Alien-XGBoost
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xgboost/demo/binary_classification/README.md view on Meta::CPAN
Run the command again, we can find the log file becomes
```
[0] test-error:0.016139 trainname-error:0.014433
[1] test-error:0.000000 trainname-error:0.001228
```
The rule is eval[name-printed-in-log] = filename, then the file will be added to monitoring process, and evaluated each round.
xgboost also supports monitoring multiple metrics, suppose we also want to monitor average log-likelihood of each prediction during training, simply add ```eval_metric=logloss``` to configure. Run again, we can find the log file becomes
```
[0] test-error:0.016139 test-negllik:0.029795 trainname-error:0.014433 trainname-negllik:0.027023
[1] test-error:0.000000 test-negllik:0.000000 trainname-error:0.001228 trainname-negllik:0.002457
```
### Saving Progress Models
If you want to save model every two round, simply set save_period=2. You will find 0002.model in the current folder. If you want to change the output folder of models, add model_dir=foldername. By default xgboost saves the model of last round.
#### Continue from Existing Model
If you want to continue boosting from existing model, say 0002.model, use
```
../../xgboost mushroom.conf model_in=0002.model num_round=2 model_out=continue.model
```
xgboost will load from 0002.model continue boosting for 2 rounds, and save output to continue.model. However, beware that the training and evaluation data specified in mushroom.conf should not change when you use this function.
xgboost/demo/regression/machine.data view on Meta::CPAN
amdahl,470v/7,29,8000,32000,32,8,32,269,253
amdahl,470v/7a,29,8000,32000,32,8,32,220,253
amdahl,470v/7b,29,8000,32000,32,8,32,172,253
amdahl,470v/7c,29,8000,16000,32,8,16,132,132
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amdahl,580-5880,23,32000,64000,128,32,64,1144,1238
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burroughs,b2925,143,1000,2000,0,5,16,31,22
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burroughs,b5900,143,1500,6300,0,5,32,30,35
burroughs,b5920,143,3100,6200,0,5,20,33,39
xgboost/demo/regression/machine.data view on Meta::CPAN
honeywell,dps:7/45,330,1000,4000,0,3,6,22,25
honeywell,dps:7/55,140,2000,4000,0,3,6,29,28
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xgboost/demo/regression/machine.data view on Meta::CPAN
ibm,4361-4,25,2000,12000,8,1,4,49,59
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ipl,4436,50,2000,4000,0,3,6,27,30
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ipl,4446,50,2000,16000,24,1,6,70,82
ipl,4460,50,2000,16000,24,1,6,80,82
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magnuson,m80/43,50,2000,16000,24,6,16,54,88
magnuson,m80/44,50,2000,16000,24,6,16,65,88
microdata,seq.ms/3200,150,512,4000,0,8,128,30,33
nas,as/3000,115,2000,8000,16,1,3,50,46
nas,as/3000-n,115,2000,4000,2,1,5,40,29
nas,as/5000,92,2000,8000,32,1,6,62,53
nas,as/5000-e,92,2000,8000,32,1,6,60,53
nas,as/5000-n,92,2000,8000,4,1,6,50,41
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nas,as/7000-n,72,2000,8000,16,6,8,105,48
nas,as/8040,40,8000,16000,32,8,16,214,126
nas,as/8050,40,8000,32000,64,8,24,277,266
nas,as/8060,35,8000,32000,64,8,24,370,270
nas,as/9000-dpc,38,16000,32000,128,16,32,510,426
nas,as/9000-n,48,4000,24000,32,8,24,214,151
nas,as/9040,38,8000,32000,64,8,24,326,267
nas,as/9060,30,16000,32000,256,16,24,510,603
ncr,v8535:ii,112,1000,1000,0,1,4,8,19
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ncr,v8555:ii,56,1000,4000,0,1,6,17,26
ncr,v8565:ii,56,2000,6000,0,1,8,21,35
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prime,50-2250,160,512,4000,2,1,5,30,25
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siemens,7.561,52,4000,16000,32,4,12,130,99
siemens,7.865-2,70,4000,12000,8,6,8,75,67
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siemens,7.872-2,59,8000,16000,64,12,24,188,149
siemens,7.875-2,26,8000,24000,32,8,16,173,183
siemens,7.880-2,26,8000,32000,64,12,16,248,275
siemens,7.881-2,26,8000,32000,128,24,32,405,382
sperry,1100/61-h1,116,2000,8000,32,5,28,70,56
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xgboost/jvm-packages/xgboost4j-spark/src/test/resources/dermatology.data view on Meta::CPAN
2,1,0,2,0,0,0,0,0,0,0,0,0,0,3,1,2,1,0,0,2,0,0,0,0,0,0,0,0,0,0,1,0,8,5
2,1,1,3,0,0,1,0,0,0,0,0,0,0,2,0,2,0,0,0,3,0,0,0,0,0,0,0,0,0,0,2,0,19,5
1,1,2,2,0,0,0,0,1,0,0,0,1,0,3,0,3,2,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,36,5
2,1,0,3,0,0,0,0,0,0,0,0,0,0,3,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,70,5
2,2,1,3,0,0,0,0,0,0,0,0,0,0,3,0,2,1,0,0,3,0,0,0,0,0,0,0,0,0,0,2,0,52,5
2,2,3,3,1,2,0,1,0,0,0,2,0,0,0,2,2,0,1,0,0,0,0,0,1,0,2,0,2,0,0,2,3,25,3
3,2,2,2,0,2,0,2,0,0,0,3,0,0,0,3,2,0,1,0,0,0,0,0,1,0,2,0,3,0,0,3,3,36,3
2,2,2,3,1,2,0,1,0,0,0,2,0,0,0,2,2,0,1,0,0,0,0,0,1,0,2,0,3,0,0,1,2,50,3
2,1,2,0,1,0,0,0,0,0,0,0,0,0,0,2,2,0,1,0,0,0,0,0,0,0,0,2,0,0,0,2,0,34,4
3,1,2,1,0,0,0,0,2,3,0,0,0,0,0,0,2,0,2,3,2,2,0,3,0,2,0,0,0,0,0,2,0,17,1
2,2,2,0,1,0,0,0,2,1,0,0,0,0,0,0,3,1,1,3,2,2,0,2,0,0,0,0,0,0,0,2,0,24,1
2,1,1,0,1,0,0,0,0,0,0,0,0,0,0,3,1,0,0,0,0,0,0,0,0,1,0,3,0,0,0,2,0,22,4
2,1,2,1,1,0,0,0,0,0,0,0,0,0,0,2,2,0,1,0,0,0,0,0,0,1,0,3,0,0,0,2,0,55,4
2,1,1,0,1,0,0,0,0,0,0,0,0,0,0,3,2,0,0,0,0,0,0,0,0,0,0,2,0,0,0,2,0,12,4
2,3,2,0,1,0,0,0,0,1,0,0,0,0,0,0,2,0,2,3,3,2,0,2,0,2,0,0,0,0,0,2,0,43,1
3,2,2,0,0,0,0,0,0,1,1,0,0,0,0,0,3,0,2,2,3,2,0,1,0,2,0,0,0,0,0,2,0,50,1
2,2,2,1,0,0,0,0,2,2,0,0,0,0,0,0,2,0,2,2,3,2,0,0,0,2,0,0,0,0,0,2,0,36,1
2,2,3,3,2,3,0,1,0,0,0,2,0,0,0,3,2,0,1,0,0,0,0,0,2,0,3,0,3,0,0,2,3,26,3
3,1,2,3,2,2,0,2,0,0,0,2,0,0,0,2,2,0,1,0,0,0,0,0,2,0,2,0,2,0,0,3,3,16,3
2,2,2,3,2,3,0,2,0,0,0,2,0,0,0,2,2,0,0,0,0,0,0,0,3,0,2,0,2,0,0,2,3,32,3
2,1,2,3,3,2,0,2,0,0,0,3,0,0,0,3,1,0,0,0,0,0,0,0,2,0,3,0,2,0,0,2,3,51,3
xgboost/jvm-packages/xgboost4j-spark/src/test/resources/dermatology.data view on Meta::CPAN
3,2,2,0,0,0,3,0,1,0,1,0,0,0,0,1,3,1,1,0,0,0,0,0,0,0,0,2,0,3,2,2,0,10,6
2,2,1,0,0,0,2,0,2,0,0,0,0,0,0,3,2,0,1,0,0,0,0,0,0,0,0,3,0,2,2,2,0,7,6
1,2,2,2,0,0,0,0,2,2,0,0,0,1,0,0,2,1,3,3,3,2,0,2,0,2,0,0,0,0,0,2,0,25,1
2,2,2,3,2,0,0,0,2,3,1,0,0,1,0,0,2,2,2,2,2,2,0,2,0,3,0,0,0,0,0,2,0,9,1
3,2,2,3,2,0,0,0,2,3,0,0,0,0,0,0,3,0,2,2,3,2,0,3,0,2,0,0,0,0,0,1,0,55,1
1,1,1,2,0,0,0,0,0,0,0,0,0,0,3,2,2,0,0,0,2,0,0,0,0,0,0,1,0,0,0,2,0,45,5
2,0,1,2,0,0,0,0,0,0,0,0,0,0,3,1,2,0,0,0,2,0,0,0,0,0,0,0,0,0,0,2,0,56,5
2,3,2,3,2,0,0,0,3,2,0,0,0,1,0,0,3,2,3,2,2,2,0,3,0,3,0,0,0,0,0,0,0,36,1
2,2,2,2,2,0,0,0,3,0,1,0,0,0,0,0,2,2,2,2,3,3,0,2,0,3,0,0,0,0,0,0,0,75,1
2,2,2,0,1,0,0,0,0,1,0,0,0,1,0,0,2,0,2,3,2,3,2,1,0,1,0,0,0,0,0,2,0,45,1
2,3,2,1,0,0,0,0,2,2,0,0,0,1,0,0,2,0,2,2,2,2,0,2,0,2,0,0,0,0,0,3,0,24,1
2,2,0,0,0,0,0,0,0,0,0,0,1,0,0,3,2,0,0,0,0,0,0,0,0,0,0,2,0,0,0,1,0,40,2
2,2,1,1,0,0,0,0,0,0,0,0,1,1,0,2,2,1,0,0,0,0,0,0,0,0,0,3,0,0,0,1,0,25,2
2,1,1,0,1,0,0,0,0,0,0,0,0,1,0,1,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,2,0,25,4
3,2,1,0,1,0,0,0,0,0,0,0,0,0,0,2,2,0,0,0,0,0,0,0,0,1,0,1,0,0,0,2,0,36,4
3,2,2,2,3,2,0,2,0,0,0,2,2,0,0,3,3,0,0,0,0,0,0,0,3,0,3,0,3,0,0,2,3,28,3
2,1,3,1,2,3,0,2,0,0,0,2,0,0,0,3,2,0,0,0,0,0,0,0,3,0,2,0,1,0,0,2,3,50,3
3,2,2,0,0,0,0,0,3,3,0,0,0,1,0,0,2,0,2,3,2,3,0,2,0,2,0,0,0,0,0,3,0,35,1
xgboost/jvm-packages/xgboost4j-spark/src/test/resources/rank-demo-0.txt.train view on Meta::CPAN
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xgboost/jvm-packages/xgboost4j-spark/src/test/resources/rank-demo-0.txt.train view on Meta::CPAN
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xgboost/jvm-packages/xgboost4j-spark/src/test/resources/rank-demo-1.txt.train view on Meta::CPAN
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xgboost/jvm-packages/xgboost4j-spark/src/test/resources/rank-demo.txt.test view on Meta::CPAN
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0 1:10.0449715742 2:8.40373928536 3:0.384457573667
1 1:996.398498791 2:941.976309154 3:0.230269231292
0 1:1005.11269468 2:900.093680877 3:0.265031528873
0 1:997.160349441 2:891.331101688 3:2.19362017313
0 1:993.754139031 2:44.8000165317 3:1.03868009875
1 1:994.831299184 2:241.959208453 3:0.667631827024
0 1:995.948333283 2:7.94326917112 3:0.750490877118
0 1:989.733981273 2:7.52077625436 3:0.0126335967282
0 1:1003.54086516 2:6.48177510564 3:1.19441696788
0 1:996.56177804 2:9.71959812613 3:1.33082465111
0 1:1005.61382467 2:0.234339369309 3:1.17987797356
1 1:980.215758708 2:6.85554542926 3:2.63965085259
1 1:987.776408872 2:2.23354609991 3:0.841885278028
0 1:1006.54260396 2:8.12142049834 3:2.26639471174
0 1:1009.87927639 2:6.40028519044 3:0.775155669615
0 1:9.95006244393 2:928.76896718 3:234.948458244
1 1:10.0749152258 2:255.294574476 3:62.9728604166
1 1:10.1916541988 2:312.682867085 3:92.299413677
0 1:9.95646724484 2:742.263188416 3:53.3310473654
0 1:9.86211293222 2:996.237023866 3:2.00760301168
1 1:9.91801019468 2:303.971783709 3:50.3147230679
0 1:996.983996934 2:9.52188222766 3:1.33588120981
0 1:995.704388126 2:9.49260524915 3:0.908498516541
0 1:987.86480767 2:0.0870786716821 3:0.108859297837
0 1:1000.99561307 2:2.85272694575 3:0.171134518956
0 1:1011.05508066 2:7.55336771768 3:1.04950084825
1 1:985.52199365 2:0.763305780608 3:1.7402424375
0 1:10.0430321467 2:813.185427181 3:4.97728254185
0 1:10.0812334228 2:258.297288417 3:0.127477670549
0 1:9.84210504292 2:887.205815261 3:0.991689193955
1 1:9.94625332613 2:0.298622762132 3:0.147881353231
0 1:9.97800659954 2:727.619819757 3:0.0718361141866
1 1:9.8037938472 2:957.385549617 3:0.0618862028941
xgboost/tests/cpp/objective/test_regression_obj.cc view on Meta::CPAN
TEST(Objective, LogisticRegressionGPair) {
xgboost::ObjFunction * obj = xgboost::ObjFunction::Create("reg:logistic");
std::vector<std::pair<std::string, std::string> > args;
obj->Configure(args);
CheckObjFunction(obj,
{ 0, 0.1f, 0.9f, 1, 0, 0.1f, 0.9f, 1},
{ 0, 0, 0, 0, 1, 1, 1, 1},
{ 1, 1, 1, 1, 1, 1, 1, 1},
{ 0.5f, 0.52f, 0.71f, 0.73f, -0.5f, -0.47f, -0.28f, -0.26f},
{0.25f, 0.24f, 0.20f, 0.19f, 0.25f, 0.24f, 0.20f, 0.19f});
}
TEST(Objective, LogisticRegressionBasic) {
xgboost::ObjFunction * obj = xgboost::ObjFunction::Create("reg:logistic");
std::vector<std::pair<std::string, std::string> > args;
obj->Configure(args);
// test label validation
EXPECT_ANY_THROW(CheckObjFunction(obj, {0}, {10}, {1}, {0}, {0}))
<< "Expected error when label not in range [0,1f] for LogisticRegression";
xgboost/tests/cpp/objective/test_regression_obj.cc view on Meta::CPAN
TEST(Objective, LogisticRawGPair) {
xgboost::ObjFunction * obj = xgboost::ObjFunction::Create("binary:logitraw");
std::vector<std::pair<std::string, std::string> > args;
obj->Configure(args);
CheckObjFunction(obj,
{ 0, 0.1f, 0.9f, 1, 0, 0.1f, 0.9f, 1},
{ 0, 0, 0, 0, 1, 1, 1, 1},
{ 1, 1, 1, 1, 1, 1, 1, 1},
{ 0.5f, 0.52f, 0.71f, 0.73f, -0.5f, -0.47f, -0.28f, -0.26f},
{0.25f, 0.24f, 0.20f, 0.19f, 0.25f, 0.24f, 0.20f, 0.19f});
}
TEST(Objective, PoissonRegressionGPair) {
xgboost::ObjFunction * obj = xgboost::ObjFunction::Create("count:poisson");
std::vector<std::pair<std::string, std::string> > args;
args.push_back(std::make_pair("max_delta_step", "0.1f"));
obj->Configure(args);
CheckObjFunction(obj,
{ 0, 0.1f, 0.9f, 1, 0, 0.1f, 0.9f, 1},
{ 0, 0, 0, 0, 1, 1, 1, 1},
( run in 1.437 second using v1.01-cache-2.11-cpan-7add2cbd662 )