集成学习：lightGBM（二）

柔光的暖阳◎ 2023-07-11 06:26 76阅读 0赞

# ![20191009191333910.png][][日萌社][Link 1] #

[人工智能AI：Keras PyTorch MXNet TensorFlow PaddlePaddle 深度学习实战（不定时更新）][AI_Keras PyTorch MXNet TensorFlow PaddlePaddle]

--------------------

[集成学习：Bagging、随机森林、Boosting、GBDT][Bagging_Boosting_GBDT]

[集成学习：XGBoost][XGBoost]

[集成学习：lightGBM（一）][lightGBM]

[集成学习：lightGBM（二）][lightGBM 1]

--------------------

# 5.8 《绝地求生》玩家排名预测 #

## 1 项目背景 ##

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3ppbWlhbzU1MjE0NzU3Mg_size_16_color_FFFFFF_t_70][]

绝地求生(Player unknown's Battlegrounds)，俗称吃鸡，是一款战术竞技型射击类沙盒游戏。

这款游戏是一款大逃杀类型的游戏，每一局游戏将有最多100名玩家参与，他们将被投放在绝地岛(battlegrounds)上，在游戏的开始时所有人都一无所有。玩家需要在岛上收集各种资源，在不断缩小的安全区域内对抗其他玩家，让自己生存到最后。

本作拥有很高的自由度，玩家可以体验飞机跳伞、开越野车、丛林射击、抢夺战利品等玩法，小心四周埋伏的敌人，尽可能成为最后1个存活的人。

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3ppbWlhbzU1MjE0NzU3Mg_size_16_color_FFFFFF_t_70 1][]

该游戏中，玩家需要在游戏地图上收集各种资源，并在不断缩小的安全区域内对抗其他玩家，让自己生存到最后。

## 2 数据集介绍 ##

本项目中，将为您提供大量匿名的《绝地求生》游戏统计数据。

其格式为每行包含一个玩家的游戏后统计数据，列为数据的特征值。

数据来自所有类型的比赛：单排，双排，四排；不保证每场比赛有100名人员，每组最多4名成员。

文件说明:

*  train\_V2.csv - 训练集
 *  test\_V2.csv - 测试集

数据集局部图如下图所示:

![20200304180850186.png][]

数据集中字段解释：

*  Id \[用户id\]
    
     *  Player’s Id
 *  groupId \[所处小队id\]
    
     *  ID to identify a group within a match. If the same group of players plays in different matches, they will have a different groupId each time.
 *  matchId \[该场比赛id\]
    
     *  ID to identify match. There are no matches that are in both the training and testing set.
 *  assists \[助攻数\]
    
     *  Number of enemy players this player damaged that were killed by teammates.
 *  boosts \[使用能量,道具数量\]
    
     *  Number of boost items used.
 *  damageDealt \[总伤害\]
    
     *  Total damage dealt. Note: Self inflicted damage is subtracted.
 *  DBNOs \[击倒敌人数量\]
    
     *  Number of enemy players knocked.
 *  headshotKills \[爆头数\]
    
     *  Number of enemy players killed with headshots.
 *  heals \[使用治疗药品数量\]
    
     *  Number of healing items used.
 *  killPlace \[本厂比赛杀敌排行\]
    
     *  Ranking in match of number of enemy players killed.
 *  killPoints \[Elo杀敌排名\]
    
     *  Kills-based external ranking of player. (Think of this as an Elo ranking where only kills matter.) If there is a value other than -1 in rankPoints, then any 0 in killPoints should be treated as a “None”.
 *  kills \[杀敌数\]
    
     *  Number of enemy players killed.
 *  killStreaks \[连续杀敌数\]
    
     *  Max number of enemy players killed in a short amount of time.
 *  longestKill \[最远杀敌距离\]
    
     *  Longest distance between player and player killed at time of death. This may be misleading, as downing a player and driving away may lead to a large longestKill stat.
 *  matchDuration \[比赛时长\]
    
     *  Duration of match in seconds.
 *  matchType \[比赛类型(小组人数)\]
    
     *  String identifying the game mode that the data comes from. The standard modes are “solo”, “duo”, “squad”, “solo-fpp”, “duo-fpp”, and “squad-fpp”; other modes are from events or custom matches.
 *  maxPlace \[本局最差名次\]
    
     *  Worst placement we have data for in the match. This may not match with numGroups, as sometimes the data skips over placements.
 *  numGroups \[小组数量\]
    
     *  Number of groups we have data for in the match.
 *  rankPoints \[Elo排名\]
    
     *  Elo-like ranking of player. This ranking is inconsistent and is being deprecated in the API’s next version, so use with caution. Value of -1 takes place of “None”.
 *  revives \[救活队员的次数\]
    
     *  Number of times this player revived teammates.
 *  rideDistance \[驾车距离\]
    
     *  Total distance traveled in vehicles measured in meters.
 *  roadKills \[驾车杀敌数\]
    
     *  Number of kills while in a vehicle.
 *  swimDistance \[游泳距离\]
    
     *  Total distance traveled by swimming measured in meters.
 *  teamKills \[杀死队友的次数\]
    
     *  Number of times this player killed a teammate.
 *  vehicleDestroys \[毁坏机动车的数量\]
    
     *  Number of vehicles destroyed.
 *  walkDistance \[步行距离\]
    
     *  Total distance traveled on foot measured in meters.
 *  weaponsAcquired \[收集武器的数量\]
    
     *  Number of weapons picked up.
 *  winPoints \[胜率Elo排名\]
    
     *  Win-based external ranking of player. (Think of this as an Elo ranking where only winning matters.) If there is a value other than -1 in rankPoints, then any 0 in winPoints should be treated as a “None”.
 *  winPlacePerc \[百分比排名\]
    
     *  The target of prediction. This is a percentile winning placement, where 1 corresponds to 1st place, and 0 corresponds to last place in the match. It is calculated off of maxPlace, not numGroups, so it is possible to have missing chunks in a match.

## 3 项目评估方式 ##

你必须创建一个模型，根据他们的最终统计数据预测玩家的排名，从1（第一名）到0（最后一名）。

最后结果通过平均绝对误差（MAE）进行评估，即通过预测的winPlacePerc和真实的winPlacePerc之间的平均绝对误差

关于MAE:

*  sklearn.metrics.mean\_absolute\_error

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3ppbWlhbzU1MjE0NzU3Mg_size_16_color_FFFFFF_t_70 2][]

## 4 代码实现 ##

### 项目实现 ###

### 获取数据、基本数据信息查看 ###

In \[1\]:

import numpy as np  
import matplotlib.pyplot as plt  
import pandas as pd  
import seaborn as sns

In \[2\]:

train = pd.read\_csv("./data/train\_V2.csv")

In \[3\]:

train.head()

Out\[3\]:

5 rows × 29 columns

In \[4\]:

train.tail()

Out\[4\]:

5 rows × 29 columns

In \[5\]:

train.describe()

Out\[5\]:

<table> 
 <thead> 
  <tr> 
   <th></th> 
   <th>assists</th> 
   <th>boosts</th> 
   <th>damageDealt</th> 
   <th>DBNOs</th> 
   <th>headshotKills</th> 
   <th>heals</th> 
   <th>killPlace</th> 
   <th>killPoints</th> 
   <th>kills</th> 
   <th>killStreaks</th> 
   <th>...</th> 
   <th>revives</th> 
   <th>rideDistance</th> 
   <th>roadKills</th> 
   <th>swimDistance</th> 
   <th>teamKills</th> 
   <th>vehicleDestroys</th> 
   <th>walkDistance</th> 
   <th>weaponsAcquired</th> 
   <th>winPoints</th> 
   <th>winPlacePerc</th> 
  </tr> 
 </thead> 
 <tbody> 
  <tr> 
   <th>count</th> 
   <td>4.446966e+06</td> 
   <td>4.446966e+06</td> 
   <td>4.446966e+06</td> 
   <td>4.446966e+06</td> 
   <td>4.446966e+06</td> 
   <td>4.446966e+06</td> 
   <td>4.446966e+06</td> 
   <td>4.446966e+06</td> 
   <td>4.446966e+06</td> 
   <td>4.446966e+06</td> 
   <td>...</td> 
   <td>4.446966e+06</td> 
   <td>4.446966e+06</td> 
   <td>4.446966e+06</td> 
   <td>4.446966e+06</td> 
   <td>4.446966e+06</td> 
   <td>4.446966e+06</td> 
   <td>4.446966e+06</td> 
   <td>4.446966e+06</td> 
   <td>4.446966e+06</td> 
   <td>4.446965e+06</td> 
  </tr> 
  <tr> 
   <th>mean</th> 
   <td>2.338149e-01</td> 
   <td>1.106908e+00</td> 
   <td>1.307171e+02</td> 
   <td>6.578755e-01</td> 
   <td>2.268196e-01</td> 
   <td>1.370147e+00</td> 
   <td>4.759935e+01</td> 
   <td>5.050060e+02</td> 
   <td>9.247833e-01</td> 
   <td>5.439551e-01</td> 
   <td>...</td> 
   <td>1.646590e-01</td> 
   <td>6.061157e+02</td> 
   <td>3.496091e-03</td> 
   <td>4.509322e+00</td> 
   <td>2.386841e-02</td> 
   <td>7.918208e-03</td> 
   <td>1.154218e+03</td> 
   <td>3.660488e+00</td> 
   <td>6.064601e+02</td> 
   <td>4.728216e-01</td> 
  </tr> 
  <tr> 
   <th>std</th> 
   <td>5.885731e-01</td> 
   <td>1.715794e+00</td> 
   <td>1.707806e+02</td> 
   <td>1.145743e+00</td> 
   <td>6.021553e-01</td> 
   <td>2.679982e+00</td> 
   <td>2.746294e+01</td> 
   <td>6.275049e+02</td> 
   <td>1.558445e+00</td> 
   <td>7.109721e-01</td> 
   <td>...</td> 
   <td>4.721671e-01</td> 
   <td>1.498344e+03</td> 
   <td>7.337297e-02</td> 
   <td>3.050220e+01</td> 
   <td>1.673935e-01</td> 
   <td>9.261157e-02</td> 
   <td>1.183497e+03</td> 
   <td>2.456544e+00</td> 
   <td>7.397004e+02</td> 
   <td>3.074050e-01</td> 
  </tr> 
  <tr> 
   <th>min</th> 
   <td>0.000000e+00</td> 
   <td>0.000000e+00</td> 
   <td>0.000000e+00</td> 
   <td>0.000000e+00</td> 
   <td>0.000000e+00</td> 
   <td>0.000000e+00</td> 
   <td>1.000000e+00</td> 
   <td>0.000000e+00</td> 
   <td>0.000000e+00</td> 
   <td>0.000000e+00</td> 
   <td>...</td> 
   <td>0.000000e+00</td> 
   <td>0.000000e+00</td> 
   <td>0.000000e+00</td> 
   <td>0.000000e+00</td> 
   <td>0.000000e+00</td> 
   <td>0.000000e+00</td> 
   <td>0.000000e+00</td> 
   <td>0.000000e+00</td> 
   <td>0.000000e+00</td> 
   <td>0.000000e+00</td> 
  </tr> 
  <tr> 
   <th>25%</th> 
   <td>0.000000e+00</td> 
   <td>0.000000e+00</td> 
   <td>0.000000e+00</td> 
   <td>0.000000e+00</td> 
   <td>0.000000e+00</td> 
   <td>0.000000e+00</td> 
   <td>2.400000e+01</td> 
   <td>0.000000e+00</td> 
   <td>0.000000e+00</td> 
   <td>0.000000e+00</td> 
   <td>...</td> 
   <td>0.000000e+00</td> 
   <td>0.000000e+00</td> 
   <td>0.000000e+00</td> 
   <td>0.000000e+00</td> 
   <td>0.000000e+00</td> 
   <td>0.000000e+00</td> 
   <td>1.551000e+02</td> 
   <td>2.000000e+00</td> 
   <td>0.000000e+00</td> 
   <td>2.000000e-01</td> 
  </tr> 
  <tr> 
   <th>50%</th> 
   <td>0.000000e+00</td> 
   <td>0.000000e+00</td> 
   <td>8.424000e+01</td> 
   <td>0.000000e+00</td> 
   <td>0.000000e+00</td> 
   <td>0.000000e+00</td> 
   <td>4.700000e+01</td> 
   <td>0.000000e+00</td> 
   <td>0.000000e+00</td> 
   <td>0.000000e+00</td> 
   <td>...</td> 
   <td>0.000000e+00</td> 
   <td>0.000000e+00</td> 
   <td>0.000000e+00</td> 
   <td>0.000000e+00</td> 
   <td>0.000000e+00</td> 
   <td>0.000000e+00</td> 
   <td>6.856000e+02</td> 
   <td>3.000000e+00</td> 
   <td>0.000000e+00</td> 
   <td>4.583000e-01</td> 
  </tr> 
  <tr> 
   <th>75%</th> 
   <td>0.000000e+00</td> 
   <td>2.000000e+00</td> 
   <td>1.860000e+02</td> 
   <td>1.000000e+00</td> 
   <td>0.000000e+00</td> 
   <td>2.000000e+00</td> 
   <td>7.100000e+01</td> 
   <td>1.172000e+03</td> 
   <td>1.000000e+00</td> 
   <td>1.000000e+00</td> 
   <td>...</td> 
   <td>0.000000e+00</td> 
   <td>1.909750e-01</td> 
   <td>0.000000e+00</td> 
   <td>0.000000e+00</td> 
   <td>0.000000e+00</td> 
   <td>0.000000e+00</td> 
   <td>1.976000e+03</td> 
   <td>5.000000e+00</td> 
   <td>1.495000e+03</td> 
   <td>7.407000e-01</td> 
  </tr> 
  <tr> 
   <th>max</th> 
   <td>2.200000e+01</td> 
   <td>3.300000e+01</td> 
   <td>6.616000e+03</td> 
   <td>5.300000e+01</td> 
   <td>6.400000e+01</td> 
   <td>8.000000e+01</td> 
   <td>1.010000e+02</td> 
   <td>2.170000e+03</td> 
   <td>7.200000e+01</td> 
   <td>2.000000e+01</td> 
   <td>...</td> 
   <td>3.900000e+01</td> 
   <td>4.071000e+04</td> 
   <td>1.800000e+01</td> 
   <td>3.823000e+03</td> 
   <td>1.200000e+01</td> 
   <td>5.000000e+00</td> 
   <td>2.578000e+04</td> 
   <td>2.360000e+02</td> 
   <td>2.013000e+03</td> 
   <td>1.000000e+00</td> 
  </tr> 
 </tbody> 
</table>

8 rows × 25 columns

In \[6\]:

train.info()

<class 'pandas.core.frame.DataFrame'>
    RangeIndex: 4446966 entries, 0 to 4446965
    Data columns (total 29 columns):
    Id                 object
    groupId            object
    matchId            object
    assists            int64
    boosts             int64
    damageDealt        float64
    DBNOs              int64
    headshotKills      int64
    heals              int64
    killPlace          int64
    killPoints         int64
    kills              int64
    killStreaks        int64
    longestKill        float64
    matchDuration      int64
    matchType          object
    maxPlace           int64
    numGroups          int64
    rankPoints         int64
    revives            int64
    rideDistance       float64
    roadKills          int64
    swimDistance       float64
    teamKills          int64
    vehicleDestroys    int64
    walkDistance       float64
    weaponsAcquired    int64
    winPoints          int64
    winPlacePerc       float64
    dtypes: float64(6), int64(19), object(4)
    memory usage: 983.9+ MB

In \[7\]:

\# 查看一共要多少条数据  
train.shape

Out\[7\]:

(4446966, 29)

In \[8\]:

\# 有多少场比赛  
np.unique(train\["matchId"\]).shape

Out\[8\]:

(47965,)

In \[9\]:

\# 有多少支队伍  
np.unique(train\["groupId"\]).shape

Out\[9\]:

(2026745,)

## 数据基本处理 ##

### 数据缺失值处理 ###

In \[10\]:

\# 判断哪列有缺失值,发现只有winPlacePerc有   
np.any(train.isnull())

Out\[10\]:

Id                 False
    groupId            False
    matchId            False
    assists            False
    boosts             False
    damageDealt        False
    DBNOs              False
    headshotKills      False
    heals              False
    killPlace          False
    killPoints         False
    kills              False
    killStreaks        False
    longestKill        False
    matchDuration      False
    matchType          False
    maxPlace           False
    numGroups          False
    rankPoints         False
    revives            False
    rideDistance       False
    roadKills          False
    swimDistance       False
    teamKills          False
    vehicleDestroys    False
    walkDistance       False
    weaponsAcquired    False
    winPoints          False
    winPlacePerc        True
    dtype: bool

In \[11\]:

\# 查找缺失值  
train\[train\["winPlacePerc"\].isnull()\]

Out\[11\]:

1 rows × 29 columns

In \[12\]:

\# 删除  
train = train.drop(2744604)

In \[13\]:

train.shape

Out\[13\]:

(4446965, 29)

### 特征数据规范化处理 ###

查看每场比赛参加的人数

In \[14\]:

count = train.groupby("matchId")\["matchId"\].transform("count")

In \[15\]:

train\["playersJoined"\] = count

In \[16\]:

train.head()

Out\[16\]:

5 rows × 30 columns

In \[17\]:

train\["playersJoined"\].sort\_values().head()

Out\[17\]:

1206365    2
    2109739    2
    3956552    5
    3620228    5
    696000     5
    Name: playersJoined, dtype: int64

In \[18\]:

plt.figure(figsize=(20, 8))  
sns.countplot(train\["playersJoined"\])  
plt.grid()  
plt.show()

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3ppbWlhbzU1MjE0NzU3Mg_size_16_color_FFFFFF_t_70 3][]

In \[19\]:

\# train\[train\["playersJoined"\]>=75\]\["playersJoined"\]

In \[20\]:

plt.figure(figsize=(20, 8))  
sns.countplot(train\[train\["playersJoined"\]>=75\]\["playersJoined"\])  
plt.grid()  
plt.show()

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3ppbWlhbzU1MjE0NzU3Mg_size_16_color_FFFFFF_t_70 4][]

规范化输出部分数据

In \[21\]:

train\["killsNorm"\] = train\["kills"\] \* ((100-train\["playersJoined"\])/100+1)

In \[22\]:

train\["damageDealtNorm"\] = train\["damageDealt"\] \* ((100-train\["playersJoined"\])/100+1)  
train\["maxPlaceNorm"\] = train\["maxPlace"\] \* ((100-train\["playersJoined"\])/100+1)  
train\["matchDurationNorm"\] = train\["matchDuration"\] \* ((100-train\["playersJoined"\])/100+1)

In \[23\]:

train.head()

Out\[23\]:

5 rows × 34 columns

In \[24\]:

\# 比较经过规范化的特征值和原始特征值的值  
to\_show = \['Id', 'kills','killsNorm','damageDealt', 'damageDealtNorm', 'maxPlace', 'maxPlaceNorm', 'matchDuration', 'matchDurationNorm'\]  
train\[to\_show\]\[0:11\]

Out\[24\]:

<table> 
 <thead> 
  <tr> 
   <th></th> 
   <th>Id</th> 
   <th>kills</th> 
   <th>killsNorm</th> 
   <th>damageDealt</th> 
   <th>damageDealtNorm</th> 
   <th>maxPlace</th> 
   <th>maxPlaceNorm</th> 
   <th>matchDuration</th> 
   <th>matchDurationNorm</th> 
  </tr> 
 </thead> 
 <tbody> 
  <tr> 
   <th>0</th> 
   <td>7f96b2f878858a</td> 
   <td>0</td> 
   <td>0.00</td> 
   <td>0.000</td> 
   <td>0.00000</td> 
   <td>28</td> 
   <td>29.12</td> 
   <td>1306</td> 
   <td>1358.24</td> 
  </tr> 
  <tr> 
   <th>1</th> 
   <td>eef90569b9d03c</td> 
   <td>0</td> 
   <td>0.00</td> 
   <td>91.470</td> 
   <td>99.70230</td> 
   <td>26</td> 
   <td>28.34</td> 
   <td>1777</td> 
   <td>1936.93</td> 
  </tr> 
  <tr> 
   <th>2</th> 
   <td>1eaf90ac73de72</td> 
   <td>0</td> 
   <td>0.00</td> 
   <td>68.000</td> 
   <td>69.36000</td> 
   <td>50</td> 
   <td>51.00</td> 
   <td>1318</td> 
   <td>1344.36</td> 
  </tr> 
  <tr> 
   <th>3</th> 
   <td>4616d365dd2853</td> 
   <td>0</td> 
   <td>0.00</td> 
   <td>32.900</td> 
   <td>35.86100</td> 
   <td>31</td> 
   <td>33.79</td> 
   <td>1436</td> 
   <td>1565.24</td> 
  </tr> 
  <tr> 
   <th>4</th> 
   <td>315c96c26c9aac</td> 
   <td>1</td> 
   <td>1.03</td> 
   <td>100.000</td> 
   <td>103.00000</td> 
   <td>97</td> 
   <td>99.91</td> 
   <td>1424</td> 
   <td>1466.72</td> 
  </tr> 
  <tr> 
   <th>5</th> 
   <td>ff79c12f326506</td> 
   <td>1</td> 
   <td>1.05</td> 
   <td>100.000</td> 
   <td>105.00000</td> 
   <td>28</td> 
   <td>29.40</td> 
   <td>1395</td> 
   <td>1464.75</td> 
  </tr> 
  <tr> 
   <th>6</th> 
   <td>95959be0e21ca3</td> 
   <td>0</td> 
   <td>0.00</td> 
   <td>0.000</td> 
   <td>0.00000</td> 
   <td>28</td> 
   <td>28.84</td> 
   <td>1316</td> 
   <td>1355.48</td> 
  </tr> 
  <tr> 
   <th>7</th> 
   <td>311b84c6ff4390</td> 
   <td>0</td> 
   <td>0.00</td> 
   <td>8.538</td> 
   <td>8.87952</td> 
   <td>96</td> 
   <td>99.84</td> 
   <td>1967</td> 
   <td>2045.68</td> 
  </tr> 
  <tr> 
   <th>8</th> 
   <td>1a68204ccf9891</td> 
   <td>0</td> 
   <td>0.00</td> 
   <td>51.600</td> 
   <td>53.14800</td> 
   <td>28</td> 
   <td>28.84</td> 
   <td>1375</td> 
   <td>1416.25</td> 
  </tr> 
  <tr> 
   <th>9</th> 
   <td>e5bb5a43587253</td> 
   <td>0</td> 
   <td>0.00</td> 
   <td>37.270</td> 
   <td>38.38810</td> 
   <td>29</td> 
   <td>29.87</td> 
   <td>1930</td> 
   <td>1987.90</td> 
  </tr> 
  <tr> 
   <th>10</th> 
   <td>2b574d43972813</td> 
   <td>0</td> 
   <td>0.00</td> 
   <td>28.380</td> 
   <td>28.66380</td> 
   <td>29</td> 
   <td>29.29</td> 
   <td>1811</td> 
   <td>1829.11</td> 
  </tr> 
 </tbody> 
</table>

### 部分变量合成 ###

In \[25\]:

train\["healsandboosts"\] = train\["heals"\] + train\["boosts"\]

In \[57\]:

train\[\["heals", "boosts", "healsandboosts"\]\].tail(10)

Out\[57\]:

<table> 
 <thead> 
  <tr> 
   <th></th> 
   <th>heals</th> 
   <th>boosts</th> 
   <th>healsandboosts</th> 
  </tr> 
 </thead> 
 <tbody> 
  <tr> 
   <th>4446956</th> 
   <td>1</td> 
   <td>0</td> 
   <td>1</td> 
  </tr> 
  <tr> 
   <th>4446957</th> 
   <td>0</td> 
   <td>1</td> 
   <td>1</td> 
  </tr> 
  <tr> 
   <th>4446958</th> 
   <td>0</td> 
   <td>0</td> 
   <td>0</td> 
  </tr> 
  <tr> 
   <th>4446959</th> 
   <td>0</td> 
   <td>0</td> 
   <td>0</td> 
  </tr> 
  <tr> 
   <th>4446960</th> 
   <td>0</td> 
   <td>0</td> 
   <td>0</td> 
  </tr> 
  <tr> 
   <th>4446961</th> 
   <td>0</td> 
   <td>0</td> 
   <td>0</td> 
  </tr> 
  <tr> 
   <th>4446962</th> 
   <td>0</td> 
   <td>1</td> 
   <td>1</td> 
  </tr> 
  <tr> 
   <th>4446963</th> 
   <td>0</td> 
   <td>0</td> 
   <td>0</td> 
  </tr> 
  <tr> 
   <th>4446964</th> 
   <td>2</td> 
   <td>4</td> 
   <td>6</td> 
  </tr> 
  <tr> 
   <th>4446965</th> 
   <td>1</td> 
   <td>2</td> 
   <td>3</td> 
  </tr> 
 </tbody> 
</table>

### 异常值处理 ###

异常值处理：删除有击杀，但是完全没有移动的玩家

In \[58\]:

train\["totalDistance"\] = train\["rideDistance"\] + train\["walkDistance"\] + train\["swimDistance"\]

In \[59\]:

train.head()

Out\[59\]:

5 rows × 37 columns

In \[60\]:

\# (train\["kills"\] > 0) & (train\["totalDistance"\] == 0)

In \[61\]:

train\["killwithoutMoving"\] = (train\["kills"\] > 0) & (train\["totalDistance"\] == 0)

In \[64\]:

train\[train\["killwithoutMoving"\] == True\].head()

Out\[64\]:

5 rows × 37 columns

In \[65\]:

train\[train\["killwithoutMoving"\] == True\].shape

Out\[65\]:

(1535, 37)

In \[66\]:

train\[train\["killwithoutMoving"\] == True\].index

Out\[66\]:

Int64Index([   1824,    6673,   11892,   14631,   15591,   20881,   23298,
                  24640,   25659,   30079,
                ...
                4426500, 4429697, 4432954, 4436511, 4437516, 4440232, 4440898,
                4440927, 4441511, 4446682],
               dtype='int64', length=1535)

In \[67\]:

train.drop(train\[train\["killwithoutMoving"\] == True\].index, inplace=True)

In \[68\]:

train.shape

Out\[68\]:

(4445430, 37)

异常值处理：删除驾车杀敌数异常的数据

In \[70\]:

\# train\["roadKills"\] > 10

In \[71\]:

train.drop(train\[train\["roadKills"\] > 10\].index, inplace=True)

In \[72\]:

train.shape

Out\[72\]:

(4445426, 37)

异常值处理：删除玩家在一局中杀敌数超过30人的数据

In \[75\]:

train\[train\["kills"\] > 30\].head()

Out\[75\]:

5 rows × 37 columns

In \[76\]:

train.drop(train\[train\["kills"\] > 30\].index, inplace=True)  
train.shape

Out\[76\]:

(4445331, 37)

异常值处理：删除爆头率异常数据

In \[79\]:

train\["headshot\_rate"\] = train\["headshotKills"\]/train\["kills"\]  
train\["headshot\_rate"\].head()

Out\[79\]:

0    NaN
    1    NaN
    2    NaN
    3    NaN
    4    0.0
    Name: headshot_rate, dtype: float64

In \[81\]:

train\["headshot\_rate"\] = train\["headshot\_rate"\].fillna(0)

In \[82\]:

train.head()

Out\[82\]:

5 rows × 38 columns

In \[85\]:

train\[(train\["headshot\_rate"\] == 1) & (train\["kills"\] > 9)\].head()

Out\[85\]:

5 rows × 38 columns

In \[86\]:

train\[(train\["headshot\_rate"\] == 1) & (train\["kills"\] > 9)\].index

Out\[86\]:

Int64Index([ 281570,  346124,  871244,  908815,  963463, 1079403, 1167959,
                1348164, 1380385, 1483199, 1581850, 1622232, 1753322, 2256755,
                2375749, 2647056, 2825200, 3288424, 3594399, 3926325, 4036281,
                4351048, 4387092, 4428741],
               dtype='int64')

In \[87\]:

train.drop(train\[(train\["headshot\_rate"\] == 1) & (train\["kills"\] > 9)\].index, inplace=True)

train.shape

Out\[87\]:

(4445307, 38)

异常值处理：删除最远杀敌距离异常数据

In \[89\]:

train\[train\["longestKill"\] >=1000\]

Out\[89\]:

<table> 
 <thead> 
  <tr> 
   <th></th> 
   <th>Id</th> 
   <th>groupId</th> 
   <th>matchId</th> 
   <th>assists</th> 
   <th>boosts</th> 
   <th>damageDealt</th> 
   <th>DBNOs</th> 
   <th>headshotKills</th> 
   <th>heals</th> 
   <th>killPlace</th> 
   <th>...</th> 
   <th>winPlacePerc</th> 
   <th>playersJoined</th> 
   <th>killsNorm</th> 
   <th>damageDealtNorm</th> 
   <th>maxPlaceNorm</th> 
   <th>matchDurationNorm</th> 
   <th>healsandboosts</th> 
   <th>totalDistance</th> 
   <th>killwithoutMoving</th> 
   <th>headshot_rate</th> 
  </tr> 
 </thead> 
 <tbody> 
  <tr> 
   <th>202281</th> 
   <td>88e2af7d78af5a</td> 
   <td>34ddeede52c042</td> 
   <td>4346bc63bc67fa</td> 
   <td>0</td> 
   <td>3</td> 
   <td>783.9</td> 
   <td>5</td> 
   <td>1</td> 
   <td>1</td> 
   <td>5</td> 
   <td>...</td> 
   <td>0.9231</td> 
   <td>88</td> 
   <td>4.48</td> 
   <td>877.968</td> 
   <td>30.24</td> 
   <td>2087.68</td> 
   <td>4</td> 
   <td>3775.20</td> 
   <td>False</td> 
   <td>0.250000</td> 
  </tr> 
  <tr> 
   <th>240005</th> 
   <td>41c2f5c0699807</td> 
   <td>9faecf87ab4275</td> 
   <td>634edab75860b3</td> 
   <td>5</td> 
   <td>0</td> 
   <td>1284.0</td> 
   <td>8</td> 
   <td>5</td> 
   <td>7</td> 
   <td>18</td> 
   <td>...</td> 
   <td>0.5385</td> 
   <td>29</td> 
   <td>18.81</td> 
   <td>2195.640</td> 
   <td>23.94</td> 
   <td>2236.68</td> 
   <td>7</td> 
   <td>48.87</td> 
   <td>False</td> 
   <td>0.454545</td> 
  </tr> 
  <tr> 
   <th>324313</th> 
   <td>ef390c152bcc3d</td> 
   <td>30fd444be3bbc1</td> 
   <td>4f7f8d6cf558b4</td> 
   <td>2</td> 
   <td>0</td> 
   <td>1028.0</td> 
   <td>0</td> 
   <td>0</td> 
   <td>0</td> 
   <td>9</td> 
   <td>...</td> 
   <td>1.0000</td> 
   <td>51</td> 
   <td>14.90</td> 
   <td>1531.720</td> 
   <td>19.37</td> 
   <td>1040.02</td> 
   <td>0</td> 
   <td>2981.00</td> 
   <td>False</td> 
   <td>0.000000</td> 
  </tr> 
  <tr> 
   <th>656553</th> 
   <td>9948b058562163</td> 
   <td>c8cb8491112bf6</td> 
   <td>0104eeb664494d</td> 
   <td>6</td> 
   <td>0</td> 
   <td>1410.0</td> 
   <td>17</td> 
   <td>5</td> 
   <td>0</td> 
   <td>3</td> 
   <td>...</td> 
   <td>0.6000</td> 
   <td>41</td> 
   <td>25.44</td> 
   <td>2241.900</td> 
   <td>9.54</td> 
   <td>1734.69</td> 
   <td>0</td> 
   <td>29.21</td> 
   <td>False</td> 
   <td>0.312500</td> 
  </tr> 
  <tr> 
   <th>803632</th> 
   <td>4e7e6c74e3c57d</td> 
   <td>94698690918933</td> 
   <td>da91b0c3d875f8</td> 
   <td>0</td> 
   <td>0</td> 
   <td>196.8</td> 
   <td>0</td> 
   <td>0</td> 
   <td>0</td> 
   <td>51</td> 
   <td>...</td> 
   <td>0.0000</td> 
   <td>61</td> 
   <td>1.39</td> 
   <td>273.552</td> 
   <td>11.12</td> 
   <td>654.69</td> 
   <td>0</td> 
   <td>3159.00</td> 
   <td>False</td> 
   <td>0.000000</td> 
  </tr> 
  <tr> 
   <th>895411</th> 
   <td>1f5ba6e0cfb968</td> 
   <td>512ea24b831be3</td> 
   <td>5fb0d8b1fc16cf</td> 
   <td>4</td> 
   <td>0</td> 
   <td>1012.0</td> 
   <td>11</td> 
   <td>5</td> 
   <td>0</td> 
   <td>5</td> 
   <td>...</td> 
   <td>0.9091</td> 
   <td>86</td> 
   <td>11.40</td> 
   <td>1153.680</td> 
   <td>13.68</td> 
   <td>1163.94</td> 
   <td>0</td> 
   <td>569.50</td> 
   <td>False</td> 
   <td>0.500000</td> 
  </tr> 
  <tr> 
   <th>1172437</th> 
   <td>303a93cfa1f46c</td> 
   <td>8795d39fd0df86</td> 
   <td>9c8962b58bb3e3</td> 
   <td>2</td> 
   <td>1</td> 
   <td>329.3</td> 
   <td>0</td> 
   <td>0</td> 
   <td>2</td> 
   <td>45</td> 
   <td>...</td> 
   <td>0.2857</td> 
   <td>58</td> 
   <td>4.26</td> 
   <td>467.606</td> 
   <td>11.36</td> 
   <td>825.02</td> 
   <td>3</td> 
   <td>832.50</td> 
   <td>False</td> 
   <td>0.000000</td> 
  </tr> 
  <tr> 
   <th>1209416</th> 
   <td>528659ff1c1aec</td> 
   <td>7d1ba83423551d</td> 
   <td>ea9386587d5888</td> 
   <td>0</td> 
   <td>6</td> 
   <td>1640.0</td> 
   <td>0</td> 
   <td>7</td> 
   <td>0</td> 
   <td>1</td> 
   <td>...</td> 
   <td>0.9412</td> 
   <td>52</td> 
   <td>22.20</td> 
   <td>2427.200</td> 
   <td>76.96</td> 
   <td>1827.80</td> 
   <td>6</td> 
   <td>2848.00</td> 
   <td>False</td> 
   <td>0.466667</td> 
  </tr> 
  <tr> 
   <th>1642712</th> 
   <td>91966848e08e2f</td> 
   <td>0ee4fbd27657c9</td> 
   <td>17dea22cefe62a</td> 
   <td>3</td> 
   <td>2</td> 
   <td>2103.0</td> 
   <td>0</td> 
   <td>4</td> 
   <td>11</td> 
   <td>11</td> 
   <td>...</td> 
   <td>0.5000</td> 
   <td>28</td> 
   <td>39.56</td> 
   <td>3617.160</td> 
   <td>25.80</td> 
   <td>3092.56</td> 
   <td>13</td> 
   <td>235.30</td> 
   <td>False</td> 
   <td>0.173913</td> 
  </tr> 
  <tr> 
   <th>2015559</th> 
   <td>5ff0c1a9fab2ba</td> 
   <td>2d8119b1544f87</td> 
   <td>904cecf36217df</td> 
   <td>3</td> 
   <td>3</td> 
   <td>1302.0</td> 
   <td>0</td> 
   <td>6</td> 
   <td>5</td> 
   <td>15</td> 
   <td>...</td> 
   <td>0.6000</td> 
   <td>42</td> 
   <td>17.38</td> 
   <td>2057.160</td> 
   <td>17.38</td> 
   <td>2834.52</td> 
   <td>8</td> 
   <td>133.20</td> 
   <td>False</td> 
   <td>0.545455</td> 
  </tr> 
  <tr> 
   <th>2122128</th> 
   <td>42df3102cb540b</td> 
   <td>7d9b2be15b355b</td> 
   <td>610d78f3affd2e</td> 
   <td>5</td> 
   <td>0</td> 
   <td>2500.0</td> 
   <td>0</td> 
   <td>7</td> 
   <td>1</td> 
   <td>2</td> 
   <td>...</td> 
   <td>0.0000</td> 
   <td>10</td> 
   <td>41.80</td> 
   <td>4750.000</td> 
   <td>3.80</td> 
   <td>3416.20</td> 
   <td>1</td> 
   <td>464.50</td> 
   <td>False</td> 
   <td>0.318182</td> 
  </tr> 
  <tr> 
   <th>2152425</th> 
   <td>4b9f61bac5eb0a</td> 
   <td>bc717b964f3bbe</td> 
   <td>838cb9a3c94598</td> 
   <td>3</td> 
   <td>0</td> 
   <td>945.4</td> 
   <td>0</td> 
   <td>0</td> 
   <td>0</td> 
   <td>11</td> 
   <td>...</td> 
   <td>0.5714</td> 
   <td>60</td> 
   <td>18.20</td> 
   <td>1323.560</td> 
   <td>11.20</td> 
   <td>1673.00</td> 
   <td>0</td> 
   <td>844.70</td> 
   <td>False</td> 
   <td>0.000000</td> 
  </tr> 
  <tr> 
   <th>2592718</th> 
   <td>24e0fec84c18e9</td> 
   <td>8404855ca02e48</td> 
   <td>e886a8ebb702cf</td> 
   <td>7</td> 
   <td>0</td> 
   <td>1684.0</td> 
   <td>0</td> 
   <td>4</td> 
   <td>7</td> 
   <td>11</td> 
   <td>...</td> 
   <td>0.5714</td> 
   <td>26</td> 
   <td>22.62</td> 
   <td>2930.160</td> 
   <td>38.28</td> 
   <td>3118.08</td> 
   <td>7</td> 
   <td>4851.00</td> 
   <td>False</td> 
   <td>0.307692</td> 
  </tr> 
  <tr> 
   <th>2981715</th> 
   <td>7f77051c7cef52</td> 
   <td>d6579a630399b5</td> 
   <td>4784f7d9a06b51</td> 
   <td>3</td> 
   <td>5</td> 
   <td>1025.0</td> 
   <td>5</td> 
   <td>2</td> 
   <td>5</td> 
   <td>2</td> 
   <td>...</td> 
   <td>1.0000</td> 
   <td>93</td> 
   <td>6.42</td> 
   <td>1096.750</td> 
   <td>50.29</td> 
   <td>1453.06</td> 
   <td>10</td> 
   <td>4085.96</td> 
   <td>False</td> 
   <td>0.333333</td> 
  </tr> 
  <tr> 
   <th>3081503</th> 
   <td>f19a76e8d7ac52</td> 
   <td>624d65c529f87c</td> 
   <td>de19b70121c40f</td> 
   <td>3</td> 
   <td>0</td> 
   <td>1038.0</td> 
   <td>0</td> 
   <td>0</td> 
   <td>0</td> 
   <td>32</td> 
   <td>...</td> 
   <td>0.8571</td> 
   <td>57</td> 
   <td>8.58</td> 
   <td>1484.340</td> 
   <td>11.44</td> 
   <td>945.23</td> 
   <td>0</td> 
   <td>270.00</td> 
   <td>False</td> 
   <td>0.000000</td> 
  </tr> 
  <tr> 
   <th>3255171</th> 
   <td>5524c154448425</td> 
   <td>674195558ad41b</td> 
   <td>db6f6d1f0d4904</td> 
   <td>1</td> 
   <td>0</td> 
   <td>1355.0</td> 
   <td>0</td> 
   <td>2</td> 
   <td>0</td> 
   <td>9</td> 
   <td>...</td> 
   <td>0.5789</td> 
   <td>21</td> 
   <td>25.06</td> 
   <td>2425.450</td> 
   <td>35.80</td> 
   <td>1607.42</td> 
   <td>0</td> 
   <td>1039.00</td> 
   <td>False</td> 
   <td>0.142857</td> 
  </tr> 
  <tr> 
   <th>3304284</th> 
   <td>d0c286ce498e17</td> 
   <td>17fdd45e612bab</td> 
   <td>3eaaa2f7a360fe</td> 
   <td>7</td> 
   <td>0</td> 
   <td>2330.0</td> 
   <td>0</td> 
   <td>2</td> 
   <td>0</td> 
   <td>2</td> 
   <td>...</td> 
   <td>1.0000</td> 
   <td>53</td> 
   <td>29.40</td> 
   <td>3425.100</td> 
   <td>26.46</td> 
   <td>1321.53</td> 
   <td>0</td> 
   <td>68.02</td> 
   <td>False</td> 
   <td>0.100000</td> 
  </tr> 
  <tr> 
   <th>3320960</th> 
   <td>0040e53dfe7b5d</td> 
   <td>650661c2351eb7</td> 
   <td>2daabf3a7852e6</td> 
   <td>0</td> 
   <td>0</td> 
   <td>399.0</td> 
   <td>2</td> 
   <td>0</td> 
   <td>6</td> 
   <td>14</td> 
   <td>...</td> 
   <td>0.0000</td> 
   <td>15</td> 
   <td>7.40</td> 
   <td>738.150</td> 
   <td>14.80</td> 
   <td>2763.90</td> 
   <td>6</td> 
   <td>5481.00</td> 
   <td>False</td> 
   <td>0.000000</td> 
  </tr> 
  <tr> 
   <th>3552532</th> 
   <td>db638834c62f6f</td> 
   <td>0614b611d6a935</td> 
   <td>ff80300f8262f5</td> 
   <td>2</td> 
   <td>0</td> 
   <td>517.0</td> 
   <td>0</td> 
   <td>0</td> 
   <td>0</td> 
   <td>10</td> 
   <td>...</td> 
   <td>0.0000</td> 
   <td>30</td> 
   <td>8.50</td> 
   <td>878.900</td> 
   <td>6.80</td> 
   <td>612.00</td> 
   <td>0</td> 
   <td>1344.88</td> 
   <td>False</td> 
   <td>0.000000</td> 
  </tr> 
  <tr> 
   <th>4332473</th> 
   <td>d8857d3d7e31b6</td> 
   <td>085de7a36897e6</td> 
   <td>42f997c16d8a0e</td> 
   <td>5</td> 
   <td>0</td> 
   <td>1685.0</td> 
   <td>11</td> 
   <td>3</td> 
   <td>18</td> 
   <td>8</td> 
   <td>...</td> 
   <td>0.9091</td> 
   <td>26</td> 
   <td>27.84</td> 
   <td>2931.900</td> 
   <td>20.88</td> 
   <td>3119.82</td> 
   <td>18</td> 
   <td>523.30</td> 
   <td>False</td> 
   <td>0.187500</td> 
  </tr> 
 </tbody> 
</table>

20 rows × 38 columns

In \[90\]:

train\[train\["longestKill"\] >=1000\].index

Out\[90\]:

Int64Index([ 202281,  240005,  324313,  656553,  803632,  895411, 1172437,
                1209416, 1642712, 2015559, 2122128, 2152425, 2592718, 2981715,
                3081503, 3255171, 3304284, 3320960, 3552532, 4332473],
               dtype='int64')

In \[91\]:

train.drop(train\[train\["longestKill"\] >=1000\].index, inplace=True)

train.shape

Out\[91\]:

(4445287, 38)

异常值处理：删除关于运动距离的异常值

In \[93\]:

\# 行走

train\[train\["walkDistance"\] >=10000\].index

Out\[93\]:

Int64Index([  23026,   34344,   49312,   68590,   94400,  125103,  136421,
                 136476,  154080,  154128,
                ...
                4181311, 4230073, 4259976, 4284974, 4288445, 4306598, 4370543,
                4380785, 4405009, 4415088],
               dtype='int64', length=219)

In \[94\]:

train.drop(train\[train\["walkDistance"\] >=10000\].index, inplace=True)

train.shape

Out\[94\]:

(4445068, 38)

In \[95\]:

\# 载具

train\[train\["rideDistance"\] >=20000\].index

Out\[95\]:

Int64Index([  28588,   63015,   70507,   72763,   95276,  140097,  297186,
                 371098,  403647,  426708,
                ...
                4154459, 4191491, 4239725, 4248221, 4256764, 4270943, 4301013,
                4386384, 4404738, 4440261],
               dtype='int64', length=150)

In \[96\]:

train.drop(train\[train\["rideDistance"\] >=20000\].index, inplace=True)

train.shape

Out\[96\]:

(4444918, 38)

In \[97\]:

\# 游泳

train\[train\["swimDistance"\] >=2000\].index

Out\[97\]:

Int64Index([ 177973,  274258, 1005337, 1195818, 1227362, 1889163, 2065940,
                2327586, 2784855, 3359439, 3513522, 4132225],
               dtype='int64')

In \[98\]:

train.drop(train\[train\["swimDistance"\] >=20000\].index, inplace=True)

train.shape

Out\[98\]:

(4444918, 38)

异常值处理：武器收集异常值处理

In \[99\]:

train\[train\["weaponsAcquired"\] >=80\].index

Out\[99\]:

Int64Index([ 233643,  588387, 1437471, 1449293, 1592744, 1834515, 2373240,
                2442962, 2743408, 2749693, 2797867, 2973445, 2977084, 2982525,
                3230315, 3405716, 3951710, 4022031, 4288517],
               dtype='int64')

In \[100\]:

train.drop(train\[train\["weaponsAcquired"\] >=80\].index, inplace=True)

train.shape

Out\[100\]:

(4444899, 38)

异常值处理：删除使用治疗药品数量异常值

In \[101\]:

train\[train\["heals"\] >=80\].index

Out\[101\]:

Int64Index([4262662], dtype='int64')

In \[102\]:

train.drop(train\[train\["heals"\] >=80\].index, inplace=True)

train.shape

Out\[102\]:

(4444898, 38)

### 类别型数据处理 ###

比赛类型one-hot处理

In \[104\]:

train\["matchType"\].unique()

Out\[104\]:

array(['squad-fpp', 'duo', 'solo-fpp', 'squad', 'duo-fpp', 'solo',
           'normal-squad-fpp', 'crashfpp', 'flaretpp', 'normal-solo-fpp',
           'flarefpp', 'normal-duo-fpp', 'normal-duo', 'normal-squad',
           'crashtpp', 'normal-solo'], dtype=object)

In \[106\]:

train = pd.get\_dummies(train, columns=\["matchType"\])

In \[107\]:

train.head()

Out\[107\]:

5 rows × 53 columns

In \[108\]:

matchType\_encoding = train.filter(regex="matchType")

In \[109\]:

matchType\_encoding.head()

Out\[109\]:

<table> 
 <thead> 
  <tr> 
   <th></th> 
   <th>matchType_crashfpp</th> 
   <th>matchType_crashtpp</th> 
   <th>matchType_duo</th> 
   <th>matchType_duo-fpp</th> 
   <th>matchType_flarefpp</th> 
   <th>matchType_flaretpp</th> 
   <th>matchType_normal-duo</th> 
   <th>matchType_normal-duo-fpp</th> 
   <th>matchType_normal-solo</th> 
   <th>matchType_normal-solo-fpp</th> 
   <th>matchType_normal-squad</th> 
   <th>matchType_normal-squad-fpp</th> 
   <th>matchType_solo</th> 
   <th>matchType_solo-fpp</th> 
   <th>matchType_squad</th> 
   <th>matchType_squad-fpp</th> 
  </tr> 
 </thead> 
 <tbody> 
  <tr> 
   <th>0</th> 
   <td>0</td> 
   <td>0</td> 
   <td>0</td> 
   <td>0</td> 
   <td>0</td> 
   <td>0</td> 
   <td>0</td> 
   <td>0</td> 
   <td>0</td> 
   <td>0</td> 
   <td>0</td> 
   <td>0</td> 
   <td>0</td> 
   <td>0</td> 
   <td>0</td> 
   <td>1</td> 
  </tr> 
  <tr> 
   <th>1</th> 
   <td>0</td> 
   <td>0</td> 
   <td>0</td> 
   <td>0</td> 
   <td>0</td> 
   <td>0</td> 
   <td>0</td> 
   <td>0</td> 
   <td>0</td> 
   <td>0</td> 
   <td>0</td> 
   <td>0</td> 
   <td>0</td> 
   <td>0</td> 
   <td>0</td> 
   <td>1</td> 
  </tr> 
  <tr> 
   <th>2</th> 
   <td>0</td> 
   <td>0</td> 
   <td>1</td> 
   <td>0</td> 
   <td>0</td> 
   <td>0</td> 
   <td>0</td> 
   <td>0</td> 
   <td>0</td> 
   <td>0</td> 
   <td>0</td> 
   <td>0</td> 
   <td>0</td> 
   <td>0</td> 
   <td>0</td> 
   <td>0</td> 
  </tr> 
  <tr> 
   <th>3</th> 
   <td>0</td> 
   <td>0</td> 
   <td>0</td> 
   <td>0</td> 
   <td>0</td> 
   <td>0</td> 
   <td>0</td> 
   <td>0</td> 
   <td>0</td> 
   <td>0</td> 
   <td>0</td> 
   <td>0</td> 
   <td>0</td> 
   <td>0</td> 
   <td>0</td> 
   <td>1</td> 
  </tr> 
  <tr> 
   <th>4</th> 
   <td>0</td> 
   <td>0</td> 
   <td>0</td> 
   <td>0</td> 
   <td>0</td> 
   <td>0</td> 
   <td>0</td> 
   <td>0</td> 
   <td>0</td> 
   <td>0</td> 
   <td>0</td> 
   <td>0</td> 
   <td>0</td> 
   <td>1</td> 
   <td>0</td> 
   <td>0</td> 
  </tr> 
 </tbody> 
</table>

对groupId,matchId等数据进行处理

In \[110\]:

train\["groupId"\].head()

Out\[110\]:

0    4d4b580de459be
    1    684d5656442f9e
    2    6a4a42c3245a74
    3    a930a9c79cd721
    4    de04010b3458dd
    Name: groupId, dtype: object

In \[112\]:

\# train\["groupId"\].astype("category")

In \[113\]:

train\["groupId"\] = train\["groupId"\].astype("category")

In \[114\]:

train\["groupId\_cat"\] = train\["groupId"\].cat.codes

In \[116\]:

train\["groupId\_cat"\].head()

Out\[116\]:

0     613619
    1     827616
    2     843307
    3    1340122
    4    1757411
    Name: groupId_cat, dtype: int32

In \[117\]:

train\["matchId"\] = train\["matchId"\].astype("category")  
train\["matchId\_cat"\] = train\["matchId"\].cat.codes  
train\["matchId\_cat"\].head()

Out\[117\]:

0    30085
    1    32751
    2     3143
    3    45260
    4    20531
    Name: matchId_cat, dtype: int32

In \[118\]:

train.head()

Out\[118\]:

5 rows × 55 columns

In \[120\]:

train.drop(\["groupId", "matchId"\], axis=1, inplace=True)

### 数据截取 ###

取部分数据进行使用（100000）

In \[122\]:

df\_sample = train.sample(100000)

In \[123\]:

df\_sample.shape

Out\[123\]:

(100000, 53)

### 确定特征值和目标值 ###

In \[124\]:

df = df\_sample.drop(\["winPlacePerc", "Id"\], axis=1)

y = df\_sample\["winPlacePerc"\]

In \[125\]:

df.shape

Out\[125\]:

(100000, 51)

In \[126\]:

y.shape

Out\[126\]:

(100000,)

### 分割训练集和测试集 ###

In \[127\]:

from sklearn.model\_selection import train\_test\_split

In \[128\]:

X\_train, X\_valid, y\_train, y\_valid = train\_test\_split(df, y, test\_size=0.2)

In \[129\]:

X\_train.shape

Out\[129\]:

(80000, 51)

In \[131\]:

y\_train.shape

Out\[131\]:

(80000,)

## 机器学习（模型训练）和评估 ##

In \[132\]:

from sklearn.ensemble import RandomForestRegressor  
from sklearn.metrics import mean\_absolute\_error

### 使用随机森林对模型进行训练 ###

初步使用随机森林进行模型训练

In \[134\]:

m1 = RandomForestRegressor(n\_estimators=40,   
                           min\_samples\_leaf=3,   
                           max\_features='sqrt',  
                           n\_jobs=-1)

m1.fit(X\_train, y\_train)

Out\[134\]:

RandomForestRegressor(bootstrap=True, ccp_alpha=0.0, criterion='mse',
                          max_depth=None, max_features='sqrt', max_leaf_nodes=None,
                          max_samples=None, min_impurity_decrease=0.0,
                          min_impurity_split=None, min_samples_leaf=3,
                          min_samples_split=2, min_weight_fraction_leaf=0.0,
                          n_estimators=40, n_jobs=-1, oob_score=False,
                          random_state=None, verbose=0, warm_start=False)

In \[137\]:

y\_pre = m1.predict(X\_valid)  
m1.score(X\_valid, y\_valid)

Out\[137\]:

0.907159951456783

In \[138\]:

mean\_absolute\_error(y\_valid, y\_pre)

Out\[138\]:

0.06647584387091089

再次使用随机森林，进行模型训练

In \[139\]:

m1.feature\_importances\_

Out\[139\]:

array([1.87658119e-03, 8.63746064e-02, 2.57685962e-02, 2.27532062e-03,
           8.80290888e-04, 2.81013428e-02, 2.35573150e-01, 2.07083462e-03,
           1.22714874e-02, 1.09702183e-02, 2.53353780e-02, 1.03767737e-02,
           6.77020063e-03, 7.45172312e-03, 4.28638708e-03, 3.28563034e-03,
           2.12925123e-02, 1.99967979e-05, 3.98400033e-03, 1.36372746e-04,
           1.32592980e-04, 1.71940999e-01, 4.20790087e-02, 2.52040557e-03,
           6.33213818e-03, 7.30402941e-03, 1.11477263e-02, 7.52171932e-03,
           1.19465432e-02, 5.30382552e-02, 1.81348675e-01, 0.00000000e+00,
           2.22747761e-03, 3.44612223e-05, 0.00000000e+00, 2.02507233e-04,
           5.97533128e-04, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
           3.54286394e-05, 0.00000000e+00, 4.94182811e-07, 0.00000000e+00,
           3.11300574e-04, 1.93840925e-04, 9.02659739e-04, 1.04955041e-03,
           9.87217660e-04, 4.53628891e-03, 4.50774311e-03])

In \[140\]:

imp\_df = pd.DataFrame(\{"cols":df.columns, "imp":m1.feature\_importances\_\})

In \[141\]:

imp\_df.head()

Out\[141\]:

<table> 
 <thead> 
  <tr> 
   <th></th> 
   <th>cols</th> 
   <th>imp</th> 
  </tr> 
 </thead> 
 <tbody> 
  <tr> 
   <th>0</th> 
   <td>assists</td> 
   <td>0.001877</td> 
  </tr> 
  <tr> 
   <th>1</th> 
   <td>boosts</td> 
   <td>0.086375</td> 
  </tr> 
  <tr> 
   <th>2</th> 
   <td>damageDealt</td> 
   <td>0.025769</td> 
  </tr> 
  <tr> 
   <th>3</th> 
   <td>DBNOs</td> 
   <td>0.002275</td> 
  </tr> 
  <tr> 
   <th>4</th> 
   <td>headshotKills</td> 
   <td>0.000880</td> 
  </tr> 
 </tbody> 
</table>

In \[142\]:

imp\_df = imp\_df.sort\_values("imp", ascending=False)

In \[143\]:

imp\_df.head()

Out\[143\]:

<table> 
 <thead> 
  <tr> 
   <th></th> 
   <th>cols</th> 
   <th>imp</th> 
  </tr> 
 </thead> 
 <tbody> 
  <tr> 
   <th>6</th> 
   <td>killPlace</td> 
   <td>0.235573</td> 
  </tr> 
  <tr> 
   <th>30</th> 
   <td>totalDistance</td> 
   <td>0.181349</td> 
  </tr> 
  <tr> 
   <th>21</th> 
   <td>walkDistance</td> 
   <td>0.171941</td> 
  </tr> 
  <tr> 
   <th>1</th> 
   <td>boosts</td> 
   <td>0.086375</td> 
  </tr> 
  <tr> 
   <th>29</th> 
   <td>healsandboosts</td> 
   <td>0.053038</td> 
  </tr> 
 </tbody> 
</table>

In \[145\]:

imp\_df\[:20\].plot("cols", "imp", figsize=(20, 8), kind="barh")

Out\[145\]:

<matplotlib.axes._subplots.AxesSubplot at 0x1a1500a080>

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3ppbWlhbzU1MjE0NzU3Mg_size_16_color_FFFFFF_t_70 4][]

In \[146\]:

to\_keep = imp\_df\[imp\_df.imp > 0.005\].cols

In \[148\]:

to\_keep.shape

Out\[148\]:

(20,)

In \[150\]:

df\_keep = df\[to\_keep\]

In \[151\]:

X\_train, X\_valid, y\_train, y\_valid = train\_test\_split(df\_keep, y, test\_size=0.2)

In \[153\]:

X\_train.shape

Out\[153\]:

(80000, 20)

In \[154\]:

m2 = RandomForestRegressor(n\_estimators=40,   
                           min\_samples\_leaf=3,   
                           max\_features='sqrt',  
                           n\_jobs=-1)

m2.fit(X\_train, y\_train)

Out\[154\]:

In \[158\]:

y\_pre = m2.predict(X\_valid)  
m2.score(X\_valid, y\_valid)

Out\[158\]:

0.9125654968172906

In \[159\]:

mean\_absolute\_error(y\_valid, y\_pre)

Out\[159\]:

0.06408683094647326

### 使用lightGBM对模型进行训练 ###

In \[160\]:

X\_train, X\_valid, y\_train, y\_valid = train\_test\_split(df, y, test\_size=0.2)

In \[161\]:

X\_train.shape

Out\[161\]:

(80000, 51)

模型初次尝试

In \[162\]:

import lightgbm as lgb

In \[163\]:

gbm = lgb.LGBMRegressor(objective="regression", num\_leaves=31, learning\_rate=0.05, n\_estimators=20)

gbm.fit(X\_train, y\_train, eval\_set=\[(X\_valid, y\_valid)\], eval\_metric="l1", early\_stopping\_rounds=5)

[1]	valid_0's l1: 0.255801	valid_0's l2: 0.0863836
    Training until validation scores don't improve for 5 rounds
    [2]	valid_0's l1: 0.244604	valid_0's l2: 0.0792314
    [3]	valid_0's l1: 0.234038	valid_0's l2: 0.072761
    [4]	valid_0's l1: 0.224123	valid_0's l2: 0.0669453
    [5]	valid_0's l1: 0.214716	valid_0's l2: 0.0616647
    [6]	valid_0's l1: 0.205802	valid_0's l2: 0.0568409
    [7]	valid_0's l1: 0.197424	valid_0's l2: 0.0525102
    [8]	valid_0's l1: 0.189497	valid_0's l2: 0.0485595
    [9]	valid_0's l1: 0.18208	valid_0's l2: 0.0450087
    [10]	valid_0's l1: 0.175038	valid_0's l2: 0.0417809
    [11]	valid_0's l1: 0.168411	valid_0's l2: 0.0388494
    [12]	valid_0's l1: 0.162014	valid_0's l2: 0.0361473
    [13]	valid_0's l1: 0.156139	valid_0's l2: 0.0337388
    [14]	valid_0's l1: 0.150548	valid_0's l2: 0.031546
    [15]	valid_0's l1: 0.145259	valid_0's l2: 0.0295381
    [16]	valid_0's l1: 0.140261	valid_0's l2: 0.0277049
    [17]	valid_0's l1: 0.135596	valid_0's l2: 0.0260668
    [18]	valid_0's l1: 0.131269	valid_0's l2: 0.0245903
    [19]	valid_0's l1: 0.127159	valid_0's l2: 0.0232428
    [20]	valid_0's l1: 0.123315	valid_0's l2: 0.0220185
    Did not meet early stopping. Best iteration is:
    [20]	valid_0's l1: 0.123315	valid_0's l2: 0.0220185

Out\[163\]:

LGBMRegressor(boosting_type='gbdt', class_weight=None, colsample_bytree=1.0,
                  importance_type='split', learning_rate=0.05, max_depth=-1,
                  min_child_samples=20, min_child_weight=0.001, min_split_gain=0.0,
                  n_estimators=20, n_jobs=-1, num_leaves=31, objective='regression',
                  random_state=None, reg_alpha=0.0, reg_lambda=0.0, silent=True,
                  subsample=1.0, subsample_for_bin=200000, subsample_freq=0)

In \[164\]:

y\_pre = gbm.predict(X\_valid, num\_iteration=gbm.best\_iteration\_)

In \[165\]:

mean\_absolute\_error(y\_valid, y\_pre)

Out\[165\]:

0.12331524150224461

模型二次调优

In \[166\]:

from sklearn.model\_selection import GridSearchCV

In \[167\]:

estimator = lgb.LGBMRegressor(num\_leaves=31)  
param\_grid = \{  
    "learning\_rate":\[0.01, 0.1, 1\],  
    "n\_estimators":\[40, 60, 80, 100, 200, 300\]  
\}

gbm = GridSearchCV(estimator, param\_grid, cv=5, n\_jobs=-1)

gbm.fit(X\_train, y\_train)

Out\[167\]:

GridSearchCV(cv=5, error_score=nan,
                 estimator=LGBMRegressor(boosting_type='gbdt', class_weight=None,
                                         colsample_bytree=1.0,
                                         importance_type='split', learning_rate=0.1,
                                         max_depth=-1, min_child_samples=20,
                                         min_child_weight=0.001, min_split_gain=0.0,
                                         n_estimators=100, n_jobs=-1, num_leaves=31,
                                         objective=None, random_state=None,
                                         reg_alpha=0.0, reg_lambda=0.0, silent=True,
                                         subsample=1.0, subsample_for_bin=200000,
                                         subsample_freq=0),
                 iid='deprecated', n_jobs=-1,
                 param_grid={'learning_rate': [0.01, 0.1, 1],
                             'n_estimators': [40, 60, 80, 100, 200, 300]},
                 pre_dispatch='2*n_jobs', refit=True, return_train_score=False,
                 scoring=None, verbose=0)

In \[168\]:

y\_pre = gbm.predict(X\_valid)  
mean\_absolute\_error(y\_valid, y\_pre)

Out\[168\]:

0.05685004010605751

In \[169\]:

gbm.best\_params\_

Out\[169\]:

{'learning_rate': 0.1, 'n_estimators': 300}

模型三次调优

In \[173\]:

\# n\_estimators

scores = \[\]  
n\_estimators = \[100, 300, 500, 800\]

for nes in  n\_estimators:  
    lgbm = lgb.LGBMRegressor(boosting\_type='gbdt',   
                      num\_leaves=31,  
                      max\_depth=5,  
                      learning\_rate=0.1,  
                      n\_estimators=nes,  
                      min\_child\_samples=20,  
                      n\_jobs=-1)  
      
    lgbm.fit(X\_train, y\_train, eval\_set=\[(X\_valid, y\_valid)\], eval\_metric="l1", early\_stopping\_rounds=5)  
      
    y\_pre = lgbm.predict(X\_valid)  
      
    mae = mean\_absolute\_error(y\_valid, y\_pre)  
      
    scores.append(mae)  
    print("本次结果输出的mae值是:\\n", mae)

[1]	valid_0's l1: 0.244506	valid_0's l2: 0.0790859
    Training until validation scores don't improve for 5 rounds
    [2]	valid_0's l1: 0.223857	valid_0's l2: 0.0667341
    [3]	valid_0's l1: 0.205626	valid_0's l2: 0.0566941
    [4]	valid_0's l1: 0.189298	valid_0's l2: 0.0484539
    [5]	valid_0's l1: 0.175104	valid_0's l2: 0.0417921
    [6]	valid_0's l1: 0.16227	valid_0's l2: 0.036285
    [7]	valid_0's l1: 0.150898	valid_0's l2: 0.0317221
    [8]	valid_0's l1: 0.140883	valid_0's l2: 0.0280196
    [9]	valid_0's l1: 0.131939	valid_0's l2: 0.0249613
    [10]	valid_0's l1: 0.124375	valid_0's l2: 0.0224944
    [11]	valid_0's l1: 0.117467	valid_0's l2: 0.0204263
    [12]	valid_0's l1: 0.110957	valid_0's l2: 0.0185453
    [13]	valid_0's l1: 0.105077	valid_0's l2: 0.016929
    [14]	valid_0's l1: 0.100272	valid_0's l2: 0.0157152
    [15]	valid_0's l1: 0.0960833	valid_0's l2: 0.0147431
    [16]	valid_0's l1: 0.0920567	valid_0's l2: 0.0137979
    [17]	valid_0's l1: 0.088268	valid_0's l2: 0.0129121
    [18]	valid_0's l1: 0.0854499	valid_0's l2: 0.0123437
    [19]	valid_0's l1: 0.0827065	valid_0's l2: 0.0117755
    [20]	valid_0's l1: 0.0800967	valid_0's l2: 0.0112162
    [21]	valid_0's l1: 0.0781237	valid_0's l2: 0.0108399
    [22]	valid_0's l1: 0.0763776	valid_0's l2: 0.0105204
    [23]	valid_0's l1: 0.074906	valid_0's l2: 0.010265
    [24]	valid_0's l1: 0.0732985	valid_0's l2: 0.00994854
    [25]	valid_0's l1: 0.0720481	valid_0's l2: 0.00970346
    [26]	valid_0's l1: 0.0710837	valid_0's l2: 0.00953733
    [27]	valid_0's l1: 0.0703066	valid_0's l2: 0.00939487
    [28]	valid_0's l1: 0.0694075	valid_0's l2: 0.009231
    [29]	valid_0's l1: 0.0685866	valid_0's l2: 0.00907982
    [30]	valid_0's l1: 0.06779	valid_0's l2: 0.0089391
    [31]	valid_0's l1: 0.067074	valid_0's l2: 0.00880224
    [32]	valid_0's l1: 0.0665149	valid_0's l2: 0.00871073
    [33]	valid_0's l1: 0.0659205	valid_0's l2: 0.00858992
    [34]	valid_0's l1: 0.0652426	valid_0's l2: 0.00843778
    [35]	valid_0's l1: 0.0648768	valid_0's l2: 0.00836372
    [36]	valid_0's l1: 0.0645306	valid_0's l2: 0.00830614
    [37]	valid_0's l1: 0.0640175	valid_0's l2: 0.00818645
    [38]	valid_0's l1: 0.0637947	valid_0's l2: 0.00814636
    [39]	valid_0's l1: 0.0635605	valid_0's l2: 0.00810447
    [40]	valid_0's l1: 0.0633449	valid_0's l2: 0.00806796
    [41]	valid_0's l1: 0.0631446	valid_0's l2: 0.00802436
    [42]	valid_0's l1: 0.0627575	valid_0's l2: 0.00792715
    [43]	valid_0's l1: 0.062611	valid_0's l2: 0.00789809
    [44]	valid_0's l1: 0.0623997	valid_0's l2: 0.00785422
    [45]	valid_0's l1: 0.0622728	valid_0's l2: 0.00783369
    [46]	valid_0's l1: 0.0621336	valid_0's l2: 0.00781042
    [47]	valid_0's l1: 0.0619906	valid_0's l2: 0.00777463
    [48]	valid_0's l1: 0.0618556	valid_0's l2: 0.00774471
    [49]	valid_0's l1: 0.0617449	valid_0's l2: 0.00772713
    [50]	valid_0's l1: 0.0616454	valid_0's l2: 0.00770345
    [51]	valid_0's l1: 0.0615492	valid_0's l2: 0.00768433
    [52]	valid_0's l1: 0.0614452	valid_0's l2: 0.00766572
    [53]	valid_0's l1: 0.0613725	valid_0's l2: 0.00764408
    [54]	valid_0's l1: 0.0612796	valid_0's l2: 0.00762834
    [55]	valid_0's l1: 0.0611706	valid_0's l2: 0.00760066
    [56]	valid_0's l1: 0.0611121	valid_0's l2: 0.00759012
    [57]	valid_0's l1: 0.0610472	valid_0's l2: 0.00757864
    [58]	valid_0's l1: 0.0609801	valid_0's l2: 0.00756664
    [59]	valid_0's l1: 0.0608993	valid_0's l2: 0.00755207
    [60]	valid_0's l1: 0.0608206	valid_0's l2: 0.00753343
    [61]	valid_0's l1: 0.0607751	valid_0's l2: 0.00752435
    [62]	valid_0's l1: 0.0607163	valid_0's l2: 0.00751128
    [63]	valid_0's l1: 0.0605746	valid_0's l2: 0.00747752
    [64]	valid_0's l1: 0.0604651	valid_0's l2: 0.0074507
    [65]	valid_0's l1: 0.0604263	valid_0's l2: 0.00744162
    [66]	valid_0's l1: 0.0603599	valid_0's l2: 0.00742889
    [67]	valid_0's l1: 0.0602701	valid_0's l2: 0.00741169
    [68]	valid_0's l1: 0.0602334	valid_0's l2: 0.00739977
    [69]	valid_0's l1: 0.060112	valid_0's l2: 0.007374
    [70]	valid_0's l1: 0.0600257	valid_0's l2: 0.00735234
    [71]	valid_0's l1: 0.059928	valid_0's l2: 0.00732775
    [72]	valid_0's l1: 0.0598568	valid_0's l2: 0.00731406
    [73]	valid_0's l1: 0.0598019	valid_0's l2: 0.007307
    [74]	valid_0's l1: 0.0597175	valid_0's l2: 0.00728248
    [75]	valid_0's l1: 0.0596246	valid_0's l2: 0.00726385
    [76]	valid_0's l1: 0.0596029	valid_0's l2: 0.00725546
    [77]	valid_0's l1: 0.0595231	valid_0's l2: 0.00723235
    [78]	valid_0's l1: 0.0595014	valid_0's l2: 0.00722831
    [79]	valid_0's l1: 0.0594712	valid_0's l2: 0.00722155
    [80]	valid_0's l1: 0.0594512	valid_0's l2: 0.00721585
    [81]	valid_0's l1: 0.0594197	valid_0's l2: 0.00720856
    [82]	valid_0's l1: 0.0593932	valid_0's l2: 0.00720475
    [83]	valid_0's l1: 0.0593157	valid_0's l2: 0.00718559
    [84]	valid_0's l1: 0.0592926	valid_0's l2: 0.00718174
    [85]	valid_0's l1: 0.0592627	valid_0's l2: 0.00717558
    [86]	valid_0's l1: 0.0592466	valid_0's l2: 0.00716984
    [87]	valid_0's l1: 0.0592043	valid_0's l2: 0.00716193
    [88]	valid_0's l1: 0.0591298	valid_0's l2: 0.00714342
    [89]	valid_0's l1: 0.059097	valid_0's l2: 0.00713607
    [90]	valid_0's l1: 0.0589982	valid_0's l2: 0.00711745
    [91]	valid_0's l1: 0.0589505	valid_0's l2: 0.00710413
    [92]	valid_0's l1: 0.0588884	valid_0's l2: 0.00708708
    [93]	valid_0's l1: 0.0588084	valid_0's l2: 0.00707062
    [94]	valid_0's l1: 0.0587967	valid_0's l2: 0.00707269
    [95]	valid_0's l1: 0.0587689	valid_0's l2: 0.00706628
    [96]	valid_0's l1: 0.058749	valid_0's l2: 0.00705746
    [97]	valid_0's l1: 0.0587121	valid_0's l2: 0.00704977
    [98]	valid_0's l1: 0.0586712	valid_0's l2: 0.00703874
    [99]	valid_0's l1: 0.05861	valid_0's l2: 0.0070237
    [100]	valid_0's l1: 0.0585889	valid_0's l2: 0.00701449
    Did not meet early stopping. Best iteration is:
    [100]	valid_0's l1: 0.0585889	valid_0's l2: 0.00701449
    本次结果输出的mae值是:
     0.05858885784299778
    [1]	valid_0's l1: 0.244506	valid_0's l2: 0.0790859
    Training until validation scores don't improve for 5 rounds
    [2]	valid_0's l1: 0.223857	valid_0's l2: 0.0667341
    [3]	valid_0's l1: 0.205626	valid_0's l2: 0.0566941
    [4]	valid_0's l1: 0.189298	valid_0's l2: 0.0484539
    [5]	valid_0's l1: 0.175104	valid_0's l2: 0.0417921
    [6]	valid_0's l1: 0.16227	valid_0's l2: 0.036285
    [7]	valid_0's l1: 0.150898	valid_0's l2: 0.0317221
    [8]	valid_0's l1: 0.140883	valid_0's l2: 0.0280196
    [9]	valid_0's l1: 0.131939	valid_0's l2: 0.0249613
    [10]	valid_0's l1: 0.124375	valid_0's l2: 0.0224944
    [11]	valid_0's l1: 0.117467	valid_0's l2: 0.0204263
    [12]	valid_0's l1: 0.110957	valid_0's l2: 0.0185453
    [13]	valid_0's l1: 0.105077	valid_0's l2: 0.016929
    [14]	valid_0's l1: 0.100272	valid_0's l2: 0.0157152
    [15]	valid_0's l1: 0.0960833	valid_0's l2: 0.0147431
    [16]	valid_0's l1: 0.0920567	valid_0's l2: 0.0137979
    [17]	valid_0's l1: 0.088268	valid_0's l2: 0.0129121
    [18]	valid_0's l1: 0.0854499	valid_0's l2: 0.0123437
    [19]	valid_0's l1: 0.0827065	valid_0's l2: 0.0117755
    [20]	valid_0's l1: 0.0800967	valid_0's l2: 0.0112162
    [21]	valid_0's l1: 0.0781237	valid_0's l2: 0.0108399
    [22]	valid_0's l1: 0.0763776	valid_0's l2: 0.0105204
    [23]	valid_0's l1: 0.074906	valid_0's l2: 0.010265
    [24]	valid_0's l1: 0.0732985	valid_0's l2: 0.00994854
    [25]	valid_0's l1: 0.0720481	valid_0's l2: 0.00970346
    [26]	valid_0's l1: 0.0710837	valid_0's l2: 0.00953733
    [27]	valid_0's l1: 0.0703066	valid_0's l2: 0.00939487
    [28]	valid_0's l1: 0.0694075	valid_0's l2: 0.009231
    [29]	valid_0's l1: 0.0685866	valid_0's l2: 0.00907982
    [30]	valid_0's l1: 0.06779	valid_0's l2: 0.0089391
    [31]	valid_0's l1: 0.067074	valid_0's l2: 0.00880224
    [32]	valid_0's l1: 0.0665149	valid_0's l2: 0.00871073
    [33]	valid_0's l1: 0.0659205	valid_0's l2: 0.00858992
    [34]	valid_0's l1: 0.0652426	valid_0's l2: 0.00843778
    [35]	valid_0's l1: 0.0648768	valid_0's l2: 0.00836372
    [36]	valid_0's l1: 0.0645306	valid_0's l2: 0.00830614
    [37]	valid_0's l1: 0.0640175	valid_0's l2: 0.00818645
    [38]	valid_0's l1: 0.0637947	valid_0's l2: 0.00814636
    [39]	valid_0's l1: 0.0635605	valid_0's l2: 0.00810447
    [40]	valid_0's l1: 0.0633449	valid_0's l2: 0.00806796
    [41]	valid_0's l1: 0.0631446	valid_0's l2: 0.00802436
    [42]	valid_0's l1: 0.0627575	valid_0's l2: 0.00792715
    [43]	valid_0's l1: 0.062611	valid_0's l2: 0.00789809
    [44]	valid_0's l1: 0.0623997	valid_0's l2: 0.00785422
    [45]	valid_0's l1: 0.0622728	valid_0's l2: 0.00783369
    [46]	valid_0's l1: 0.0621336	valid_0's l2: 0.00781042
    [47]	valid_0's l1: 0.0619906	valid_0's l2: 0.00777463
    [48]	valid_0's l1: 0.0618556	valid_0's l2: 0.00774471
    [49]	valid_0's l1: 0.0617449	valid_0's l2: 0.00772713
    [50]	valid_0's l1: 0.0616454	valid_0's l2: 0.00770345
    [51]	valid_0's l1: 0.0615492	valid_0's l2: 0.00768433
    [52]	valid_0's l1: 0.0614452	valid_0's l2: 0.00766572
    [53]	valid_0's l1: 0.0613725	valid_0's l2: 0.00764408
    [54]	valid_0's l1: 0.0612796	valid_0's l2: 0.00762834
    [55]	valid_0's l1: 0.0611706	valid_0's l2: 0.00760066
    [56]	valid_0's l1: 0.0611121	valid_0's l2: 0.00759012
    [57]	valid_0's l1: 0.0610472	valid_0's l2: 0.00757864
    [58]	valid_0's l1: 0.0609801	valid_0's l2: 0.00756664
    [59]	valid_0's l1: 0.0608993	valid_0's l2: 0.00755207
    [60]	valid_0's l1: 0.0608206	valid_0's l2: 0.00753343
    [61]	valid_0's l1: 0.0607751	valid_0's l2: 0.00752435
    [62]	valid_0's l1: 0.0607163	valid_0's l2: 0.00751128

[63]	valid_0's l1: 0.0605746	valid_0's l2: 0.00747752
    [64]	valid_0's l1: 0.0604651	valid_0's l2: 0.0074507
    [65]	valid_0's l1: 0.0604263	valid_0's l2: 0.00744162
    [66]	valid_0's l1: 0.0603599	valid_0's l2: 0.00742889
    [67]	valid_0's l1: 0.0602701	valid_0's l2: 0.00741169
    [68]	valid_0's l1: 0.0602334	valid_0's l2: 0.00739977
    [69]	valid_0's l1: 0.060112	valid_0's l2: 0.007374
    [70]	valid_0's l1: 0.0600257	valid_0's l2: 0.00735234
    [71]	valid_0's l1: 0.059928	valid_0's l2: 0.00732775
    [72]	valid_0's l1: 0.0598568	valid_0's l2: 0.00731406
    [73]	valid_0's l1: 0.0598019	valid_0's l2: 0.007307
    [74]	valid_0's l1: 0.0597175	valid_0's l2: 0.00728248
    [75]	valid_0's l1: 0.0596246	valid_0's l2: 0.00726385
    [76]	valid_0's l1: 0.0596029	valid_0's l2: 0.00725546
    [77]	valid_0's l1: 0.0595231	valid_0's l2: 0.00723235
    [78]	valid_0's l1: 0.0595014	valid_0's l2: 0.00722831
    [79]	valid_0's l1: 0.0594712	valid_0's l2: 0.00722155
    [80]	valid_0's l1: 0.0594512	valid_0's l2: 0.00721585
    [81]	valid_0's l1: 0.0594197	valid_0's l2: 0.00720856
    [82]	valid_0's l1: 0.0593932	valid_0's l2: 0.00720475
    [83]	valid_0's l1: 0.0593157	valid_0's l2: 0.00718559
    [84]	valid_0's l1: 0.0592926	valid_0's l2: 0.00718174
    [85]	valid_0's l1: 0.0592627	valid_0's l2: 0.00717558
    [86]	valid_0's l1: 0.0592466	valid_0's l2: 0.00716984
    [87]	valid_0's l1: 0.0592043	valid_0's l2: 0.00716193
    [88]	valid_0's l1: 0.0591298	valid_0's l2: 0.00714342
    [89]	valid_0's l1: 0.059097	valid_0's l2: 0.00713607
    [90]	valid_0's l1: 0.0589982	valid_0's l2: 0.00711745
    [91]	valid_0's l1: 0.0589505	valid_0's l2: 0.00710413
    [92]	valid_0's l1: 0.0588884	valid_0's l2: 0.00708708
    [93]	valid_0's l1: 0.0588084	valid_0's l2: 0.00707062
    [94]	valid_0's l1: 0.0587967	valid_0's l2: 0.00707269
    [95]	valid_0's l1: 0.0587689	valid_0's l2: 0.00706628
    [96]	valid_0's l1: 0.058749	valid_0's l2: 0.00705746
    [97]	valid_0's l1: 0.0587121	valid_0's l2: 0.00704977
    [98]	valid_0's l1: 0.0586712	valid_0's l2: 0.00703874
    [99]	valid_0's l1: 0.05861	valid_0's l2: 0.0070237
    [100]	valid_0's l1: 0.0585889	valid_0's l2: 0.00701449
    [101]	valid_0's l1: 0.0585771	valid_0's l2: 0.00701003
    [102]	valid_0's l1: 0.0585635	valid_0's l2: 0.0070058
    [103]	valid_0's l1: 0.0585479	valid_0's l2: 0.00700123
    [104]	valid_0's l1: 0.0585384	valid_0's l2: 0.00699934
    [105]	valid_0's l1: 0.0585209	valid_0's l2: 0.00699689
    [106]	valid_0's l1: 0.0585063	valid_0's l2: 0.00699365
    [107]	valid_0's l1: 0.0584905	valid_0's l2: 0.00698833
    [108]	valid_0's l1: 0.0584738	valid_0's l2: 0.00698361
    [109]	valid_0's l1: 0.0584563	valid_0's l2: 0.00697852
    [110]	valid_0's l1: 0.0584379	valid_0's l2: 0.00697721
    [111]	valid_0's l1: 0.0584333	valid_0's l2: 0.00697385
    [112]	valid_0's l1: 0.0584316	valid_0's l2: 0.00697371
    [113]	valid_0's l1: 0.0584076	valid_0's l2: 0.00696515
    [114]	valid_0's l1: 0.0583903	valid_0's l2: 0.00696145
    [115]	valid_0's l1: 0.0583817	valid_0's l2: 0.00695705
    [116]	valid_0's l1: 0.0583585	valid_0's l2: 0.00695171
    [117]	valid_0's l1: 0.0583329	valid_0's l2: 0.00694439
    [118]	valid_0's l1: 0.0582953	valid_0's l2: 0.00693451
    [119]	valid_0's l1: 0.0582302	valid_0's l2: 0.00691471
    [120]	valid_0's l1: 0.0582053	valid_0's l2: 0.00690717
    [121]	valid_0's l1: 0.058165	valid_0's l2: 0.00690191
    [122]	valid_0's l1: 0.0581507	valid_0's l2: 0.00689919
    [123]	valid_0's l1: 0.0581202	valid_0's l2: 0.00689344
    [124]	valid_0's l1: 0.0580842	valid_0's l2: 0.00688742
    [125]	valid_0's l1: 0.0580695	valid_0's l2: 0.00688381
    [126]	valid_0's l1: 0.0580556	valid_0's l2: 0.0068812
    [127]	valid_0's l1: 0.0580404	valid_0's l2: 0.00687439
    [128]	valid_0's l1: 0.0580276	valid_0's l2: 0.00687128
    [129]	valid_0's l1: 0.0579982	valid_0's l2: 0.00686654
    [130]	valid_0's l1: 0.0579886	valid_0's l2: 0.00686163
    [131]	valid_0's l1: 0.0579795	valid_0's l2: 0.00686265
    [132]	valid_0's l1: 0.0579773	valid_0's l2: 0.00686113
    [133]	valid_0's l1: 0.0579718	valid_0's l2: 0.00686023
    [134]	valid_0's l1: 0.0579558	valid_0's l2: 0.00685794
    [135]	valid_0's l1: 0.0579452	valid_0's l2: 0.00685478
    [136]	valid_0's l1: 0.0579375	valid_0's l2: 0.00685219
    [137]	valid_0's l1: 0.0579311	valid_0's l2: 0.00685004
    [138]	valid_0's l1: 0.0578929	valid_0's l2: 0.0068395
    [139]	valid_0's l1: 0.0578863	valid_0's l2: 0.00683737
    [140]	valid_0's l1: 0.0578618	valid_0's l2: 0.00682973
    [141]	valid_0's l1: 0.057814	valid_0's l2: 0.00682006
    [142]	valid_0's l1: 0.0577865	valid_0's l2: 0.00681178
    [143]	valid_0's l1: 0.0577836	valid_0's l2: 0.00681092
    [144]	valid_0's l1: 0.0577732	valid_0's l2: 0.00680807
    [145]	valid_0's l1: 0.05775	valid_0's l2: 0.00680476
    [146]	valid_0's l1: 0.0577295	valid_0's l2: 0.00679983
    [147]	valid_0's l1: 0.0577274	valid_0's l2: 0.00679906
    [148]	valid_0's l1: 0.0577282	valid_0's l2: 0.00680071
    [149]	valid_0's l1: 0.0577027	valid_0's l2: 0.00679154
    [150]	valid_0's l1: 0.0577023	valid_0's l2: 0.00679031
    [151]	valid_0's l1: 0.0576895	valid_0's l2: 0.0067869
    [152]	valid_0's l1: 0.0576863	valid_0's l2: 0.00678669
    [153]	valid_0's l1: 0.057681	valid_0's l2: 0.00678298
    [154]	valid_0's l1: 0.0576461	valid_0's l2: 0.00677587
    [155]	valid_0's l1: 0.0576281	valid_0's l2: 0.00676922
    [156]	valid_0's l1: 0.0576071	valid_0's l2: 0.00676582
    [157]	valid_0's l1: 0.0575831	valid_0's l2: 0.00676433
    [158]	valid_0's l1: 0.0575486	valid_0's l2: 0.00675594
    [159]	valid_0's l1: 0.0575371	valid_0's l2: 0.00675428
    [160]	valid_0's l1: 0.0575191	valid_0's l2: 0.00675119
    [161]	valid_0's l1: 0.0575124	valid_0's l2: 0.00675132
    [162]	valid_0's l1: 0.0575015	valid_0's l2: 0.00674851
    [163]	valid_0's l1: 0.0574785	valid_0's l2: 0.00674612
    [164]	valid_0's l1: 0.0574678	valid_0's l2: 0.00674357
    [165]	valid_0's l1: 0.0574633	valid_0's l2: 0.00674366
    [166]	valid_0's l1: 0.0574458	valid_0's l2: 0.00673905
    [167]	valid_0's l1: 0.0574112	valid_0's l2: 0.00672946
    [168]	valid_0's l1: 0.0573985	valid_0's l2: 0.00672684
    [169]	valid_0's l1: 0.0573967	valid_0's l2: 0.00672339
    [170]	valid_0's l1: 0.0573916	valid_0's l2: 0.00672143
    [171]	valid_0's l1: 0.0573821	valid_0's l2: 0.00671896
    [172]	valid_0's l1: 0.0573703	valid_0's l2: 0.00671675
    [173]	valid_0's l1: 0.0573778	valid_0's l2: 0.00671835
    [174]	valid_0's l1: 0.0573707	valid_0's l2: 0.00671666
    [175]	valid_0's l1: 0.0573572	valid_0's l2: 0.00671479
    [176]	valid_0's l1: 0.0573574	valid_0's l2: 0.0067135
    [177]	valid_0's l1: 0.0573542	valid_0's l2: 0.00671297
    [178]	valid_0's l1: 0.057355	valid_0's l2: 0.00671458
    [179]	valid_0's l1: 0.0573403	valid_0's l2: 0.00671148
    [180]	valid_0's l1: 0.0573278	valid_0's l2: 0.00670699
    [181]	valid_0's l1: 0.0573277	valid_0's l2: 0.00670656
    [182]	valid_0's l1: 0.0573192	valid_0's l2: 0.00670432
    [183]	valid_0's l1: 0.0573012	valid_0's l2: 0.00670044
    [184]	valid_0's l1: 0.0573018	valid_0's l2: 0.00669917
    [185]	valid_0's l1: 0.0572933	valid_0's l2: 0.00669791
    [186]	valid_0's l1: 0.0572937	valid_0's l2: 0.00669628
    [187]	valid_0's l1: 0.0572959	valid_0's l2: 0.006696
    [188]	valid_0's l1: 0.0572946	valid_0's l2: 0.00669529
    [189]	valid_0's l1: 0.057284	valid_0's l2: 0.00669419
    [190]	valid_0's l1: 0.0572756	valid_0's l2: 0.00669183
    [191]	valid_0's l1: 0.0572536	valid_0's l2: 0.00668716
    [192]	valid_0's l1: 0.0572549	valid_0's l2: 0.0066889
    [193]	valid_0's l1: 0.0572407	valid_0's l2: 0.00668565
    [194]	valid_0's l1: 0.0572137	valid_0's l2: 0.00668092
    [195]	valid_0's l1: 0.0572176	valid_0's l2: 0.00668088
    [196]	valid_0's l1: 0.0571987	valid_0's l2: 0.00667881
    [197]	valid_0's l1: 0.0571904	valid_0's l2: 0.00667394
    [198]	valid_0's l1: 0.0571825	valid_0's l2: 0.00667086
    [199]	valid_0's l1: 0.0571814	valid_0's l2: 0.0066715
    [200]	valid_0's l1: 0.0571754	valid_0's l2: 0.0066712
    [201]	valid_0's l1: 0.0571679	valid_0's l2: 0.00666813
    [202]	valid_0's l1: 0.0571538	valid_0's l2: 0.00666858
    [203]	valid_0's l1: 0.0571505	valid_0's l2: 0.00666737
    [204]	valid_0's l1: 0.0571496	valid_0's l2: 0.00666642
    [205]	valid_0's l1: 0.0571438	valid_0's l2: 0.00666369
    [206]	valid_0's l1: 0.0571522	valid_0's l2: 0.0066647
    [207]	valid_0's l1: 0.0571443	valid_0's l2: 0.00666353
    [208]	valid_0's l1: 0.057147	valid_0's l2: 0.00666341
    [209]	valid_0's l1: 0.0571413	valid_0's l2: 0.00666194
    [210]	valid_0's l1: 0.0571264	valid_0's l2: 0.00666014
    [211]	valid_0's l1: 0.0571122	valid_0's l2: 0.00665585
    [212]	valid_0's l1: 0.0571118	valid_0's l2: 0.00665518
    [213]	valid_0's l1: 0.0571002	valid_0's l2: 0.00665338
    [214]	valid_0's l1: 0.0570964	valid_0's l2: 0.00665241
    [215]	valid_0's l1: 0.05709	valid_0's l2: 0.00665055
    [216]	valid_0's l1: 0.0570856	valid_0's l2: 0.00665057
    [217]	valid_0's l1: 0.0570826	valid_0's l2: 0.00665004
    [218]	valid_0's l1: 0.0570747	valid_0's l2: 0.00664898
    [219]	valid_0's l1: 0.057068	valid_0's l2: 0.00664791
    [220]	valid_0's l1: 0.05707	valid_0's l2: 0.00664691
    [221]	valid_0's l1: 0.0570675	valid_0's l2: 0.00664523
    [222]	valid_0's l1: 0.0570426	valid_0's l2: 0.00664251
    [223]	valid_0's l1: 0.0570167	valid_0's l2: 0.00663508
    [224]	valid_0's l1: 0.0570101	valid_0's l2: 0.00663261
    [225]	valid_0's l1: 0.0570103	valid_0's l2: 0.00663457
    [226]	valid_0's l1: 0.0569972	valid_0's l2: 0.00663136
    [227]	valid_0's l1: 0.0569882	valid_0's l2: 0.00663009
    [228]	valid_0's l1: 0.0569898	valid_0's l2: 0.00662954
    [229]	valid_0's l1: 0.0569865	valid_0's l2: 0.00662705
    [230]	valid_0's l1: 0.0569757	valid_0's l2: 0.00662507
    [231]	valid_0's l1: 0.0569731	valid_0's l2: 0.00662383
    [232]	valid_0's l1: 0.0569676	valid_0's l2: 0.00662384
    [233]	valid_0's l1: 0.0569664	valid_0's l2: 0.00662459
    [234]	valid_0's l1: 0.0569611	valid_0's l2: 0.00662457
    [235]	valid_0's l1: 0.0569633	valid_0's l2: 0.00662413
    [236]	valid_0's l1: 0.0569615	valid_0's l2: 0.00662337
    [237]	valid_0's l1: 0.0569616	valid_0's l2: 0.00662352
    [238]	valid_0's l1: 0.0569541	valid_0's l2: 0.00662248
    [239]	valid_0's l1: 0.0569478	valid_0's l2: 0.0066219
    [240]	valid_0's l1: 0.0569487	valid_0's l2: 0.00662202
    [241]	valid_0's l1: 0.0569517	valid_0's l2: 0.00662222
    [242]	valid_0's l1: 0.0569443	valid_0's l2: 0.00662104
    [243]	valid_0's l1: 0.0569249	valid_0's l2: 0.00661558
    [244]	valid_0's l1: 0.0569245	valid_0's l2: 0.00661384
    [245]	valid_0's l1: 0.0569247	valid_0's l2: 0.00661414

[246]	valid_0's l1: 0.0569229	valid_0's l2: 0.00661358
    [247]	valid_0's l1: 0.0569195	valid_0's l2: 0.00661233
    [248]	valid_0's l1: 0.0569077	valid_0's l2: 0.00660972
    [249]	valid_0's l1: 0.0569058	valid_0's l2: 0.00660907
    [250]	valid_0's l1: 0.0569022	valid_0's l2: 0.00660765
    [251]	valid_0's l1: 0.056903	valid_0's l2: 0.00660776
    [252]	valid_0's l1: 0.0569011	valid_0's l2: 0.00660744
    [253]	valid_0's l1: 0.0569056	valid_0's l2: 0.00660932
    [254]	valid_0's l1: 0.0569014	valid_0's l2: 0.00660959
    [255]	valid_0's l1: 0.0568977	valid_0's l2: 0.00660912
    [256]	valid_0's l1: 0.0568991	valid_0's l2: 0.00660947
    [257]	valid_0's l1: 0.0568659	valid_0's l2: 0.00660135
    [258]	valid_0's l1: 0.0568594	valid_0's l2: 0.00660015
    [259]	valid_0's l1: 0.0568588	valid_0's l2: 0.00659937
    [260]	valid_0's l1: 0.0568588	valid_0's l2: 0.00659942
    [261]	valid_0's l1: 0.056858	valid_0's l2: 0.00659948
    [262]	valid_0's l1: 0.0568521	valid_0's l2: 0.00659886
    [263]	valid_0's l1: 0.0568516	valid_0's l2: 0.00659866
    [264]	valid_0's l1: 0.0568237	valid_0's l2: 0.00659403
    [265]	valid_0's l1: 0.0568185	valid_0's l2: 0.00659247
    [266]	valid_0's l1: 0.0568207	valid_0's l2: 0.00659355
    [267]	valid_0's l1: 0.0568153	valid_0's l2: 0.00659291
    [268]	valid_0's l1: 0.0568104	valid_0's l2: 0.00659252
    [269]	valid_0's l1: 0.0568035	valid_0's l2: 0.00659123
    [270]	valid_0's l1: 0.0567872	valid_0's l2: 0.00658465
    [271]	valid_0's l1: 0.0567721	valid_0's l2: 0.00658252
    [272]	valid_0's l1: 0.0567615	valid_0's l2: 0.00658002
    [273]	valid_0's l1: 0.0567417	valid_0's l2: 0.00657648
    [274]	valid_0's l1: 0.0567421	valid_0's l2: 0.00657556
    [275]	valid_0's l1: 0.0567452	valid_0's l2: 0.00657642
    [276]	valid_0's l1: 0.0567469	valid_0's l2: 0.00657607
    [277]	valid_0's l1: 0.056743	valid_0's l2: 0.00657593
    [278]	valid_0's l1: 0.0567391	valid_0's l2: 0.00657495
    [279]	valid_0's l1: 0.056739	valid_0's l2: 0.00657478
    [280]	valid_0's l1: 0.0567325	valid_0's l2: 0.00657337
    [281]	valid_0's l1: 0.0567323	valid_0's l2: 0.00657334
    [282]	valid_0's l1: 0.0567366	valid_0's l2: 0.00657365
    [283]	valid_0's l1: 0.0567364	valid_0's l2: 0.00657325
    [284]	valid_0's l1: 0.0567233	valid_0's l2: 0.00657079
    [285]	valid_0's l1: 0.0567246	valid_0's l2: 0.0065718
    [286]	valid_0's l1: 0.0567265	valid_0's l2: 0.00657222
    [287]	valid_0's l1: 0.0567223	valid_0's l2: 0.00657141
    [288]	valid_0's l1: 0.056714	valid_0's l2: 0.00656963
    [289]	valid_0's l1: 0.0567092	valid_0's l2: 0.00656967
    [290]	valid_0's l1: 0.0566989	valid_0's l2: 0.00656775
    [291]	valid_0's l1: 0.0566969	valid_0's l2: 0.00656714
    [292]	valid_0's l1: 0.0566972	valid_0's l2: 0.00656797
    [293]	valid_0's l1: 0.0566986	valid_0's l2: 0.00656812
    [294]	valid_0's l1: 0.056693	valid_0's l2: 0.00656709
    [295]	valid_0's l1: 0.0566943	valid_0's l2: 0.00656743
    [296]	valid_0's l1: 0.0566987	valid_0's l2: 0.0065679
    [297]	valid_0's l1: 0.056698	valid_0's l2: 0.00656738
    [298]	valid_0's l1: 0.0566927	valid_0's l2: 0.00656645
    [299]	valid_0's l1: 0.0566939	valid_0's l2: 0.00656589
    [300]	valid_0's l1: 0.0566834	valid_0's l2: 0.00656346
    Did not meet early stopping. Best iteration is:
    [300]	valid_0's l1: 0.0566834	valid_0's l2: 0.00656346
    本次结果输出的mae值是:
     0.056683443527655
    [1]	valid_0's l1: 0.244506	valid_0's l2: 0.0790859
    Training until validation scores don't improve for 5 rounds
    [2]	valid_0's l1: 0.223857	valid_0's l2: 0.0667341
    [3]	valid_0's l1: 0.205626	valid_0's l2: 0.0566941
    [4]	valid_0's l1: 0.189298	valid_0's l2: 0.0484539
    [5]	valid_0's l1: 0.175104	valid_0's l2: 0.0417921
    [6]	valid_0's l1: 0.16227	valid_0's l2: 0.036285
    [7]	valid_0's l1: 0.150898	valid_0's l2: 0.0317221
    [8]	valid_0's l1: 0.140883	valid_0's l2: 0.0280196
    [9]	valid_0's l1: 0.131939	valid_0's l2: 0.0249613
    [10]	valid_0's l1: 0.124375	valid_0's l2: 0.0224944
    [11]	valid_0's l1: 0.117467	valid_0's l2: 0.0204263
    [12]	valid_0's l1: 0.110957	valid_0's l2: 0.0185453
    [13]	valid_0's l1: 0.105077	valid_0's l2: 0.016929
    [14]	valid_0's l1: 0.100272	valid_0's l2: 0.0157152
    [15]	valid_0's l1: 0.0960833	valid_0's l2: 0.0147431
    [16]	valid_0's l1: 0.0920567	valid_0's l2: 0.0137979
    [17]	valid_0's l1: 0.088268	valid_0's l2: 0.0129121
    [18]	valid_0's l1: 0.0854499	valid_0's l2: 0.0123437
    [19]	valid_0's l1: 0.0827065	valid_0's l2: 0.0117755
    [20]	valid_0's l1: 0.0800967	valid_0's l2: 0.0112162
    [21]	valid_0's l1: 0.0781237	valid_0's l2: 0.0108399
    [22]	valid_0's l1: 0.0763776	valid_0's l2: 0.0105204
    [23]	valid_0's l1: 0.074906	valid_0's l2: 0.010265
    [24]	valid_0's l1: 0.0732985	valid_0's l2: 0.00994854
    [25]	valid_0's l1: 0.0720481	valid_0's l2: 0.00970346
    [26]	valid_0's l1: 0.0710837	valid_0's l2: 0.00953733
    [27]	valid_0's l1: 0.0703066	valid_0's l2: 0.00939487
    [28]	valid_0's l1: 0.0694075	valid_0's l2: 0.009231
    [29]	valid_0's l1: 0.0685866	valid_0's l2: 0.00907982
    [30]	valid_0's l1: 0.06779	valid_0's l2: 0.0089391
    [31]	valid_0's l1: 0.067074	valid_0's l2: 0.00880224
    [32]	valid_0's l1: 0.0665149	valid_0's l2: 0.00871073
    [33]	valid_0's l1: 0.0659205	valid_0's l2: 0.00858992
    [34]	valid_0's l1: 0.0652426	valid_0's l2: 0.00843778
    [35]	valid_0's l1: 0.0648768	valid_0's l2: 0.00836372
    [36]	valid_0's l1: 0.0645306	valid_0's l2: 0.00830614
    [37]	valid_0's l1: 0.0640175	valid_0's l2: 0.00818645
    [38]	valid_0's l1: 0.0637947	valid_0's l2: 0.00814636
    [39]	valid_0's l1: 0.0635605	valid_0's l2: 0.00810447
    [40]	valid_0's l1: 0.0633449	valid_0's l2: 0.00806796
    [41]	valid_0's l1: 0.0631446	valid_0's l2: 0.00802436
    [42]	valid_0's l1: 0.0627575	valid_0's l2: 0.00792715
    [43]	valid_0's l1: 0.062611	valid_0's l2: 0.00789809
    [44]	valid_0's l1: 0.0623997	valid_0's l2: 0.00785422
    [45]	valid_0's l1: 0.0622728	valid_0's l2: 0.00783369
    [46]	valid_0's l1: 0.0621336	valid_0's l2: 0.00781042
    [47]	valid_0's l1: 0.0619906	valid_0's l2: 0.00777463
    [48]	valid_0's l1: 0.0618556	valid_0's l2: 0.00774471
    [49]	valid_0's l1: 0.0617449	valid_0's l2: 0.00772713
    [50]	valid_0's l1: 0.0616454	valid_0's l2: 0.00770345
    [51]	valid_0's l1: 0.0615492	valid_0's l2: 0.00768433
    [52]	valid_0's l1: 0.0614452	valid_0's l2: 0.00766572
    [53]	valid_0's l1: 0.0613725	valid_0's l2: 0.00764408
    [54]	valid_0's l1: 0.0612796	valid_0's l2: 0.00762834
    [55]	valid_0's l1: 0.0611706	valid_0's l2: 0.00760066
    [56]	valid_0's l1: 0.0611121	valid_0's l2: 0.00759012
    [57]	valid_0's l1: 0.0610472	valid_0's l2: 0.00757864
    [58]	valid_0's l1: 0.0609801	valid_0's l2: 0.00756664
    [59]	valid_0's l1: 0.0608993	valid_0's l2: 0.00755207
    [60]	valid_0's l1: 0.0608206	valid_0's l2: 0.00753343
    [61]	valid_0's l1: 0.0607751	valid_0's l2: 0.00752435
    [62]	valid_0's l1: 0.0607163	valid_0's l2: 0.00751128
    [63]	valid_0's l1: 0.0605746	valid_0's l2: 0.00747752
    [64]	valid_0's l1: 0.0604651	valid_0's l2: 0.0074507
    [65]	valid_0's l1: 0.0604263	valid_0's l2: 0.00744162
    [66]	valid_0's l1: 0.0603599	valid_0's l2: 0.00742889
    [67]	valid_0's l1: 0.0602701	valid_0's l2: 0.00741169
    [68]	valid_0's l1: 0.0602334	valid_0's l2: 0.00739977
    [69]	valid_0's l1: 0.060112	valid_0's l2: 0.007374
    [70]	valid_0's l1: 0.0600257	valid_0's l2: 0.00735234
    [71]	valid_0's l1: 0.059928	valid_0's l2: 0.00732775
    [72]	valid_0's l1: 0.0598568	valid_0's l2: 0.00731406
    [73]	valid_0's l1: 0.0598019	valid_0's l2: 0.007307
    [74]	valid_0's l1: 0.0597175	valid_0's l2: 0.00728248
    [75]	valid_0's l1: 0.0596246	valid_0's l2: 0.00726385
    [76]	valid_0's l1: 0.0596029	valid_0's l2: 0.00725546
    [77]	valid_0's l1: 0.0595231	valid_0's l2: 0.00723235
    [78]	valid_0's l1: 0.0595014	valid_0's l2: 0.00722831
    [79]	valid_0's l1: 0.0594712	valid_0's l2: 0.00722155
    [80]	valid_0's l1: 0.0594512	valid_0's l2: 0.00721585
    [81]	valid_0's l1: 0.0594197	valid_0's l2: 0.00720856
    [82]	valid_0's l1: 0.0593932	valid_0's l2: 0.00720475
    [83]	valid_0's l1: 0.0593157	valid_0's l2: 0.00718559
    [84]	valid_0's l1: 0.0592926	valid_0's l2: 0.00718174
    [85]	valid_0's l1: 0.0592627	valid_0's l2: 0.00717558
    [86]	valid_0's l1: 0.0592466	valid_0's l2: 0.00716984
    [87]	valid_0's l1: 0.0592043	valid_0's l2: 0.00716193
    [88]	valid_0's l1: 0.0591298	valid_0's l2: 0.00714342
    [89]	valid_0's l1: 0.059097	valid_0's l2: 0.00713607
    [90]	valid_0's l1: 0.0589982	valid_0's l2: 0.00711745
    [91]	valid_0's l1: 0.0589505	valid_0's l2: 0.00710413
    [92]	valid_0's l1: 0.0588884	valid_0's l2: 0.00708708
    [93]	valid_0's l1: 0.0588084	valid_0's l2: 0.00707062
    [94]	valid_0's l1: 0.0587967	valid_0's l2: 0.00707269
    [95]	valid_0's l1: 0.0587689	valid_0's l2: 0.00706628
    [96]	valid_0's l1: 0.058749	valid_0's l2: 0.00705746
    [97]	valid_0's l1: 0.0587121	valid_0's l2: 0.00704977
    [98]	valid_0's l1: 0.0586712	valid_0's l2: 0.00703874
    [99]	valid_0's l1: 0.05861	valid_0's l2: 0.0070237
    [100]	valid_0's l1: 0.0585889	valid_0's l2: 0.00701449
    [101]	valid_0's l1: 0.0585771	valid_0's l2: 0.00701003
    [102]	valid_0's l1: 0.0585635	valid_0's l2: 0.0070058
    [103]	valid_0's l1: 0.0585479	valid_0's l2: 0.00700123
    [104]	valid_0's l1: 0.0585384	valid_0's l2: 0.00699934
    [105]	valid_0's l1: 0.0585209	valid_0's l2: 0.00699689
    [106]	valid_0's l1: 0.0585063	valid_0's l2: 0.00699365
    [107]	valid_0's l1: 0.0584905	valid_0's l2: 0.00698833
    [108]	valid_0's l1: 0.0584738	valid_0's l2: 0.00698361
    [109]	valid_0's l1: 0.0584563	valid_0's l2: 0.00697852
    [110]	valid_0's l1: 0.0584379	valid_0's l2: 0.00697721
    [111]	valid_0's l1: 0.0584333	valid_0's l2: 0.00697385
    [112]	valid_0's l1: 0.0584316	valid_0's l2: 0.00697371
    [113]	valid_0's l1: 0.0584076	valid_0's l2: 0.00696515
    [114]	valid_0's l1: 0.0583903	valid_0's l2: 0.00696145
    [115]	valid_0's l1: 0.0583817	valid_0's l2: 0.00695705
    [116]	valid_0's l1: 0.0583585	valid_0's l2: 0.00695171
    [117]	valid_0's l1: 0.0583329	valid_0's l2: 0.00694439

[118]	valid_0's l1: 0.0582953	valid_0's l2: 0.00693451
    [119]	valid_0's l1: 0.0582302	valid_0's l2: 0.00691471
    [120]	valid_0's l1: 0.0582053	valid_0's l2: 0.00690717
    [121]	valid_0's l1: 0.058165	valid_0's l2: 0.00690191
    [122]	valid_0's l1: 0.0581507	valid_0's l2: 0.00689919
    [123]	valid_0's l1: 0.0581202	valid_0's l2: 0.00689344
    [124]	valid_0's l1: 0.0580842	valid_0's l2: 0.00688742
    [125]	valid_0's l1: 0.0580695	valid_0's l2: 0.00688381
    [126]	valid_0's l1: 0.0580556	valid_0's l2: 0.0068812
    [127]	valid_0's l1: 0.0580404	valid_0's l2: 0.00687439
    [128]	valid_0's l1: 0.0580276	valid_0's l2: 0.00687128
    [129]	valid_0's l1: 0.0579982	valid_0's l2: 0.00686654
    [130]	valid_0's l1: 0.0579886	valid_0's l2: 0.00686163
    [131]	valid_0's l1: 0.0579795	valid_0's l2: 0.00686265
    [132]	valid_0's l1: 0.0579773	valid_0's l2: 0.00686113
    [133]	valid_0's l1: 0.0579718	valid_0's l2: 0.00686023
    [134]	valid_0's l1: 0.0579558	valid_0's l2: 0.00685794
    [135]	valid_0's l1: 0.0579452	valid_0's l2: 0.00685478
    [136]	valid_0's l1: 0.0579375	valid_0's l2: 0.00685219
    [137]	valid_0's l1: 0.0579311	valid_0's l2: 0.00685004
    [138]	valid_0's l1: 0.0578929	valid_0's l2: 0.0068395
    [139]	valid_0's l1: 0.0578863	valid_0's l2: 0.00683737
    [140]	valid_0's l1: 0.0578618	valid_0's l2: 0.00682973
    [141]	valid_0's l1: 0.057814	valid_0's l2: 0.00682006
    [142]	valid_0's l1: 0.0577865	valid_0's l2: 0.00681178
    [143]	valid_0's l1: 0.0577836	valid_0's l2: 0.00681092
    [144]	valid_0's l1: 0.0577732	valid_0's l2: 0.00680807
    [145]	valid_0's l1: 0.05775	valid_0's l2: 0.00680476
    [146]	valid_0's l1: 0.0577295	valid_0's l2: 0.00679983
    [147]	valid_0's l1: 0.0577274	valid_0's l2: 0.00679906
    [148]	valid_0's l1: 0.0577282	valid_0's l2: 0.00680071
    [149]	valid_0's l1: 0.0577027	valid_0's l2: 0.00679154
    [150]	valid_0's l1: 0.0577023	valid_0's l2: 0.00679031
    [151]	valid_0's l1: 0.0576895	valid_0's l2: 0.0067869
    [152]	valid_0's l1: 0.0576863	valid_0's l2: 0.00678669
    [153]	valid_0's l1: 0.057681	valid_0's l2: 0.00678298
    [154]	valid_0's l1: 0.0576461	valid_0's l2: 0.00677587
    [155]	valid_0's l1: 0.0576281	valid_0's l2: 0.00676922
    [156]	valid_0's l1: 0.0576071	valid_0's l2: 0.00676582
    [157]	valid_0's l1: 0.0575831	valid_0's l2: 0.00676433
    [158]	valid_0's l1: 0.0575486	valid_0's l2: 0.00675594
    [159]	valid_0's l1: 0.0575371	valid_0's l2: 0.00675428
    [160]	valid_0's l1: 0.0575191	valid_0's l2: 0.00675119
    [161]	valid_0's l1: 0.0575124	valid_0's l2: 0.00675132
    [162]	valid_0's l1: 0.0575015	valid_0's l2: 0.00674851
    [163]	valid_0's l1: 0.0574785	valid_0's l2: 0.00674612
    [164]	valid_0's l1: 0.0574678	valid_0's l2: 0.00674357
    [165]	valid_0's l1: 0.0574633	valid_0's l2: 0.00674366
    [166]	valid_0's l1: 0.0574458	valid_0's l2: 0.00673905
    [167]	valid_0's l1: 0.0574112	valid_0's l2: 0.00672946
    [168]	valid_0's l1: 0.0573985	valid_0's l2: 0.00672684
    [169]	valid_0's l1: 0.0573967	valid_0's l2: 0.00672339
    [170]	valid_0's l1: 0.0573916	valid_0's l2: 0.00672143
    [171]	valid_0's l1: 0.0573821	valid_0's l2: 0.00671896
    [172]	valid_0's l1: 0.0573703	valid_0's l2: 0.00671675
    [173]	valid_0's l1: 0.0573778	valid_0's l2: 0.00671835
    [174]	valid_0's l1: 0.0573707	valid_0's l2: 0.00671666
    [175]	valid_0's l1: 0.0573572	valid_0's l2: 0.00671479
    [176]	valid_0's l1: 0.0573574	valid_0's l2: 0.0067135
    [177]	valid_0's l1: 0.0573542	valid_0's l2: 0.00671297
    [178]	valid_0's l1: 0.057355	valid_0's l2: 0.00671458
    [179]	valid_0's l1: 0.0573403	valid_0's l2: 0.00671148
    [180]	valid_0's l1: 0.0573278	valid_0's l2: 0.00670699
    [181]	valid_0's l1: 0.0573277	valid_0's l2: 0.00670656
    [182]	valid_0's l1: 0.0573192	valid_0's l2: 0.00670432
    [183]	valid_0's l1: 0.0573012	valid_0's l2: 0.00670044
    [184]	valid_0's l1: 0.0573018	valid_0's l2: 0.00669917
    [185]	valid_0's l1: 0.0572933	valid_0's l2: 0.00669791
    [186]	valid_0's l1: 0.0572937	valid_0's l2: 0.00669628
    [187]	valid_0's l1: 0.0572959	valid_0's l2: 0.006696
    [188]	valid_0's l1: 0.0572946	valid_0's l2: 0.00669529
    [189]	valid_0's l1: 0.057284	valid_0's l2: 0.00669419
    [190]	valid_0's l1: 0.0572756	valid_0's l2: 0.00669183
    [191]	valid_0's l1: 0.0572536	valid_0's l2: 0.00668716
    [192]	valid_0's l1: 0.0572549	valid_0's l2: 0.0066889
    [193]	valid_0's l1: 0.0572407	valid_0's l2: 0.00668565
    [194]	valid_0's l1: 0.0572137	valid_0's l2: 0.00668092
    [195]	valid_0's l1: 0.0572176	valid_0's l2: 0.00668088
    [196]	valid_0's l1: 0.0571987	valid_0's l2: 0.00667881
    [197]	valid_0's l1: 0.0571904	valid_0's l2: 0.00667394
    [198]	valid_0's l1: 0.0571825	valid_0's l2: 0.00667086
    [199]	valid_0's l1: 0.0571814	valid_0's l2: 0.0066715
    [200]	valid_0's l1: 0.0571754	valid_0's l2: 0.0066712
    [201]	valid_0's l1: 0.0571679	valid_0's l2: 0.00666813
    [202]	valid_0's l1: 0.0571538	valid_0's l2: 0.00666858
    [203]	valid_0's l1: 0.0571505	valid_0's l2: 0.00666737
    [204]	valid_0's l1: 0.0571496	valid_0's l2: 0.00666642
    [205]	valid_0's l1: 0.0571438	valid_0's l2: 0.00666369
    [206]	valid_0's l1: 0.0571522	valid_0's l2: 0.0066647
    [207]	valid_0's l1: 0.0571443	valid_0's l2: 0.00666353
    [208]	valid_0's l1: 0.057147	valid_0's l2: 0.00666341
    [209]	valid_0's l1: 0.0571413	valid_0's l2: 0.00666194
    [210]	valid_0's l1: 0.0571264	valid_0's l2: 0.00666014
    [211]	valid_0's l1: 0.0571122	valid_0's l2: 0.00665585
    [212]	valid_0's l1: 0.0571118	valid_0's l2: 0.00665518
    [213]	valid_0's l1: 0.0571002	valid_0's l2: 0.00665338
    [214]	valid_0's l1: 0.0570964	valid_0's l2: 0.00665241
    [215]	valid_0's l1: 0.05709	valid_0's l2: 0.00665055
    [216]	valid_0's l1: 0.0570856	valid_0's l2: 0.00665057
    [217]	valid_0's l1: 0.0570826	valid_0's l2: 0.00665004
    [218]	valid_0's l1: 0.0570747	valid_0's l2: 0.00664898
    [219]	valid_0's l1: 0.057068	valid_0's l2: 0.00664791
    [220]	valid_0's l1: 0.05707	valid_0's l2: 0.00664691
    [221]	valid_0's l1: 0.0570675	valid_0's l2: 0.00664523
    [222]	valid_0's l1: 0.0570426	valid_0's l2: 0.00664251
    [223]	valid_0's l1: 0.0570167	valid_0's l2: 0.00663508
    [224]	valid_0's l1: 0.0570101	valid_0's l2: 0.00663261
    [225]	valid_0's l1: 0.0570103	valid_0's l2: 0.00663457
    [226]	valid_0's l1: 0.0569972	valid_0's l2: 0.00663136
    [227]	valid_0's l1: 0.0569882	valid_0's l2: 0.00663009
    [228]	valid_0's l1: 0.0569898	valid_0's l2: 0.00662954
    [229]	valid_0's l1: 0.0569865	valid_0's l2: 0.00662705
    [230]	valid_0's l1: 0.0569757	valid_0's l2: 0.00662507
    [231]	valid_0's l1: 0.0569731	valid_0's l2: 0.00662383
    [232]	valid_0's l1: 0.0569676	valid_0's l2: 0.00662384
    [233]	valid_0's l1: 0.0569664	valid_0's l2: 0.00662459
    [234]	valid_0's l1: 0.0569611	valid_0's l2: 0.00662457
    [235]	valid_0's l1: 0.0569633	valid_0's l2: 0.00662413
    [236]	valid_0's l1: 0.0569615	valid_0's l2: 0.00662337
    [237]	valid_0's l1: 0.0569616	valid_0's l2: 0.00662352
    [238]	valid_0's l1: 0.0569541	valid_0's l2: 0.00662248
    [239]	valid_0's l1: 0.0569478	valid_0's l2: 0.0066219
    [240]	valid_0's l1: 0.0569487	valid_0's l2: 0.00662202
    [241]	valid_0's l1: 0.0569517	valid_0's l2: 0.00662222
    [242]	valid_0's l1: 0.0569443	valid_0's l2: 0.00662104
    [243]	valid_0's l1: 0.0569249	valid_0's l2: 0.00661558
    [244]	valid_0's l1: 0.0569245	valid_0's l2: 0.00661384
    [245]	valid_0's l1: 0.0569247	valid_0's l2: 0.00661414
    [246]	valid_0's l1: 0.0569229	valid_0's l2: 0.00661358
    [247]	valid_0's l1: 0.0569195	valid_0's l2: 0.00661233
    [248]	valid_0's l1: 0.0569077	valid_0's l2: 0.00660972
    [249]	valid_0's l1: 0.0569058	valid_0's l2: 0.00660907
    [250]	valid_0's l1: 0.0569022	valid_0's l2: 0.00660765
    [251]	valid_0's l1: 0.056903	valid_0's l2: 0.00660776
    [252]	valid_0's l1: 0.0569011	valid_0's l2: 0.00660744
    [253]	valid_0's l1: 0.0569056	valid_0's l2: 0.00660932
    [254]	valid_0's l1: 0.0569014	valid_0's l2: 0.00660959
    [255]	valid_0's l1: 0.0568977	valid_0's l2: 0.00660912
    [256]	valid_0's l1: 0.0568991	valid_0's l2: 0.00660947
    [257]	valid_0's l1: 0.0568659	valid_0's l2: 0.00660135
    [258]	valid_0's l1: 0.0568594	valid_0's l2: 0.00660015
    [259]	valid_0's l1: 0.0568588	valid_0's l2: 0.00659937
    [260]	valid_0's l1: 0.0568588	valid_0's l2: 0.00659942
    [261]	valid_0's l1: 0.056858	valid_0's l2: 0.00659948
    [262]	valid_0's l1: 0.0568521	valid_0's l2: 0.00659886
    [263]	valid_0's l1: 0.0568516	valid_0's l2: 0.00659866
    [264]	valid_0's l1: 0.0568237	valid_0's l2: 0.00659403
    [265]	valid_0's l1: 0.0568185	valid_0's l2: 0.00659247
    [266]	valid_0's l1: 0.0568207	valid_0's l2: 0.00659355
    [267]	valid_0's l1: 0.0568153	valid_0's l2: 0.00659291
    [268]	valid_0's l1: 0.0568104	valid_0's l2: 0.00659252
    [269]	valid_0's l1: 0.0568035	valid_0's l2: 0.00659123
    [270]	valid_0's l1: 0.0567872	valid_0's l2: 0.00658465
    [271]	valid_0's l1: 0.0567721	valid_0's l2: 0.00658252
    [272]	valid_0's l1: 0.0567615	valid_0's l2: 0.00658002
    [273]	valid_0's l1: 0.0567417	valid_0's l2: 0.00657648
    [274]	valid_0's l1: 0.0567421	valid_0's l2: 0.00657556
    [275]	valid_0's l1: 0.0567452	valid_0's l2: 0.00657642
    [276]	valid_0's l1: 0.0567469	valid_0's l2: 0.00657607
    [277]	valid_0's l1: 0.056743	valid_0's l2: 0.00657593
    [278]	valid_0's l1: 0.0567391	valid_0's l2: 0.00657495
    [279]	valid_0's l1: 0.056739	valid_0's l2: 0.00657478
    [280]	valid_0's l1: 0.0567325	valid_0's l2: 0.00657337
    [281]	valid_0's l1: 0.0567323	valid_0's l2: 0.00657334
    [282]	valid_0's l1: 0.0567366	valid_0's l2: 0.00657365
    [283]	valid_0's l1: 0.0567364	valid_0's l2: 0.00657325
    [284]	valid_0's l1: 0.0567233	valid_0's l2: 0.00657079
    [285]	valid_0's l1: 0.0567246	valid_0's l2: 0.0065718
    [286]	valid_0's l1: 0.0567265	valid_0's l2: 0.00657222
    [287]	valid_0's l1: 0.0567223	valid_0's l2: 0.00657141
    [288]	valid_0's l1: 0.056714	valid_0's l2: 0.00656963
    [289]	valid_0's l1: 0.0567092	valid_0's l2: 0.00656967
    [290]	valid_0's l1: 0.0566989	valid_0's l2: 0.00656775
    [291]	valid_0's l1: 0.0566969	valid_0's l2: 0.00656714
    [292]	valid_0's l1: 0.0566972	valid_0's l2: 0.00656797
    [293]	valid_0's l1: 0.0566986	valid_0's l2: 0.00656812
    [294]	valid_0's l1: 0.056693	valid_0's l2: 0.00656709
    [295]	valid_0's l1: 0.0566943	valid_0's l2: 0.00656743

[296]	valid_0's l1: 0.0566987	valid_0's l2: 0.0065679
    [297]	valid_0's l1: 0.056698	valid_0's l2: 0.00656738
    [298]	valid_0's l1: 0.0566927	valid_0's l2: 0.00656645
    [299]	valid_0's l1: 0.0566939	valid_0's l2: 0.00656589
    [300]	valid_0's l1: 0.0566834	valid_0's l2: 0.00656346
    [301]	valid_0's l1: 0.0566872	valid_0's l2: 0.0065663
    [302]	valid_0's l1: 0.0566856	valid_0's l2: 0.00656487
    [303]	valid_0's l1: 0.0566822	valid_0's l2: 0.00656261
    [304]	valid_0's l1: 0.0566848	valid_0's l2: 0.00656279
    [305]	valid_0's l1: 0.0566818	valid_0's l2: 0.00656087
    [306]	valid_0's l1: 0.0566818	valid_0's l2: 0.00656023
    [307]	valid_0's l1: 0.0566825	valid_0's l2: 0.00655957
    [308]	valid_0's l1: 0.0566804	valid_0's l2: 0.00655989
    [309]	valid_0's l1: 0.0566693	valid_0's l2: 0.00655513
    [310]	valid_0's l1: 0.0566608	valid_0's l2: 0.00655397
    [311]	valid_0's l1: 0.0566546	valid_0's l2: 0.00655309
    [312]	valid_0's l1: 0.0566519	valid_0's l2: 0.00655274
    [313]	valid_0's l1: 0.05665	valid_0's l2: 0.00655275
    [314]	valid_0's l1: 0.0566492	valid_0's l2: 0.00655257
    [315]	valid_0's l1: 0.0566516	valid_0's l2: 0.00655293
    [316]	valid_0's l1: 0.0566499	valid_0's l2: 0.00655212
    [317]	valid_0's l1: 0.0566385	valid_0's l2: 0.00655
    [318]	valid_0's l1: 0.0566288	valid_0's l2: 0.00654956
    [319]	valid_0's l1: 0.0566317	valid_0's l2: 0.00655049
    [320]	valid_0's l1: 0.056631	valid_0's l2: 0.00655062
    [321]	valid_0's l1: 0.0566272	valid_0's l2: 0.00654966
    [322]	valid_0's l1: 0.0566247	valid_0's l2: 0.00654857
    [323]	valid_0's l1: 0.0566318	valid_0's l2: 0.00654932
    [324]	valid_0's l1: 0.0566344	valid_0's l2: 0.00655044
    [325]	valid_0's l1: 0.0566239	valid_0's l2: 0.00654858
    [326]	valid_0's l1: 0.0566241	valid_0's l2: 0.00654811
    [327]	valid_0's l1: 0.056621	valid_0's l2: 0.00654755
    [328]	valid_0's l1: 0.056627	valid_0's l2: 0.00655085
    [329]	valid_0's l1: 0.0566305	valid_0's l2: 0.00655121
    [330]	valid_0's l1: 0.0566281	valid_0's l2: 0.00655078
    [331]	valid_0's l1: 0.0566228	valid_0's l2: 0.00655045
    [332]	valid_0's l1: 0.0566242	valid_0's l2: 0.00654992
    Early stopping, best iteration is:
    [327]	valid_0's l1: 0.056621	valid_0's l2: 0.00654755
    本次结果输出的mae值是:
     0.0566209902947507
    [1]	valid_0's l1: 0.244506	valid_0's l2: 0.0790859
    Training until validation scores don't improve for 5 rounds
    [2]	valid_0's l1: 0.223857	valid_0's l2: 0.0667341
    [3]	valid_0's l1: 0.205626	valid_0's l2: 0.0566941
    [4]	valid_0's l1: 0.189298	valid_0's l2: 0.0484539
    [5]	valid_0's l1: 0.175104	valid_0's l2: 0.0417921
    [6]	valid_0's l1: 0.16227	valid_0's l2: 0.036285
    [7]	valid_0's l1: 0.150898	valid_0's l2: 0.0317221
    [8]	valid_0's l1: 0.140883	valid_0's l2: 0.0280196
    [9]	valid_0's l1: 0.131939	valid_0's l2: 0.0249613
    [10]	valid_0's l1: 0.124375	valid_0's l2: 0.0224944
    [11]	valid_0's l1: 0.117467	valid_0's l2: 0.0204263
    [12]	valid_0's l1: 0.110957	valid_0's l2: 0.0185453
    [13]	valid_0's l1: 0.105077	valid_0's l2: 0.016929
    [14]	valid_0's l1: 0.100272	valid_0's l2: 0.0157152
    [15]	valid_0's l1: 0.0960833	valid_0's l2: 0.0147431
    [16]	valid_0's l1: 0.0920567	valid_0's l2: 0.0137979
    [17]	valid_0's l1: 0.088268	valid_0's l2: 0.0129121
    [18]	valid_0's l1: 0.0854499	valid_0's l2: 0.0123437
    [19]	valid_0's l1: 0.0827065	valid_0's l2: 0.0117755
    [20]	valid_0's l1: 0.0800967	valid_0's l2: 0.0112162
    [21]	valid_0's l1: 0.0781237	valid_0's l2: 0.0108399
    [22]	valid_0's l1: 0.0763776	valid_0's l2: 0.0105204
    [23]	valid_0's l1: 0.074906	valid_0's l2: 0.010265
    [24]	valid_0's l1: 0.0732985	valid_0's l2: 0.00994854
    [25]	valid_0's l1: 0.0720481	valid_0's l2: 0.00970346
    [26]	valid_0's l1: 0.0710837	valid_0's l2: 0.00953733
    [27]	valid_0's l1: 0.0703066	valid_0's l2: 0.00939487
    [28]	valid_0's l1: 0.0694075	valid_0's l2: 0.009231
    [29]	valid_0's l1: 0.0685866	valid_0's l2: 0.00907982
    [30]	valid_0's l1: 0.06779	valid_0's l2: 0.0089391
    [31]	valid_0's l1: 0.067074	valid_0's l2: 0.00880224
    [32]	valid_0's l1: 0.0665149	valid_0's l2: 0.00871073
    [33]	valid_0's l1: 0.0659205	valid_0's l2: 0.00858992
    [34]	valid_0's l1: 0.0652426	valid_0's l2: 0.00843778
    [35]	valid_0's l1: 0.0648768	valid_0's l2: 0.00836372
    [36]	valid_0's l1: 0.0645306	valid_0's l2: 0.00830614
    [37]	valid_0's l1: 0.0640175	valid_0's l2: 0.00818645
    [38]	valid_0's l1: 0.0637947	valid_0's l2: 0.00814636
    [39]	valid_0's l1: 0.0635605	valid_0's l2: 0.00810447
    [40]	valid_0's l1: 0.0633449	valid_0's l2: 0.00806796
    [41]	valid_0's l1: 0.0631446	valid_0's l2: 0.00802436
    [42]	valid_0's l1: 0.0627575	valid_0's l2: 0.00792715
    [43]	valid_0's l1: 0.062611	valid_0's l2: 0.00789809
    [44]	valid_0's l1: 0.0623997	valid_0's l2: 0.00785422
    [45]	valid_0's l1: 0.0622728	valid_0's l2: 0.00783369
    [46]	valid_0's l1: 0.0621336	valid_0's l2: 0.00781042
    [47]	valid_0's l1: 0.0619906	valid_0's l2: 0.00777463
    [48]	valid_0's l1: 0.0618556	valid_0's l2: 0.00774471
    [49]	valid_0's l1: 0.0617449	valid_0's l2: 0.00772713
    [50]	valid_0's l1: 0.0616454	valid_0's l2: 0.00770345
    [51]	valid_0's l1: 0.0615492	valid_0's l2: 0.00768433
    [52]	valid_0's l1: 0.0614452	valid_0's l2: 0.00766572
    [53]	valid_0's l1: 0.0613725	valid_0's l2: 0.00764408
    [54]	valid_0's l1: 0.0612796	valid_0's l2: 0.00762834
    [55]	valid_0's l1: 0.0611706	valid_0's l2: 0.00760066
    [56]	valid_0's l1: 0.0611121	valid_0's l2: 0.00759012
    [57]	valid_0's l1: 0.0610472	valid_0's l2: 0.00757864
    [58]	valid_0's l1: 0.0609801	valid_0's l2: 0.00756664
    [59]	valid_0's l1: 0.0608993	valid_0's l2: 0.00755207
    [60]	valid_0's l1: 0.0608206	valid_0's l2: 0.00753343
    [61]	valid_0's l1: 0.0607751	valid_0's l2: 0.00752435
    [62]	valid_0's l1: 0.0607163	valid_0's l2: 0.00751128
    [63]	valid_0's l1: 0.0605746	valid_0's l2: 0.00747752
    [64]	valid_0's l1: 0.0604651	valid_0's l2: 0.0074507
    [65]	valid_0's l1: 0.0604263	valid_0's l2: 0.00744162
    [66]	valid_0's l1: 0.0603599	valid_0's l2: 0.00742889
    [67]	valid_0's l1: 0.0602701	valid_0's l2: 0.00741169
    [68]	valid_0's l1: 0.0602334	valid_0's l2: 0.00739977
    [69]	valid_0's l1: 0.060112	valid_0's l2: 0.007374
    [70]	valid_0's l1: 0.0600257	valid_0's l2: 0.00735234
    [71]	valid_0's l1: 0.059928	valid_0's l2: 0.00732775
    [72]	valid_0's l1: 0.0598568	valid_0's l2: 0.00731406
    [73]	valid_0's l1: 0.0598019	valid_0's l2: 0.007307
    [74]	valid_0's l1: 0.0597175	valid_0's l2: 0.00728248
    [75]	valid_0's l1: 0.0596246	valid_0's l2: 0.00726385
    [76]	valid_0's l1: 0.0596029	valid_0's l2: 0.00725546
    [77]	valid_0's l1: 0.0595231	valid_0's l2: 0.00723235
    [78]	valid_0's l1: 0.0595014	valid_0's l2: 0.00722831
    [79]	valid_0's l1: 0.0594712	valid_0's l2: 0.00722155
    [80]	valid_0's l1: 0.0594512	valid_0's l2: 0.00721585
    [81]	valid_0's l1: 0.0594197	valid_0's l2: 0.00720856
    [82]	valid_0's l1: 0.0593932	valid_0's l2: 0.00720475
    [83]	valid_0's l1: 0.0593157	valid_0's l2: 0.00718559
    [84]	valid_0's l1: 0.0592926	valid_0's l2: 0.00718174
    [85]	valid_0's l1: 0.0592627	valid_0's l2: 0.00717558
    [86]	valid_0's l1: 0.0592466	valid_0's l2: 0.00716984
    [87]	valid_0's l1: 0.0592043	valid_0's l2: 0.00716193
    [88]	valid_0's l1: 0.0591298	valid_0's l2: 0.00714342
    [89]	valid_0's l1: 0.059097	valid_0's l2: 0.00713607
    [90]	valid_0's l1: 0.0589982	valid_0's l2: 0.00711745
    [91]	valid_0's l1: 0.0589505	valid_0's l2: 0.00710413
    [92]	valid_0's l1: 0.0588884	valid_0's l2: 0.00708708
    [93]	valid_0's l1: 0.0588084	valid_0's l2: 0.00707062
    [94]	valid_0's l1: 0.0587967	valid_0's l2: 0.00707269
    [95]	valid_0's l1: 0.0587689	valid_0's l2: 0.00706628
    [96]	valid_0's l1: 0.058749	valid_0's l2: 0.00705746
    [97]	valid_0's l1: 0.0587121	valid_0's l2: 0.00704977
    [98]	valid_0's l1: 0.0586712	valid_0's l2: 0.00703874
    [99]	valid_0's l1: 0.05861	valid_0's l2: 0.0070237
    [100]	valid_0's l1: 0.0585889	valid_0's l2: 0.00701449
    [101]	valid_0's l1: 0.0585771	valid_0's l2: 0.00701003
    [102]	valid_0's l1: 0.0585635	valid_0's l2: 0.0070058
    [103]	valid_0's l1: 0.0585479	valid_0's l2: 0.00700123
    [104]	valid_0's l1: 0.0585384	valid_0's l2: 0.00699934
    [105]	valid_0's l1: 0.0585209	valid_0's l2: 0.00699689
    [106]	valid_0's l1: 0.0585063	valid_0's l2: 0.00699365
    [107]	valid_0's l1: 0.0584905	valid_0's l2: 0.00698833
    [108]	valid_0's l1: 0.0584738	valid_0's l2: 0.00698361
    [109]	valid_0's l1: 0.0584563	valid_0's l2: 0.00697852
    [110]	valid_0's l1: 0.0584379	valid_0's l2: 0.00697721
    [111]	valid_0's l1: 0.0584333	valid_0's l2: 0.00697385
    [112]	valid_0's l1: 0.0584316	valid_0's l2: 0.00697371
    [113]	valid_0's l1: 0.0584076	valid_0's l2: 0.00696515
    [114]	valid_0's l1: 0.0583903	valid_0's l2: 0.00696145
    [115]	valid_0's l1: 0.0583817	valid_0's l2: 0.00695705
    [116]	valid_0's l1: 0.0583585	valid_0's l2: 0.00695171
    [117]	valid_0's l1: 0.0583329	valid_0's l2: 0.00694439
    [118]	valid_0's l1: 0.0582953	valid_0's l2: 0.00693451
    [119]	valid_0's l1: 0.0582302	valid_0's l2: 0.00691471
    [120]	valid_0's l1: 0.0582053	valid_0's l2: 0.00690717
    [121]	valid_0's l1: 0.058165	valid_0's l2: 0.00690191
    [122]	valid_0's l1: 0.0581507	valid_0's l2: 0.00689919
    [123]	valid_0's l1: 0.0581202	valid_0's l2: 0.00689344

[124]	valid_0's l1: 0.0580842	valid_0's l2: 0.00688742
    [125]	valid_0's l1: 0.0580695	valid_0's l2: 0.00688381
    [126]	valid_0's l1: 0.0580556	valid_0's l2: 0.0068812
    [127]	valid_0's l1: 0.0580404	valid_0's l2: 0.00687439
    [128]	valid_0's l1: 0.0580276	valid_0's l2: 0.00687128
    [129]	valid_0's l1: 0.0579982	valid_0's l2: 0.00686654
    [130]	valid_0's l1: 0.0579886	valid_0's l2: 0.00686163
    [131]	valid_0's l1: 0.0579795	valid_0's l2: 0.00686265
    [132]	valid_0's l1: 0.0579773	valid_0's l2: 0.00686113
    [133]	valid_0's l1: 0.0579718	valid_0's l2: 0.00686023
    [134]	valid_0's l1: 0.0579558	valid_0's l2: 0.00685794
    [135]	valid_0's l1: 0.0579452	valid_0's l2: 0.00685478
    [136]	valid_0's l1: 0.0579375	valid_0's l2: 0.00685219
    [137]	valid_0's l1: 0.0579311	valid_0's l2: 0.00685004
    [138]	valid_0's l1: 0.0578929	valid_0's l2: 0.0068395
    [139]	valid_0's l1: 0.0578863	valid_0's l2: 0.00683737
    [140]	valid_0's l1: 0.0578618	valid_0's l2: 0.00682973
    [141]	valid_0's l1: 0.057814	valid_0's l2: 0.00682006
    [142]	valid_0's l1: 0.0577865	valid_0's l2: 0.00681178
    [143]	valid_0's l1: 0.0577836	valid_0's l2: 0.00681092
    [144]	valid_0's l1: 0.0577732	valid_0's l2: 0.00680807
    [145]	valid_0's l1: 0.05775	valid_0's l2: 0.00680476
    [146]	valid_0's l1: 0.0577295	valid_0's l2: 0.00679983
    [147]	valid_0's l1: 0.0577274	valid_0's l2: 0.00679906
    [148]	valid_0's l1: 0.0577282	valid_0's l2: 0.00680071
    [149]	valid_0's l1: 0.0577027	valid_0's l2: 0.00679154
    [150]	valid_0's l1: 0.0577023	valid_0's l2: 0.00679031
    [151]	valid_0's l1: 0.0576895	valid_0's l2: 0.0067869
    [152]	valid_0's l1: 0.0576863	valid_0's l2: 0.00678669
    [153]	valid_0's l1: 0.057681	valid_0's l2: 0.00678298
    [154]	valid_0's l1: 0.0576461	valid_0's l2: 0.00677587
    [155]	valid_0's l1: 0.0576281	valid_0's l2: 0.00676922
    [156]	valid_0's l1: 0.0576071	valid_0's l2: 0.00676582
    [157]	valid_0's l1: 0.0575831	valid_0's l2: 0.00676433
    [158]	valid_0's l1: 0.0575486	valid_0's l2: 0.00675594
    [159]	valid_0's l1: 0.0575371	valid_0's l2: 0.00675428
    [160]	valid_0's l1: 0.0575191	valid_0's l2: 0.00675119
    [161]	valid_0's l1: 0.0575124	valid_0's l2: 0.00675132
    [162]	valid_0's l1: 0.0575015	valid_0's l2: 0.00674851
    [163]	valid_0's l1: 0.0574785	valid_0's l2: 0.00674612
    [164]	valid_0's l1: 0.0574678	valid_0's l2: 0.00674357
    [165]	valid_0's l1: 0.0574633	valid_0's l2: 0.00674366
    [166]	valid_0's l1: 0.0574458	valid_0's l2: 0.00673905
    [167]	valid_0's l1: 0.0574112	valid_0's l2: 0.00672946
    [168]	valid_0's l1: 0.0573985	valid_0's l2: 0.00672684
    [169]	valid_0's l1: 0.0573967	valid_0's l2: 0.00672339
    [170]	valid_0's l1: 0.0573916	valid_0's l2: 0.00672143
    [171]	valid_0's l1: 0.0573821	valid_0's l2: 0.00671896
    [172]	valid_0's l1: 0.0573703	valid_0's l2: 0.00671675
    [173]	valid_0's l1: 0.0573778	valid_0's l2: 0.00671835
    [174]	valid_0's l1: 0.0573707	valid_0's l2: 0.00671666
    [175]	valid_0's l1: 0.0573572	valid_0's l2: 0.00671479
    [176]	valid_0's l1: 0.0573574	valid_0's l2: 0.0067135
    [177]	valid_0's l1: 0.0573542	valid_0's l2: 0.00671297
    [178]	valid_0's l1: 0.057355	valid_0's l2: 0.00671458
    [179]	valid_0's l1: 0.0573403	valid_0's l2: 0.00671148
    [180]	valid_0's l1: 0.0573278	valid_0's l2: 0.00670699
    [181]	valid_0's l1: 0.0573277	valid_0's l2: 0.00670656
    [182]	valid_0's l1: 0.0573192	valid_0's l2: 0.00670432
    [183]	valid_0's l1: 0.0573012	valid_0's l2: 0.00670044
    [184]	valid_0's l1: 0.0573018	valid_0's l2: 0.00669917
    [185]	valid_0's l1: 0.0572933	valid_0's l2: 0.00669791
    [186]	valid_0's l1: 0.0572937	valid_0's l2: 0.00669628
    [187]	valid_0's l1: 0.0572959	valid_0's l2: 0.006696
    [188]	valid_0's l1: 0.0572946	valid_0's l2: 0.00669529
    [189]	valid_0's l1: 0.057284	valid_0's l2: 0.00669419
    [190]	valid_0's l1: 0.0572756	valid_0's l2: 0.00669183
    [191]	valid_0's l1: 0.0572536	valid_0's l2: 0.00668716
    [192]	valid_0's l1: 0.0572549	valid_0's l2: 0.0066889
    [193]	valid_0's l1: 0.0572407	valid_0's l2: 0.00668565
    [194]	valid_0's l1: 0.0572137	valid_0's l2: 0.00668092
    [195]	valid_0's l1: 0.0572176	valid_0's l2: 0.00668088
    [196]	valid_0's l1: 0.0571987	valid_0's l2: 0.00667881
    [197]	valid_0's l1: 0.0571904	valid_0's l2: 0.00667394
    [198]	valid_0's l1: 0.0571825	valid_0's l2: 0.00667086
    [199]	valid_0's l1: 0.0571814	valid_0's l2: 0.0066715
    [200]	valid_0's l1: 0.0571754	valid_0's l2: 0.0066712
    [201]	valid_0's l1: 0.0571679	valid_0's l2: 0.00666813
    [202]	valid_0's l1: 0.0571538	valid_0's l2: 0.00666858
    [203]	valid_0's l1: 0.0571505	valid_0's l2: 0.00666737
    [204]	valid_0's l1: 0.0571496	valid_0's l2: 0.00666642
    [205]	valid_0's l1: 0.0571438	valid_0's l2: 0.00666369
    [206]	valid_0's l1: 0.0571522	valid_0's l2: 0.0066647
    [207]	valid_0's l1: 0.0571443	valid_0's l2: 0.00666353
    [208]	valid_0's l1: 0.057147	valid_0's l2: 0.00666341
    [209]	valid_0's l1: 0.0571413	valid_0's l2: 0.00666194
    [210]	valid_0's l1: 0.0571264	valid_0's l2: 0.00666014
    [211]	valid_0's l1: 0.0571122	valid_0's l2: 0.00665585
    [212]	valid_0's l1: 0.0571118	valid_0's l2: 0.00665518
    [213]	valid_0's l1: 0.0571002	valid_0's l2: 0.00665338
    [214]	valid_0's l1: 0.0570964	valid_0's l2: 0.00665241
    [215]	valid_0's l1: 0.05709	valid_0's l2: 0.00665055
    [216]	valid_0's l1: 0.0570856	valid_0's l2: 0.00665057
    [217]	valid_0's l1: 0.0570826	valid_0's l2: 0.00665004
    [218]	valid_0's l1: 0.0570747	valid_0's l2: 0.00664898
    [219]	valid_0's l1: 0.057068	valid_0's l2: 0.00664791
    [220]	valid_0's l1: 0.05707	valid_0's l2: 0.00664691
    [221]	valid_0's l1: 0.0570675	valid_0's l2: 0.00664523
    [222]	valid_0's l1: 0.0570426	valid_0's l2: 0.00664251
    [223]	valid_0's l1: 0.0570167	valid_0's l2: 0.00663508
    [224]	valid_0's l1: 0.0570101	valid_0's l2: 0.00663261
    [225]	valid_0's l1: 0.0570103	valid_0's l2: 0.00663457
    [226]	valid_0's l1: 0.0569972	valid_0's l2: 0.00663136
    [227]	valid_0's l1: 0.0569882	valid_0's l2: 0.00663009
    [228]	valid_0's l1: 0.0569898	valid_0's l2: 0.00662954
    [229]	valid_0's l1: 0.0569865	valid_0's l2: 0.00662705
    [230]	valid_0's l1: 0.0569757	valid_0's l2: 0.00662507
    [231]	valid_0's l1: 0.0569731	valid_0's l2: 0.00662383
    [232]	valid_0's l1: 0.0569676	valid_0's l2: 0.00662384
    [233]	valid_0's l1: 0.0569664	valid_0's l2: 0.00662459
    [234]	valid_0's l1: 0.0569611	valid_0's l2: 0.00662457
    [235]	valid_0's l1: 0.0569633	valid_0's l2: 0.00662413
    [236]	valid_0's l1: 0.0569615	valid_0's l2: 0.00662337
    [237]	valid_0's l1: 0.0569616	valid_0's l2: 0.00662352
    [238]	valid_0's l1: 0.0569541	valid_0's l2: 0.00662248
    [239]	valid_0's l1: 0.0569478	valid_0's l2: 0.0066219
    [240]	valid_0's l1: 0.0569487	valid_0's l2: 0.00662202
    [241]	valid_0's l1: 0.0569517	valid_0's l2: 0.00662222
    [242]	valid_0's l1: 0.0569443	valid_0's l2: 0.00662104
    [243]	valid_0's l1: 0.0569249	valid_0's l2: 0.00661558
    [244]	valid_0's l1: 0.0569245	valid_0's l2: 0.00661384
    [245]	valid_0's l1: 0.0569247	valid_0's l2: 0.00661414
    [246]	valid_0's l1: 0.0569229	valid_0's l2: 0.00661358
    [247]	valid_0's l1: 0.0569195	valid_0's l2: 0.00661233
    [248]	valid_0's l1: 0.0569077	valid_0's l2: 0.00660972
    [249]	valid_0's l1: 0.0569058	valid_0's l2: 0.00660907
    [250]	valid_0's l1: 0.0569022	valid_0's l2: 0.00660765
    [251]	valid_0's l1: 0.056903	valid_0's l2: 0.00660776
    [252]	valid_0's l1: 0.0569011	valid_0's l2: 0.00660744
    [253]	valid_0's l1: 0.0569056	valid_0's l2: 0.00660932
    [254]	valid_0's l1: 0.0569014	valid_0's l2: 0.00660959
    [255]	valid_0's l1: 0.0568977	valid_0's l2: 0.00660912
    [256]	valid_0's l1: 0.0568991	valid_0's l2: 0.00660947
    [257]	valid_0's l1: 0.0568659	valid_0's l2: 0.00660135
    [258]	valid_0's l1: 0.0568594	valid_0's l2: 0.00660015
    [259]	valid_0's l1: 0.0568588	valid_0's l2: 0.00659937
    [260]	valid_0's l1: 0.0568588	valid_0's l2: 0.00659942
    [261]	valid_0's l1: 0.056858	valid_0's l2: 0.00659948
    [262]	valid_0's l1: 0.0568521	valid_0's l2: 0.00659886
    [263]	valid_0's l1: 0.0568516	valid_0's l2: 0.00659866
    [264]	valid_0's l1: 0.0568237	valid_0's l2: 0.00659403
    [265]	valid_0's l1: 0.0568185	valid_0's l2: 0.00659247
    [266]	valid_0's l1: 0.0568207	valid_0's l2: 0.00659355
    [267]	valid_0's l1: 0.0568153	valid_0's l2: 0.00659291
    [268]	valid_0's l1: 0.0568104	valid_0's l2: 0.00659252
    [269]	valid_0's l1: 0.0568035	valid_0's l2: 0.00659123
    [270]	valid_0's l1: 0.0567872	valid_0's l2: 0.00658465
    [271]	valid_0's l1: 0.0567721	valid_0's l2: 0.00658252
    [272]	valid_0's l1: 0.0567615	valid_0's l2: 0.00658002
    [273]	valid_0's l1: 0.0567417	valid_0's l2: 0.00657648
    [274]	valid_0's l1: 0.0567421	valid_0's l2: 0.00657556
    [275]	valid_0's l1: 0.0567452	valid_0's l2: 0.00657642
    [276]	valid_0's l1: 0.0567469	valid_0's l2: 0.00657607

[277]	valid_0's l1: 0.056743	valid_0's l2: 0.00657593
    [278]	valid_0's l1: 0.0567391	valid_0's l2: 0.00657495
    [279]	valid_0's l1: 0.056739	valid_0's l2: 0.00657478
    [280]	valid_0's l1: 0.0567325	valid_0's l2: 0.00657337
    [281]	valid_0's l1: 0.0567323	valid_0's l2: 0.00657334
    [282]	valid_0's l1: 0.0567366	valid_0's l2: 0.00657365
    [283]	valid_0's l1: 0.0567364	valid_0's l2: 0.00657325
    [284]	valid_0's l1: 0.0567233	valid_0's l2: 0.00657079
    [285]	valid_0's l1: 0.0567246	valid_0's l2: 0.0065718
    [286]	valid_0's l1: 0.0567265	valid_0's l2: 0.00657222
    [287]	valid_0's l1: 0.0567223	valid_0's l2: 0.00657141
    [288]	valid_0's l1: 0.056714	valid_0's l2: 0.00656963
    [289]	valid_0's l1: 0.0567092	valid_0's l2: 0.00656967
    [290]	valid_0's l1: 0.0566989	valid_0's l2: 0.00656775
    [291]	valid_0's l1: 0.0566969	valid_0's l2: 0.00656714
    [292]	valid_0's l1: 0.0566972	valid_0's l2: 0.00656797
    [293]	valid_0's l1: 0.0566986	valid_0's l2: 0.00656812
    [294]	valid_0's l1: 0.056693	valid_0's l2: 0.00656709
    [295]	valid_0's l1: 0.0566943	valid_0's l2: 0.00656743
    [296]	valid_0's l1: 0.0566987	valid_0's l2: 0.0065679
    [297]	valid_0's l1: 0.056698	valid_0's l2: 0.00656738
    [298]	valid_0's l1: 0.0566927	valid_0's l2: 0.00656645
    [299]	valid_0's l1: 0.0566939	valid_0's l2: 0.00656589
    [300]	valid_0's l1: 0.0566834	valid_0's l2: 0.00656346
    [301]	valid_0's l1: 0.0566872	valid_0's l2: 0.0065663
    [302]	valid_0's l1: 0.0566856	valid_0's l2: 0.00656487
    [303]	valid_0's l1: 0.0566822	valid_0's l2: 0.00656261
    [304]	valid_0's l1: 0.0566848	valid_0's l2: 0.00656279
    [305]	valid_0's l1: 0.0566818	valid_0's l2: 0.00656087
    [306]	valid_0's l1: 0.0566818	valid_0's l2: 0.00656023
    [307]	valid_0's l1: 0.0566825	valid_0's l2: 0.00655957
    [308]	valid_0's l1: 0.0566804	valid_0's l2: 0.00655989
    [309]	valid_0's l1: 0.0566693	valid_0's l2: 0.00655513
    [310]	valid_0's l1: 0.0566608	valid_0's l2: 0.00655397
    [311]	valid_0's l1: 0.0566546	valid_0's l2: 0.00655309
    [312]	valid_0's l1: 0.0566519	valid_0's l2: 0.00655274
    [313]	valid_0's l1: 0.05665	valid_0's l2: 0.00655275
    [314]	valid_0's l1: 0.0566492	valid_0's l2: 0.00655257
    [315]	valid_0's l1: 0.0566516	valid_0's l2: 0.00655293
    [316]	valid_0's l1: 0.0566499	valid_0's l2: 0.00655212
    [317]	valid_0's l1: 0.0566385	valid_0's l2: 0.00655
    [318]	valid_0's l1: 0.0566288	valid_0's l2: 0.00654956
    [319]	valid_0's l1: 0.0566317	valid_0's l2: 0.00655049
    [320]	valid_0's l1: 0.056631	valid_0's l2: 0.00655062
    [321]	valid_0's l1: 0.0566272	valid_0's l2: 0.00654966
    [322]	valid_0's l1: 0.0566247	valid_0's l2: 0.00654857
    [323]	valid_0's l1: 0.0566318	valid_0's l2: 0.00654932
    [324]	valid_0's l1: 0.0566344	valid_0's l2: 0.00655044
    [325]	valid_0's l1: 0.0566239	valid_0's l2: 0.00654858
    [326]	valid_0's l1: 0.0566241	valid_0's l2: 0.00654811
    [327]	valid_0's l1: 0.056621	valid_0's l2: 0.00654755
    [328]	valid_0's l1: 0.056627	valid_0's l2: 0.00655085
    [329]	valid_0's l1: 0.0566305	valid_0's l2: 0.00655121
    [330]	valid_0's l1: 0.0566281	valid_0's l2: 0.00655078
    [331]	valid_0's l1: 0.0566228	valid_0's l2: 0.00655045
    [332]	valid_0's l1: 0.0566242	valid_0's l2: 0.00654992
    Early stopping, best iteration is:
    [327]	valid_0's l1: 0.056621	valid_0's l2: 0.00654755
    本次结果输出的mae值是:
     0.0566209902947507

In \[174\]:

plt.plot(n\_estimators,scores,'o-')  
plt.ylabel("mae")  
plt.xlabel("n\_estimator")  
print("best n\_estimator \{\}".format(n\_estimators\[np.argmin(scores)\]))

best n_estimator 500

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3ppbWlhbzU1MjE0NzU3Mg_size_16_color_FFFFFF_t_70 4][]

In \[175\]:

\# max\_depth

scores = \[\]  
max\_depth = \[3, 5, 7, 9, 11\]

for md in  max\_depth:  
    lgbm = lgb.LGBMRegressor(boosting\_type='gbdt',   
                      num\_leaves=31,  
                      max\_depth=md,  
                      learning\_rate=0.1,  
                      n\_estimators=500,  
                      min\_child\_samples=20,  
                      n\_jobs=-1)  
      
    lgbm.fit(X\_train, y\_train, eval\_set=\[(X\_valid, y\_valid)\], eval\_metric="l1", early\_stopping\_rounds=5)  
      
    y\_pre = lgbm.predict(X\_valid)  
      
    mae = mean\_absolute\_error(y\_valid, y\_pre)  
      
    scores.append(mae)  
    print("本次结果输出的mae值是:\\n", mae)

[1]	valid_0's l1: 0.245951	valid_0's l2: 0.0799026
    Training until validation scores don't improve for 5 rounds
    [2]	valid_0's l1: 0.226678	valid_0's l2: 0.0682131
    [3]	valid_0's l1: 0.209595	valid_0's l2: 0.0586855
    [4]	valid_0's l1: 0.194338	valid_0's l2: 0.0508607
    [5]	valid_0's l1: 0.181069	valid_0's l2: 0.0445219
    [6]	valid_0's l1: 0.169189	valid_0's l2: 0.0392848
    [7]	valid_0's l1: 0.158873	valid_0's l2: 0.0350431
    [8]	valid_0's l1: 0.149769	valid_0's l2: 0.031557
    [9]	valid_0's l1: 0.141415	valid_0's l2: 0.0285897
    [10]	valid_0's l1: 0.134067	valid_0's l2: 0.0261583
    [11]	valid_0's l1: 0.127481	valid_0's l2: 0.0240992
    [12]	valid_0's l1: 0.121933	valid_0's l2: 0.0224835
    [13]	valid_0's l1: 0.116908	valid_0's l2: 0.0210707
    [14]	valid_0's l1: 0.112637	valid_0's l2: 0.0199038
    [15]	valid_0's l1: 0.108706	valid_0's l2: 0.0188823
    [16]	valid_0's l1: 0.105431	valid_0's l2: 0.0180432
    [17]	valid_0's l1: 0.102307	valid_0's l2: 0.0172914
    [18]	valid_0's l1: 0.0996523	valid_0's l2: 0.0166384
    [19]	valid_0's l1: 0.0972029	valid_0's l2: 0.0160783
    [20]	valid_0's l1: 0.0950796	valid_0's l2: 0.0156012
    [21]	valid_0's l1: 0.0928554	valid_0's l2: 0.0150882
    [22]	valid_0's l1: 0.0912364	valid_0's l2: 0.0147276
    [23]	valid_0's l1: 0.0893773	valid_0's l2: 0.0143482
    [24]	valid_0's l1: 0.087748	valid_0's l2: 0.0139913
    [25]	valid_0's l1: 0.0867009	valid_0's l2: 0.0137646
    [26]	valid_0's l1: 0.0855458	valid_0's l2: 0.0135216
    [27]	valid_0's l1: 0.0845268	valid_0's l2: 0.0132951
    [28]	valid_0's l1: 0.0834586	valid_0's l2: 0.01308
    [29]	valid_0's l1: 0.0820494	valid_0's l2: 0.0126823
    [30]	valid_0's l1: 0.0810451	valid_0's l2: 0.0124618
    [31]	valid_0's l1: 0.0802479	valid_0's l2: 0.0122846
    [32]	valid_0's l1: 0.0796107	valid_0's l2: 0.0121644
    [33]	valid_0's l1: 0.0790837	valid_0's l2: 0.0120704
    [34]	valid_0's l1: 0.0784538	valid_0's l2: 0.0119457
    [35]	valid_0's l1: 0.0780126	valid_0's l2: 0.0118636
    [36]	valid_0's l1: 0.077716	valid_0's l2: 0.0117917
    [37]	valid_0's l1: 0.077197	valid_0's l2: 0.0116888
    [38]	valid_0's l1: 0.0768649	valid_0's l2: 0.0116143
    [39]	valid_0's l1: 0.0760565	valid_0's l2: 0.0114137
    [40]	valid_0's l1: 0.0752823	valid_0's l2: 0.0112145
    [41]	valid_0's l1: 0.0749631	valid_0's l2: 0.0111483
    [42]	valid_0's l1: 0.0746633	valid_0's l2: 0.0110827
    [43]	valid_0's l1: 0.0744063	valid_0's l2: 0.011016
    [44]	valid_0's l1: 0.074134	valid_0's l2: 0.0109585
    [45]	valid_0's l1: 0.0738961	valid_0's l2: 0.010913
    [46]	valid_0's l1: 0.0737403	valid_0's l2: 0.0108762
    [47]	valid_0's l1: 0.0731932	valid_0's l2: 0.0107357
    [48]	valid_0's l1: 0.0729765	valid_0's l2: 0.0106948
    [49]	valid_0's l1: 0.0728205	valid_0's l2: 0.0106548
    [50]	valid_0's l1: 0.0722648	valid_0's l2: 0.0105136
    [51]	valid_0's l1: 0.0721261	valid_0's l2: 0.0104765
    [52]	valid_0's l1: 0.071956	valid_0's l2: 0.0104392
    [53]	valid_0's l1: 0.0717767	valid_0's l2: 0.0104094
    [54]	valid_0's l1: 0.0716089	valid_0's l2: 0.0103866
    [55]	valid_0's l1: 0.071301	valid_0's l2: 0.0103142
    [56]	valid_0's l1: 0.0711698	valid_0's l2: 0.0102875
    [57]	valid_0's l1: 0.0710264	valid_0's l2: 0.0102616
    [58]	valid_0's l1: 0.0708755	valid_0's l2: 0.0102362
    [59]	valid_0's l1: 0.0707723	valid_0's l2: 0.0102076
    [60]	valid_0's l1: 0.0706688	valid_0's l2: 0.0101851
    [61]	valid_0's l1: 0.0705466	valid_0's l2: 0.0101611
    [62]	valid_0's l1: 0.070417	valid_0's l2: 0.0101418
    [63]	valid_0's l1: 0.0703099	valid_0's l2: 0.0101184
    [64]	valid_0's l1: 0.070014	valid_0's l2: 0.0100408
    [65]	valid_0's l1: 0.0698032	valid_0's l2: 0.00998559
    [66]	valid_0's l1: 0.0695586	valid_0's l2: 0.00992395
    [67]	valid_0's l1: 0.0694659	valid_0's l2: 0.00991016
    [68]	valid_0's l1: 0.0693805	valid_0's l2: 0.00988416
    [69]	valid_0's l1: 0.0692109	valid_0's l2: 0.00983863
    [70]	valid_0's l1: 0.0691033	valid_0's l2: 0.00981832
    [71]	valid_0's l1: 0.0688559	valid_0's l2: 0.00974502
    [72]	valid_0's l1: 0.0687006	valid_0's l2: 0.00970238
    [73]	valid_0's l1: 0.06848	valid_0's l2: 0.0096301
    [74]	valid_0's l1: 0.0684223	valid_0's l2: 0.00961455
    [75]	valid_0's l1: 0.0683508	valid_0's l2: 0.00958615
    [76]	valid_0's l1: 0.0683052	valid_0's l2: 0.009572
    [77]	valid_0's l1: 0.0681153	valid_0's l2: 0.00952969
    [78]	valid_0's l1: 0.0680296	valid_0's l2: 0.00951449
    [79]	valid_0's l1: 0.0678274	valid_0's l2: 0.00944451
    [80]	valid_0's l1: 0.0677938	valid_0's l2: 0.00943237
    [81]	valid_0's l1: 0.0676954	valid_0's l2: 0.00940965
    [82]	valid_0's l1: 0.0676028	valid_0's l2: 0.0093877
    [83]	valid_0's l1: 0.0674349	valid_0's l2: 0.0093459
    [84]	valid_0's l1: 0.0673912	valid_0's l2: 0.00932776
    [85]	valid_0's l1: 0.0673384	valid_0's l2: 0.00931617
    [86]	valid_0's l1: 0.0672796	valid_0's l2: 0.00930222
    [87]	valid_0's l1: 0.0672013	valid_0's l2: 0.00928555
    [88]	valid_0's l1: 0.067058	valid_0's l2: 0.00923902
    [89]	valid_0's l1: 0.0669635	valid_0's l2: 0.0092177
    [90]	valid_0's l1: 0.0668587	valid_0's l2: 0.0091912
    [91]	valid_0's l1: 0.0667102	valid_0's l2: 0.00913804
    [92]	valid_0's l1: 0.0666526	valid_0's l2: 0.00911653
    [93]	valid_0's l1: 0.0666176	valid_0's l2: 0.0091075
    [94]	valid_0's l1: 0.0665381	valid_0's l2: 0.00908938
    [95]	valid_0's l1: 0.0664094	valid_0's l2: 0.00905902
    [96]	valid_0's l1: 0.06634	valid_0's l2: 0.00903684
    [97]	valid_0's l1: 0.0662206	valid_0's l2: 0.00899158
    [98]	valid_0's l1: 0.0661688	valid_0's l2: 0.0089855
    [99]	valid_0's l1: 0.0660773	valid_0's l2: 0.00896682
    [100]	valid_0's l1: 0.0659306	valid_0's l2: 0.00892838
    [101]	valid_0's l1: 0.0658745	valid_0's l2: 0.0089192
    [102]	valid_0's l1: 0.065791	valid_0's l2: 0.00888833
    [103]	valid_0's l1: 0.0656774	valid_0's l2: 0.00885243
    [104]	valid_0's l1: 0.0655935	valid_0's l2: 0.00882508
    [105]	valid_0's l1: 0.0655746	valid_0's l2: 0.00881604
    [106]	valid_0's l1: 0.0655173	valid_0's l2: 0.00880362
    [107]	valid_0's l1: 0.0653877	valid_0's l2: 0.00877138
    [108]	valid_0's l1: 0.065285	valid_0's l2: 0.00874944
    [109]	valid_0's l1: 0.0652656	valid_0's l2: 0.00874508
    [110]	valid_0's l1: 0.0652144	valid_0's l2: 0.00873215
    [111]	valid_0's l1: 0.0650925	valid_0's l2: 0.00870511
    [112]	valid_0's l1: 0.0650302	valid_0's l2: 0.00867355
    [113]	valid_0's l1: 0.064989	valid_0's l2: 0.00866171
    [114]	valid_0's l1: 0.0649491	valid_0's l2: 0.00864751
    [115]	valid_0's l1: 0.0649154	valid_0's l2: 0.00864013
    [116]	valid_0's l1: 0.0647886	valid_0's l2: 0.00860299
    [117]	valid_0's l1: 0.0647248	valid_0's l2: 0.00859226
    [118]	valid_0's l1: 0.0646513	valid_0's l2: 0.00856838
    [119]	valid_0's l1: 0.0645208	valid_0's l2: 0.00853253
    [120]	valid_0's l1: 0.0644689	valid_0's l2: 0.00852473
    [121]	valid_0's l1: 0.0643448	valid_0's l2: 0.00849375
    [122]	valid_0's l1: 0.064289	valid_0's l2: 0.00847424
    [123]	valid_0's l1: 0.0642224	valid_0's l2: 0.00844912
    [124]	valid_0's l1: 0.0642043	valid_0's l2: 0.0084414
    [125]	valid_0's l1: 0.0641053	valid_0's l2: 0.00841524
    [126]	valid_0's l1: 0.0640266	valid_0's l2: 0.00838405
    [127]	valid_0's l1: 0.0639821	valid_0's l2: 0.00837852
    [128]	valid_0's l1: 0.0639547	valid_0's l2: 0.0083617
    [129]	valid_0's l1: 0.0639358	valid_0's l2: 0.00835842
    [130]	valid_0's l1: 0.0638841	valid_0's l2: 0.00834969
    [131]	valid_0's l1: 0.0638538	valid_0's l2: 0.0083397
    [132]	valid_0's l1: 0.063819	valid_0's l2: 0.00833278
    [133]	valid_0's l1: 0.0637716	valid_0's l2: 0.00832131
    [134]	valid_0's l1: 0.0637488	valid_0's l2: 0.0083152
    [135]	valid_0's l1: 0.0637281	valid_0's l2: 0.0083078
    [136]	valid_0's l1: 0.0636638	valid_0's l2: 0.00828725
    [137]	valid_0's l1: 0.0636322	valid_0's l2: 0.00828142
    [138]	valid_0's l1: 0.0635598	valid_0's l2: 0.00826705
    [139]	valid_0's l1: 0.0635517	valid_0's l2: 0.00826326
    [140]	valid_0's l1: 0.0635109	valid_0's l2: 0.00825556
    [141]	valid_0's l1: 0.0634626	valid_0's l2: 0.0082434
    [142]	valid_0's l1: 0.063427	valid_0's l2: 0.0082356
    [143]	valid_0's l1: 0.0633992	valid_0's l2: 0.00823178
    [144]	valid_0's l1: 0.0633714	valid_0's l2: 0.00822117
    [145]	valid_0's l1: 0.0632689	valid_0's l2: 0.00819275
    [146]	valid_0's l1: 0.0632355	valid_0's l2: 0.0081842
    [147]	valid_0's l1: 0.0631734	valid_0's l2: 0.00816914
    [148]	valid_0's l1: 0.0631554	valid_0's l2: 0.00816342
    [149]	valid_0's l1: 0.0630915	valid_0's l2: 0.00814223
    [150]	valid_0's l1: 0.0630257	valid_0's l2: 0.00812864
    [151]	valid_0's l1: 0.0629674	valid_0's l2: 0.00810772
    [152]	valid_0's l1: 0.0628928	valid_0's l2: 0.00809157
    [153]	valid_0's l1: 0.062862	valid_0's l2: 0.00808326
    [154]	valid_0's l1: 0.0628367	valid_0's l2: 0.00807544
    [155]	valid_0's l1: 0.0628224	valid_0's l2: 0.00807054
    [156]	valid_0's l1: 0.0627607	valid_0's l2: 0.00805709
    [157]	valid_0's l1: 0.0627235	valid_0's l2: 0.00804171
    [158]	valid_0's l1: 0.0626681	valid_0's l2: 0.00802948
    [159]	valid_0's l1: 0.0626548	valid_0's l2: 0.00802659
    [160]	valid_0's l1: 0.0625744	valid_0's l2: 0.00800215
    [161]	valid_0's l1: 0.0625507	valid_0's l2: 0.00799773
    [162]	valid_0's l1: 0.0625062	valid_0's l2: 0.00798584
    [163]	valid_0's l1: 0.0624752	valid_0's l2: 0.00797277
    [164]	valid_0's l1: 0.0624702	valid_0's l2: 0.0079688
    [165]	valid_0's l1: 0.0624439	valid_0's l2: 0.00796345
    [166]	valid_0's l1: 0.0624375	valid_0's l2: 0.00796167
    [167]	valid_0's l1: 0.0624021	valid_0's l2: 0.00795622
    [168]	valid_0's l1: 0.0623861	valid_0's l2: 0.0079532
    [169]	valid_0's l1: 0.0623754	valid_0's l2: 0.00795102
    [170]	valid_0's l1: 0.0623463	valid_0's l2: 0.00794603
    [171]	valid_0's l1: 0.0622781	valid_0's l2: 0.00793724
    [172]	valid_0's l1: 0.0622417	valid_0's l2: 0.00792782
    [173]	valid_0's l1: 0.0622139	valid_0's l2: 0.00791893
    [174]	valid_0's l1: 0.0621415	valid_0's l2: 0.00790142
    [175]	valid_0's l1: 0.0621292	valid_0's l2: 0.00790014

[176]	valid_0's l1: 0.0621091	valid_0's l2: 0.00789475
    [177]	valid_0's l1: 0.0620937	valid_0's l2: 0.00789072
    [178]	valid_0's l1: 0.0620923	valid_0's l2: 0.00788742
    [179]	valid_0's l1: 0.0620695	valid_0's l2: 0.00788345
    [180]	valid_0's l1: 0.0620561	valid_0's l2: 0.00787245
    [181]	valid_0's l1: 0.0619938	valid_0's l2: 0.00785701
    [182]	valid_0's l1: 0.0619728	valid_0's l2: 0.00785054
    [183]	valid_0's l1: 0.0619483	valid_0's l2: 0.00784426
    [184]	valid_0's l1: 0.0618916	valid_0's l2: 0.00782893
    [185]	valid_0's l1: 0.0618882	valid_0's l2: 0.00782821
    [186]	valid_0's l1: 0.0617958	valid_0's l2: 0.00780013
    [187]	valid_0's l1: 0.0617874	valid_0's l2: 0.00779428
    [188]	valid_0's l1: 0.0617613	valid_0's l2: 0.00778537
    [189]	valid_0's l1: 0.0617498	valid_0's l2: 0.00778327
    [190]	valid_0's l1: 0.0617415	valid_0's l2: 0.00778013
    [191]	valid_0's l1: 0.0617271	valid_0's l2: 0.00777387
    [192]	valid_0's l1: 0.0617194	valid_0's l2: 0.00777251
    [193]	valid_0's l1: 0.0617034	valid_0's l2: 0.00776778
    [194]	valid_0's l1: 0.0616932	valid_0's l2: 0.00776213
    [195]	valid_0's l1: 0.0616458	valid_0's l2: 0.00775323
    [196]	valid_0's l1: 0.0616342	valid_0's l2: 0.0077498
    [197]	valid_0's l1: 0.0616099	valid_0's l2: 0.00774467
    [198]	valid_0's l1: 0.0615941	valid_0's l2: 0.00774147
    [199]	valid_0's l1: 0.0615867	valid_0's l2: 0.00773916
    [200]	valid_0's l1: 0.0615496	valid_0's l2: 0.00773484
    [201]	valid_0's l1: 0.0615442	valid_0's l2: 0.00773016
    [202]	valid_0's l1: 0.0615308	valid_0's l2: 0.00772869
    [203]	valid_0's l1: 0.061527	valid_0's l2: 0.00772763
    [204]	valid_0's l1: 0.0615127	valid_0's l2: 0.00772413
    [205]	valid_0's l1: 0.0614676	valid_0's l2: 0.00770868
    [206]	valid_0's l1: 0.0614456	valid_0's l2: 0.00770356
    [207]	valid_0's l1: 0.0614349	valid_0's l2: 0.00770067
    [208]	valid_0's l1: 0.0614148	valid_0's l2: 0.00769728
    [209]	valid_0's l1: 0.061393	valid_0's l2: 0.00769306
    [210]	valid_0's l1: 0.0613878	valid_0's l2: 0.00769176
    [211]	valid_0's l1: 0.0613715	valid_0's l2: 0.00768739
    [212]	valid_0's l1: 0.0613692	valid_0's l2: 0.00768421
    [213]	valid_0's l1: 0.0613009	valid_0's l2: 0.00767403
    [214]	valid_0's l1: 0.061259	valid_0's l2: 0.00766591
    [215]	valid_0's l1: 0.0612563	valid_0's l2: 0.00766338
    [216]	valid_0's l1: 0.0612416	valid_0's l2: 0.00766072
    [217]	valid_0's l1: 0.0612324	valid_0's l2: 0.00765844
    [218]	valid_0's l1: 0.0612154	valid_0's l2: 0.00765249
    [219]	valid_0's l1: 0.0611909	valid_0's l2: 0.00764667
    [220]	valid_0's l1: 0.0611841	valid_0's l2: 0.00764471
    [221]	valid_0's l1: 0.0611569	valid_0's l2: 0.00763774
    [222]	valid_0's l1: 0.0611428	valid_0's l2: 0.00763603
    [223]	valid_0's l1: 0.0611038	valid_0's l2: 0.00762949
    [224]	valid_0's l1: 0.0610883	valid_0's l2: 0.00762472
    [225]	valid_0's l1: 0.0610858	valid_0's l2: 0.00762472
    [226]	valid_0's l1: 0.0610785	valid_0's l2: 0.00762216
    [227]	valid_0's l1: 0.0610739	valid_0's l2: 0.00762143
    [228]	valid_0's l1: 0.0610678	valid_0's l2: 0.00761936
    [229]	valid_0's l1: 0.0610439	valid_0's l2: 0.00761486
    [230]	valid_0's l1: 0.0610175	valid_0's l2: 0.00760908
    [231]	valid_0's l1: 0.0609923	valid_0's l2: 0.00760436
    [232]	valid_0's l1: 0.0609525	valid_0's l2: 0.00759502
    [233]	valid_0's l1: 0.0609434	valid_0's l2: 0.00759245
    [234]	valid_0's l1: 0.0609346	valid_0's l2: 0.00758803
    [235]	valid_0's l1: 0.0609314	valid_0's l2: 0.00758588
    [236]	valid_0's l1: 0.0609282	valid_0's l2: 0.0075855
    [237]	valid_0's l1: 0.0609238	valid_0's l2: 0.00758473
    [238]	valid_0's l1: 0.060858	valid_0's l2: 0.0075607
    [239]	valid_0's l1: 0.0608407	valid_0's l2: 0.00755626
    [240]	valid_0's l1: 0.0608279	valid_0's l2: 0.00755207
    [241]	valid_0's l1: 0.060826	valid_0's l2: 0.0075503
    [242]	valid_0's l1: 0.0607948	valid_0's l2: 0.00754333
    [243]	valid_0's l1: 0.0607598	valid_0's l2: 0.00753284
    [244]	valid_0's l1: 0.0607413	valid_0's l2: 0.00753085
    [245]	valid_0's l1: 0.0607301	valid_0's l2: 0.00752813
    [246]	valid_0's l1: 0.0607071	valid_0's l2: 0.00752255
    [247]	valid_0's l1: 0.0607026	valid_0's l2: 0.00752137
    [248]	valid_0's l1: 0.0606923	valid_0's l2: 0.00751851
    [249]	valid_0's l1: 0.0606846	valid_0's l2: 0.00751692
    [250]	valid_0's l1: 0.0606751	valid_0's l2: 0.00751755
    [251]	valid_0's l1: 0.0606774	valid_0's l2: 0.00751856
    [252]	valid_0's l1: 0.0606561	valid_0's l2: 0.00751333
    [253]	valid_0's l1: 0.0606549	valid_0's l2: 0.00751255
    [254]	valid_0's l1: 0.0606527	valid_0's l2: 0.0075117
    [255]	valid_0's l1: 0.0606451	valid_0's l2: 0.00750979
    [256]	valid_0's l1: 0.0606465	valid_0's l2: 0.00750681
    [257]	valid_0's l1: 0.0606421	valid_0's l2: 0.00750484
    [258]	valid_0's l1: 0.0605991	valid_0's l2: 0.00749648
    [259]	valid_0's l1: 0.0605624	valid_0's l2: 0.00748836
    [260]	valid_0's l1: 0.0605564	valid_0's l2: 0.00748607
    [261]	valid_0's l1: 0.060547	valid_0's l2: 0.00747954
    [262]	valid_0's l1: 0.060511	valid_0's l2: 0.00747297
    [263]	valid_0's l1: 0.0604954	valid_0's l2: 0.00746919
    [264]	valid_0's l1: 0.0604873	valid_0's l2: 0.00746591
    [265]	valid_0's l1: 0.0604839	valid_0's l2: 0.00746563
    [266]	valid_0's l1: 0.0604628	valid_0's l2: 0.00745981
    [267]	valid_0's l1: 0.0604221	valid_0's l2: 0.0074482
    [268]	valid_0's l1: 0.0604221	valid_0's l2: 0.00744695
    [269]	valid_0's l1: 0.0604059	valid_0's l2: 0.00744225
    [270]	valid_0's l1: 0.060387	valid_0's l2: 0.00743872
    [271]	valid_0's l1: 0.0603783	valid_0's l2: 0.00743707
    [272]	valid_0's l1: 0.0603586	valid_0's l2: 0.00743586
    [273]	valid_0's l1: 0.0603492	valid_0's l2: 0.00743342
    [274]	valid_0's l1: 0.0603445	valid_0's l2: 0.00743217
    [275]	valid_0's l1: 0.0603204	valid_0's l2: 0.00742736
    [276]	valid_0's l1: 0.0602933	valid_0's l2: 0.0074182
    [277]	valid_0's l1: 0.0602856	valid_0's l2: 0.00741636
    [278]	valid_0's l1: 0.0602808	valid_0's l2: 0.00741444
    [279]	valid_0's l1: 0.0602755	valid_0's l2: 0.00741346
    [280]	valid_0's l1: 0.060249	valid_0's l2: 0.0074111
    [281]	valid_0's l1: 0.060246	valid_0's l2: 0.00740991
    [282]	valid_0's l1: 0.0602039	valid_0's l2: 0.00740377
    [283]	valid_0's l1: 0.0601958	valid_0's l2: 0.00739957
    [284]	valid_0's l1: 0.0601844	valid_0's l2: 0.00739784
    [285]	valid_0's l1: 0.0601649	valid_0's l2: 0.00738912
    [286]	valid_0's l1: 0.0601514	valid_0's l2: 0.00738531
    [287]	valid_0's l1: 0.060144	valid_0's l2: 0.0073847
    [288]	valid_0's l1: 0.0601185	valid_0's l2: 0.00737609
    [289]	valid_0's l1: 0.060093	valid_0's l2: 0.00736866
    [290]	valid_0's l1: 0.0600861	valid_0's l2: 0.00736682
    [291]	valid_0's l1: 0.060082	valid_0's l2: 0.00736437
    [292]	valid_0's l1: 0.0600697	valid_0's l2: 0.00736088
    [293]	valid_0's l1: 0.0600644	valid_0's l2: 0.00736019
    [294]	valid_0's l1: 0.0600478	valid_0's l2: 0.00735573
    [295]	valid_0's l1: 0.0600324	valid_0's l2: 0.00735283
    [296]	valid_0's l1: 0.0600261	valid_0's l2: 0.00735116
    [297]	valid_0's l1: 0.060015	valid_0's l2: 0.00734777
    [298]	valid_0's l1: 0.0599943	valid_0's l2: 0.00734292
    [299]	valid_0's l1: 0.0599897	valid_0's l2: 0.0073379
    [300]	valid_0's l1: 0.0599743	valid_0's l2: 0.00733289
    [301]	valid_0's l1: 0.0599677	valid_0's l2: 0.00733165
    [302]	valid_0's l1: 0.0599564	valid_0's l2: 0.00732815
    [303]	valid_0's l1: 0.0599186	valid_0's l2: 0.00731642
    [304]	valid_0's l1: 0.0599057	valid_0's l2: 0.00731443
    [305]	valid_0's l1: 0.0598899	valid_0's l2: 0.00731051
    [306]	valid_0's l1: 0.0598758	valid_0's l2: 0.00730785
    [307]	valid_0's l1: 0.059867	valid_0's l2: 0.00730554
    [308]	valid_0's l1: 0.0598299	valid_0's l2: 0.00729924
    [309]	valid_0's l1: 0.0598213	valid_0's l2: 0.00729622
    [310]	valid_0's l1: 0.0598019	valid_0's l2: 0.00729078
    [311]	valid_0's l1: 0.0597985	valid_0's l2: 0.00729032
    [312]	valid_0's l1: 0.0597968	valid_0's l2: 0.00728993
    [313]	valid_0's l1: 0.0597889	valid_0's l2: 0.00728781
    [314]	valid_0's l1: 0.0597828	valid_0's l2: 0.00728647
    [315]	valid_0's l1: 0.0597752	valid_0's l2: 0.00728308
    [316]	valid_0's l1: 0.0597313	valid_0's l2: 0.00727217
    [317]	valid_0's l1: 0.0597099	valid_0's l2: 0.00727033
    [318]	valid_0's l1: 0.0597052	valid_0's l2: 0.00726923
    [319]	valid_0's l1: 0.0596815	valid_0's l2: 0.00726406
    [320]	valid_0's l1: 0.0596641	valid_0's l2: 0.00725938
    [321]	valid_0's l1: 0.0596434	valid_0's l2: 0.00725662
    [322]	valid_0's l1: 0.0596271	valid_0's l2: 0.00725326
    [323]	valid_0's l1: 0.0596116	valid_0's l2: 0.00724937
    [324]	valid_0's l1: 0.0596097	valid_0's l2: 0.00724817
    [325]	valid_0's l1: 0.0596057	valid_0's l2: 0.00724688
    [326]	valid_0's l1: 0.0596048	valid_0's l2: 0.00724418
    [327]	valid_0's l1: 0.0596012	valid_0's l2: 0.00724299
    [328]	valid_0's l1: 0.059593	valid_0's l2: 0.00724168
    [329]	valid_0's l1: 0.0595802	valid_0's l2: 0.00723676
    [330]	valid_0's l1: 0.0595807	valid_0's l2: 0.00723609
    [331]	valid_0's l1: 0.0595654	valid_0's l2: 0.00723407
    [332]	valid_0's l1: 0.059557	valid_0's l2: 0.00723122
    [333]	valid_0's l1: 0.0595544	valid_0's l2: 0.00723015
    [334]	valid_0's l1: 0.0595488	valid_0's l2: 0.00722858
    [335]	valid_0's l1: 0.0595438	valid_0's l2: 0.00722837
    [336]	valid_0's l1: 0.0595339	valid_0's l2: 0.00722712
    [337]	valid_0's l1: 0.0595153	valid_0's l2: 0.00722262
    [338]	valid_0's l1: 0.0595139	valid_0's l2: 0.00722096
    [339]	valid_0's l1: 0.0594884	valid_0's l2: 0.0072162
    [340]	valid_0's l1: 0.059482	valid_0's l2: 0.00721469
    [341]	valid_0's l1: 0.0594803	valid_0's l2: 0.00721375
    [342]	valid_0's l1: 0.0594763	valid_0's l2: 0.0072131
    [343]	valid_0's l1: 0.0594707	valid_0's l2: 0.00721182
    [344]	valid_0's l1: 0.0594612	valid_0's l2: 0.00721005
    [345]	valid_0's l1: 0.0594647	valid_0's l2: 0.00721021
    [346]	valid_0's l1: 0.0594596	valid_0's l2: 0.00721017
    [347]	valid_0's l1: 0.0594374	valid_0's l2: 0.00720848
    [348]	valid_0's l1: 0.0594418	valid_0's l2: 0.00720804
    [349]	valid_0's l1: 0.0594385	valid_0's l2: 0.00720749
    [350]	valid_0's l1: 0.0594349	valid_0's l2: 0.00720642
    [351]	valid_0's l1: 0.0594172	valid_0's l2: 0.00720432
    [352]	valid_0's l1: 0.0594158	valid_0's l2: 0.00720268
    [353]	valid_0's l1: 0.0594119	valid_0's l2: 0.00720244
    [354]	valid_0's l1: 0.0594084	valid_0's l2: 0.00720234
    [355]	valid_0's l1: 0.0594068	valid_0's l2: 0.00720142
    [356]	valid_0's l1: 0.0593827	valid_0's l2: 0.00719698
    [357]	valid_0's l1: 0.0593672	valid_0's l2: 0.00719361
    [358]	valid_0's l1: 0.0593657	valid_0's l2: 0.0071925
    [359]	valid_0's l1: 0.0593621	valid_0's l2: 0.00719183
    [360]	valid_0's l1: 0.0593578	valid_0's l2: 0.00719048
    [361]	valid_0's l1: 0.0593317	valid_0's l2: 0.00718509
    [362]	valid_0's l1: 0.0593165	valid_0's l2: 0.00718096
    [363]	valid_0's l1: 0.0592964	valid_0's l2: 0.00717612
    [364]	valid_0's l1: 0.0592904	valid_0's l2: 0.00717273
    [365]	valid_0's l1: 0.0592745	valid_0's l2: 0.00716989
    [366]	valid_0's l1: 0.0592595	valid_0's l2: 0.00716674
    [367]	valid_0's l1: 0.0592458	valid_0's l2: 0.00716225
    [368]	valid_0's l1: 0.059247	valid_0's l2: 0.00716195
    [369]	valid_0's l1: 0.0592436	valid_0's l2: 0.00716071
    [370]	valid_0's l1: 0.0592314	valid_0's l2: 0.00715802

[371]	valid_0's l1: 0.0592317	valid_0's l2: 0.00715905
    [372]	valid_0's l1: 0.0592257	valid_0's l2: 0.00715697
    [373]	valid_0's l1: 0.0592199	valid_0's l2: 0.00715637
    [374]	valid_0's l1: 0.0592168	valid_0's l2: 0.00715498
    [375]	valid_0's l1: 0.0592069	valid_0's l2: 0.0071537
    [376]	valid_0's l1: 0.0591999	valid_0's l2: 0.00715176
    [377]	valid_0's l1: 0.0591984	valid_0's l2: 0.00714892
    [378]	valid_0's l1: 0.0591692	valid_0's l2: 0.00713848
    [379]	valid_0's l1: 0.0591685	valid_0's l2: 0.00713698
    [380]	valid_0's l1: 0.0591578	valid_0's l2: 0.00713306
    [381]	valid_0's l1: 0.0591421	valid_0's l2: 0.00713099
    [382]	valid_0's l1: 0.0591172	valid_0's l2: 0.00712579
    [383]	valid_0's l1: 0.0591006	valid_0's l2: 0.0071224
    [384]	valid_0's l1: 0.059097	valid_0's l2: 0.00712188
    [385]	valid_0's l1: 0.059095	valid_0's l2: 0.00712107
    [386]	valid_0's l1: 0.0590862	valid_0's l2: 0.00711945
    [387]	valid_0's l1: 0.0590644	valid_0's l2: 0.00711505
    [388]	valid_0's l1: 0.0590607	valid_0's l2: 0.00711506
    [389]	valid_0's l1: 0.0590575	valid_0's l2: 0.00711392
    [390]	valid_0's l1: 0.0590417	valid_0's l2: 0.00711146
    [391]	valid_0's l1: 0.0590355	valid_0's l2: 0.00711067
    [392]	valid_0's l1: 0.0590286	valid_0's l2: 0.00710778
    [393]	valid_0's l1: 0.0590216	valid_0's l2: 0.00710659
    [394]	valid_0's l1: 0.0590099	valid_0's l2: 0.00710301
    [395]	valid_0's l1: 0.0590069	valid_0's l2: 0.00710208
    [396]	valid_0's l1: 0.0590034	valid_0's l2: 0.00710047
    [397]	valid_0's l1: 0.0589909	valid_0's l2: 0.0070936
    [398]	valid_0's l1: 0.0589901	valid_0's l2: 0.0070914
    [399]	valid_0's l1: 0.0589854	valid_0's l2: 0.00708941
    [400]	valid_0's l1: 0.0589705	valid_0's l2: 0.00708773
    [401]	valid_0's l1: 0.0589625	valid_0's l2: 0.00708597
    [402]	valid_0's l1: 0.0589644	valid_0's l2: 0.00708488
    [403]	valid_0's l1: 0.0589634	valid_0's l2: 0.00708438
    [404]	valid_0's l1: 0.0589565	valid_0's l2: 0.00708323
    [405]	valid_0's l1: 0.0589561	valid_0's l2: 0.00708292
    [406]	valid_0's l1: 0.0589485	valid_0's l2: 0.00708057
    [407]	valid_0's l1: 0.0589426	valid_0's l2: 0.00707874
    [408]	valid_0's l1: 0.0589328	valid_0's l2: 0.0070761
    [409]	valid_0's l1: 0.058926	valid_0's l2: 0.007073
    [410]	valid_0's l1: 0.0589203	valid_0's l2: 0.00707211
    [411]	valid_0's l1: 0.0588941	valid_0's l2: 0.00706425
    [412]	valid_0's l1: 0.058887	valid_0's l2: 0.00706222
    [413]	valid_0's l1: 0.0588788	valid_0's l2: 0.00705912
    [414]	valid_0's l1: 0.0588677	valid_0's l2: 0.00705691
    [415]	valid_0's l1: 0.0588715	valid_0's l2: 0.00706103
    [416]	valid_0's l1: 0.0588646	valid_0's l2: 0.00705964
    [417]	valid_0's l1: 0.0588604	valid_0's l2: 0.00705951
    [418]	valid_0's l1: 0.0588378	valid_0's l2: 0.00705833
    [419]	valid_0's l1: 0.0588364	valid_0's l2: 0.00705713
    Early stopping, best iteration is:
    [414]	valid_0's l1: 0.0588677	valid_0's l2: 0.00705691
    本次结果输出的mae值是:
     0.058867698663447106
    [1]	valid_0's l1: 0.244506	valid_0's l2: 0.0790859
    Training until validation scores don't improve for 5 rounds
    [2]	valid_0's l1: 0.223857	valid_0's l2: 0.0667341
    [3]	valid_0's l1: 0.205626	valid_0's l2: 0.0566941
    [4]	valid_0's l1: 0.189298	valid_0's l2: 0.0484539
    [5]	valid_0's l1: 0.175104	valid_0's l2: 0.0417921
    [6]	valid_0's l1: 0.16227	valid_0's l2: 0.036285
    [7]	valid_0's l1: 0.150898	valid_0's l2: 0.0317221
    [8]	valid_0's l1: 0.140883	valid_0's l2: 0.0280196
    [9]	valid_0's l1: 0.131939	valid_0's l2: 0.0249613
    [10]	valid_0's l1: 0.124375	valid_0's l2: 0.0224944
    [11]	valid_0's l1: 0.117467	valid_0's l2: 0.0204263
    [12]	valid_0's l1: 0.110957	valid_0's l2: 0.0185453
    [13]	valid_0's l1: 0.105077	valid_0's l2: 0.016929
    [14]	valid_0's l1: 0.100272	valid_0's l2: 0.0157152
    [15]	valid_0's l1: 0.0960833	valid_0's l2: 0.0147431
    [16]	valid_0's l1: 0.0920567	valid_0's l2: 0.0137979
    [17]	valid_0's l1: 0.088268	valid_0's l2: 0.0129121
    [18]	valid_0's l1: 0.0854499	valid_0's l2: 0.0123437
    [19]	valid_0's l1: 0.0827065	valid_0's l2: 0.0117755
    [20]	valid_0's l1: 0.0800967	valid_0's l2: 0.0112162
    [21]	valid_0's l1: 0.0781237	valid_0's l2: 0.0108399
    [22]	valid_0's l1: 0.0763776	valid_0's l2: 0.0105204
    [23]	valid_0's l1: 0.074906	valid_0's l2: 0.010265
    [24]	valid_0's l1: 0.0732985	valid_0's l2: 0.00994854
    [25]	valid_0's l1: 0.0720481	valid_0's l2: 0.00970346
    [26]	valid_0's l1: 0.0710837	valid_0's l2: 0.00953733
    [27]	valid_0's l1: 0.0703066	valid_0's l2: 0.00939487
    [28]	valid_0's l1: 0.0694075	valid_0's l2: 0.009231
    [29]	valid_0's l1: 0.0685866	valid_0's l2: 0.00907982
    [30]	valid_0's l1: 0.06779	valid_0's l2: 0.0089391
    [31]	valid_0's l1: 0.067074	valid_0's l2: 0.00880224
    [32]	valid_0's l1: 0.0665149	valid_0's l2: 0.00871073
    [33]	valid_0's l1: 0.0659205	valid_0's l2: 0.00858992
    [34]	valid_0's l1: 0.0652426	valid_0's l2: 0.00843778
    [35]	valid_0's l1: 0.0648768	valid_0's l2: 0.00836372
    [36]	valid_0's l1: 0.0645306	valid_0's l2: 0.00830614
    [37]	valid_0's l1: 0.0640175	valid_0's l2: 0.00818645
    [38]	valid_0's l1: 0.0637947	valid_0's l2: 0.00814636
    [39]	valid_0's l1: 0.0635605	valid_0's l2: 0.00810447
    [40]	valid_0's l1: 0.0633449	valid_0's l2: 0.00806796
    [41]	valid_0's l1: 0.0631446	valid_0's l2: 0.00802436
    [42]	valid_0's l1: 0.0627575	valid_0's l2: 0.00792715
    [43]	valid_0's l1: 0.062611	valid_0's l2: 0.00789809
    [44]	valid_0's l1: 0.0623997	valid_0's l2: 0.00785422
    [45]	valid_0's l1: 0.0622728	valid_0's l2: 0.00783369
    [46]	valid_0's l1: 0.0621336	valid_0's l2: 0.00781042
    [47]	valid_0's l1: 0.0619906	valid_0's l2: 0.00777463
    [48]	valid_0's l1: 0.0618556	valid_0's l2: 0.00774471
    [49]	valid_0's l1: 0.0617449	valid_0's l2: 0.00772713
    [50]	valid_0's l1: 0.0616454	valid_0's l2: 0.00770345
    [51]	valid_0's l1: 0.0615492	valid_0's l2: 0.00768433
    [52]	valid_0's l1: 0.0614452	valid_0's l2: 0.00766572
    [53]	valid_0's l1: 0.0613725	valid_0's l2: 0.00764408
    [54]	valid_0's l1: 0.0612796	valid_0's l2: 0.00762834
    [55]	valid_0's l1: 0.0611706	valid_0's l2: 0.00760066
    [56]	valid_0's l1: 0.0611121	valid_0's l2: 0.00759012
    [57]	valid_0's l1: 0.0610472	valid_0's l2: 0.00757864
    [58]	valid_0's l1: 0.0609801	valid_0's l2: 0.00756664
    [59]	valid_0's l1: 0.0608993	valid_0's l2: 0.00755207
    [60]	valid_0's l1: 0.0608206	valid_0's l2: 0.00753343
    [61]	valid_0's l1: 0.0607751	valid_0's l2: 0.00752435
    [62]	valid_0's l1: 0.0607163	valid_0's l2: 0.00751128
    [63]	valid_0's l1: 0.0605746	valid_0's l2: 0.00747752
    [64]	valid_0's l1: 0.0604651	valid_0's l2: 0.0074507
    [65]	valid_0's l1: 0.0604263	valid_0's l2: 0.00744162
    [66]	valid_0's l1: 0.0603599	valid_0's l2: 0.00742889
    [67]	valid_0's l1: 0.0602701	valid_0's l2: 0.00741169
    [68]	valid_0's l1: 0.0602334	valid_0's l2: 0.00739977
    [69]	valid_0's l1: 0.060112	valid_0's l2: 0.007374
    [70]	valid_0's l1: 0.0600257	valid_0's l2: 0.00735234
    [71]	valid_0's l1: 0.059928	valid_0's l2: 0.00732775
    [72]	valid_0's l1: 0.0598568	valid_0's l2: 0.00731406
    [73]	valid_0's l1: 0.0598019	valid_0's l2: 0.007307
    [74]	valid_0's l1: 0.0597175	valid_0's l2: 0.00728248
    [75]	valid_0's l1: 0.0596246	valid_0's l2: 0.00726385
    [76]	valid_0's l1: 0.0596029	valid_0's l2: 0.00725546
    [77]	valid_0's l1: 0.0595231	valid_0's l2: 0.00723235
    [78]	valid_0's l1: 0.0595014	valid_0's l2: 0.00722831
    [79]	valid_0's l1: 0.0594712	valid_0's l2: 0.00722155
    [80]	valid_0's l1: 0.0594512	valid_0's l2: 0.00721585
    [81]	valid_0's l1: 0.0594197	valid_0's l2: 0.00720856
    [82]	valid_0's l1: 0.0593932	valid_0's l2: 0.00720475
    [83]	valid_0's l1: 0.0593157	valid_0's l2: 0.00718559
    [84]	valid_0's l1: 0.0592926	valid_0's l2: 0.00718174
    [85]	valid_0's l1: 0.0592627	valid_0's l2: 0.00717558
    [86]	valid_0's l1: 0.0592466	valid_0's l2: 0.00716984
    [87]	valid_0's l1: 0.0592043	valid_0's l2: 0.00716193
    [88]	valid_0's l1: 0.0591298	valid_0's l2: 0.00714342
    [89]	valid_0's l1: 0.059097	valid_0's l2: 0.00713607
    [90]	valid_0's l1: 0.0589982	valid_0's l2: 0.00711745
    [91]	valid_0's l1: 0.0589505	valid_0's l2: 0.00710413
    [92]	valid_0's l1: 0.0588884	valid_0's l2: 0.00708708
    [93]	valid_0's l1: 0.0588084	valid_0's l2: 0.00707062
    [94]	valid_0's l1: 0.0587967	valid_0's l2: 0.00707269
    [95]	valid_0's l1: 0.0587689	valid_0's l2: 0.00706628
    [96]	valid_0's l1: 0.058749	valid_0's l2: 0.00705746
    [97]	valid_0's l1: 0.0587121	valid_0's l2: 0.00704977
    [98]	valid_0's l1: 0.0586712	valid_0's l2: 0.00703874
    [99]	valid_0's l1: 0.05861	valid_0's l2: 0.0070237
    [100]	valid_0's l1: 0.0585889	valid_0's l2: 0.00701449
    [101]	valid_0's l1: 0.0585771	valid_0's l2: 0.00701003
    [102]	valid_0's l1: 0.0585635	valid_0's l2: 0.0070058
    [103]	valid_0's l1: 0.0585479	valid_0's l2: 0.00700123
    [104]	valid_0's l1: 0.0585384	valid_0's l2: 0.00699934
    [105]	valid_0's l1: 0.0585209	valid_0's l2: 0.00699689
    [106]	valid_0's l1: 0.0585063	valid_0's l2: 0.00699365
    [107]	valid_0's l1: 0.0584905	valid_0's l2: 0.00698833
    [108]	valid_0's l1: 0.0584738	valid_0's l2: 0.00698361
    [109]	valid_0's l1: 0.0584563	valid_0's l2: 0.00697852
    [110]	valid_0's l1: 0.0584379	valid_0's l2: 0.00697721
    [111]	valid_0's l1: 0.0584333	valid_0's l2: 0.00697385
    [112]	valid_0's l1: 0.0584316	valid_0's l2: 0.00697371
    [113]	valid_0's l1: 0.0584076	valid_0's l2: 0.00696515
    [114]	valid_0's l1: 0.0583903	valid_0's l2: 0.00696145
    [115]	valid_0's l1: 0.0583817	valid_0's l2: 0.00695705

[116]	valid_0's l1: 0.0583585	valid_0's l2: 0.00695171
    [117]	valid_0's l1: 0.0583329	valid_0's l2: 0.00694439
    [118]	valid_0's l1: 0.0582953	valid_0's l2: 0.00693451
    [119]	valid_0's l1: 0.0582302	valid_0's l2: 0.00691471
    [120]	valid_0's l1: 0.0582053	valid_0's l2: 0.00690717
    [121]	valid_0's l1: 0.058165	valid_0's l2: 0.00690191
    [122]	valid_0's l1: 0.0581507	valid_0's l2: 0.00689919
    [123]	valid_0's l1: 0.0581202	valid_0's l2: 0.00689344
    [124]	valid_0's l1: 0.0580842	valid_0's l2: 0.00688742
    [125]	valid_0's l1: 0.0580695	valid_0's l2: 0.00688381
    [126]	valid_0's l1: 0.0580556	valid_0's l2: 0.0068812
    [127]	valid_0's l1: 0.0580404	valid_0's l2: 0.00687439
    [128]	valid_0's l1: 0.0580276	valid_0's l2: 0.00687128
    [129]	valid_0's l1: 0.0579982	valid_0's l2: 0.00686654
    [130]	valid_0's l1: 0.0579886	valid_0's l2: 0.00686163
    [131]	valid_0's l1: 0.0579795	valid_0's l2: 0.00686265
    [132]	valid_0's l1: 0.0579773	valid_0's l2: 0.00686113
    [133]	valid_0's l1: 0.0579718	valid_0's l2: 0.00686023
    [134]	valid_0's l1: 0.0579558	valid_0's l2: 0.00685794
    [135]	valid_0's l1: 0.0579452	valid_0's l2: 0.00685478
    [136]	valid_0's l1: 0.0579375	valid_0's l2: 0.00685219
    [137]	valid_0's l1: 0.0579311	valid_0's l2: 0.00685004
    [138]	valid_0's l1: 0.0578929	valid_0's l2: 0.0068395
    [139]	valid_0's l1: 0.0578863	valid_0's l2: 0.00683737
    [140]	valid_0's l1: 0.0578618	valid_0's l2: 0.00682973
    [141]	valid_0's l1: 0.057814	valid_0's l2: 0.00682006
    [142]	valid_0's l1: 0.0577865	valid_0's l2: 0.00681178
    [143]	valid_0's l1: 0.0577836	valid_0's l2: 0.00681092
    [144]	valid_0's l1: 0.0577732	valid_0's l2: 0.00680807
    [145]	valid_0's l1: 0.05775	valid_0's l2: 0.00680476
    [146]	valid_0's l1: 0.0577295	valid_0's l2: 0.00679983
    [147]	valid_0's l1: 0.0577274	valid_0's l2: 0.00679906
    [148]	valid_0's l1: 0.0577282	valid_0's l2: 0.00680071
    [149]	valid_0's l1: 0.0577027	valid_0's l2: 0.00679154
    [150]	valid_0's l1: 0.0577023	valid_0's l2: 0.00679031
    [151]	valid_0's l1: 0.0576895	valid_0's l2: 0.0067869
    [152]	valid_0's l1: 0.0576863	valid_0's l2: 0.00678669
    [153]	valid_0's l1: 0.057681	valid_0's l2: 0.00678298
    [154]	valid_0's l1: 0.0576461	valid_0's l2: 0.00677587
    [155]	valid_0's l1: 0.0576281	valid_0's l2: 0.00676922
    [156]	valid_0's l1: 0.0576071	valid_0's l2: 0.00676582
    [157]	valid_0's l1: 0.0575831	valid_0's l2: 0.00676433
    [158]	valid_0's l1: 0.0575486	valid_0's l2: 0.00675594
    [159]	valid_0's l1: 0.0575371	valid_0's l2: 0.00675428
    [160]	valid_0's l1: 0.0575191	valid_0's l2: 0.00675119
    [161]	valid_0's l1: 0.0575124	valid_0's l2: 0.00675132
    [162]	valid_0's l1: 0.0575015	valid_0's l2: 0.00674851
    [163]	valid_0's l1: 0.0574785	valid_0's l2: 0.00674612
    [164]	valid_0's l1: 0.0574678	valid_0's l2: 0.00674357
    [165]	valid_0's l1: 0.0574633	valid_0's l2: 0.00674366
    [166]	valid_0's l1: 0.0574458	valid_0's l2: 0.00673905
    [167]	valid_0's l1: 0.0574112	valid_0's l2: 0.00672946
    [168]	valid_0's l1: 0.0573985	valid_0's l2: 0.00672684
    [169]	valid_0's l1: 0.0573967	valid_0's l2: 0.00672339
    [170]	valid_0's l1: 0.0573916	valid_0's l2: 0.00672143
    [171]	valid_0's l1: 0.0573821	valid_0's l2: 0.00671896
    [172]	valid_0's l1: 0.0573703	valid_0's l2: 0.00671675
    [173]	valid_0's l1: 0.0573778	valid_0's l2: 0.00671835
    [174]	valid_0's l1: 0.0573707	valid_0's l2: 0.00671666
    [175]	valid_0's l1: 0.0573572	valid_0's l2: 0.00671479
    [176]	valid_0's l1: 0.0573574	valid_0's l2: 0.0067135
    [177]	valid_0's l1: 0.0573542	valid_0's l2: 0.00671297
    [178]	valid_0's l1: 0.057355	valid_0's l2: 0.00671458
    [179]	valid_0's l1: 0.0573403	valid_0's l2: 0.00671148
    [180]	valid_0's l1: 0.0573278	valid_0's l2: 0.00670699
    [181]	valid_0's l1: 0.0573277	valid_0's l2: 0.00670656
    [182]	valid_0's l1: 0.0573192	valid_0's l2: 0.00670432
    [183]	valid_0's l1: 0.0573012	valid_0's l2: 0.00670044
    [184]	valid_0's l1: 0.0573018	valid_0's l2: 0.00669917
    [185]	valid_0's l1: 0.0572933	valid_0's l2: 0.00669791
    [186]	valid_0's l1: 0.0572937	valid_0's l2: 0.00669628
    [187]	valid_0's l1: 0.0572959	valid_0's l2: 0.006696
    [188]	valid_0's l1: 0.0572946	valid_0's l2: 0.00669529
    [189]	valid_0's l1: 0.057284	valid_0's l2: 0.00669419
    [190]	valid_0's l1: 0.0572756	valid_0's l2: 0.00669183
    [191]	valid_0's l1: 0.0572536	valid_0's l2: 0.00668716
    [192]	valid_0's l1: 0.0572549	valid_0's l2: 0.0066889
    [193]	valid_0's l1: 0.0572407	valid_0's l2: 0.00668565
    [194]	valid_0's l1: 0.0572137	valid_0's l2: 0.00668092
    [195]	valid_0's l1: 0.0572176	valid_0's l2: 0.00668088
    [196]	valid_0's l1: 0.0571987	valid_0's l2: 0.00667881
    [197]	valid_0's l1: 0.0571904	valid_0's l2: 0.00667394
    [198]	valid_0's l1: 0.0571825	valid_0's l2: 0.00667086
    [199]	valid_0's l1: 0.0571814	valid_0's l2: 0.0066715
    [200]	valid_0's l1: 0.0571754	valid_0's l2: 0.0066712
    [201]	valid_0's l1: 0.0571679	valid_0's l2: 0.00666813
    [202]	valid_0's l1: 0.0571538	valid_0's l2: 0.00666858
    [203]	valid_0's l1: 0.0571505	valid_0's l2: 0.00666737
    [204]	valid_0's l1: 0.0571496	valid_0's l2: 0.00666642
    [205]	valid_0's l1: 0.0571438	valid_0's l2: 0.00666369
    [206]	valid_0's l1: 0.0571522	valid_0's l2: 0.0066647
    [207]	valid_0's l1: 0.0571443	valid_0's l2: 0.00666353
    [208]	valid_0's l1: 0.057147	valid_0's l2: 0.00666341
    [209]	valid_0's l1: 0.0571413	valid_0's l2: 0.00666194
    [210]	valid_0's l1: 0.0571264	valid_0's l2: 0.00666014
    [211]	valid_0's l1: 0.0571122	valid_0's l2: 0.00665585
    [212]	valid_0's l1: 0.0571118	valid_0's l2: 0.00665518
    [213]	valid_0's l1: 0.0571002	valid_0's l2: 0.00665338
    [214]	valid_0's l1: 0.0570964	valid_0's l2: 0.00665241
    [215]	valid_0's l1: 0.05709	valid_0's l2: 0.00665055
    [216]	valid_0's l1: 0.0570856	valid_0's l2: 0.00665057
    [217]	valid_0's l1: 0.0570826	valid_0's l2: 0.00665004
    [218]	valid_0's l1: 0.0570747	valid_0's l2: 0.00664898
    [219]	valid_0's l1: 0.057068	valid_0's l2: 0.00664791
    [220]	valid_0's l1: 0.05707	valid_0's l2: 0.00664691
    [221]	valid_0's l1: 0.0570675	valid_0's l2: 0.00664523
    [222]	valid_0's l1: 0.0570426	valid_0's l2: 0.00664251
    [223]	valid_0's l1: 0.0570167	valid_0's l2: 0.00663508
    [224]	valid_0's l1: 0.0570101	valid_0's l2: 0.00663261
    [225]	valid_0's l1: 0.0570103	valid_0's l2: 0.00663457
    [226]	valid_0's l1: 0.0569972	valid_0's l2: 0.00663136
    [227]	valid_0's l1: 0.0569882	valid_0's l2: 0.00663009
    [228]	valid_0's l1: 0.0569898	valid_0's l2: 0.00662954
    [229]	valid_0's l1: 0.0569865	valid_0's l2: 0.00662705
    [230]	valid_0's l1: 0.0569757	valid_0's l2: 0.00662507
    [231]	valid_0's l1: 0.0569731	valid_0's l2: 0.00662383
    [232]	valid_0's l1: 0.0569676	valid_0's l2: 0.00662384
    [233]	valid_0's l1: 0.0569664	valid_0's l2: 0.00662459
    [234]	valid_0's l1: 0.0569611	valid_0's l2: 0.00662457
    [235]	valid_0's l1: 0.0569633	valid_0's l2: 0.00662413
    [236]	valid_0's l1: 0.0569615	valid_0's l2: 0.00662337
    [237]	valid_0's l1: 0.0569616	valid_0's l2: 0.00662352
    [238]	valid_0's l1: 0.0569541	valid_0's l2: 0.00662248
    [239]	valid_0's l1: 0.0569478	valid_0's l2: 0.0066219
    [240]	valid_0's l1: 0.0569487	valid_0's l2: 0.00662202
    [241]	valid_0's l1: 0.0569517	valid_0's l2: 0.00662222
    [242]	valid_0's l1: 0.0569443	valid_0's l2: 0.00662104
    [243]	valid_0's l1: 0.0569249	valid_0's l2: 0.00661558
    [244]	valid_0's l1: 0.0569245	valid_0's l2: 0.00661384
    [245]	valid_0's l1: 0.0569247	valid_0's l2: 0.00661414
    [246]	valid_0's l1: 0.0569229	valid_0's l2: 0.00661358
    [247]	valid_0's l1: 0.0569195	valid_0's l2: 0.00661233
    [248]	valid_0's l1: 0.0569077	valid_0's l2: 0.00660972
    [249]	valid_0's l1: 0.0569058	valid_0's l2: 0.00660907
    [250]	valid_0's l1: 0.0569022	valid_0's l2: 0.00660765
    [251]	valid_0's l1: 0.056903	valid_0's l2: 0.00660776
    [252]	valid_0's l1: 0.0569011	valid_0's l2: 0.00660744
    [253]	valid_0's l1: 0.0569056	valid_0's l2: 0.00660932
    [254]	valid_0's l1: 0.0569014	valid_0's l2: 0.00660959
    [255]	valid_0's l1: 0.0568977	valid_0's l2: 0.00660912
    [256]	valid_0's l1: 0.0568991	valid_0's l2: 0.00660947
    [257]	valid_0's l1: 0.0568659	valid_0's l2: 0.00660135
    [258]	valid_0's l1: 0.0568594	valid_0's l2: 0.00660015
    [259]	valid_0's l1: 0.0568588	valid_0's l2: 0.00659937
    [260]	valid_0's l1: 0.0568588	valid_0's l2: 0.00659942
    [261]	valid_0's l1: 0.056858	valid_0's l2: 0.00659948
    [262]	valid_0's l1: 0.0568521	valid_0's l2: 0.00659886
    [263]	valid_0's l1: 0.0568516	valid_0's l2: 0.00659866
    [264]	valid_0's l1: 0.0568237	valid_0's l2: 0.00659403
    [265]	valid_0's l1: 0.0568185	valid_0's l2: 0.00659247
    [266]	valid_0's l1: 0.0568207	valid_0's l2: 0.00659355
    [267]	valid_0's l1: 0.0568153	valid_0's l2: 0.00659291
    [268]	valid_0's l1: 0.0568104	valid_0's l2: 0.00659252
    [269]	valid_0's l1: 0.0568035	valid_0's l2: 0.00659123
    [270]	valid_0's l1: 0.0567872	valid_0's l2: 0.00658465
    [271]	valid_0's l1: 0.0567721	valid_0's l2: 0.00658252
    [272]	valid_0's l1: 0.0567615	valid_0's l2: 0.00658002
    [273]	valid_0's l1: 0.0567417	valid_0's l2: 0.00657648
    [274]	valid_0's l1: 0.0567421	valid_0's l2: 0.00657556
    [275]	valid_0's l1: 0.0567452	valid_0's l2: 0.00657642
    [276]	valid_0's l1: 0.0567469	valid_0's l2: 0.00657607
    [277]	valid_0's l1: 0.056743	valid_0's l2: 0.00657593
    [278]	valid_0's l1: 0.0567391	valid_0's l2: 0.00657495
    [279]	valid_0's l1: 0.056739	valid_0's l2: 0.00657478
    [280]	valid_0's l1: 0.0567325	valid_0's l2: 0.00657337
    [281]	valid_0's l1: 0.0567323	valid_0's l2: 0.00657334
    [282]	valid_0's l1: 0.0567366	valid_0's l2: 0.00657365
    [283]	valid_0's l1: 0.0567364	valid_0's l2: 0.00657325
    [284]	valid_0's l1: 0.0567233	valid_0's l2: 0.00657079
    [285]	valid_0's l1: 0.0567246	valid_0's l2: 0.0065718
    [286]	valid_0's l1: 0.0567265	valid_0's l2: 0.00657222
    [287]	valid_0's l1: 0.0567223	valid_0's l2: 0.00657141
    [288]	valid_0's l1: 0.056714	valid_0's l2: 0.00656963
    [289]	valid_0's l1: 0.0567092	valid_0's l2: 0.00656967
    [290]	valid_0's l1: 0.0566989	valid_0's l2: 0.00656775
    [291]	valid_0's l1: 0.0566969	valid_0's l2: 0.00656714
    [292]	valid_0's l1: 0.0566972	valid_0's l2: 0.00656797
    [293]	valid_0's l1: 0.0566986	valid_0's l2: 0.00656812
    [294]	valid_0's l1: 0.056693	valid_0's l2: 0.00656709
    [295]	valid_0's l1: 0.0566943	valid_0's l2: 0.00656743
    [296]	valid_0's l1: 0.0566987	valid_0's l2: 0.0065679
    [297]	valid_0's l1: 0.056698	valid_0's l2: 0.00656738
    [298]	valid_0's l1: 0.0566927	valid_0's l2: 0.00656645
    [299]	valid_0's l1: 0.0566939	valid_0's l2: 0.00656589

[300]	valid_0's l1: 0.0566834	valid_0's l2: 0.00656346
    [301]	valid_0's l1: 0.0566872	valid_0's l2: 0.0065663
    [302]	valid_0's l1: 0.0566856	valid_0's l2: 0.00656487
    [303]	valid_0's l1: 0.0566822	valid_0's l2: 0.00656261
    [304]	valid_0's l1: 0.0566848	valid_0's l2: 0.00656279
    [305]	valid_0's l1: 0.0566818	valid_0's l2: 0.00656087
    [306]	valid_0's l1: 0.0566818	valid_0's l2: 0.00656023
    [307]	valid_0's l1: 0.0566825	valid_0's l2: 0.00655957
    [308]	valid_0's l1: 0.0566804	valid_0's l2: 0.00655989
    [309]	valid_0's l1: 0.0566693	valid_0's l2: 0.00655513
    [310]	valid_0's l1: 0.0566608	valid_0's l2: 0.00655397
    [311]	valid_0's l1: 0.0566546	valid_0's l2: 0.00655309
    [312]	valid_0's l1: 0.0566519	valid_0's l2: 0.00655274
    [313]	valid_0's l1: 0.05665	valid_0's l2: 0.00655275
    [314]	valid_0's l1: 0.0566492	valid_0's l2: 0.00655257
    [315]	valid_0's l1: 0.0566516	valid_0's l2: 0.00655293
    [316]	valid_0's l1: 0.0566499	valid_0's l2: 0.00655212
    [317]	valid_0's l1: 0.0566385	valid_0's l2: 0.00655
    [318]	valid_0's l1: 0.0566288	valid_0's l2: 0.00654956
    [319]	valid_0's l1: 0.0566317	valid_0's l2: 0.00655049
    [320]	valid_0's l1: 0.056631	valid_0's l2: 0.00655062
    [321]	valid_0's l1: 0.0566272	valid_0's l2: 0.00654966
    [322]	valid_0's l1: 0.0566247	valid_0's l2: 0.00654857
    [323]	valid_0's l1: 0.0566318	valid_0's l2: 0.00654932
    [324]	valid_0's l1: 0.0566344	valid_0's l2: 0.00655044
    [325]	valid_0's l1: 0.0566239	valid_0's l2: 0.00654858
    [326]	valid_0's l1: 0.0566241	valid_0's l2: 0.00654811
    [327]	valid_0's l1: 0.056621	valid_0's l2: 0.00654755
    [328]	valid_0's l1: 0.056627	valid_0's l2: 0.00655085
    [329]	valid_0's l1: 0.0566305	valid_0's l2: 0.00655121
    [330]	valid_0's l1: 0.0566281	valid_0's l2: 0.00655078
    [331]	valid_0's l1: 0.0566228	valid_0's l2: 0.00655045
    [332]	valid_0's l1: 0.0566242	valid_0's l2: 0.00654992
    Early stopping, best iteration is:
    [327]	valid_0's l1: 0.056621	valid_0's l2: 0.00654755
    本次结果输出的mae值是:
     0.0566209902947507
    [1]	valid_0's l1: 0.244105	valid_0's l2: 0.0788929
    Training until validation scores don't improve for 5 rounds
    [2]	valid_0's l1: 0.223433	valid_0's l2: 0.0665048
    [3]	valid_0's l1: 0.204693	valid_0's l2: 0.056241
    [4]	valid_0's l1: 0.188138	valid_0's l2: 0.0479084
    [5]	valid_0's l1: 0.173652	valid_0's l2: 0.0411735
    [6]	valid_0's l1: 0.160452	valid_0's l2: 0.0355248
    [7]	valid_0's l1: 0.149048	valid_0's l2: 0.0309893
    [8]	valid_0's l1: 0.138672	valid_0's l2: 0.0271575
    [9]	valid_0's l1: 0.129588	valid_0's l2: 0.0240739
    [10]	valid_0's l1: 0.12149	valid_0's l2: 0.0214714
    [11]	valid_0's l1: 0.11444	valid_0's l2: 0.0193639
    [12]	valid_0's l1: 0.108472	valid_0's l2: 0.0177015
    [13]	valid_0's l1: 0.102859	valid_0's l2: 0.0162138
    [14]	valid_0's l1: 0.0979989	valid_0's l2: 0.0149889
    [15]	valid_0's l1: 0.093631	valid_0's l2: 0.0139367
    [16]	valid_0's l1: 0.0897701	valid_0's l2: 0.0130511
    [17]	valid_0's l1: 0.0866436	valid_0's l2: 0.0123709
    [18]	valid_0's l1: 0.0837132	valid_0's l2: 0.011743
    [19]	valid_0's l1: 0.081271	valid_0's l2: 0.0112436
    [20]	valid_0's l1: 0.0787173	valid_0's l2: 0.0106819
    [21]	valid_0's l1: 0.0765278	valid_0's l2: 0.0102272
    [22]	valid_0's l1: 0.0747217	valid_0's l2: 0.00988702
    [23]	valid_0's l1: 0.0732441	valid_0's l2: 0.009621
    [24]	valid_0's l1: 0.0719183	valid_0's l2: 0.00938525
    [25]	valid_0's l1: 0.0706699	valid_0's l2: 0.00915448
    [26]	valid_0's l1: 0.0697174	valid_0's l2: 0.0089885
    [27]	valid_0's l1: 0.068879	valid_0's l2: 0.00884727
    [28]	valid_0's l1: 0.067942	valid_0's l2: 0.00866247
    [29]	valid_0's l1: 0.0673086	valid_0's l2: 0.00855939
    [30]	valid_0's l1: 0.0666173	valid_0's l2: 0.0084259
    [31]	valid_0's l1: 0.0659214	valid_0's l2: 0.00829166
    [32]	valid_0's l1: 0.065314	valid_0's l2: 0.00818122
    [33]	valid_0's l1: 0.0648871	valid_0's l2: 0.00811447
    [34]	valid_0's l1: 0.064382	valid_0's l2: 0.00802105
    [35]	valid_0's l1: 0.0640183	valid_0's l2: 0.00795961
    [36]	valid_0's l1: 0.0636983	valid_0's l2: 0.0079045
    [37]	valid_0's l1: 0.0633309	valid_0's l2: 0.00783605
    [38]	valid_0's l1: 0.0630379	valid_0's l2: 0.00777941
    [39]	valid_0's l1: 0.0627646	valid_0's l2: 0.00773424
    [40]	valid_0's l1: 0.0625134	valid_0's l2: 0.00769552
    [41]	valid_0's l1: 0.0622952	valid_0's l2: 0.00765205
    [42]	valid_0's l1: 0.0621023	valid_0's l2: 0.00761986
    [43]	valid_0's l1: 0.0617965	valid_0's l2: 0.00755124
    [44]	valid_0's l1: 0.0616577	valid_0's l2: 0.0075282
    [45]	valid_0's l1: 0.0614919	valid_0's l2: 0.00749925
    [46]	valid_0's l1: 0.0613431	valid_0's l2: 0.00747765
    [47]	valid_0's l1: 0.0610837	valid_0's l2: 0.00742058
    [48]	valid_0's l1: 0.0609448	valid_0's l2: 0.00739924
    [49]	valid_0's l1: 0.0608516	valid_0's l2: 0.00738553
    [50]	valid_0's l1: 0.0606495	valid_0's l2: 0.00733807
    [51]	valid_0's l1: 0.060549	valid_0's l2: 0.00731766
    [52]	valid_0's l1: 0.0604562	valid_0's l2: 0.0073052
    [53]	valid_0's l1: 0.0603682	valid_0's l2: 0.00729467
    [54]	valid_0's l1: 0.0602269	valid_0's l2: 0.00725959
    [55]	valid_0's l1: 0.060151	valid_0's l2: 0.00724768
    [56]	valid_0's l1: 0.060094	valid_0's l2: 0.00723379
    [57]	valid_0's l1: 0.059947	valid_0's l2: 0.00720518
    [58]	valid_0's l1: 0.0598583	valid_0's l2: 0.00719324
    [59]	valid_0's l1: 0.0597802	valid_0's l2: 0.00717468
    [60]	valid_0's l1: 0.0596954	valid_0's l2: 0.00715649
    [61]	valid_0's l1: 0.0596543	valid_0's l2: 0.00714967
    [62]	valid_0's l1: 0.0596053	valid_0's l2: 0.00714439
    [63]	valid_0's l1: 0.0594822	valid_0's l2: 0.00712291
    [64]	valid_0's l1: 0.0594105	valid_0's l2: 0.00710717
    [65]	valid_0's l1: 0.0593536	valid_0's l2: 0.00709356
    [66]	valid_0's l1: 0.0593072	valid_0's l2: 0.00708164
    [67]	valid_0's l1: 0.0592132	valid_0's l2: 0.00706076
    [68]	valid_0's l1: 0.0591506	valid_0's l2: 0.00704672
    [69]	valid_0's l1: 0.0591161	valid_0's l2: 0.00704004
    [70]	valid_0's l1: 0.0590643	valid_0's l2: 0.00702763
    [71]	valid_0's l1: 0.0590467	valid_0's l2: 0.00702267
    [72]	valid_0's l1: 0.0589565	valid_0's l2: 0.00700425
    [73]	valid_0's l1: 0.0589145	valid_0's l2: 0.00699761
    [74]	valid_0's l1: 0.0588782	valid_0's l2: 0.00698942
    [75]	valid_0's l1: 0.0588522	valid_0's l2: 0.00698468
    [76]	valid_0's l1: 0.0587967	valid_0's l2: 0.00696597
    [77]	valid_0's l1: 0.0587412	valid_0's l2: 0.00695178
    [78]	valid_0's l1: 0.0586989	valid_0's l2: 0.00694183
    [79]	valid_0's l1: 0.0586632	valid_0's l2: 0.00693235
    [80]	valid_0's l1: 0.0586331	valid_0's l2: 0.00692453
    [81]	valid_0's l1: 0.0586072	valid_0's l2: 0.00692157
    [82]	valid_0's l1: 0.0585562	valid_0's l2: 0.00690726
    [83]	valid_0's l1: 0.0585418	valid_0's l2: 0.00690549
    [84]	valid_0's l1: 0.058509	valid_0's l2: 0.00689496
    [85]	valid_0's l1: 0.0584869	valid_0's l2: 0.0068896
    [86]	valid_0's l1: 0.0584263	valid_0's l2: 0.00687405
    [87]	valid_0's l1: 0.0584199	valid_0's l2: 0.00687463
    [88]	valid_0's l1: 0.0583738	valid_0's l2: 0.00686202
    [89]	valid_0's l1: 0.0583086	valid_0's l2: 0.00684491
    [90]	valid_0's l1: 0.0582755	valid_0's l2: 0.00683798
    [91]	valid_0's l1: 0.0582677	valid_0's l2: 0.00683774
    [92]	valid_0's l1: 0.0582571	valid_0's l2: 0.00683567
    [93]	valid_0's l1: 0.0582269	valid_0's l2: 0.00683224
    [94]	valid_0's l1: 0.0581795	valid_0's l2: 0.00682475
    [95]	valid_0's l1: 0.0581728	valid_0's l2: 0.00682114
    [96]	valid_0's l1: 0.0581201	valid_0's l2: 0.00680408
    [97]	valid_0's l1: 0.0581001	valid_0's l2: 0.00680022
    [98]	valid_0's l1: 0.0580769	valid_0's l2: 0.00679834
    [99]	valid_0's l1: 0.0580318	valid_0's l2: 0.00678817
    [100]	valid_0's l1: 0.0580005	valid_0's l2: 0.00677894
    [101]	valid_0's l1: 0.0579874	valid_0's l2: 0.00677555
    [102]	valid_0's l1: 0.0579807	valid_0's l2: 0.00677678
    [103]	valid_0's l1: 0.0579638	valid_0's l2: 0.00677095
    [104]	valid_0's l1: 0.0579293	valid_0's l2: 0.00676342
    [105]	valid_0's l1: 0.05791	valid_0's l2: 0.00676344
    [106]	valid_0's l1: 0.0578992	valid_0's l2: 0.00676211
    [107]	valid_0's l1: 0.0578498	valid_0's l2: 0.00674959
    [108]	valid_0's l1: 0.057827	valid_0's l2: 0.00674559
    [109]	valid_0's l1: 0.0578117	valid_0's l2: 0.00674041
    [110]	valid_0's l1: 0.0578044	valid_0's l2: 0.00673802
    [111]	valid_0's l1: 0.0577842	valid_0's l2: 0.00673412
    [112]	valid_0's l1: 0.0577507	valid_0's l2: 0.00672687
    [113]	valid_0's l1: 0.0577474	valid_0's l2: 0.00672595
    [114]	valid_0's l1: 0.0577255	valid_0's l2: 0.00672198
    [115]	valid_0's l1: 0.0577231	valid_0's l2: 0.00672109
    [116]	valid_0's l1: 0.0577055	valid_0's l2: 0.00672055
    [117]	valid_0's l1: 0.0576466	valid_0's l2: 0.00670775
    [118]	valid_0's l1: 0.057628	valid_0's l2: 0.00670329
    [119]	valid_0's l1: 0.0575997	valid_0's l2: 0.00669591
    [120]	valid_0's l1: 0.0575961	valid_0's l2: 0.00669584
    [121]	valid_0's l1: 0.0575791	valid_0's l2: 0.00669389
    [122]	valid_0's l1: 0.0575359	valid_0's l2: 0.0066821
    [123]	valid_0's l1: 0.0575284	valid_0's l2: 0.00667967
    [124]	valid_0's l1: 0.057513	valid_0's l2: 0.00667581
    [125]	valid_0's l1: 0.0574838	valid_0's l2: 0.00666955
    [126]	valid_0's l1: 0.0574864	valid_0's l2: 0.00666745
    [127]	valid_0's l1: 0.0574734	valid_0's l2: 0.00666472
    [128]	valid_0's l1: 0.0574586	valid_0's l2: 0.00666244
    [129]	valid_0's l1: 0.0574407	valid_0's l2: 0.00666013
    [130]	valid_0's l1: 0.0574323	valid_0's l2: 0.00665867
    [131]	valid_0's l1: 0.0574333	valid_0's l2: 0.0066599
    [132]	valid_0's l1: 0.0574278	valid_0's l2: 0.00665876
    [133]	valid_0's l1: 0.0574076	valid_0's l2: 0.00665516
    [134]	valid_0's l1: 0.0574125	valid_0's l2: 0.00665794
    [135]	valid_0's l1: 0.0574071	valid_0's l2: 0.00665589
    [136]	valid_0's l1: 0.0573937	valid_0's l2: 0.00665473
    [137]	valid_0's l1: 0.0573902	valid_0's l2: 0.00665233
    [138]	valid_0's l1: 0.0573902	valid_0's l2: 0.00665279
    [139]	valid_0's l1: 0.0573865	valid_0's l2: 0.00665121
    [140]	valid_0's l1: 0.0573731	valid_0's l2: 0.0066489
    [141]	valid_0's l1: 0.0573604	valid_0's l2: 0.0066455
    [142]	valid_0's l1: 0.0573624	valid_0's l2: 0.00664686
    [143]	valid_0's l1: 0.0573593	valid_0's l2: 0.00664809
    [144]	valid_0's l1: 0.0573456	valid_0's l2: 0.00664556
    [145]	valid_0's l1: 0.0573137	valid_0's l2: 0.006639
    [146]	valid_0's l1: 0.0573132	valid_0's l2: 0.00663865
    [147]	valid_0's l1: 0.0573074	valid_0's l2: 0.00663662

[148]	valid_0's l1: 0.0573049	valid_0's l2: 0.00663523
    [149]	valid_0's l1: 0.0572911	valid_0's l2: 0.0066343
    [150]	valid_0's l1: 0.0572924	valid_0's l2: 0.00663432
    [151]	valid_0's l1: 0.0572662	valid_0's l2: 0.00662862
    [152]	valid_0's l1: 0.0572543	valid_0's l2: 0.00662574
    [153]	valid_0's l1: 0.057226	valid_0's l2: 0.00661834
    [154]	valid_0's l1: 0.0572214	valid_0's l2: 0.00661939
    [155]	valid_0's l1: 0.0572233	valid_0's l2: 0.00661946
    [156]	valid_0's l1: 0.057212	valid_0's l2: 0.00661578
    [157]	valid_0's l1: 0.0572076	valid_0's l2: 0.00661554
    [158]	valid_0's l1: 0.0571993	valid_0's l2: 0.00661361
    [159]	valid_0's l1: 0.057202	valid_0's l2: 0.00661621
    [160]	valid_0's l1: 0.0571892	valid_0's l2: 0.00661211
    [161]	valid_0's l1: 0.0571876	valid_0's l2: 0.00661046
    [162]	valid_0's l1: 0.0571866	valid_0's l2: 0.00661041
    [163]	valid_0's l1: 0.0571878	valid_0's l2: 0.00661
    [164]	valid_0's l1: 0.0571826	valid_0's l2: 0.00661015
    [165]	valid_0's l1: 0.0571824	valid_0's l2: 0.00661016
    [166]	valid_0's l1: 0.0571745	valid_0's l2: 0.00660962
    [167]	valid_0's l1: 0.0571554	valid_0's l2: 0.00660598
    [168]	valid_0's l1: 0.0571587	valid_0's l2: 0.00660641
    [169]	valid_0's l1: 0.0571557	valid_0's l2: 0.00660595
    [170]	valid_0's l1: 0.0571399	valid_0's l2: 0.00660777
    [171]	valid_0's l1: 0.057132	valid_0's l2: 0.00660611
    [172]	valid_0's l1: 0.057128	valid_0's l2: 0.00660516
    [173]	valid_0's l1: 0.0571253	valid_0's l2: 0.0066045
    [174]	valid_0's l1: 0.0571231	valid_0's l2: 0.00660414
    [175]	valid_0's l1: 0.0571209	valid_0's l2: 0.00660406
    [176]	valid_0's l1: 0.0571253	valid_0's l2: 0.00660481
    [177]	valid_0's l1: 0.0571189	valid_0's l2: 0.00660444
    [178]	valid_0's l1: 0.0571045	valid_0's l2: 0.00660155
    [179]	valid_0's l1: 0.0571037	valid_0's l2: 0.00660023
    [180]	valid_0's l1: 0.0571058	valid_0's l2: 0.00660109
    [181]	valid_0's l1: 0.0570763	valid_0's l2: 0.00659708
    [182]	valid_0's l1: 0.0570642	valid_0's l2: 0.00659508
    [183]	valid_0's l1: 0.0570605	valid_0's l2: 0.00659514
    [184]	valid_0's l1: 0.0570409	valid_0's l2: 0.00659086
    [185]	valid_0's l1: 0.0570447	valid_0's l2: 0.00659105
    [186]	valid_0's l1: 0.0570373	valid_0's l2: 0.00659125
    [187]	valid_0's l1: 0.0570329	valid_0's l2: 0.00659046
    [188]	valid_0's l1: 0.0570322	valid_0's l2: 0.00659034
    [189]	valid_0's l1: 0.0570349	valid_0's l2: 0.00659091
    [190]	valid_0's l1: 0.0570327	valid_0's l2: 0.00659076
    [191]	valid_0's l1: 0.0570249	valid_0's l2: 0.00658946
    [192]	valid_0's l1: 0.0570263	valid_0's l2: 0.00658941
    [193]	valid_0's l1: 0.0570234	valid_0's l2: 0.00658936
    [194]	valid_0's l1: 0.057018	valid_0's l2: 0.00658878
    [195]	valid_0's l1: 0.0570113	valid_0's l2: 0.00658768
    [196]	valid_0's l1: 0.0570008	valid_0's l2: 0.00658621
    [197]	valid_0's l1: 0.0570017	valid_0's l2: 0.00658597
    [198]	valid_0's l1: 0.0570015	valid_0's l2: 0.00658509
    [199]	valid_0's l1: 0.0569963	valid_0's l2: 0.00658326
    [200]	valid_0's l1: 0.0569934	valid_0's l2: 0.00658282
    [201]	valid_0's l1: 0.0569784	valid_0's l2: 0.00657947
    [202]	valid_0's l1: 0.0569634	valid_0's l2: 0.00657955
    [203]	valid_0's l1: 0.0569635	valid_0's l2: 0.00657958
    [204]	valid_0's l1: 0.0569585	valid_0's l2: 0.00657733
    [205]	valid_0's l1: 0.0569617	valid_0's l2: 0.0065785
    [206]	valid_0's l1: 0.0569614	valid_0's l2: 0.00657886
    [207]	valid_0's l1: 0.0569627	valid_0's l2: 0.00657942
    [208]	valid_0's l1: 0.0569641	valid_0's l2: 0.00657974
    [209]	valid_0's l1: 0.0569645	valid_0's l2: 0.00657976
    Early stopping, best iteration is:
    [204]	valid_0's l1: 0.0569585	valid_0's l2: 0.00657733
    本次结果输出的mae值是:
     0.05695850296967709
    [1]	valid_0's l1: 0.244063	valid_0's l2: 0.0788856
    Training until validation scores don't improve for 5 rounds
    [2]	valid_0's l1: 0.223176	valid_0's l2: 0.0664122
    [3]	valid_0's l1: 0.204455	valid_0's l2: 0.0561456
    [4]	valid_0's l1: 0.187824	valid_0's l2: 0.0478148
    [5]	valid_0's l1: 0.173277	valid_0's l2: 0.0410717
    [6]	valid_0's l1: 0.160081	valid_0's l2: 0.0354211
    [7]	valid_0's l1: 0.148661	valid_0's l2: 0.0308521
    [8]	valid_0's l1: 0.138527	valid_0's l2: 0.0271285
    [9]	valid_0's l1: 0.129282	valid_0's l2: 0.0239689
    [10]	valid_0's l1: 0.121392	valid_0's l2: 0.0214621
    [11]	valid_0's l1: 0.114518	valid_0's l2: 0.0194071
    [12]	valid_0's l1: 0.10824	valid_0's l2: 0.0176404
    [13]	valid_0's l1: 0.102699	valid_0's l2: 0.0161276
    [14]	valid_0's l1: 0.0978181	valid_0's l2: 0.0149099
    [15]	valid_0's l1: 0.0935616	valid_0's l2: 0.0138972
    [16]	valid_0's l1: 0.0897781	valid_0's l2: 0.0130326
    [17]	valid_0's l1: 0.0865207	valid_0's l2: 0.0123139
    [18]	valid_0's l1: 0.0835797	valid_0's l2: 0.0116615
    [19]	valid_0's l1: 0.0806617	valid_0's l2: 0.0110192
    [20]	valid_0's l1: 0.0783251	valid_0's l2: 0.010561
    [21]	valid_0's l1: 0.0764453	valid_0's l2: 0.010213
    [22]	valid_0's l1: 0.0745763	valid_0's l2: 0.0098424
    [23]	valid_0's l1: 0.0730364	valid_0's l2: 0.0095608
    [24]	valid_0's l1: 0.0717259	valid_0's l2: 0.00932012
    [25]	valid_0's l1: 0.070659	valid_0's l2: 0.00913589
    [26]	valid_0's l1: 0.0695184	valid_0's l2: 0.00890158
    [27]	valid_0's l1: 0.068521	valid_0's l2: 0.00872442
    [28]	valid_0's l1: 0.0677593	valid_0's l2: 0.00860031
    [29]	valid_0's l1: 0.0670563	valid_0's l2: 0.00846547
    [30]	valid_0's l1: 0.0663721	valid_0's l2: 0.0083437
    [31]	valid_0's l1: 0.0657946	valid_0's l2: 0.00824613
    [32]	valid_0's l1: 0.0652387	valid_0's l2: 0.00814615
    [33]	valid_0's l1: 0.064785	valid_0's l2: 0.00807145
    [34]	valid_0's l1: 0.0643381	valid_0's l2: 0.00799664
    [35]	valid_0's l1: 0.0639428	valid_0's l2: 0.00792993
    [36]	valid_0's l1: 0.0636731	valid_0's l2: 0.0078904
    [37]	valid_0's l1: 0.0633901	valid_0's l2: 0.00784526
    [38]	valid_0's l1: 0.0630453	valid_0's l2: 0.00778262
    [39]	valid_0's l1: 0.0627991	valid_0's l2: 0.00773648
    [40]	valid_0's l1: 0.0625745	valid_0's l2: 0.00769476
    [41]	valid_0's l1: 0.0623287	valid_0's l2: 0.00765658
    [42]	valid_0's l1: 0.0621665	valid_0's l2: 0.00762669
    [43]	valid_0's l1: 0.0618116	valid_0's l2: 0.00754486
    [44]	valid_0's l1: 0.0615524	valid_0's l2: 0.0074952
    [45]	valid_0's l1: 0.0614	valid_0's l2: 0.00746806
    [46]	valid_0's l1: 0.0612735	valid_0's l2: 0.00744893
    [47]	valid_0's l1: 0.0611022	valid_0's l2: 0.00742157
    [48]	valid_0's l1: 0.060977	valid_0's l2: 0.00739853
    [49]	valid_0's l1: 0.0607192	valid_0's l2: 0.00733779
    [50]	valid_0's l1: 0.0605926	valid_0's l2: 0.00731349
    [51]	valid_0's l1: 0.0604936	valid_0's l2: 0.00729716
    [52]	valid_0's l1: 0.0604034	valid_0's l2: 0.00728515
    [53]	valid_0's l1: 0.0602887	valid_0's l2: 0.0072663
    [54]	valid_0's l1: 0.0601539	valid_0's l2: 0.00723567
    [55]	valid_0's l1: 0.060007	valid_0's l2: 0.00720224
    [56]	valid_0's l1: 0.0599055	valid_0's l2: 0.00717916
    [57]	valid_0's l1: 0.0598399	valid_0's l2: 0.00716383
    [58]	valid_0's l1: 0.0597649	valid_0's l2: 0.00715059
    [59]	valid_0's l1: 0.0597	valid_0's l2: 0.00713486
    [60]	valid_0's l1: 0.0595987	valid_0's l2: 0.00711665
    [61]	valid_0's l1: 0.0595196	valid_0's l2: 0.0071022
    [62]	valid_0's l1: 0.059383	valid_0's l2: 0.00707138
    [63]	valid_0's l1: 0.0592932	valid_0's l2: 0.00705331
    [64]	valid_0's l1: 0.0592234	valid_0's l2: 0.00704094
    [65]	valid_0's l1: 0.0591341	valid_0's l2: 0.00702208
    [66]	valid_0's l1: 0.059073	valid_0's l2: 0.00700961
    [67]	valid_0's l1: 0.059019	valid_0's l2: 0.00699905
    [68]	valid_0's l1: 0.0589748	valid_0's l2: 0.00699518
    [69]	valid_0's l1: 0.0589165	valid_0's l2: 0.00698031
    [70]	valid_0's l1: 0.0588849	valid_0's l2: 0.00697689
    [71]	valid_0's l1: 0.058853	valid_0's l2: 0.00697075
    [72]	valid_0's l1: 0.0588092	valid_0's l2: 0.00696225
    [73]	valid_0's l1: 0.0587735	valid_0's l2: 0.00695234
    [74]	valid_0's l1: 0.0587286	valid_0's l2: 0.00694158
    [75]	valid_0's l1: 0.0586749	valid_0's l2: 0.00693199
    [76]	valid_0's l1: 0.058612	valid_0's l2: 0.00691603
    [77]	valid_0's l1: 0.0585808	valid_0's l2: 0.00691155
    [78]	valid_0's l1: 0.0585173	valid_0's l2: 0.00689848
    [79]	valid_0's l1: 0.0584979	valid_0's l2: 0.0068898
    [80]	valid_0's l1: 0.0584677	valid_0's l2: 0.00688132
    [81]	valid_0's l1: 0.0584299	valid_0's l2: 0.00687103
    [82]	valid_0's l1: 0.0584147	valid_0's l2: 0.00686414
    [83]	valid_0's l1: 0.0583878	valid_0's l2: 0.00686026
    [84]	valid_0's l1: 0.0583368	valid_0's l2: 0.00684628
    [85]	valid_0's l1: 0.0582898	valid_0's l2: 0.00683886
    [86]	valid_0's l1: 0.0582692	valid_0's l2: 0.0068303
    [87]	valid_0's l1: 0.0582186	valid_0's l2: 0.0068213
    [88]	valid_0's l1: 0.0581802	valid_0's l2: 0.00681356
    [89]	valid_0's l1: 0.0581544	valid_0's l2: 0.00680582
    [90]	valid_0's l1: 0.0581256	valid_0's l2: 0.00680097
    [91]	valid_0's l1: 0.0580926	valid_0's l2: 0.00679245
    [92]	valid_0's l1: 0.0580769	valid_0's l2: 0.00678742
    [93]	valid_0's l1: 0.0580553	valid_0's l2: 0.00678329

[94]	valid_0's l1: 0.0580344	valid_0's l2: 0.00678133
    [95]	valid_0's l1: 0.0580178	valid_0's l2: 0.0067792
    [96]	valid_0's l1: 0.0579945	valid_0's l2: 0.00677705
    [97]	valid_0's l1: 0.0579599	valid_0's l2: 0.00677002
    [98]	valid_0's l1: 0.0579278	valid_0's l2: 0.00676346
    [99]	valid_0's l1: 0.0578941	valid_0's l2: 0.00675483
    [100]	valid_0's l1: 0.0578671	valid_0's l2: 0.00675051
    [101]	valid_0's l1: 0.057862	valid_0's l2: 0.00674761
    [102]	valid_0's l1: 0.0578421	valid_0's l2: 0.00674354
    [103]	valid_0's l1: 0.0578054	valid_0's l2: 0.00673438
    [104]	valid_0's l1: 0.0577867	valid_0's l2: 0.00672863
    [105]	valid_0's l1: 0.057759	valid_0's l2: 0.00672193
    [106]	valid_0's l1: 0.0577344	valid_0's l2: 0.00671761
    [107]	valid_0's l1: 0.0577158	valid_0's l2: 0.0067133
    [108]	valid_0's l1: 0.0577095	valid_0's l2: 0.00671315
    [109]	valid_0's l1: 0.0576939	valid_0's l2: 0.00670815
    [110]	valid_0's l1: 0.0576888	valid_0's l2: 0.00670707
    [111]	valid_0's l1: 0.0576814	valid_0's l2: 0.00670523
    [112]	valid_0's l1: 0.0576824	valid_0's l2: 0.00670641
    [113]	valid_0's l1: 0.0576709	valid_0's l2: 0.00670404
    [114]	valid_0's l1: 0.0576689	valid_0's l2: 0.00670389
    [115]	valid_0's l1: 0.0576702	valid_0's l2: 0.00670487
    [116]	valid_0's l1: 0.057659	valid_0's l2: 0.00670353
    [117]	valid_0's l1: 0.0576338	valid_0's l2: 0.00669865
    [118]	valid_0's l1: 0.0576315	valid_0's l2: 0.00669768
    [119]	valid_0's l1: 0.057629	valid_0's l2: 0.00669664
    [120]	valid_0's l1: 0.0576168	valid_0's l2: 0.0066933
    [121]	valid_0's l1: 0.0575981	valid_0's l2: 0.00669042
    [122]	valid_0's l1: 0.0575889	valid_0's l2: 0.00668857
    [123]	valid_0's l1: 0.0575823	valid_0's l2: 0.00668705
    [124]	valid_0's l1: 0.0575609	valid_0's l2: 0.00668314
    [125]	valid_0's l1: 0.057543	valid_0's l2: 0.00667645
    [126]	valid_0's l1: 0.0575474	valid_0's l2: 0.00667774
    [127]	valid_0's l1: 0.0575349	valid_0's l2: 0.00667439
    [128]	valid_0's l1: 0.057525	valid_0's l2: 0.00667356
    [129]	valid_0's l1: 0.0575154	valid_0's l2: 0.00667239
    [130]	valid_0's l1: 0.0574792	valid_0's l2: 0.00666311
    [131]	valid_0's l1: 0.0574741	valid_0's l2: 0.00666224
    [132]	valid_0's l1: 0.057441	valid_0's l2: 0.00665561
    [133]	valid_0's l1: 0.0574263	valid_0's l2: 0.00665145
    [134]	valid_0's l1: 0.0574211	valid_0's l2: 0.00664973
    [135]	valid_0's l1: 0.0574148	valid_0's l2: 0.00664785
    [136]	valid_0's l1: 0.0574146	valid_0's l2: 0.0066487
    [137]	valid_0's l1: 0.057421	valid_0's l2: 0.00665064
    [138]	valid_0's l1: 0.0574108	valid_0's l2: 0.00664897
    [139]	valid_0's l1: 0.0574112	valid_0's l2: 0.00665038
    [140]	valid_0's l1: 0.0574058	valid_0's l2: 0.00665055
    Early stopping, best iteration is:
    [135]	valid_0's l1: 0.0574148	valid_0's l2: 0.00664785
    本次结果输出的mae值是:
     0.057414793402343275
    [1]	valid_0's l1: 0.244063	valid_0's l2: 0.0788856
    Training until validation scores don't improve for 5 rounds
    [2]	valid_0's l1: 0.223176	valid_0's l2: 0.0664122
    [3]	valid_0's l1: 0.204455	valid_0's l2: 0.0561456
    [4]	valid_0's l1: 0.187824	valid_0's l2: 0.0478148
    [5]	valid_0's l1: 0.173277	valid_0's l2: 0.0410717
    [6]	valid_0's l1: 0.160081	valid_0's l2: 0.0354211
    [7]	valid_0's l1: 0.148661	valid_0's l2: 0.0308521
    [8]	valid_0's l1: 0.138527	valid_0's l2: 0.0271285
    [9]	valid_0's l1: 0.129282	valid_0's l2: 0.0239689
    [10]	valid_0's l1: 0.121392	valid_0's l2: 0.0214621
    [11]	valid_0's l1: 0.114518	valid_0's l2: 0.0194071
    [12]	valid_0's l1: 0.10824	valid_0's l2: 0.0176404
    [13]	valid_0's l1: 0.102699	valid_0's l2: 0.0161276
    [14]	valid_0's l1: 0.0978181	valid_0's l2: 0.0149099
    [15]	valid_0's l1: 0.0935616	valid_0's l2: 0.0138972
    [16]	valid_0's l1: 0.0897781	valid_0's l2: 0.0130326
    [17]	valid_0's l1: 0.0865207	valid_0's l2: 0.0123139
    [18]	valid_0's l1: 0.0835797	valid_0's l2: 0.0116615
    [19]	valid_0's l1: 0.0806617	valid_0's l2: 0.0110192
    [20]	valid_0's l1: 0.0783251	valid_0's l2: 0.010561
    [21]	valid_0's l1: 0.0764453	valid_0's l2: 0.010213
    [22]	valid_0's l1: 0.0745763	valid_0's l2: 0.0098424
    [23]	valid_0's l1: 0.0730364	valid_0's l2: 0.0095608
    [24]	valid_0's l1: 0.0717334	valid_0's l2: 0.00931933
    [25]	valid_0's l1: 0.0706655	valid_0's l2: 0.00913462
    [26]	valid_0's l1: 0.0695224	valid_0's l2: 0.0089011
    [27]	valid_0's l1: 0.0685251	valid_0's l2: 0.00872392
    [28]	valid_0's l1: 0.0677621	valid_0's l2: 0.00859975
    [29]	valid_0's l1: 0.0670479	valid_0's l2: 0.00846277
    [30]	valid_0's l1: 0.0663683	valid_0's l2: 0.00833923
    [31]	valid_0's l1: 0.0657891	valid_0's l2: 0.00824289
    [32]	valid_0's l1: 0.065241	valid_0's l2: 0.0081429
    [33]	valid_0's l1: 0.0647888	valid_0's l2: 0.00806954
    [34]	valid_0's l1: 0.0643411	valid_0's l2: 0.00799501
    [35]	valid_0's l1: 0.0639537	valid_0's l2: 0.00792677
    [36]	valid_0's l1: 0.0636382	valid_0's l2: 0.00788136
    [37]	valid_0's l1: 0.0631577	valid_0's l2: 0.00778751
    [38]	valid_0's l1: 0.062859	valid_0's l2: 0.00773368
    [39]	valid_0's l1: 0.0626296	valid_0's l2: 0.00769338
    [40]	valid_0's l1: 0.0624051	valid_0's l2: 0.00765182
    [41]	valid_0's l1: 0.0622352	valid_0's l2: 0.00762209
    [42]	valid_0's l1: 0.0619461	valid_0's l2: 0.00756547
    [43]	valid_0's l1: 0.0617797	valid_0's l2: 0.00753276
    [44]	valid_0's l1: 0.0614276	valid_0's l2: 0.00744623
    [45]	valid_0's l1: 0.0612517	valid_0's l2: 0.00742016
    [46]	valid_0's l1: 0.0610947	valid_0's l2: 0.00739592
    [47]	valid_0's l1: 0.0609663	valid_0's l2: 0.00737358
    [48]	valid_0's l1: 0.060769	valid_0's l2: 0.00733339
    [49]	valid_0's l1: 0.0606285	valid_0's l2: 0.00730529
    [50]	valid_0's l1: 0.0604584	valid_0's l2: 0.00727351
    [51]	valid_0's l1: 0.0602991	valid_0's l2: 0.00724508
    [52]	valid_0's l1: 0.0602221	valid_0's l2: 0.00722567
    [53]	valid_0's l1: 0.0600667	valid_0's l2: 0.00719851
    [54]	valid_0's l1: 0.0599871	valid_0's l2: 0.00718487
    [55]	valid_0's l1: 0.0598913	valid_0's l2: 0.00716622
    [56]	valid_0's l1: 0.0597944	valid_0's l2: 0.00715016
    [57]	valid_0's l1: 0.0596758	valid_0's l2: 0.00713336
    [58]	valid_0's l1: 0.0596	valid_0's l2: 0.00711917
    [59]	valid_0's l1: 0.0595392	valid_0's l2: 0.00710513
    [60]	valid_0's l1: 0.0594717	valid_0's l2: 0.00709746
    [61]	valid_0's l1: 0.0594232	valid_0's l2: 0.00708731
    [62]	valid_0's l1: 0.0593787	valid_0's l2: 0.00707836
    [63]	valid_0's l1: 0.0592889	valid_0's l2: 0.00705705
    [64]	valid_0's l1: 0.0592268	valid_0's l2: 0.00704876
    [65]	valid_0's l1: 0.0591686	valid_0's l2: 0.0070416
    [66]	valid_0's l1: 0.0590772	valid_0's l2: 0.00702161
    [67]	valid_0's l1: 0.0589662	valid_0's l2: 0.00699605
    [68]	valid_0's l1: 0.0588787	valid_0's l2: 0.00697071
    [69]	valid_0's l1: 0.0588323	valid_0's l2: 0.00696029
    [70]	valid_0's l1: 0.0587533	valid_0's l2: 0.00694215
    [71]	valid_0's l1: 0.0586919	valid_0's l2: 0.00692878
    [72]	valid_0's l1: 0.0586246	valid_0's l2: 0.00691109
    [73]	valid_0's l1: 0.0585868	valid_0's l2: 0.0069041
    [74]	valid_0's l1: 0.0585397	valid_0's l2: 0.00689259
    [75]	valid_0's l1: 0.0584929	valid_0's l2: 0.00688262
    [76]	valid_0's l1: 0.0584706	valid_0's l2: 0.00687733
    [77]	valid_0's l1: 0.0584299	valid_0's l2: 0.0068677
    [78]	valid_0's l1: 0.0583709	valid_0's l2: 0.00685618
    [79]	valid_0's l1: 0.0583309	valid_0's l2: 0.00684923
    [80]	valid_0's l1: 0.0582852	valid_0's l2: 0.00683956
    [81]	valid_0's l1: 0.0582757	valid_0's l2: 0.00683683
    [82]	valid_0's l1: 0.0582213	valid_0's l2: 0.00682707
    [83]	valid_0's l1: 0.0581754	valid_0's l2: 0.00681631
    [84]	valid_0's l1: 0.0581571	valid_0's l2: 0.00681542
    [85]	valid_0's l1: 0.0581403	valid_0's l2: 0.00681774
    [86]	valid_0's l1: 0.0581176	valid_0's l2: 0.00681333
    [87]	valid_0's l1: 0.0580879	valid_0's l2: 0.00680352
    [88]	valid_0's l1: 0.0580737	valid_0's l2: 0.00680133
    [89]	valid_0's l1: 0.0580218	valid_0's l2: 0.00678954
    [90]	valid_0's l1: 0.0580105	valid_0's l2: 0.00678592
    [91]	valid_0's l1: 0.0579909	valid_0's l2: 0.00678153
    [92]	valid_0's l1: 0.0579343	valid_0's l2: 0.00676701
    [93]	valid_0's l1: 0.0579158	valid_0's l2: 0.00676113
    [94]	valid_0's l1: 0.0578677	valid_0's l2: 0.00674938
    [95]	valid_0's l1: 0.0578526	valid_0's l2: 0.00674681
    [96]	valid_0's l1: 0.0578378	valid_0's l2: 0.00674361
    [97]	valid_0's l1: 0.057815	valid_0's l2: 0.00673954
    [98]	valid_0's l1: 0.0578017	valid_0's l2: 0.00673705
    [99]	valid_0's l1: 0.0577899	valid_0's l2: 0.00673524
    [100]	valid_0's l1: 0.057765	valid_0's l2: 0.0067305
    [101]	valid_0's l1: 0.0577622	valid_0's l2: 0.0067312
    [102]	valid_0's l1: 0.0577477	valid_0's l2: 0.00672642
    [103]	valid_0's l1: 0.0577449	valid_0's l2: 0.0067272
    [104]	valid_0's l1: 0.0577327	valid_0's l2: 0.00672531
    [105]	valid_0's l1: 0.0577088	valid_0's l2: 0.00672135
    [106]	valid_0's l1: 0.0576958	valid_0's l2: 0.00671529
    [107]	valid_0's l1: 0.0576723	valid_0's l2: 0.00670975
    [108]	valid_0's l1: 0.0576425	valid_0's l2: 0.00670291
    [109]	valid_0's l1: 0.0576288	valid_0's l2: 0.00670083
    [110]	valid_0's l1: 0.0576223	valid_0's l2: 0.00669825
    [111]	valid_0's l1: 0.0575739	valid_0's l2: 0.0066865

[112]	valid_0's l1: 0.0575511	valid_0's l2: 0.00668169
    [113]	valid_0's l1: 0.0575504	valid_0's l2: 0.00668156
    [114]	valid_0's l1: 0.0575315	valid_0's l2: 0.00667443
    [115]	valid_0's l1: 0.0575035	valid_0's l2: 0.00666914
    [116]	valid_0's l1: 0.0574776	valid_0's l2: 0.00666205
    [117]	valid_0's l1: 0.0574713	valid_0's l2: 0.00666035
    [118]	valid_0's l1: 0.0574586	valid_0's l2: 0.00665749
    [119]	valid_0's l1: 0.057462	valid_0's l2: 0.00665968
    [120]	valid_0's l1: 0.0574539	valid_0's l2: 0.00665788
    [121]	valid_0's l1: 0.0574465	valid_0's l2: 0.00665674
    [122]	valid_0's l1: 0.0574396	valid_0's l2: 0.00665355
    [123]	valid_0's l1: 0.0574137	valid_0's l2: 0.00665023
    [124]	valid_0's l1: 0.0574152	valid_0's l2: 0.00665045
    [125]	valid_0's l1: 0.0573915	valid_0's l2: 0.00664429
    [126]	valid_0's l1: 0.0573724	valid_0's l2: 0.00663949
    [127]	valid_0's l1: 0.0573824	valid_0's l2: 0.00664405
    [128]	valid_0's l1: 0.0573845	valid_0's l2: 0.00664359
    [129]	valid_0's l1: 0.0573786	valid_0's l2: 0.00664196
    [130]	valid_0's l1: 0.0573559	valid_0's l2: 0.00663834
    [131]	valid_0's l1: 0.0573331	valid_0's l2: 0.00663644
    [132]	valid_0's l1: 0.0573199	valid_0's l2: 0.0066319
    [133]	valid_0's l1: 0.0573067	valid_0's l2: 0.00663149
    [134]	valid_0's l1: 0.057294	valid_0's l2: 0.00662884
    [135]	valid_0's l1: 0.0572883	valid_0's l2: 0.0066279
    [136]	valid_0's l1: 0.0572797	valid_0's l2: 0.006626
    [137]	valid_0's l1: 0.0572703	valid_0's l2: 0.00662318
    [138]	valid_0's l1: 0.0572728	valid_0's l2: 0.00662225
    [139]	valid_0's l1: 0.0572561	valid_0's l2: 0.0066201
    [140]	valid_0's l1: 0.0572488	valid_0's l2: 0.00661893
    [141]	valid_0's l1: 0.0572553	valid_0's l2: 0.00662052
    [142]	valid_0's l1: 0.0572455	valid_0's l2: 0.00662221
    [143]	valid_0's l1: 0.0572391	valid_0's l2: 0.00662034
    [144]	valid_0's l1: 0.0572357	valid_0's l2: 0.0066178
    [145]	valid_0's l1: 0.0572336	valid_0's l2: 0.00661707
    [146]	valid_0's l1: 0.0572169	valid_0's l2: 0.00661278
    [147]	valid_0's l1: 0.0572176	valid_0's l2: 0.00661231
    [148]	valid_0's l1: 0.0572168	valid_0's l2: 0.00661204
    [149]	valid_0's l1: 0.0572201	valid_0's l2: 0.00661197
    [150]	valid_0's l1: 0.0572184	valid_0's l2: 0.00661064
    [151]	valid_0's l1: 0.0572125	valid_0's l2: 0.00660915
    [152]	valid_0's l1: 0.0572116	valid_0's l2: 0.00660901
    [153]	valid_0's l1: 0.057203	valid_0's l2: 0.00660772
    [154]	valid_0's l1: 0.0572018	valid_0's l2: 0.00660706
    [155]	valid_0's l1: 0.0571923	valid_0's l2: 0.00660642
    [156]	valid_0's l1: 0.0571933	valid_0's l2: 0.00660578
    [157]	valid_0's l1: 0.0571993	valid_0's l2: 0.00660681
    [158]	valid_0's l1: 0.0572	valid_0's l2: 0.0066077
    [159]	valid_0's l1: 0.0571927	valid_0's l2: 0.00660645
    [160]	valid_0's l1: 0.0571965	valid_0's l2: 0.00660616
    Early stopping, best iteration is:
    [155]	valid_0's l1: 0.0571923	valid_0's l2: 0.00660642
    本次结果输出的mae值是:
     0.0571923061736829

In \[178\]:

plt.plot(max\_depth,scores,'o-')  
plt.ylabel("mae")  
plt.xlabel("max\_depths")  
print("best max\_depths \{\}".format(max\_depth\[np.argmin(scores)\]))

best max_depths 5

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3ppbWlhbzU1MjE0NzU3Mg_size_16_color_FFFFFF_t_70 5][]

In \[179\]:

scores

Out\[179\]:

[0.058867698663447106,
     0.0566209902947507,
     0.05695850296967709,
     0.057414793402343275,
     0.0571923061736829]

[20191009191333910.png]: https://img-blog.csdnimg.cn/20191009191333910.png
[Link 1]: http://www.rimengshe.com/
[AI_Keras PyTorch MXNet TensorFlow PaddlePaddle]: https://blog.csdn.net/zimiao552147572/article/details/88867161
[Bagging_Boosting_GBDT]: https://blog.csdn.net/zimiao552147572/article/details/104529615
[XGBoost]: https://blog.csdn.net/zimiao552147572/article/details/104658658
[lightGBM]: https://blog.csdn.net/zimiao552147572/article/details/104659336
[lightGBM 1]: https://blog.csdn.net/zimiao552147572/article/details/104659386
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3ppbWlhbzU1MjE0NzU3Mg_size_16_color_FFFFFF_t_70]: https://img-blog.csdnimg.cn/20200304180824431.png?x-oss-process=image/watermark,type_ZmFuZ3poZW5naGVpdGk,shadow_10,text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3ppbWlhbzU1MjE0NzU3Mg==,size_16,color_FFFFFF,t_70
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3ppbWlhbzU1MjE0NzU3Mg_size_16_color_FFFFFF_t_70 1]: https://img-blog.csdnimg.cn/20200304180833550.png?x-oss-process=image/watermark,type_ZmFuZ3poZW5naGVpdGk,shadow_10,text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3ppbWlhbzU1MjE0NzU3Mg==,size_16,color_FFFFFF,t_70
[20200304180850186.png]: https://img-blog.csdnimg.cn/20200304180850186.png
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3ppbWlhbzU1MjE0NzU3Mg_size_16_color_FFFFFF_t_70 2]: https://img-blog.csdnimg.cn/20200304180919604.png?x-oss-process=image/watermark,type_ZmFuZ3poZW5naGVpdGk,shadow_10,text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3ppbWlhbzU1MjE0NzU3Mg==,size_16,color_FFFFFF,t_70
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3ppbWlhbzU1MjE0NzU3Mg_size_16_color_FFFFFF_t_70 3]: https://img-blog.csdnimg.cn/2020030418150856.png?x-oss-process=image/watermark,type_ZmFuZ3poZW5naGVpdGk,shadow_10,text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3ppbWlhbzU1MjE0NzU3Mg==,size_16,color_FFFFFF,t_70
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3ppbWlhbzU1MjE0NzU3Mg_size_16_color_FFFFFF_t_70 4]: https://img-blog.csdnimg.cn/2020030418150881.png?x-oss-process=image/watermark,type_ZmFuZ3poZW5naGVpdGk,shadow_10,text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3ppbWlhbzU1MjE0NzU3Mg==,size_16,color_FFFFFF,t_70
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3ppbWlhbzU1MjE0NzU3Mg_size_16_color_FFFFFF_t_70 5]: https://img-blog.csdnimg.cn/2020030418150880.png?x-oss-process=image/watermark,type_ZmFuZ3poZW5naGVpdGk,shadow_10,text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3ppbWlhbzU1MjE0NzU3Mg==,size_16,color_FFFFFF,t_70