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>
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)
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
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
[163] train-rmse:4.655665 val-rmse:4.789880
[164] train-rmse:4.643551 val-rmse:4.778129
[165] train-rmse:4.631861 val-rmse:4.766302
[166] train-rmse:4.619789 val-rmse:4.753952
[167] train-rmse:4.608164 val-rmse:4.742253
[168] train-rmse:4.596863 val-rmse:4.730800
[169] train-rmse:4.585907 val-rmse:4.719892
[170] train-rmse:4.574670 val-rmse:4.708724
[171] train-rmse:4.562812 val-rmse:4.697246
[172] train-rmse:4.550556 val-rmse:4.684882
[173] train-rmse:4.538766 val-rmse:4.673412
[174] train-rmse:4.526935 val-rmse:4.661990
[175] train-rmse:4.516529 val-rmse:4.651709
[176] train-rmse:4.505424 val-rmse:4.640927
[177] train-rmse:4.494192 val-rmse:4.629838
[178] train-rmse:4.483749 val-rmse:4.619508
[179] train-rmse:4.473160 val-rmse:4.609185
[180] train-rmse:4.461061 val-rmse:4.597197
[181] train-rmse:4.451606 val-rmse:4.587699
[182] train-rmse:4.441091 val-rmse:4.577253
[183] train-rmse:4.431334 val-rmse:4.567564
[184] train-rmse:4.420961 val-rmse:4.557477
[185] train-rmse:4.411724 val-rmse:4.548355
[186] train-rmse:4.401867 val-rmse:4.538667
[187] train-rmse:4.392283 val-rmse:4.529509
[188] train-rmse:4.382382 val-rmse:4.519881
[189] train-rmse:4.372919 val-rmse:4.510668
[190] train-rmse:4.363767 val-rmse:4.501463
[191] train-rmse:4.354137 val-rmse:4.492111
[192] train-rmse:4.344435 val-rmse:4.482800
[193] train-rmse:4.334926 val-rmse:4.473263
[194] train-rmse:4.325314 val-rmse:4.463747
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[2814] train-rmse:1.166234 val-rmse:1.929512
[2815] train-rmse:1.166082 val-rmse:1.929403
[2816] train-rmse:1.165928 val-rmse:1.929307
[2817] train-rmse:1.165401 val-rmse:1.929083
[2818] train-rmse:1.164836 val-rmse:1.928827
[2819] train-rmse:1.164606 val-rmse:1.928698
[2820] train-rmse:1.164466 val-rmse:1.928613
[2821] train-rmse:1.163877 val-rmse:1.928316
[2822] train-rmse:1.163651 val-rmse:1.928209
[2823] train-rmse:1.163214 val-rmse:1.927953
[2824] train-rmse:1.163100 val-rmse:1.927895
[2825] train-rmse:1.162336 val-rmse:1.927440
[2826] train-rmse:1.161927 val-rmse:1.927151
[2827] train-rmse:1.161763 val-rmse:1.927059
[2828] train-rmse:1.161537 val-rmse:1.926965
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[2833] train-rmse:1.160660 val-rmse:1.926606
[2834] train-rmse:1.160218 val-rmse:1.926384
[2835] train-rmse:1.160019 val-rmse:1.926250
[2836] train-rmse:1.159550 val-rmse:1.926022
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[2870] train-rmse:1.147993 val-rmse:1.920012
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[2877] train-rmse:1.145716 val-rmse:1.918781
[2878] train-rmse:1.145153 val-rmse:1.918434
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[2880] train-rmse:1.144800 val-rmse:1.918214
[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
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[2972] train-rmse:1.115380 val-rmse:1.903283
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[2983] train-rmse:1.111728 val-rmse:1.901404
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[2985] train-rmse:1.110774 val-rmse:1.900968
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[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'
Content source: leandroohf/Public_Liberty_Mutual_Group_Property_Inspection_Prediction
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