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 [25]:
%ls
1.5_create_lag.ipynb preprocessed_products.csv
1_predata.ipynb RF_model/
1_predata_whole.ipynb ruta_for_cliente_producto.csv
3_xgb_43fea.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 train_pivot_45678_to_9_whole_zero.csv
8_svm_linearSVR.ipynb train_pivot_56789_to_10_44fea.pickle
agencia_for_cliente_producto.csv train_pivot_56789_to_10_44fea_zero.pickle
bst_use_all_train.model train_pivot_56789_to_10_new.pickle
canal_for_cliente_producto.csv train_pivot_6789_to_11_new.pickle
old_submission/ train_pivot_xgb_time1_44fea.csv
origin/ train_pivot_xgb_time1_44fea_zero.csv
pivot_test.pickle train_pivot_xgb_time1.pickle
pivot_train_with_nan.pickle train_pivot_xgb_time2_38fea.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
[166] train-rmse:0.455599 test-rmse:0.459238
[167] train-rmse:0.455578 test-rmse:0.459229
[168] train-rmse:0.455557 test-rmse:0.459222
[169] train-rmse:0.455519 test-rmse:0.459205
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[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
1.5_create_lag.ipynb preprocessed_products.csv
1_predata.ipynb RF_model/
1_predata_whole.ipynb ruta_for_cliente_producto.csv
3_xgb_43fea.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 train_pivot_56789_to_10_44fea.pickle
8_svm_linearSVR.ipynb train_pivot_56789_to_10_44fea_zero.pickle
agencia_for_cliente_producto.csv train_pivot_56789_to_10_new.pickle
bst_use_all_train.model train_pivot_6789_to_11_new.pickle
canal_for_cliente_producto.csv train_pivot_xgb_time1_44fea.csv
old_submission/ train_pivot_xgb_time1_44fea_zero.csv
origin/ train_pivot_xgb_time1.pickle
pivot_test.pickle train_pivot_xgb_time2_38fea.csv
pivot_train_with_nan.pickle
In [4]:
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 [5]:
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 [28]:
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],copy = False)
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()
test_dataset_normalize['id'] = test_dataset['id'].copy()
# target = train_dataset['target']
return train_dataset_normalize,test_dataset_normalize
In [29]:
f = lambda x : (x-x.mean())/x.std(ddof=0)
In [6]:
train_pivot_xgb_time1 = pd.read_csv('train_pivot_45678_to_9_whole_zero.csv',
dtype=np.float32,usecols = predictors_target_10)
In [19]:
train_pivot_56789_to_10 = pd.read_pickle('train_pivot_56789_to_10_44fea_zero.pickle')
In [20]:
train_pivot_56789_to_10['id'] = train_pivot_56789_to_10['id'].astype(int)
train_pivot_56789_to_10.head()
Out[20]:
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_5_cum
t_m_4_cum
t_m_3_cum
t_m_2_cum
t_m_1_cum
NombreCliente
weight
weight_per_piece
pieces
0
1569352
10.0
166.0
3604.0
143.0
4.001407
3.411275
2.890955
2.498162
3.411275
...
0.106650
0.000000
0.000000
0.000000
0.000000
0.000000
131.0
691.0
NaN
NaN
1
6667200
713.0
166.0
12208.0
10842.0
3.523074
3.222417
2.890955
4.361940
2.835826
...
21.111349
3.784190
7.280697
10.714684
14.403563
18.092443
6027.0
740.0
NaN
NaN
2
1592616
713.0
166.0
12208.0
10780.0
3.523074
3.222417
2.890955
3.987424
2.835826
...
19.113272
0.000000
2.833213
6.165418
8.730368
11.949243
6027.0
480.0
NaN
NaN
3
3909690
713.0
166.0
12208.0
13222.0
3.523074
3.222417
2.890955
4.529289
2.835826
...
21.542048
4.430817
8.144389
12.461877
16.556221
20.886955
6027.0
680.0
NaN
NaN
4
3659672
713.0
166.0
12208.0
10881.0
3.523074
3.222417
2.890955
3.238592
2.835826
...
14.471490
3.583519
7.138867
10.827746
14.656388
18.319950
6027.0
567.0
NaN
NaN
5 rows × 44 columns
In [6]:
train_pivot_56789_to_10.columns.values
Out[6]:
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_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 [7]:
train_pivot_xgb_time1.columns.values
Out[7]:
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_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 [9]:
%ls
1.5_create_lag.ipynb pivot_train_with_nan.pickle
1_predata.ipynb preprocessed_products.csv
1_predata_whole.ipynb RF_model/
3_xgb_43fea.ipynb ruta_for_cliente_producto.csv
3_xgb.ipynb stack_sub/
3_xgb_prediction.ipynb submission_10_new.csv
3_xgb_test.ipynb submission_11_new.csv
4_keras_nn.ipynb submission_44fea.csv
5_random_forest.ipynb submission_all_train_10.csv
6_stack_model.ipynb submission_all_train_11.csv
7_SGD_regressor.ipynb submission_all_train_12.csv
8_svm_linearSVR.ipynb submission_all_train_13.csv
9_private_board.ipynb submission_all_train_14.csv
agencia_for_cliente_producto.csv submission_all_train_2.csv
bst_1000_eta001_2.model submission_all_train_3.csv
bst_1000_eta001.model submission_all_train_4.csv
bst_1000_eta002.model submission_all_train_5.csv
bst_1000.model submission_all_train_6.csv
bst_1200_eta0015.model submission_all_train_7.csv
bst_1200_eta005.model submission_all_train_8.csv
bst_1400_eta0015.model submission_all_train_9.csv
bst_1400_eta002.model submission_all_train.csv
bst_1800_eta0015.model submission_nn.csv
bst_200_eta005.model submission_nn_xgb
bst_400_eta002.model train_pivot_34567_to_9.csv
bst_400_eta005.model train_pivot_45678_to_9_whole_zero.csv
bst_551_eta02.model train_pivot_56789_to_10_44fea.pickle
bst_600_eta001.model train_pivot_56789_to_10_44fea_zero.pickle
bst_600_eta002.model train_pivot_56789_to_10_new.pickle
bst_600_eta005.model train_pivot_56789_to_11_private.csv
bst_800_eta002.model train_pivot_6789_to_11_new.pickle
bst_use_all_train.model train_pivot_xgb_time1_44fea.csv
canal_for_cliente_producto.csv train_pivot_xgb_time1_44fea_zero.csv
old_submission/ train_pivot_xgb_time1.pickle
origin/ train_pivot_xgb_time2_38fea.csv
pivot_test.pickle
In [38]:
#right now the best is this parameter set with 700 round
param_10 = {'booster':'gbtree',
'nthread': 12,
'max_depth':8,
'eta':0.1,
'silent':1,
'subsample':0.5,
'objective':'reg:linear',
'eval_metric':'rmse',
'colsample_bytree':0.7}
In [10]:
train_feature_10 = train_pivot_xgb_time1.drop(['target'],axis = 1)
train_label_10 = train_pivot_xgb_time1['target']
dtrain_10 = xgb.DMatrix(train_feature_10,label = train_label_10,missing= np.nan)
In [11]:
bst = xgb.Booster({'nthread':8}) #init model
bst.load_model("bst_1400_eta0015.model") # load data
In [67]:
bst
Out[67]:
<xgboost.core.Booster at 0x7fa7b52cbd90>
In [21]:
dtest_10 = xgb.DMatrix(train_pivot_56789_to_10.drop(['id'],axis =1), missing=np.nan)
In [35]:
submission_10_all_train = pd.DataFrame()
submission_10_all_train = train_pivot_56789_to_10[['id']].copy()
submission_10_all_train['predict'] = bst.predict(dtest_10)
submission_10_all_train.reset_index(drop = True,inplace = True)
In [36]:
submission_10_all_train.to_csv('week10_private2.csv',index = False)
In [37]:
submission_10_all_train['predict'].describe()
Out[37]:
count 3.538385e+06
mean 1.573562e+00
std 7.086894e-01
min -3.240995e-01
25% 1.061112e+00
50% 1.408914e+00
75% 1.898488e+00
max 7.382428e+00
Name: predict, dtype: float64
In [14]:
submission_10_all_train.head()
Out[14]:
Semana
id
predict
0
1569352
2.389577
1
6667200
3.532464
2
1592616
2.962314
3
3909690
4.135859
4
3659672
3.563721
In [15]:
submission_10_all_train.to_csv('week10_private.csv')
In [33]:
num_round = 1600
evallist = [(dtrain_10,'train')]
bst = xgb.train(param_10, dtrain_10, num_round,evallist)
[0] train-rmse:1.35052
[1] train-rmse:1.33273
[2] train-rmse:1.31522
[3] train-rmse:1.29818
[4] train-rmse:1.2813
[5] train-rmse:1.26485
[6] train-rmse:1.24863
[7] train-rmse:1.23255
[8] train-rmse:1.21692
[9] train-rmse:1.20137
[10] train-rmse:1.18612
[11] train-rmse:1.17115
[12] train-rmse:1.1564
[13] train-rmse:1.1419
[14] train-rmse:1.12764
[15] train-rmse:1.11363
[16] train-rmse:1.09985
[17] train-rmse:1.08633
[18] train-rmse:1.07303
[19] train-rmse:1.05999
[20] train-rmse:1.04719
[21] train-rmse:1.03474
[22] train-rmse:1.02235
[23] train-rmse:1.01018
[24] train-rmse:0.998356
[25] train-rmse:0.986818
[26] train-rmse:0.975354
[27] train-rmse:0.964049
[28] train-rmse:0.952932
[29] train-rmse:0.942135
[30] train-rmse:0.93145
[31] train-rmse:0.92095
[32] train-rmse:0.910629
[33] train-rmse:0.900503
[34] train-rmse:0.890605
[35] train-rmse:0.880889
[36] train-rmse:0.871322
[37] train-rmse:0.861935
[38] train-rmse:0.852721
[39] train-rmse:0.843735
[40] train-rmse:0.834869
[41] train-rmse:0.826338
[42] train-rmse:0.817924
[43] train-rmse:0.809639
[44] train-rmse:0.801448
[45] train-rmse:0.793433
[46] train-rmse:0.785558
[47] train-rmse:0.777833
[48] train-rmse:0.77039
[49] train-rmse:0.763075
[50] train-rmse:0.755813
[51] train-rmse:0.748692
[52] train-rmse:0.741711
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[54] train-rmse:0.728327
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[1192] train-rmse:0.438967
[1193] train-rmse:0.438965
[1194] train-rmse:0.438962
[1195] train-rmse:0.438958
[1196] train-rmse:0.438955
[1197] train-rmse:0.438949
[1198] train-rmse:0.438944
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[1200] train-rmse:0.438935
[1201] train-rmse:0.438932
[1202] train-rmse:0.438929
[1203] train-rmse:0.438924
[1204] train-rmse:0.438918
[1205] train-rmse:0.438914
[1206] train-rmse:0.438907
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[1208] train-rmse:0.438898
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[1210] train-rmse:0.438891
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[1275] train-rmse:0.438608
[1276] train-rmse:0.438606
[1277] train-rmse:0.438603
[1278] train-rmse:0.438598
[1279] train-rmse:0.438596
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[1281] train-rmse:0.438589
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[1297] train-rmse:0.438531
[1298] train-rmse:0.438529
[1299] train-rmse:0.438527
[1300] train-rmse:0.438521
[1301] train-rmse:0.438519
[1302] train-rmse:0.438516
[1303] train-rmse:0.438511
[1304] train-rmse:0.438505
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[1306] train-rmse:0.438496
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[1308] train-rmse:0.438485
[1309] train-rmse:0.438482
[1310] train-rmse:0.438478
[1311] train-rmse:0.438474
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[1313] train-rmse:0.438466
[1314] train-rmse:0.438462
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[1319] train-rmse:0.438444
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[1321] train-rmse:0.438438
[1322] train-rmse:0.438435
[1323] train-rmse:0.438431
[1324] train-rmse:0.438428
[1325] train-rmse:0.438423
[1326] train-rmse:0.43842
[1327] train-rmse:0.438416
[1328] train-rmse:0.438413
[1329] train-rmse:0.43841
[1330] train-rmse:0.438407
[1331] train-rmse:0.438403
[1332] train-rmse:0.438399
[1333] train-rmse:0.438397
[1334] train-rmse:0.438393
[1335] train-rmse:0.43839
[1336] train-rmse:0.438388
[1337] train-rmse:0.438386
[1338] train-rmse:0.438382
[1339] train-rmse:0.438378
[1340] train-rmse:0.438375
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[1343] train-rmse:0.438363
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[1345] train-rmse:0.438356
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[1348] train-rmse:0.438345
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[1350] train-rmse:0.438334
[1351] train-rmse:0.438331
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[1353] train-rmse:0.438324
[1354] train-rmse:0.438319
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[1356] train-rmse:0.438311
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[1358] train-rmse:0.438307
[1359] train-rmse:0.438302
[1360] train-rmse:0.438299
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[1364] train-rmse:0.438287
[1365] train-rmse:0.438285
[1366] train-rmse:0.438281
[1367] train-rmse:0.438276
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[1369] train-rmse:0.43827
[1370] train-rmse:0.438267
[1371] train-rmse:0.438265
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[1373] train-rmse:0.438255
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[1375] train-rmse:0.438249
[1376] train-rmse:0.438245
[1377] train-rmse:0.438242
[1378] train-rmse:0.438237
[1379] train-rmse:0.438233
[1380] train-rmse:0.438227
[1381] train-rmse:0.438225
[1382] train-rmse:0.438218
[1383] train-rmse:0.438215
[1384] train-rmse:0.438213
[1385] train-rmse:0.438211
[1386] train-rmse:0.438208
[1387] train-rmse:0.438205
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[1410] train-rmse:0.438123
[1411] train-rmse:0.43812
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[1413] train-rmse:0.43811
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[1416] train-rmse:0.4381
[1417] train-rmse:0.438096
[1418] train-rmse:0.43809
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[1420] train-rmse:0.438084
[1421] train-rmse:0.43808
[1422] train-rmse:0.438075
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[1427] train-rmse:0.438056
[1428] train-rmse:0.438053
[1429] train-rmse:0.43805
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[1431] train-rmse:0.438044
[1432] train-rmse:0.43804
[1433] train-rmse:0.438036
[1434] train-rmse:0.438033
[1435] train-rmse:0.438029
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[1438] train-rmse:0.438021
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[1464] train-rmse:0.43794
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[1466] train-rmse:0.437933
[1467] train-rmse:0.43793
[1468] train-rmse:0.437926
[1469] train-rmse:0.437924
[1470] train-rmse:0.437918
[1471] train-rmse:0.437914
[1472] train-rmse:0.43791
[1473] train-rmse:0.437907
[1474] train-rmse:0.437905
[1475] train-rmse:0.437899
[1476] train-rmse:0.437895
[1477] train-rmse:0.437894
[1478] train-rmse:0.437891
[1479] train-rmse:0.43789
[1480] train-rmse:0.437885
[1481] train-rmse:0.437882
[1482] train-rmse:0.437878
[1483] train-rmse:0.437875
[1484] train-rmse:0.43787
[1485] train-rmse:0.437866
[1486] train-rmse:0.437863
[1487] train-rmse:0.43786
[1488] train-rmse:0.437858
[1489] train-rmse:0.437853
[1490] train-rmse:0.437849
[1491] train-rmse:0.437843
[1492] train-rmse:0.43784
[1493] train-rmse:0.437837
[1494] train-rmse:0.437835
[1495] train-rmse:0.437831
[1496] train-rmse:0.437828
[1497] train-rmse:0.437825
[1498] train-rmse:0.437822
[1499] train-rmse:0.437818
[1500] train-rmse:0.437813
[1501] train-rmse:0.43781
[1502] train-rmse:0.437806
[1503] train-rmse:0.437802
[1504] train-rmse:0.437799
[1505] train-rmse:0.437795
[1506] train-rmse:0.437792
[1507] train-rmse:0.437788
[1508] train-rmse:0.437784
[1509] train-rmse:0.437782
[1510] train-rmse:0.437777
[1511] train-rmse:0.437772
[1512] train-rmse:0.437768
[1513] train-rmse:0.437766
[1514] train-rmse:0.437762
[1515] train-rmse:0.437759
[1516] train-rmse:0.437755
[1517] train-rmse:0.437754
[1518] train-rmse:0.43775
[1519] train-rmse:0.437749
[1520] train-rmse:0.437746
[1521] train-rmse:0.437744
[1522] train-rmse:0.43774
[1523] train-rmse:0.437737
[1524] train-rmse:0.437733
[1525] train-rmse:0.437731
[1526] train-rmse:0.437727
[1527] train-rmse:0.437724
[1528] train-rmse:0.437721
[1529] train-rmse:0.437718
[1530] train-rmse:0.437714
[1531] train-rmse:0.437711
[1532] train-rmse:0.437708
[1533] train-rmse:0.437705
[1534] train-rmse:0.437703
[1535] train-rmse:0.437699
[1536] train-rmse:0.437697
[1537] train-rmse:0.437693
[1538] train-rmse:0.437689
[1539] train-rmse:0.437687
[1540] train-rmse:0.437682
[1541] train-rmse:0.437679
[1542] train-rmse:0.437676
[1543] train-rmse:0.437674
[1544] train-rmse:0.437669
[1545] train-rmse:0.437663
[1546] train-rmse:0.437659
[1547] train-rmse:0.437657
[1548] train-rmse:0.437655
[1549] train-rmse:0.43765
[1550] train-rmse:0.437646
[1551] train-rmse:0.437643
[1552] train-rmse:0.437639
[1553] train-rmse:0.437636
[1554] train-rmse:0.437633
[1555] train-rmse:0.43763
[1556] train-rmse:0.437628
[1557] train-rmse:0.437624
[1558] train-rmse:0.437622
[1559] train-rmse:0.437618
[1560] train-rmse:0.437614
[1561] train-rmse:0.437609
[1562] train-rmse:0.437603
[1563] train-rmse:0.4376
[1564] train-rmse:0.437596
[1565] train-rmse:0.437592
[1566] train-rmse:0.437589
[1567] train-rmse:0.437585
[1568] train-rmse:0.437583
[1569] train-rmse:0.437581
[1570] train-rmse:0.437578
[1571] train-rmse:0.437574
[1572] train-rmse:0.437571
[1573] train-rmse:0.437567
[1574] train-rmse:0.437564
[1575] train-rmse:0.43756
[1576] train-rmse:0.437556
[1577] train-rmse:0.437554
[1578] train-rmse:0.43755
[1579] train-rmse:0.437547
[1580] train-rmse:0.437545
[1581] train-rmse:0.437542
[1582] train-rmse:0.437539
[1583] train-rmse:0.437536
[1584] train-rmse:0.437532
[1585] train-rmse:0.43753
[1586] train-rmse:0.437527
[1587] train-rmse:0.437522
[1588] train-rmse:0.43752
[1589] train-rmse:0.437518
[1590] train-rmse:0.437516
[1591] train-rmse:0.437512
[1592] train-rmse:0.437509
[1593] train-rmse:0.437506
[1594] train-rmse:0.437503
[1595] train-rmse:0.437498
[1596] train-rmse:0.437495
[1597] train-rmse:0.43749
[1598] train-rmse:0.437488
[1599] train-rmse:0.437483
In [34]:
bst.save_model('bst_1600_eta0015.model')
In [10]:
gc.collect()
Out[10]:
16
In [32]:
#right now the best is this parameter set with 700 round
param_10 = {'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 [149]:
num_round = 400
cvresult = xgb.cv(param_10, dtrain_10, num_round, nfold=4,show_stdv=False,
seed = 0, early_stopping_rounds=5,verbose_eval = 1)
print(cvresult.tail())
[0] train-rmse:1.34436 test-rmse:1.34436
[1] train-rmse:1.32079 test-rmse:1.32079
[2] train-rmse:1.29786 test-rmse:1.29787
[3] train-rmse:1.27532 test-rmse:1.27533
[4] train-rmse:1.2535 test-rmse:1.2535
[5] train-rmse:1.2321 test-rmse:1.23211
[6] train-rmse:1.21113 test-rmse:1.21114
[7] train-rmse:1.19076 test-rmse:1.19077
[8] train-rmse:1.17074 test-rmse:1.17076
[9] train-rmse:1.15127 test-rmse:1.15128
[10] train-rmse:1.13219 test-rmse:1.13221
[11] train-rmse:1.11348 test-rmse:1.1135
[12] train-rmse:1.09533 test-rmse:1.09535
[13] train-rmse:1.07753 test-rmse:1.07755
[14] train-rmse:1.06016 test-rmse:1.06018
[15] train-rmse:1.04318 test-rmse:1.04321
[16] train-rmse:1.02652 test-rmse:1.02655
[17] train-rmse:1.01034 test-rmse:1.01037
[18] train-rmse:0.994443 test-rmse:0.994482
[19] train-rmse:0.978944 test-rmse:0.978986
[20] train-rmse:0.963866 test-rmse:0.96391
[21] train-rmse:0.949097 test-rmse:0.949142
[22] train-rmse:0.934742 test-rmse:0.93479
[23] train-rmse:0.920769 test-rmse:0.920819
[24] train-rmse:0.907041 test-rmse:0.907093
[25] train-rmse:0.893764 test-rmse:0.89382
[26] train-rmse:0.880777 test-rmse:0.880837
[27] train-rmse:0.868099 test-rmse:0.868161
[28] train-rmse:0.855725 test-rmse:0.855788
[29] train-rmse:0.843641 test-rmse:0.843709
[30] train-rmse:0.831897 test-rmse:0.831968
[31] train-rmse:0.820507 test-rmse:0.820581
[32] train-rmse:0.809323 test-rmse:0.8094
[33] train-rmse:0.798422 test-rmse:0.798503
[34] train-rmse:0.78787 test-rmse:0.787955
[35] train-rmse:0.77759 test-rmse:0.777678
[36] train-rmse:0.767551 test-rmse:0.767641
[37] train-rmse:0.757767 test-rmse:0.757861
[38] train-rmse:0.748245 test-rmse:0.748342
[39] train-rmse:0.739 test-rmse:0.7391
[40] train-rmse:0.730001 test-rmse:0.730103
[41] train-rmse:0.721251 test-rmse:0.721357
[42] train-rmse:0.712736 test-rmse:0.712846
[43] train-rmse:0.704461 test-rmse:0.704573
[44] train-rmse:0.696408 test-rmse:0.696525
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[383] train-rmse:0.443876 test-rmse:0.44511
[384] train-rmse:0.44386 test-rmse:0.445098
[385] train-rmse:0.443845 test-rmse:0.445084
[386] train-rmse:0.443827 test-rmse:0.445071
[387] train-rmse:0.443812 test-rmse:0.445058
[388] train-rmse:0.443796 test-rmse:0.445044
[389] train-rmse:0.443783 test-rmse:0.445035
[390] train-rmse:0.443768 test-rmse:0.445022
[391] train-rmse:0.443748 test-rmse:0.445005
[392] train-rmse:0.443727 test-rmse:0.444988
[393] train-rmse:0.443713 test-rmse:0.444976
[394] train-rmse:0.4437 test-rmse:0.444966
[395] train-rmse:0.443683 test-rmse:0.444952
[396] train-rmse:0.44367 test-rmse:0.444942
[397] train-rmse:0.443654 test-rmse:0.444929
[398] train-rmse:0.443639 test-rmse:0.444916
[399] train-rmse:0.443624 test-rmse:0.444904
test-rmse-mean test-rmse-std train-rmse-mean train-rmse-std
395 0.444952 0.000282 0.443683 0.000118
396 0.444942 0.000283 0.443670 0.000118
397 0.444929 0.000287 0.443654 0.000114
398 0.444916 0.000289 0.443639 0.000112
399 0.444904 0.000287 0.443624 0.000115
In [83]:
cvresult[['test-rmse-mean','train-rmse-mean']].plot()
Out[83]:
<matplotlib.axes.AxesSubplot at 0x7fa7b4f11750>
In [72]:
cvresult[['test-rmse-mean','train-rmse-mean']].plot()
Out[72]:
<matplotlib.axes.AxesSubplot at 0x7fa7b62e1b90>
In [79]:
curve_all = cvresult[['test-rmse-mean','train-rmse-mean']].copy()
In [24]:
submission_11 = pd.read_csv('submission_11_new.csv',index_col = 0)
In [25]:
submission_11.reset_index(inplace = True)
submission_11.head()
Out[25]:
id
predict
0
1547831
4.406201
1
6825659
3.053817
2
5853787
2.684612
3
2316053
1.259826
4
900676
2.301486
In [26]:
submission_all_train = pd.concat([submission_10_all_train,submission_11],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['Demanda_uni_equil'] = submission_all_train['Demanda_uni_equil'].round(1)
In [27]:
submission_all_train.loc[submission_all_train['Demanda_uni_equil']<0,'Demanda_uni_equil'] = 0
In [28]:
submission_all_train['Demanda_uni_equil'].describe()
Out[28]:
count 6.999251e+06
mean 6.120201e+00
std 1.580884e+01
min 0.000000e+00
25% 1.900000e+00
50% 3.100000e+00
75% 5.700000e+00
max 2.598100e+03
Name: Demanda_uni_equil, dtype: float64
In [29]:
submission_all_train['id'] = submission_all_train['id'].astype(int)
In [30]:
submission_all_train.head()
Out[30]:
Semana
id
Demanda_uni_equil
0
1569352
8.4
1
6667200
33.8
2
1592616
18.4
3
3909690
62.3
4
3659672
34.5
In [31]:
submission_all_train.to_csv('submission_all_train_15.csv',index = False)
In [136]:
%ls
1.5_create_lag.ipynb pivot_test.pickle
1_predata.ipynb pivot_train_with_nan.pickle
1_predata_whole.ipynb preprocessed_products.csv
3_xgb_43fea.ipynb RF_model/
3_xgb.ipynb ruta_for_cliente_producto.csv
3_xgb_prediction.ipynb stack_sub/
3_xgb_test.ipynb submission_10_new.csv
4_keras_nn.ipynb submission_11_new.csv
5_random_forest.ipynb submission_44fea.csv
6_stack_model.ipynb submission_all_train_2.csv
7_SGD_regressor.ipynb submission_all_train_3.csv
8_svm_linearSVR.ipynb submission_all_train.csv
agencia_for_cliente_producto.csv submission_nn.csv
bst_1000.model submission_nn_xgb
bst_1200_eta005.model train_pivot_45678_to_9_whole_zero.csv
bst_200_eta005.model train_pivot_56789_to_10_44fea.pickle
bst_400_eta005.model train_pivot_56789_to_10_44fea_zero.pickle
bst_551_eta02.model train_pivot_56789_to_10_new.pickle
bst_600_eta005.model train_pivot_6789_to_11_new.pickle
bst_use_all_train.model train_pivot_xgb_time1_44fea.csv
canal_for_cliente_producto.csv train_pivot_xgb_time1_44fea_zero.csv
old_submission/ train_pivot_xgb_time1.pickle
origin/ train_pivot_xgb_time2_38fea.csv
In [ ]:
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)
Content source: boya-zhou/kaggle_bimbo_reformat
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