Modelo com base balanciada

Considerei apenas algumas Hazard < 21 E mantive demais parametros.

Treinou parou por estouro num_rounds

xgval com 10k amostras

retornei objecitve reg:linear

[2999] train-rmse:1.105964 val-rmse:1.898553

kaglle score: 0.306974

Meu melhor kaglle score: 0.3850


In [69]:
# Setup
import pandas as pd
import numpy as np 
from sklearn import preprocessing
import xgboost as xgb
import random

In [70]:
# Defining some functions

print 'Defining functions (shameless stolen from the script pages)'

## Gini: Shameless stolen from jpopham91's script
def Gini1(y_true, y_pred):
    # check and get number of samples
    assert y_true.shape == y_pred.shape
    n_samples = y_true.shape[0]
    
    # sort rows on prediction column 
    # (from largest to smallest)
    arr = np.array([y_true, y_pred]).transpose()
    true_order = arr[arr[:,0].argsort()][::-1,0]
    pred_order = arr[arr[:,1].argsort()][::-1,0]
    
    # get Lorenz curves
    L_true = np.cumsum(true_order) / np.sum(true_order)
    L_pred = np.cumsum(pred_order) / np.sum(pred_order)
    L_ones = np.linspace(1/n_samples, 1, n_samples)
    
    # get Gini coefficients (area between curves)
    G_true = np.sum(L_ones - L_true)
    G_pred = np.sum(L_ones - L_pred)
    
    # normalize to true Gini coefficient
    return G_pred/G_true

# Source script: justfor que por sua vez pegou da fonte abaixo
# Source of good version: https://www.kaggle.com/c/ClaimPredictionChallenge/forums/t/703/code-to-calculate-normalizedgini    
def Gini2_aux(actual, pred, cmpcol = 0, sortcol = 1):
     assert( len(actual) == len(pred) )
     all = np.asarray(np.c_[ actual, pred, np.arange(len(actual)) ], dtype=np.float)
     all = all[ np.lexsort((all[:,2], -1*all[:,1])) ]
     totalLosses = all[:,0].sum()
     giniSum = all[:,0].cumsum().sum() / totalLosses
     giniSum -= (len(actual) + 1) / 2.
     return giniSum / len(actual)
 
def Gini2(y_true, y_pred):
     return Gini2_aux(y_true, y_pred) / Gini2_aux(y_true, y_true)


Defining functions (shameless stolen from the script pages)

In [71]:
#load train and test 
train  = pd.read_csv('../data/raw/train.csv', index_col=0)
test   = pd.read_csv('../data/raw/test.csv', index_col=0)

print train.shape
## XXX Parece BUG head() nao mostra a ultimas colunas: 10,20,30
## Entao eu tive que duplicar para imprimir todas as colunas
print train.iloc[:,0:10].head()
print train.iloc[:,10:20].head()
print train.iloc[:,20:30].head()
print train.iloc[:,30:33].head()
print "=========================="

labels = train.Hazard
%matplotlib inline
labels.hist(bins=69)


(50999, 33)
    Hazard  T1_V1  T1_V2  T1_V3 T1_V4 T1_V5 T1_V6 T1_V7 T1_V8 T1_V9
Id                                                                 
1        1     15      3      2     N     B     N     B     B     D
2        4     16     14      5     H     B     N     B     B     C
3        1     10     10      5     N     K     N     B     B     E
4        1     18     18      5     N     K     N     B     B     E
5        1     13     19      5     N     H     N     B     B     E
    T1_V10 T1_V11 T1_V12  T1_V13  T1_V14 T1_V15 T1_V16 T1_V17  T2_V1  T2_V2
Id                                                                         
1        7      B      B      15       1      A      B      N     36     11
2       12      B      B      10       3      A      B      Y     78     10
3       12      H      B      15       1      A      R      Y     71     21
4        3      H      B      15       1      A      R      N     71     13
5        7      H      B      10       1      A      J      N     75     10
   T2_V3  T2_V4 T2_V5  T2_V6  T2_V7  T2_V8  T2_V9  T2_V10 T2_V11 T2_V12
Id                                                                     
1      N     10     B      2     37      1     11       6      Y      N
2      Y     17     C      2     22      1     18       5      Y      Y
3      Y     13     C      6     37      2     14       6      Y      Y
4      N     15     A      2     25      1      1       6      Y      N
5      Y     11     B      1     22      1      2       7      N      N
   T2_V13  T2_V14  T2_V15
Id                       
1       E       2       2
2       E       2       1
3       E       6       1
4       C       2       6
5       E       1       1
==========================
Out[71]:
<matplotlib.axes.AxesSubplot at 0x7fdc1f528610>

Prepaing training data


In [78]:
## Fazendo copia dos dados
train_pre = train.copy()

hazard_thr = 21
sample_size = 3000 # sample size per class

print "Printing intial train dim:"
print train_pre.shape
print "Considering Hazard < " + str(hazard_thr) + " in train data"

# verifcar a capacidade de gerar hazard acima de hazard_thr
test_pre  = train_pre[ train_pre['Hazard'] > hazard_thr]
train_pre = train_pre[ train_pre['Hazard'] < hazard_thr]

h = 1
print "Hazard: " + str(h)
train_aux = train_pre[train_pre.Hazard == h]
rows_sampled = np.random.choice(train_aux.index,sample_size)
train_balanced_pre = train_pre.ix[rows_sampled]

for h in range(2,hazard_thr):
    print "Hazard: " + str(h)
    train_aux = train_pre[train_pre.Hazard == h]
    rows_sampled = np.random.choice(train_aux.index,sample_size)
    train_balanced_pre = train_balanced_pre.append(train_pre.ix[rows_sampled])

print train_balanced_pre.shape
labels_balanced = train_balanced_pre['Hazard'].copy()
test_labels = test_pre['Hazard'].copy()


Printing intial train dim:
(50999, 33)
Considering Hazard < 21 in train data
Hazard: 1
Hazard: 2
Hazard: 3
Hazard: 4
Hazard: 5
Hazard: 6
Hazard: 7
Hazard: 8
Hazard: 9
Hazard: 10
Hazard: 11
Hazard: 12
Hazard: 13
Hazard: 14
Hazard: 15
Hazard: 16
Hazard: 17
Hazard: 18
Hazard: 19
Hazard: 20
(60000, 33)

In [79]:
submit_pre = test.copy()

print "Droping columns"

train_balanced_pre.drop('Hazard', axis=1, inplace=True)
train_balanced_pre.drop('T2_V10', axis=1, inplace=True)
train_balanced_pre.drop('T2_V7', axis=1, inplace=True)
train_balanced_pre.drop('T1_V13', axis=1, inplace=True)
train_balanced_pre.drop('T1_V10', axis=1, inplace=True)

test_pre.drop('Hazard', axis=1, inplace=True)
test_pre.drop('T2_V10', axis=1, inplace=True)
test_pre.drop('T2_V7', axis=1, inplace=True)
test_pre.drop('T1_V13', axis=1, inplace=True)
test_pre.drop('T1_V10', axis=1, inplace=True)

submit_pre.drop('T2_V10', axis=1, inplace=True)
submit_pre.drop('T2_V7', axis=1, inplace=True)
submit_pre.drop('T1_V13', axis=1, inplace=True)
submit_pre.drop('T1_V10', axis=1, inplace=True)

print "Printing labels dim:"
print labels_balanced.shape
print "Printing train dim:"
print train_balanced_pre.shape
print "Printing test dim:"
print test_pre.shape
print "Printing submit dim:"
print submit_pre.shape
    
columns = train_balanced_pre.columns
submit_ind = submit_pre.index

print "Converting to numpy array"

train_balanced_pre = np.array(train_balanced_pre)
labels_balanced = np.array(labels_balanced)

test_pre = np.array(test_pre)
test_labels = np.array(test_labels)

submit_pre = np.array(submit_pre)


Droping columns
Printing labels dim:
(60000,)
Printing train dim:
(60000, 28)
Printing test dim:
(305, 28)
Printing submit dim:
(51000, 28)
Converting to numpy array

In [80]:
lbl = preprocessing.LabelEncoder()

print "Converting string columns to numerical levels (train_balanced_pre)"
# label encode the categorical variables
for i in range(train_balanced_pre.shape[1]):
    lbl = preprocessing.LabelEncoder()
    lbl.fit(list(train_balanced_pre[:,i]) + list(submit_pre[:,i]) + list(test_pre[:,i]))
    train_balanced_pre[:,i] = lbl.transform(train_balanced_pre[:,i])
    test_pre[:,i] = lbl.transform(test_pre[:,i])    
    submit_pre[:,i] = lbl.transform(submit_pre[:,i])

train_balanced_pre = train_balanced_pre.astype(float)
test_pre = test_pre.astype(float)
submit_pre = submit_pre.astype(float)

print train_balanced_pre.shape
print test_pre.shape


Converting string columns to numerical levels (train_balanced_pre)
(60000, 28)
(305, 28)

Seting model parameters


In [81]:
params = {}
params["objective"] = "reg:linear" # "count:poisson" 
params["eta"] = 0.01
params["min_child_weight"] = 5
params["subsample"] = 0.8
params["colsample_bytree"] = 0.8
params["scale_pos_weight"] = 1.0
params["silent"] = 1
params["booster"] = "gbtree"
params["max_depth"] = 9

params["seed"] = 13

plst = list(params.items())
print "Prameters list"
print plst

num_rounds = 3000
est = 11

print 'num_rounds: ' + str(num_rounds)
print 'early_stopping_rounds: ' + str(est)


Prameters list
[('colsample_bytree', 0.8), ('silent', 1), ('scale_pos_weight', 1.0), ('min_child_weight', 5), ('subsample', 0.8), ('eta', 0.01), ('objective', 'reg:linear'), ('seed', 13), ('max_depth', 9), ('booster', 'gbtree')]
num_rounds: 3000
early_stopping_rounds: 11

In [82]:
test_rows_sampled = np.random.choice(range(len(train_balanced_pre)),10000)

xgsubmit = xgb.DMatrix(submit_pre)
xgtest = xgb.DMatrix(test_pre)

#create a train and validation dmatrices 
print 'Setting validation and training data'
xgval = xgb.DMatrix(train_balanced_pre[test_rows_sampled,:],
				      label=labels_balanced[test_rows_sampled])
xgtrain = xgb.DMatrix(np.delete(train_balanced_pre,test_rows_sampled,0),
			       label=np.delete(labels_balanced,test_rows_sampled,0))


print "number of row xgsubmit"
print xgsubmit.num_row()

print "number of row xgtest"
print xgtest.num_row()

print "number of row xgtrain"
print xgtrain.num_row()

print "number of row xgval"
print xgval.num_row()


Setting validation and training data
number of row xgsubmit
51000
number of row xgtest
305
number of row xgtrain
50766
number of row xgval
10000

Training XGBoost


In [83]:
#train using early stopping and predict
watchlist = [(xgtrain, 'train'),(xgval, 'val')]
model = xgb.train(plst, xgtrain, num_rounds, watchlist, early_stopping_rounds=est)


Will train until val error hasn't decreased in 11 rounds.
[0]	train-rmse:11.420278	val-rmse:11.607869
[1]	train-rmse:11.327220	val-rmse:11.513988
[2]	train-rmse:11.234998	val-rmse:11.420287
[3]	train-rmse:11.143738	val-rmse:11.328167
[4]	train-rmse:11.053298	val-rmse:11.236904
[5]	train-rmse:10.963988	val-rmse:11.146788
[6]	train-rmse:10.875762	val-rmse:11.057503
[7]	train-rmse:10.788413	val-rmse:10.968929
[8]	train-rmse:10.702023	val-rmse:10.882018
[9]	train-rmse:10.616765	val-rmse:10.795618
[10]	train-rmse:10.532366	val-rmse:10.710501
[11]	train-rmse:10.449328	val-rmse:10.626732
[12]	train-rmse:10.366737	val-rmse:10.543026
[13]	train-rmse:10.284745	val-rmse:10.460431
[14]	train-rmse:10.203990	val-rmse:10.378748
[15]	train-rmse:10.124277	val-rmse:10.298087
[16]	train-rmse:10.045329	val-rmse:10.218133
[17]	train-rmse:9.967393	val-rmse:10.139464
[18]	train-rmse:9.890088	val-rmse:10.060884
[19]	train-rmse:9.813949	val-rmse:9.983804
[20]	train-rmse:9.738643	val-rmse:9.907499
[21]	train-rmse:9.664410	val-rmse:9.832494
[22]	train-rmse:9.590709	val-rmse:9.757861
[23]	train-rmse:9.517856	val-rmse:9.684668
[24]	train-rmse:9.446395	val-rmse:9.612333
[25]	train-rmse:9.375482	val-rmse:9.540519
[26]	train-rmse:9.305270	val-rmse:9.469521
[27]	train-rmse:9.235826	val-rmse:9.399468
[28]	train-rmse:9.167105	val-rmse:9.330237
[29]	train-rmse:9.099583	val-rmse:9.262111
[30]	train-rmse:9.032965	val-rmse:9.194465
[31]	train-rmse:8.966397	val-rmse:9.127297
[32]	train-rmse:8.901225	val-rmse:9.061385
[33]	train-rmse:8.836326	val-rmse:8.995824
[34]	train-rmse:8.772210	val-rmse:8.931086
[35]	train-rmse:8.708403	val-rmse:8.866675
[36]	train-rmse:8.645843	val-rmse:8.803664
[37]	train-rmse:8.584430	val-rmse:8.741311
[38]	train-rmse:8.523167	val-rmse:8.679375
[39]	train-rmse:8.463674	val-rmse:8.619238
[40]	train-rmse:8.403858	val-rmse:8.558820
[41]	train-rmse:8.344560	val-rmse:8.498936
[42]	train-rmse:8.285776	val-rmse:8.439453
[43]	train-rmse:8.228264	val-rmse:8.381702
[44]	train-rmse:8.171051	val-rmse:8.323915
[45]	train-rmse:8.114499	val-rmse:8.266496
[46]	train-rmse:8.059668	val-rmse:8.211078
[47]	train-rmse:8.004497	val-rmse:8.155423
[48]	train-rmse:7.949891	val-rmse:8.100339
[49]	train-rmse:7.896591	val-rmse:8.046816
[50]	train-rmse:7.843237	val-rmse:7.992666
[51]	train-rmse:7.790645	val-rmse:7.939781
[52]	train-rmse:7.738732	val-rmse:7.887678
[53]	train-rmse:7.688071	val-rmse:7.836663
[54]	train-rmse:7.637918	val-rmse:7.785783
[55]	train-rmse:7.588081	val-rmse:7.735547
[56]	train-rmse:7.538823	val-rmse:7.685649
[57]	train-rmse:7.490683	val-rmse:7.637323
[58]	train-rmse:7.441819	val-rmse:7.588126
[59]	train-rmse:7.394026	val-rmse:7.540039
[60]	train-rmse:7.347291	val-rmse:7.492799
[61]	train-rmse:7.301218	val-rmse:7.446409
[62]	train-rmse:7.255850	val-rmse:7.400788
[63]	train-rmse:7.210669	val-rmse:7.355294
[64]	train-rmse:7.165981	val-rmse:7.310040
[65]	train-rmse:7.121630	val-rmse:7.265166
[66]	train-rmse:7.078074	val-rmse:7.221290
[67]	train-rmse:7.035086	val-rmse:7.178141
[68]	train-rmse:6.993080	val-rmse:7.135776
[69]	train-rmse:6.951047	val-rmse:7.093567
[70]	train-rmse:6.909626	val-rmse:7.051881
[71]	train-rmse:6.869378	val-rmse:7.011499
[72]	train-rmse:6.829181	val-rmse:6.971206
[73]	train-rmse:6.789486	val-rmse:6.930777
[74]	train-rmse:6.750058	val-rmse:6.891233
[75]	train-rmse:6.710716	val-rmse:6.851651
[76]	train-rmse:6.672447	val-rmse:6.813017
[77]	train-rmse:6.634272	val-rmse:6.774973
[78]	train-rmse:6.596488	val-rmse:6.736772
[79]	train-rmse:6.559574	val-rmse:6.699841
[80]	train-rmse:6.522688	val-rmse:6.662411
[81]	train-rmse:6.486504	val-rmse:6.625845
[82]	train-rmse:6.451230	val-rmse:6.590301
[83]	train-rmse:6.415155	val-rmse:6.553990
[84]	train-rmse:6.380031	val-rmse:6.518972
[85]	train-rmse:6.345704	val-rmse:6.484279
[86]	train-rmse:6.311529	val-rmse:6.449943
[87]	train-rmse:6.277737	val-rmse:6.416045
[88]	train-rmse:6.244621	val-rmse:6.382372
[89]	train-rmse:6.211464	val-rmse:6.349411
[90]	train-rmse:6.179574	val-rmse:6.317398
[91]	train-rmse:6.148008	val-rmse:6.285753
[92]	train-rmse:6.116827	val-rmse:6.254164
[93]	train-rmse:6.085588	val-rmse:6.222544
[94]	train-rmse:6.055392	val-rmse:6.192102
[95]	train-rmse:6.024465	val-rmse:6.161241
[96]	train-rmse:5.994828	val-rmse:6.131451
[97]	train-rmse:5.965024	val-rmse:6.101520
[98]	train-rmse:5.936260	val-rmse:6.072193
[99]	train-rmse:5.906865	val-rmse:6.042837
[100]	train-rmse:5.878117	val-rmse:6.013616
[101]	train-rmse:5.849909	val-rmse:5.985036
[102]	train-rmse:5.822598	val-rmse:5.957683
[103]	train-rmse:5.794988	val-rmse:5.930198
[104]	train-rmse:5.768232	val-rmse:5.903271
[105]	train-rmse:5.740964	val-rmse:5.876062
[106]	train-rmse:5.714239	val-rmse:5.849265
[107]	train-rmse:5.688502	val-rmse:5.823516
[108]	train-rmse:5.663200	val-rmse:5.797785
[109]	train-rmse:5.638158	val-rmse:5.772784
[110]	train-rmse:5.612610	val-rmse:5.747009
[111]	train-rmse:5.587595	val-rmse:5.721837
[112]	train-rmse:5.563134	val-rmse:5.697262
[113]	train-rmse:5.539075	val-rmse:5.673138
[114]	train-rmse:5.515770	val-rmse:5.649637
[115]	train-rmse:5.492365	val-rmse:5.626124
[116]	train-rmse:5.469679	val-rmse:5.603441
[117]	train-rmse:5.447266	val-rmse:5.581019
[118]	train-rmse:5.424981	val-rmse:5.558619
[119]	train-rmse:5.403025	val-rmse:5.536461
[120]	train-rmse:5.381306	val-rmse:5.514720
[121]	train-rmse:5.359980	val-rmse:5.493241
[122]	train-rmse:5.338612	val-rmse:5.471810
[123]	train-rmse:5.316825	val-rmse:5.450174
[124]	train-rmse:5.295835	val-rmse:5.428816
[125]	train-rmse:5.275197	val-rmse:5.408215
[126]	train-rmse:5.254004	val-rmse:5.386556
[127]	train-rmse:5.234472	val-rmse:5.366716
[128]	train-rmse:5.213769	val-rmse:5.346353
[129]	train-rmse:5.194470	val-rmse:5.327252
[130]	train-rmse:5.174590	val-rmse:5.307080
[131]	train-rmse:5.155139	val-rmse:5.287448
[132]	train-rmse:5.136289	val-rmse:5.268632
[133]	train-rmse:5.117630	val-rmse:5.250028
[134]	train-rmse:5.099591	val-rmse:5.232044
[135]	train-rmse:5.080525	val-rmse:5.212737
[136]	train-rmse:5.062132	val-rmse:5.193926
[137]	train-rmse:5.044414	val-rmse:5.176353
[138]	train-rmse:5.026895	val-rmse:5.158984
[139]	train-rmse:5.010222	val-rmse:5.142392
[140]	train-rmse:4.993148	val-rmse:5.125398
[141]	train-rmse:4.976615	val-rmse:5.108908
[142]	train-rmse:4.960152	val-rmse:5.092420
[143]	train-rmse:4.943490	val-rmse:5.075904
[144]	train-rmse:4.927501	val-rmse:5.059897
[145]	train-rmse:4.911263	val-rmse:5.043758
[146]	train-rmse:4.895329	val-rmse:5.028041
[147]	train-rmse:4.880358	val-rmse:5.012979
[148]	train-rmse:4.865286	val-rmse:4.998021
[149]	train-rmse:4.849907	val-rmse:4.982947
[150]	train-rmse:4.835485	val-rmse:4.968807
[151]	train-rmse:4.820704	val-rmse:4.953784
[152]	train-rmse:4.806257	val-rmse:4.939498
[153]	train-rmse:4.791951	val-rmse:4.925125
[154]	train-rmse:4.777924	val-rmse:4.910904
[155]	train-rmse:4.763650	val-rmse:4.896841
[156]	train-rmse:4.749698	val-rmse:4.883020
[157]	train-rmse:4.735842	val-rmse:4.869585
[158]	train-rmse:4.721500	val-rmse:4.855488
[159]	train-rmse:4.708182	val-rmse:4.842100
[160]	train-rmse:4.694930	val-rmse:4.828897
[161]	train-rmse:4.681375	val-rmse:4.815401
[162]	train-rmse:4.668317	val-rmse:4.802428
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[2881]	train-rmse:1.144122	val-rmse:1.917917
[2882]	train-rmse:1.143980	val-rmse:1.917855
[2883]	train-rmse:1.143820	val-rmse:1.917790
[2884]	train-rmse:1.143350	val-rmse:1.917565
[2885]	train-rmse:1.143109	val-rmse:1.917418
[2886]	train-rmse:1.142899	val-rmse:1.917316
[2887]	train-rmse:1.142302	val-rmse:1.917003
[2888]	train-rmse:1.142047	val-rmse:1.916855
[2889]	train-rmse:1.141611	val-rmse:1.916605
[2890]	train-rmse:1.141469	val-rmse:1.916535
[2891]	train-rmse:1.141167	val-rmse:1.916395
[2892]	train-rmse:1.140743	val-rmse:1.916181
[2893]	train-rmse:1.140235	val-rmse:1.915992
[2894]	train-rmse:1.139899	val-rmse:1.915851
[2895]	train-rmse:1.139239	val-rmse:1.915494
[2896]	train-rmse:1.138757	val-rmse:1.915210
[2897]	train-rmse:1.138463	val-rmse:1.915036
[2898]	train-rmse:1.138302	val-rmse:1.914983
[2899]	train-rmse:1.138152	val-rmse:1.914901
[2900]	train-rmse:1.138029	val-rmse:1.914846
[2901]	train-rmse:1.137200	val-rmse:1.914381
[2902]	train-rmse:1.136985	val-rmse:1.914282
[2903]	train-rmse:1.136712	val-rmse:1.914139
[2904]	train-rmse:1.135962	val-rmse:1.913716
[2905]	train-rmse:1.135430	val-rmse:1.913419
[2906]	train-rmse:1.135217	val-rmse:1.913275
[2907]	train-rmse:1.134832	val-rmse:1.913077
[2908]	train-rmse:1.134587	val-rmse:1.912942
[2909]	train-rmse:1.134359	val-rmse:1.912825
[2910]	train-rmse:1.134202	val-rmse:1.912732
[2911]	train-rmse:1.133879	val-rmse:1.912576
[2912]	train-rmse:1.133490	val-rmse:1.912337
[2913]	train-rmse:1.133278	val-rmse:1.912214
[2914]	train-rmse:1.132849	val-rmse:1.912011
[2915]	train-rmse:1.132709	val-rmse:1.911920
[2916]	train-rmse:1.132295	val-rmse:1.911717
[2917]	train-rmse:1.132111	val-rmse:1.911599
[2918]	train-rmse:1.131616	val-rmse:1.911324
[2919]	train-rmse:1.131482	val-rmse:1.911269
[2920]	train-rmse:1.130998	val-rmse:1.910993
[2921]	train-rmse:1.130869	val-rmse:1.910960
[2922]	train-rmse:1.130718	val-rmse:1.910917
[2923]	train-rmse:1.130382	val-rmse:1.910732
[2924]	train-rmse:1.130098	val-rmse:1.910618
[2925]	train-rmse:1.129768	val-rmse:1.910446
[2926]	train-rmse:1.129535	val-rmse:1.910326
[2927]	train-rmse:1.129405	val-rmse:1.910277
[2928]	train-rmse:1.129034	val-rmse:1.910064
[2929]	train-rmse:1.128774	val-rmse:1.909943
[2930]	train-rmse:1.128474	val-rmse:1.909788
[2931]	train-rmse:1.128153	val-rmse:1.909622
[2932]	train-rmse:1.127496	val-rmse:1.909252
[2933]	train-rmse:1.127425	val-rmse:1.909212
[2934]	train-rmse:1.127239	val-rmse:1.909108
[2935]	train-rmse:1.127025	val-rmse:1.909003
[2936]	train-rmse:1.126396	val-rmse:1.908720
[2937]	train-rmse:1.125974	val-rmse:1.908546
[2938]	train-rmse:1.125418	val-rmse:1.908280
[2939]	train-rmse:1.125313	val-rmse:1.908221
[2940]	train-rmse:1.125199	val-rmse:1.908151
[2941]	train-rmse:1.124689	val-rmse:1.907925
[2942]	train-rmse:1.124564	val-rmse:1.907879
[2943]	train-rmse:1.124440	val-rmse:1.907840
[2944]	train-rmse:1.124329	val-rmse:1.907777
[2945]	train-rmse:1.124091	val-rmse:1.907700
[2946]	train-rmse:1.123652	val-rmse:1.907527
[2947]	train-rmse:1.123401	val-rmse:1.907414
[2948]	train-rmse:1.123246	val-rmse:1.907334
[2949]	train-rmse:1.123143	val-rmse:1.907282
[2950]	train-rmse:1.122809	val-rmse:1.907152
[2951]	train-rmse:1.122323	val-rmse:1.906884
[2952]	train-rmse:1.122161	val-rmse:1.906812
[2953]	train-rmse:1.122035	val-rmse:1.906741
[2954]	train-rmse:1.121827	val-rmse:1.906609
[2955]	train-rmse:1.121544	val-rmse:1.906468
[2956]	train-rmse:1.121307	val-rmse:1.906343
[2957]	train-rmse:1.120659	val-rmse:1.905958
[2958]	train-rmse:1.120300	val-rmse:1.905802
[2959]	train-rmse:1.119813	val-rmse:1.905557
[2960]	train-rmse:1.119510	val-rmse:1.905405
[2961]	train-rmse:1.119169	val-rmse:1.905234
[2962]	train-rmse:1.118878	val-rmse:1.905065
[2963]	train-rmse:1.118263	val-rmse:1.904752
[2964]	train-rmse:1.117974	val-rmse:1.904590
[2965]	train-rmse:1.117837	val-rmse:1.904518
[2966]	train-rmse:1.117594	val-rmse:1.904426
[2967]	train-rmse:1.117226	val-rmse:1.904248
[2968]	train-rmse:1.116768	val-rmse:1.904002
[2969]	train-rmse:1.116510	val-rmse:1.903869
[2970]	train-rmse:1.116232	val-rmse:1.903743
[2971]	train-rmse:1.115979	val-rmse:1.903588
[2972]	train-rmse:1.115380	val-rmse:1.903283
[2973]	train-rmse:1.115098	val-rmse:1.903131
[2974]	train-rmse:1.114872	val-rmse:1.902996
[2975]	train-rmse:1.114313	val-rmse:1.902712
[2976]	train-rmse:1.113842	val-rmse:1.902476
[2977]	train-rmse:1.113484	val-rmse:1.902304
[2978]	train-rmse:1.113259	val-rmse:1.902230
[2979]	train-rmse:1.112554	val-rmse:1.901920
[2980]	train-rmse:1.112334	val-rmse:1.901756
[2981]	train-rmse:1.112227	val-rmse:1.901716
[2982]	train-rmse:1.112079	val-rmse:1.901635
[2983]	train-rmse:1.111728	val-rmse:1.901404
[2984]	train-rmse:1.111145	val-rmse:1.901106
[2985]	train-rmse:1.110774	val-rmse:1.900968
[2986]	train-rmse:1.110170	val-rmse:1.900674
[2987]	train-rmse:1.109895	val-rmse:1.900536
[2988]	train-rmse:1.109473	val-rmse:1.900335
[2989]	train-rmse:1.109084	val-rmse:1.900147
[2990]	train-rmse:1.108722	val-rmse:1.899970
[2991]	train-rmse:1.108266	val-rmse:1.899706
[2992]	train-rmse:1.107821	val-rmse:1.899486
[2993]	train-rmse:1.107690	val-rmse:1.899420
[2994]	train-rmse:1.107382	val-rmse:1.899257
[2995]	train-rmse:1.107074	val-rmse:1.899088
[2996]	train-rmse:1.106919	val-rmse:1.899021
[2997]	train-rmse:1.106617	val-rmse:1.898885
[2998]	train-rmse:1.106498	val-rmse:1.898817
[2999]	train-rmse:1.105964	val-rmse:1.898553

In [14]:
xgb.plot_importance(model)

## importance: f1,f15,f0,f2,f18,f16,f27,f13
col_k = [1,15,0,2,18,16,27,13,3,4,26,9,25,20,11,5,19,8,17,12,14]

print columns[col_k]


Index([u'T1_V2', u'T2_V1', u'T1_V1', u'T1_V3', u'T2_V4', u'T2_V2', u'T2_V15',
       u'T1_V16', u'T1_V4', u'T1_V5', u'T2_V14', u'T1_V11', u'T2_V13',
       u'T2_V6', u'T1_V14', u'T1_V6', u'T2_V5', u'T1_V9', u'T2_V3', u'T1_V15',
       u'T1_V17'],
      dtype='object')

In [84]:
print 'Submit'
preds1 = model.predict(xgsubmit)

# generate solution file
preds = pd.DataFrame({"Id": submit_ind, "Hazard": preds1})
preds = preds.set_index('Id')
print preds.head()
preds.to_csv('submit_dev_20150824_1.csv')

print 'Computing lables in test data'
preds2 = model.predict(xgtest)

# generate solution file
preds = pd.DataFrame({"Hazard": test_labels, "Model_Hazard": preds2})
preds = preds.set_index('Hazard')
print preds.head(11)

print 'Gini (Duvidoso)'
print Gini1(test_labels,preds2)
print Gini2(test_labels,preds2)

print 'rmse' 
print np.sqrt( np.mean((test_labels - preds2)**2) )

preds.to_csv('test_dev_20150824_1.csv')

print 'Current dir:'
%pwd


Submit
       Hazard
Id           
6    3.066651
7    9.934229
8   12.975117
9    6.175351
10   4.229500
Computing lables in test data
        Model_Hazard
Hazard              
22          8.750013
37         11.065730
23          7.737857
23          9.968647
22          8.284258
Gini (Duvidoso)
0.166500195883
0.141624442974
rmse
19.6027104661
Current dir:
Out[84]:
u'/home/leandroohf/Documents/kaggle/Liberty_Mutual_Group_Property_Inspection_Prediction/dev'