T85_code/特征分组建模_lightgbm.ipynb at master

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In [1]:

import warnings

warnings.filterwarnings("ignore")

In [2]:

import pandas as pd
import lightgbm as lgb
import numpy as np
import xgboost as xgb
import seaborn as sns
from sklearn.model_selection import train_test_split
from sklearn.model_selection import KFold
from sklearn.metrics import mean_absolute_error, mean_squared_error, mean_absolute_percentage_error, r2_score

In [3]:

total_data = pd.read_csv('./train_data_processed.csv')
total_data.head()

Out[3]:

	铭牌容量 (MW)	入炉煤低位热值(kJ/kg)	燃煤挥发份Var(%)	燃煤灰份Aar(%)	longitude	latitude	altitude	发电碳排放因子(kg/kWh)	供热碳排放因子(kg/MJ)	所处地区_上海市	...	机组类型_供热式	参数分类_亚临界	冷凝器型式_水冷
0	5.70711	9.818311	3.297687	2.815409	4.807875	3.467769	1.386294	0.537574	0.070992	1.0	...	1.0	1.0	1.0
1	5.70711	9.821572	3.297687	2.815409	4.807875	3.467769	1.386294	0.545516	0.072476	1.0	...	1.0	1.0	1.0
2	5.70711	9.878580	3.310543	2.769459	4.807875	3.467769	1.386294	0.595849	0.064745	1.0	...	1.0	1.0	1.0
3	5.70711	9.883285	3.324316	2.532108	4.807875	3.467769	1.386294	0.584432	0.068390	1.0	...	1.0	1.0	1.0
4	5.70711	9.909768	3.255015	2.766319	4.807875	3.467769	1.386294	0.605369	0.066996	1.0	...	1.0	1.0	1.0

5 rows × 60 columns

In [4]:

total_data.shape

Out[4]:

(3080, 60)

In [5]:

total_data.columns

Out[5]:

Index(['铭牌容量 (MW)', '入炉煤低位热值(kJ/kg)', '燃煤挥发份Var(%)', '燃煤灰份Aar(%)', 'longitude',
       'latitude', 'altitude', '发电碳排放因子(kg/kWh)', '供热碳排放因子(kg/MJ)', '所处地区_上海市',
       '所处地区_云南省', '所处地区_内蒙古', '所处地区_内蒙古自治区', '所处地区_北京市', '所处地区_吉林省',
       '所处地区_四川省', '所处地区_天津市', '所处地区_宁夏', '所处地区_宁夏回族自治区', '所处地区_安徽省',
       '所处地区_山东省', '所处地区_山西', '所处地区_山西省', '所处地区_广东省', '所处地区_广西', '所处地区_广西省',
       '所处地区_新疆', '所处地区_新疆维吾尔自治区', '所处地区_江苏省', '所处地区_江西省', '所处地区_河北',
       '所处地区_河北省', '所处地区_河南', '所处地区_河南省', '所处地区_浙江省', '所处地区_海南省', '所处地区_湖北',
       '所处地区_湖北省', '所处地区_湖南', '所处地区_湖南省', '所处地区_甘肃省', '所处地区_福建省', '所处地区_贵州省',
       '所处地区_辽宁省', '所处地区_重庆市', '所处地区_陕西省', '所处地区_青海省', '所处地区_黑龙江', '所处地区_黑龙江省',
       '机组类型_供热', '机组类型_供热式', '机组类型_纯凝式', '参数分类_亚临界', '参数分类_超临界', '参数分类_超超临界',
       '参数分类_超高压', '参数分类_高压', '冷凝器型式_水冷', '冷凝器型式_直接空冷', '冷凝器型式_间接空冷'],
      dtype='object')

In [6]:

feature_cols = [x for x in total_data.columns if '因子' not in x]
target_cols = [x for x in total_data.columns if x not in feature_cols]

In [7]:

use_data = total_data.groupby(feature_cols)[target_cols].mean().reset_index()
use_data

Out[7]:

	铭牌容量 (MW)	入炉煤低位热值(kJ/kg)	燃煤挥发份Var(%)	燃煤灰份Aar(%)	longitude	latitude	altitude	所处地区_上海市	所处地区_云南省	所处地区_内蒙古	...	参数分类_亚临界	参数分类_超临界	参数分类_超超临界	参数分类_超高压	参数分类_高压	冷凝器型式_水冷	冷凝器型式_直接空冷	冷凝器型式_间接空冷	发电碳排放因子(kg/kWh)	供热碳排放因子(kg/MJ)
0	4.615121	9.527411	3.823629	3.007661	4.834910	3.862442	4.983607	0.0	0.0	0.0	...	0.0	0.0	0.0	0.0	1.0	1.0	0.0	0.0	0.483547	0.058613
1	4.836282	9.920745	3.625673	3.201526	4.700990	3.563714	5.981414	0.0	0.0	0.0	...	0.0	0.0	0.0	1.0	0.0	1.0	0.0	0.0	0.575553	0.085880
2	4.836282	9.923023	3.623807	3.231200	4.700990	3.563714	5.981414	0.0	0.0	0.0	...	0.0	0.0	0.0	1.0	0.0	1.0	0.0	0.0	0.607741	0.084890
3	4.836282	9.932727	3.272227	3.236716	4.700990	3.563714	5.981414	0.0	0.0	0.0	...	0.0	0.0	0.0	1.0	0.0	1.0	0.0	0.0	0.595382	0.082342
4	4.836282	9.936819	3.278653	3.173460	4.700990	3.563714	5.981414	0.0	0.0	0.0	...	0.0	0.0	0.0	1.0	0.0	1.0	0.0	0.0	0.578838	0.082685
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
3075	6.966967	9.754581	3.100543	3.378270	4.676091	3.667429	7.020191	0.0	0.0	0.0	...	0.0	0.0	1.0	0.0	0.0	0.0	1.0	0.0	0.426880	0.061722
3076	6.966967	9.755162	3.082827	3.361070	4.676091	3.667429	7.020191	0.0	0.0	0.0	...	0.0	0.0	1.0	0.0	0.0	0.0	1.0	0.0	0.456768	0.060739
3077	6.966967	9.762903	3.095125	3.288775	4.676091	3.667429	7.020191	0.0	0.0	0.0	...	0.0	0.0	1.0	0.0	0.0	0.0	1.0	0.0	0.455534	0.061277
3078	6.966967	9.776506	3.096934	3.328268	4.676091	3.667429	7.020191	0.0	0.0	0.0	...	0.0	0.0	1.0	0.0	0.0	0.0	1.0	0.0	0.450064	0.062032
3079	6.966967	9.792277	3.073156	3.384051	4.676091	3.667429	7.020191	0.0	0.0	0.0	...	0.0	0.0	1.0	0.0	0.0	0.0	1.0	0.0	0.468720	0.063016

3080 rows × 60 columns

In [8]:

for col in use_data.columns:
    use_data[col] = use_data[col].astype(float)

In [15]:

train_data, test_data = train_test_split(use_data.dropna(), test_size=0.1, shuffle=True, random_state=666)
train_data, valid_data = train_test_split(train_data.dropna(), test_size=0.2, shuffle=True, random_state=666)

In [18]:

X_train, Y_train = train_data[feature_cols], train_data[target_cols[0]]
X_valid, Y_valid = valid_data[feature_cols], valid_data[target_cols[0]]
X_test, Y_test = test_data[feature_cols], test_data[target_cols[0]]

In [19]:

lgb_train = lgb.Dataset(X_train, Y_train)
lgb_eval = lgb.Dataset(X_valid, Y_valid)
lgb_test = lgb.Dataset(X_test, Y_test)

In [20]:

params_gbm = {
    'task': 'train',
    'boosting_type': 'gbdt',  # 设置提升类型
    'objective': 'l1',  # 目标函数
    'metric': {'rmse'},  # 评估函数
    'max_depth': 12,
    'num_leaves': 20,  # 叶子节点数
    'learning_rate': 0.05,  # 学习速率
    'feature_fraction': 0.9,  # 建树的特征选择比例
    'bagging_fraction': 0.9,  # 建树的样本采样比例
    'bagging_freq': 10,  # k 意味着每 k 次迭代执行bagging
    'verbose': -1  # <0 显示致命的, =0 显示错误 (警告), >0 显示信息
}

In [21]:

gbm = lgb.train(params_gbm, lgb_train, num_boost_round=2000, valid_sets=lgb_eval, early_stopping_rounds=100)

[1]	valid_0's rmse: 0.0692875
Training until validation scores don't improve for 100 rounds
[2]	valid_0's rmse: 0.06714
[3]	valid_0's rmse: 0.0646839
[4]	valid_0's rmse: 0.0623338
[5]	valid_0's rmse: 0.0600964
[6]	valid_0's rmse: 0.0580108
[7]	valid_0's rmse: 0.056067
[8]	valid_0's rmse: 0.0544344
[9]	valid_0's rmse: 0.0529408
[10]	valid_0's rmse: 0.051276
[11]	valid_0's rmse: 0.0497692
[12]	valid_0's rmse: 0.0483588
[13]	valid_0's rmse: 0.0470211
[14]	valid_0's rmse: 0.0460061
[15]	valid_0's rmse: 0.0448745
[16]	valid_0's rmse: 0.043796
[17]	valid_0's rmse: 0.0428645
[18]	valid_0's rmse: 0.0419008
[19]	valid_0's rmse: 0.0409544
[20]	valid_0's rmse: 0.0400698
[21]	valid_0's rmse: 0.0392848
[22]	valid_0's rmse: 0.038578
[23]	valid_0's rmse: 0.0378727
[24]	valid_0's rmse: 0.0371929
[25]	valid_0's rmse: 0.0366533
[26]	valid_0's rmse: 0.0360842
[27]	valid_0's rmse: 0.0355757
[28]	valid_0's rmse: 0.0350562
[29]	valid_0's rmse: 0.0345382
[30]	valid_0's rmse: 0.0340975
[31]	valid_0's rmse: 0.0337632
[32]	valid_0's rmse: 0.0334232
[33]	valid_0's rmse: 0.0330998
[34]	valid_0's rmse: 0.0328678
[35]	valid_0's rmse: 0.0325827
[36]	valid_0's rmse: 0.0323483
[37]	valid_0's rmse: 0.0321363
[38]	valid_0's rmse: 0.0318823
[39]	valid_0's rmse: 0.0316983
[40]	valid_0's rmse: 0.0315094
[41]	valid_0's rmse: 0.0313339
[42]	valid_0's rmse: 0.0311663
[43]	valid_0's rmse: 0.031002
[44]	valid_0's rmse: 0.0308446
[45]	valid_0's rmse: 0.0307193
[46]	valid_0's rmse: 0.03058
[47]	valid_0's rmse: 0.0304975
[48]	valid_0's rmse: 0.0303807
[49]	valid_0's rmse: 0.0302476
[50]	valid_0's rmse: 0.0301379
[51]	valid_0's rmse: 0.03
[52]	valid_0's rmse: 0.0299129
[53]	valid_0's rmse: 0.0298092
[54]	valid_0's rmse: 0.0297318
[55]	valid_0's rmse: 0.0296587
[56]	valid_0's rmse: 0.0295906
[57]	valid_0's rmse: 0.0295262
[58]	valid_0's rmse: 0.0294317
[59]	valid_0's rmse: 0.0293666
[60]	valid_0's rmse: 0.029295
[61]	valid_0's rmse: 0.0292621
[62]	valid_0's rmse: 0.0291822
[63]	valid_0's rmse: 0.0291453
[64]	valid_0's rmse: 0.029071
[65]	valid_0's rmse: 0.0289955
[66]	valid_0's rmse: 0.0289425
[67]	valid_0's rmse: 0.0288803
[68]	valid_0's rmse: 0.0288438
[69]	valid_0's rmse: 0.0288004
[70]	valid_0's rmse: 0.0287685
[71]	valid_0's rmse: 0.0287379
[72]	valid_0's rmse: 0.0286942
[73]	valid_0's rmse: 0.028654
[74]	valid_0's rmse: 0.0286255
[75]	valid_0's rmse: 0.0285826
[76]	valid_0's rmse: 0.0285438
[77]	valid_0's rmse: 0.0284903
[78]	valid_0's rmse: 0.0284767
[79]	valid_0's rmse: 0.0284401
[80]	valid_0's rmse: 0.0284152
[81]	valid_0's rmse: 0.0283845
[82]	valid_0's rmse: 0.028375
[83]	valid_0's rmse: 0.0283271
[84]	valid_0's rmse: 0.0283098
[85]	valid_0's rmse: 0.0282848
[86]	valid_0's rmse: 0.0282564
[87]	valid_0's rmse: 0.0282311
[88]	valid_0's rmse: 0.0281999
[89]	valid_0's rmse: 0.0281744
[90]	valid_0's rmse: 0.0281694
[91]	valid_0's rmse: 0.0281849
[92]	valid_0's rmse: 0.0281936
[93]	valid_0's rmse: 0.0281859
[94]	valid_0's rmse: 0.028193
[95]	valid_0's rmse: 0.0281768
[96]	valid_0's rmse: 0.0281729
[97]	valid_0's rmse: 0.0281829
[98]	valid_0's rmse: 0.0281698
[99]	valid_0's rmse: 0.0281678
[100]	valid_0's rmse: 0.0281451
[101]	valid_0's rmse: 0.0281243
[102]	valid_0's rmse: 0.028098
[103]	valid_0's rmse: 0.028089
[104]	valid_0's rmse: 0.0280947
[105]	valid_0's rmse: 0.0280915
[106]	valid_0's rmse: 0.0280942
[107]	valid_0's rmse: 0.0280905
[108]	valid_0's rmse: 0.0280888
[109]	valid_0's rmse: 0.0280827
[110]	valid_0's rmse: 0.028075
[111]	valid_0's rmse: 0.0280506
[112]	valid_0's rmse: 0.0280414
[113]	valid_0's rmse: 0.0280254
[114]	valid_0's rmse: 0.0280016
[115]	valid_0's rmse: 0.0279858
[116]	valid_0's rmse: 0.027973
[117]	valid_0's rmse: 0.027962
[118]	valid_0's rmse: 0.0279404
[119]	valid_0's rmse: 0.0279082
[120]	valid_0's rmse: 0.0279064
[121]	valid_0's rmse: 0.0279041
[122]	valid_0's rmse: 0.0278874
[123]	valid_0's rmse: 0.0278608
[124]	valid_0's rmse: 0.0278517
[125]	valid_0's rmse: 0.0278507
[126]	valid_0's rmse: 0.0278408
[127]	valid_0's rmse: 0.0278322
[128]	valid_0's rmse: 0.0278089
[129]	valid_0's rmse: 0.0278084
[130]	valid_0's rmse: 0.0277843
[131]	valid_0's rmse: 0.0277892
[132]	valid_0's rmse: 0.0277827
[133]	valid_0's rmse: 0.0277758
[134]	valid_0's rmse: 0.0277766
[135]	valid_0's rmse: 0.0277853
[136]	valid_0's rmse: 0.0277744
[137]	valid_0's rmse: 0.0277624
[138]	valid_0's rmse: 0.0277481
[139]	valid_0's rmse: 0.027733
[140]	valid_0's rmse: 0.0277201
[141]	valid_0's rmse: 0.0277112
[142]	valid_0's rmse: 0.0277081
[143]	valid_0's rmse: 0.0276965
[144]	valid_0's rmse: 0.0276911
[145]	valid_0's rmse: 0.0276786
[146]	valid_0's rmse: 0.0276798
[147]	valid_0's rmse: 0.0276724
[148]	valid_0's rmse: 0.0276479
[149]	valid_0's rmse: 0.0276436
[150]	valid_0's rmse: 0.0276115
[151]	valid_0's rmse: 0.0275966
[152]	valid_0's rmse: 0.0275874
[153]	valid_0's rmse: 0.0275693
[154]	valid_0's rmse: 0.0275769
[155]	valid_0's rmse: 0.0275677
[156]	valid_0's rmse: 0.0275517
[157]	valid_0's rmse: 0.0275422
[158]	valid_0's rmse: 0.0275326
[159]	valid_0's rmse: 0.0275205
[160]	valid_0's rmse: 0.0275234
[161]	valid_0's rmse: 0.0275164
[162]	valid_0's rmse: 0.0275097
[163]	valid_0's rmse: 0.0275092
[164]	valid_0's rmse: 0.0274879
[165]	valid_0's rmse: 0.0274696
[166]	valid_0's rmse: 0.0274685
[167]	valid_0's rmse: 0.0274698
[168]	valid_0's rmse: 0.0274655
[169]	valid_0's rmse: 0.0274796
[170]	valid_0's rmse: 0.0274609
[171]	valid_0's rmse: 0.0274455
[172]	valid_0's rmse: 0.0274493
[173]	valid_0's rmse: 0.0274369
[174]	valid_0's rmse: 0.0274299
[175]	valid_0's rmse: 0.0274234
[176]	valid_0's rmse: 0.0274104
[177]	valid_0's rmse: 0.0273984
[178]	valid_0's rmse: 0.0273957
[179]	valid_0's rmse: 0.0273894
[180]	valid_0's rmse: 0.0273696
[181]	valid_0's rmse: 0.0273432
[182]	valid_0's rmse: 0.027342
[183]	valid_0's rmse: 0.0273113
[184]	valid_0's rmse: 0.0273034
[185]	valid_0's rmse: 0.0272787
[186]	valid_0's rmse: 0.027264
[187]	valid_0's rmse: 0.0272687
[188]	valid_0's rmse: 0.0272646
[189]	valid_0's rmse: 0.027269
[190]	valid_0's rmse: 0.0272657
[191]	valid_0's rmse: 0.0272644
[192]	valid_0's rmse: 0.027266
[193]	valid_0's rmse: 0.0272565
[194]	valid_0's rmse: 0.0272468
[195]	valid_0's rmse: 0.0272463
[196]	valid_0's rmse: 0.027222
[197]	valid_0's rmse: 0.0271824
[198]	valid_0's rmse: 0.02718
[199]	valid_0's rmse: 0.0271605
[200]	valid_0's rmse: 0.0271487
[201]	valid_0's rmse: 0.0271442
[202]	valid_0's rmse: 0.0271446
[203]	valid_0's rmse: 0.0271367
[204]	valid_0's rmse: 0.0271474
[205]	valid_0's rmse: 0.0271404
[206]	valid_0's rmse: 0.0271376
[207]	valid_0's rmse: 0.0271251
[208]	valid_0's rmse: 0.0271296
[209]	valid_0's rmse: 0.0271322
[210]	valid_0's rmse: 0.0271364
[211]	valid_0's rmse: 0.027128
[212]	valid_0's rmse: 0.0271156
[213]	valid_0's rmse: 0.0271112
[214]	valid_0's rmse: 0.0271093
[215]	valid_0's rmse: 0.0271047
[216]	valid_0's rmse: 0.0270906
[217]	valid_0's rmse: 0.0270941
[218]	valid_0's rmse: 0.0270903
[219]	valid_0's rmse: 0.0270865
[220]	valid_0's rmse: 0.0270923
[221]	valid_0's rmse: 0.0270943
[222]	valid_0's rmse: 0.0270857
[223]	valid_0's rmse: 0.0270803
[224]	valid_0's rmse: 0.0270701
[225]	valid_0's rmse: 0.0270644
[226]	valid_0's rmse: 0.0270723
[227]	valid_0's rmse: 0.0270654
[228]	valid_0's rmse: 0.027069
[229]	valid_0's rmse: 0.0270634
[230]	valid_0's rmse: 0.027059
[231]	valid_0's rmse: 0.0270559
[232]	valid_0's rmse: 0.0270541
[233]	valid_0's rmse: 0.0270546
[234]	valid_0's rmse: 0.0270555
[235]	valid_0's rmse: 0.0270554
[236]	valid_0's rmse: 0.0270527
[237]	valid_0's rmse: 0.027045
[238]	valid_0's rmse: 0.0270457
[239]	valid_0's rmse: 0.0270406
[240]	valid_0's rmse: 0.0270462
[241]	valid_0's rmse: 0.0270405
[242]	valid_0's rmse: 0.0270448
[243]	valid_0's rmse: 0.0270406
[244]	valid_0's rmse: 0.0270415
[245]	valid_0's rmse: 0.0270421
[246]	valid_0's rmse: 0.0270327
[247]	valid_0's rmse: 0.0270246
[248]	valid_0's rmse: 0.0270194
[249]	valid_0's rmse: 0.0270177
[250]	valid_0's rmse: 0.0270092
[251]	valid_0's rmse: 0.0270089
[252]	valid_0's rmse: 0.0270085
[253]	valid_0's rmse: 0.0269901
[254]	valid_0's rmse: 0.0269891
[255]	valid_0's rmse: 0.0269845
[256]	valid_0's rmse: 0.0269845
[257]	valid_0's rmse: 0.0269555
[258]	valid_0's rmse: 0.026949
[259]	valid_0's rmse: 0.0269442
[260]	valid_0's rmse: 0.0269473
[261]	valid_0's rmse: 0.026946
[262]	valid_0's rmse: 0.0269368
[263]	valid_0's rmse: 0.0269311
[264]	valid_0's rmse: 0.0269294
[265]	valid_0's rmse: 0.0269236
[266]	valid_0's rmse: 0.0269203
[267]	valid_0's rmse: 0.0269202
[268]	valid_0's rmse: 0.0269171
[269]	valid_0's rmse: 0.0269116
[270]	valid_0's rmse: 0.026909
[271]	valid_0's rmse: 0.0269102
[272]	valid_0's rmse: 0.0269057
[273]	valid_0's rmse: 0.0269039
[274]	valid_0's rmse: 0.0269003
[275]	valid_0's rmse: 0.0268963
[276]	valid_0's rmse: 0.0268905
[277]	valid_0's rmse: 0.0268955
[278]	valid_0's rmse: 0.0268977
[279]	valid_0's rmse: 0.0269015
[280]	valid_0's rmse: 0.0269013
[281]	valid_0's rmse: 0.0268988
[282]	valid_0's rmse: 0.0268985
[283]	valid_0's rmse: 0.0268988
[284]	valid_0's rmse: 0.0268935
[285]	valid_0's rmse: 0.0268928
[286]	valid_0's rmse: 0.0268898
[287]	valid_0's rmse: 0.0268862
[288]	valid_0's rmse: 0.0268827
[289]	valid_0's rmse: 0.0268775
[290]	valid_0's rmse: 0.0268797
[291]	valid_0's rmse: 0.0268748
[292]	valid_0's rmse: 0.0268375
[293]	valid_0's rmse: 0.026812
[294]	valid_0's rmse: 0.0268085
[295]	valid_0's rmse: 0.0268076
[296]	valid_0's rmse: 0.026803
[297]	valid_0's rmse: 0.0267955
[298]	valid_0's rmse: 0.0267948
[299]	valid_0's rmse: 0.0267962
[300]	valid_0's rmse: 0.0267929
[301]	valid_0's rmse: 0.026792
[302]	valid_0's rmse: 0.026785
[303]	valid_0's rmse: 0.0267811
[304]	valid_0's rmse: 0.0267687
[305]	valid_0's rmse: 0.0267677
[306]	valid_0's rmse: 0.0267618
[307]	valid_0's rmse: 0.0267611
[308]	valid_0's rmse: 0.0267278
[309]	valid_0's rmse: 0.026727
[310]	valid_0's rmse: 0.0267222
[311]	valid_0's rmse: 0.0267172
[312]	valid_0's rmse: 0.0267138
[313]	valid_0's rmse: 0.0267119
[314]	valid_0's rmse: 0.0267091
[315]	valid_0's rmse: 0.0267093
[316]	valid_0's rmse: 0.0267089
[317]	valid_0's rmse: 0.0267078
[318]	valid_0's rmse: 0.0267068
[319]	valid_0's rmse: 0.0267062
[320]	valid_0's rmse: 0.0267035
[321]	valid_0's rmse: 0.0267021
[322]	valid_0's rmse: 0.0266997
[323]	valid_0's rmse: 0.026701
[324]	valid_0's rmse: 0.0266997
[325]	valid_0's rmse: 0.0266999
[326]	valid_0's rmse: 0.0267043
[327]	valid_0's rmse: 0.0267048
[328]	valid_0's rmse: 0.0266922
[329]	valid_0's rmse: 0.0266828
[330]	valid_0's rmse: 0.0266837
[331]	valid_0's rmse: 0.0266863
[332]	valid_0's rmse: 0.0266764
[333]	valid_0's rmse: 0.0266769
[334]	valid_0's rmse: 0.0266686
[335]	valid_0's rmse: 0.0266701
[336]	valid_0's rmse: 0.0266739
[337]	valid_0's rmse: 0.0266749
[338]	valid_0's rmse: 0.0266749
[339]	valid_0's rmse: 0.0266745
[340]	valid_0's rmse: 0.0266731
[341]	valid_0's rmse: 0.0266707
[342]	valid_0's rmse: 0.0266627
[343]	valid_0's rmse: 0.0266618
[344]	valid_0's rmse: 0.0266607
[345]	valid_0's rmse: 0.0266595
[346]	valid_0's rmse: 0.0266483
[347]	valid_0's rmse: 0.0266501
[348]	valid_0's rmse: 0.0266484
[349]	valid_0's rmse: 0.0266469
[350]	valid_0's rmse: 0.0266446
[351]	valid_0's rmse: 0.0266422
[352]	valid_0's rmse: 0.0266445
[353]	valid_0's rmse: 0.026642
[354]	valid_0's rmse: 0.0266332
[355]	valid_0's rmse: 0.0266333
[356]	valid_0's rmse: 0.0266291
[357]	valid_0's rmse: 0.0266298
[358]	valid_0's rmse: 0.0266302
[359]	valid_0's rmse: 0.026626
[360]	valid_0's rmse: 0.0266191
[361]	valid_0's rmse: 0.0266188
[362]	valid_0's rmse: 0.0266132
[363]	valid_0's rmse: 0.0266094
[364]	valid_0's rmse: 0.0266022
[365]	valid_0's rmse: 0.0266027
[366]	valid_0's rmse: 0.0266001
[367]	valid_0's rmse: 0.0266011
[368]	valid_0's rmse: 0.0265957
[369]	valid_0's rmse: 0.026593
[370]	valid_0's rmse: 0.0265889
[371]	valid_0's rmse: 0.0265887
[372]	valid_0's rmse: 0.0265821
[373]	valid_0's rmse: 0.026579
[374]	valid_0's rmse: 0.0265765
[375]	valid_0's rmse: 0.0265742
[376]	valid_0's rmse: 0.0265724
[377]	valid_0's rmse: 0.0265683
[378]	valid_0's rmse: 0.0265671
[379]	valid_0's rmse: 0.0265605
[380]	valid_0's rmse: 0.026561
[381]	valid_0's rmse: 0.0265544
[382]	valid_0's rmse: 0.026555
[383]	valid_0's rmse: 0.0265526
[384]	valid_0's rmse: 0.0265483
[385]	valid_0's rmse: 0.0265519
[386]	valid_0's rmse: 0.0265494
[387]	valid_0's rmse: 0.0265502
[388]	valid_0's rmse: 0.0265525
[389]	valid_0's rmse: 0.0265567
[390]	valid_0's rmse: 0.0265403
[391]	valid_0's rmse: 0.0265361
[392]	valid_0's rmse: 0.0265342
[393]	valid_0's rmse: 0.026529
[394]	valid_0's rmse: 0.0265267
[395]	valid_0's rmse: 0.0265303
[396]	valid_0's rmse: 0.0265306
[397]	valid_0's rmse: 0.0265338
[398]	valid_0's rmse: 0.0265294
[399]	valid_0's rmse: 0.0265253
[400]	valid_0's rmse: 0.0265248
[401]	valid_0's rmse: 0.0265266
[402]	valid_0's rmse: 0.0265279
[403]	valid_0's rmse: 0.0265289
[404]	valid_0's rmse: 0.0265279
[405]	valid_0's rmse: 0.0265228
[406]	valid_0's rmse: 0.0265323
[407]	valid_0's rmse: 0.0265335
[408]	valid_0's rmse: 0.0265318
[409]	valid_0's rmse: 0.0265298
[410]	valid_0's rmse: 0.0265275
[411]	valid_0's rmse: 0.0265259
[412]	valid_0's rmse: 0.0265261
[413]	valid_0's rmse: 0.0265267
[414]	valid_0's rmse: 0.0265261
[415]	valid_0's rmse: 0.0265255
[416]	valid_0's rmse: 0.0265275
[417]	valid_0's rmse: 0.0265225
[418]	valid_0's rmse: 0.0265226
[419]	valid_0's rmse: 0.0265222
[420]	valid_0's rmse: 0.026521
[421]	valid_0's rmse: 0.0265169
[422]	valid_0's rmse: 0.0265139
[423]	valid_0's rmse: 0.0265126
[424]	valid_0's rmse: 0.0265136
[425]	valid_0's rmse: 0.0265079
[426]	valid_0's rmse: 0.0265017
[427]	valid_0's rmse: 0.0264914
[428]	valid_0's rmse: 0.026489
[429]	valid_0's rmse: 0.0264918
[430]	valid_0's rmse: 0.0264906
[431]	valid_0's rmse: 0.0264809
[432]	valid_0's rmse: 0.0264809
[433]	valid_0's rmse: 0.0264819
[434]	valid_0's rmse: 0.0264775
[435]	valid_0's rmse: 0.0264744
[436]	valid_0's rmse: 0.026474
[437]	valid_0's rmse: 0.0264713
[438]	valid_0's rmse: 0.0264702
[439]	valid_0's rmse: 0.0264686
[440]	valid_0's rmse: 0.0264654
[441]	valid_0's rmse: 0.0264663
[442]	valid_0's rmse: 0.0264543
[443]	valid_0's rmse: 0.0264538
[444]	valid_0's rmse: 0.0264507
[445]	valid_0's rmse: 0.0264509
[446]	valid_0's rmse: 0.0264456
[447]	valid_0's rmse: 0.0264483
[448]	valid_0's rmse: 0.0264169
[449]	valid_0's rmse: 0.0264151
[450]	valid_0's rmse: 0.0264172
[451]	valid_0's rmse: 0.0264171
[452]	valid_0's rmse: 0.0264175
[453]	valid_0's rmse: 0.0264149
[454]	valid_0's rmse: 0.0264144
[455]	valid_0's rmse: 0.0264154
[456]	valid_0's rmse: 0.0264147
[457]	valid_0's rmse: 0.0264118
[458]	valid_0's rmse: 0.0264138
[459]	valid_0's rmse: 0.0264151
[460]	valid_0's rmse: 0.026415
[461]	valid_0's rmse: 0.0264159
[462]	valid_0's rmse: 0.0264121
[463]	valid_0's rmse: 0.026414
[464]	valid_0's rmse: 0.0264093
[465]	valid_0's rmse: 0.0264118
[466]	valid_0's rmse: 0.0264118
[467]	valid_0's rmse: 0.0264099
[468]	valid_0's rmse: 0.0264113
[469]	valid_0's rmse: 0.0264101
[470]	valid_0's rmse: 0.0264118
[471]	valid_0's rmse: 0.0264092
[472]	valid_0's rmse: 0.0264044
[473]	valid_0's rmse: 0.0263975
[474]	valid_0's rmse: 0.0263909
[475]	valid_0's rmse: 0.0263866
[476]	valid_0's rmse: 0.0263848
[477]	valid_0's rmse: 0.0263839
[478]	valid_0's rmse: 0.0263787
[479]	valid_0's rmse: 0.0263797
[480]	valid_0's rmse: 0.0263769
[481]	valid_0's rmse: 0.0263744
[482]	valid_0's rmse: 0.0263693
[483]	valid_0's rmse: 0.0263673
[484]	valid_0's rmse: 0.0263626
[485]	valid_0's rmse: 0.0263591
[486]	valid_0's rmse: 0.0263569
[487]	valid_0's rmse: 0.0263557
[488]	valid_0's rmse: 0.0263559
[489]	valid_0's rmse: 0.026358
[490]	valid_0's rmse: 0.0263566
[491]	valid_0's rmse: 0.0263564
[492]	valid_0's rmse: 0.0263568
[493]	valid_0's rmse: 0.0263562
[494]	valid_0's rmse: 0.0263561
[495]	valid_0's rmse: 0.0263508
[496]	valid_0's rmse: 0.0263498
[497]	valid_0's rmse: 0.026346
[498]	valid_0's rmse: 0.0263474
[499]	valid_0's rmse: 0.026346
[500]	valid_0's rmse: 0.026342
[501]	valid_0's rmse: 0.0263415
[502]	valid_0's rmse: 0.0263404
[503]	valid_0's rmse: 0.0263355
[504]	valid_0's rmse: 0.0263363
[505]	valid_0's rmse: 0.0263362
[506]	valid_0's rmse: 0.0263356
[507]	valid_0's rmse: 0.0263345
[508]	valid_0's rmse: 0.0263343
[509]	valid_0's rmse: 0.0263294
[510]	valid_0's rmse: 0.0263279
[511]	valid_0's rmse: 0.0263274
[512]	valid_0's rmse: 0.0263227
[513]	valid_0's rmse: 0.0263228
[514]	valid_0's rmse: 0.0263178
[515]	valid_0's rmse: 0.0263175
[516]	valid_0's rmse: 0.0263152
[517]	valid_0's rmse: 0.0263062
[518]	valid_0's rmse: 0.0263098
[519]	valid_0's rmse: 0.0263065
[520]	valid_0's rmse: 0.0263043
[521]	valid_0's rmse: 0.0263029
[522]	valid_0's rmse: 0.0263005
[523]	valid_0's rmse: 0.0263013
[524]	valid_0's rmse: 0.0263
[525]	valid_0's rmse: 0.0262944
[526]	valid_0's rmse: 0.0262956
[527]	valid_0's rmse: 0.0262945
[528]	valid_0's rmse: 0.0262948
[529]	valid_0's rmse: 0.0262927
[530]	valid_0's rmse: 0.0262942
[531]	valid_0's rmse: 0.0262821
[532]	valid_0's rmse: 0.0262828
[533]	valid_0's rmse: 0.0262794
[534]	valid_0's rmse: 0.0262778
[535]	valid_0's rmse: 0.0262769
[536]	valid_0's rmse: 0.0262763
[537]	valid_0's rmse: 0.0262754
[538]	valid_0's rmse: 0.026275
[539]	valid_0's rmse: 0.0262742
[540]	valid_0's rmse: 0.02625
[541]	valid_0's rmse: 0.0262449
[542]	valid_0's rmse: 0.0262456
[543]	valid_0's rmse: 0.0262468
[544]	valid_0's rmse: 0.0262448
[545]	valid_0's rmse: 0.0262438
[546]	valid_0's rmse: 0.0262417
[547]	valid_0's rmse: 0.026231
[548]	valid_0's rmse: 0.0262339
[549]	valid_0's rmse: 0.0262327
[550]	valid_0's rmse: 0.0262289
[551]	valid_0's rmse: 0.0262244
[552]	valid_0's rmse: 0.0262075
[553]	valid_0's rmse: 0.0262031
[554]	valid_0's rmse: 0.0262028
[555]	valid_0's rmse: 0.0261984
[556]	valid_0's rmse: 0.0261981
[557]	valid_0's rmse: 0.0261977
[558]	valid_0's rmse: 0.0262004
[559]	valid_0's rmse: 0.0261955
[560]	valid_0's rmse: 0.0261955
[561]	valid_0's rmse: 0.0261947
[562]	valid_0's rmse: 0.0261983
[563]	valid_0's rmse: 0.0261981
[564]	valid_0's rmse: 0.0261992
[565]	valid_0's rmse: 0.0261974
[566]	valid_0's rmse: 0.0261936
[567]	valid_0's rmse: 0.0261954
[568]	valid_0's rmse: 0.0261987
[569]	valid_0's rmse: 0.0261837
[570]	valid_0's rmse: 0.0261839
[571]	valid_0's rmse: 0.026185
[572]	valid_0's rmse: 0.0261849
[573]	valid_0's rmse: 0.0261842
[574]	valid_0's rmse: 0.0261826
[575]	valid_0's rmse: 0.0261834
[576]	valid_0's rmse: 0.0261825
[577]	valid_0's rmse: 0.0261717
[578]	valid_0's rmse: 0.026171
[579]	valid_0's rmse: 0.0261609
[580]	valid_0's rmse: 0.02616
[581]	valid_0's rmse: 0.0261573
[582]	valid_0's rmse: 0.026159
[583]	valid_0's rmse: 0.0261576
[584]	valid_0's rmse: 0.0261557
[585]	valid_0's rmse: 0.0261582
[586]	valid_0's rmse: 0.026158
[587]	valid_0's rmse: 0.0261573
[588]	valid_0's rmse: 0.0261571
[589]	valid_0's rmse: 0.0261535
[590]	valid_0's rmse: 0.0261534
[591]	valid_0's rmse: 0.0261534
[592]	valid_0's rmse: 0.0261436
[593]	valid_0's rmse: 0.0261423
[594]	valid_0's rmse: 0.0261409
[595]	valid_0's rmse: 0.0261377
[596]	valid_0's rmse: 0.0261358
[597]	valid_0's rmse: 0.0261367
[598]	valid_0's rmse: 0.026137
[599]	valid_0's rmse: 0.0261357
[600]	valid_0's rmse: 0.0261344
[601]	valid_0's rmse: 0.0261345
[602]	valid_0's rmse: 0.026133
[603]	valid_0's rmse: 0.0261313
[604]	valid_0's rmse: 0.0261344
[605]	valid_0's rmse: 0.0261339
[606]	valid_0's rmse: 0.0261321
[607]	valid_0's rmse: 0.0261288
[608]	valid_0's rmse: 0.0261285
[609]	valid_0's rmse: 0.0261298
[610]	valid_0's rmse: 0.026131
[611]	valid_0's rmse: 0.0261265
[612]	valid_0's rmse: 0.0261043
[613]	valid_0's rmse: 0.0261023
[614]	valid_0's rmse: 0.0261013
[615]	valid_0's rmse: 0.0260971
[616]	valid_0's rmse: 0.0260979
[617]	valid_0's rmse: 0.0260987
[618]	valid_0's rmse: 0.0260728
[619]	valid_0's rmse: 0.026069
[620]	valid_0's rmse: 0.0260678
[621]	valid_0's rmse: 0.0260587
[622]	valid_0's rmse: 0.0260571
[623]	valid_0's rmse: 0.0260564
[624]	valid_0's rmse: 0.026054
[625]	valid_0's rmse: 0.0260544
[626]	valid_0's rmse: 0.0260502
[627]	valid_0's rmse: 0.0260444
[628]	valid_0's rmse: 0.026044
[629]	valid_0's rmse: 0.02604
[630]	valid_0's rmse: 0.0260386
[631]	valid_0's rmse: 0.0260394
[632]	valid_0's rmse: 0.0260378
[633]	valid_0's rmse: 0.0260397
[634]	valid_0's rmse: 0.0260395
[635]	valid_0's rmse: 0.0260398
[636]	valid_0's rmse: 0.0260376
[637]	valid_0's rmse: 0.026039
[638]	valid_0's rmse: 0.0260362
[639]	valid_0's rmse: 0.0260345
[640]	valid_0's rmse: 0.0260342
[641]	valid_0's rmse: 0.0260336
[642]	valid_0's rmse: 0.0260337
[643]	valid_0's rmse: 0.0260325
[644]	valid_0's rmse: 0.0260305
[645]	valid_0's rmse: 0.0260308
[646]	valid_0's rmse: 0.0260319
[647]	valid_0's rmse: 0.0260334
[648]	valid_0's rmse: 0.0260338
[649]	valid_0's rmse: 0.0260325
[650]	valid_0's rmse: 0.0260265
[651]	valid_0's rmse: 0.0260269
[652]	valid_0's rmse: 0.0260251
[653]	valid_0's rmse: 0.0260252
[654]	valid_0's rmse: 0.0260251
[655]	valid_0's rmse: 0.0260257
[656]	valid_0's rmse: 0.0260234
[657]	valid_0's rmse: 0.0260219
[658]	valid_0's rmse: 0.0260211
[659]	valid_0's rmse: 0.0260209
[660]	valid_0's rmse: 0.0260217
[661]	valid_0's rmse: 0.0260234
[662]	valid_0's rmse: 0.0260244
[663]	valid_0's rmse: 0.0260219
[664]	valid_0's rmse: 0.0260216
[665]	valid_0's rmse: 0.026023
[666]	valid_0's rmse: 0.026025
[667]	valid_0's rmse: 0.0260245
[668]	valid_0's rmse: 0.026022
[669]	valid_0's rmse: 0.0260216
[670]	valid_0's rmse: 0.0260231
[671]	valid_0's rmse: 0.0260226
[672]	valid_0's rmse: 0.0260197
[673]	valid_0's rmse: 0.0260191
[674]	valid_0's rmse: 0.0260193
[675]	valid_0's rmse: 0.0260178
[676]	valid_0's rmse: 0.0260171
[677]	valid_0's rmse: 0.0260153
[678]	valid_0's rmse: 0.0260153
[679]	valid_0's rmse: 0.026013
[680]	valid_0's rmse: 0.0260116
[681]	valid_0's rmse: 0.0260089
[682]	valid_0's rmse: 0.0260046
[683]	valid_0's rmse: 0.0260029
[684]	valid_0's rmse: 0.0260038
[685]	valid_0's rmse: 0.0260018
[686]	valid_0's rmse: 0.0260058
[687]	valid_0's rmse: 0.0260083
[688]	valid_0's rmse: 0.0260081
[689]	valid_0's rmse: 0.0260076
[690]	valid_0's rmse: 0.0260032
[691]	valid_0's rmse: 0.0260018
[692]	valid_0's rmse: 0.0260013
[693]	valid_0's rmse: 0.0260024
[694]	valid_0's rmse: 0.026003
[695]	valid_0's rmse: 0.0260023
[696]	valid_0's rmse: 0.0260022
[697]	valid_0's rmse: 0.0260018
[698]	valid_0's rmse: 0.0260004
[699]	valid_0's rmse: 0.0259998
[700]	valid_0's rmse: 0.0259961
[701]	valid_0's rmse: 0.0259964
[702]	valid_0's rmse: 0.0259942
[703]	valid_0's rmse: 0.0259951
[704]	valid_0's rmse: 0.0259918
[705]	valid_0's rmse: 0.0259913
[706]	valid_0's rmse: 0.0259895
[707]	valid_0's rmse: 0.0259881
[708]	valid_0's rmse: 0.0259869
[709]	valid_0's rmse: 0.0259796
[710]	valid_0's rmse: 0.0259789
[711]	valid_0's rmse: 0.0259766
[712]	valid_0's rmse: 0.0259758
[713]	valid_0's rmse: 0.0259746
[714]	valid_0's rmse: 0.0259744
[715]	valid_0's rmse: 0.0259761
[716]	valid_0's rmse: 0.0259832
[717]	valid_0's rmse: 0.0259813
[718]	valid_0's rmse: 0.0259823
[719]	valid_0's rmse: 0.0259815
[720]	valid_0's rmse: 0.0259701
[721]	valid_0's rmse: 0.0259693
[722]	valid_0's rmse: 0.0259679
[723]	valid_0's rmse: 0.0259668
[724]	valid_0's rmse: 0.0259646
[725]	valid_0's rmse: 0.0259639
[726]	valid_0's rmse: 0.0259672
[727]	valid_0's rmse: 0.025969
[728]	valid_0's rmse: 0.0259709
[729]	valid_0's rmse: 0.0259705
[730]	valid_0's rmse: 0.0259611
[731]	valid_0's rmse: 0.0259601
[732]	valid_0's rmse: 0.0259605
[733]	valid_0's rmse: 0.02596
[734]	valid_0's rmse: 0.0259589
[735]	valid_0's rmse: 0.0259593
[736]	valid_0's rmse: 0.0259612
[737]	valid_0's rmse: 0.0259617
[738]	valid_0's rmse: 0.0259604
[739]	valid_0's rmse: 0.0259609
[740]	valid_0's rmse: 0.0259575
[741]	valid_0's rmse: 0.0259552
[742]	valid_0's rmse: 0.025958
[743]	valid_0's rmse: 0.0259575
[744]	valid_0's rmse: 0.0259551
[745]	valid_0's rmse: 0.0259555
[746]	valid_0's rmse: 0.0259564
[747]	valid_0's rmse: 0.0259554
[748]	valid_0's rmse: 0.0259536
[749]	valid_0's rmse: 0.0259524
[750]	valid_0's rmse: 0.0259526
[751]	valid_0's rmse: 0.0259521
[752]	valid_0's rmse: 0.0259515
[753]	valid_0's rmse: 0.0259512
[754]	valid_0's rmse: 0.0259504
[755]	valid_0's rmse: 0.0259508
[756]	valid_0's rmse: 0.0259495
[757]	valid_0's rmse: 0.0259432
[758]	valid_0's rmse: 0.0259428
[759]	valid_0's rmse: 0.0259422
[760]	valid_0's rmse: 0.0259443
[761]	valid_0's rmse: 0.0259459
[762]	valid_0's rmse: 0.0259443
[763]	valid_0's rmse: 0.0259442
[764]	valid_0's rmse: 0.0259432
[765]	valid_0's rmse: 0.025944
[766]	valid_0's rmse: 0.0259433
[767]	valid_0's rmse: 0.0259438
[768]	valid_0's rmse: 0.0259408
[769]	valid_0's rmse: 0.0259404
[770]	valid_0's rmse: 0.0259398
[771]	valid_0's rmse: 0.0259375
[772]	valid_0's rmse: 0.025935
[773]	valid_0's rmse: 0.0259347
[774]	valid_0's rmse: 0.0259332
[775]	valid_0's rmse: 0.0259335
[776]	valid_0's rmse: 0.0259349
[777]	valid_0's rmse: 0.0259345
[778]	valid_0's rmse: 0.0259353
[779]	valid_0's rmse: 0.0259353
[780]	valid_0's rmse: 0.0259354
[781]	valid_0's rmse: 0.025935
[782]	valid_0's rmse: 0.0259362
[783]	valid_0's rmse: 0.0259348
[784]	valid_0's rmse: 0.0259347
[785]	valid_0's rmse: 0.0259361
[786]	valid_0's rmse: 0.0259417
[787]	valid_0's rmse: 0.0259418
[788]	valid_0's rmse: 0.0259422
[789]	valid_0's rmse: 0.0259422
[790]	valid_0's rmse: 0.0259419
[791]	valid_0's rmse: 0.0259409
[792]	valid_0's rmse: 0.0259409
[793]	valid_0's rmse: 0.0259433
[794]	valid_0's rmse: 0.0259438
[795]	valid_0's rmse: 0.0259415
[796]	valid_0's rmse: 0.0259423
[797]	valid_0's rmse: 0.0259435
[798]	valid_0's rmse: 0.0259416
[799]	valid_0's rmse: 0.0259469
[800]	valid_0's rmse: 0.0259488
[801]	valid_0's rmse: 0.0259505
[802]	valid_0's rmse: 0.025947
[803]	valid_0's rmse: 0.0259453
[804]	valid_0's rmse: 0.0259434
[805]	valid_0's rmse: 0.0259429
[806]	valid_0's rmse: 0.0259445
[807]	valid_0's rmse: 0.0259469
[808]	valid_0's rmse: 0.0259436
[809]	valid_0's rmse: 0.0259414
[810]	valid_0's rmse: 0.0259419
[811]	valid_0's rmse: 0.0259498
[812]	valid_0's rmse: 0.0259524
[813]	valid_0's rmse: 0.025951
[814]	valid_0's rmse: 0.0259468
[815]	valid_0's rmse: 0.0259462
[816]	valid_0's rmse: 0.0259387
[817]	valid_0's rmse: 0.0259382
[818]	valid_0's rmse: 0.0259381
[819]	valid_0's rmse: 0.0259391
[820]	valid_0's rmse: 0.0259437
[821]	valid_0's rmse: 0.0259455
[822]	valid_0's rmse: 0.0259458
[823]	valid_0's rmse: 0.0259459
[824]	valid_0's rmse: 0.0259441
[825]	valid_0's rmse: 0.0259408
[826]	valid_0's rmse: 0.0259412
[827]	valid_0's rmse: 0.0259419
[828]	valid_0's rmse: 0.0259434
[829]	valid_0's rmse: 0.0259429
[830]	valid_0's rmse: 0.0259448
[831]	valid_0's rmse: 0.0259442
[832]	valid_0's rmse: 0.0259424
[833]	valid_0's rmse: 0.0259416
[834]	valid_0's rmse: 0.0259425
[835]	valid_0's rmse: 0.025941
[836]	valid_0's rmse: 0.02594
[837]	valid_0's rmse: 0.0259396
[838]	valid_0's rmse: 0.0259382
[839]	valid_0's rmse: 0.0259367
[840]	valid_0's rmse: 0.0259381
[841]	valid_0's rmse: 0.0259379
[842]	valid_0's rmse: 0.0259268
[843]	valid_0's rmse: 0.0259259
[844]	valid_0's rmse: 0.0259228
[845]	valid_0's rmse: 0.0259228
[846]	valid_0's rmse: 0.0259187
[847]	valid_0's rmse: 0.0259171
[848]	valid_0's rmse: 0.0259177
[849]	valid_0's rmse: 0.0259164
[850]	valid_0's rmse: 0.0259161
[851]	valid_0's rmse: 0.0259161
[852]	valid_0's rmse: 0.0259147
[853]	valid_0's rmse: 0.0259145
[854]	valid_0's rmse: 0.0259144
[855]	valid_0's rmse: 0.0259125
[856]	valid_0's rmse: 0.0259127
[857]	valid_0's rmse: 0.0259115
[858]	valid_0's rmse: 0.0259104
[859]	valid_0's rmse: 0.0259119
[860]	valid_0's rmse: 0.0259109
[861]	valid_0's rmse: 0.02591
[862]	valid_0's rmse: 0.0259099
[863]	valid_0's rmse: 0.0259097
[864]	valid_0's rmse: 0.0259133
[865]	valid_0's rmse: 0.0259116
[866]	valid_0's rmse: 0.0259111
[867]	valid_0's rmse: 0.0259095
[868]	valid_0's rmse: 0.0258982
[869]	valid_0's rmse: 0.0258979
[870]	valid_0's rmse: 0.0258956
[871]	valid_0's rmse: 0.0258967
[872]	valid_0's rmse: 0.0258972
[873]	valid_0's rmse: 0.0258971
[874]	valid_0's rmse: 0.0259015
[875]	valid_0's rmse: 0.0258999
[876]	valid_0's rmse: 0.0258987
[877]	valid_0's rmse: 0.0258987
[878]	valid_0's rmse: 0.0258985
[879]	valid_0's rmse: 0.0259
[880]	valid_0's rmse: 0.0259008
[881]	valid_0's rmse: 0.0259018
[882]	valid_0's rmse: 0.0259037
[883]	valid_0's rmse: 0.0259048
[884]	valid_0's rmse: 0.0259063
[885]	valid_0's rmse: 0.0259055
[886]	valid_0's rmse: 0.0259052
[887]	valid_0's rmse: 0.0259047
[888]	valid_0's rmse: 0.0259042
[889]	valid_0's rmse: 0.0259046
[890]	valid_0's rmse: 0.0259049
[891]	valid_0's rmse: 0.0259044
[892]	valid_0's rmse: 0.0259046
[893]	valid_0's rmse: 0.0259035
[894]	valid_0's rmse: 0.0259016
[895]	valid_0's rmse: 0.0259031
[896]	valid_0's rmse: 0.0259025
[897]	valid_0's rmse: 0.0259047
[898]	valid_0's rmse: 0.0259051
[899]	valid_0's rmse: 0.0259101
[900]	valid_0's rmse: 0.0259099
[901]	valid_0's rmse: 0.0259106
[902]	valid_0's rmse: 0.0259101
[903]	valid_0's rmse: 0.0259044
[904]	valid_0's rmse: 0.0259034
[905]	valid_0's rmse: 0.0259038
[906]	valid_0's rmse: 0.0259047
[907]	valid_0's rmse: 0.0259061
[908]	valid_0's rmse: 0.025906
[909]	valid_0's rmse: 0.025901
[910]	valid_0's rmse: 0.0258971
[911]	valid_0's rmse: 0.0258968
[912]	valid_0's rmse: 0.0258973
[913]	valid_0's rmse: 0.0258965
[914]	valid_0's rmse: 0.025898
[915]	valid_0's rmse: 0.0258982
[916]	valid_0's rmse: 0.0258981
[917]	valid_0's rmse: 0.0258952
[918]	valid_0's rmse: 0.0258949
[919]	valid_0's rmse: 0.0258947
[920]	valid_0's rmse: 0.0258959
[921]	valid_0's rmse: 0.0258954
[922]	valid_0's rmse: 0.0258947
[923]	valid_0's rmse: 0.0258946
[924]	valid_0's rmse: 0.0258931
[925]	valid_0's rmse: 0.0258945
[926]	valid_0's rmse: 0.0258925
[927]	valid_0's rmse: 0.0258899
[928]	valid_0's rmse: 0.0258898
[929]	valid_0's rmse: 0.0258914
[930]	valid_0's rmse: 0.0258912
[931]	valid_0's rmse: 0.025892
[932]	valid_0's rmse: 0.025893
[933]	valid_0's rmse: 0.0258918
[934]	valid_0's rmse: 0.0258882
[935]	valid_0's rmse: 0.0258882
[936]	valid_0's rmse: 0.0258871
[937]	valid_0's rmse: 0.0258879
[938]	valid_0's rmse: 0.0258857
[939]	valid_0's rmse: 0.0258855
[940]	valid_0's rmse: 0.0258856
[941]	valid_0's rmse: 0.0258855
[942]	valid_0's rmse: 0.0258857
[943]	valid_0's rmse: 0.0258857
[944]	valid_0's rmse: 0.0258861
[945]	valid_0's rmse: 0.0258858
[946]	valid_0's rmse: 0.0258865
[947]	valid_0's rmse: 0.0258875
[948]	valid_0's rmse: 0.0258872
[949]	valid_0's rmse: 0.0258872
[950]	valid_0's rmse: 0.0258866
[951]	valid_0's rmse: 0.0258888
[952]	valid_0's rmse: 0.0258892
[953]	valid_0's rmse: 0.0258835
[954]	valid_0's rmse: 0.0258817
[955]	valid_0's rmse: 0.0258817
[956]	valid_0's rmse: 0.0258786
[957]	valid_0's rmse: 0.0258788
[958]	valid_0's rmse: 0.0258788
[959]	valid_0's rmse: 0.0258798
[960]	valid_0's rmse: 0.0258797
[961]	valid_0's rmse: 0.0258797
[962]	valid_0's rmse: 0.0258776
[963]	valid_0's rmse: 0.0258773
[964]	valid_0's rmse: 0.025877
[965]	valid_0's rmse: 0.0258773
[966]	valid_0's rmse: 0.025879
[967]	valid_0's rmse: 0.0258802
[968]	valid_0's rmse: 0.0258794
[969]	valid_0's rmse: 0.02588
[970]	valid_0's rmse: 0.0258797
[971]	valid_0's rmse: 0.0258782
[972]	valid_0's rmse: 0.0258827
[973]	valid_0's rmse: 0.0258842
[974]	valid_0's rmse: 0.0258837
[975]	valid_0's rmse: 0.0258827
[976]	valid_0's rmse: 0.0258818
[977]	valid_0's rmse: 0.0258811
[978]	valid_0's rmse: 0.0258813
[979]	valid_0's rmse: 0.0258813
[980]	valid_0's rmse: 0.0258805
[981]	valid_0's rmse: 0.0258805
[982]	valid_0's rmse: 0.0258791
[983]	valid_0's rmse: 0.0258764
[984]	valid_0's rmse: 0.0258765
[985]	valid_0's rmse: 0.0258748
[986]	valid_0's rmse: 0.025877
[987]	valid_0's rmse: 0.025878
[988]	valid_0's rmse: 0.0258776
[989]	valid_0's rmse: 0.0258761
[990]	valid_0's rmse: 0.0258762
[991]	valid_0's rmse: 0.0258591
[992]	valid_0's rmse: 0.0258595
[993]	valid_0's rmse: 0.0258594
[994]	valid_0's rmse: 0.0258605
[995]	valid_0's rmse: 0.02586
[996]	valid_0's rmse: 0.0258582
[997]	valid_0's rmse: 0.0258576
[998]	valid_0's rmse: 0.0258556
[999]	valid_0's rmse: 0.0258562
[1000]	valid_0's rmse: 0.0258543
[1001]	valid_0's rmse: 0.0258523
[1002]	valid_0's rmse: 0.0258534
[1003]	valid_0's rmse: 0.0258537
[1004]	valid_0's rmse: 0.0258546
[1005]	valid_0's rmse: 0.0258533
[1006]	valid_0's rmse: 0.0258519
[1007]	valid_0's rmse: 0.0258508
[1008]	valid_0's rmse: 0.0258508
[1009]	valid_0's rmse: 0.0258509
[1010]	valid_0's rmse: 0.0258469
[1011]	valid_0's rmse: 0.025851
[1012]	valid_0's rmse: 0.0258512
[1013]	valid_0's rmse: 0.0258474
[1014]	valid_0's rmse: 0.0258468
[1015]	valid_0's rmse: 0.0258432
[1016]	valid_0's rmse: 0.0258409
[1017]	valid_0's rmse: 0.0258283
[1018]	valid_0's rmse: 0.0258284
[1019]	valid_0's rmse: 0.0258254
[1020]	valid_0's rmse: 0.0258244
[1021]	valid_0's rmse: 0.0258246
[1022]	valid_0's rmse: 0.0258249
[1023]	valid_0's rmse: 0.0258246
[1024]	valid_0's rmse: 0.0258215
[1025]	valid_0's rmse: 0.0258211
[1026]	valid_0's rmse: 0.0258215
[1027]	valid_0's rmse: 0.0258213
[1028]	valid_0's rmse: 0.0258215
[1029]	valid_0's rmse: 0.0258233
[1030]	valid_0's rmse: 0.0258232
[1031]	valid_0's rmse: 0.0258233
[1032]	valid_0's rmse: 0.0258191
[1033]	valid_0's rmse: 0.0258196
[1034]	valid_0's rmse: 0.0258169
[1035]	valid_0's rmse: 0.025816
[1036]	valid_0's rmse: 0.0258137
[1037]	valid_0's rmse: 0.0258143
[1038]	valid_0's rmse: 0.0258121
[1039]	valid_0's rmse: 0.0258055
[1040]	valid_0's rmse: 0.0258055
[1041]	valid_0's rmse: 0.0258079
[1042]	valid_0's rmse: 0.0258097
[1043]	valid_0's rmse: 0.0258097
[1044]	valid_0's rmse: 0.0258109
[1045]	valid_0's rmse: 0.0258118
[1046]	valid_0's rmse: 0.0258121
[1047]	valid_0's rmse: 0.0258112
[1048]	valid_0's rmse: 0.0258103
[1049]	valid_0's rmse: 0.0258102
[1050]	valid_0's rmse: 0.0258113
[1051]	valid_0's rmse: 0.0258119
[1052]	valid_0's rmse: 0.0258115
[1053]	valid_0's rmse: 0.0258116
[1054]	valid_0's rmse: 0.0258114
[1055]	valid_0's rmse: 0.0258098
[1056]	valid_0's rmse: 0.0258097
[1057]	valid_0's rmse: 0.0258085
[1058]	valid_0's rmse: 0.0258088
[1059]	valid_0's rmse: 0.0258058
[1060]	valid_0's rmse: 0.0258033
[1061]	valid_0's rmse: 0.0257999
[1062]	valid_0's rmse: 0.025795
[1063]	valid_0's rmse: 0.0257936
[1064]	valid_0's rmse: 0.0257928
[1065]	valid_0's rmse: 0.025793
[1066]	valid_0's rmse: 0.0257934
[1067]	valid_0's rmse: 0.0257928
[1068]	valid_0's rmse: 0.0257786
[1069]	valid_0's rmse: 0.0257783
[1070]	valid_0's rmse: 0.0257778
[1071]	valid_0's rmse: 0.025777
[1072]	valid_0's rmse: 0.0257782
[1073]	valid_0's rmse: 0.0257767
[1074]	valid_0's rmse: 0.0257763
[1075]	valid_0's rmse: 0.0257764
[1076]	valid_0's rmse: 0.025776
[1077]	valid_0's rmse: 0.0257776
[1078]	valid_0's rmse: 0.0257782
[1079]	valid_0's rmse: 0.0257782
[1080]	valid_0's rmse: 0.0257781
[1081]	valid_0's rmse: 0.025776
[1082]	valid_0's rmse: 0.0257761
[1083]	valid_0's rmse: 0.0257762
[1084]	valid_0's rmse: 0.0257773
[1085]	valid_0's rmse: 0.0257783
[1086]	valid_0's rmse: 0.0257785
[1087]	valid_0's rmse: 0.0257788
[1088]	valid_0's rmse: 0.0257792
[1089]	valid_0's rmse: 0.02578
[1090]	valid_0's rmse: 0.0257788
[1091]	valid_0's rmse: 0.0257776
[1092]	valid_0's rmse: 0.0257795
[1093]	valid_0's rmse: 0.0257788
[1094]	valid_0's rmse: 0.0257782
[1095]	valid_0's rmse: 0.025778
[1096]	valid_0's rmse: 0.0257811
[1097]	valid_0's rmse: 0.0257814
[1098]	valid_0's rmse: 0.0257792
[1099]	valid_0's rmse: 0.0257788
[1100]	valid_0's rmse: 0.0257798
[1101]	valid_0's rmse: 0.0257804
[1102]	valid_0's rmse: 0.0257804
[1103]	valid_0's rmse: 0.0257781
[1104]	valid_0's rmse: 0.0257786
[1105]	valid_0's rmse: 0.0257794
[1106]	valid_0's rmse: 0.0257793
[1107]	valid_0's rmse: 0.0257795
[1108]	valid_0's rmse: 0.0257792
[1109]	valid_0's rmse: 0.0257754
[1110]	valid_0's rmse: 0.0257772
[1111]	valid_0's rmse: 0.0257766
[1112]	valid_0's rmse: 0.0257761
[1113]	valid_0's rmse: 0.0257759
[1114]	valid_0's rmse: 0.0257754
[1115]	valid_0's rmse: 0.0257751
[1116]	valid_0's rmse: 0.0257731
[1117]	valid_0's rmse: 0.0257728
[1118]	valid_0's rmse: 0.0257725
[1119]	valid_0's rmse: 0.025771
[1120]	valid_0's rmse: 0.0257698
[1121]	valid_0's rmse: 0.0257699
[1122]	valid_0's rmse: 0.0257698
[1123]	valid_0's rmse: 0.0257685
[1124]	valid_0's rmse: 0.0257678
[1125]	valid_0's rmse: 0.0257679
[1126]	valid_0's rmse: 0.0257667
[1127]	valid_0's rmse: 0.0257669
[1128]	valid_0's rmse: 0.0257648
[1129]	valid_0's rmse: 0.0257647
[1130]	valid_0's rmse: 0.0257651
[1131]	valid_0's rmse: 0.0257653
[1132]	valid_0's rmse: 0.0257657
[1133]	valid_0's rmse: 0.0257652
[1134]	valid_0's rmse: 0.0257653
[1135]	valid_0's rmse: 0.0257593
[1136]	valid_0's rmse: 0.0257585
[1137]	valid_0's rmse: 0.0257583
[1138]	valid_0's rmse: 0.0257575
[1139]	valid_0's rmse: 0.0257571
[1140]	valid_0's rmse: 0.0257562
[1141]	valid_0's rmse: 0.0257562
[1142]	valid_0's rmse: 0.0257561
[1143]	valid_0's rmse: 0.025755
[1144]	valid_0's rmse: 0.025754
[1145]	valid_0's rmse: 0.0257534
[1146]	valid_0's rmse: 0.0257535
[1147]	valid_0's rmse: 0.0257503
[1148]	valid_0's rmse: 0.0257519
[1149]	valid_0's rmse: 0.0257486
[1150]	valid_0's rmse: 0.0257485
[1151]	valid_0's rmse: 0.0257492
[1152]	valid_0's rmse: 0.0257531
[1153]	valid_0's rmse: 0.0257529
[1154]	valid_0's rmse: 0.0257521
[1155]	valid_0's rmse: 0.0257517
[1156]	valid_0's rmse: 0.0257545
[1157]	valid_0's rmse: 0.0257556
[1158]	valid_0's rmse: 0.0257559
[1159]	valid_0's rmse: 0.0257578
[1160]	valid_0's rmse: 0.0257567
[1161]	valid_0's rmse: 0.0257569
[1162]	valid_0's rmse: 0.0257559
[1163]	valid_0's rmse: 0.0257577
[1164]	valid_0's rmse: 0.0257551
[1165]	valid_0's rmse: 0.025756
[1166]	valid_0's rmse: 0.0257558
[1167]	valid_0's rmse: 0.0257561
[1168]	valid_0's rmse: 0.0257562
[1169]	valid_0's rmse: 0.0257558
[1170]	valid_0's rmse: 0.0257527
[1171]	valid_0's rmse: 0.0257479
[1172]	valid_0's rmse: 0.0257481
[1173]	valid_0's rmse: 0.0257445
[1174]	valid_0's rmse: 0.0257442
[1175]	valid_0's rmse: 0.0257454
[1176]	valid_0's rmse: 0.0257446
[1177]	valid_0's rmse: 0.0257455
[1178]	valid_0's rmse: 0.0257465
[1179]	valid_0's rmse: 0.0257483
[1180]	valid_0's rmse: 0.0257494
[1181]	valid_0's rmse: 0.025749
[1182]	valid_0's rmse: 0.0257492
[1183]	valid_0's rmse: 0.0257497
[1184]	valid_0's rmse: 0.02575
[1185]	valid_0's rmse: 0.0257441
[1186]	valid_0's rmse: 0.0257412
[1187]	valid_0's rmse: 0.0257376
[1188]	valid_0's rmse: 0.025734
[1189]	valid_0's rmse: 0.0257333
[1190]	valid_0's rmse: 0.0257326
[1191]	valid_0's rmse: 0.0257325
[1192]	valid_0's rmse: 0.0257347
[1193]	valid_0's rmse: 0.0257189
[1194]	valid_0's rmse: 0.0257085
[1195]	valid_0's rmse: 0.0257073
[1196]	valid_0's rmse: 0.025707
[1197]	valid_0's rmse: 0.0257055
[1198]	valid_0's rmse: 0.0257056
[1199]	valid_0's rmse: 0.0257043
[1200]	valid_0's rmse: 0.0257063
[1201]	valid_0's rmse: 0.0257056
[1202]	valid_0's rmse: 0.0257059
[1203]	valid_0's rmse: 0.0257041
[1204]	valid_0's rmse: 0.0257018
[1205]	valid_0's rmse: 0.025702
[1206]	valid_0's rmse: 0.0257017
[1207]	valid_0's rmse: 0.0256966
[1208]	valid_0's rmse: 0.0256931
[1209]	valid_0's rmse: 0.0256931
[1210]	valid_0's rmse: 0.025693
[1211]	valid_0's rmse: 0.0256934
[1212]	valid_0's rmse: 0.0256969
[1213]	valid_0's rmse: 0.0256973
[1214]	valid_0's rmse: 0.0256982
[1215]	valid_0's rmse: 0.0256965
[1216]	valid_0's rmse: 0.0256955
[1217]	valid_0's rmse: 0.0256956
[1218]	valid_0's rmse: 0.0256956
[1219]	valid_0's rmse: 0.0256943
[1220]	valid_0's rmse: 0.0256932
[1221]	valid_0's rmse: 0.0256944
[1222]	valid_0's rmse: 0.0256935
[1223]	valid_0's rmse: 0.0256947
[1224]	valid_0's rmse: 0.0256951
[1225]	valid_0's rmse: 0.0256953
[1226]	valid_0's rmse: 0.0256967
[1227]	valid_0's rmse: 0.0256974
[1228]	valid_0's rmse: 0.0256971
[1229]	valid_0's rmse: 0.025697
[1230]	valid_0's rmse: 0.0256973
[1231]	valid_0's rmse: 0.0256971
[1232]	valid_0's rmse: 0.0256976
[1233]	valid_0's rmse: 0.0256976
[1234]	valid_0's rmse: 0.025696
[1235]	valid_0's rmse: 0.0256965
[1236]	valid_0's rmse: 0.0256961
[1237]	valid_0's rmse: 0.0256962
[1238]	valid_0's rmse: 0.0256996
[1239]	valid_0's rmse: 0.0257003
[1240]	valid_0's rmse: 0.0257023
[1241]	valid_0's rmse: 0.0257018
[1242]	valid_0's rmse: 0.0257016
[1243]	valid_0's rmse: 0.0257023
[1244]	valid_0's rmse: 0.0257013
[1245]	valid_0's rmse: 0.0256968
[1246]	valid_0's rmse: 0.0256967
[1247]	valid_0's rmse: 0.0256935
[1248]	valid_0's rmse: 0.0256932
[1249]	valid_0's rmse: 0.0256959
[1250]	valid_0's rmse: 0.025695
[1251]	valid_0's rmse: 0.025695
[1252]	valid_0's rmse: 0.0256954
[1253]	valid_0's rmse: 0.0256932
[1254]	valid_0's rmse: 0.0256933
[1255]	valid_0's rmse: 0.0256942
[1256]	valid_0's rmse: 0.0256929
[1257]	valid_0's rmse: 0.0256918
[1258]	valid_0's rmse: 0.0256916
[1259]	valid_0's rmse: 0.0256913
[1260]	valid_0's rmse: 0.0256924
[1261]	valid_0's rmse: 0.0256909
[1262]	valid_0's rmse: 0.0256907
[1263]	valid_0's rmse: 0.0256914
[1264]	valid_0's rmse: 0.0256819
[1265]	valid_0's rmse: 0.0256823
[1266]	valid_0's rmse: 0.0256822
[1267]	valid_0's rmse: 0.0256828
[1268]	valid_0's rmse: 0.025683
[1269]	valid_0's rmse: 0.0256841
[1270]	valid_0's rmse: 0.0256839
[1271]	valid_0's rmse: 0.0256837
[1272]	valid_0's rmse: 0.0256835
[1273]	valid_0's rmse: 0.0256819
[1274]	valid_0's rmse: 0.0256814
[1275]	valid_0's rmse: 0.0256859
[1276]	valid_0's rmse: 0.0256845
[1277]	valid_0's rmse: 0.0256854
[1278]	valid_0's rmse: 0.0256899
[1279]	valid_0's rmse: 0.0256912
[1280]	valid_0's rmse: 0.0256951
[1281]	valid_0's rmse: 0.0256952
[1282]	valid_0's rmse: 0.0256956
[1283]	valid_0's rmse: 0.0256958
[1284]	valid_0's rmse: 0.0256956
[1285]	valid_0's rmse: 0.025695
[1286]	valid_0's rmse: 0.0256955
[1287]	valid_0's rmse: 0.0256955
[1288]	valid_0's rmse: 0.0256966
[1289]	valid_0's rmse: 0.0256969
[1290]	valid_0's rmse: 0.0256961
[1291]	valid_0's rmse: 0.0256955
[1292]	valid_0's rmse: 0.025695
[1293]	valid_0's rmse: 0.0256959
[1294]	valid_0's rmse: 0.0256953
[1295]	valid_0's rmse: 0.0256943
[1296]	valid_0's rmse: 0.0256935
[1297]	valid_0's rmse: 0.0256928
[1298]	valid_0's rmse: 0.0256922
[1299]	valid_0's rmse: 0.0256921
[1300]	valid_0's rmse: 0.0256929
[1301]	valid_0's rmse: 0.0256929
[1302]	valid_0's rmse: 0.0256922
[1303]	valid_0's rmse: 0.0256922
[1304]	valid_0's rmse: 0.0256903
[1305]	valid_0's rmse: 0.0256902
[1306]	valid_0's rmse: 0.025689
[1307]	valid_0's rmse: 0.0256867
[1308]	valid_0's rmse: 0.025687
[1309]	valid_0's rmse: 0.0256871
[1310]	valid_0's rmse: 0.0256871
[1311]	valid_0's rmse: 0.0256937
[1312]	valid_0's rmse: 0.0256927
[1313]	valid_0's rmse: 0.0256883
[1314]	valid_0's rmse: 0.0256881
[1315]	valid_0's rmse: 0.0256876
[1316]	valid_0's rmse: 0.0256871
[1317]	valid_0's rmse: 0.025685
[1318]	valid_0's rmse: 0.0256843
[1319]	valid_0's rmse: 0.0256852
[1320]	valid_0's rmse: 0.0256852
[1321]	valid_0's rmse: 0.0256852
[1322]	valid_0's rmse: 0.0256842
[1323]	valid_0's rmse: 0.0256825
[1324]	valid_0's rmse: 0.0256824
[1325]	valid_0's rmse: 0.0256792
[1326]	valid_0's rmse: 0.0256781
[1327]	valid_0's rmse: 0.0256776
[1328]	valid_0's rmse: 0.0256776
[1329]	valid_0's rmse: 0.0256782
[1330]	valid_0's rmse: 0.0256781
[1331]	valid_0's rmse: 0.0256777
[1332]	valid_0's rmse: 0.0256777
[1333]	valid_0's rmse: 0.0256772
[1334]	valid_0's rmse: 0.025677
[1335]	valid_0's rmse: 0.0256771
[1336]	valid_0's rmse: 0.0256768
[1337]	valid_0's rmse: 0.0256775
[1338]	valid_0's rmse: 0.0256776
[1339]	valid_0's rmse: 0.0256774
[1340]	valid_0's rmse: 0.0256753
[1341]	valid_0's rmse: 0.0256751
[1342]	valid_0's rmse: 0.0256747
[1343]	valid_0's rmse: 0.0256749
[1344]	valid_0's rmse: 0.0256746
[1345]	valid_0's rmse: 0.0256722
[1346]	valid_0's rmse: 0.0256697
[1347]	valid_0's rmse: 0.0256704
[1348]	valid_0's rmse: 0.0256681
[1349]	valid_0's rmse: 0.025668
[1350]	valid_0's rmse: 0.0256667
[1351]	valid_0's rmse: 0.0256684
[1352]	valid_0's rmse: 0.0256685
[1353]	valid_0's rmse: 0.0256673
[1354]	valid_0's rmse: 0.0256673
[1355]	valid_0's rmse: 0.025667
[1356]	valid_0's rmse: 0.0256675
[1357]	valid_0's rmse: 0.0256686
[1358]	valid_0's rmse: 0.0256681
[1359]	valid_0's rmse: 0.0256681
[1360]	valid_0's rmse: 0.0256682
[1361]	valid_0's rmse: 0.025668
[1362]	valid_0's rmse: 0.0256671
[1363]	valid_0's rmse: 0.0256675
[1364]	valid_0's rmse: 0.0256638
[1365]	valid_0's rmse: 0.0256638
[1366]	valid_0's rmse: 0.0256526
[1367]	valid_0's rmse: 0.0256534
[1368]	valid_0's rmse: 0.0256534
[1369]	valid_0's rmse: 0.025653
[1370]	valid_0's rmse: 0.0256528
[1371]	valid_0's rmse: 0.0256532
[1372]	valid_0's rmse: 0.025647
[1373]	valid_0's rmse: 0.0256454
[1374]	valid_0's rmse: 0.0256457
[1375]	valid_0's rmse: 0.0256426
[1376]	valid_0's rmse: 0.0256425
[1377]	valid_0's rmse: 0.0256441
[1378]	valid_0's rmse: 0.0256431
[1379]	valid_0's rmse: 0.0256452
[1380]	valid_0's rmse: 0.0256455
[1381]	valid_0's rmse: 0.0256454
[1382]	valid_0's rmse: 0.0256441
[1383]	valid_0's rmse: 0.0256446
[1384]	valid_0's rmse: 0.0256443
[1385]	valid_0's rmse: 0.0256444
[1386]	valid_0's rmse: 0.0256445
[1387]	valid_0's rmse: 0.0256436
[1388]	valid_0's rmse: 0.0256418
[1389]	valid_0's rmse: 0.0256422
[1390]	valid_0's rmse: 0.0256363
[1391]	valid_0's rmse: 0.0256359
[1392]	valid_0's rmse: 0.0256348
[1393]	valid_0's rmse: 0.0256345
[1394]	valid_0's rmse: 0.0256347
[1395]	valid_0's rmse: 0.025635
[1396]	valid_0's rmse: 0.0256333
[1397]	valid_0's rmse: 0.025633
[1398]	valid_0's rmse: 0.025633
[1399]	valid_0's rmse: 0.0256312
[1400]	valid_0's rmse: 0.025631
[1401]	valid_0's rmse: 0.025631
[1402]	valid_0's rmse: 0.0256313
[1403]	valid_0's rmse: 0.025627
[1404]	valid_0's rmse: 0.0256275
[1405]	valid_0's rmse: 0.0256277
[1406]	valid_0's rmse: 0.0256274
[1407]	valid_0's rmse: 0.0256277
[1408]	valid_0's rmse: 0.0256266
[1409]	valid_0's rmse: 0.025626
[1410]	valid_0's rmse: 0.0256258
[1411]	valid_0's rmse: 0.0256246
[1412]	valid_0's rmse: 0.0256245
[1413]	valid_0's rmse: 0.0256243
[1414]	valid_0's rmse: 0.0256237
[1415]	valid_0's rmse: 0.0256244
[1416]	valid_0's rmse: 0.0256238
[1417]	valid_0's rmse: 0.0256171
[1418]	valid_0's rmse: 0.0256115
[1419]	valid_0's rmse: 0.0256106
[1420]	valid_0's rmse: 0.0256105
[1421]	valid_0's rmse: 0.02561
[1422]	valid_0's rmse: 0.0256113
[1423]	valid_0's rmse: 0.0256111
[1424]	valid_0's rmse: 0.025611
[1425]	valid_0's rmse: 0.0256113
[1426]	valid_0's rmse: 0.0256108
[1427]	valid_0's rmse: 0.0256105
[1428]	valid_0's rmse: 0.0256095
[1429]	valid_0's rmse: 0.0256065
[1430]	valid_0's rmse: 0.0256062
[1431]	valid_0's rmse: 0.025607
[1432]	valid_0's rmse: 0.0256074
[1433]	valid_0's rmse: 0.025607
[1434]	valid_0's rmse: 0.0256081
[1435]	valid_0's rmse: 0.0256045
[1436]	valid_0's rmse: 0.0256057
[1437]	valid_0's rmse: 0.0256067
[1438]	valid_0's rmse: 0.0256063
[1439]	valid_0's rmse: 0.0256066
[1440]	valid_0's rmse: 0.0256061
[1441]	valid_0's rmse: 0.025605
[1442]	valid_0's rmse: 0.0256045
[1443]	valid_0's rmse: 0.0256032
[1444]	valid_0's rmse: 0.0256063
[1445]	valid_0's rmse: 0.0256076
[1446]	valid_0's rmse: 0.025608
[1447]	valid_0's rmse: 0.0256077
[1448]	valid_0's rmse: 0.0256093
[1449]	valid_0's rmse: 0.0256077
[1450]	valid_0's rmse: 0.0256074
[1451]	valid_0's rmse: 0.0256078
[1452]	valid_0's rmse: 0.025608
[1453]	valid_0's rmse: 0.0256081
[1454]	valid_0's rmse: 0.0256081
[1455]	valid_0's rmse: 0.0256079
[1456]	valid_0's rmse: 0.0256087
[1457]	valid_0's rmse: 0.0256062
[1458]	valid_0's rmse: 0.025602
[1459]	valid_0's rmse: 0.0256021
[1460]	valid_0's rmse: 0.0256041
[1461]	valid_0's rmse: 0.0256042
[1462]	valid_0's rmse: 0.025605
[1463]	valid_0's rmse: 0.0256056
[1464]	valid_0's rmse: 0.0256053
[1465]	valid_0's rmse: 0.0256077
[1466]	valid_0's rmse: 0.0256076
[1467]	valid_0's rmse: 0.0256083
[1468]	valid_0's rmse: 0.0256082
[1469]	valid_0's rmse: 0.0256074
[1470]	valid_0's rmse: 0.0256074
[1471]	valid_0's rmse: 0.025608
[1472]	valid_0's rmse: 0.0256081
[1473]	valid_0's rmse: 0.0256084
[1474]	valid_0's rmse: 0.0256081
[1475]	valid_0's rmse: 0.0256084
[1476]	valid_0's rmse: 0.0256083
[1477]	valid_0's rmse: 0.0256086
[1478]	valid_0's rmse: 0.0256084
[1479]	valid_0's rmse: 0.025608
[1480]	valid_0's rmse: 0.02561
[1481]	valid_0's rmse: 0.0256062
[1482]	valid_0's rmse: 0.0256062
[1483]	valid_0's rmse: 0.0256062
[1484]	valid_0's rmse: 0.0256056
[1485]	valid_0's rmse: 0.0256048
[1486]	valid_0's rmse: 0.0256054
[1487]	valid_0's rmse: 0.025605
[1488]	valid_0's rmse: 0.0256026
[1489]	valid_0's rmse: 0.0255999
[1490]	valid_0's rmse: 0.0255993
[1491]	valid_0's rmse: 0.0255995
[1492]	valid_0's rmse: 0.0256009
[1493]	valid_0's rmse: 0.0256006
[1494]	valid_0's rmse: 0.0256027
[1495]	valid_0's rmse: 0.0256021
[1496]	valid_0's rmse: 0.0256017
[1497]	valid_0's rmse: 0.0256016
[1498]	valid_0's rmse: 0.0256018
[1499]	valid_0's rmse: 0.0256011
[1500]	valid_0's rmse: 0.025602
[1501]	valid_0's rmse: 0.0256019
[1502]	valid_0's rmse: 0.025602
[1503]	valid_0's rmse: 0.0256027
[1504]	valid_0's rmse: 0.0255921
[1505]	valid_0's rmse: 0.0255919
[1506]	valid_0's rmse: 0.025592
[1507]	valid_0's rmse: 0.0255918
[1508]	valid_0's rmse: 0.0255914
[1509]	valid_0's rmse: 0.0255913
[1510]	valid_0's rmse: 0.0255907
[1511]	valid_0's rmse: 0.0255905
[1512]	valid_0's rmse: 0.0255883
[1513]	valid_0's rmse: 0.0255877
[1514]	valid_0's rmse: 0.025587
[1515]	valid_0's rmse: 0.0255873
[1516]	valid_0's rmse: 0.025587
[1517]	valid_0's rmse: 0.0255872
[1518]	valid_0's rmse: 0.0255876
[1519]	valid_0's rmse: 0.0255883
[1520]	valid_0's rmse: 0.0255884
[1521]	valid_0's rmse: 0.0255852
[1522]	valid_0's rmse: 0.0255853
[1523]	valid_0's rmse: 0.0255852
[1524]	valid_0's rmse: 0.0255875
[1525]	valid_0's rmse: 0.025588
[1526]	valid_0's rmse: 0.0255894
[1527]	valid_0's rmse: 0.0255891
[1528]	valid_0's rmse: 0.0255891
[1529]	valid_0's rmse: 0.0255892
[1530]	valid_0's rmse: 0.0255908
[1531]	valid_0's rmse: 0.0255902
[1532]	valid_0's rmse: 0.0255903
[1533]	valid_0's rmse: 0.0255905
[1534]	valid_0's rmse: 0.0255906
[1535]	valid_0's rmse: 0.0255913
[1536]	valid_0's rmse: 0.0255906
[1537]	valid_0's rmse: 0.0255919
[1538]	valid_0's rmse: 0.0255919
[1539]	valid_0's rmse: 0.0255936
[1540]	valid_0's rmse: 0.025594
[1541]	valid_0's rmse: 0.0255927
[1542]	valid_0's rmse: 0.0255924
[1543]	valid_0's rmse: 0.0255929
[1544]	valid_0's rmse: 0.0255937
[1545]	valid_0's rmse: 0.0255927
[1546]	valid_0's rmse: 0.025592
[1547]	valid_0's rmse: 0.0255914
[1548]	valid_0's rmse: 0.0255914
[1549]	valid_0's rmse: 0.0255913
[1550]	valid_0's rmse: 0.0255909
[1551]	valid_0's rmse: 0.0255915
[1552]	valid_0's rmse: 0.0255916
[1553]	valid_0's rmse: 0.0255916
[1554]	valid_0's rmse: 0.0255915
[1555]	valid_0's rmse: 0.0255921
[1556]	valid_0's rmse: 0.0255909
[1557]	valid_0's rmse: 0.0255908
[1558]	valid_0's rmse: 0.0255916
[1559]	valid_0's rmse: 0.0255904
[1560]	valid_0's rmse: 0.0255898
[1561]	valid_0's rmse: 0.0255908
[1562]	valid_0's rmse: 0.0255909
[1563]	valid_0's rmse: 0.0255911
[1564]	valid_0's rmse: 0.0255908
[1565]	valid_0's rmse: 0.0255928
[1566]	valid_0's rmse: 0.0255909
[1567]	valid_0's rmse: 0.0255908
[1568]	valid_0's rmse: 0.0255925
[1569]	valid_0's rmse: 0.0255903
[1570]	valid_0's rmse: 0.0255904
[1571]	valid_0's rmse: 0.0255902
[1572]	valid_0's rmse: 0.0255895
[1573]	valid_0's rmse: 0.0255941
[1574]	valid_0's rmse: 0.025596
[1575]	valid_0's rmse: 0.0255966
[1576]	valid_0's rmse: 0.0255966
[1577]	valid_0's rmse: 0.0255965
[1578]	valid_0's rmse: 0.0255957
[1579]	valid_0's rmse: 0.0255949
[1580]	valid_0's rmse: 0.0255931
[1581]	valid_0's rmse: 0.0255936
[1582]	valid_0's rmse: 0.0255936
[1583]	valid_0's rmse: 0.0255941
[1584]	valid_0's rmse: 0.0255942
[1585]	valid_0's rmse: 0.0255976
[1586]	valid_0's rmse: 0.0255974
[1587]	valid_0's rmse: 0.0255956
[1588]	valid_0's rmse: 0.025595
[1589]	valid_0's rmse: 0.0255943
[1590]	valid_0's rmse: 0.0255946
[1591]	valid_0's rmse: 0.0255945
[1592]	valid_0's rmse: 0.0255938
[1593]	valid_0's rmse: 0.0255907
[1594]	valid_0's rmse: 0.0255832
[1595]	valid_0's rmse: 0.0255833
[1596]	valid_0's rmse: 0.0255824
[1597]	valid_0's rmse: 0.025583
[1598]	valid_0's rmse: 0.0255812
[1599]	valid_0's rmse: 0.0255811
[1600]	valid_0's rmse: 0.0255808
[1601]	valid_0's rmse: 0.0255761
[1602]	valid_0's rmse: 0.0255687
[1603]	valid_0's rmse: 0.0255698
[1604]	valid_0's rmse: 0.0255697
[1605]	valid_0's rmse: 0.0255691
[1606]	valid_0's rmse: 0.0255697
[1607]	valid_0's rmse: 0.0255554
[1608]	valid_0's rmse: 0.0255555
[1609]	valid_0's rmse: 0.0255572
[1610]	valid_0's rmse: 0.0255572
[1611]	valid_0's rmse: 0.0255571
[1612]	valid_0's rmse: 0.0255571
[1613]	valid_0's rmse: 0.0255573
[1614]	valid_0's rmse: 0.0255553
[1615]	valid_0's rmse: 0.0255563
[1616]	valid_0's rmse: 0.0255559
[1617]	valid_0's rmse: 0.0255553
[1618]	valid_0's rmse: 0.0255544
[1619]	valid_0's rmse: 0.0255544
[1620]	valid_0's rmse: 0.0255537
[1621]	valid_0's rmse: 0.0255486
[1622]	valid_0's rmse: 0.0255496
[1623]	valid_0's rmse: 0.0255495
[1624]	valid_0's rmse: 0.0255509
[1625]	valid_0's rmse: 0.0255513
[1626]	valid_0's rmse: 0.0255499
[1627]	valid_0's rmse: 0.0255497
[1628]	valid_0's rmse: 0.0255489
[1629]	valid_0's rmse: 0.0255457
[1630]	valid_0's rmse: 0.0255384
[1631]	valid_0's rmse: 0.0255383
[1632]	valid_0's rmse: 0.0255377
[1633]	valid_0's rmse: 0.025538
[1634]	valid_0's rmse: 0.0255383
[1635]	valid_0's rmse: 0.0255381
[1636]	valid_0's rmse: 0.0255379
[1637]	valid_0's rmse: 0.0255386
[1638]	valid_0's rmse: 0.0255391
[1639]	valid_0's rmse: 0.0255386
[1640]	valid_0's rmse: 0.0255322
[1641]	valid_0's rmse: 0.0255328
[1642]	valid_0's rmse: 0.0255273
[1643]	valid_0's rmse: 0.0255264
[1644]	valid_0's rmse: 0.0255262
[1645]	valid_0's rmse: 0.0255239
[1646]	valid_0's rmse: 0.0255234
[1647]	valid_0's rmse: 0.0255245
[1648]	valid_0's rmse: 0.0255188
[1649]	valid_0's rmse: 0.0255174
[1650]	valid_0's rmse: 0.0255231
[1651]	valid_0's rmse: 0.0255231
[1652]	valid_0's rmse: 0.0255237
[1653]	valid_0's rmse: 0.0255217
[1654]	valid_0's rmse: 0.025521
[1655]	valid_0's rmse: 0.0255201
[1656]	valid_0's rmse: 0.02552
[1657]	valid_0's rmse: 0.0255204
[1658]	valid_0's rmse: 0.0255194
[1659]	valid_0's rmse: 0.0255194
[1660]	valid_0's rmse: 0.0255194
[1661]	valid_0's rmse: 0.0255189
[1662]	valid_0's rmse: 0.0255192
[1663]	valid_0's rmse: 0.0255183
[1664]	valid_0's rmse: 0.0255186
[1665]	valid_0's rmse: 0.0255179
[1666]	valid_0's rmse: 0.0255182
[1667]	valid_0's rmse: 0.0255178
[1668]	valid_0's rmse: 0.0255175
[1669]	valid_0's rmse: 0.0255181
[1670]	valid_0's rmse: 0.0255179
[1671]	valid_0's rmse: 0.025517
[1672]	valid_0's rmse: 0.0255169
[1673]	valid_0's rmse: 0.0255012
[1674]	valid_0's rmse: 0.0255018
[1675]	valid_0's rmse: 0.0255017
[1676]	valid_0's rmse: 0.0255032
[1677]	valid_0's rmse: 0.0255028
[1678]	valid_0's rmse: 0.0255035
[1679]	valid_0's rmse: 0.0255038
[1680]	valid_0's rmse: 0.0255043
[1681]	valid_0's rmse: 0.0255043
[1682]	valid_0's rmse: 0.0255052
[1683]	valid_0's rmse: 0.0255043
[1684]	valid_0's rmse: 0.0255045
[1685]	valid_0's rmse: 0.0255044
[1686]	valid_0's rmse: 0.0255039
[1687]	valid_0's rmse: 0.0255027
[1688]	valid_0's rmse: 0.0255026
[1689]	valid_0's rmse: 0.0255028
[1690]	valid_0's rmse: 0.0255036
[1691]	valid_0's rmse: 0.0255024
[1692]	valid_0's rmse: 0.0255021
[1693]	valid_0's rmse: 0.0255018
[1694]	valid_0's rmse: 0.0255018
[1695]	valid_0's rmse: 0.0255012
[1696]	valid_0's rmse: 0.0255006
[1697]	valid_0's rmse: 0.0255006
[1698]	valid_0's rmse: 0.0255005
[1699]	valid_0's rmse: 0.0254974
[1700]	valid_0's rmse: 0.0254964
[1701]	valid_0's rmse: 0.0254971
[1702]	valid_0's rmse: 0.0254974
[1703]	valid_0's rmse: 0.0254974
[1704]	valid_0's rmse: 0.0254945
[1705]	valid_0's rmse: 0.0254948
[1706]	valid_0's rmse: 0.0254947
[1707]	valid_0's rmse: 0.025495
[1708]	valid_0's rmse: 0.0254952
[1709]	valid_0's rmse: 0.025495
[1710]	valid_0's rmse: 0.0254946
[1711]	valid_0's rmse: 0.0254946
[1712]	valid_0's rmse: 0.0254923
[1713]	valid_0's rmse: 0.0254919
[1714]	valid_0's rmse: 0.0254932
[1715]	valid_0's rmse: 0.025493
[1716]	valid_0's rmse: 0.0254935
[1717]	valid_0's rmse: 0.025492
[1718]	valid_0's rmse: 0.0254914
[1719]	valid_0's rmse: 0.0254918
[1720]	valid_0's rmse: 0.0254917
[1721]	valid_0's rmse: 0.0254922
[1722]	valid_0's rmse: 0.0254925
[1723]	valid_0's rmse: 0.0254928
[1724]	valid_0's rmse: 0.0254932
[1725]	valid_0's rmse: 0.0254931
[1726]	valid_0's rmse: 0.0254933
[1727]	valid_0's rmse: 0.0254931
[1728]	valid_0's rmse: 0.0254962
[1729]	valid_0's rmse: 0.0254961
[1730]	valid_0's rmse: 0.0254956
[1731]	valid_0's rmse: 0.025495
[1732]	valid_0's rmse: 0.0254947
[1733]	valid_0's rmse: 0.0254938
[1734]	valid_0's rmse: 0.0254942
[1735]	valid_0's rmse: 0.0254946
[1736]	valid_0's rmse: 0.0254936
[1737]	valid_0's rmse: 0.0254922
[1738]	valid_0's rmse: 0.0254917
[1739]	valid_0's rmse: 0.025492
[1740]	valid_0's rmse: 0.025492
[1741]	valid_0's rmse: 0.0254923
[1742]	valid_0's rmse: 0.0254932
[1743]	valid_0's rmse: 0.0254933
[1744]	valid_0's rmse: 0.0254935
[1745]	valid_0's rmse: 0.0254933
[1746]	valid_0's rmse: 0.0254937
[1747]	valid_0's rmse: 0.0254928
[1748]	valid_0's rmse: 0.0254926
[1749]	valid_0's rmse: 0.0254945
[1750]	valid_0's rmse: 0.0254948
[1751]	valid_0's rmse: 0.025495
[1752]	valid_0's rmse: 0.025487
[1753]	valid_0's rmse: 0.0254868
[1754]	valid_0's rmse: 0.025486
[1755]	valid_0's rmse: 0.0254842
[1756]	valid_0's rmse: 0.0254837
[1757]	valid_0's rmse: 0.025483
[1758]	valid_0's rmse: 0.0254827
[1759]	valid_0's rmse: 0.0254805
[1760]	valid_0's rmse: 0.02548
[1761]	valid_0's rmse: 0.0254799
[1762]	valid_0's rmse: 0.0254799
[1763]	valid_0's rmse: 0.0254794
[1764]	valid_0's rmse: 0.0254783
[1765]	valid_0's rmse: 0.0254772
[1766]	valid_0's rmse: 0.0254773
[1767]	valid_0's rmse: 0.0254773
[1768]	valid_0's rmse: 0.0254767
[1769]	valid_0's rmse: 0.0254775
[1770]	valid_0's rmse: 0.0254774
[1771]	valid_0's rmse: 0.0254775
[1772]	valid_0's rmse: 0.0254769
[1773]	valid_0's rmse: 0.025477
[1774]	valid_0's rmse: 0.0254779
[1775]	valid_0's rmse: 0.025477
[1776]	valid_0's rmse: 0.0254767
[1777]	valid_0's rmse: 0.025474
[1778]	valid_0's rmse: 0.0254756
[1779]	valid_0's rmse: 0.0254761
[1780]	valid_0's rmse: 0.025476
[1781]	valid_0's rmse: 0.0254763
[1782]	valid_0's rmse: 0.0254763
[1783]	valid_0's rmse: 0.0254762
[1784]	valid_0's rmse: 0.0254749
[1785]	valid_0's rmse: 0.025473
[1786]	valid_0's rmse: 0.0254723
[1787]	valid_0's rmse: 0.0254712
[1788]	valid_0's rmse: 0.0254711
[1789]	valid_0's rmse: 0.0254718
[1790]	valid_0's rmse: 0.0254716
[1791]	valid_0's rmse: 0.0254721
[1792]	valid_0's rmse: 0.0254709
[1793]	valid_0's rmse: 0.0254738
[1794]	valid_0's rmse: 0.0254739
[1795]	valid_0's rmse: 0.025474
[1796]	valid_0's rmse: 0.0254719
[1797]	valid_0's rmse: 0.0254719
[1798]	valid_0's rmse: 0.0254734
[1799]	valid_0's rmse: 0.0254738
[1800]	valid_0's rmse: 0.0254739
[1801]	valid_0's rmse: 0.0254722
[1802]	valid_0's rmse: 0.0254725
[1803]	valid_0's rmse: 0.0254716
[1804]	valid_0's rmse: 0.0254717
[1805]	valid_0's rmse: 0.0254718
[1806]	valid_0's rmse: 0.025471
[1807]	valid_0's rmse: 0.0254714
[1808]	valid_0's rmse: 0.0254714
[1809]	valid_0's rmse: 0.0254713
[1810]	valid_0's rmse: 0.0254711
[1811]	valid_0's rmse: 0.0254716
[1812]	valid_0's rmse: 0.025472
[1813]	valid_0's rmse: 0.0254719
[1814]	valid_0's rmse: 0.0254712
[1815]	valid_0's rmse: 0.0254712
[1816]	valid_0's rmse: 0.0254708
[1817]	valid_0's rmse: 0.0254711
[1818]	valid_0's rmse: 0.0254701
[1819]	valid_0's rmse: 0.0254683
[1820]	valid_0's rmse: 0.0254685
[1821]	valid_0's rmse: 0.0254685
[1822]	valid_0's rmse: 0.0254687
[1823]	valid_0's rmse: 0.0254688
[1824]	valid_0's rmse: 0.0254686
[1825]	valid_0's rmse: 0.0254686
[1826]	valid_0's rmse: 0.0254685
[1827]	valid_0's rmse: 0.0254681
[1828]	valid_0's rmse: 0.0254681
[1829]	valid_0's rmse: 0.025468
[1830]	valid_0's rmse: 0.0254683
[1831]	valid_0's rmse: 0.025464
[1832]	valid_0's rmse: 0.0254641
[1833]	valid_0's rmse: 0.0254636
[1834]	valid_0's rmse: 0.0254633
[1835]	valid_0's rmse: 0.0254625
[1836]	valid_0's rmse: 0.0254622
[1837]	valid_0's rmse: 0.0254617
[1838]	valid_0's rmse: 0.0254617
[1839]	valid_0's rmse: 0.0254609
[1840]	valid_0's rmse: 0.025452
[1841]	valid_0's rmse: 0.0254516
[1842]	valid_0's rmse: 0.0254517
[1843]	valid_0's rmse: 0.0254523
[1844]	valid_0's rmse: 0.0254516
[1845]	valid_0's rmse: 0.0254519
[1846]	valid_0's rmse: 0.0254519
[1847]	valid_0's rmse: 0.0254506
[1848]	valid_0's rmse: 0.0254508
[1849]	valid_0's rmse: 0.0254503
[1850]	valid_0's rmse: 0.0254484
[1851]	valid_0's rmse: 0.0254485
[1852]	valid_0's rmse: 0.0254486
[1853]	valid_0's rmse: 0.0254492
[1854]	valid_0's rmse: 0.0254493
[1855]	valid_0's rmse: 0.0254488
[1856]	valid_0's rmse: 0.0254492
[1857]	valid_0's rmse: 0.0254538
[1858]	valid_0's rmse: 0.0254541
[1859]	valid_0's rmse: 0.0254591
[1860]	valid_0's rmse: 0.0254593
[1861]	valid_0's rmse: 0.0254593
[1862]	valid_0's rmse: 0.0254589
[1863]	valid_0's rmse: 0.0254589
[1864]	valid_0's rmse: 0.0254596
[1865]	valid_0's rmse: 0.0254593
[1866]	valid_0's rmse: 0.02546
[1867]	valid_0's rmse: 0.0254596
[1868]	valid_0's rmse: 0.0254609
[1869]	valid_0's rmse: 0.0254586
[1870]	valid_0's rmse: 0.0254583
[1871]	valid_0's rmse: 0.0254584
[1872]	valid_0's rmse: 0.0254582
[1873]	valid_0's rmse: 0.025458
[1874]	valid_0's rmse: 0.0254559
[1875]	valid_0's rmse: 0.0254556
[1876]	valid_0's rmse: 0.0254552
[1877]	valid_0's rmse: 0.0254551
[1878]	valid_0's rmse: 0.0254557
[1879]	valid_0's rmse: 0.0254539
[1880]	valid_0's rmse: 0.0254533
[1881]	valid_0's rmse: 0.0254524
[1882]	valid_0's rmse: 0.0254525
[1883]	valid_0's rmse: 0.0254542
[1884]	valid_0's rmse: 0.0254548
[1885]	valid_0's rmse: 0.0254539
[1886]	valid_0's rmse: 0.0254536
[1887]	valid_0's rmse: 0.0254537
[1888]	valid_0's rmse: 0.0254532
[1889]	valid_0's rmse: 0.0254555
[1890]	valid_0's rmse: 0.0254548
[1891]	valid_0's rmse: 0.0254549
[1892]	valid_0's rmse: 0.0254548
[1893]	valid_0's rmse: 0.0254545
[1894]	valid_0's rmse: 0.0254543
[1895]	valid_0's rmse: 0.0254553
[1896]	valid_0's rmse: 0.0254551
[1897]	valid_0's rmse: 0.0254553
[1898]	valid_0's rmse: 0.0254557
[1899]	valid_0's rmse: 0.0254553
[1900]	valid_0's rmse: 0.0254554
[1901]	valid_0's rmse: 0.025455
[1902]	valid_0's rmse: 0.0254548
[1903]	valid_0's rmse: 0.0254559
[1904]	valid_0's rmse: 0.025455
[1905]	valid_0's rmse: 0.0254548
[1906]	valid_0's rmse: 0.0254548
[1907]	valid_0's rmse: 0.025454
[1908]	valid_0's rmse: 0.0254535
[1909]	valid_0's rmse: 0.0254534
[1910]	valid_0's rmse: 0.0254536
[1911]	valid_0's rmse: 0.0254536
[1912]	valid_0's rmse: 0.0254531
[1913]	valid_0's rmse: 0.0254532
[1914]	valid_0's rmse: 0.0254535
[1915]	valid_0's rmse: 0.0254525
[1916]	valid_0's rmse: 0.025452
[1917]	valid_0's rmse: 0.0254519
[1918]	valid_0's rmse: 0.0254518
[1919]	valid_0's rmse: 0.0254515
[1920]	valid_0's rmse: 0.0254513
[1921]	valid_0's rmse: 0.0254524
[1922]	valid_0's rmse: 0.0254529
[1923]	valid_0's rmse: 0.0254551
[1924]	valid_0's rmse: 0.0254534
[1925]	valid_0's rmse: 0.0254535
[1926]	valid_0's rmse: 0.0254536
[1927]	valid_0's rmse: 0.0254536
[1928]	valid_0's rmse: 0.0254538
[1929]	valid_0's rmse: 0.0254538
[1930]	valid_0's rmse: 0.0254529
[1931]	valid_0's rmse: 0.0254529
[1932]	valid_0's rmse: 0.0254527
[1933]	valid_0's rmse: 0.0254525
[1934]	valid_0's rmse: 0.0254524
[1935]	valid_0's rmse: 0.0254518
[1936]	valid_0's rmse: 0.0254518
[1937]	valid_0's rmse: 0.0254518
[1938]	valid_0's rmse: 0.0254512
[1939]	valid_0's rmse: 0.0254511
[1940]	valid_0's rmse: 0.0254517
[1941]	valid_0's rmse: 0.0254514
[1942]	valid_0's rmse: 0.0254517
[1943]	valid_0's rmse: 0.0254503
[1944]	valid_0's rmse: 0.0254474
[1945]	valid_0's rmse: 0.0254471
[1946]	valid_0's rmse: 0.0254472
[1947]	valid_0's rmse: 0.0254473
[1948]	valid_0's rmse: 0.0254469
[1949]	valid_0's rmse: 0.0254462
[1950]	valid_0's rmse: 0.0254464
[1951]	valid_0's rmse: 0.025446
[1952]	valid_0's rmse: 0.025446
[1953]	valid_0's rmse: 0.0254422
[1954]	valid_0's rmse: 0.0254356
[1955]	valid_0's rmse: 0.0254358
[1956]	valid_0's rmse: 0.0254357
[1957]	valid_0's rmse: 0.0254344
[1958]	valid_0's rmse: 0.0254348
[1959]	valid_0's rmse: 0.0254348
[1960]	valid_0's rmse: 0.0254347
[1961]	valid_0's rmse: 0.0254346
[1962]	valid_0's rmse: 0.0254346
[1963]	valid_0's rmse: 0.0254344
[1964]	valid_0's rmse: 0.0254341
[1965]	valid_0's rmse: 0.0254337
[1966]	valid_0's rmse: 0.0254337
[1967]	valid_0's rmse: 0.0254335
[1968]	valid_0's rmse: 0.0254336
[1969]	valid_0's rmse: 0.0254336
[1970]	valid_0's rmse: 0.0254333
[1971]	valid_0's rmse: 0.0254335
[1972]	valid_0's rmse: 0.0254333
[1973]	valid_0's rmse: 0.0254328
[1974]	valid_0's rmse: 0.0254329
[1975]	valid_0's rmse: 0.0254329
[1976]	valid_0's rmse: 0.0254334
[1977]	valid_0's rmse: 0.0254333
[1978]	valid_0's rmse: 0.0254336
[1979]	valid_0's rmse: 0.0254342
[1980]	valid_0's rmse: 0.0254343
[1981]	valid_0's rmse: 0.0254338
[1982]	valid_0's rmse: 0.0254341
[1983]	valid_0's rmse: 0.0254341
[1984]	valid_0's rmse: 0.0254343
[1985]	valid_0's rmse: 0.0254342
[1986]	valid_0's rmse: 0.0254341
[1987]	valid_0's rmse: 0.0254347
[1988]	valid_0's rmse: 0.025435
[1989]	valid_0's rmse: 0.0254349
[1990]	valid_0's rmse: 0.0254338
[1991]	valid_0's rmse: 0.0254339
[1992]	valid_0's rmse: 0.0254342
[1993]	valid_0's rmse: 0.0254341
[1994]	valid_0's rmse: 0.0254341
[1995]	valid_0's rmse: 0.0254339
[1996]	valid_0's rmse: 0.0254349
[1997]	valid_0's rmse: 0.025434
[1998]	valid_0's rmse: 0.0254327
[1999]	valid_0's rmse: 0.0254326
[2000]	valid_0's rmse: 0.025432
Did not meet early stopping. Best iteration is:
[2000]	valid_0's rmse: 0.025432

In [23]:

y_pred = gbm.predict(X_test)
y_true = Y_test.values

In [24]:

MSE = mean_squared_error(y_true, y_pred)
RMSE = np.sqrt(mean_squared_error(y_true, y_pred))
MAE = mean_absolute_error(y_true, y_pred)
MAPE = mean_absolute_percentage_error(y_true, y_pred)
R_2 = r2_score(y_true, y_pred)
print('MSE:', format(MSE, '.1E'))
print('RMSE:', round(RMSE, 3))
print('MAE:', round(MAE, 3))
print('MAPE:', round(MAPE*100, 2), '%')
print('R_2:', round(R_2, 3))  #R方为负就说明拟合效果比平均值差a

MSE: 3.7E-04
RMSE: 0.019
MAE: 0.013
MAPE: 2.64 %
R_2: 0.93

In [25]:

dtrain = xgb.DMatrix(X_train, Y_train)
dvalid = xgb.DMatrix(X_valid, Y_valid)
dtest = xgb.DMatrix(X_test, Y_test)

In [102]:

from sklearn.model_selection import cross_val_score
from xgboost import XGBRegressor
from bayes_opt import BayesianOptimization

In [103]:

def xgb_cv(max_depth, learning_rate, n_estimators, min_child_weight, subsample, colsample_bytree, reg_alpha, gamma):
    val = cross_val_score(estimator=XGBRegressor(max_depth=int(max_depth),
                                                 learning_rate=learning_rate,
                                                 n_estimators=int(n_estimators),
                                                 min_child_weight=min_child_weight,
                                                 subsample=max(min(subsample, 1), 0),
                                                 colsample_bytree=max(min(colsample_bytree, 1), 0),
                                                 reg_alpha=max(reg_alpha, 0), gamma=gamma, objective='reg:squarederror',
                                                 booster='gbtree',
                                                 seed=666), X=use_data[feature_cols], y=use_data.values[:1], scoring='r2',
                          cv=10).max()
    return val

In [104]:

xgb_bo = BayesianOptimization(xgb_cv, pbounds={'max_depth': (20, 60),
                                               'learning_rate': (0.005, 0.1),
                                               'n_estimators': (100, 2000),
                                               'min_child_weight': (0, 30),
                                               'subsample': (0.05, 1),
                                               'colsample_bytree': (0.1, 1),
                                               'reg_alpha': (0.001, 10),
                                               'gamma': (0.001, 10)})
xgb_bo.maximize(n_iter=100, init_points=10)

|   iter    |  target   | colsam... |   gamma   | learni... | max_depth | min_ch... | n_esti... | reg_alpha | subsample |
-------------------------------------------------------------------------------------------------------------------------

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
~\AppData\Local\Temp\ipykernel_17148\1576227182.py in <module>
      7                                                'reg_alpha': (0.001, 10),
      8                                                'gamma': (0.001, 10)})
----> 9 xgb_bo.maximize(n_iter=100, init_points=10)

D:\miniconda3\envs\py37\lib\site-packages\bayes_opt\bayesian_optimization.py in maximize(self, init_points, n_iter, acquisition_function, acq, kappa, kappa_decay, kappa_decay_delay, xi, **gp_params)
    309                 x_probe = self.suggest(util)
    310                 iteration += 1
--> 311             self.probe(x_probe, lazy=False)
    312 
    313             if self._bounds_transformer and iteration > 0:

D:\miniconda3\envs\py37\lib\site-packages\bayes_opt\bayesian_optimization.py in probe(self, params, lazy)
    206             self._queue.add(params)
    207         else:
--> 208             self._space.probe(params)
    209             self.dispatch(Events.OPTIMIZATION_STEP)
    210 

D:\miniconda3\envs\py37\lib\site-packages\bayes_opt\target_space.py in probe(self, params)
    234         x = self._as_array(params)
    235         params = dict(zip(self._keys, x))
--> 236         target = self.target_func(**params)
    237 
    238         if self._constraint is None:

~\AppData\Local\Temp\ipykernel_17148\2288155185.py in xgb_cv(max_depth, learning_rate, n_estimators, min_child_weight, subsample, colsample_bytree, reg_alpha, gamma)
      9                                                  booster='gbtree',
     10                                                  seed=666), X=use_data[feature_cols], y=use_data.values[:1], scoring='r2',
---> 11                           cv=10).max()
     12     return val

D:\miniconda3\envs\py37\lib\site-packages\sklearn\model_selection\_validation.py in cross_val_score(estimator, X, y, groups, scoring, cv, n_jobs, verbose, fit_params, pre_dispatch, error_score)
    518         fit_params=fit_params,
    519         pre_dispatch=pre_dispatch,
--> 520         error_score=error_score,
    521     )
    522     return cv_results["test_score"]

D:\miniconda3\envs\py37\lib\site-packages\sklearn\model_selection\_validation.py in cross_validate(estimator, X, y, groups, scoring, cv, n_jobs, verbose, fit_params, pre_dispatch, return_train_score, return_estimator, error_score)
    251 
    252     """
--> 253     X, y, groups = indexable(X, y, groups)
    254 
    255     cv = check_cv(cv, y, classifier=is_classifier(estimator))

D:\miniconda3\envs\py37\lib\site-packages\sklearn\utils\validation.py in indexable(*iterables)
    376 
    377     result = [_make_indexable(X) for X in iterables]
--> 378     check_consistent_length(*result)
    379     return result
    380 

D:\miniconda3\envs\py37\lib\site-packages\sklearn\utils\validation.py in check_consistent_length(*arrays)
    332         raise ValueError(
    333             "Found input variables with inconsistent numbers of samples: %r"
--> 334             % [int(l) for l in lengths]
    335         )
    336 

ValueError: Found input variables with inconsistent numbers of samples: [3080, 1]

In [105]:

params_xgb = {'objective': 'reg:squarederror',
              'booster': 'gbtree',
              'eta': 0.037,
              'max_depth': 30,
              'subsample': 1.0,
              'colsample_bytree': 0.47,
              'min_child_weight': 30,
              'seed': 42}
num_boost_round = 2000

dtrain = xgb.DMatrix(X_train, Y_train)
dvalid = xgb.DMatrix(X_valid, Y_valid)
watchlist = [(dtrain, 'train'), (dvalid, 'eval')]

gb_model = xgb.train(params_xgb, dtrain, num_boost_round, evals=watchlist,
                early_stopping_rounds=100, verbose_eval=False)

In [106]:

y_pred_xgb = np.expm1(gb_model.predict(xgb.DMatrix(X_test)))
y_true_xgb = np.expm1(Y_test.values)

In [107]:

MSE = mean_squared_error(y_true_xgb, y_pred_xgb)
RMSE = np.sqrt(mean_squared_error(y_true_xgb, y_pred_xgb))
MAE = mean_absolute_error(y_true_xgb, y_pred_xgb)
MAPE = mean_absolute_percentage_error(y_true_xgb, y_pred_xgb)
R_2 = r2_score(y_true_xgb, y_pred_xgb)
print('MSE:', format(MSE, '.1E'))
print('RMSE:', round(RMSE, 3))
print('MAE:', round(MAE, 3))
print('MAPE:', round(MAPE*100, 2), '%')
print('R_2:', round(R_2, 3))  #R方为负就说明拟合效果比平均值差a

MSE: 1.1E-05
RMSE: 0.003
MAE: 0.002
MAPE: 2.99 %
R_2: 0.88

In [108]:

In [109]:

kf = KFold(n_splits=10, shuffle=True, random_state=42)
eva_list = list()
for (train_index, test_index) in kf.split(use_data):
    train = use_data.loc[train_index]
    test = use_data.loc[test_index]
    train, valid = train_test_split(train, test_size=0.15, random_state=42)
    X_train, Y_train = train[feature_cols], train[target_cols[1]]
    X_valid, Y_valid = valid[feature_cols], valid[target_cols[1]]
    X_test, Y_test = test[feature_cols], test[target_cols[1]]
    dtrain = xgb.DMatrix(X_train, Y_train)
    dvalid = xgb.DMatrix(X_valid, Y_valid)
    watchlist = [(dvalid, 'eval')]
    gb_model = xgb.train(params_xgb, dtrain, num_boost_round, evals=watchlist,
                    early_stopping_rounds=100, verbose_eval=False)
    y_pred = gb_model.predict(xgb.DMatrix(X_test))
    y_true = Y_test.values
    MSE = mean_squared_error(y_true, y_pred)
    RMSE = np.sqrt(mean_squared_error(y_true, y_pred))
    MAE = mean_absolute_error(y_true, y_pred)
    MAPE = mean_absolute_percentage_error(y_true, y_pred)
    R_2 = r2_score(y_true, y_pred)
    print('MSE:', format(MSE, '.1E'), end=', ')
    print('RMSE:', round(RMSE, 3), end=', ')
    print('MAE:', round(MAE, 3), end=', ')
    print('MAPE:', round(MAPE*100, 2), '%', end=', ')
    print('R_2:', round(R_2, 3))  #R方为负就说明拟合效果比平均值差
    eva_list.append([MSE, RMSE, MAE, MAPE, R_2])

MSE: 1.8E-05, RMSE: 0.004, MAE: 0.002, MAPE: 3.47 %, R_2: 0.776
MSE: 1.8E-05, RMSE: 0.004, MAE: 0.002, MAPE: 3.19 %, R_2: 0.83
MSE: 1.8E-05, RMSE: 0.004, MAE: 0.002, MAPE: 3.87 %, R_2: 0.811
MSE: 1.2E-05, RMSE: 0.003, MAE: 0.002, MAPE: 2.96 %, R_2: 0.861
MSE: 1.9E-05, RMSE: 0.004, MAE: 0.002, MAPE: 3.65 %, R_2: 0.775
MSE: 1.9E-05, RMSE: 0.004, MAE: 0.002, MAPE: 3.56 %, R_2: 0.789
MSE: 2.3E-05, RMSE: 0.005, MAE: 0.002, MAPE: 3.05 %, R_2: 0.723
MSE: 2.5E-05, RMSE: 0.005, MAE: 0.002, MAPE: 3.94 %, R_2: 0.717
MSE: 1.0E-05, RMSE: 0.003, MAE: 0.002, MAPE: 2.9 %, R_2: 0.864
MSE: 9.4E-06, RMSE: 0.003, MAE: 0.002, MAPE: 2.89 %, R_2: 0.881

In [110]:

record = pd.DataFrame.from_records(eva_list, columns=['MSE', 'RMSE', 'MAE', 'MAPE', 'R2'])

In [120]:

record

Out[120]:

	MSE	RMSE	MAE	MAPE	R2
0	0.000018	0.004221	0.002394	0.034705	0.775560
1	0.000018	0.004191	0.002405	0.031921	0.829931
2	0.000018	0.004249	0.002235	0.038677	0.810649
3	0.000012	0.003395	0.002090	0.029607	0.861337
4	0.000019	0.004334	0.002302	0.036496	0.775066
5	0.000019	0.004367	0.002260	0.035588	0.789063
6	0.000023	0.004806	0.002272	0.030522	0.723082
7	0.000025	0.004968	0.002401	0.039428	0.717094
8	0.000010	0.003207	0.002037	0.029033	0.863679
9	0.000009	0.003072	0.002008	0.028871	0.880821

In [124]:

Out[124]:

	MSE	RMSE	MAE	MAPE	R2
8	0.00001	0.003207	0.002037	0.029033	0.863679

In [126]:

index = [0, 1, 2, 3, 4, 5, 6, 8]

In [128]:

record.loc[index].mean()

Out[128]:

MSE     0.000017
RMSE    0.004096
MAE     0.002249
MAPE    0.033319
R2      0.803546
dtype: float64

In [63]:

record.mean()

Out[63]:

MSE     0.000552
RMSE    0.022978
MAE     0.014251
MAPE    0.034105
R2      0.896138
dtype: float64

In [57]:

import matplotlib.pyplot as plt
#新增加的两行
from pylab import mpl
# 设置显示中文字体
mpl.rcParams["font.sans-serif"] = ["SimHei"]

mpl.rcParams["axes.unicode_minus"] = False

In [58]:

plt.figure(figsize=(16, 10))
plt.plot(range(len(y_true)), y_true, 'o-', label='真实值')
plt.plot(range(len(y_pred)), y_pred, '*-', label='预测值')
plt.legend(loc='best')
plt.title('预测结果')
plt.savefig('./figure/CO2排放强度预测结果.png')

No description has been provided for this image

In [59]:

pd.DataFrame.from_records([y_pred, y_true]).T.to_csv('pred.csv')

In [60]:

rst = pd.DataFrame.from_records(([y_true_xgb, y_pred_xgb])).T
rst.columns = ['y_true', 'y_pred']

In [61]:

rst['mAP'] = abs(rst.y_pred - rst.y_true) / rst.y_true

In [62]:

rst.sort_values(by='mAP').sample(10)

Out[62]:

	y_true	y_pred	mAP
23	0.233161	0.228589	0.019609
46	0.242031	0.260373	0.075782
42	0.233845	0.215675	0.077700
1	0.233773	0.237715	0.016864
58	0.258407	0.259042	0.002460
41	0.233404	0.246465	0.055956
15	0.249245	0.248289	0.003837
63	0.237670	0.284324	0.196296
59	0.244008	0.242001	0.008228
37	0.252681	0.251169	0.005983

In [63]:

plt.figure(figsize=(16, 10))
plt.plot(range(len(y_true_xgb)), y_true_xgb, 'o-', label='真实值')
plt.plot(range(len(y_pred_xgb)), y_pred_xgb, '*-', label='预测值')
plt.legend(loc='best')
plt.title('预测结果')
plt.savefig('./figure/CO2排放强度预测结果.png')

煤种标准化工程¶

In [73]:

new_values = total_data.groupby(['煤种', '入炉煤低位热值_new', '燃煤挥发份Var_new', '燃煤灰份Aar_new']).CO2_em_air.mean()

In [74]:

coal_df = new_values.reset_index().drop(columns='CO2_em_air')
coal_df

Out[74]:

	煤种	入炉煤低位热值_new	燃煤挥发份Var_new	燃煤灰份Aar_new
0	无烟煤	17050.00	6.51	31.330000
1	无烟煤	18440.00	9.13	21.240189
2	无烟煤	19335.65	7.06	21.400000
3	无烟煤	20125.07	5.70	29.850000
4	无烟煤	20463.30	5.70	29.790000
...	...	...	...	...
622	贫煤	21772.91	10.66	26.320000
623	贫煤	21907.00	10.64	28.100000
624	贫煤	22042.72	12.96	25.690000
625	贫煤	23215.00	11.00	19.310000
626	贫煤	23791.00	11.00	19.310000

627 rows × 4 columns

In [75]:

coal_params_dict = dict()
for coal_type in coal_df['煤种'].unique().tolist():
    options = coal_df[coal_df['煤种']==coal_type][['入炉煤低位热值_new', '燃煤挥发份Var_new', '燃煤灰份Aar_new']].values
    coal_params_dict[coal_type] = options

In [76]:

coal_params_dict

Out[76]:

{'无烟煤': array([[1.70500000e+04, 6.51000000e+00, 3.13300000e+01],
        [1.84400000e+04, 9.13000000e+00, 2.12401894e+01],
        [1.93356500e+04, 7.06000000e+00, 2.14000000e+01],
        [2.01250700e+04, 5.70000000e+00, 2.98500000e+01],
        [2.04633000e+04, 5.70000000e+00, 2.97900000e+01]]),
 '烟煤': array([[1.277100e+04, 2.126000e+01, 3.355000e+01],
        [1.500000e+04, 2.346000e+01, 1.904000e+01],
        [1.610000e+04, 2.333000e+01, 1.873000e+01],
        ...,
        [2.348751e+04, 2.927000e+01, 2.097000e+01],
        [2.365000e+04, 2.887000e+01, 7.910000e+00],
        [2.365614e+04, 2.927000e+01, 2.097000e+01]]),
 '褐煤': array([[1.059800e+04, 2.476000e+01, 2.179000e+01],
        [1.129000e+04, 4.764000e+01, 3.079000e+01],
        [1.160400e+04, 4.758000e+01, 3.025000e+01],
        [1.172435e+04, 4.601000e+01, 3.673000e+01],
        [1.203000e+04, 4.726000e+01, 3.119000e+01],
        [1.213546e+04, 4.642000e+01, 1.113000e+01],
        [1.217290e+04, 4.642000e+01, 1.113000e+01],
        [1.219256e+04, 4.642000e+01, 1.113000e+01],
        [1.221131e+04, 4.642000e+01, 1.113000e+01],
        [1.230939e+04, 4.642000e+01, 1.113000e+01],
        [1.233780e+04, 4.642000e+01, 1.113000e+01],
        [1.267400e+04, 4.324000e+01, 1.237000e+01],
        [1.278700e+04, 4.884000e+01, 4.117000e+01],
        [1.295100e+04, 2.228000e+01, 1.287000e+01],
        [1.299880e+04, 2.256000e+01, 1.716000e+01],
        [1.311100e+04, 2.367000e+01, 2.107000e+01],
        [1.313000e+04, 2.417000e+01, 1.630000e+01],
        [1.318000e+04, 2.445000e+01, 1.794000e+01],
        [1.320830e+04, 2.451000e+01, 1.429000e+01],
        [1.325722e+04, 1.703000e+01, 3.660000e+01],
        [1.327000e+04, 3.204000e+01, 1.709000e+01],
        [1.327300e+04, 2.364000e+01, 1.622000e+01],
        [1.327300e+04, 2.458000e+01, 1.261000e+01],
        [1.332771e+04, 4.090000e+01, 2.507000e+01],
        [1.333064e+04, 1.680000e+01, 3.741000e+01],
        [1.335883e+04, 2.301000e+01, 1.841000e+01],
        [1.336864e+04, 2.301000e+01, 1.841000e+01],
        [1.343787e+04, 2.336000e+01, 1.705000e+01],
        [1.344000e+04, 4.782000e+01, 2.290000e+01],
        [1.345749e+04, 2.388000e+01, 1.652000e+01],
        [1.357000e+04, 1.799000e+01, 2.177000e+01],
        [1.364000e+04, 2.526000e+01, 2.108000e+01],
        [1.365410e+04, 2.232000e+01, 1.171000e+01],
        [1.369000e+04, 4.771000e+01, 2.205000e+01],
        [1.382000e+04, 2.420000e+01, 1.104000e+01],
        [1.389597e+04, 2.232000e+01, 1.171000e+01],
        [1.390000e+04, 3.683000e+01, 4.441000e+01],
        [1.395400e+04, 2.310000e+01, 1.011000e+01],
        [1.396000e+04, 4.665000e+01, 1.890000e+01],
        [1.400000e+04, 4.520000e+01, 1.364000e+01],
        [1.404100e+04, 2.346000e+01, 1.046000e+01],
        [1.410900e+04, 4.520000e+01, 1.364000e+01],
        [1.412200e+04, 2.478000e+01, 1.916000e+01],
        [1.419900e+04, 4.733000e+01, 1.697000e+01],
        [1.433937e+04, 2.476000e+01, 3.371000e+01],
        [1.440000e+04, 2.589000e+01, 1.643000e+01],
        [1.442729e+04, 4.474000e+01, 1.193000e+01],
        [1.446814e+04, 2.484000e+01, 3.331000e+01],
        [1.448810e+04, 3.554000e+01, 1.171000e+01],
        [1.458200e+04, 2.834000e+01, 2.320000e+01],
        [1.460000e+04, 2.714000e+01, 4.346000e+01],
        [1.462400e+04, 4.613000e+01, 2.700000e+01],
        [1.463500e+04, 4.613000e+01, 2.700000e+01],
        [1.464000e+04, 4.439000e+01, 1.684000e+01],
        [1.470100e+04, 2.210000e+01, 4.588000e+01],
        [1.481078e+04, 4.501000e+01, 1.325000e+01],
        [1.489878e+04, 2.386000e+01, 3.161000e+01],
        [1.507938e+04, 4.501000e+01, 1.325000e+01],
        [1.512117e+04, 2.355000e+01, 1.472000e+01],
        [1.517400e+04, 3.126000e+01, 1.696000e+01],
        [1.523800e+04, 2.492000e+01, 2.378000e+01],
        [1.524041e+04, 2.355000e+01, 1.472000e+01],
        [1.528927e+04, 2.345000e+01, 1.554000e+01],
        [1.534700e+04, 2.492000e+01, 2.378000e+01],
        [1.536708e+04, 4.501000e+01, 8.590000e+00],
        [1.540000e+04, 2.450000e+01, 2.085000e+01],
        [1.560165e+04, 2.345000e+01, 1.554000e+01],
        [1.562100e+04, 4.409000e+01, 1.019000e+01],
        [1.568455e+04, 1.865000e+01, 3.545000e+01],
        [1.599544e+04, 1.865000e+01, 3.545000e+01],
        [1.619823e+04, 2.032000e+01, 3.297000e+01],
        [1.619823e+04, 2.075000e+01, 3.310000e+01],
        [1.619951e+04, 1.790000e+01, 3.976000e+01],
        [1.620200e+04, 1.268000e+01, 4.012000e+01],
        [1.638000e+04, 2.264000e+01, 2.024000e+01],
        [1.644918e+04, 2.061000e+01, 3.224000e+01],
        [1.644918e+04, 2.087000e+01, 3.238000e+01],
        [1.660450e+04, 3.484000e+01, 9.590000e+00],
        [1.662400e+04, 1.287000e+01, 3.909000e+01],
        [1.667800e+04, 1.320000e+01, 3.884000e+01],
        [1.701000e+04, 2.721000e+01, 4.295000e+01],
        [1.711359e+04, 3.560000e+01, 9.440000e+00],
        [1.721702e+04, 3.266000e+01, 6.030000e+00],
        [1.732699e+04, 3.266000e+01, 6.030000e+00],
        [1.769205e+04, 3.632000e+01, 8.880000e+00],
        [1.783200e+04, 3.564000e+01, 2.418000e+01],
        [1.792600e+04, 3.563000e+01, 2.488000e+01],
        [1.802919e+04, 3.526000e+01, 7.680000e+00],
        [1.811583e+04, 3.348000e+01, 1.236000e+01],
        [1.815944e+04, 3.348000e+01, 1.236000e+01],
        [1.834900e+04, 3.542000e+01, 1.152000e+01],
        [1.862400e+04, 3.951000e+01, 1.937000e+01],
        [1.877383e+04, 2.676000e+01, 3.448000e+01],
        [1.877602e+04, 2.676000e+01, 3.448000e+01],
        [1.882100e+04, 2.678000e+01, 3.445000e+01],
        [1.884200e+04, 2.685000e+01, 3.451000e+01],
        [1.896000e+04, 3.951000e+01, 1.937000e+01],
        [1.903900e+04, 2.580000e+01, 2.420000e+01],
        [1.908760e+04, 3.426000e+01, 4.580000e+00],
        [1.918000e+04, 2.670000e+01, 2.480000e+01],
        [1.922827e+04, 3.426000e+01, 4.580000e+00],
        [1.924675e+04, 3.243000e+01, 7.700000e+00],
        [1.927600e+04, 3.200000e+01, 7.700000e+00],
        [1.959900e+04, 3.514000e+01, 1.065000e+01],
        [1.964010e+04, 3.446000e+01, 4.600000e+00],
        [1.965200e+04, 2.990000e+01, 2.406000e+01],
        [1.974233e+04, 3.422000e+01, 2.892000e+01],
        [1.976235e+04, 3.414000e+01, 2.934000e+01],
        [1.977612e+04, 3.446000e+01, 4.600000e+00],
        [1.993700e+04, 3.514000e+01, 1.065000e+01],
        [1.997000e+04, 3.533000e+01, 9.050000e+00],
        [2.003000e+04, 3.948000e+01, 3.080000e+01],
        [2.006000e+04, 3.911000e+01, 3.080000e+01],
        [2.011300e+04, 2.560000e+01, 2.312000e+01],
        [2.017338e+04, 2.979000e+01, 1.814000e+01],
        [2.025484e+04, 2.979000e+01, 1.814000e+01],
        [2.028500e+04, 3.009000e+01, 1.125000e+01],
        [2.057100e+04, 3.147000e+01, 2.478000e+01],
        [2.062600e+04, 2.627000e+01, 2.050000e+01],
        [2.066423e+04, 2.752000e+01, 2.014000e+01],
        [2.067360e+04, 2.840000e+01, 2.165000e+01],
        [2.068200e+04, 2.960000e+01, 1.603000e+01],
        [2.068600e+04, 3.124000e+01, 2.445000e+01],
        [2.070300e+04, 3.000000e+01, 1.125000e+01],
        [2.073600e+04, 2.627000e+01, 2.050000e+01],
        [2.075090e+04, 2.780000e+01, 2.254000e+01],
        [2.076000e+04, 2.977000e+01, 1.291000e+01],
        [2.078500e+04, 3.871000e+01, 1.575000e+01],
        [2.083648e+04, 2.780000e+01, 2.254000e+01],
        [2.089200e+04, 3.252000e+01, 9.680000e+00],
        [2.089200e+04, 3.255000e+01, 9.380000e+00],
        [2.089200e+04, 3.262000e+01, 1.026000e+01],
        [2.089200e+04, 3.324000e+01, 8.560000e+00],
        [2.090000e+04, 3.100000e+01, 1.981000e+01],
        [2.093990e+04, 2.840000e+01, 2.165000e+01],
        [2.094100e+04, 2.977000e+01, 1.291000e+01],
        [2.094900e+04, 3.100000e+01, 2.007000e+01],
        [2.107400e+04, 3.830000e+01, 1.525000e+01],
        [2.110000e+04, 2.470000e+01, 2.599000e+01],
        [2.114300e+04, 2.580000e+01, 2.196000e+01],
        [2.114300e+04, 2.580000e+01, 2.197000e+01],
        [2.121740e+04, 3.279000e+01, 1.334000e+01],
        [2.127156e+04, 3.844000e+01, 1.186000e+01],
        [2.134680e+04, 3.885000e+01, 1.243000e+01],
        [2.137900e+04, 2.944000e+01, 1.436000e+01],
        [2.147400e+04, 2.944000e+01, 1.436000e+01],
        [2.166129e+04, 3.124000e+01, 1.849000e+01],
        [2.176000e+04, 3.213000e+01, 1.785000e+01],
        [2.208167e+04, 3.176000e+01, 1.816000e+01],
        [2.214783e+04, 3.736000e+01, 1.390000e+01],
        [2.219619e+04, 3.736000e+01, 1.390000e+01],
        [2.240000e+04, 3.052000e+01, 1.785000e+01],
        [2.248200e+04, 3.010000e+01, 1.125000e+01],
        [2.261900e+04, 3.047000e+01, 1.303000e+01],
        [2.274200e+04, 3.028000e+01, 1.057000e+01]]),
 '贫煤': array([[1.695900e+04, 9.310000e+00, 4.477000e+01],
        [1.742404e+04, 1.058000e+01, 2.268000e+01],
        [1.742931e+04, 7.900000e+00, 3.840000e+01],
        [1.799800e+04, 1.175000e+01, 2.981000e+01],
        [1.875700e+04, 1.185000e+01, 3.122000e+01],
        [1.912518e+04, 7.810000e+00, 3.145000e+01],
        [1.928076e+04, 7.930000e+00, 3.137000e+01],
        [1.935228e+04, 1.119000e+01, 3.202000e+01],
        [1.938269e+04, 1.127000e+01, 3.192000e+01],
        [1.983535e+04, 1.152000e+01, 3.052000e+01],
        [1.986900e+04, 1.161000e+01, 3.042000e+01],
        [1.994000e+04, 9.370000e+00, 3.426000e+01],
        [1.994300e+04, 9.370000e+00, 3.426000e+01],
        [2.003700e+04, 1.125000e+01, 3.067000e+01],
        [2.024590e+04, 1.058000e+01, 2.654000e+01],
        [2.028730e+04, 1.120000e+01, 2.698000e+01],
        [2.031000e+04, 1.123000e+01, 3.357000e+01],
        [2.031700e+04, 1.125000e+01, 3.067000e+01],
        [2.036000e+04, 9.450000e+00, 3.077000e+01],
        [2.057000e+04, 1.185000e+01, 2.786000e+01],
        [2.075500e+04, 1.174000e+01, 2.817000e+01],
        [2.086230e+04, 1.040000e+01, 2.583000e+01],
        [2.092670e+04, 9.510000e+00, 2.515000e+01],
        [2.096500e+04, 1.258000e+01, 2.965000e+01],
        [2.097590e+04, 1.017000e+01, 2.491000e+01],
        [2.098100e+04, 1.258000e+01, 2.965000e+01],
        [2.101000e+04, 1.209000e+01, 2.169000e+01],
        [2.101980e+04, 9.410000e+00, 2.489000e+01],
        [2.103908e+04, 7.010000e+00, 2.714000e+01],
        [2.105200e+04, 1.074000e+01, 3.136000e+01],
        [2.106690e+04, 1.034000e+01, 2.481000e+01],
        [2.107710e+04, 1.017000e+01, 2.478000e+01],
        [2.110900e+04, 7.670000e+00, 2.597000e+01],
        [2.110900e+04, 1.209000e+01, 2.169000e+01],
        [2.119000e+04, 7.170000e+00, 2.591000e+01],
        [2.119400e+04, 7.190000e+00, 2.597000e+01],
        [2.119433e+04, 7.010000e+00, 2.667000e+01],
        [2.122400e+04, 1.256000e+01, 2.636000e+01],
        [2.126600e+04, 7.260000e+00, 2.567000e+01],
        [2.126900e+04, 1.174000e+01, 2.817000e+01],
        [2.157900e+04, 1.189000e+01, 2.689000e+01],
        [2.174500e+04, 1.074000e+01, 2.850000e+01],
        [2.176688e+04, 1.062000e+01, 2.687000e+01],
        [2.177291e+04, 1.066000e+01, 2.632000e+01],
        [2.190700e+04, 1.064000e+01, 2.810000e+01],
        [2.204272e+04, 1.296000e+01, 2.569000e+01],
        [2.321500e+04, 1.100000e+01, 1.931000e+01],
        [2.379100e+04, 1.100000e+01, 1.931000e+01]])}

In [77]:

total_data

Out[77]:

	地区	所属集团	投产时间	机组容量	机组类型	参数分类	冷却方式	锅炉类型	时间	发电量	...	标煤量	出力系数	煤种	入炉煤低位热值	燃煤挥发份Var	燃煤灰份Aar	CO2_em_air	入炉煤低位热值_new	燃煤挥发份Var_new	燃煤灰份Aar_new
0	北京	华能	1998/1/20 0:00	165	供热式	超高压	水冷	煤粉	2016.0	51841.70000	...	2.580497e+05	75.84	烟煤	23380.0	27.59	9.94	0.235066	23380.0	27.59	9.94
1	北京	华能	1998/1/20 0:00	165	供热式	超高压	水冷	煤粉	2016.0	47387.95000	...	2.126813e+05	74.50	烟煤	23380.0	27.59	9.94	0.226207	23380.0	27.59	9.94
2	北京	华能	1998/12/20 0:00	220	供热式	超高压	水冷	煤粉	2016.0	115498.04000	...	4.410925e+05	78.76	烟煤	23380.0	27.59	9.94	0.220954	23380.0	27.59	9.94
3	北京	华能	1999/6/26 0:00	220	供热式	超高压	水冷	煤粉	2016.0	120884.07000	...	4.707218e+05	81.41	烟煤	23380.0	27.59	9.94	0.216298	23380.0	27.59	9.94
4	辽宁	大唐	2009/4/30 0:00	300	供热式	亚临界	水冷	煤粉	2016.0	111218.55000	...	3.726990e+05	71.27	褐煤	14122.0	24.78	19.16	0.238755	14122.0	24.78	19.16
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
847	新疆	NaN	NaN	1320	纯凝式	超临界	间接空冷	煤粉	NaN	704381.26290	...	2.283076e+06	NaN	褐煤	19970.0	35.33	9.05	0.196452	19970.0	35.33	9.05
848	辽宁	NaN	NaN	700	供热式	超临界	水冷	煤粉	NaN	350000.00000	...	1.328747e+06	NaN	褐煤	14640.0	44.39	16.84	0.185688	14640.0	44.39	16.84
849	内蒙	NaN	NaN	700	供热式	超临界	直接空冷	煤粉	NaN	385000.00000	...	1.362009e+06	NaN	褐煤	13960.0	46.65	18.90	0.181214	13960.0	46.65	18.90
850	山东	NaN	NaN	40	供热式	超高压	水冷	循环流化床	NaN	17000.00000	...	1.810834e+05	NaN	烟煤	21060.0	19.12	20.27	0.347570	21060.0	19.12	20.27
851	浙江	NaN	NaN	70	供热式	超高压	水冷	循环流化床	NaN	35788.81469	...	3.502535e+05	NaN	烟煤	22021.0	19.12	21.77	0.253057	22021.0	19.12	21.77

852 rows × 21 columns

In [78]:

new_use_data = total_data.groupby(use_col + ['煤种'])['CO2_em_air'].mean().reset_index().drop(columns=['入炉煤低位热值_new', '燃煤挥发份Var_new', '燃煤灰份Aar_new'])
new_use_data.rename(columns={0:'CO2_em_air'}, inplace=True)
new_use_data['coal_params'] = new_use_data['煤种'].apply(lambda x: coal_params_dict.get(x))
new_use_data.drop(columns='煤种', inplace=True)

In [79]:

new_data = new_use_data.explode(column='coal_params')

In [80]:

new_data.drop(columns=['CO2_em_air'])

Out[80]:

	地区	机组类型	参数分类	冷却方式	锅炉类型	机组容量	coal_params
0	上海	纯凝式	亚临界	水冷	煤粉	320	[12771.0, 21.26, 33.55]
0	上海	纯凝式	亚临界	水冷	煤粉	320	[15000.0, 23.46, 19.04]
0	上海	纯凝式	亚临界	水冷	煤粉	320	[16100.0, 23.33, 18.73]
0	上海	纯凝式	亚临界	水冷	煤粉	320	[16190.0, 23.33, 18.73]
0	上海	纯凝式	亚临界	水冷	煤粉	320	[16641.0, 19.13, 39.12]
...	...	...	...	...	...	...	...
646	黑龙江	纯凝式	超高压	水冷	煤粉	210	[23253.68, 23.72, 18.45]
646	黑龙江	纯凝式	超高压	水冷	煤粉	210	[23380.0, 27.59, 9.94]
646	黑龙江	纯凝式	超高压	水冷	煤粉	210	[23487.51, 29.27, 20.97]
646	黑龙江	纯凝式	超高压	水冷	煤粉	210	[23650.0, 28.87, 7.91]
646	黑龙江	纯凝式	超高压	水冷	煤粉	210	[23656.14, 29.27, 20.97]

208875 rows × 7 columns

In [81]:

norm_data = pd.concat([new_data, new_data.coal_params.apply(pd.Series, index=['入炉煤低位热值_new', '燃煤挥发份Var_new', '燃煤灰份Aar_new'])], axis=1).drop(columns='coal_params')

In [82]:

norm_data

Out[82]:

	地区	机组类型	参数分类	冷却方式	锅炉类型	机组容量	CO2_em_air	入炉煤低位热值_new	燃煤挥发份Var_new	燃煤灰份Aar_new
0	上海	纯凝式	亚临界	水冷	煤粉	320	0.266602	12771.00	21.26	33.55
0	上海	纯凝式	亚临界	水冷	煤粉	320	0.266602	15000.00	23.46	19.04
0	上海	纯凝式	亚临界	水冷	煤粉	320	0.266602	16100.00	23.33	18.73
0	上海	纯凝式	亚临界	水冷	煤粉	320	0.266602	16190.00	23.33	18.73
0	上海	纯凝式	亚临界	水冷	煤粉	320	0.266602	16641.00	19.13	39.12
...	...	...	...	...	...	...	...	...	...	...
646	黑龙江	纯凝式	超高压	水冷	煤粉	210	0.278763	23253.68	23.72	18.45
646	黑龙江	纯凝式	超高压	水冷	煤粉	210	0.278763	23380.00	27.59	9.94
646	黑龙江	纯凝式	超高压	水冷	煤粉	210	0.278763	23487.51	29.27	20.97
646	黑龙江	纯凝式	超高压	水冷	煤粉	210	0.278763	23650.00	28.87	7.91
646	黑龙江	纯凝式	超高压	水冷	煤粉	210	0.278763	23656.14	29.27	20.97

208875 rows × 10 columns

In [83]:

for col in num_cols:
    norm_data[col] = np.log1p(norm_data[col])
    # total_data[col] = (total_data[col] - total_data[col].min()) / (total_data[col].max() - total_data[col].min())
norm_data_dummy = pd.get_dummies(norm_data, columns=object_cols)

In [84]:

norm_data_dummy

Out[84]:

	机组容量	CO2_em_air	入炉煤低位热值_new	燃煤挥发份Var_new	燃煤灰份Aar_new	地区_上海	地区_云南	地区_内蒙	地区_北京	地区_吉林	...	机组类型_纯凝式	参数分类_亚临界	参数分类_超临界	参数分类_超超临界	参数分类_超高压	冷却方式_水冷	冷却方式_直接空冷	冷却方式_间接空冷	锅炉类型_循环流化床	锅炉类型_煤粉
0	5.771441	0.236338	9.455011	3.102791	3.542408	1	0	0	0	0	...	1	1	0	0	0	1	0	0	0	1
0	5.771441	0.236338	9.615872	3.197039	2.997730	1	0	0	0	0	...	1	1	0	0	0	1	0	0	0	1
0	5.771441	0.236338	9.686637	3.191710	2.982140	1	0	0	0	0	...	1	1	0	0	0	1	0	0	0	1
0	5.771441	0.236338	9.692211	3.191710	2.982140	1	0	0	0	0	...	1	1	0	0	0	1	0	0	0	1
0	5.771441	0.236338	9.719685	3.002211	3.691875	1	0	0	0	0	...	1	1	0	0	0	1	0	0	0	1
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
646	5.351858	0.245893	10.054262	3.207613	2.967847	0	0	0	0	0	...	1	0	0	0	1	1	0	0	0	1
646	5.351858	0.245893	10.059679	3.353057	2.392426	0	0	0	0	0	...	1	0	0	0	1	1	0	0	0	1
646	5.351858	0.245893	10.064267	3.410157	3.089678	0	0	0	0	0	...	1	0	0	0	1	1	0	0	0	1
646	5.351858	0.245893	10.071161	3.396855	2.187174	0	0	0	0	0	...	1	0	0	0	1	1	0	0	0	1
646	5.351858	0.245893	10.071420	3.410157	3.089678	0	0	0	0	0	...	1	0	0	0	1	1	0	0	0	1

208875 rows × 45 columns

In [85]:

new_xgb_data = xgb.DMatrix(norm_data_dummy[feature_cols])

In [86]:

norm_data.drop(columns='CO2_em_air', inplace=True)

In [87]:

norm_data['co2_pred'] = gb_model.predict(new_xgb_data)
normaled_data = norm_data.drop(columns=['入炉煤低位热值_new', '燃煤挥发份Var_new', '燃煤灰份Aar_new']).groupby([x for x in use_col if x not in ['CO2_em_air', '入炉煤低位热值_new', '燃煤挥发份Var_new', '燃煤灰份Aar_new']])['co2_pred'].mean()

In [ ]:

normaled_data.reset_index().to_csv('./data/去煤种化数据.csv', encoding='utf-8-sig', index=False)

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煤种标准化工程¶

459 KiB

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