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/4-模型训练和预测.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/4-模型训练和预测.ipynb
deleted file mode 100644
index 7984da2..0000000
--- 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/4-模型训练和预测.ipynb
+++ /dev/null
@@ -1,2505 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import sys\n",
-    "import pandas as pd\n",
-    "import numpy as np\n",
-    "import xgboost as xgb\n",
-    "from sklearn.model_selection import train_test_split\n",
-    "import operator\n",
-    "from matplotlib import pylab as plt\n",
-    "from datetime import datetime\n",
-    "import time\n",
-    "from sklearn.model_selection import GridSearchCV"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
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-      "text/plain": [
-       "    user_id    sku_id  cate  action_before_3_1.0_x  action_before_3_2.0_x  \\\n",
-       "0  202633.0   12564.0   8.0                    1.0                    0.0   \n",
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-       "\n",
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-       "1                    4.0                    2.0  ...           20.866667   \n",
-       "2                    5.0                   79.0  ...           20.866667   \n",
-       "3                   26.0                   10.0  ...           20.866667   \n",
-       "4                   28.0                   55.0  ...           20.866667   \n",
-       "\n",
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-       "0              5167.6              1.0            0.0260            0.0   \n",
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-       "\n",
-       "   comment_num_1  comment_num_2  comment_num_3  comment_num_4  label  \n",
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-       "\n",
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-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
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-   ],
-   "source": [
-    "data = pd.read_csv('data/train_set.csv')  # 读取训练数据\n",
-    "data.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
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-       "Index(['user_id', 'sku_id', 'cate', 'action_before_3_1.0_x',\n",
-       "       'action_before_3_2.0_x', 'action_before_3_3.0_x',\n",
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-       "       ...\n",
-       "       'cate_action_5_mean', 'cate_action_6_mean', 'has_bad_comment',\n",
-       "       'bad_comment_rate', 'comment_num_0', 'comment_num_1', 'comment_num_2',\n",
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-       "      dtype='object', length=237)"
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-    "data.columns"
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-       "    user_id    sku_id  cate  action_before_3_1.0_x  action_before_3_2.0_x  \\\n",
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-       "\n",
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-       "4                    0.0                    0.0                    0.0   \n",
-       "\n",
-       "   action_before_3_6.0_x  action_before_3_1.0_y  ...  cate_action_4_mean  \\\n",
-       "0                    2.0                    1.0  ...                 8.4   \n",
-       "1                    4.0                    2.0  ...                 8.4   \n",
-       "2                    5.0                   79.0  ...                 8.4   \n",
-       "3                   26.0                   10.0  ...                 8.4   \n",
-       "4                   28.0                   55.0  ...                 8.4   \n",
-       "\n",
-       "   cate_action_5_mean  cate_action_6_mean  has_bad_comment  bad_comment_rate  \\\n",
-       "0           20.866667              5167.6              1.0            0.0260   \n",
-       "1           20.866667              5167.6              1.0            0.0403   \n",
-       "2           20.866667              5167.6              1.0            0.0245   \n",
-       "3           20.866667              5167.6              1.0            0.0208   \n",
-       "4           20.866667              5167.6              1.0            0.0166   \n",
-       "\n",
-       "   comment_num_0  comment_num_1  comment_num_2  comment_num_3  comment_num_4  \n",
-       "0            0.0            0.0            0.0            0.0            1.0  \n",
-       "1            0.0            0.0            0.0            0.0            1.0  \n",
-       "2            0.0            0.0            0.0            0.0            1.0  \n",
-       "3            0.0            0.0            0.0            0.0            1.0  \n",
-       "4            0.0            0.0            0.0            0.0            1.0  \n",
-       "\n",
-       "[5 rows x 236 columns]"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "data_x = data.loc[:,data.columns != 'label']  # 将训练数据集分成特征和标签\n",
-    "data_y = data.loc[:,data.columns == 'label']\n",
-    "data_x.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "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>label</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>1.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>1.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>1.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>1.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>1.0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   label\n",
-       "0    1.0\n",
-       "1    1.0\n",
-       "2    1.0\n",
-       "3    1.0\n",
-       "4    1.0"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "data_y.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "(11695, 236)\n",
-      "(2924, 236)\n"
-     ]
-    }
-   ],
-   "source": [
-    "x_train, x_test, y_train, y_test = train_test_split(data_x,data_y,test_size = 0.2, random_state = 0)  # 数据切分成两份，训练和测试，8：2切分\n",
-    "print(x_train.shape)\n",
-    "print(x_test.shape)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# 上面测试集刚好有近3000条，二切分成训练时的验证和预测\n",
-    "x_val = x_test.iloc[:1500,:]\n",
-    "y_val = y_test.iloc[:1500,:]\n",
-    "\n",
-    "x_test = x_test.iloc[1500:,:] \n",
-    "y_test = y_test.iloc[1500:,:]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "(1500, 236)\n",
-      "(1424, 236)\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(x_val.shape)\n",
-    "print(x_test.shape)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "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>cate</th>\n",
-       "      <th>action_before_3_1.0_x</th>\n",
-       "      <th>action_before_3_2.0_x</th>\n",
-       "      <th>action_before_3_3.0_x</th>\n",
-       "      <th>action_before_3_4.0_x</th>\n",
-       "      <th>action_before_3_5.0_x</th>\n",
-       "      <th>action_before_3_6.0_x</th>\n",
-       "      <th>action_before_3_1.0_y</th>\n",
-       "      <th>action_before_3_2.0_y</th>\n",
-       "      <th>action_before_3_3.0_y</th>\n",
-       "      <th>...</th>\n",
-       "      <th>cate_action_4_mean</th>\n",
-       "      <th>cate_action_5_mean</th>\n",
-       "      <th>cate_action_6_mean</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>2157</th>\n",
-       "      <td>4.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>48.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>25.300000</td>\n",
-       "      <td>52.133333</td>\n",
-       "      <td>16112.033333</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0344</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>2464</th>\n",
-       "      <td>10.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>7.0</td>\n",
-       "      <td>288.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.433333</td>\n",
-       "      <td>5.066667</td>\n",
-       "      <td>1273.500000</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0132</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>10326</th>\n",
-       "      <td>8.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>42.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>35.233333</td>\n",
-       "      <td>149.266667</td>\n",
-       "      <td>32299.233333</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0213</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>7025</th>\n",
-       "      <td>8.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>36.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>25.733333</td>\n",
-       "      <td>116.800000</td>\n",
-       "      <td>24942.666667</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0000</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",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6625</th>\n",
-       "      <td>7.0</td>\n",
-       "      <td>13.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>22.0</td>\n",
-       "      <td>92.0</td>\n",
-       "      <td>5.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>17.000000</td>\n",
-       "      <td>36.166667</td>\n",
-       "      <td>9447.266667</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0800</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",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>5 rows × 234 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "       cate  action_before_3_1.0_x  action_before_3_2.0_x  \\\n",
-       "2157    4.0                    2.0                    0.0   \n",
-       "2464   10.0                    2.0                    0.0   \n",
-       "10326   8.0                    2.0                    1.0   \n",
-       "7025    8.0                    2.0                    0.0   \n",
-       "6625    7.0                   13.0                    0.0   \n",
-       "\n",
-       "       action_before_3_3.0_x  action_before_3_4.0_x  action_before_3_5.0_x  \\\n",
-       "2157                     0.0                    0.0                    0.0   \n",
-       "2464                     0.0                    0.0                    0.0   \n",
-       "10326                    1.0                    0.0                    0.0   \n",
-       "7025                     0.0                    0.0                    0.0   \n",
-       "6625                     0.0                    0.0                    0.0   \n",
-       "\n",
-       "       action_before_3_6.0_x  action_before_3_1.0_y  action_before_3_2.0_y  \\\n",
-       "2157                     4.0                   48.0                    0.0   \n",
-       "2464                     7.0                  288.0                    0.0   \n",
-       "10326                    3.0                   42.0                    2.0   \n",
-       "7025                     2.0                   36.0                    1.0   \n",
-       "6625                    22.0                   92.0                    5.0   \n",
-       "\n",
-       "       action_before_3_3.0_y  ...  cate_action_4_mean  cate_action_5_mean  \\\n",
-       "2157                     0.0  ...           25.300000           52.133333   \n",
-       "2464                     0.0  ...            0.433333            5.066667   \n",
-       "10326                    1.0  ...           35.233333          149.266667   \n",
-       "7025                     0.0  ...           25.733333          116.800000   \n",
-       "6625                     1.0  ...           17.000000           36.166667   \n",
-       "\n",
-       "       cate_action_6_mean  has_bad_comment  bad_comment_rate  comment_num_0  \\\n",
-       "2157         16112.033333              1.0            0.0344            0.0   \n",
-       "2464          1273.500000              1.0            0.0132            0.0   \n",
-       "10326        32299.233333              1.0            0.0213            0.0   \n",
-       "7025         24942.666667              0.0            0.0000            0.0   \n",
-       "6625          9447.266667              1.0            0.0800            0.0   \n",
-       "\n",
-       "       comment_num_1  comment_num_2  comment_num_3  comment_num_4  \n",
-       "2157             0.0            0.0            0.0            1.0  \n",
-       "2464             0.0            0.0            0.0            1.0  \n",
-       "10326            0.0            0.0            0.0            1.0  \n",
-       "7025             0.0            1.0            0.0            0.0  \n",
-       "6625             0.0            0.0            0.0            1.0  \n",
-       "\n",
-       "[5 rows x 234 columns]"
-      ]
-     },
-     "execution_count": 18,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "del x_train['user_id']\n",
-    "del x_train['sku_id']\n",
-    "\n",
-    "del x_val['user_id']\n",
-    "del x_val['sku_id']\n",
-    "\n",
-    "x_train.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "dtrain = xgb.DMatrix(x_train, label=y_train)\n",
-    "dvalid = xgb.DMatrix(x_val, label=y_val)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "param = {'n_estimators': 4000, 'max_depth': 3, 'min_child_weight': 5, 'gamma': 0, 'subsample': 1.0, \n",
-    "             'colsample_bytree': 0.8, 'scale_pos_weight':10, 'eta': 0.1, 'silent': 1, 'objective': 'binary:logistic',\n",
-    "             'eval_metric':'auc'}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[0]\ttrain-auc:0.938547\teval-auc:0.934522\n",
-      "Multiple eval metrics have been passed: 'eval-auc' will be used for early stopping.\n",
-      "\n",
-      "Will train until eval-auc hasn't improved in 10 rounds.\n",
-      "[1]\ttrain-auc:0.947568\teval-auc:0.944769\n",
-      "[2]\ttrain-auc:0.952758\teval-auc:0.949358\n",
-      "[3]\ttrain-auc:0.955704\teval-auc:0.952481\n",
-      "[4]\ttrain-auc:0.95525\teval-auc:0.952343\n",
-      "[5]\ttrain-auc:0.957462\teval-auc:0.95475\n",
-      "[6]\ttrain-auc:0.957636\teval-auc:0.955133\n",
-      "[7]\ttrain-auc:0.958327\teval-auc:0.955077\n",
-      "[8]\ttrain-auc:0.958339\teval-auc:0.95549\n",
-      "[9]\ttrain-auc:0.958235\teval-auc:0.955479\n",
-      "[10]\ttrain-auc:0.958922\teval-auc:0.955668\n",
-      "[11]\ttrain-auc:0.959378\teval-auc:0.956423\n",
-      "[12]\ttrain-auc:0.959666\teval-auc:0.956724\n",
-      "[13]\ttrain-auc:0.959674\teval-auc:0.957192\n",
-      "[14]\ttrain-auc:0.960227\teval-auc:0.957447\n",
-      "[15]\ttrain-auc:0.960316\teval-auc:0.957583\n",
-      "[16]\ttrain-auc:0.961338\teval-auc:0.959102\n",
-      "[17]\ttrain-auc:0.961986\teval-auc:0.958905\n",
-      "[18]\ttrain-auc:0.962297\teval-auc:0.959365\n",
-      "[19]\ttrain-auc:0.962798\teval-auc:0.959863\n",
-      "[20]\ttrain-auc:0.963266\teval-auc:0.960243\n",
-      "[21]\ttrain-auc:0.963661\teval-auc:0.96069\n",
-      "[22]\ttrain-auc:0.964377\teval-auc:0.961733\n",
-      "[23]\ttrain-auc:0.964741\teval-auc:0.962419\n",
-      "[24]\ttrain-auc:0.964886\teval-auc:0.962837\n",
-      "[25]\ttrain-auc:0.965193\teval-auc:0.963015\n",
-      "[26]\ttrain-auc:0.965453\teval-auc:0.963356\n",
-      "[27]\ttrain-auc:0.965766\teval-auc:0.963154\n",
-      "[28]\ttrain-auc:0.965954\teval-auc:0.963015\n",
-      "[29]\ttrain-auc:0.966586\teval-auc:0.963877\n",
-      "[30]\ttrain-auc:0.966813\teval-auc:0.963994\n",
-      "[31]\ttrain-auc:0.967003\teval-auc:0.96425\n",
-      "[32]\ttrain-auc:0.967469\teval-auc:0.965021\n",
-      "[33]\ttrain-auc:0.967952\teval-auc:0.964936\n",
-      "[34]\ttrain-auc:0.968365\teval-auc:0.965487\n",
-      "[35]\ttrain-auc:0.968576\teval-auc:0.965891\n",
-      "[36]\ttrain-auc:0.968961\teval-auc:0.966418\n",
-      "[37]\ttrain-auc:0.969475\teval-auc:0.966474\n",
-      "[38]\ttrain-auc:0.96979\teval-auc:0.966923\n",
-      "[39]\ttrain-auc:0.970028\teval-auc:0.967189\n",
-      "[40]\ttrain-auc:0.970177\teval-auc:0.967397\n",
-      "[41]\ttrain-auc:0.970596\teval-auc:0.967048\n",
-      "[42]\ttrain-auc:0.970871\teval-auc:0.967607\n",
-      "[43]\ttrain-auc:0.971206\teval-auc:0.968102\n",
-      "[44]\ttrain-auc:0.971298\teval-auc:0.968176\n",
-      "[45]\ttrain-auc:0.971754\teval-auc:0.968493\n",
-      "[46]\ttrain-auc:0.971813\teval-auc:0.968546\n",
-      "[47]\ttrain-auc:0.972115\teval-auc:0.968902\n",
-      "[48]\ttrain-auc:0.972266\teval-auc:0.968961\n",
-      "[49]\ttrain-auc:0.972328\teval-auc:0.969046\n",
-      "[50]\ttrain-auc:0.972632\teval-auc:0.968804\n",
-      "[51]\ttrain-auc:0.973076\teval-auc:0.968977\n",
-      "[52]\ttrain-auc:0.973468\teval-auc:0.969573\n",
-      "[53]\ttrain-auc:0.973681\teval-auc:0.969738\n",
-      "[54]\ttrain-auc:0.973826\teval-auc:0.970062\n",
-      "[55]\ttrain-auc:0.974159\teval-auc:0.970142\n",
-      "[56]\ttrain-auc:0.974259\teval-auc:0.970254\n",
-      "[57]\ttrain-auc:0.974533\teval-auc:0.970278\n",
-      "[58]\ttrain-auc:0.974716\teval-auc:0.970485\n",
-      "[59]\ttrain-auc:0.974808\teval-auc:0.970538\n",
-      "[60]\ttrain-auc:0.975131\teval-auc:0.9709\n",
-      "[61]\ttrain-auc:0.975251\teval-auc:0.97106\n",
-      "[62]\ttrain-auc:0.97532\teval-auc:0.971113\n",
-      "[63]\ttrain-auc:0.975468\teval-auc:0.971262\n",
-      "[64]\ttrain-auc:0.975523\teval-auc:0.971342\n",
-      "[65]\ttrain-auc:0.975621\teval-auc:0.971342\n",
-      "[66]\ttrain-auc:0.975726\teval-auc:0.97132\n",
-      "[67]\ttrain-auc:0.975945\teval-auc:0.971496\n",
-      "[68]\ttrain-auc:0.976067\teval-auc:0.971703\n",
-      "[69]\ttrain-auc:0.976234\teval-auc:0.971991\n",
-      "[70]\ttrain-auc:0.976296\teval-auc:0.972044\n",
-      "[71]\ttrain-auc:0.976386\teval-auc:0.972129\n",
-      "[72]\ttrain-auc:0.976636\teval-auc:0.972087\n",
-      "[73]\ttrain-auc:0.976809\teval-auc:0.972156\n",
-      "[74]\ttrain-auc:0.97688\teval-auc:0.972251\n",
-      "[75]\ttrain-auc:0.977256\teval-auc:0.972459\n",
-      "[76]\ttrain-auc:0.977306\teval-auc:0.972507\n",
-      "[77]\ttrain-auc:0.977407\teval-auc:0.972603\n",
-      "[78]\ttrain-auc:0.977514\teval-auc:0.972656\n",
-      "[79]\ttrain-auc:0.977588\teval-auc:0.972757\n",
-      "[80]\ttrain-auc:0.977843\teval-auc:0.972972\n",
-      "[81]\ttrain-auc:0.977938\teval-auc:0.973036\n",
-      "[82]\ttrain-auc:0.978056\teval-auc:0.972962\n",
-      "[83]\ttrain-auc:0.97829\teval-auc:0.973058\n",
-      "[84]\ttrain-auc:0.978366\teval-auc:0.973132\n",
-      "[85]\ttrain-auc:0.97844\teval-auc:0.973132\n",
-      "[86]\ttrain-auc:0.978461\teval-auc:0.973143\n",
-      "[87]\ttrain-auc:0.97852\teval-auc:0.973207\n",
-      "[88]\ttrain-auc:0.978731\teval-auc:0.973457\n",
-      "[89]\ttrain-auc:0.978776\teval-auc:0.973499\n",
-      "[90]\ttrain-auc:0.978881\teval-auc:0.973446\n",
-      "[91]\ttrain-auc:0.979052\teval-auc:0.973494\n",
-      "[92]\ttrain-auc:0.979078\teval-auc:0.973499\n",
-      "[93]\ttrain-auc:0.979186\teval-auc:0.973637\n",
-      "[94]\ttrain-auc:0.9793\teval-auc:0.973712\n",
-      "[95]\ttrain-auc:0.979578\teval-auc:0.973733\n",
-      "[96]\ttrain-auc:0.979638\teval-auc:0.973797\n",
-      "[97]\ttrain-auc:0.979718\teval-auc:0.974021\n",
-      "[98]\ttrain-auc:0.979887\teval-auc:0.973978\n",
-      "[99]\ttrain-auc:0.9799\teval-auc:0.973957\n",
-      "[100]\ttrain-auc:0.979966\teval-auc:0.974106\n",
-      "[101]\ttrain-auc:0.980003\teval-auc:0.974159\n",
-      "[102]\ttrain-auc:0.98012\teval-auc:0.973994\n",
-      "[103]\ttrain-auc:0.980258\teval-auc:0.973962\n",
-      "[104]\ttrain-auc:0.980323\teval-auc:0.973903\n",
-      "[105]\ttrain-auc:0.980386\teval-auc:0.973999\n",
-      "[106]\ttrain-auc:0.980468\teval-auc:0.973946\n",
-      "[107]\ttrain-auc:0.980523\teval-auc:0.974058\n",
-      "[108]\ttrain-auc:0.980577\teval-auc:0.974116\n",
-      "[109]\ttrain-auc:0.98073\teval-auc:0.974239\n",
-      "[110]\ttrain-auc:0.98088\teval-auc:0.974244\n",
-      "[111]\ttrain-auc:0.980953\teval-auc:0.974377\n",
-      "[112]\ttrain-auc:0.981079\teval-auc:0.974409\n",
-      "[113]\ttrain-auc:0.981224\teval-auc:0.974499\n",
-      "[114]\ttrain-auc:0.981241\teval-auc:0.974515\n",
-      "[115]\ttrain-auc:0.981318\teval-auc:0.97434\n",
-      "[116]\ttrain-auc:0.981389\teval-auc:0.97451\n",
-      "[117]\ttrain-auc:0.981489\teval-auc:0.974537\n",
-      "[118]\ttrain-auc:0.981613\teval-auc:0.974654\n",
-      "[119]\ttrain-auc:0.981645\teval-auc:0.974765\n",
-      "[120]\ttrain-auc:0.981738\teval-auc:0.974739\n",
-      "[121]\ttrain-auc:0.98188\teval-auc:0.974707\n",
-      "[122]\ttrain-auc:0.98195\teval-auc:0.974643\n",
-      "[123]\ttrain-auc:0.982098\teval-auc:0.974659\n",
-      "[124]\ttrain-auc:0.982177\teval-auc:0.974723\n",
-      "[125]\ttrain-auc:0.982389\teval-auc:0.974941\n",
-      "[126]\ttrain-auc:0.982517\teval-auc:0.97509\n",
-      "[127]\ttrain-auc:0.982527\teval-auc:0.975132\n",
-      "[128]\ttrain-auc:0.982643\teval-auc:0.97517\n",
-      "[129]\ttrain-auc:0.982795\teval-auc:0.97509\n",
-      "[130]\ttrain-auc:0.982866\teval-auc:0.975122\n",
-      "[131]\ttrain-auc:0.98296\teval-auc:0.975186\n",
-      "[132]\ttrain-auc:0.983059\teval-auc:0.975223\n",
-      "[133]\ttrain-auc:0.983209\teval-auc:0.975143\n",
-      "[134]\ttrain-auc:0.983343\teval-auc:0.975239\n",
-      "[135]\ttrain-auc:0.983497\teval-auc:0.975266\n",
-      "[136]\ttrain-auc:0.983545\teval-auc:0.975228\n",
-      "[137]\ttrain-auc:0.98368\teval-auc:0.975196\n",
-      "[138]\ttrain-auc:0.983674\teval-auc:0.975244\n",
-      "[139]\ttrain-auc:0.983737\teval-auc:0.975223\n",
-      "[140]\ttrain-auc:0.983804\teval-auc:0.97518\n",
-      "[141]\ttrain-auc:0.983939\teval-auc:0.975143\n",
-      "[142]\ttrain-auc:0.983985\teval-auc:0.975159\n",
-      "[143]\ttrain-auc:0.984077\teval-auc:0.975095\n",
-      "[144]\ttrain-auc:0.984248\teval-auc:0.975074\n",
-      "[145]\ttrain-auc:0.984285\teval-auc:0.975042\n",
-      "Stopping. Best iteration:\n",
-      "[135]\ttrain-auc:0.983497\teval-auc:0.975266\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "num_round = param['n_estimators']\n",
-    "\n",
-    "plst = param.items()\n",
-    "evallist = [(dtrain, 'train'), (dvalid, 'eval')]\n",
-    "best = xgb.train(plst, dtrain, num_round, evallist, early_stopping_rounds=10)  # 寻找最优参\n",
-    "best.save_model('bst.model')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'best_iteration': '135',\n",
-       " 'best_msg': '[135]\\ttrain-auc:0.983497\\teval-auc:0.975266',\n",
-       " 'best_score': '0.975266'}"
-      ]
-     },
-     "execution_count": 23,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "best.attributes()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def create_feature_map(features):\n",
-    "    outfile = open(r'xgb.fmap', 'w')\n",
-    "    i = 0\n",
-    "    for feat in features:\n",
-    "        outfile.write('{0}\\t{1}\\tq\\n'.format(i, feat))\n",
-    "        i = i + 1\n",
-    "    outfile.close()\n",
-    "\n",
-    "\n",
-    "features = list(x_train.columns[:])\n",
-    "create_feature_map(features)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def feature_importance(best_xgb):\n",
-    "    importance = best_xgb.get_fscore(fmap=r'xgb.fmap')\n",
-    "    importance = sorted(importance.items(), key=operator.itemgetter(1), reverse=True)\n",
-    "\n",
-    "    df = pd.DataFrame(importance, columns=['feature', 'fscore'])\n",
-    "    df['fscore'] = df['fscore'] / df['fscore'].sum()\n",
-    "    file_name = 'data/feature_importance_' + str(datetime.now().date())[5:] + '.csv'\n",
-    "    df.to_csv(file_name)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "feature_importance(best)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
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-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Unnamed: 0</th>\n",
-       "      <th>feature</th>\n",
-       "      <th>fscore</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>0</td>\n",
-       "      <td>brand</td>\n",
-       "      <td>0.077852</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>1</td>\n",
-       "      <td>action_before_10_5.0_x</td>\n",
-       "      <td>0.041611</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>2</td>\n",
-       "      <td>bad_comment_rate</td>\n",
-       "      <td>0.038926</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>3</td>\n",
-       "      <td>product_action_5_ratio</td>\n",
-       "      <td>0.028188</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>4</td>\n",
-       "      <td>user_lv_cd_2</td>\n",
-       "      <td>0.025503</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   Unnamed: 0                 feature    fscore\n",
-       "0           0                   brand  0.077852\n",
-       "1           1  action_before_10_5.0_x  0.041611\n",
-       "2           2        bad_comment_rate  0.038926\n",
-       "3           3  product_action_5_ratio  0.028188\n",
-       "4           4            user_lv_cd_2  0.025503"
-      ]
-     },
-     "execution_count": 30,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "fi = pd.read_csv('data/feature_importance_02-05.csv')\n",
-    "fi.sort_values(\"fscore\", inplace=True, ascending=False)\n",
-    "fi.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
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-       "      <th>cate_action_5_mean</th>\n",
-       "      <th>cate_action_6_mean</th>\n",
-       "      <th>has_bad_comment</th>\n",
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-       "      <th>comment_num_1</th>\n",
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-       "      <th>comment_num_4</th>\n",
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-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>6765</th>\n",
-       "      <td>235394.0</td>\n",
-       "      <td>24371.0</td>\n",
-       "      <td>8.0</td>\n",
-       "      <td>8.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>18.0</td>\n",
-       "      <td>12.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>25.733333</td>\n",
-       "      <td>116.8</td>\n",
-       "      <td>24942.666667</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0206</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",
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-       "    <tr>\n",
-       "      <th>13767</th>\n",
-       "      <td>272948.0</td>\n",
-       "      <td>108907.0</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>8.0</td>\n",
-       "      <td>0.0</td>\n",
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-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>7.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>61.633333</td>\n",
-       "      <td>143.9</td>\n",
-       "      <td>41669.233333</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0000</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
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-       "    <tr>\n",
-       "      <th>9672</th>\n",
-       "      <td>245846.0</td>\n",
-       "      <td>63026.0</td>\n",
-       "      <td>10.0</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>47.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>1.100000</td>\n",
-       "      <td>9.0</td>\n",
-       "      <td>2361.600000</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0938</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",
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-       "    <tr>\n",
-       "      <th>9116</th>\n",
-       "      <td>272178.0</td>\n",
-       "      <td>66704.0</td>\n",
-       "      <td>9.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>112.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>10.400000</td>\n",
-       "      <td>32.5</td>\n",
-       "      <td>7264.200000</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0000</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",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>10055</th>\n",
-       "      <td>216485.0</td>\n",
-       "      <td>131364.0</td>\n",
-       "      <td>6.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>6.0</td>\n",
-       "      <td>28.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>38.233333</td>\n",
-       "      <td>88.0</td>\n",
-       "      <td>17558.033333</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0473</td>\n",
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-       "      <td>1.0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>5 rows × 236 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "        user_id    sku_id  cate  action_before_3_1.0_x  action_before_3_2.0_x  \\\n",
-       "6765   235394.0   24371.0   8.0                    8.0                    0.0   \n",
-       "13767  272948.0  108907.0   4.0                    8.0                    0.0   \n",
-       "9672   245846.0   63026.0  10.0                    1.0                    0.0   \n",
-       "9116   272178.0   66704.0   9.0                    2.0                    0.0   \n",
-       "10055  216485.0  131364.0   6.0                    2.0                    1.0   \n",
-       "\n",
-       "       action_before_3_3.0_x  action_before_3_4.0_x  action_before_3_5.0_x  \\\n",
-       "6765                     0.0                    0.0                    1.0   \n",
-       "13767                    0.0                    0.0                    0.0   \n",
-       "9672                     0.0                    0.0                    0.0   \n",
-       "9116                     0.0                    0.0                    0.0   \n",
-       "10055                    1.0                    0.0                    0.0   \n",
-       "\n",
-       "       action_before_3_6.0_x  action_before_3_1.0_y  ...  cate_action_4_mean  \\\n",
-       "6765                    18.0                   12.0  ...           25.733333   \n",
-       "13767                    7.0                    0.0  ...           61.633333   \n",
-       "9672                     0.0                   47.0  ...            1.100000   \n",
-       "9116                     4.0                  112.0  ...           10.400000   \n",
-       "10055                    6.0                   28.0  ...           38.233333   \n",
-       "\n",
-       "       cate_action_5_mean  cate_action_6_mean  has_bad_comment  \\\n",
-       "6765                116.8        24942.666667              1.0   \n",
-       "13767               143.9        41669.233333              0.0   \n",
-       "9672                  9.0         2361.600000              1.0   \n",
-       "9116                 32.5         7264.200000              0.0   \n",
-       "10055                88.0        17558.033333              1.0   \n",
-       "\n",
-       "       bad_comment_rate  comment_num_0  comment_num_1  comment_num_2  \\\n",
-       "6765             0.0206            0.0            0.0            0.0   \n",
-       "13767            0.0000            0.0            0.0            1.0   \n",
-       "9672             0.0938            0.0            0.0            0.0   \n",
-       "9116             0.0000            0.0            0.0            0.0   \n",
-       "10055            0.0473            0.0            0.0            0.0   \n",
-       "\n",
-       "       comment_num_3  comment_num_4  \n",
-       "6765             0.0            1.0  \n",
-       "13767            0.0            0.0  \n",
-       "9672             1.0            0.0  \n",
-       "9116             0.0            0.0  \n",
-       "10055            0.0            1.0  \n",
-       "\n",
-       "[5 rows x 236 columns]"
-      ]
-     },
-     "execution_count": 31,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "x_test.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "users = x_test[['user_id', 'sku_id', 'cate']].copy()\n",
-    "del x_test['user_id']\n",
-    "del x_test['sku_id']\n",
-    "x_test_DMatrix = xgb.DMatrix(x_test)\n",
-    "y_pred = bst.predict(x_test_DMatrix, ntree_limit=bst.best_ntree_limit)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
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-       "      <th>pred_label</th>\n",
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-       "  </tbody>\n",
-       "</table>\n",
-       "<p>5 rows × 235 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "       cate  action_before_3_1.0_x  action_before_3_2.0_x  \\\n",
-       "6765    8.0                    8.0                    0.0   \n",
-       "13767   4.0                    8.0                    0.0   \n",
-       "9672   10.0                    1.0                    0.0   \n",
-       "9116    9.0                    2.0                    0.0   \n",
-       "10055   6.0                    2.0                    1.0   \n",
-       "\n",
-       "       action_before_3_3.0_x  action_before_3_4.0_x  action_before_3_5.0_x  \\\n",
-       "6765                     0.0                    0.0                    1.0   \n",
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-       "9672                     0.0                    0.0                    0.0   \n",
-       "9116                     0.0                    0.0                    0.0   \n",
-       "10055                    1.0                    0.0                    0.0   \n",
-       "\n",
-       "       action_before_3_6.0_x  action_before_3_1.0_y  action_before_3_2.0_y  \\\n",
-       "6765                    18.0                   12.0                    0.0   \n",
-       "13767                    7.0                    0.0                    0.0   \n",
-       "9672                     0.0                   47.0                    0.0   \n",
-       "9116                     4.0                  112.0                    0.0   \n",
-       "10055                    6.0                   28.0                    1.0   \n",
-       "\n",
-       "       action_before_3_3.0_y  ...  cate_action_5_mean  cate_action_6_mean  \\\n",
-       "6765                     0.0  ...               116.8        24942.666667   \n",
-       "13767                    0.0  ...               143.9        41669.233333   \n",
-       "9672                     0.0  ...                 9.0         2361.600000   \n",
-       "9116                     0.0  ...                32.5         7264.200000   \n",
-       "10055                    0.0  ...                88.0        17558.033333   \n",
-       "\n",
-       "       has_bad_comment  bad_comment_rate  comment_num_0  comment_num_1  \\\n",
-       "6765               1.0            0.0206            0.0            0.0   \n",
-       "13767              0.0            0.0000            0.0            0.0   \n",
-       "9672               1.0            0.0938            0.0            0.0   \n",
-       "9116               0.0            0.0000            0.0            0.0   \n",
-       "10055              1.0            0.0473            0.0            0.0   \n",
-       "\n",
-       "       comment_num_2  comment_num_3  comment_num_4  pred_label  \n",
-       "6765             0.0            0.0            1.0    0.453736  \n",
-       "13767            1.0            0.0            0.0    0.002793  \n",
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-       "10055            0.0            0.0            1.0    0.000507  \n",
-       "\n",
-       "[5 rows x 235 columns]"
-      ]
-     },
-     "execution_count": 33,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "x_test['pred_label'] = y_pred\n",
-    "x_test.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
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-       "</table>\n",
-       "<p>5 rows × 235 columns</p>\n",
-       "</div>"
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-      "text/plain": [
-       "       cate  action_before_3_1.0_x  action_before_3_2.0_x  \\\n",
-       "6765    8.0                    8.0                    0.0   \n",
-       "13767   4.0                    8.0                    0.0   \n",
-       "9672   10.0                    1.0                    0.0   \n",
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-       "10055   6.0                    2.0                    1.0   \n",
-       "\n",
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-       "6765                     0.0                    0.0                    1.0   \n",
-       "13767                    0.0                    0.0                    0.0   \n",
-       "9672                     0.0                    0.0                    0.0   \n",
-       "9116                     0.0                    0.0                    0.0   \n",
-       "10055                    1.0                    0.0                    0.0   \n",
-       "\n",
-       "       action_before_3_6.0_x  action_before_3_1.0_y  action_before_3_2.0_y  \\\n",
-       "6765                    18.0                   12.0                    0.0   \n",
-       "13767                    7.0                    0.0                    0.0   \n",
-       "9672                     0.0                   47.0                    0.0   \n",
-       "9116                     4.0                  112.0                    0.0   \n",
-       "10055                    6.0                   28.0                    1.0   \n",
-       "\n",
-       "       action_before_3_3.0_y  ...  cate_action_5_mean  cate_action_6_mean  \\\n",
-       "6765                     0.0  ...               116.8        24942.666667   \n",
-       "13767                    0.0  ...               143.9        41669.233333   \n",
-       "9672                     0.0  ...                 9.0         2361.600000   \n",
-       "9116                     0.0  ...                32.5         7264.200000   \n",
-       "10055                    0.0  ...                88.0        17558.033333   \n",
-       "\n",
-       "       has_bad_comment  bad_comment_rate  comment_num_0  comment_num_1  \\\n",
-       "6765               1.0            0.0206            0.0            0.0   \n",
-       "13767              0.0            0.0000            0.0            0.0   \n",
-       "9672               1.0            0.0938            0.0            0.0   \n",
-       "9116               0.0            0.0000            0.0            0.0   \n",
-       "10055              1.0            0.0473            0.0            0.0   \n",
-       "\n",
-       "       comment_num_2  comment_num_3  comment_num_4  pred_label  \n",
-       "6765             0.0            0.0            1.0         0.0  \n",
-       "13767            1.0            0.0            0.0         0.0  \n",
-       "9672             0.0            1.0            0.0         0.0  \n",
-       "9116             0.0            0.0            0.0         0.0  \n",
-       "10055            0.0            0.0            1.0         0.0  \n",
-       "\n",
-       "[5 rows x 235 columns]"
-      ]
-     },
-     "execution_count": 34,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "def label(column):\n",
-    "    if column['pred_label'] > 0.5:\n",
-    "        #rint ('yes')\n",
-    "        column['pred_label'] = 1\n",
-    "    else:\n",
-    "        column['pred_label'] = 0\n",
-    "    return column\n",
-    "x_test = x_test.apply(label,axis = 1)\n",
-    "x_test.head()  "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {},
-   "outputs": [
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-       "</table>\n",
-       "<p>5 rows × 236 columns</p>\n",
-       "</div>"
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-      "text/plain": [
-       "       cate  action_before_3_1.0_x  action_before_3_2.0_x  \\\n",
-       "6765    8.0                    8.0                    0.0   \n",
-       "13767   4.0                    8.0                    0.0   \n",
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-       "10055   6.0                    2.0                    1.0   \n",
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-       "6765                     0.0                    0.0                    1.0   \n",
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-       "10055                    1.0                    0.0                    0.0   \n",
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-       "6765                    18.0                   12.0                    0.0   \n",
-       "13767                    7.0                    0.0                    0.0   \n",
-       "9672                     0.0                   47.0                    0.0   \n",
-       "9116                     4.0                  112.0                    0.0   \n",
-       "10055                    6.0                   28.0                    1.0   \n",
-       "\n",
-       "       action_before_3_3.0_y  ...  cate_action_6_mean  has_bad_comment  \\\n",
-       "6765                     0.0  ...        24942.666667              1.0   \n",
-       "13767                    0.0  ...        41669.233333              0.0   \n",
-       "9672                     0.0  ...         2361.600000              1.0   \n",
-       "9116                     0.0  ...         7264.200000              0.0   \n",
-       "10055                    0.0  ...        17558.033333              1.0   \n",
-       "\n",
-       "       bad_comment_rate  comment_num_0  comment_num_1  comment_num_2  \\\n",
-       "6765             0.0206            0.0            0.0            0.0   \n",
-       "13767            0.0000            0.0            0.0            1.0   \n",
-       "9672             0.0938            0.0            0.0            0.0   \n",
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-       "10055            0.0473            0.0            0.0            0.0   \n",
-       "\n",
-       "       comment_num_3  comment_num_4  pred_label  true_label  \n",
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-       "10055            0.0            1.0         0.0         0.0  \n",
-       "\n",
-       "[5 rows x 236 columns]"
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-     },
-     "execution_count": 35,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "x_test['true_label'] = y_test\n",
-    "x_test.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
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-       "</table>\n",
-       "<p>5 rows × 238 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "       cate  action_before_3_1.0_x  action_before_3_2.0_x  \\\n",
-       "6765    8.0                    8.0                    0.0   \n",
-       "13767   4.0                    8.0                    0.0   \n",
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-       "10055   6.0                    2.0                    1.0   \n",
-       "\n",
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-       "6765                     0.0                    0.0                    1.0   \n",
-       "13767                    0.0                    0.0                    0.0   \n",
-       "9672                     0.0                    0.0                    0.0   \n",
-       "9116                     0.0                    0.0                    0.0   \n",
-       "10055                    1.0                    0.0                    0.0   \n",
-       "\n",
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-       "6765                    18.0                   12.0                    0.0   \n",
-       "13767                    7.0                    0.0                    0.0   \n",
-       "9672                     0.0                   47.0                    0.0   \n",
-       "9116                     4.0                  112.0                    0.0   \n",
-       "10055                    6.0                   28.0                    1.0   \n",
-       "\n",
-       "       action_before_3_3.0_y  ...  bad_comment_rate  comment_num_0  \\\n",
-       "6765                     0.0  ...            0.0206            0.0   \n",
-       "13767                    0.0  ...            0.0000            0.0   \n",
-       "9672                     0.0  ...            0.0938            0.0   \n",
-       "9116                     0.0  ...            0.0000            0.0   \n",
-       "10055                    0.0  ...            0.0473            0.0   \n",
-       "\n",
-       "       comment_num_1  comment_num_2  comment_num_3  comment_num_4  pred_label  \\\n",
-       "6765             0.0            0.0            0.0            1.0         0.0   \n",
-       "13767            0.0            1.0            0.0            0.0         0.0   \n",
-       "9672             0.0            0.0            1.0            0.0         0.0   \n",
-       "9116             0.0            0.0            0.0            0.0         0.0   \n",
-       "10055            0.0            0.0            0.0            1.0         0.0   \n",
-       "\n",
-       "       true_label   user_id    sku_id  \n",
-       "6765          0.0  235394.0   24371.0  \n",
-       "13767         0.0  272948.0  108907.0  \n",
-       "9672          0.0  245846.0   63026.0  \n",
-       "9116          0.0  272178.0   66704.0  \n",
-       "10055         0.0  216485.0  131364.0  \n",
-       "\n",
-       "[5 rows x 238 columns]"
-      ]
-     },
-     "execution_count": 36,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "x_test['user_id'] = users['user_id']\n",
-    "x_test['sku_id'] = users['sku_id']\n",
-    "x_test.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "126\n",
-      "248\n",
-      "267\n"
-     ]
-    }
-   ],
-   "source": [
-    "# 所有购买用户\n",
-    "all_user_set = x_test[x_test['true_label']==1]['user_id'].unique()\n",
-    "print (len(all_user_set))\n",
-    "# 所有预测购买的用户\n",
-    "all_user_test_set = x_test[x_test['pred_label'] == 1]['user_id'].unique()\n",
-    "print (len(all_user_test_set))\n",
-    "all_user_test_item_pair = x_test[x_test['pred_label'] == 1]['user_id'].map(str) + '-' + x_test[x_test['pred_label'] == 1]['sku_id'].map(str)\n",
-    "all_user_test_item_pair = np.array(all_user_test_item_pair)\n",
-    "print (len(all_user_test_item_pair))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "所有用户中预测购买用户的准确率为 0.4838709677419355\n",
-      "所有用户中预测购买用户的召回率0.9523809523809523\n"
-     ]
-    }
-   ],
-   "source": [
-    "pos, neg = 0,0\n",
-    "for user_id in all_user_test_set:\n",
-    "    if user_id in all_user_set:\n",
-    "        pos += 1\n",
-    "    else:\n",
-    "        neg += 1\n",
-    "all_user_acc = 1.0 * pos / (pos + neg)\n",
-    "all_user_recall = 1.0 * pos / len(all_user_set)\n",
-    "print ('所有用户中预测购买用户的准确率为 ' + str(all_user_acc))\n",
-    "print ('所有用户中预测购买用户的召回率' + str(all_user_recall))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 40,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "144\n",
-      "所有用户中预测购买商品的准确率为 0.5131086142322098\n",
-      "所有用户中预测购买商品的召回率0.9513888888888888\n",
-      "F11=0.527086383601757\n",
-      "F12=0.7091097308488614\n",
-      "score=0.6363003919500196\n"
-     ]
-    }
-   ],
-   "source": [
-    "#所有实际商品对\n",
-    "all_user_item_pair = x_test[x_test['true_label']==1]['user_id'].map(str) + '-' + x_test[x_test['true_label']==1]['sku_id'].map(str)\n",
-    "all_user_item_pair = np.array(all_user_item_pair)\n",
-    "print (len(all_user_item_pair))\n",
-    "pos, neg = 0, 0\n",
-    "for user_item_pair in all_user_test_item_pair:\n",
-    "    #print (user_item_pair)\n",
-    "    if user_item_pair in all_user_item_pair:\n",
-    "        pos += 1\n",
-    "    else:\n",
-    "        neg += 1\n",
-    "all_item_acc = 1.0 * pos / ( pos + neg)\n",
-    "all_item_recall = 1.0 * pos / len(all_user_item_pair)\n",
-    "print ('所有用户中预测购买商品的准确率为 ' + str(all_item_acc))\n",
-    "print ('所有用户中预测购买商品的召回率' + str(all_item_recall))\n",
-    "F11 = 6.0 * all_user_recall * all_user_acc / (5.0 * all_user_recall + all_user_acc)\n",
-    "F12 = 5.0 * all_item_acc * all_item_recall / (2.0 * all_item_recall + 3 * all_item_acc)\n",
-    "score = 0.4 * F11 + 0.6 * F12\n",
-    "print ('F11=' + str(F11))\n",
-    "print ('F12=' + str(F12))\n",
-    "print ('score=' + str(score))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
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-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
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-   "nbconvert_exporter": "python",
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