \n",
-    "    user_cate = get_user_cate_feature(train_start_date, train_end_date, all_actions)\n",
-    "    print ('get_user_cate_feature finished')\n",
-    "    \n",
-    "    product_acc = get_accumulate_product_feat(start_days, train_end_date, all_actions)\n",
-    "    print ('get_accumulate_product_feat finsihed')\n",
-    "    cate_acc = get_accumulate_cate_feat(start_days, train_end_date, all_actions)\n",
-    "    print ('get_accumulate_cate_feat finsihed')\n",
-    "    comment_acc = get_comments_product_feat(train_end_date)\n",
-    "    print ('get_comments_product_feat finished')\n",
-    "    # 标记\n",
-    "    test_start_date = train_end_date\n",
-    "    test_end_date = datetime.strptime(test_start_date, '%Y-%m-%d') + timedelta(days=5)\n",
-    "    test_end_date = test_end_date.strftime('%Y-%m-%d')\n",
-    "    labels = get_labels(test_start_date, test_end_date, all_actions)\n",
-    "    print (\"get labels\")\n",
-    "    \n",
-    "    actions = None\n",
-    "    for i in (3, 5, 7, 10, 15, 21, 30):\n",
-    "        start_days = datetime.strptime(train_end_date, '%Y-%m-%d') - timedelta(days=i)\n",
-    "        start_days = start_days.strftime('%Y-%m-%d')\n",
-    "        if actions is None:\n",
-    "            actions = get_action_feat(start_days, train_end_date, all_actions, i)\n",
-    "        else:\n",
-    "            # 注意这里的拼接key\n",
-    "            actions = pd.merge(actions, get_action_feat(start_days, train_end_date, all_actions, i), how='left',\n",
-    "                               on=['user_id', 'sku_id', 'cate'])\n",
-    "\n",
-    "    actions = pd.merge(actions, user, how='left', on='user_id')\n",
-    "    actions = pd.merge(actions, user_acc, how='left', on='user_id')\n",
-    "#     actions = pd.merge(actions, user_cate, how='left', on='user_id')\n",
-    "    actions.append(user_cate)\n",
-    "    # 注意这里的拼接key\n",
-    "    actions = pd.merge(actions, product, how='left', on=['sku_id', 'cate'])\n",
-    "    actions = pd.merge(actions, product_acc, how='left', on='sku_id')\n",
-    "    actions = pd.merge(actions, cate_acc, how='left', on='cate')\n",
-    "    actions = pd.merge(actions, comment_acc, how='left', on='sku_id')\n",
-    "    actions = pd.merge(actions, labels, how='left', on=['user_id', 'sku_id'])\n",
-    "    # 主要是填充拼接商品基本特征、评论特征、标签之后的空值\n",
-    "    actions = actions.fillna(0)\n",
-    "#     return actions\n",
-    "    # 采样\n",
-    "    action_postive = actions[actions['label'] == 1]\n",
-    "    action_negative = actions[actions['label'] == 0]\n",
-    "    del actions\n",
-    "    neg_len = len(action_postive) * 10\n",
-    "    action_negative = action_negative.sample(n=neg_len)\n",
-    "    action_sample = pd.concat([action_postive, action_negative], ignore_index=True)    \n",
-    "    \n",
-    "    return action_sample"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 44,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def make_train_set(train_start_date, setNums ,f_path, all_actions):\n",
-    "    train_actions = None\n",
-    "    #all_actions = get_all_action()\n",
-    "    #print (\"get all actions!\")\n",
-    "    user = get_basic_user_feat()\n",
-    "    print ('get_basic_user_feat finsihed')\n",
-    "    product = get_basic_product_feat()\n",
-    "    print ('get_basic_product_feat finsihed')\n",
-    "    # 滑窗,构造多组训练集/验证集\n",
-    "    for i in range(setNums):\n",
-    "        print (train_start_date)\n",
-    "        if train_actions is None:\n",
-    "            train_actions = make_actions(user, product, all_actions, train_start_date)\n",
-    "        else:\n",
-    "            train_actions = pd.concat([train_actions, make_actions(user, product, all_actions, train_start_date)],\n",
-    "                                          ignore_index=True)\n",
-    "        # 接下来每次移动一天\n",
-    "        train_start_date = datetime.strptime(train_start_date, '%Y-%m-%d') + timedelta(days=1)\n",
-    "        train_start_date = train_start_date.strftime('%Y-%m-%d')\n",
-    "        print (\"round {0}/{1} over!\".format(i+1, setNums))\n",
-    "\n",
-    "    train_actions.to_csv(f_path, index=False)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 45,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "get_basic_user_feat finsihed\n",
-      "get_basic_product_feat finsihed\n",
-      "2016-02-01\n",
-      "2016-02-04\n",
-      "get_recent_user_feat finsihed\n",
-      "get_user_cate_feature finished\n",
-      "get_accumulate_product_feat finsihed\n",
-      "get_accumulate_cate_feat finsihed\n",
-      "get_comments_product_feat finished\n",
-      "get labels\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "D:\\Anaconda3\\lib\\site-packages\\pandas\\core\\frame.py:6692: FutureWarning: Sorting because non-concatenation axis is not aligned. A future version\n",
-      "of pandas will change to not sort by default.\n",
-      "\n",
-      "To accept the future behavior, pass 'sort=False'.\n",
-      "\n",
-      "To retain the current behavior and silence the warning, pass 'sort=True'.\n",
-      "\n",
-      "  sort=sort)\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "round 1/20 over!\n",
-      "2016-02-02\n",
-      "2016-02-05\n",
-      "get_recent_user_feat finsihed\n",
-      "get_user_cate_feature finished\n",
-      "get_accumulate_product_feat finsihed\n",
-      "get_accumulate_cate_feat finsihed\n",
-      "get_comments_product_feat finished\n",
-      "get labels\n",
-      "round 2/20 over!\n",
-      "2016-02-03\n",
-      "2016-02-06\n",
-      "get_recent_user_feat finsihed\n",
-      "get_user_cate_feature finished\n",
-      "get_accumulate_product_feat finsihed\n",
-      "get_accumulate_cate_feat finsihed\n",
-      "get_comments_product_feat finished\n",
-      "get labels\n",
-      "round 3/20 over!\n",
-      "2016-02-04\n",
-      "2016-02-07\n",
-      "get_recent_user_feat finsihed\n",
-      "get_user_cate_feature finished\n",
-      "get_accumulate_product_feat finsihed\n",
-      "get_accumulate_cate_feat finsihed\n",
-      "get_comments_product_feat finished\n",
-      "get labels\n",
-      "round 4/20 over!\n",
-      "2016-02-05\n",
-      "2016-02-08\n",
-      "get_recent_user_feat finsihed\n",
-      "get_user_cate_feature finished\n",
-      "get_accumulate_product_feat finsihed\n",
-      "get_accumulate_cate_feat finsihed\n",
-      "get_comments_product_feat finished\n",
-      "get labels\n",
-      "round 5/20 over!\n",
-      "2016-02-06\n",
-      "2016-02-09\n",
-      "get_recent_user_feat finsihed\n",
-      "get_user_cate_feature finished\n",
-      "get_accumulate_product_feat finsihed\n",
-      "get_accumulate_cate_feat finsihed\n",
-      "get_comments_product_feat finished\n",
-      "get labels\n",
-      "round 6/20 over!\n",
-      "2016-02-07\n",
-      "2016-02-10\n",
-      "get_recent_user_feat finsihed\n",
-      "get_user_cate_feature finished\n",
-      "get_accumulate_product_feat finsihed\n",
-      "get_accumulate_cate_feat finsihed\n",
-      "get_comments_product_feat finished\n",
-      "get labels\n",
-      "round 7/20 over!\n",
-      "2016-02-08\n",
-      "2016-02-11\n",
-      "get_recent_user_feat finsihed\n",
-      "get_user_cate_feature finished\n",
-      "get_accumulate_product_feat finsihed\n",
-      "get_accumulate_cate_feat finsihed\n",
-      "get_comments_product_feat finished\n",
-      "get labels\n",
-      "round 8/20 over!\n",
-      "2016-02-09\n",
-      "2016-02-12\n",
-      "get_recent_user_feat finsihed\n",
-      "get_user_cate_feature finished\n",
-      "get_accumulate_product_feat finsihed\n",
-      "get_accumulate_cate_feat finsihed\n",
-      "get_comments_product_feat finished\n",
-      "get labels\n",
-      "round 9/20 over!\n",
-      "2016-02-10\n",
-      "2016-02-13\n",
-      "get_recent_user_feat finsihed\n",
-      "get_user_cate_feature finished\n",
-      "get_accumulate_product_feat finsihed\n",
-      "get_accumulate_cate_feat finsihed\n",
-      "get_comments_product_feat finished\n",
-      "get labels\n",
-      "round 10/20 over!\n",
-      "2016-02-11\n",
-      "2016-02-14\n",
-      "get_recent_user_feat finsihed\n",
-      "get_user_cate_feature finished\n",
-      "get_accumulate_product_feat finsihed\n",
-      "get_accumulate_cate_feat finsihed\n",
-      "get_comments_product_feat finished\n",
-      "get labels\n",
-      "round 11/20 over!\n",
-      "2016-02-12\n",
-      "2016-02-15\n",
-      "get_recent_user_feat finsihed\n",
-      "get_user_cate_feature finished\n",
-      "get_accumulate_product_feat finsihed\n",
-      "get_accumulate_cate_feat finsihed\n",
-      "get_comments_product_feat finished\n",
-      "get labels\n",
-      "round 12/20 over!\n",
-      "2016-02-13\n",
-      "2016-02-16\n",
-      "get_recent_user_feat finsihed\n",
-      "get_user_cate_feature finished\n",
-      "get_accumulate_product_feat finsihed\n",
-      "get_accumulate_cate_feat finsihed\n",
-      "get_comments_product_feat finished\n",
-      "get labels\n",
-      "round 13/20 over!\n",
-      "2016-02-14\n",
-      "2016-02-17\n",
-      "get_recent_user_feat finsihed\n",
-      "get_user_cate_feature finished\n",
-      "get_accumulate_product_feat finsihed\n",
-      "get_accumulate_cate_feat finsihed\n",
-      "get_comments_product_feat finished\n",
-      "get labels\n",
-      "round 14/20 over!\n",
-      "2016-02-15\n",
-      "2016-02-18\n",
-      "get_recent_user_feat finsihed\n",
-      "get_user_cate_feature finished\n",
-      "get_accumulate_product_feat finsihed\n",
-      "get_accumulate_cate_feat finsihed\n",
-      "get_comments_product_feat finished\n",
-      "get labels\n",
-      "round 15/20 over!\n",
-      "2016-02-16\n",
-      "2016-02-19\n",
-      "get_recent_user_feat finsihed\n",
-      "get_user_cate_feature finished\n",
-      "get_accumulate_product_feat finsihed\n",
-      "get_accumulate_cate_feat finsihed\n",
-      "get_comments_product_feat finished\n",
-      "get labels\n",
-      "round 16/20 over!\n",
-      "2016-02-17\n",
-      "2016-02-20\n",
-      "get_recent_user_feat finsihed\n",
-      "get_user_cate_feature finished\n",
-      "get_accumulate_product_feat finsihed\n",
-      "get_accumulate_cate_feat finsihed\n",
-      "get_comments_product_feat finished\n",
-      "get labels\n",
-      "round 17/20 over!\n",
-      "2016-02-18\n",
-      "2016-02-21\n",
-      "get_recent_user_feat finsihed\n",
-      "get_user_cate_feature finished\n",
-      "get_accumulate_product_feat finsihed\n",
-      "get_accumulate_cate_feat finsihed\n",
-      "get_comments_product_feat finished\n",
-      "get labels\n",
-      "round 18/20 over!\n",
-      "2016-02-19\n",
-      "2016-02-22\n",
-      "get_recent_user_feat finsihed\n",
-      "get_user_cate_feature finished\n",
-      "get_accumulate_product_feat finsihed\n",
-      "get_accumulate_cate_feat finsihed\n",
-      "get_comments_product_feat finished\n",
-      "get labels\n",
-      "round 19/20 over!\n",
-      "2016-02-20\n",
-      "2016-02-23\n",
-      "get_recent_user_feat finsihed\n",
-      "get_user_cate_feature finished\n",
-      "get_accumulate_product_feat finsihed\n",
-      "get_accumulate_cate_feat finsihed\n",
-      "get_comments_product_feat finished\n",
-      "get labels\n",
-      "round 20/20 over!\n"
-     ]
-    }
-   ],
-   "source": [
-    "# 训练集\n",
-    "train_start_date = '2016-02-01'\n",
-    "make_train_set(train_start_date, 20, 'data/train_set.csv',all_actions)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 构造验证集(线下测试集)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 46,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def make_val_answer(val_start_date, val_end_date, all_actions, label_val_s1_path):\n",
-    "    actions = get_actions(val_start_date, val_end_date,all_actions)\n",
-    "    actions = actions[(actions['type'] == 4) & (actions['cate'] == 8)]\n",
-    "    actions = actions[['user_id', 'sku_id']]\n",
-    "    actions = actions.drop_duplicates()\n",
-    "    actions.to_csv(label_val_s1_path, index=False)\n",
-    "\n",
-    "def make_val_set(train_start_date, train_end_date, val_s1_path):\n",
-    "    # 修改时间跨度\n",
-    "    start_days = datetime.strptime(train_end_date, '%Y-%m-%d') - timedelta(days=30)\n",
-    "    start_days = start_days.strftime('%Y-%m-%d')\n",
-    "    all_actions = get_all_action()\n",
-    "    print (\"get all actions!\")\n",
-    "    user = get_basic_user_feat()\n",
-    "    print ('get_basic_user_feat finsihed')\n",
-    "    \n",
-    "    product = get_basic_product_feat()\n",
-    "    print ('get_basic_product_feat finsihed')\n",
-    "    user_acc = get_recent_user_feat(train_end_date, all_actions)\n",
-    "    print ('get_recent_user_feat finsihed')\n",
-    "    user_cate = get_user_cate_feature(train_start_date, train_end_date, all_actions)\n",
-    "    print ('get_user_cate_feature finished')\n",
-    " \n",
-    "    product_acc = get_accumulate_product_feat(start_days, train_end_date, all_actions)\n",
-    "    print ('get_accumulate_product_feat finsihed')\n",
-    "    cate_acc = get_accumulate_cate_feat(start_days, train_end_date, all_actions)\n",
-    "    print ('get_accumulate_cate_feat finsihed')\n",
-    "    comment_acc = get_comments_product_feat(train_end_date)\n",
-    "    print ('get_comments_product_feat finished')\n",
-    "    \n",
-    "    actions = None\n",
-    "    for i in (3, 5, 7, 10, 15, 21, 30):\n",
-    "        start_days = datetime.strptime(train_end_date, '%Y-%m-%d') - timedelta(days=i)\n",
-    "        start_days = start_days.strftime('%Y-%m-%d')\n",
-    "        if actions is None:\n",
-    "            actions = get_action_feat(start_days, train_end_date, all_actions,i)\n",
-    "        else:\n",
-    "            actions = pd.merge(actions, get_action_feat(start_days, train_end_date,all_actions,i), how='left',\n",
-    "                               on=['user_id', 'sku_id', 'cate'])\n",
-    "\n",
-    "    actions = pd.merge(actions, user, how='left', on='user_id')\n",
-    "    actions = pd.merge(actions, user_acc, how='left', on='user_id')\n",
-    "#     actions = pd.merge(actions, user_cate, how='left', on='user_id')\n",
-    "    actions.append(user_cate)\n",
-    "    # 注意这里的拼接key\n",
-    "    actions = pd.merge(actions, product, how='left', on=['sku_id', 'cate'])\n",
-    "    actions = pd.merge(actions, product_acc, how='left', on='sku_id')\n",
-    "    actions = pd.merge(actions, cate_acc, how='left', on='cate')\n",
-    "    actions = pd.merge(actions, comment_acc, how='left', on='sku_id')\n",
-    "    actions = actions.fillna(0)\n",
-    "   \n",
-    "    \n",
-    "#     print actions\n",
-    "    # 构造真实用户购买情况作为后续验证\n",
-    "    val_start_date = train_end_date\n",
-    "    val_end_date = datetime.strptime(val_start_date, '%Y-%m-%d') + timedelta(days=5)\n",
-    "    val_end_date = val_end_date.strftime('%Y-%m-%d')\n",
-    "    make_val_answer(val_start_date, val_end_date, all_actions, val_s1_path)\n",
-    "    \n",
-    "    actions.to_csv(val_s1_path, index=False)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 47,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "get all actions!\n",
-      "get_basic_user_feat finsihed\n",
-      "get_basic_product_feat finsihed\n",
-      "get_recent_user_feat finsihed\n",
-      "get_user_cate_feature finished\n",
-      "get_accumulate_product_feat finsihed\n",
-      "get_accumulate_cate_feat finsihed\n",
-      "get_comments_product_feat finished\n"
-     ]
-    }
-   ],
-   "source": [
-    "# 验证集\n",
-    "make_val_set('2016-02-23', '2016-02-26', 'data/val_set.csv')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 49,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def make_test_set(train_start_date, train_end_date):\n",
-    "    start_days = datetime.strptime(train_end_date, '%Y-%m-%d') - timedelta(days=30)\n",
-    "    start_days = start_days.strftime('%Y-%m-%d')\n",
-    "    all_actions = get_all_action()\n",
-    "    print(\"get all actions!\")\n",
-    "    user = get_basic_user_feat()\n",
-    "    print('get_basic_user_feat finsihed')\n",
-    "    product = get_basic_product_feat()\n",
-    "    print('get_basic_product_feat finsihed')\n",
-    "    \n",
-    "    user_acc = get_recent_user_feat(train_end_date, all_actions)\n",
-    "    print('get_accumulate_user_feat finsihed')\n",
-    "    \n",
-    "    user_cate = get_user_cate_feature(train_start_date, train_end_date, all_actions)\n",
-    "    print('get_user_cate_feature finished')\n",
-    "    \n",
-    "    product_acc = get_accumulate_product_feat(start_days, train_end_date, all_actions)\n",
-    "    print('get_accumulate_product_feat finsihed')\n",
-    "    cate_acc = get_accumulate_cate_feat(start_days, train_end_date, all_actions)\n",
-    "    print('get_accumulate_cate_feat finsihed')\n",
-    "    comment_acc = get_comments_product_feat(train_end_date)\n",
-    "\n",
-    "    actions = None\n",
-    "    for i in (3, 5, 7, 10, 15, 21, 30):\n",
-    "        start_days = datetime.strptime(train_end_date, '%Y-%m-%d') - timedelta(days=i)\n",
-    "        start_days = start_days.strftime('%Y-%m-%d')\n",
-    "        if actions is None:\n",
-    "            actions = get_action_feat(start_days, train_end_date, all_actions,i)\n",
-    "        else:\n",
-    "            actions = pd.merge(actions, get_action_feat(start_days, train_end_date,all_actions,i), how='left',\n",
-    "                               on=['user_id', 'sku_id', 'cate'])\n",
-    "\n",
-    "    actions = pd.merge(actions, user, how='left', on='user_id')\n",
-    "    actions = pd.merge(actions, user_acc, how='left', on='user_id')\n",
-    "#     actions = pd.merge(actions, user_cate, how='left', on='user_id')\n",
-    "    actions.append(user_cate)\n",
-    "    # 注意这里的拼接key\n",
-    "    actions = pd.merge(actions, product, how='left', on=['sku_id', 'cate'])\n",
-    "    actions = pd.merge(actions, product_acc, how='left', on='sku_id')\n",
-    "    actions = pd.merge(actions, cate_acc, how='left', on='cate')\n",
-    "    actions = pd.merge(actions, comment_acc, how='left', on='sku_id')\n",
-    "\n",
-    "    actions = actions.fillna(0)\n",
-    "    \n",
-    "\n",
-    "    actions.to_csv(\"data/test_set.csv\", index=False)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 50,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "get all actions!\n",
-      "get_basic_user_feat finsihed\n",
-      "get_basic_product_feat finsihed\n",
-      "get_accumulate_user_feat finsihed\n",
-      "get_user_cate_feature finished\n",
-      "get_accumulate_product_feat finsihed\n",
-      "get_accumulate_cate_feat finsihed\n"
-     ]
-    }
-   ],
-   "source": [
-    "# 预测结果\n",
-    "sub_start_date = '2016-04-13'\n",
-    "sub_end_date = '2016-04-16'\n",
-    "make_test_set(sub_start_date, sub_end_date)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
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-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
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