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In [1]:



    


import pandas as pd
import numpy as np
import tensorflow as tf
from sklearn.cross_validation import train_test_split
import xgboost as xgb
from scipy import sparse
from sklearn.feature_extraction import FeatureHasher
from scipy.sparse import coo_matrix,csr_matrix,csc_matrix, hstack
from sklearn.preprocessing import normalize
from sklearn.utils import shuffle
from sklearn import linear_model
import gc
from sklearn import preprocessing



    











In [2]:



    


import matplotlib.pyplot as plt
%matplotlib inline



    











In [3]:



    


predictors_target_11 = ['ruta_freq', 'clien_freq', 'agen_freq', 'prod_freq',
       'agen_for_log_de', 'ruta_for_log_de', 'cliente_for_log_de',
       'producto_for_log_de', 'agen_ruta_for_log_de',
       'agen_cliente_for_log_de', 'agen_producto_for_log_de',
       'ruta_cliente_for_log_de', 'ruta_producto_for_log_de',
       'cliente_producto_for_log_de', 'cliente_for_log_sum',
       'cliente_producto_agen_for_log_sum', 'corr', 't_min_6', 't_min_2',
       't_min_3', 't_min_4', 't_min_5', 't2_min_t6', 't3_min_t6',
       't4_min_t6', 't5_min_t6', 't2_min_t3', 't2_min_t4', 't2_min_t5',
       't3_min_t4', 't3_min_t5', 't4_min_t5', 'LR_prod', 'LR_prod_corr',
       'target', 't_m_6_cum', 't_m_5_cum', 't_m_4_cum', 't_m_3_cum',
       't_m_2_cum', 'NombreCliente', 'weight', 'weight_per_piece', 'pieces']



    











In [4]:



    


predictors_11 = ['ruta_freq', 'clien_freq', 'agen_freq',
       'prod_freq', 'agen_for_log_de', 'ruta_for_log_de',
       'cliente_for_log_de', 'producto_for_log_de', 'agen_ruta_for_log_de',
       'agen_cliente_for_log_de', 'agen_producto_for_log_de',
       'ruta_cliente_for_log_de', 'ruta_producto_for_log_de',
       'cliente_producto_for_log_de', 'cliente_for_log_sum',
       'cliente_producto_agen_for_log_sum', 'corr', 't_min_6', 't_min_2',
       't_min_3', 't_min_4', 't_min_5', 't2_min_t6', 't3_min_t6',
       't4_min_t6', 't5_min_t6', 't2_min_t3', 't2_min_t4', 't2_min_t5',
       't3_min_t4', 't3_min_t5', 't4_min_t5', 'LR_prod', 'LR_prod_corr',
       't_m_6_cum', 't_m_5_cum', 't_m_4_cum', 't_m_3_cum', 't_m_2_cum',
       'NombreCliente', 'weight', 'weight_per_piece', 'pieces']



    











In [5]:



    


train_pivot_xgb_time2 = pd.read_csv('train_pivot_34567_to_9.csv',usecols = predictors_target_11,dtype = np.float32)



    











In [6]:



    


train_pivot_xgb_time2.columns.values



    


















    
Out[6]:








array(['ruta_freq', 'clien_freq', 'agen_freq', 'prod_freq',
       'agen_for_log_de', 'ruta_for_log_de', 'cliente_for_log_de',
       'producto_for_log_de', 'agen_ruta_for_log_de',
       'agen_cliente_for_log_de', 'agen_producto_for_log_de',
       'ruta_cliente_for_log_de', 'ruta_producto_for_log_de',
       'cliente_producto_for_log_de', 'cliente_for_log_sum',
       'cliente_producto_agen_for_log_sum', 'corr', 't_min_6', 't_min_2',
       't_min_3', 't_min_4', 't_min_5', 't2_min_t6', 't3_min_t6',
       't4_min_t6', 't5_min_t6', 't2_min_t3', 't2_min_t4', 't2_min_t5',
       't3_min_t4', 't3_min_t5', 't4_min_t5', 'LR_prod', 'LR_prod_corr',
       'target', 't_m_6_cum', 't_m_5_cum', 't_m_4_cum', 't_m_3_cum',
       't_m_2_cum', 'NombreCliente', 'weight', 'weight_per_piece', 'pieces'], dtype=object)

















In [7]:



    


train_pivot_xgb_time2.head()



    


















    
Out[7]:










  
    

      
      ruta_freq
      clien_freq
      agen_freq
      prod_freq
      agen_for_log_de
      ruta_for_log_de
      cliente_for_log_de
      producto_for_log_de
      agen_ruta_for_log_de
      agen_cliente_for_log_de
      ...
      target
      t_m_6_cum
      t_m_5_cum
      t_m_4_cum
      t_m_3_cum
      t_m_2_cum
      NombreCliente
      weight
      weight_per_piece
      pieces
    

  
  
    

      0
      11.0
      160.0
      4017.0
      2159.0
      3.936934
      3.095841
      2.897803
      3.106304
      3.095841
      3.015667
      ...
      4.574711
      0.000000
      0.000000
      0.000000
      0.000000
      0.000000
      233.0
      600.0
      75.00
      8.0
    

    

      1
      548.0
      160.0
      12574.0
      15736.0
      3.489749
      3.208231
      2.897803
      1.563950
      2.877003
      2.877003
      ...
      2.639057
      3.044523
      5.877736
      8.650325
      11.422914
      14.367352
      18989.0
      640.0
      NaN
      NaN
    

    

      2
      548.0
      160.0
      12574.0
      7432.0
      3.489749
      3.208231
      2.897803
      2.637196
      2.877003
      2.877003
      ...
      2.397895
      1.386294
      1.386294
      1.386294
      1.386294
      1.386294
      18989.0
      180.0
      30.00
      6.0
    

    

      3
      548.0
      160.0
      12574.0
      17261.0
      3.489749
      3.208231
      2.897803
      2.674463
      2.877003
      2.877003
      ...
      3.784190
      3.784190
      7.218177
      11.024839
      14.580188
      18.191105
      18989.0
      450.0
      56.25
      8.0
    

    

      4
      548.0
      160.0
      12574.0
      87803.0
      3.489749
      3.208231
      2.897803
      1.730373
      2.877003
      2.877003
      ...
      4.682131
      4.795791
      9.202510
      12.963710
      17.212206
      21.786917
      18989.0
      340.0
      42.50
      8.0
    

  



5 rows × 44 columns



















In [8]:



    


train_pivot_xgb_time2.shape



    


















    
Out[8]:








(10385350, 44)

















In [9]:



    


train_pivot_56789_to_11 = pd.read_csv('train_pivot_56789_to_11_private.csv',dtype=np.float32)



    











In [10]:



    


train_pivot_56789_to_11.head()



    


















    
Out[10]:










  
    

      
      id
      ruta_freq
      clien_freq
      agen_freq
      prod_freq
      agen_for_log_de
      ruta_for_log_de
      cliente_for_log_de
      producto_for_log_de
      agen_ruta_for_log_de
      ...
      LR_prod_corr
      t_m_6_cum
      t_m_5_cum
      t_m_4_cum
      t_m_3_cum
      t_m_2_cum
      NombreCliente
      weight
      weight_per_piece
      pieces
    

  
  
    

      0
      1547831.0
      713.0
      166.0
      12208.0
      91578.0
      3.523074
      3.222417
      2.890955
      1.724493
      2.835826
      ...
      7.372772
      3.761200
      8.009695
      12.584406
      17.084215
      21.766348
      5954.0
      340.0
      42.500
      8.0
    

    

      1
      6825659.0
      713.0
      166.0
      12208.0
      10780.0
      3.523074
      3.222417
      2.890955
      3.987424
      2.835826
      ...
      18.803841
      0.000000
      2.833213
      6.165418
      8.730368
      11.949243
      5954.0
      480.0
      NaN
      NaN
    

    

      2
      5853787.0
      713.0
      166.0
      12208.0
      11047.0
      3.523074
      3.222417
      2.890955
      2.694399
      2.835826
      ...
      11.708699
      1.791759
      4.564348
      6.962244
      10.006766
      13.002499
      5954.0
      567.0
      NaN
      NaN
    

    

      3
      2316053.0
      713.0
      166.0
      12208.0
      4912.0
      3.523074
      3.222417
      2.890955
      2.392465
      2.835826
      ...
      10.535754
      0.000000
      0.000000
      0.000000
      0.000000
      1.386294
      5954.0
      435.0
      54.375
      8.0
    

    

      4
      900676.0
      713.0
      166.0
      12208.0
      10614.0
      3.523074
      3.222417
      2.890955
      2.745177
      2.835826
      ...
      12.040730
      1.098612
      1.098612
      1.098612
      2.890372
      5.934894
      5954.0
      248.0
      62.000
      4.0
    

  



5 rows × 44 columns



















In [18]:



    


train_pivot_56789_to_11.columns.values



    


















    
Out[18]:








array(['id', 'ruta_freq', 'clien_freq', 'agen_freq', 'prod_freq',
       'agen_for_log_de', 'ruta_for_log_de', 'cliente_for_log_de',
       'producto_for_log_de', 'agen_ruta_for_log_de',
       'agen_cliente_for_log_de', 'agen_producto_for_log_de',
       'ruta_cliente_for_log_de', 'ruta_producto_for_log_de',
       'cliente_producto_for_log_de', 'cliente_for_log_sum',
       'cliente_producto_agen_for_log_sum', 'corr', 't_min_6', 't_min_2',
       't_min_3', 't_min_4', 't_min_5', 't2_min_t6', 't3_min_t6',
       't4_min_t6', 't5_min_t6', 't2_min_t3', 't2_min_t4', 't2_min_t5',
       't3_min_t4', 't3_min_t5', 't4_min_t5', 'LR_prod', 'LR_prod_corr',
       't_m_6_cum', 't_m_5_cum', 't_m_4_cum', 't_m_3_cum', 't_m_2_cum',
       'NombreCliente', 'weight', 'weight_per_piece', 'pieces'], dtype=object)

begin xgboost





In [11]:



    


train_feature_11 = train_pivot_xgb_time2.drop(['target'],axis = 1)
train_label_11 = train_pivot_xgb_time2['target']

dtrain_11 = xgb.DMatrix(train_feature_11,label = train_label_11,missing= np.nan)



    











In [92]:



    


#right now the best is this parameter set with 700 round
param_11 = {'booster':'gbtree',
         'nthread': 8,
         'max_depth':8, 
         'eta':0.015,
         'silent':1,
         'subsample':0.5, 
         'objective':'reg:linear',
         'eval_metric':'rmse',
         'colsample_bytree':0.5}



    











In [43]:



    


# # for cv
# num_round = 1400

# cvresult = xgb.cv(param_11, dtrain_11, num_round, nfold=4,show_stdv=False,
#                         seed = 0, verbose_eval = 1)
# print(cvresult.tail())



    











In [131]:



    


# for real train
num_round = 2200
evallist = [(dtrain_11,'train')]
bst = xgb.train(param_11, dtrain_11, num_round,evallist)



    


















    






[0]	train-rmse:1.35068
[1]	train-rmse:1.33297
[2]	train-rmse:1.31555
[3]	train-rmse:1.29857
[4]	train-rmse:1.28177
[5]	train-rmse:1.26544
[6]	train-rmse:1.2493
[7]	train-rmse:1.23334
[8]	train-rmse:1.21778
[9]	train-rmse:1.20234
[10]	train-rmse:1.18717
[11]	train-rmse:1.17227
[12]	train-rmse:1.15764
[13]	train-rmse:1.14325
[14]	train-rmse:1.12907
[15]	train-rmse:1.11514
[16]	train-rmse:1.10143
[17]	train-rmse:1.08799
[18]	train-rmse:1.07478
[19]	train-rmse:1.06182
[20]	train-rmse:1.0491
[21]	train-rmse:1.03677
[22]	train-rmse:1.02445
[23]	train-rmse:1.01235
[24]	train-rmse:1.00063
[25]	train-rmse:0.989205
[26]	train-rmse:0.977832
[27]	train-rmse:0.966595
[28]	train-rmse:0.955548
[29]	train-rmse:0.944862
[30]	train-rmse:0.934253
[31]	train-rmse:0.923844
[32]	train-rmse:0.913594
[33]	train-rmse:0.903533
[34]	train-rmse:0.893702
[35]	train-rmse:0.884046
[36]	train-rmse:0.874552
[37]	train-rmse:0.865223
[38]	train-rmse:0.856063
[39]	train-rmse:0.847138
[40]	train-rmse:0.838336
[41]	train-rmse:0.829915
[42]	train-rmse:0.821547
[43]	train-rmse:0.813318
[44]	train-rmse:0.80518
[45]	train-rmse:0.797244
[46]	train-rmse:0.78942
[47]	train-rmse:0.781744
[48]	train-rmse:0.774356
[49]	train-rmse:0.767098
[50]	train-rmse:0.759885
[51]	train-rmse:0.752821
[52]	train-rmse:0.745895
[53]	train-rmse:0.739203
[54]	train-rmse:0.732639
[55]	train-rmse:0.726235
[56]	train-rmse:0.719865
[57]	train-rmse:0.713614
[58]	train-rmse:0.707488
[59]	train-rmse:0.701498
[60]	train-rmse:0.695686
[61]	train-rmse:0.689953
[62]	train-rmse:0.68439
[63]	train-rmse:0.678897
[64]	train-rmse:0.673515
[65]	train-rmse:0.668255
[66]	train-rmse:0.663121
[67]	train-rmse:0.658086
[68]	train-rmse:0.653165
[69]	train-rmse:0.648399
[70]	train-rmse:0.643812
[71]	train-rmse:0.639186
[72]	train-rmse:0.634686
[73]	train-rmse:0.630329
[74]	train-rmse:0.626033
[75]	train-rmse:0.621863
[76]	train-rmse:0.617788
[77]	train-rmse:0.613783
[78]	train-rmse:0.609819
[79]	train-rmse:0.606018
[80]	train-rmse:0.602257
[81]	train-rmse:0.598558
[82]	train-rmse:0.594973
[83]	train-rmse:0.59147
[84]	train-rmse:0.588031
[85]	train-rmse:0.584705
[86]	train-rmse:0.581433
[87]	train-rmse:0.578254
[88]	train-rmse:0.575145
[89]	train-rmse:0.572117
[90]	train-rmse:0.569167
[91]	train-rmse:0.56629
[92]	train-rmse:0.563492
[93]	train-rmse:0.560733
[94]	train-rmse:0.5581
[95]	train-rmse:0.555454
[96]	train-rmse:0.55289
[97]	train-rmse:0.550383
[98]	train-rmse:0.548054
[99]	train-rmse:0.545672
[100]	train-rmse:0.543342
[101]	train-rmse:0.541081
[102]	train-rmse:0.53888
[103]	train-rmse:0.536719
[104]	train-rmse:0.534629
[105]	train-rmse:0.532617
[106]	train-rmse:0.530637
[107]	train-rmse:0.528736
[108]	train-rmse:0.526832
[109]	train-rmse:0.525011
[110]	train-rmse:0.523212
[111]	train-rmse:0.521474
[112]	train-rmse:0.519767
[113]	train-rmse:0.518162
[114]	train-rmse:0.516555
[115]	train-rmse:0.514982
[116]	train-rmse:0.513459
[117]	train-rmse:0.511949
[118]	train-rmse:0.510484
[119]	train-rmse:0.50906
[120]	train-rmse:0.507668
[121]	train-rmse:0.506307
[122]	train-rmse:0.504989
[123]	train-rmse:0.503691
[124]	train-rmse:0.502438
[125]	train-rmse:0.501222
[126]	train-rmse:0.500051
[127]	train-rmse:0.498912
[128]	train-rmse:0.497779
[129]	train-rmse:0.496706
[130]	train-rmse:0.495631
[131]	train-rmse:0.494587
[132]	train-rmse:0.493579
[133]	train-rmse:0.492593
[134]	train-rmse:0.491649
[135]	train-rmse:0.490717
[136]	train-rmse:0.48979
[137]	train-rmse:0.488886
[138]	train-rmse:0.488035
[139]	train-rmse:0.487175
[140]	train-rmse:0.486352
[141]	train-rmse:0.485541
[142]	train-rmse:0.484764
[143]	train-rmse:0.483985
[144]	train-rmse:0.483258
[145]	train-rmse:0.482556
[146]	train-rmse:0.481845
[147]	train-rmse:0.481166
[148]	train-rmse:0.480512
[149]	train-rmse:0.479875
[150]	train-rmse:0.479245
[151]	train-rmse:0.478633
[152]	train-rmse:0.47807
[153]	train-rmse:0.477503
[154]	train-rmse:0.476946
[155]	train-rmse:0.476377
[156]	train-rmse:0.475862
[157]	train-rmse:0.47534
[158]	train-rmse:0.474827
[159]	train-rmse:0.474322
[160]	train-rmse:0.473832
[161]	train-rmse:0.47334
[162]	train-rmse:0.472885
[163]	train-rmse:0.472447
[164]	train-rmse:0.471997
[165]	train-rmse:0.471552
[166]	train-rmse:0.471146
[167]	train-rmse:0.470766
[168]	train-rmse:0.470375
[169]	train-rmse:0.469985
[170]	train-rmse:0.46959
[171]	train-rmse:0.469227
[172]	train-rmse:0.468872
[173]	train-rmse:0.468508
[174]	train-rmse:0.468161
[175]	train-rmse:0.467822
[176]	train-rmse:0.467495
[177]	train-rmse:0.46719
[178]	train-rmse:0.46688
[179]	train-rmse:0.466585
[180]	train-rmse:0.466278
[181]	train-rmse:0.465991
[182]	train-rmse:0.465704
[183]	train-rmse:0.465437
[184]	train-rmse:0.465171
[185]	train-rmse:0.464897
[186]	train-rmse:0.464644
[187]	train-rmse:0.464391
[188]	train-rmse:0.464139
[189]	train-rmse:0.463906
[190]	train-rmse:0.463663
[191]	train-rmse:0.463436
[192]	train-rmse:0.463216
[193]	train-rmse:0.463003
[194]	train-rmse:0.462801
[195]	train-rmse:0.462603
[196]	train-rmse:0.462394
[197]	train-rmse:0.46219
[198]	train-rmse:0.462002
[199]	train-rmse:0.461822
[200]	train-rmse:0.461637
[201]	train-rmse:0.46145
[202]	train-rmse:0.461287
[203]	train-rmse:0.461123
[204]	train-rmse:0.460949
[205]	train-rmse:0.460794
[206]	train-rmse:0.460621
[207]	train-rmse:0.460464
[208]	train-rmse:0.46032
[209]	train-rmse:0.460172
[210]	train-rmse:0.460022
[211]	train-rmse:0.459879
[212]	train-rmse:0.459753
[213]	train-rmse:0.459624
[214]	train-rmse:0.459491
[215]	train-rmse:0.459363
[216]	train-rmse:0.459249
[217]	train-rmse:0.459121
[218]	train-rmse:0.458988
[219]	train-rmse:0.458878
[220]	train-rmse:0.458762
[221]	train-rmse:0.458652
[222]	train-rmse:0.458522
[223]	train-rmse:0.458402
[224]	train-rmse:0.458291
[225]	train-rmse:0.458198
[226]	train-rmse:0.458085
[227]	train-rmse:0.457982
[228]	train-rmse:0.457896
[229]	train-rmse:0.457791
[230]	train-rmse:0.457678
[231]	train-rmse:0.457577
[232]	train-rmse:0.457478
[233]	train-rmse:0.457396
[234]	train-rmse:0.457306
[235]	train-rmse:0.457217
[236]	train-rmse:0.457114
[237]	train-rmse:0.457039
[238]	train-rmse:0.456972
[239]	train-rmse:0.456867
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[1918]	train-rmse:0.44197
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[1920]	train-rmse:0.441963
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[1927]	train-rmse:0.441943
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[1929]	train-rmse:0.441936
[1930]	train-rmse:0.441934
[1931]	train-rmse:0.441929
[1932]	train-rmse:0.441927
[1933]	train-rmse:0.441925
[1934]	train-rmse:0.441923
[1935]	train-rmse:0.441919
[1936]	train-rmse:0.441917
[1937]	train-rmse:0.441913
[1938]	train-rmse:0.441909
[1939]	train-rmse:0.441907
[1940]	train-rmse:0.441904
[1941]	train-rmse:0.441902
[1942]	train-rmse:0.441899
[1943]	train-rmse:0.441897
[1944]	train-rmse:0.441894
[1945]	train-rmse:0.441892
[1946]	train-rmse:0.441889
[1947]	train-rmse:0.441887
[1948]	train-rmse:0.441885
[1949]	train-rmse:0.441881
[1950]	train-rmse:0.441877
[1951]	train-rmse:0.441874
[1952]	train-rmse:0.441872
[1953]	train-rmse:0.441869
[1954]	train-rmse:0.441867
[1955]	train-rmse:0.441864
[1956]	train-rmse:0.441862
[1957]	train-rmse:0.441857
[1958]	train-rmse:0.441855
[1959]	train-rmse:0.441852
[1960]	train-rmse:0.441851
[1961]	train-rmse:0.441849
[1962]	train-rmse:0.441846
[1963]	train-rmse:0.441843
[1964]	train-rmse:0.441841
[1965]	train-rmse:0.441838
[1966]	train-rmse:0.441834
[1967]	train-rmse:0.441832
[1968]	train-rmse:0.441829
[1969]	train-rmse:0.441826
[1970]	train-rmse:0.441822
[1971]	train-rmse:0.441818
[1972]	train-rmse:0.441816
[1973]	train-rmse:0.441813
[1974]	train-rmse:0.44181
[1975]	train-rmse:0.441806
[1976]	train-rmse:0.441803
[1977]	train-rmse:0.441801
[1978]	train-rmse:0.441797
[1979]	train-rmse:0.441795
[1980]	train-rmse:0.441793
[1981]	train-rmse:0.441792
[1982]	train-rmse:0.44179
[1983]	train-rmse:0.441787
[1984]	train-rmse:0.441782
[1985]	train-rmse:0.44178
[1986]	train-rmse:0.441777
[1987]	train-rmse:0.441776
[1988]	train-rmse:0.441774
[1989]	train-rmse:0.441771
[1990]	train-rmse:0.441769
[1991]	train-rmse:0.441769
[1992]	train-rmse:0.441766
[1993]	train-rmse:0.441764
[1994]	train-rmse:0.441763
[1995]	train-rmse:0.441761
[1996]	train-rmse:0.441758
[1997]	train-rmse:0.441756
[1998]	train-rmse:0.441753
[1999]	train-rmse:0.441752
[2000]	train-rmse:0.441749
[2001]	train-rmse:0.441747
[2002]	train-rmse:0.441745
[2003]	train-rmse:0.441741
[2004]	train-rmse:0.44174
[2005]	train-rmse:0.441737
[2006]	train-rmse:0.441733
[2007]	train-rmse:0.441731
[2008]	train-rmse:0.441729
[2009]	train-rmse:0.441728
[2010]	train-rmse:0.441724
[2011]	train-rmse:0.441721
[2012]	train-rmse:0.441719
[2013]	train-rmse:0.441716
[2014]	train-rmse:0.441714
[2015]	train-rmse:0.441712
[2016]	train-rmse:0.441708
[2017]	train-rmse:0.441706
[2018]	train-rmse:0.441704
[2019]	train-rmse:0.441702
[2020]	train-rmse:0.441698
[2021]	train-rmse:0.441697
[2022]	train-rmse:0.441695
[2023]	train-rmse:0.44169
[2024]	train-rmse:0.441687
[2025]	train-rmse:0.441682
[2026]	train-rmse:0.441678
[2027]	train-rmse:0.441676
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[2029]	train-rmse:0.441672
[2030]	train-rmse:0.441667
[2031]	train-rmse:0.441665
[2032]	train-rmse:0.441662
[2033]	train-rmse:0.44166
[2034]	train-rmse:0.441659
[2035]	train-rmse:0.441655
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[2054]	train-rmse:0.441608
[2055]	train-rmse:0.441605
[2056]	train-rmse:0.441601
[2057]	train-rmse:0.441598
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[2084]	train-rmse:0.441519
[2085]	train-rmse:0.441516
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[2087]	train-rmse:0.441509
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[2091]	train-rmse:0.4415
[2092]	train-rmse:0.441498
[2093]	train-rmse:0.441496
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[2097]	train-rmse:0.441487
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[2099]	train-rmse:0.441482
[2100]	train-rmse:0.441479
[2101]	train-rmse:0.441474
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[2111]	train-rmse:0.441448
[2112]	train-rmse:0.441446
[2113]	train-rmse:0.441445
[2114]	train-rmse:0.441444
[2115]	train-rmse:0.441442
[2116]	train-rmse:0.44144
[2117]	train-rmse:0.441437
[2118]	train-rmse:0.441436
[2119]	train-rmse:0.441433
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[2122]	train-rmse:0.441424
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[2143]	train-rmse:0.441366
[2144]	train-rmse:0.441363
[2145]	train-rmse:0.441361
[2146]	train-rmse:0.44136
[2147]	train-rmse:0.441357
[2148]	train-rmse:0.441355
[2149]	train-rmse:0.441353
[2150]	train-rmse:0.441351
[2151]	train-rmse:0.441347
[2152]	train-rmse:0.441344
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[2156]	train-rmse:0.441334
[2157]	train-rmse:0.44133
[2158]	train-rmse:0.441326
[2159]	train-rmse:0.441324
[2160]	train-rmse:0.441322
[2161]	train-rmse:0.44132
[2162]	train-rmse:0.441317
[2163]	train-rmse:0.441315
[2164]	train-rmse:0.441313
[2165]	train-rmse:0.44131
[2166]	train-rmse:0.441308
[2167]	train-rmse:0.441306
[2168]	train-rmse:0.441303
[2169]	train-rmse:0.441302
[2170]	train-rmse:0.441298
[2171]	train-rmse:0.441296
[2172]	train-rmse:0.441294
[2173]	train-rmse:0.441292
[2174]	train-rmse:0.44129
[2175]	train-rmse:0.441288
[2176]	train-rmse:0.441285
[2177]	train-rmse:0.441282
[2178]	train-rmse:0.441279
[2179]	train-rmse:0.441276
[2180]	train-rmse:0.441274
[2181]	train-rmse:0.441273
[2182]	train-rmse:0.441269
[2183]	train-rmse:0.441267
[2184]	train-rmse:0.441266
[2185]	train-rmse:0.441263
[2186]	train-rmse:0.441261
[2187]	train-rmse:0.441259
[2188]	train-rmse:0.441256
[2189]	train-rmse:0.441253
[2190]	train-rmse:0.441252
[2191]	train-rmse:0.441249
[2192]	train-rmse:0.441247
[2193]	train-rmse:0.441245
[2194]	train-rmse:0.441244
[2195]	train-rmse:0.441243
[2196]	train-rmse:0.441241
[2197]	train-rmse:0.441238
[2198]	train-rmse:0.441236
[2199]	train-rmse:0.441234
  • 1600 : 0.442933
  • 1800 : 0.442292
  • 2200 : 0.441234




In [141]:



    


bst.save_model('bst11_2200_eta0015.model')



    











In [56]:



    


dtest_11 = xgb.DMatrix(train_pivot_56789_to_11.drop(['id'],axis =1), missing=np.nan)



    











In [132]:



    


submission_11_all_train = pd.DataFrame()
submission_11_all_train = train_pivot_56789_to_11[['id']].copy()
submission_11_all_train['predict'] = bst.predict(dtest_11)
submission_11_all_train.reset_index(drop = True,inplace = True)



    











In [96]:



    


submission_11_all_train['predict'].describe()



    


















    
Out[96]:








count    3.460866e+06
mean     1.561886e+00
std      7.065410e-01
min     -3.176572e-01
25%      1.056087e+00
50%      1.396147e+00
75%      1.880355e+00
max      7.308497e+00
Name: predict, dtype: float64

















In [97]:



    


submission_11_all_train.head()



    


















    
Out[97]:










  
    

      
      id
      predict
    

  
  
    

      0
      1547831.0
      4.339578
    

    

      1
      6825659.0
      2.999748
    

    

      2
      5853787.0
      2.638007
    

    

      3
      2316053.0
      1.133329
    

    

      4
      900676.0
      2.027621
    

  




















In [133]:



    


submission_11_all_train['id'] = submission_11_all_train['id'].astype(int)

concat result





In [29]:



    


submission_10 = pd.read_csv('week10_private.csv',index_col = 0)



    











In [38]:



    


submission_10.head()



    


















    
Out[38]:










  
    

      
      id
      predict
    

  
  
    

      0
      1569352
      2.389577
    

    

      1
      6667200
      3.532464
    

    

      2
      1592616
      2.962314
    

    

      3
      3909690
      4.135859
    

    

      4
      3659672
      3.563721
    

  




















In [33]:



    


submission_all_train = pd.concat([submission_10,submission_11_all_train],axis =0)
submission_all_train['predict'] = submission_all_train['predict'].apply(np.expm1)
submission_all_train.rename(columns = {'predict':'Demanda_uni_equil'},inplace = True)



    











In [ ]:



    


submission_all_train['Demanda_uni_equil'] = submission_all_train['Demanda_uni_equil'].round(1)
submission_all_train.loc[submission_all_train['Demanda_uni_equil']<0,'Demanda_uni_equil'] = 0
submission_all_train['Demanda_uni_equil'].describe()



    











In [ ]:



    


submission_all_train['id'] = submission_all_train['id'].astype(int)



    











In [36]:



    


submission_all_train.head()



    


















    
Out[36]:










  
    

      
      id
      Demanda_uni_equil
    

  
  
    

      0
      1569352
      9.9
    

    

      1
      6667200
      33.2
    

    

      2
      1592616
      18.3
    

    

      3
      3909690
      61.5
    

    

      4
      3659672
      34.3
    

  




















In [ ]:



    


submission_all_train.to('')

validation 45678 to 10





In [45]:



    


train_pivot_45678_to_10 = pd.read_pickle('validation_45678_10.pickle')
train_pivot_45678_to_10.head()



    


















    
Out[45]:










  
    

      Semana
      id
      ruta_freq
      clien_freq
      agen_freq
      prod_freq
      agen_for_log_de
      ruta_for_log_de
      cliente_for_log_de
      producto_for_log_de
      agen_ruta_for_log_de
      ...
      LR_prod_corr
      t_m_6_cum
      t_m_5_cum
      t_m_4_cum
      t_m_3_cum
      t_m_2_cum
      NombreCliente
      weight
      weight_per_piece
      pieces
    

  
  
    

      0
      1569352
      10.0
      158.0
      3861.0
      169.0
      3.951774
      3.165635
      2.83547
      2.493198
      3.165635
      ...
      -1.349546
      2.397895
      2.397895
      2.397895
      2.397895
      2.397895
      131.0
      691.0
      NaN
      NaN
    

    

      1
      6667200
      658.0
      158.0
      12429.0
      10746.0
      3.509364
      3.211487
      2.83547
      4.374220
      2.805172
      ...
      19.839575
      3.761200
      7.545390
      11.041898
      14.475884
      18.164764
      6027.0
      740.0
      NaN
      NaN
    

    

      2
      1592616
      658.0
      158.0
      12429.0
      10736.0
      3.509364
      3.211487
      2.83547
      3.987994
      2.805172
      ...
      17.888412
      0.000000
      0.000000
      2.833213
      6.165418
      8.730368
      6027.0
      480.0
      NaN
      NaN
    

    

      3
      3909690
      658.0
      158.0
      12429.0
      13169.0
      3.509364
      3.211487
      2.83547
      4.535940
      2.805172
      ...
      20.221937
      4.158883
      8.589700
      12.303272
      16.620760
      20.715105
      6027.0
      680.0
      NaN
      NaN
    

    

      4
      3659672
      658.0
      158.0
      12429.0
      10624.0
      3.509364
      3.211487
      2.83547
      3.268587
      2.805172
      ...
      13.291507
      1.945910
      5.529429
      9.084777
      12.773657
      16.602299
      6027.0
      567.0
      NaN
      NaN
    

  



5 rows × 44 columns



















In [46]:



    


dtest_valid = xgb.DMatrix(train_pivot_45678_to_10.drop(['id'],axis =1), missing=np.nan)



    











In [134]:



    


submission_10_valid = pd.DataFrame()
submission_10_valid = train_pivot_45678_to_10[['id']].copy()
submission_10_valid['predict'] = bst.predict(dtest_valid)
submission_10_valid.reset_index(drop = True,inplace = True)



    











In [100]:



    


submission_10_valid.head()



    


















    
Out[100]:










  
    

      Semana
      id
      predict
    

  
  
    

      0
      1569352
      2.259041
    

    

      1
      6667200
      3.567400
    

    

      2
      1592616
      2.877977
    

    

      3
      3909690
      4.066172
    

    

      4
      3659672
      3.513400
    

  




















In [135]:



    


submission_all_train = pd.DataFrame()
submission_all_train = pd.concat([submission_10_valid,submission_11_all_train],axis =0)
submission_all_train['predict'] = submission_all_train['predict'].apply(np.expm1)
submission_all_train.rename(columns = {'predict':'Demanda_uni_equil'},inplace = True)
submission_all_train.head()



    


















    
Out[135]:










  
    

      Semana
      id
      Demanda_uni_equil
    

  
  
    

      0
      1569352
      8.406385
    

    

      1
      6667200
      34.465729
    

    

      2
      1592616
      16.756153
    

    

      3
      3909690
      57.540695
    

    

      4
      3659672
      33.211864
    

  




















In [136]:



    


submission_all_train.loc[submission_all_train['Demanda_uni_equil']<0,'Demanda_uni_equil'] = 0
submission_all_train['Demanda_uni_equil'].describe()



    


















    
Out[136]:








count    6.999251e+06
mean     6.043300e+00
std      1.571901e+01
min      0.000000e+00
25%      1.877748e+00
50%      3.051292e+00
75%      5.550261e+00
max      5.625943e+03
Name: Demanda_uni_equil, dtype: float64

















In [137]:



    


submission_all_train['id'] = submission_all_train['id'].astype(int)



    











In [138]:



    


submission_all_train['Demanda_uni_equil'].describe()



    


















    
Out[138]:








count    6.999251e+06
mean     6.043300e+00
std      1.571901e+01
min      0.000000e+00
25%      1.877748e+00
50%      3.051292e+00
75%      5.550261e+00
max      5.625943e+03
Name: Demanda_uni_equil, dtype: float64

















In [139]:



    


submission_all_train.head()



    


















    
Out[139]:










  
    

      Semana
      id
      Demanda_uni_equil
    

  
  
    

      0
      1569352
      8.406385
    

    

      1
      6667200
      34.465729
    

    

      2
      1592616
      16.756153
    

    

      3
      3909690
      57.540695
    

    

      4
      3659672
      33.211864
    

  




















In [123]:



    


submission_all_train['Demanda_uni_equil'] = submission_all_train['Demanda_uni_equil'].round(1)



    











In [140]:



    


submission_all_train.to_csv('valid10_6.csv',index = False)



    











In [ ]:



    






    











In [ ]:



    






    











In [ ]:



    






    











In [ ]:



    






    











In [ ]:

Content source: boya-zhou/kaggle_bimbo_reformat

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