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 [3]:
%ls
1_predata.ipynb submission_10_new.csv
3_prediction.ipynb submission_11.csv
44fea_bst.model submission_11_new.csv
4_keras_nn.ipynb submission_nn_2.csv
5_random_forest.ipynb submission_nn.csv
6_random_forest.ipynb submission_nn_xgb
agencia_for_cliente_producto.csv submission_xgb_10.pickle
agen_freq_semana.pickle submission_xgb_2.csv
canal_for_cliente_producto.csv submission_xgb.csv
clien_freq_semana.pickle submission_xgb_nn_10.pickle
model_nn_10_after_l2reg.h5 submission_xgb_with_nn.csv
model_nn_10.h5 train_pivot_3456_to_8.csv
model_nn_10_whole.h5 train_pivot_45678_to_9_new.csv
origin/ train_pivot_56789_to_10_44fea.pickle
pivot_test.pickle train_pivot_56789_to_10_new.pickle
pivot_train_with_nan.pickle train_pivot_56789_to_10.pickle
pivot_train_with_zero.pickle train_pivot_6789_to_11_new.pickle
preprocessed_products.csv train_pivot_6789_to_11.pickle
prod_freq_semana.pickle train_pivot_xgb_time1_44fea.csv
ruta_for_cliente_producto.csv train_pivot_xgb_time1.csv
ruta_freq_semana.pickle train_pivot_xgb_time2_38fea.csv
stack_train_nn_10.pickle train_pivot_xgb_time2.csv
submission_10.csv
In [18]:
predictors_target_11 = ['LR_prod', 'LR_prod_corr',
'NombreCliente',
'agen_cliente_for_log_de', 'agen_for_log_de',
'agen_producto_for_log_de', 'agen_ruta_for_log_de',
'cliente_for_log_de', 'cliente_for_log_sum',
'cliente_producto_for_log_de', 'corr', 'pieces',
'producto_for_log_de', 'ruta_cliente_for_log_de', 'ruta_for_log_de',
'ruta_producto_for_log_de', 't2_min_t3', 't2_min_t4', 't2_min_t5',
't3_min_t4', 't3_min_t5', 't4_min_t5', 't_m_2_cum', 't_m_3_cum',
't_m_4_cum', 't_m_5_cum', 't_min_2', 't_min_3', 't_min_4',
't_min_5', 'target', 'weight', 'weight_per_piece']
In [19]:
predictors_11 = ['LR_prod', 'LR_prod_corr',
'NombreCliente',
'agen_cliente_for_log_de', 'agen_for_log_de',
'agen_producto_for_log_de', 'agen_ruta_for_log_de',
'cliente_for_log_de', 'cliente_for_log_sum',
'cliente_producto_for_log_de', 'corr', 'pieces',
'producto_for_log_de', 'ruta_cliente_for_log_de', 'ruta_for_log_de',
'ruta_producto_for_log_de', 't2_min_t3', 't2_min_t4', 't2_min_t5',
't3_min_t4', 't3_min_t5', 't4_min_t5', 't_m_2_cum', 't_m_3_cum',
't_m_4_cum', 't_m_5_cum', 't_min_2', 't_min_3', 't_min_4',
't_min_5', 'weight', 'weight_per_piece']
In [10]:
f = lambda x : (x-x.mean())/x.std(ddof=0)
In [14]:
train_pivot_xgb_time2 = pd.read_csv('train_pivot_xgb_time2.csv',index_col = 0)
In [7]:
train_pivot_6789_to_11 = pd.read_pickle('train_pivot_6789_to_11_new.pickle')
In [8]:
train_pivot_xgb_time2.head()
Out[8]:
Agencia_ID
Canal_ID
Cliente_ID
LR_prod
LR_prod_corr
NombreCliente
Producto_ID
Ruta_SAK
agen_cliente_for_log_de
agen_for_log_de
...
t_m_3_cum
t_m_4_cum
t_m_5_cum
t_min_2
t_min_3
t_min_4
t_min_5
target
weight
weight_per_piece
0
2061
2
26
2.001190
7.293554
18434
1182
7212
2.852285
3.491654
...
NaN
NaN
3.688879
NaN
NaN
NaN
3.688879
0.000000
210.0
210.00
1
2061
2
26
1.839411
6.703932
18434
4767
7212
2.852285
3.491654
...
NaN
NaN
3.761200
NaN
NaN
NaN
3.761200
3.761200
250.0
NaN
2
2061
2
26
1.911283
6.965878
18434
31393
7212
2.852285
3.491654
...
8.650325
5.877736
3.044522
2.772589
2.772589
2.833213
3.044522
3.135494
640.0
NaN
3
2061
2
26
3.113374
11.347029
18434
34204
7212
2.852285
3.491654
...
11.024839
7.218177
3.784190
3.555348
3.806662
3.433987
3.784190
3.828641
450.0
56.25
4
2061
2
26
2.031231
7.403043
18434
34206
7212
2.852285
3.491654
...
12.963710
9.202510
4.795791
4.248495
3.761200
4.406719
4.795791
4.499810
340.0
42.50
5 rows × 38 columns
In [15]:
train_pivot_xgb_time2.columns.values
Out[15]:
array(['Agencia_ID', 'Canal_ID', 'Cliente_ID', 'LR_prod', 'LR_prod_corr',
'NombreCliente', 'Producto_ID', 'Ruta_SAK',
'agen_cliente_for_log_de', 'agen_for_log_de',
'agen_producto_for_log_de', 'agen_ruta_for_log_de',
'cliente_for_log_de', 'cliente_for_log_sum',
'cliente_producto_for_log_de', 'corr', 'pieces',
'producto_for_log_de', 'ruta_cliente_for_log_de', 'ruta_for_log_de',
'ruta_producto_for_log_de', 't2_min_t3', 't2_min_t4', 't2_min_t5',
't3_min_t4', 't3_min_t5', 't4_min_t5', 't_m_2_cum', 't_m_3_cum',
't_m_4_cum', 't_m_5_cum', 't_min_2', 't_min_3', 't_min_4',
't_min_5', 'target', 'weight', 'weight_per_piece'], dtype=object)
In [4]:
def normalize_dataset(train_dataset,test_dataset):
train_dataset_normalize = train_dataset[predictors_11].copy()
train_dataset_normalize['label'] = 0
test_dataset_normalize = test_dataset[predictors_11].copy()
test_dataset_normalize['label'] = 1
whole_dataset = pd.concat([train_dataset_normalize,test_dataset_normalize])
whole_dataset_normalize = whole_dataset.apply(f,axis = 0)
train_dataset_normalize = whole_dataset_normalize.loc[whole_dataset['label'] == 0]
test_dataset_normalize = whole_dataset_normalize.loc[whole_dataset['label']==1]
train_dataset_normalize.drop(['label'],axis = 1,inplace = True)
test_dataset_normalize.drop(['label'],axis =1,inplace = True)
train_dataset_normalize['target'] = train_dataset['target'].copy()
# target = train_dataset['target']
return train_dataset_normalize,test_dataset_normalize
In [21]:
train_dataset_normalize, test_dataset_normalize = normalize_dataset(train_pivot_xgb_time2,train_pivot_6789_to_11)
/usr/local/lib/python2.7/dist-packages/ipykernel/__main__.py:14: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame
See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
/usr/local/lib/python2.7/dist-packages/ipykernel/__main__.py:15: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame
See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
/usr/local/lib/python2.7/dist-packages/ipykernel/__main__.py:17: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead
See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
In [22]:
train_dataset_normalize.head()
Out[22]:
Semana
LR_prod
LR_prod_corr
NombreCliente
agen_cliente_for_log_de
agen_for_log_de
agen_producto_for_log_de
agen_ruta_for_log_de
cliente_for_log_de
cliente_for_log_sum
cliente_producto_for_log_de
...
t_m_3_cum
t_m_4_cum
t_m_5_cum
t_min_2
t_min_3
t_min_4
t_min_5
weight
weight_per_piece
target
0
0.440004
0.007984
-1.198863
2.893915
7.040262
4.922515
3.707101
2.951726
0.023732
2.468511
...
NaN
NaN
0.841755
NaN
NaN
NaN
2.148223
0.041948
0.552880
0.000000
1
0.136858
0.004843
-1.198863
2.893915
7.040262
4.561988
3.707101
2.951726
0.023732
2.558317
...
NaN
NaN
0.888582
NaN
NaN
NaN
2.230611
0.180385
NaN
3.761200
2
0.271533
0.006239
-1.198863
2.893915
7.040262
3.000979
3.707101
2.951726
0.023732
1.433925
...
1.028141
0.851082
0.424536
1.090852
1.086921
1.149949
1.414172
1.530144
NaN
3.135494
3
2.524041
0.029578
-1.198863
2.893915
7.040262
3.917930
3.707101
2.951726
0.023732
2.414081
...
1.752516
1.424506
0.903467
1.968861
2.245187
1.825675
2.256800
0.872569
-0.290791
3.828641
4
0.496296
0.008568
-1.198863
2.893915
7.040262
5.719378
3.707101
2.951726
0.023732
3.231175
...
2.343992
2.273380
1.558475
2.746354
2.194265
2.919764
3.409216
0.491868
-0.366242
4.499810
5 rows × 33 columns
In [26]:
train_pivot_xgb_time2_sample = train_dataset_normalize.sample(2000000)
train_feature_11 = train_pivot_xgb_time2_sample.drop(['target'],axis = 1)
train_label_11 = train_pivot_xgb_time2_sample[['target']]
dtrain_11 = xgb.DMatrix(train_feature_11,label = train_label_11,missing=np.nan)
In [27]:
num_round = 1000
cvresult = xgb.cv(param_11, dtrain_11, num_round, nfold=5,verbose_eval = 1,show_stdv=False,
seed = 0, early_stopping_rounds=5)
print(cvresult.tail())
[0] train-rmse:1.14019 test-rmse:1.14021
[1] train-rmse:0.961458 test-rmse:0.961473
[2] train-rmse:0.824843 test-rmse:0.824873
[3] train-rmse:0.722328 test-rmse:0.722397
[4] train-rmse:0.647489 test-rmse:0.647603
[5] train-rmse:0.592862 test-rmse:0.593041
[6] train-rmse:0.555021 test-rmse:0.555254
[7] train-rmse:0.528662 test-rmse:0.528924
[8] train-rmse:0.510662 test-rmse:0.510977
[9] train-rmse:0.498551 test-rmse:0.498903
[10] train-rmse:0.49003 test-rmse:0.490423
[11] train-rmse:0.484163 test-rmse:0.484588
[12] train-rmse:0.480003 test-rmse:0.480457
[13] train-rmse:0.477039 test-rmse:0.47752
[14] train-rmse:0.474897 test-rmse:0.475405
[15] train-rmse:0.473305 test-rmse:0.473844
[16] train-rmse:0.472061 test-rmse:0.472641
[17] train-rmse:0.471142 test-rmse:0.471752
[18] train-rmse:0.470369 test-rmse:0.471
[19] train-rmse:0.469729 test-rmse:0.470375
[20] train-rmse:0.469123 test-rmse:0.469783
[21] train-rmse:0.468683 test-rmse:0.469369
[22] train-rmse:0.468239 test-rmse:0.468944
[23] train-rmse:0.467862 test-rmse:0.468596
[24] train-rmse:0.467516 test-rmse:0.468284
[25] train-rmse:0.467183 test-rmse:0.467968
[26] train-rmse:0.46683 test-rmse:0.467642
[27] train-rmse:0.466558 test-rmse:0.467384
[28] train-rmse:0.466233 test-rmse:0.467087
[29] train-rmse:0.465967 test-rmse:0.46684
[30] train-rmse:0.46573 test-rmse:0.466622
[31] train-rmse:0.465487 test-rmse:0.466401
[32] train-rmse:0.465309 test-rmse:0.466247
[33] train-rmse:0.465145 test-rmse:0.466103
[34] train-rmse:0.464955 test-rmse:0.465931
[35] train-rmse:0.464779 test-rmse:0.465778
[36] train-rmse:0.464622 test-rmse:0.465647
[37] train-rmse:0.46445 test-rmse:0.465496
[38] train-rmse:0.464265 test-rmse:0.465335
[39] train-rmse:0.464089 test-rmse:0.46519
[40] train-rmse:0.463953 test-rmse:0.465087
[41] train-rmse:0.463803 test-rmse:0.464965
[42] train-rmse:0.463669 test-rmse:0.46485
[43] train-rmse:0.463526 test-rmse:0.46473
[44] train-rmse:0.463345 test-rmse:0.46458
[45] train-rmse:0.46323 test-rmse:0.464488
[46] train-rmse:0.463083 test-rmse:0.46436
[47] train-rmse:0.462961 test-rmse:0.464254
[48] train-rmse:0.462844 test-rmse:0.464164
[49] train-rmse:0.462671 test-rmse:0.464006
[50] train-rmse:0.462573 test-rmse:0.463937
[51] train-rmse:0.462433 test-rmse:0.463811
[52] train-rmse:0.462326 test-rmse:0.463717
[53] train-rmse:0.462213 test-rmse:0.46363
[54] train-rmse:0.462113 test-rmse:0.463553
[55] train-rmse:0.462006 test-rmse:0.463481
[56] train-rmse:0.461917 test-rmse:0.463415
[57] train-rmse:0.461803 test-rmse:0.463323
[58] train-rmse:0.461723 test-rmse:0.463264
[59] train-rmse:0.461629 test-rmse:0.463187
[60] train-rmse:0.461547 test-rmse:0.463137
[61] train-rmse:0.461431 test-rmse:0.46305
[62] train-rmse:0.461339 test-rmse:0.462979
[63] train-rmse:0.461268 test-rmse:0.462928
[64] train-rmse:0.461137 test-rmse:0.462813
[65] train-rmse:0.461038 test-rmse:0.462732
[66] train-rmse:0.460964 test-rmse:0.462681
[67] train-rmse:0.460849 test-rmse:0.462587
[68] train-rmse:0.460744 test-rmse:0.462503
[69] train-rmse:0.460662 test-rmse:0.462446
[70] train-rmse:0.460593 test-rmse:0.462395
[71] train-rmse:0.460521 test-rmse:0.462347
[72] train-rmse:0.460432 test-rmse:0.462281
[73] train-rmse:0.460351 test-rmse:0.46221
[74] train-rmse:0.46028 test-rmse:0.462161
[75] train-rmse:0.460207 test-rmse:0.462113
[76] train-rmse:0.460113 test-rmse:0.462038
[77] train-rmse:0.460044 test-rmse:0.461992
[78] train-rmse:0.459984 test-rmse:0.461954
[79] train-rmse:0.459917 test-rmse:0.461911
[80] train-rmse:0.45985 test-rmse:0.461865
[81] train-rmse:0.459802 test-rmse:0.461841
[82] train-rmse:0.459757 test-rmse:0.461811
[83] train-rmse:0.4597 test-rmse:0.461779
[84] train-rmse:0.459629 test-rmse:0.461723
[85] train-rmse:0.459556 test-rmse:0.46167
[86] train-rmse:0.45951 test-rmse:0.461643
[87] train-rmse:0.459459 test-rmse:0.461609
[88] train-rmse:0.459389 test-rmse:0.461553
[89] train-rmse:0.459321 test-rmse:0.461508
[90] train-rmse:0.45926 test-rmse:0.46146
[91] train-rmse:0.459208 test-rmse:0.461435
[92] train-rmse:0.459144 test-rmse:0.461394
[93] train-rmse:0.459064 test-rmse:0.461334
[94] train-rmse:0.458977 test-rmse:0.461258
[95] train-rmse:0.458892 test-rmse:0.461193
[96] train-rmse:0.45883 test-rmse:0.461158
[97] train-rmse:0.458763 test-rmse:0.461109
[98] train-rmse:0.458715 test-rmse:0.461084
[99] train-rmse:0.458667 test-rmse:0.461051
[100] train-rmse:0.45862 test-rmse:0.461025
[101] train-rmse:0.458561 test-rmse:0.460985
[102] train-rmse:0.458485 test-rmse:0.460929
[103] train-rmse:0.458429 test-rmse:0.46089
[104] train-rmse:0.458358 test-rmse:0.46085
[105] train-rmse:0.458298 test-rmse:0.46081
[106] train-rmse:0.458249 test-rmse:0.460781
[107] train-rmse:0.458208 test-rmse:0.460761
[108] train-rmse:0.458152 test-rmse:0.460726
[109] train-rmse:0.458104 test-rmse:0.460691
[110] train-rmse:0.458062 test-rmse:0.460668
[111] train-rmse:0.458015 test-rmse:0.46064
[112] train-rmse:0.45795 test-rmse:0.460591
[113] train-rmse:0.457892 test-rmse:0.460552
[114] train-rmse:0.457838 test-rmse:0.460513
[115] train-rmse:0.457792 test-rmse:0.460485
[116] train-rmse:0.457736 test-rmse:0.460451
[117] train-rmse:0.457677 test-rmse:0.460414
[118] train-rmse:0.457629 test-rmse:0.460385
[119] train-rmse:0.457591 test-rmse:0.460364
[120] train-rmse:0.457548 test-rmse:0.460338
[121] train-rmse:0.457504 test-rmse:0.460321
[122] train-rmse:0.457451 test-rmse:0.46029
[123] train-rmse:0.4574 test-rmse:0.460258
[124] train-rmse:0.457357 test-rmse:0.460242
[125] train-rmse:0.457313 test-rmse:0.460215
[126] train-rmse:0.457275 test-rmse:0.460195
[127] train-rmse:0.457239 test-rmse:0.460181
[128] train-rmse:0.457175 test-rmse:0.460133
[129] train-rmse:0.457127 test-rmse:0.460101
[130] train-rmse:0.457072 test-rmse:0.460069
[131] train-rmse:0.45704 test-rmse:0.460059
[132] train-rmse:0.456989 test-rmse:0.460023
[133] train-rmse:0.456961 test-rmse:0.46001
[134] train-rmse:0.456914 test-rmse:0.459983
[135] train-rmse:0.456855 test-rmse:0.459941
[136] train-rmse:0.4568 test-rmse:0.459908
[137] train-rmse:0.456758 test-rmse:0.459893
[138] train-rmse:0.456722 test-rmse:0.459872
[139] train-rmse:0.456691 test-rmse:0.459851
[140] train-rmse:0.456637 test-rmse:0.459813
[141] train-rmse:0.456591 test-rmse:0.459779
[142] train-rmse:0.456547 test-rmse:0.459753
[143] train-rmse:0.456497 test-rmse:0.459717
[144] train-rmse:0.456462 test-rmse:0.4597
[145] train-rmse:0.456428 test-rmse:0.459689
[146] train-rmse:0.45639 test-rmse:0.459668
[147] train-rmse:0.456343 test-rmse:0.459636
[148] train-rmse:0.456296 test-rmse:0.459606
[149] train-rmse:0.456264 test-rmse:0.459592
[150] train-rmse:0.456216 test-rmse:0.459563
[151] train-rmse:0.45618 test-rmse:0.459541
[152] train-rmse:0.456143 test-rmse:0.459521
[153] train-rmse:0.456107 test-rmse:0.459505
[154] train-rmse:0.45607 test-rmse:0.45948
[155] train-rmse:0.456035 test-rmse:0.459458
[156] train-rmse:0.455993 test-rmse:0.459439
[157] train-rmse:0.455953 test-rmse:0.459421
[158] train-rmse:0.455928 test-rmse:0.459423
[159] train-rmse:0.455896 test-rmse:0.459411
[160] train-rmse:0.455849 test-rmse:0.459382
[161] train-rmse:0.455798 test-rmse:0.459353
[162] train-rmse:0.455758 test-rmse:0.459332
[163] train-rmse:0.45572 test-rmse:0.459312
[164] train-rmse:0.455671 test-rmse:0.459274
[165] train-rmse:0.455643 test-rmse:0.459267
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[504] train-rmse:0.447619 test-rmse:0.456581
[505] train-rmse:0.447602 test-rmse:0.456581
[506] train-rmse:0.447585 test-rmse:0.456579
[507] train-rmse:0.447569 test-rmse:0.456579
[508] train-rmse:0.447555 test-rmse:0.456575
[509] train-rmse:0.447539 test-rmse:0.456571
[510] train-rmse:0.447523 test-rmse:0.456566
[511] train-rmse:0.447508 test-rmse:0.456564
[512] train-rmse:0.447489 test-rmse:0.456559
[513] train-rmse:0.447473 test-rmse:0.456559
[514] train-rmse:0.447455 test-rmse:0.456555
[515] train-rmse:0.447437 test-rmse:0.456552
[516] train-rmse:0.447422 test-rmse:0.45655
[517] train-rmse:0.447405 test-rmse:0.45655
[518] train-rmse:0.447389 test-rmse:0.456549
[519] train-rmse:0.447374 test-rmse:0.456546
[520] train-rmse:0.447357 test-rmse:0.45654
[521] train-rmse:0.447342 test-rmse:0.456538
[522] train-rmse:0.447329 test-rmse:0.456535
[523] train-rmse:0.44731 test-rmse:0.456529
[524] train-rmse:0.447293 test-rmse:0.456528
[525] train-rmse:0.447278 test-rmse:0.456527
[526] train-rmse:0.447257 test-rmse:0.456523
[527] train-rmse:0.447241 test-rmse:0.456522
[528] train-rmse:0.447226 test-rmse:0.456519
[529] train-rmse:0.447208 test-rmse:0.456513
[530] train-rmse:0.447191 test-rmse:0.456511
[531] train-rmse:0.447175 test-rmse:0.45651
[532] train-rmse:0.447157 test-rmse:0.456505
[533] train-rmse:0.447134 test-rmse:0.456498
[534] train-rmse:0.447115 test-rmse:0.456495
[535] train-rmse:0.447096 test-rmse:0.456494
[536] train-rmse:0.447084 test-rmse:0.456493
[537] train-rmse:0.447066 test-rmse:0.456491
[538] train-rmse:0.447051 test-rmse:0.456491
[539] train-rmse:0.447028 test-rmse:0.456484
[540] train-rmse:0.447012 test-rmse:0.456482
[541] train-rmse:0.446996 test-rmse:0.456481
[542] train-rmse:0.446977 test-rmse:0.456479
[543] train-rmse:0.44696 test-rmse:0.456476
[544] train-rmse:0.446945 test-rmse:0.456477
[545] train-rmse:0.446925 test-rmse:0.456472
[546] train-rmse:0.446903 test-rmse:0.456464
[547] train-rmse:0.446884 test-rmse:0.456464
[548] train-rmse:0.446865 test-rmse:0.456464
[549] train-rmse:0.44685 test-rmse:0.456462
[550] train-rmse:0.446826 test-rmse:0.456447
[551] train-rmse:0.446809 test-rmse:0.456444
[552] train-rmse:0.44679 test-rmse:0.456443
[553] train-rmse:0.446772 test-rmse:0.456443
[554] train-rmse:0.446761 test-rmse:0.456443
[555] train-rmse:0.446743 test-rmse:0.45644
[556] train-rmse:0.446725 test-rmse:0.456437
[557] train-rmse:0.446707 test-rmse:0.456438
[558] train-rmse:0.44669 test-rmse:0.456435
[559] train-rmse:0.446669 test-rmse:0.456423
[560] train-rmse:0.446651 test-rmse:0.456424
[561] train-rmse:0.446636 test-rmse:0.456423
[562] train-rmse:0.446622 test-rmse:0.456422
[563] train-rmse:0.446608 test-rmse:0.456422
[564] train-rmse:0.446596 test-rmse:0.456422
[565] train-rmse:0.44658 test-rmse:0.45642
[566] train-rmse:0.446561 test-rmse:0.456417
[567] train-rmse:0.446544 test-rmse:0.456418
[568] train-rmse:0.446529 test-rmse:0.45642
[569] train-rmse:0.446512 test-rmse:0.45642
[570] train-rmse:0.446497 test-rmse:0.456418
test-rmse-mean test-rmse-std train-rmse-mean train-rmse-std
562 0.456422 0.000445 0.446622 0.000113
563 0.456422 0.000447 0.446608 0.000115
564 0.456422 0.000447 0.446596 0.000116
565 0.456420 0.000451 0.446580 0.000116
566 0.456417 0.000442 0.446561 0.000120
In [24]:
param_11 = {'booster':'gbtree',
'nthread': 10,
'max_depth':5,
'eta':0.2,
'silent':1,
'subsample':0.7,
'objective':'reg:linear',
'eval_metric':'rmse',
'colsample_bytree':0.7}
In [28]:
num_round = 566
dtest_11 = xgb.DMatrix(test_dataset_normalize[predictors_11], missing=np.nan)
submission_11 = train_pivot_6789_to_11[['id']].copy()
j =0
for j in range(20):
train_pivot_xgb_time2_sample = train_dataset_normalize[predictors_target_11].sample(2000000)
train_feature_11 = train_pivot_xgb_time2_sample.drop(['target'],axis = 1)
train_label_11 = train_pivot_xgb_time2_sample[['target']]
dtrain_11 = xgb.DMatrix(train_feature_11,label = train_label_11,missing= np.nan)
bst_11 = xgb.train(param_11, dtrain_11, num_round)
print str(j) + 'training finished!'
submission_11['predict_' + str(j)] = bst_11.predict(dtest_11)
print 'finished'
0training finished!
1training finished!
2training finished!
3training finished!
4training finished!
5training finished!
6training finished!
7training finished!
8training finished!
9training finished!
10training finished!
11training finished!
12training finished!
13training finished!
14training finished!
15training finished!
16training finished!
17training finished!
18training finished!
19training finished!
finished
In [12]:
# make prediction
dtest_11 = xgb.DMatrix(train_pivot_6789_to_11[predictors], missing=NaN)
submission_11 = train_pivot_6789_to_11[['id']].copy()
submission_11['predict'] = bst.predict(dtest)
xgb.plot_importance(bst)
In [29]:
submission_11.to_csv('submission_11_new.csv')
In [11]:
submission_11 = pd.read_csv('submission_11_new.csv',index_col =0)
In [12]:
submission_11.columns.values
Out[12]:
array(['id', 'predict_0', 'predict_1', 'predict_2', 'predict_3',
'predict_4', 'predict_5', 'predict_6', 'predict_7', 'predict_8',
'predict_9', 'predict_10', 'predict_11', 'predict_12', 'predict_13',
'predict_14', 'predict_15', 'predict_16', 'predict_17',
'predict_18', 'predict_19'], dtype=object)
In [2]:
%ls
1_predata.ipynb stack_sub/
3_xgb.ipynb submission_10_new.csv
3_xgb_prediction.ipynb submission_11_new.csv
3_xgb_test.ipynb submission_44fea.csv
4_keras_nn.ipynb submission_all_train.csv
5_random_forest.ipynb submission_nn.csv
6_stack_model.ipynb submission_nn_xgb
7_SGD_regressor.ipynb test_dataset_10_normalize.csv
8_svm_linearSVR.ipynb test_dataset_10_normalize_new.pickle
agencia_for_cliente_producto.csv train_dataset_10_normalize.csv
bst_use_all_train.model train_dataset_10_normalize.pickle
canal_for_cliente_producto.csv train_pivot_56789_to_10_44fea.pickle
old_submission/ train_pivot_56789_to_10_new.pickle
origin/ train_pivot_6789_to_11_new.pickle
pivot_test.pickle train_pivot_xgb_time1_44fea.csv
pivot_train_with_nan.pickle train_pivot_xgb_time1.csv
RF_model/ train_pivot_xgb_time2_38fea.csv
ruta_for_cliente_producto.csv train_pivot_xgb_time2.csv
In [2]:
predictors_target_10 = ['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_1', 't_min_2',
't_min_3', 't_min_4', 't_min_5', 't1_min_t2', 't1_min_t3',
't1_min_t4', 't1_min_t5', '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_5_cum', 't_m_4_cum', 't_m_3_cum', 't_m_2_cum',
't_m_1_cum', 'NombreCliente', 'weight', 'weight_per_piece', 'pieces']
In [3]:
predictors_10 = ['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_1', 't_min_2',
't_min_3', 't_min_4', 't_min_5', 't1_min_t2', 't1_min_t3',
't1_min_t4', 't1_min_t5', '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_5_cum', 't_m_4_cum', 't_m_3_cum', 't_m_2_cum',
't_m_1_cum', 'NombreCliente', 'weight', 'weight_per_piece', 'pieces']
In [4]:
def normalize_dataset_10(train_dataset,test_dataset):
train_dataset_normalize = train_dataset[predictors_10].copy()
train_dataset_normalize['label'] = 0
test_dataset_normalize = test_dataset[predictors_10].copy()
test_dataset_normalize['label'] = 1
whole_dataset = pd.concat([train_dataset_normalize,test_dataset_normalize])
whole_dataset_normalize = whole_dataset.apply(f,axis = 0)
train_dataset_normalize = whole_dataset_normalize.loc[whole_dataset['label'] == 0]
test_dataset_normalize = whole_dataset_normalize.loc[whole_dataset['label']==1]
train_dataset_normalize.drop(['label'],axis = 1,inplace = True)
test_dataset_normalize.drop(['label'],axis =1,inplace = True)
train_dataset_normalize['target'] = train_dataset['target'].copy()
# target = train_dataset['target']
return train_dataset_normalize,test_dataset_normalize
In [ ]:
dtypes = {'agen_for_log_de':'float32',
'ruta_for_log_de':'float32',
'cliente_for_log_de':'float32',
'producto_for_log_de':'float32',
'agen_ruta_for_log_de':'float32',
'agen_cliente_for_log_de':'float32',
'agen_producto_for_log_de':'float32',
'ruta_cliente_for_log_de':'float32',
'ruta_producto_for_log_de':"float32",
'cliente_producto_for_log_de':'float32',
'cliente_for_log_sum':'float32',
'corr':'float32',
't_min_1':'float32',
't_min_2':'float32',
't_min_3':'float32',
't_min_4':'float32',
't_min_5':'float32',
't1_min_t2':'float32',
't1_min_t3':'float32',
't1_min_t4':'float32',
't1_min_t5':'float32',
't2_min_t3':'float32',
't2_min_t4':'float32',
't2_min_t5':'float32',
't3_min_t4':'float32',
't3_min_t5':'float32',
't4_min_t5':'float32',
'LR_prod':'float32',
'LR_prod_corr':'float32',
'target':'float32',
't_m_5_cum':'float32',
't_m_4_cum' :'float32',
't_m_3_cum':'float32',
't_m_2_cum':'float32',
't_m_1_cum':'float32',
'NombreCliente':'int32',
'weight':'float32',
'weight_per_piece':'float32',
'pieces':'float32'}
In [ ]:
train_pivot_xgb_time1 = pd.read_csv('train_pivot_xgb_time1_44fea.csv',dtype = np.float32,index_col = 0)
In [3]:
train_pivot_56789_to_10 = pd.read_pickle('train_pivot_56789_to_10_44fea.pickle')
In [4]:
train_pivot_56789_to_10.columns.values
Out[4]:
array(['Cliente_ID', 'Producto_ID', 'id', 'sem10_sem11', 'Agencia_ID',
'Canal_ID', 'Ruta_SAK', '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_1', 't_min_2',
't_min_3', 't_min_4', 't_min_5', 't1_min_t2', 't1_min_t3',
't1_min_t4', 't1_min_t5', '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_5_cum', 't_m_4_cum', 't_m_3_cum', 't_m_2_cum', 't_m_1_cum',
'NombreCliente', 'weight', 'weight_per_piece', 'pieces'], dtype=object)
In [4]:
train_pivot_xgb_time1.columns.values
Out[4]:
array(['Cliente_ID', 'Producto_ID', 'Agencia_ID', 'Canal_ID', 'Ruta_SAK',
'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_1', 't_min_2',
't_min_3', 't_min_4', 't_min_5', 't1_min_t2', 't1_min_t3',
't1_min_t4', 't1_min_t5', '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_5_cum', 't_m_4_cum', 't_m_3_cum', 't_m_2_cum',
't_m_1_cum', 'NombreCliente', 'weight', 'weight_per_piece', 'pieces'], dtype=object)
In [11]:
train_dataset_10_normalize, test_dataset_10_normalize = normalize_dataset_10(train_pivot_xgb_time1,
train_pivot_56789_to_10)
/usr/local/lib/python2.7/dist-packages/ipykernel/__main__.py:14: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame
See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
/usr/local/lib/python2.7/dist-packages/ipykernel/__main__.py:15: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame
See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
/usr/local/lib/python2.7/dist-packages/ipykernel/__main__.py:17: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead
See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
In [13]:
train_dataset_10_normalize.head()
Out[13]:
Semana
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
...
t_m_5_cum
t_m_4_cum
t_m_3_cum
t_m_2_cum
t_m_1_cum
NombreCliente
weight
weight_per_piece
pieces
target
0
8.788259
5.529159
2.906576
3.130665
4.665460
3.354598
3.377005
3.292888
NaN
NaN
...
NaN
NaN
NaN
NaN
NaN
-1.822053
1.391852
-0.188302
0.470006
4.574711
1
7.134035
5.691308
2.906576
-0.117290
3.590018
2.819381
3.083452
2.530998
2.647204
1.549154
...
1.173570
1.271504
1.338973
1.462613
1.794103
-1.180468
1.530289
NaN
NaN
2.639057
2
7.134035
5.691308
2.906576
2.136123
3.590018
2.819381
1.418874
2.530998
1.075756
NaN
...
NaN
NaN
NaN
NaN
NaN
-1.180468
-0.061744
-0.435800
0.160219
2.397895
3
7.134035
5.691308
2.906576
2.236749
3.590018
2.819381
3.923257
2.530998
3.817979
2.509546
...
1.858372
2.182535
2.202313
2.287777
2.628881
-1.180468
0.872711
-0.291426
0.470006
3.784190
4
7.134035
5.691308
2.906576
0.250182
3.590018
2.819381
5.702605
2.530998
5.278630
3.337253
...
2.967155
2.699262
2.780972
2.979197
3.348328
-1.180468
0.492007
-0.367050
0.470006
4.682131
5 rows × 39 columns
In [4]:
%ls
1_predata.ipynb stack_sub/
3_xgb.ipynb submission_10_new.csv
3_xgb_prediction.ipynb submission_11_new.csv
4_keras_nn.ipynb submission_44fea.csv
5_random_forest.ipynb submission_nn.csv
6_stack_model.ipynb submission_nn_xgb
7_SGD_regressor.ipynb test_dataset_10_normalize.csv
8_svm_linearSVR.ipynb train_dataset_10_normalize.csv
agencia_for_cliente_producto.csv train_dataset_10_normalize.pickle
canal_for_cliente_producto.csv train_pivot_56789_to_10_44fea.pickle
old_submission/ train_pivot_56789_to_10_new.pickle
origin/ train_pivot_6789_to_11_new.pickle
pivot_test.pickle train_pivot_xgb_time1_44fea.csv
pivot_train_with_nan.pickle train_pivot_xgb_time1.csv
RF_model/ train_pivot_xgb_time2_38fea.csv
ruta_for_cliente_producto.csv train_pivot_xgb_time2.csv
In [ ]:
train_dataset_10_normalize = pd.read_csv('train_dataset_10_normalize.csv',index_col = 0)
train_dataset_10_normalize.head()
In [23]:
test_dataset_10_normalize = pd.read_pickle('test_dataset_10_normalize_new.pickle')
test_dataset_10_normalize.head()
Out[23]:
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
...
t_m_5_cum
t_m_4_cum
t_m_3_cum
t_m_2_cum
t_m_1_cum
NombreCliente
weight
weight_per_piece
pieces
id
0
8.973844
6.397834
3.037557
1.835223
5.398326
3.786630
1.577787
3.812083
NaN
NaN
...
NaN
NaN
NaN
NaN
NaN
-1.825542
1.706797
NaN
NaN
1569352
1
7.185299
5.729961
3.037557
5.701978
3.681476
2.891354
5.064185
2.595791
3.870621
2.473158
...
2.257555
2.204852
2.173276
2.286881
2.597253
-1.623858
1.876383
NaN
NaN
6667200
2
7.185299
5.729961
3.037557
4.924975
3.681476
2.891354
3.943551
2.595791
3.801173
1.670748
...
NaN
-0.273540
0.548709
0.768985
1.239590
-1.623858
0.976539
NaN
NaN
1592616
3
7.185299
5.729961
3.037557
6.049174
3.681476
2.891354
5.509614
2.595791
4.969614
3.183686
...
2.994623
2.686150
2.797207
2.862836
3.214847
-1.623858
1.668727
NaN
NaN
3909690
4
7.185299
5.729961
3.037557
3.371384
3.681476
2.891354
4.432333
2.595791
3.323528
2.531004
...
2.028818
2.125816
2.213651
2.354525
2.647532
-1.623858
1.277641
NaN
NaN
3659672
5 rows × 39 columns
In [24]:
test_dataset_10_normalize.columns.values
Out[24]:
array(['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', 'corr',
't_min_1', 't_min_2', 't_min_3', 't_min_4', 't_min_5', 't1_min_t2',
't1_min_t3', 't1_min_t4', 't1_min_t5', '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_5_cum', 't_m_4_cum', 't_m_3_cum', 't_m_2_cum',
't_m_1_cum', 'NombreCliente', 'weight', 'weight_per_piece',
'pieces', 'id'], dtype=object)
In [7]:
param_10 = {'booster':'gbtree',
'nthread': 11,
'max_depth':5,
'eta':0.1,
'silent':1,
'subsample':0.7,
'objective':'reg:linear',
'eval_metric':'rmse',
'colsample_bytree':0.7}
In [6]:
# train_dataset_10_normalize = train_pivot_xgb_time1[predictors_target_10].sample(2000000)
In [8]:
# train_pivot_xgb_time1_sample = train_dataset_10_normalize[predictors_target_10].sample(2000000)
train_feature_10 = train_dataset_10_normalize.drop(['target'],axis = 1)
train_label_10 = train_dataset_10_normalize[['target']]
dtrain_10 = xgb.DMatrix(train_feature_10,label = train_label_10,missing= np.nan)
In [9]:
gc.collect()
Out[9]:
324
In [10]:
num_round = 20000
cvresult = xgb.cv(param_10, dtrain_10, num_round, nfold=5,show_stdv=False,
seed = 0, early_stopping_rounds=5,verbose_eval = 1)
print(cvresult.tail())
[0] train-rmse:1.25273 test-rmse:1.25273
[1] train-rmse:1.14826 test-rmse:1.14826
[2] train-rmse:1.05531 test-rmse:1.05531
[3] train-rmse:0.972803 test-rmse:0.972797
[4] train-rmse:0.900266 test-rmse:0.900262
[5] train-rmse:0.836907 test-rmse:0.836904
[6] train-rmse:0.781313 test-rmse:0.781317
[7] train-rmse:0.732719 test-rmse:0.732726
[8] train-rmse:0.690846 test-rmse:0.690862
[9] train-rmse:0.654807 test-rmse:0.654823
[10] train-rmse:0.623818 test-rmse:0.623832
[11] train-rmse:0.597409 test-rmse:0.597424
[12] train-rmse:0.57508 test-rmse:0.575096
[13] train-rmse:0.556027 test-rmse:0.556043
[14] train-rmse:0.539989 test-rmse:0.540005
[15] train-rmse:0.526533 test-rmse:0.526549
[16] train-rmse:0.51534 test-rmse:0.515358
[17] train-rmse:0.505916 test-rmse:0.505937
[18] train-rmse:0.49811 test-rmse:0.498132
[19] train-rmse:0.491551 test-rmse:0.491573
[20] train-rmse:0.486143 test-rmse:0.486171
[21] train-rmse:0.481592 test-rmse:0.481619
[22] train-rmse:0.477847 test-rmse:0.477874
[23] train-rmse:0.474703 test-rmse:0.474729
[24] train-rmse:0.472046 test-rmse:0.472071
[25] train-rmse:0.469869 test-rmse:0.469897
[26] train-rmse:0.467985 test-rmse:0.468013
[27] train-rmse:0.466445 test-rmse:0.466473
[28] train-rmse:0.465138 test-rmse:0.46517
[29] train-rmse:0.46404 test-rmse:0.464073
[30] train-rmse:0.4631 test-rmse:0.463136
[31] train-rmse:0.462288 test-rmse:0.462327
[32] train-rmse:0.461587 test-rmse:0.461626
[33] train-rmse:0.460999 test-rmse:0.46104
[34] train-rmse:0.460476 test-rmse:0.460517
[35] train-rmse:0.460023 test-rmse:0.460065
[36] train-rmse:0.459617 test-rmse:0.45966
[37] train-rmse:0.45923 test-rmse:0.459275
[38] train-rmse:0.458913 test-rmse:0.458959
[39] train-rmse:0.45864 test-rmse:0.458687
[40] train-rmse:0.458337 test-rmse:0.458386
[41] train-rmse:0.458087 test-rmse:0.458139
[42] train-rmse:0.457854 test-rmse:0.457906
[43] train-rmse:0.457634 test-rmse:0.457688
[44] train-rmse:0.457435 test-rmse:0.45749
[45] train-rmse:0.457258 test-rmse:0.457314
[46] train-rmse:0.457092 test-rmse:0.457149
[47] train-rmse:0.456931 test-rmse:0.456989
[48] train-rmse:0.456794 test-rmse:0.456853
[49] train-rmse:0.456649 test-rmse:0.45671
[50] train-rmse:0.45652 test-rmse:0.456582
[51] train-rmse:0.456437 test-rmse:0.456501
[52] train-rmse:0.456325 test-rmse:0.456391
[53] train-rmse:0.456244 test-rmse:0.45631
[54] train-rmse:0.456157 test-rmse:0.456224
[55] train-rmse:0.45605 test-rmse:0.456117
[56] train-rmse:0.45597 test-rmse:0.456038
[57] train-rmse:0.455873 test-rmse:0.455943
[58] train-rmse:0.455793 test-rmse:0.455864
[59] train-rmse:0.455705 test-rmse:0.455778
[60] train-rmse:0.455627 test-rmse:0.455702
[61] train-rmse:0.455557 test-rmse:0.455633
[62] train-rmse:0.455486 test-rmse:0.455563
[63] train-rmse:0.455417 test-rmse:0.455496
[64] train-rmse:0.455351 test-rmse:0.455431
[65] train-rmse:0.455293 test-rmse:0.455373
[66] train-rmse:0.45523 test-rmse:0.455311
[67] train-rmse:0.455156 test-rmse:0.455239
[68] train-rmse:0.455093 test-rmse:0.455176
[69] train-rmse:0.455025 test-rmse:0.45511
[70] train-rmse:0.454917 test-rmse:0.455003
[71] train-rmse:0.454866 test-rmse:0.454954
[72] train-rmse:0.454798 test-rmse:0.454887
[73] train-rmse:0.454729 test-rmse:0.454819
[74] train-rmse:0.454675 test-rmse:0.454767
[75] train-rmse:0.454589 test-rmse:0.454682
[76] train-rmse:0.454517 test-rmse:0.45461
[77] train-rmse:0.454455 test-rmse:0.45455
[78] train-rmse:0.454386 test-rmse:0.454481
[79] train-rmse:0.454315 test-rmse:0.454411
[80] train-rmse:0.454273 test-rmse:0.45437
[81] train-rmse:0.454208 test-rmse:0.454306
[82] train-rmse:0.454152 test-rmse:0.454252
[83] train-rmse:0.454084 test-rmse:0.454184
[84] train-rmse:0.454021 test-rmse:0.454123
[85] train-rmse:0.453973 test-rmse:0.454076
[86] train-rmse:0.453923 test-rmse:0.454026
[87] train-rmse:0.453872 test-rmse:0.453976
[88] train-rmse:0.453829 test-rmse:0.453935
[89] train-rmse:0.453797 test-rmse:0.453904
[90] train-rmse:0.453744 test-rmse:0.453854
[91] train-rmse:0.453681 test-rmse:0.453792
[92] train-rmse:0.453628 test-rmse:0.45374
[93] train-rmse:0.45358 test-rmse:0.453693
[94] train-rmse:0.453543 test-rmse:0.453656
[95] train-rmse:0.45351 test-rmse:0.453623
[96] train-rmse:0.453455 test-rmse:0.453569
[97] train-rmse:0.453402 test-rmse:0.453516
[98] train-rmse:0.453351 test-rmse:0.453467
[99] train-rmse:0.453304 test-rmse:0.45342
[100] train-rmse:0.453268 test-rmse:0.453385
[101] train-rmse:0.453221 test-rmse:0.453339
[102] train-rmse:0.453174 test-rmse:0.453293
[103] train-rmse:0.453101 test-rmse:0.453221
[104] train-rmse:0.453065 test-rmse:0.453185
[105] train-rmse:0.453005 test-rmse:0.453125
[106] train-rmse:0.452947 test-rmse:0.453068
[107] train-rmse:0.452911 test-rmse:0.453034
[108] train-rmse:0.452863 test-rmse:0.452987
[109] train-rmse:0.452813 test-rmse:0.452938
[110] train-rmse:0.45277 test-rmse:0.452896
[111] train-rmse:0.452728 test-rmse:0.452855
[112] train-rmse:0.452696 test-rmse:0.452825
[113] train-rmse:0.452665 test-rmse:0.452795
[114] train-rmse:0.452626 test-rmse:0.452758
[115] train-rmse:0.452591 test-rmse:0.452723
[116] train-rmse:0.452559 test-rmse:0.452692
[117] train-rmse:0.452507 test-rmse:0.452641
[118] train-rmse:0.452474 test-rmse:0.452608
[119] train-rmse:0.452416 test-rmse:0.45255
[120] train-rmse:0.452366 test-rmse:0.452502
[121] train-rmse:0.452318 test-rmse:0.452454
[122] train-rmse:0.452282 test-rmse:0.452419
[123] train-rmse:0.452225 test-rmse:0.452363
[124] train-rmse:0.452185 test-rmse:0.452323
[125] train-rmse:0.452153 test-rmse:0.452291
[126] train-rmse:0.452117 test-rmse:0.452258
[127] train-rmse:0.45208 test-rmse:0.452221
[128] train-rmse:0.452055 test-rmse:0.452199
[129] train-rmse:0.452032 test-rmse:0.452176
[130] train-rmse:0.451981 test-rmse:0.452125
[131] train-rmse:0.451931 test-rmse:0.452076
[132] train-rmse:0.451895 test-rmse:0.452043
[133] train-rmse:0.451843 test-rmse:0.451992
[134] train-rmse:0.4518 test-rmse:0.45195
[135] train-rmse:0.451763 test-rmse:0.451914
[136] train-rmse:0.451727 test-rmse:0.451879
[137] train-rmse:0.451695 test-rmse:0.451848
[138] train-rmse:0.45166 test-rmse:0.451815
[139] train-rmse:0.451624 test-rmse:0.451779
[140] train-rmse:0.451584 test-rmse:0.45174
[141] train-rmse:0.451559 test-rmse:0.451717
[142] train-rmse:0.451526 test-rmse:0.451686
[143] train-rmse:0.451487 test-rmse:0.451648
[144] train-rmse:0.451458 test-rmse:0.45162
[145] train-rmse:0.451425 test-rmse:0.451588
[146] train-rmse:0.451396 test-rmse:0.45156
[147] train-rmse:0.451359 test-rmse:0.451524
[148] train-rmse:0.451337 test-rmse:0.451503
[149] train-rmse:0.451314 test-rmse:0.45148
[150] train-rmse:0.451282 test-rmse:0.451449
[151] train-rmse:0.451254 test-rmse:0.451422
[152] train-rmse:0.451225 test-rmse:0.451395
[153] train-rmse:0.451203 test-rmse:0.451374
[154] train-rmse:0.45117 test-rmse:0.451341
[155] train-rmse:0.451145 test-rmse:0.451316
[156] train-rmse:0.451112 test-rmse:0.451284
[157] train-rmse:0.451084 test-rmse:0.451257
[158] train-rmse:0.451051 test-rmse:0.451226
[159] train-rmse:0.451023 test-rmse:0.451199
[160] train-rmse:0.450995 test-rmse:0.451172
[161] train-rmse:0.450968 test-rmse:0.451146
[162] train-rmse:0.450937 test-rmse:0.451116
[163] train-rmse:0.450913 test-rmse:0.451094
[164] train-rmse:0.450871 test-rmse:0.451052
[165] train-rmse:0.450853 test-rmse:0.451036
[166] train-rmse:0.450821 test-rmse:0.451005
[167] train-rmse:0.450798 test-rmse:0.450983
[168] train-rmse:0.450772 test-rmse:0.450959
[169] train-rmse:0.45075 test-rmse:0.450938
[170] train-rmse:0.450721 test-rmse:0.450909
[171] train-rmse:0.450695 test-rmse:0.450884
[172] train-rmse:0.450664 test-rmse:0.450853
[173] train-rmse:0.450638 test-rmse:0.450829
[174] train-rmse:0.450613 test-rmse:0.450804
[175] train-rmse:0.450594 test-rmse:0.450787
[176] train-rmse:0.450568 test-rmse:0.450761
[177] train-rmse:0.450546 test-rmse:0.450741
[178] train-rmse:0.450512 test-rmse:0.450707
[179] train-rmse:0.450485 test-rmse:0.45068
[180] train-rmse:0.450467 test-rmse:0.450664
[181] train-rmse:0.450444 test-rmse:0.450643
[182] train-rmse:0.450417 test-rmse:0.450617
[183] train-rmse:0.450388 test-rmse:0.450587
[184] train-rmse:0.450363 test-rmse:0.450564
[185] train-rmse:0.450333 test-rmse:0.450535
[186] train-rmse:0.450298 test-rmse:0.4505
[187] train-rmse:0.450263 test-rmse:0.450465
[188] train-rmse:0.450239 test-rmse:0.450443
[189] train-rmse:0.450212 test-rmse:0.450418
[190] train-rmse:0.450197 test-rmse:0.450403
[191] train-rmse:0.45018 test-rmse:0.450388
[192] train-rmse:0.450158 test-rmse:0.450367
[193] train-rmse:0.450121 test-rmse:0.45033
[194] train-rmse:0.450093 test-rmse:0.450302
[195] train-rmse:0.450073 test-rmse:0.450283
[196] train-rmse:0.450053 test-rmse:0.450264
[197] train-rmse:0.450033 test-rmse:0.450245
[198] train-rmse:0.450014 test-rmse:0.450227
[199] train-rmse:0.44999 test-rmse:0.450203
[200] train-rmse:0.449963 test-rmse:0.450177
[201] train-rmse:0.449944 test-rmse:0.450159
[202] train-rmse:0.449929 test-rmse:0.450144
[203] train-rmse:0.449907 test-rmse:0.450123
[204] train-rmse:0.449886 test-rmse:0.450102
[205] train-rmse:0.449869 test-rmse:0.450086
[206] train-rmse:0.449845 test-rmse:0.450063
[207] train-rmse:0.449828 test-rmse:0.450047
[208] train-rmse:0.449806 test-rmse:0.450026
[209] train-rmse:0.449785 test-rmse:0.450005
[210] train-rmse:0.449762 test-rmse:0.449983
[211] train-rmse:0.449738 test-rmse:0.44996
[212] train-rmse:0.449724 test-rmse:0.449947
[213] train-rmse:0.449702 test-rmse:0.449926
[214] train-rmse:0.449681 test-rmse:0.449906
[215] train-rmse:0.449659 test-rmse:0.449884
[216] train-rmse:0.449641 test-rmse:0.449868
[217] train-rmse:0.449622 test-rmse:0.44985
[218] train-rmse:0.449605 test-rmse:0.449835
[219] train-rmse:0.44959 test-rmse:0.44982
[220] train-rmse:0.449571 test-rmse:0.449801
[221] train-rmse:0.449553 test-rmse:0.449785
[222] train-rmse:0.449536 test-rmse:0.449769
[223] train-rmse:0.449517 test-rmse:0.44975
[224] train-rmse:0.449496 test-rmse:0.44973
[225] train-rmse:0.449479 test-rmse:0.449713
[226] train-rmse:0.449463 test-rmse:0.449698
[227] train-rmse:0.449441 test-rmse:0.449677
[228] train-rmse:0.449417 test-rmse:0.449654
[229] train-rmse:0.449402 test-rmse:0.44964
[230] train-rmse:0.449385 test-rmse:0.449623
[231] train-rmse:0.449369 test-rmse:0.449608
[232] train-rmse:0.449352 test-rmse:0.449591
[233] train-rmse:0.449332 test-rmse:0.449572
[234] train-rmse:0.449318 test-rmse:0.449559
[235] train-rmse:0.449303 test-rmse:0.449545
[236] train-rmse:0.449284 test-rmse:0.449527
[237] train-rmse:0.449269 test-rmse:0.449513
[238] train-rmse:0.449249 test-rmse:0.449494
[239] train-rmse:0.449234 test-rmse:0.449479
[240] train-rmse:0.449213 test-rmse:0.449459
[241] train-rmse:0.449193 test-rmse:0.44944
[242] train-rmse:0.449174 test-rmse:0.449422
[243] train-rmse:0.449152 test-rmse:0.449401
[244] train-rmse:0.449135 test-rmse:0.449385
[245] train-rmse:0.449119 test-rmse:0.44937
[246] train-rmse:0.449107 test-rmse:0.449358
[247] train-rmse:0.449092 test-rmse:0.449344
[248] train-rmse:0.44907 test-rmse:0.449324
[249] train-rmse:0.449054 test-rmse:0.449309
[250] train-rmse:0.449032 test-rmse:0.449288
[251] train-rmse:0.449016 test-rmse:0.449272
[252] train-rmse:0.449002 test-rmse:0.449259
[253] train-rmse:0.448973 test-rmse:0.449232
[254] train-rmse:0.44896 test-rmse:0.449219
[255] train-rmse:0.448945 test-rmse:0.449205
[256] train-rmse:0.44893 test-rmse:0.449191
[257] train-rmse:0.44891 test-rmse:0.449173
[258] train-rmse:0.448895 test-rmse:0.449158
[259] train-rmse:0.448882 test-rmse:0.449146
[260] train-rmse:0.448864 test-rmse:0.449128
[261] train-rmse:0.448845 test-rmse:0.44911
[262] train-rmse:0.448831 test-rmse:0.449097
[263] train-rmse:0.448815 test-rmse:0.449081
[264] train-rmse:0.448801 test-rmse:0.44907
[265] train-rmse:0.448786 test-rmse:0.449056
[266] train-rmse:0.448769 test-rmse:0.44904
[267] train-rmse:0.448755 test-rmse:0.449027
[268] train-rmse:0.448739 test-rmse:0.44901
[269] train-rmse:0.448726 test-rmse:0.448998
[270] train-rmse:0.44871 test-rmse:0.448983
[271] train-rmse:0.448695 test-rmse:0.448969
[272] train-rmse:0.448683 test-rmse:0.448958
[273] train-rmse:0.448665 test-rmse:0.44894
[274] train-rmse:0.448644 test-rmse:0.448921
[275] train-rmse:0.448624 test-rmse:0.448902
[276] train-rmse:0.448607 test-rmse:0.448885
[277] train-rmse:0.448595 test-rmse:0.448874
[278] train-rmse:0.448578 test-rmse:0.448858
[279] train-rmse:0.448556 test-rmse:0.448837
[280] train-rmse:0.448542 test-rmse:0.448824
[281] train-rmse:0.448529 test-rmse:0.448812
[282] train-rmse:0.448518 test-rmse:0.448801
[283] train-rmse:0.448504 test-rmse:0.448788
[284] train-rmse:0.44849 test-rmse:0.448776
[285] train-rmse:0.448475 test-rmse:0.448761
[286] train-rmse:0.44846 test-rmse:0.448747
[287] train-rmse:0.448446 test-rmse:0.448734
[288] train-rmse:0.448431 test-rmse:0.44872
[289] train-rmse:0.448412 test-rmse:0.448703
[290] train-rmse:0.448396 test-rmse:0.448688
[291] train-rmse:0.448381 test-rmse:0.448674
[292] train-rmse:0.44837 test-rmse:0.448664
[293] train-rmse:0.448356 test-rmse:0.44865
[294] train-rmse:0.448342 test-rmse:0.448638
[295] train-rmse:0.448329 test-rmse:0.448625
[296] train-rmse:0.448313 test-rmse:0.44861
[297] train-rmse:0.448298 test-rmse:0.448596
[298] train-rmse:0.448287 test-rmse:0.448585
[299] train-rmse:0.448274 test-rmse:0.448573
[300] train-rmse:0.448258 test-rmse:0.448558
[301] train-rmse:0.448242 test-rmse:0.448542
[302] train-rmse:0.448229 test-rmse:0.448531
[303] train-rmse:0.448215 test-rmse:0.448518
[304] train-rmse:0.448205 test-rmse:0.448508
[305] train-rmse:0.448189 test-rmse:0.448493
[306] train-rmse:0.448177 test-rmse:0.448482
[307] train-rmse:0.448164 test-rmse:0.448469
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[1946] train-rmse:0.441458 test-rmse:0.443151
[1947] train-rmse:0.441456 test-rmse:0.44315
[1948] train-rmse:0.441453 test-rmse:0.443149
[1949] train-rmse:0.441452 test-rmse:0.443148
[1950] train-rmse:0.44145 test-rmse:0.443147
[1951] train-rmse:0.441448 test-rmse:0.443146
[1952] train-rmse:0.441447 test-rmse:0.443145
[1953] train-rmse:0.441445 test-rmse:0.443144
[1954] train-rmse:0.441443 test-rmse:0.443143
[1955] train-rmse:0.441441 test-rmse:0.443142
[1956] train-rmse:0.44144 test-rmse:0.443141
[1957] train-rmse:0.441437 test-rmse:0.44314
[1958] train-rmse:0.441436 test-rmse:0.443139
[1959] train-rmse:0.441434 test-rmse:0.443139
[1960] train-rmse:0.441432 test-rmse:0.443138
[1961] train-rmse:0.44143 test-rmse:0.443137
[1962] train-rmse:0.441427 test-rmse:0.443135
[1963] train-rmse:0.441425 test-rmse:0.443133
[1964] train-rmse:0.441423 test-rmse:0.443132
[1965] train-rmse:0.441421 test-rmse:0.443131
[1966] train-rmse:0.441419 test-rmse:0.443129
[1967] train-rmse:0.441417 test-rmse:0.443128
[1968] train-rmse:0.441415 test-rmse:0.443127
[1969] train-rmse:0.441414 test-rmse:0.443126
[1970] train-rmse:0.441412 test-rmse:0.443126
[1971] train-rmse:0.44141 test-rmse:0.443125
[1972] train-rmse:0.441408 test-rmse:0.443124
[1973] train-rmse:0.441406 test-rmse:0.443122
[1974] train-rmse:0.441405 test-rmse:0.443121
[1975] train-rmse:0.441402 test-rmse:0.44312
[1976] train-rmse:0.441401 test-rmse:0.443119
[1977] train-rmse:0.441399 test-rmse:0.443118
[1978] train-rmse:0.441397 test-rmse:0.443117
[1979] train-rmse:0.441395 test-rmse:0.443115
[1980] train-rmse:0.441392 test-rmse:0.443113
[1981] train-rmse:0.44139 test-rmse:0.443112
[1982] train-rmse:0.441389 test-rmse:0.443111
[1983] train-rmse:0.441387 test-rmse:0.44311
[1984] train-rmse:0.441385 test-rmse:0.443109
[1985] train-rmse:0.441384 test-rmse:0.443108
[1986] train-rmse:0.441382 test-rmse:0.443108
[1987] train-rmse:0.44138 test-rmse:0.443107
[1988] train-rmse:0.441378 test-rmse:0.443105
[1989] train-rmse:0.441376 test-rmse:0.443104
[1990] train-rmse:0.441375 test-rmse:0.443104
[1991] train-rmse:0.441372 test-rmse:0.443102
[1992] train-rmse:0.44137 test-rmse:0.443101
[1993] train-rmse:0.441369 test-rmse:0.4431
[1994] train-rmse:0.441366 test-rmse:0.443099
[1995] train-rmse:0.441364 test-rmse:0.443097
[1996] train-rmse:0.441362 test-rmse:0.443095
[1997] train-rmse:0.44136 test-rmse:0.443094
[1998] train-rmse:0.441357 test-rmse:0.443092
[1999] train-rmse:0.441356 test-rmse:0.443091
test-rmse-mean test-rmse-std train-rmse-mean train-rmse-std
1995 0.443097 0.000280 0.441364 0.000077
1996 0.443095 0.000280 0.441362 0.000077
1997 0.443094 0.000280 0.441360 0.000077
1998 0.443092 0.000281 0.441357 0.000077
1999 0.443091 0.000280 0.441356 0.000077
In [11]:
num_round = 1900
bst = xgb.train(param_10, dtrain_10, num_round)
In [16]:
xgb.plot_importance(bst)
Out[16]:
<matplotlib.axes.AxesSubplot at 0x7fc3ff106690>
In [12]:
bst.save_model('bst_use_all_train.model')
In [ ]:
# dump model
bst= bst.dump_model('bst_use_all_train.model')
In [6]:
bst = xgb.Booster({'nthread':10}) #init model
bst.load_model("bst_use_all_train.model") # load data
In [18]:
dtest_10 = xgb.DMatrix(test_dataset_10_normalize.drop(['id'],axis =1,inplace = True), missing=np.nan)
In [ ]:
submission_10_all_train = pd.DataFrame()
submission_10_all_train = test_dataset_10_normalize['id'].copy()
submission_10_all_train['predict'] = bst.predict(dtest_10)
In [ ]:
submission_11 = pd.read_csv('submission_11_new.csv',index_col = 0)
In [21]:
num_round = 392
dtest_10 = xgb.DMatrix(test_dataset_10_normalize[predictors_10], missing=np.nan)
submission_10 = train_pivot_56789_to_10[['id']].copy()
i = 0
for i in range(20):
train_pivot_xgb_time1_sample = train_dataset_10_normalize[predictors_target_10].sample(2000000)
train_feature_10 = train_pivot_xgb_time1_sample.drop(['target'],axis = 1)
train_label_10 = train_pivot_xgb_time1_sample[['target']]
dtrain_10 = xgb.DMatrix(train_feature_10,label = train_label_10,missing= np.nan)
bst = xgb.train(param_10, dtrain_10, num_round)
print str(i) + 'training finished!'
submission_10['predict_' + str(i)] = bst.predict(dtest_10)
print str(i) + 'predicting finished!'
print 'finished'
0training finished!
0predicting finished!
1training finished!
1predicting finished!
2training finished!
2predicting finished!
3training finished!
3predicting finished!
4training finished!
4predicting finished!
5training finished!
5predicting finished!
6training finished!
6predicting finished!
7training finished!
7predicting finished!
8training finished!
8predicting finished!
9training finished!
9predicting finished!
10training finished!
10predicting finished!
11training finished!
11predicting finished!
12training finished!
12predicting finished!
13training finished!
13predicting finished!
14training finished!
14predicting finished!
15training finished!
15predicting finished!
16training finished!
16predicting finished!
17training finished!
17predicting finished!
18training finished!
18predicting finished!
19training finished!
19predicting finished!
finished
In [22]:
submission_10.to_csv('submission_10_new.csv')
In [26]:
# make prediction
xgb.plot_importance(bst)
Out[26]:
<matplotlib.axes.AxesSubplot at 0x7fc793a07dd0>
In [13]:
submission_11['predict'] = submission_11[['predict_' + str(i) for i in range(20)]].mean(axis=1)
In [14]:
submission_11 = submission_11[['id','predict']]
gc.collect()
submission_11.head()
Out[14]:
id
predict
0
1547831
4.406201
1
6825659
3.053817
2
5853787
2.684612
3
2316053
1.259826
4
900676
2.301486
In [24]:
submission_11.to_csv('submission_11_new.csv',index = False)
In [16]:
submission_44fea = pd.concat([submission_44fea,submission_11],axis =0,copy = False)
In [17]:
submission_44fea.shape
Out[17]:
(6999251, 2)
In [18]:
submission_44fea.rename(columns = {'predict':'Demanda_uni_equil'},inplace = True)
submission_44fea['Demanda_uni_equil'] = submission_44fea['Demanda_uni_equil'].apply(np.expm1)
submission_44fea.head()
Out[18]:
Semana
id
Demanda_uni_equil
0
1569352
10.206472
1
6667200
35.766411
2
1592616
17.642273
3
3909690
62.235741
4
3659672
34.847991
In [19]:
submission_44fea['Demanda_uni_equil'] = submission_44fea['Demanda_uni_equil'].round(1)
In [20]:
submission_44fea['Demanda_uni_equil'].describe()
Out[20]:
count 6.999251e+06
mean 6.075400e+00
std 1.606870e+01
min -7.000000e-01
25% 1.900000e+00
50% 3.100000e+00
75% 5.600000e+00
max 2.879800e+03
Name: Demanda_uni_equil, dtype: float64
In [21]:
mask = submission_44fea[submission_44fea['Demanda_uni_equil'] <= 0].index
submission_44fea.loc[mask,'Demanda_uni_equil'] = 0
submission_44fea['Demanda_uni_equil'].describe()
Out[21]:
count 6.999251e+06
mean 6.074749e+00
std 1.606832e+01
min 0.000000e+00
25% 1.900000e+00
50% 3.100000e+00
75% 5.600000e+00
max 2.879800e+03
Name: Demanda_uni_equil, dtype: float64
In [22]:
submission_44fea.head()
Out[22]:
Semana
id
Demanda_uni_equil
0
1569352
10.2
1
6667200
35.8
2
1592616
17.6
3
3909690
62.2
4
3659672
34.8
In [23]:
submission_44fea.to_csv('submission_44fea.csv',index = False)
In [12]:
submission_10 = pd.read_csv('submission_10.csv',index_col = 0)
In [22]:
submission_10.shape
Out[22]:
(3538385, 21)
In [23]:
submission_10.columns.values
Out[23]:
array(['id', 'predict_0', 'predict_1', 'predict_2', 'predict_3',
'predict_4', 'predict_5', 'predict_6', 'predict_7', 'predict_8',
'predict_9', 'predict_10', 'predict_11', 'predict_12', 'predict_13',
'predict_14', 'predict_15', 'predict_16', 'predict_17',
'predict_18', 'predict_19'], dtype=object)
In [26]:
submission_11.columns.values
Out[26]:
array(['id', 'predict_0', 'predict_1', 'predict_2', 'predict_3',
'predict_4', 'predict_5', 'predict_6', 'predict_7', 'predict_8',
'predict_9', 'predict_10', 'predict_11', 'predict_12', 'predict_13',
'predict_14', 'predict_15', 'predict_16', 'predict_17',
'predict_18', 'predict_19'], dtype=object)
In [27]:
submission = pd.concat([submission_10,submission_11],axis = 0)
In [28]:
submission.head()
Out[28]:
Semana
id
predict_0
predict_1
predict_2
predict_3
predict_4
predict_5
predict_6
predict_7
predict_8
...
predict_10
predict_11
predict_12
predict_13
predict_14
predict_15
predict_16
predict_17
predict_18
predict_19
0
1569352
1.570254
2.058795
2.034031
2.030173
2.360505
2.103804
2.306178
2.905277
2.163551
...
2.337900
2.052084
1.988659
2.145079
1.940696
1.745424
1.975164
1.969749
2.098094
2.157057
1
6667200
3.562964
3.605132
3.617031
3.654797
3.647879
3.615349
3.648028
3.547087
3.555520
...
3.617610
3.678215
3.664827
3.635223
3.621170
3.587815
3.619729
3.626218
3.718844
3.676515
2
1592616
3.095027
3.052559
2.981558
2.946887
2.818832
2.871925
2.925531
2.865346
2.925287
...
3.030976
3.111521
3.083862
2.991664
3.072153
2.873937
2.984680
2.915040
3.102967
3.057709
3
3909690
4.199105
4.092796
4.173037
4.114638
4.228617
4.108154
4.186193
4.186264
4.168976
...
4.217747
4.271296
4.210735
4.131866
4.077342
4.124441
4.189803
4.178765
4.237868
4.161020
4
3659672
3.582508
3.632398
3.602768
3.671225
3.653172
3.627649
3.626319
3.613807
3.573425
...
3.626101
3.640444
3.641191
3.594605
3.645134
3.609037
3.617180
3.677435
3.683105
3.669554
5 rows × 21 columns
In [29]:
submission['predict'] = submission[['predict_' + str(i) for i in range(20)]].mean(axis=1)
In [30]:
submission.head()
Out[30]:
Semana
id
predict_0
predict_1
predict_2
predict_3
predict_4
predict_5
predict_6
predict_7
predict_8
...
predict_11
predict_12
predict_13
predict_14
predict_15
predict_16
predict_17
predict_18
predict_19
predict
0
1569352
1.570254
2.058795
2.034031
2.030173
2.360505
2.103804
2.306178
2.905277
2.163551
...
2.052084
1.988659
2.145079
1.940696
1.745424
1.975164
1.969749
2.098094
2.157057
2.122850
1
6667200
3.562964
3.605132
3.617031
3.654797
3.647879
3.615349
3.648028
3.547087
3.555520
...
3.678215
3.664827
3.635223
3.621170
3.587815
3.619729
3.626218
3.718844
3.676515
3.627489
2
1592616
3.095027
3.052559
2.981558
2.946887
2.818832
2.871925
2.925531
2.865346
2.925287
...
3.111521
3.083862
2.991664
3.072153
2.873937
2.984680
2.915040
3.102967
3.057709
2.989878
3
3909690
4.199105
4.092796
4.173037
4.114638
4.228617
4.108154
4.186193
4.186264
4.168976
...
4.271296
4.210735
4.131866
4.077342
4.124441
4.189803
4.178765
4.237868
4.161020
4.173270
4
3659672
3.582508
3.632398
3.602768
3.671225
3.653172
3.627649
3.626319
3.613807
3.573425
...
3.640444
3.641191
3.594605
3.645134
3.609037
3.617180
3.677435
3.683105
3.669554
3.633988
5 rows × 22 columns
In [32]:
submission.rename(columns = {'predict':'Demanda_uni_equil'},inplace = True)
submission['Demanda_uni_equil'] = submission['Demanda_uni_equil'].apply(np.expm1)
submission.head()
Out[32]:
Semana
id
predict_0
predict_1
predict_2
predict_3
predict_4
predict_5
predict_6
predict_7
predict_8
...
predict_11
predict_12
predict_13
predict_14
predict_15
predict_16
predict_17
predict_18
predict_19
Demanda_uni_equil
0
1569352
1.570254
2.058795
2.034031
2.030173
2.360505
2.103804
2.306178
2.905277
2.163551
...
2.052084
1.988659
2.145079
1.940696
1.745424
1.975164
1.969749
2.098094
2.157057
7.354917
1
6667200
3.562964
3.605132
3.617031
3.654797
3.647879
3.615349
3.648028
3.547087
3.555520
...
3.678215
3.664827
3.635223
3.621170
3.587815
3.619729
3.626218
3.718844
3.676515
36.618239
2
1592616
3.095027
3.052559
2.981558
2.946887
2.818832
2.871925
2.925531
2.865346
2.925287
...
3.111521
3.083862
2.991664
3.072153
2.873937
2.984680
2.915040
3.102967
3.057709
18.883251
3
3909690
4.199105
4.092796
4.173037
4.114638
4.228617
4.108154
4.186193
4.186264
4.168976
...
4.271296
4.210735
4.131866
4.077342
4.124441
4.189803
4.178765
4.237868
4.161020
63.927416
4
3659672
3.582508
3.632398
3.602768
3.671225
3.653172
3.627649
3.626319
3.613807
3.573425
...
3.640444
3.641191
3.594605
3.645134
3.609037
3.617180
3.677435
3.683105
3.669554
36.863517
5 rows × 22 columns
In [33]:
submission_final = submission[['id','Demanda_uni_equil']].copy()
In [34]:
submission_final['Demanda_uni_equil'] = submission_final['Demanda_uni_equil'].round(1)
In [35]:
submission_final.head()
Out[35]:
Semana
id
Demanda_uni_equil
0
1569352
7.4
1
6667200
36.6
2
1592616
18.9
3
3909690
63.9
4
3659672
36.9
In [36]:
submission_final.to_csv('submission_xgb_2.csv',index = False)
Content source: boya-zhou/kaggle_bimbo_reformat
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