{'colsample_bytree': 0.6215866306140633, 'eval_metric': 'logloss', 'max_delta_step': 9.0, 'nthread': 16, 'min_child_weight': 5.0, 'subsample': 0.2046354209730657, 'eta': 0.1, 'base_score': 0.2, 'objective': 'binary:logistic', 'alpha': 0.001, 'max_depth': 4, 'gamma': 0.4, 'lambda': 10}
[0] train-logloss:0.457755 valid-logloss:0.458087
Multiple eval metrics have been passed: 'valid-logloss' will be used for early stopping.
Will train until valid-logloss hasn't improved in 30 rounds.
[500] train-logloss:0.169888 valid-logloss:0.183592
Stopping. Best iteration:
[839] train-logloss:0.160773 valid-logloss:0.181412
{'colsample_bytree': 0.4942310527676672, 'eval_metric': 'logloss', 'max_delta_step': 8.0, 'nthread': 16, 'min_child_weight': 8.0, 'subsample': 0.6688748954090619, 'eta': 0.1, 'base_score': 0.2, 'objective': 'binary:logistic', 'alpha': 0.005, 'max_depth': 7, 'gamma': 0, 'lambda': 0.1}
[0] train-logloss:0.452926 valid-logloss:0.4535
Multiple eval metrics have been passed: 'valid-logloss' will be used for early stopping.
Will train until valid-logloss hasn't improved in 30 rounds.
[500] train-logloss:0.119825 valid-logloss:0.175848
Stopping. Best iteration:
[487] train-logloss:0.121232 valid-logloss:0.175664
{'colsample_bytree': 0.2262418681771136, 'eval_metric': 'logloss', 'max_delta_step': 10.0, 'nthread': 16, 'min_child_weight': 5.0, 'subsample': 0.6400081544667823, 'eta': 0.1, 'base_score': 0.2, 'objective': 'binary:logistic', 'alpha': 0.1, 'max_depth': 14, 'gamma': 0.1, 'lambda': 0.1}
[0] train-logloss:0.449829 valid-logloss:0.452885
Multiple eval metrics have been passed: 'valid-logloss' will be used for early stopping.
Will train until valid-logloss hasn't improved in 30 rounds.
Stopping. Best iteration:
[126] train-logloss:0.07523 valid-logloss:0.181878
{'colsample_bytree': 0.6524112525169689, 'eval_metric': 'logloss', 'max_delta_step': 8.0, 'nthread': 16, 'min_child_weight': 8.0, 'subsample': 0.3723787100850201, 'eta': 0.1, 'base_score': 0.2, 'objective': 'binary:logistic', 'alpha': 0, 'max_depth': 14, 'gamma': 0.4, 'lambda': 1e-05}
[0] train-logloss:0.448619 valid-logloss:0.450279
Multiple eval metrics have been passed: 'valid-logloss' will be used for early stopping.
Will train until valid-logloss hasn't improved in 30 rounds.
Stopping. Best iteration:
[108] train-logloss:0.109754 valid-logloss:0.18066
{'colsample_bytree': 0.16650529293613403, 'eval_metric': 'logloss', 'max_delta_step': 8.0, 'nthread': 16, 'min_child_weight': 8.0, 'subsample': 0.6162541363164695, 'eta': 0.1, 'base_score': 0.2, 'objective': 'binary:logistic', 'alpha': 0.001, 'max_depth': 10, 'gamma': 0.4, 'lambda': 1}
[0] train-logloss:0.483575 valid-logloss:0.483906
Multiple eval metrics have been passed: 'valid-logloss' will be used for early stopping.
Will train until valid-logloss hasn't improved in 30 rounds.
Stopping. Best iteration:
[335] train-logloss:0.093127 valid-logloss:0.176843
{'colsample_bytree': 0.2641676058323803, 'eval_metric': 'logloss', 'max_delta_step': 5.0, 'nthread': 16, 'min_child_weight': 7.0, 'subsample': 0.8773616833314521, 'eta': 0.1, 'base_score': 0.2, 'objective': 'binary:logistic', 'alpha': 0, 'max_depth': 12, 'gamma': 0, 'lambda': 10}
[0] train-logloss:0.453449 valid-logloss:0.454672
Multiple eval metrics have been passed: 'valid-logloss' will be used for early stopping.
Will train until valid-logloss hasn't improved in 30 rounds.
Stopping. Best iteration:
[195] train-logloss:0.094001 valid-logloss:0.176301
{'colsample_bytree': 0.8722642189983841, 'eval_metric': 'logloss', 'max_delta_step': 2.0, 'nthread': 16, 'min_child_weight': 5.0, 'subsample': 0.7678583799309385, 'eta': 0.1, 'base_score': 0.2, 'objective': 'binary:logistic', 'alpha': 10, 'max_depth': 9, 'gamma': 0.1, 'lambda': 100}
[0] train-logloss:0.476353 valid-logloss:0.476386
Multiple eval metrics have been passed: 'valid-logloss' will be used for early stopping.
Will train until valid-logloss hasn't improved in 30 rounds.
[500] train-logloss:0.106879 valid-logloss:0.173537
Stopping. Best iteration:
[540] train-logloss:0.102536 valid-logloss:0.173374
{'colsample_bytree': 0.5681441209744663, 'eval_metric': 'logloss', 'max_delta_step': 4.0, 'nthread': 16, 'min_child_weight': 8.0, 'subsample': 0.3793264896708848, 'eta': 0.1, 'base_score': 0.2, 'objective': 'binary:logistic', 'alpha': 0.001, 'max_depth': 11, 'gamma': 0.2, 'lambda': 0.05}
[0] train-logloss:0.456586 valid-logloss:0.457352
Multiple eval metrics have been passed: 'valid-logloss' will be used for early stopping.
Will train until valid-logloss hasn't improved in 30 rounds.
Stopping. Best iteration:
[172] train-logloss:0.114973 valid-logloss:0.18001
{'colsample_bytree': 0.6585666437547426, 'eval_metric': 'logloss', 'max_delta_step': 7.0, 'nthread': 16, 'min_child_weight': 6.0, 'subsample': 0.12249883737053519, 'eta': 0.1, 'base_score': 0.2, 'objective': 'binary:logistic', 'alpha': 0.1, 'max_depth': 10, 'gamma': 0.3, 'lambda': 0.05}
[0] train-logloss:0.45133 valid-logloss:0.452029
Multiple eval metrics have been passed: 'valid-logloss' will be used for early stopping.
Will train until valid-logloss hasn't improved in 30 rounds.
Stopping. Best iteration:
[120] train-logloss:0.154732 valid-logloss:0.18615
{'colsample_bytree': 0.42964353040548475, 'eval_metric': 'logloss', 'max_delta_step': 6.0, 'nthread': 16, 'min_child_weight': 9.0, 'subsample': 0.4616646995522957, 'eta': 0.1, 'base_score': 0.2, 'objective': 'binary:logistic', 'alpha': 0.005, 'max_depth': 9, 'gamma': 0.2, 'lambda': 0.01}
[0] train-logloss:0.452371 valid-logloss:0.453306
Multiple eval metrics have been passed: 'valid-logloss' will be used for early stopping.
Will train until valid-logloss hasn't improved in 30 rounds.
Stopping. Best iteration:
[256] train-logloss:0.121103 valid-logloss:0.176798
{'colsample_bytree': 0.8650546946731305, 'eval_metric': 'logloss', 'max_delta_step': 6.0, 'nthread': 16, 'min_child_weight': 8.0, 'subsample': 0.3618221703220311, 'eta': 0.1, 'base_score': 0.2, 'objective': 'binary:logistic', 'alpha': 1, 'max_depth': 5, 'gamma': 0.1, 'lambda': 0.05}
[0] train-logloss:0.455349 valid-logloss:0.455703
Multiple eval metrics have been passed: 'valid-logloss' will be used for early stopping.
Will train until valid-logloss hasn't improved in 30 rounds.
[500] train-logloss:0.154015 valid-logloss:0.179303
Stopping. Best iteration:
[581] train-logloss:0.149983 valid-logloss:0.17868
{'colsample_bytree': 0.7030797180234829, 'eval_metric': 'logloss', 'max_delta_step': 9.0, 'nthread': 16, 'min_child_weight': 7.0, 'subsample': 0.6995774779301367, 'eta': 0.1, 'base_score': 0.2, 'objective': 'binary:logistic', 'alpha': 0.05, 'max_depth': 4, 'gamma': 0.4, 'lambda': 1e-05}
[0] train-logloss:0.457347 valid-logloss:0.457678
Multiple eval metrics have been passed: 'valid-logloss' will be used for early stopping.
Will train until valid-logloss hasn't improved in 30 rounds.
[500] train-logloss:0.166804 valid-logloss:0.181617
[1000] train-logloss:0.152616 valid-logloss:0.177618
[1500] train-logloss:0.140951 valid-logloss:0.175593
Stopping. Best iteration:
[1494] train-logloss:0.141075 valid-logloss:0.175577
{'colsample_bytree': 0.2353829374379225, 'eval_metric': 'logloss', 'max_delta_step': 2.0, 'nthread': 16, 'min_child_weight': 6.0, 'subsample': 0.26935957632771823, 'eta': 0.1, 'base_score': 0.2, 'objective': 'binary:logistic', 'alpha': 0.01, 'max_depth': 8, 'gamma': 0.4, 'lambda': 0.1}
[0] train-logloss:0.47747 valid-logloss:0.477631
Multiple eval metrics have been passed: 'valid-logloss' will be used for early stopping.
Will train until valid-logloss hasn't improved in 30 rounds.
Stopping. Best iteration:
[270] train-logloss:0.140681 valid-logloss:0.180478
{'colsample_bytree': 0.6672717162125326, 'eval_metric': 'logloss', 'max_delta_step': 9.0, 'nthread': 16, 'min_child_weight': 6.0, 'subsample': 0.30740585129681897, 'eta': 0.1, 'base_score': 0.2, 'objective': 'binary:logistic', 'alpha': 0, 'max_depth': 6, 'gamma': 0.5, 'lambda': 1e-05}
[0] train-logloss:0.453928 valid-logloss:0.454313
Multiple eval metrics have been passed: 'valid-logloss' will be used for early stopping.
Will train until valid-logloss hasn't improved in 30 rounds.
[500] train-logloss:0.141136 valid-logloss:0.178575
Stopping. Best iteration:
[582] train-logloss:0.13594 valid-logloss:0.178281
{'colsample_bytree': 0.15880447956457236, 'eval_metric': 'logloss', 'max_delta_step': 8.0, 'nthread': 16, 'min_child_weight': 8.0, 'subsample': 0.1829600910862202, 'eta': 0.1, 'base_score': 0.2, 'objective': 'binary:logistic', 'alpha': 1, 'max_depth': 10, 'gamma': 0.5, 'lambda': 0.01}
[0] train-logloss:0.484776 valid-logloss:0.484909
Multiple eval metrics have been passed: 'valid-logloss' will be used for early stopping.
Will train until valid-logloss hasn't improved in 30 rounds.
Stopping. Best iteration:
[217] train-logloss:0.136896 valid-logloss:0.184117
{'colsample_bytree': 0.4328545889768428, 'eval_metric': 'logloss', 'max_delta_step': 8.0, 'nthread': 16, 'min_child_weight': 6.0, 'subsample': 0.24037830373984043, 'eta': 0.1, 'base_score': 0.2, 'objective': 'binary:logistic', 'alpha': 10, 'max_depth': 6, 'gamma': 0.2, 'lambda': 100}
[0] train-logloss:0.459882 valid-logloss:0.460301
Multiple eval metrics have been passed: 'valid-logloss' will be used for early stopping.
Will train until valid-logloss hasn't improved in 30 rounds.
[500] train-logloss:0.159745 valid-logloss:0.181588
Stopping. Best iteration:
[775] train-logloss:0.148607 valid-logloss:0.179812
{'colsample_bytree': 0.45627781942866086, 'eval_metric': 'logloss', 'max_delta_step': 7.0, 'nthread': 16, 'min_child_weight': 8.0, 'subsample': 0.22173515414320227, 'eta': 0.1, 'base_score': 0.2, 'objective': 'binary:logistic', 'alpha': 0.1, 'max_depth': 7, 'gamma': 0.3, 'lambda': 10}
[0] train-logloss:0.455826 valid-logloss:0.456411
Multiple eval metrics have been passed: 'valid-logloss' will be used for early stopping.
Will train until valid-logloss hasn't improved in 30 rounds.
[500] train-logloss:0.133365 valid-logloss:0.179545
Stopping. Best iteration:
[485] train-logloss:0.134569 valid-logloss:0.179385
{'colsample_bytree': 0.1931001251223502, 'eval_metric': 'logloss', 'max_delta_step': 6.0, 'nthread': 16, 'min_child_weight': 3.0, 'subsample': 0.45187782375223107, 'eta': 0.1, 'base_score': 0.2, 'objective': 'binary:logistic', 'alpha': 0.01, 'max_depth': 9, 'gamma': 0.2, 'lambda': 100}
[0] train-logloss:0.488414 valid-logloss:0.488188
Multiple eval metrics have been passed: 'valid-logloss' will be used for early stopping.
Will train until valid-logloss hasn't improved in 30 rounds.
[500] train-logloss:0.119792 valid-logloss:0.176875
Stopping. Best iteration:
[588] train-logloss:0.111819 valid-logloss:0.176281
{'colsample_bytree': 0.5287440649793352, 'eval_metric': 'logloss', 'max_delta_step': 6.0, 'nthread': 16, 'min_child_weight': 4.0, 'subsample': 0.6080693746281164, 'eta': 0.1, 'base_score': 0.2, 'objective': 'binary:logistic', 'alpha': 0.001, 'max_depth': 12, 'gamma': 0.3, 'lambda': 1}
[0] train-logloss:0.449223 valid-logloss:0.450825
Multiple eval metrics have been passed: 'valid-logloss' will be used for early stopping.
Will train until valid-logloss hasn't improved in 30 rounds.
Stopping. Best iteration:
[145] train-logloss:0.090304 valid-logloss:0.178292
{'colsample_bytree': 0.7266881793590831, 'eval_metric': 'logloss', 'max_delta_step': 3.0, 'nthread': 16, 'min_child_weight': 8.0, 'subsample': 0.39309052689513946, 'eta': 0.1, 'base_score': 0.2, 'objective': 'binary:logistic', 'alpha': 0.01, 'max_depth': 9, 'gamma': 0.5, 'lambda': 0.1}
[0] train-logloss:0.466118 valid-logloss:0.466507
Multiple eval metrics have been passed: 'valid-logloss' will be used for early stopping.
Will train until valid-logloss hasn't improved in 30 rounds.
Stopping. Best iteration:
[210] train-logloss:0.128233 valid-logloss:0.178441
{'colsample_bytree': 0.8967588020888194, 'eval_metric': 'logloss', 'max_delta_step': 2.0, 'nthread': 16, 'min_child_weight': 2.0, 'subsample': 0.8555879726482751, 'eta': 0.1, 'base_score': 0.2, 'objective': 'binary:logistic', 'alpha': 0.05, 'max_depth': 4, 'gamma': 0.1, 'lambda': 100}
[0] train-logloss:0.478228 valid-logloss:0.478229
Multiple eval metrics have been passed: 'valid-logloss' will be used for early stopping.
Will train until valid-logloss hasn't improved in 30 rounds.
[500] train-logloss:0.170527 valid-logloss:0.182535
[1000] train-logloss:0.157658 valid-logloss:0.178249
[1500] train-logloss:0.147655 valid-logloss:0.176107
{'colsample_bytree': 0.885243232976707, 'eval_metric': 'logloss', 'max_delta_step': 1.0, 'nthread': 16, 'min_child_weight': 2.0, 'subsample': 0.8780683008706107, 'eta': 0.1, 'base_score': 0.2, 'objective': 'binary:logistic', 'alpha': 10, 'max_depth': 13, 'gamma': 0.1, 'lambda': 100}
[0] train-logloss:0.488214 valid-logloss:0.488283
Multiple eval metrics have been passed: 'valid-logloss' will be used for early stopping.
Will train until valid-logloss hasn't improved in 30 rounds.
Stopping. Best iteration:
[343] train-logloss:0.081298 valid-logloss:0.173949
{'colsample_bytree': 0.809427376934251, 'eval_metric': 'logloss', 'max_delta_step': 1.0, 'nthread': 16, 'min_child_weight': 1.0, 'subsample': 0.7835642044093545, 'eta': 0.1, 'base_score': 0.2, 'objective': 'binary:logistic', 'alpha': 10, 'max_depth': 13, 'gamma': 0.1, 'lambda': 100}
[0] train-logloss:0.488249 valid-logloss:0.488313
Multiple eval metrics have been passed: 'valid-logloss' will be used for early stopping.
Will train until valid-logloss hasn't improved in 30 rounds.
Stopping. Best iteration:
[361] train-logloss:0.079592 valid-logloss:0.174906
{'colsample_bytree': 0.8081900342913997, 'eval_metric': 'logloss', 'max_delta_step': 1.0, 'nthread': 16, 'min_child_weight': 1.0, 'subsample': 0.7693709637442891, 'eta': 0.1, 'base_score': 0.2, 'objective': 'binary:logistic', 'alpha': 10, 'max_depth': 12, 'gamma': 0.1, 'lambda': 100}
[0] train-logloss:0.488346 valid-logloss:0.48836
Multiple eval metrics have been passed: 'valid-logloss' will be used for early stopping.
Will train until valid-logloss hasn't improved in 30 rounds.
Stopping. Best iteration:
[369] train-logloss:0.087433 valid-logloss:0.174648
{'colsample_bytree': 0.7852290495822306, 'eval_metric': 'logloss', 'max_delta_step': 2.0, 'nthread': 16, 'min_child_weight': 3.0, 'subsample': 0.8071297738930207, 'eta': 0.1, 'base_score': 0.2, 'objective': 'binary:logistic', 'alpha': 10, 'max_depth': 11, 'gamma': 0.1, 'lambda': 100}
[0] train-logloss:0.476087 valid-logloss:0.47621
Multiple eval metrics have been passed: 'valid-logloss' will be used for early stopping.
Will train until valid-logloss hasn't improved in 30 rounds.
[500] train-logloss:0.079612 valid-logloss:0.173976
Stopping. Best iteration:
[474] train-logloss:0.082591 valid-logloss:0.173839
{'colsample_bytree': 0.7699761681203763, 'eval_metric': 'logloss', 'max_delta_step': 3.0, 'nthread': 16, 'min_child_weight': 4.0, 'subsample': 0.5542116809010466, 'eta': 0.1, 'base_score': 0.2, 'objective': 'binary:logistic', 'alpha': 10, 'max_depth': 11, 'gamma': 0.1, 'lambda': 100}
[0] train-logloss:0.466799 valid-logloss:0.467161
Multiple eval metrics have been passed: 'valid-logloss' will be used for early stopping.
Will train until valid-logloss hasn't improved in 30 rounds.
Stopping. Best iteration:
[361] train-logloss:0.107015 valid-logloss:0.176052
{'colsample_bytree': 0.3453868164923646, 'eval_metric': 'logloss', 'max_delta_step': 4.0, 'nthread': 16, 'min_child_weight': 3.0, 'subsample': 0.8067425284677572, 'eta': 0.1, 'base_score': 0.2, 'objective': 'binary:logistic', 'alpha': 10, 'max_depth': 8, 'gamma': 0.1, 'lambda': 100}
[0] train-logloss:0.46158 valid-logloss:0.462032
Multiple eval metrics have been passed: 'valid-logloss' will be used for early stopping.
Will train until valid-logloss hasn't improved in 30 rounds.
[500] train-logloss:0.126788 valid-logloss:0.175164
Stopping. Best iteration:
[795] train-logloss:0.104325 valid-logloss:0.173645
{'colsample_bytree': 0.29786813041005694, 'eval_metric': 'logloss', 'max_delta_step': 4.0, 'nthread': 16, 'min_child_weight': 4.0, 'subsample': 0.7224425527900319, 'eta': 0.1, 'base_score': 0.2, 'objective': 'binary:logistic', 'alpha': 10, 'max_depth': 8, 'gamma': 0, 'lambda': 100}
[0] train-logloss:0.461759 valid-logloss:0.462159
Multiple eval metrics have been passed: 'valid-logloss' will be used for early stopping.
Will train until valid-logloss hasn't improved in 30 rounds.
[500] train-logloss:0.128305 valid-logloss:0.175792
Stopping. Best iteration:
[743] train-logloss:0.109823 valid-logloss:0.174752
{'colsample_bytree': 0.35255406299458536, 'eval_metric': 'logloss', 'max_delta_step': 4.0, 'nthread': 16, 'min_child_weight': 3.0, 'subsample': 0.5684920092729825, 'eta': 0.1, 'base_score': 0.2, 'objective': 'binary:logistic', 'alpha': 10, 'max_depth': 8, 'gamma': 0.1, 'lambda': 1}
[0] train-logloss:0.460017 valid-logloss:0.460579
Multiple eval metrics have been passed: 'valid-logloss' will be used for early stopping.
Will train until valid-logloss hasn't improved in 30 rounds.
Stopping. Best iteration:
[468] train-logloss:0.107501 valid-logloss:0.174734
{'colsample_bytree': 0.36121034843499156, 'eval_metric': 'logloss', 'max_delta_step': 3.0, 'nthread': 16, 'min_child_weight': 5.0, 'subsample': 0.818627908766779, 'eta': 0.1, 'base_score': 0.2, 'objective': 'binary:logistic', 'alpha': 0.005, 'max_depth': 7, 'gamma': 0.1, 'lambda': 0.01}
[0] train-logloss:0.468212 valid-logloss:0.468661
Multiple eval metrics have been passed: 'valid-logloss' will be used for early stopping.
Will train until valid-logloss hasn't improved in 30 rounds.
Stopping. Best iteration:
[424] train-logloss:0.125365 valid-logloss:0.175948
{'colsample_bytree': 0.11454188140127014, 'eval_metric': 'logloss', 'max_delta_step': 5.0, 'nthread': 16, 'min_child_weight': 2.0, 'subsample': 0.7317275135482078, 'eta': 0.1, 'base_score': 0.2, 'objective': 'binary:logistic', 'alpha': 0.05, 'max_depth': 6, 'gamma': 0, 'lambda': 10}
[0] train-logloss:0.491215 valid-logloss:0.491119
Multiple eval metrics have been passed: 'valid-logloss' will be used for early stopping.
Will train until valid-logloss hasn't improved in 30 rounds.
[500] train-logloss:0.146742 valid-logloss:0.178359
Stopping. Best iteration:
[745] train-logloss:0.133101 valid-logloss:0.176483
{'colsample_bytree': 0.35873934232776394, 'eval_metric': 'logloss', 'max_delta_step': 3.0, 'nthread': 16, 'min_child_weight': 5.0, 'subsample': 0.693189987161331, 'eta': 0.1, 'base_score': 0.2, 'objective': 'binary:logistic', 'alpha': 1, 'max_depth': 5, 'gamma': 0.1, 'lambda': 100}
[0] train-logloss:0.470408 valid-logloss:0.470681
Multiple eval metrics have been passed: 'valid-logloss' will be used for early stopping.
Will train until valid-logloss hasn't improved in 30 rounds.
[500] train-logloss:0.162439 valid-logloss:0.180049
[1000] train-logloss:0.145488 valid-logloss:0.176277
Stopping. Best iteration:
[1304] train-logloss:0.136927 valid-logloss:0.175178
{'colsample_bytree': 0.58149140413932, 'eval_metric': 'logloss', 'max_delta_step': 2.0, 'nthread': 16, 'min_child_weight': 4.0, 'subsample': 0.5370865827609025, 'eta': 0.1, 'base_score': 0.2, 'objective': 'binary:logistic', 'alpha': 10, 'max_depth': 8, 'gamma': 0.1, 'lambda': 100}
[0] train-logloss:0.476855 valid-logloss:0.476901
Multiple eval metrics have been passed: 'valid-logloss' will be used for early stopping.
Will train until valid-logloss hasn't improved in 30 rounds.
[500] train-logloss:0.126188 valid-logloss:0.17585
Stopping. Best iteration:
[518] train-logloss:0.124603 valid-logloss:0.175707
{'colsample_bytree': 0.4913478920459614, 'eval_metric': 'logloss', 'max_delta_step': 5.0, 'nthread': 16, 'min_child_weight': 3.0, 'subsample': 0.6611519161703867, 'eta': 0.1, 'base_score': 0.2, 'objective': 'binary:logistic', 'alpha': 0.005, 'max_depth': 7, 'gamma': 0.3, 'lambda': 1e-05}
[0] train-logloss:0.452886 valid-logloss:0.453421
Multiple eval metrics have been passed: 'valid-logloss' will be used for early stopping.
Will train until valid-logloss hasn't improved in 30 rounds.
[500] train-logloss:0.114178 valid-logloss:0.175691
Stopping. Best iteration:
[514] train-logloss:0.113077 valid-logloss:0.175634
{'colsample_bytree': 0.3002022906789539, 'eval_metric': 'logloss', 'max_delta_step': 4.0, 'nthread': 16, 'min_child_weight': 5.0, 'subsample': 0.7566280826568816, 'eta': 0.1, 'base_score': 0.2, 'objective': 'binary:logistic', 'alpha': 10, 'max_depth': 9, 'gamma': 0.5, 'lambda': 0.1}
[0] train-logloss:0.45933 valid-logloss:0.460163
Multiple eval metrics have been passed: 'valid-logloss' will be used for early stopping.
Will train until valid-logloss hasn't improved in 30 rounds.
Stopping. Best iteration:
[398] train-logloss:0.09748 valid-logloss:0.173972
{'colsample_bytree': 0.5631979426523986, 'eval_metric': 'logloss', 'max_delta_step': 3.0, 'nthread': 16, 'min_child_weight': 7.0, 'subsample': 0.8936894272191092, 'eta': 0.1, 'base_score': 0.2, 'objective': 'binary:logistic', 'alpha': 0, 'max_depth': 5, 'gamma': 0, 'lambda': 1}
[0] train-logloss:0.468534 valid-logloss:0.468784
Multiple eval metrics have been passed: 'valid-logloss' will be used for early stopping.
Will train until valid-logloss hasn't improved in 30 rounds.
[500] train-logloss:0.154218 valid-logloss:0.178734
[1000] train-logloss:0.13376 valid-logloss:0.175318
Stopping. Best iteration:
[1183] train-logloss:0.127649 valid-logloss:0.174781
{'colsample_bytree': 0.3872379603142309, 'eval_metric': 'logloss', 'max_delta_step': 2.0, 'nthread': 16, 'min_child_weight': 10.0, 'subsample': 0.8473402869211502, 'eta': 0.1, 'base_score': 0.2, 'objective': 'binary:logistic', 'alpha': 0.1, 'max_depth': 10, 'gamma': 0.4, 'lambda': 0.05}
[0] train-logloss:0.476375 valid-logloss:0.476886
Multiple eval metrics have been passed: 'valid-logloss' will be used for early stopping.
Will train until valid-logloss hasn't improved in 30 rounds.
Stopping. Best iteration:
[287] train-logloss:0.095166 valid-logloss:0.175476
{'colsample_bytree': 0.125240726887441, 'eval_metric': 'logloss', 'max_delta_step': 5.0, 'nthread': 16, 'min_child_weight': 2.0, 'subsample': 0.6466219377538647, 'eta': 0.1, 'base_score': 0.2, 'objective': 'binary:logistic', 'alpha': 0.001, 'max_depth': 15, 'gamma': 0.1, 'lambda': 10}
[0] train-logloss:0.481828 valid-logloss:0.483569
Multiple eval metrics have been passed: 'valid-logloss' will be used for early stopping.
Will train until valid-logloss hasn't improved in 30 rounds.
Stopping. Best iteration:
[195] train-logloss:0.055078 valid-logloss:0.182065
{'colsample_bytree': 0.6179601149157483, 'eval_metric': 'logloss', 'max_delta_step': 4.0, 'nthread': 16, 'min_child_weight': 4.0, 'subsample': 0.492464726698365, 'eta': 0.1, 'base_score': 0.2, 'objective': 'binary:logistic', 'alpha': 10, 'max_depth': 7, 'gamma': 0.1, 'lambda': 0.01}
[0] train-logloss:0.459017 valid-logloss:0.459552
Multiple eval metrics have been passed: 'valid-logloss' will be used for early stopping.
Will train until valid-logloss hasn't improved in 30 rounds.
[500] train-logloss:0.120765 valid-logloss:0.176093
Stopping. Best iteration:
[481] train-logloss:0.122537 valid-logloss:0.175998
{'colsample_bytree': 0.3011372038566066, 'eval_metric': 'logloss', 'max_delta_step': 7.0, 'nthread': 16, 'min_child_weight': 5.0, 'subsample': 0.5902261465225475, 'eta': 0.1, 'base_score': 0.2, 'objective': 'binary:logistic', 'alpha': 0, 'max_depth': 11, 'gamma': 0.2, 'lambda': 100}
[0] train-logloss:0.457606 valid-logloss:0.458251
Multiple eval metrics have been passed: 'valid-logloss' will be used for early stopping.
Will train until valid-logloss hasn't improved in 30 rounds.
Stopping. Best iteration:
[407] train-logloss:0.099604 valid-logloss:0.175287
{'colsample_bytree': 0.4815534771783838, 'eval_metric': 'logloss', 'max_delta_step': 1.0, 'nthread': 16, 'min_child_weight': 7.0, 'subsample': 0.8986936374687722, 'eta': 0.1, 'base_score': 0.2, 'objective': 'binary:logistic', 'alpha': 1, 'max_depth': 10, 'gamma': 0.4, 'lambda': 0.05}
[0] train-logloss:0.488294 valid-logloss:0.488282
Multiple eval metrics have been passed: 'valid-logloss' will be used for early stopping.
Will train until valid-logloss hasn't improved in 30 rounds.
Stopping. Best iteration:
[271] train-logloss:0.096622 valid-logloss:0.175078
{'colsample_bytree': 0.24755694526312677, 'eval_metric': 'logloss', 'max_delta_step': 10.0, 'nthread': 16, 'min_child_weight': 3.0, 'subsample': 0.8128000416673974, 'eta': 0.1, 'base_score': 0.2, 'objective': 'binary:logistic', 'alpha': 0.005, 'max_depth': 8, 'gamma': 0, 'lambda': 1e-05}
[0] train-logloss:0.454051 valid-logloss:0.454859
Multiple eval metrics have been passed: 'valid-logloss' will be used for early stopping.
Will train until valid-logloss hasn't improved in 30 rounds.
Stopping. Best iteration:
[463] train-logloss:0.09956 valid-logloss:0.175002
{'colsample_bytree': 0.41748483925514357, 'eval_metric': 'logloss', 'max_delta_step': 4.0, 'nthread': 16, 'min_child_weight': 6.0, 'subsample': 0.6286231325099906, 'eta': 0.1, 'base_score': 0.2, 'objective': 'binary:logistic', 'alpha': 0.1, 'max_depth': 9, 'gamma': 0.3, 'lambda': 0.1}
[0] train-logloss:0.458699 valid-logloss:0.459592
Multiple eval metrics have been passed: 'valid-logloss' will be used for early stopping.
Will train until valid-logloss hasn't improved in 30 rounds.
Stopping. Best iteration:
[259] train-logloss:0.111371 valid-logloss:0.176456
{'colsample_bytree': 0.5342407748737998, 'eval_metric': 'logloss', 'max_delta_step': 3.0, 'nthread': 16, 'min_child_weight': 9.0, 'subsample': 0.7307268600851822, 'eta': 0.1, 'base_score': 0.2, 'objective': 'binary:logistic', 'alpha': 0.001, 'max_depth': 6, 'gamma': 0.1, 'lambda': 1}
[0] train-logloss:0.46769 valid-logloss:0.467919
Multiple eval metrics have been passed: 'valid-logloss' will be used for early stopping.
Will train until valid-logloss hasn't improved in 30 rounds.
[500] train-logloss:0.138411 valid-logloss:0.176471
Stopping. Best iteration:
[746] train-logloss:0.124029 valid-logloss:0.175004
{'colsample_bytree': 0.19458993822937742, 'eval_metric': 'logloss', 'max_delta_step': 5.0, 'nthread': 16, 'min_child_weight': 4.0, 'subsample': 0.5091554094240125, 'eta': 0.1, 'base_score': 0.2, 'objective': 'binary:logistic', 'alpha': 0.05, 'max_depth': 14, 'gamma': 0.5, 'lambda': 10}
[0] train-logloss:0.482066 valid-logloss:0.483247
Multiple eval metrics have been passed: 'valid-logloss' will be used for early stopping.
Will train until valid-logloss hasn't improved in 30 rounds.
Stopping. Best iteration:
[172] train-logloss:0.083291 valid-logloss:0.180125
{'colsample_bytree': 0.6886365852423127, 'eval_metric': 'logloss', 'max_delta_step': 2.0, 'nthread': 16, 'min_child_weight': 1.0, 'subsample': 0.6804680742133138, 'eta': 0.1, 'base_score': 0.2, 'objective': 'binary:logistic', 'alpha': 0.01, 'max_depth': 9, 'gamma': 0.2, 'lambda': 0.01}
[0] train-logloss:0.475678 valid-logloss:0.47591
Multiple eval metrics have been passed: 'valid-logloss' will be used for early stopping.
Will train until valid-logloss hasn't improved in 30 rounds.
Stopping. Best iteration:
[220] train-logloss:0.107397 valid-logloss:0.177099
{'colsample_bytree': 0.6335685054642126, 'eval_metric': 'logloss', 'max_delta_step': 1.0, 'nthread': 16, 'min_child_weight': 7.0, 'subsample': 0.4125109664512634, 'eta': 0.1, 'base_score': 0.2, 'objective': 'binary:logistic', 'alpha': 10, 'max_depth': 5, 'gamma': 0.4, 'lambda': 100}
[0] train-logloss:0.48997 valid-logloss:0.48981
Multiple eval metrics have been passed: 'valid-logloss' will be used for early stopping.
Will train until valid-logloss hasn't improved in 30 rounds.
[500] train-logloss:0.163834 valid-logloss:0.180581
[1000] train-logloss:0.147307 valid-logloss:0.17702
Stopping. Best iteration:
[1106] train-logloss:0.144376 valid-logloss:0.17673
{'colsample_bytree': 0.272055771843968, 'eval_metric': 'logloss', 'max_delta_step': 7.0, 'nthread': 16, 'min_child_weight': 5.0, 'subsample': 0.3440531015873607, 'eta': 0.1, 'base_score': 0.2, 'objective': 'binary:logistic', 'alpha': 0, 'max_depth': 12, 'gamma': 0.1, 'lambda': 1e-05}
[0] train-logloss:0.451824 valid-logloss:0.453606
Multiple eval metrics have been passed: 'valid-logloss' will be used for early stopping.
Will train until valid-logloss hasn't improved in 30 rounds.
Stopping. Best iteration:
[166] train-logloss:0.101752 valid-logloss:0.181765
{'colsample_bytree': 0.8548416605656896, 'eval_metric': 'logloss', 'max_delta_step': 6.0, 'nthread': 16, 'min_child_weight': 9.0, 'subsample': 0.8323108705212866, 'eta': 0.1, 'base_score': 0.2, 'objective': 'binary:logistic', 'alpha': 1, 'max_depth': 10, 'gamma': 0.3, 'lambda': 0.05}
[0] train-logloss:0.450034 valid-logloss:0.451077
Multiple eval metrics have been passed: 'valid-logloss' will be used for early stopping.
Will train until valid-logloss hasn't improved in 30 rounds.
Stopping. Best iteration:
[245] train-logloss:0.096381 valid-logloss:0.174968
{'colsample_bytree': 0.7461585425861366, 'eval_metric': 'logloss', 'max_delta_step': 2.0, 'nthread': 16, 'min_child_weight': 6.0, 'subsample': 0.16357518862955528, 'eta': 0.1, 'base_score': 0.2, 'objective': 'binary:logistic', 'alpha': 0.01, 'max_depth': 7, 'gamma': 0.5, 'lambda': 100}
[0] train-logloss:0.477417 valid-logloss:0.477424
Multiple eval metrics have been passed: 'valid-logloss' will be used for early stopping.
Will train until valid-logloss hasn't improved in 30 rounds.
Stopping. Best iteration:
[424] train-logloss:0.154338 valid-logloss:0.182012
{'colsample_bytree': 0.45593481360326926, 'eval_metric': 'logloss', 'max_delta_step': 4.0, 'nthread': 16, 'min_child_weight': 2.0, 'subsample': 0.7900904488081063, 'eta': 0.1, 'base_score': 0.2, 'objective': 'binary:logistic', 'alpha': 10, 'max_depth': 4, 'gamma': 0.2, 'lambda': 0.1}
[0] train-logloss:0.463917 valid-logloss:0.464227
Multiple eval metrics have been passed: 'valid-logloss' will be used for early stopping.
Will train until valid-logloss hasn't improved in 30 rounds.
[500] train-logloss:0.168392 valid-logloss:0.181765
[1000] train-logloss:0.154509 valid-logloss:0.177592
Stopping. Best iteration:
[1426] train-logloss:0.145318 valid-logloss:0.175894
{'colsample_bytree': 0.20854333330953745, 'eval_metric': 'logloss', 'max_delta_step': 5.0, 'nthread': 16, 'min_child_weight': 3.0, 'subsample': 0.8680613469000104, 'eta': 0.1, 'base_score': 0.2, 'objective': 'binary:logistic', 'alpha': 0.1, 'max_depth': 13, 'gamma': 0.1, 'lambda': 100}
[0] train-logloss:0.483757 valid-logloss:0.484099
Multiple eval metrics have been passed: 'valid-logloss' will be used for early stopping.
Will train until valid-logloss hasn't improved in 30 rounds.
Stopping. Best iteration:
[333] train-logloss:0.086233 valid-logloss:0.175335
{'colsample_bytree': 0.16017168242118035, 'eval_metric': 'logloss', 'max_delta_step': 3.0, 'nthread': 16, 'min_child_weight': 6.0, 'subsample': 0.7471647674921095, 'eta': 0.1, 'base_score': 0.2, 'objective': 'binary:logistic', 'alpha': 0.001, 'max_depth': 6, 'gamma': 0.4, 'lambda': 10}
[0] train-logloss:0.489256 valid-logloss:0.48897
Multiple eval metrics have been passed: 'valid-logloss' will be used for early stopping.
Will train until valid-logloss hasn't improved in 30 rounds.
[500] train-logloss:0.145898 valid-logloss:0.177459
[1000] train-logloss:0.121003 valid-logloss:0.175287
Stopping. Best iteration:
[1013] train-logloss:0.120507 valid-logloss:0.175206
{'colsample_bytree': 0.32907158928619007, 'eval_metric': 'logloss', 'max_delta_step': 1.0, 'nthread': 16, 'min_child_weight': 4.0, 'subsample': 0.6104725323063749, 'eta': 0.1, 'base_score': 0.2, 'objective': 'binary:logistic', 'alpha': 0.05, 'max_depth': 15, 'gamma': 0.1, 'lambda': 100}
[0] train-logloss:0.488978 valid-logloss:0.489122
Multiple eval metrics have been passed: 'valid-logloss' will be used for early stopping.
Will train until valid-logloss hasn't improved in 30 rounds.
Stopping. Best iteration:
[344] train-logloss:0.076086 valid-logloss:0.175998
{'colsample_bytree': 0.41291172790077446, 'eval_metric': 'logloss', 'max_delta_step': 9.0, 'nthread': 16, 'min_child_weight': 1.0, 'subsample': 0.42698697481392084, 'eta': 0.1, 'base_score': 0.2, 'objective': 'binary:logistic', 'alpha': 10, 'max_depth': 11, 'gamma': 0, 'lambda': 1}
[0] train-logloss:0.45337 valid-logloss:0.454473
Multiple eval metrics have been passed: 'valid-logloss' will be used for early stopping.
Will train until valid-logloss hasn't improved in 30 rounds.
Stopping. Best iteration:
[243] train-logloss:0.097876 valid-logloss:0.178026
{'colsample_bytree': 0.5867094456305084, 'eval_metric': 'logloss', 'max_delta_step': 6.0, 'nthread': 16, 'min_child_weight': 2.0, 'subsample': 0.12001748525343353, 'eta': 0.1, 'base_score': 0.2, 'objective': 'binary:logistic', 'alpha': 0.005, 'max_depth': 8, 'gamma': 0.1, 'lambda': 0.01}
[0] train-logloss:0.4522 valid-logloss:0.452742
Multiple eval metrics have been passed: 'valid-logloss' will be used for early stopping.
Will train until valid-logloss hasn't improved in 30 rounds.
Stopping. Best iteration:
[134] train-logloss:0.161293 valid-logloss:0.186812
{'colsample_bytree': 0.4002347810637314, 'eval_metric': 'logloss', 'max_delta_step': 10.0, 'nthread': 16, 'min_child_weight': 5.0, 'subsample': 0.7055265328868927, 'eta': 0.1, 'base_score': 0.2, 'objective': 'binary:logistic', 'alpha': 10, 'max_depth': 9, 'gamma': 0.3, 'lambda': 0.05}
[0] train-logloss:0.453729 valid-logloss:0.454522
Multiple eval metrics have been passed: 'valid-logloss' will be used for early stopping.
Will train until valid-logloss hasn't improved in 30 rounds.
Stopping. Best iteration:
[407] train-logloss:0.093631 valid-logloss:0.175224
{'colsample_bytree': 0.839445229143727, 'eval_metric': 'logloss', 'max_delta_step': 2.0, 'nthread': 16, 'min_child_weight': 3.0, 'subsample': 0.28157601426944423, 'eta': 0.1, 'base_score': 0.2, 'objective': 'binary:logistic', 'alpha': 0.01, 'max_depth': 12, 'gamma': 0.5, 'lambda': 100}
[0] train-logloss:0.476458 valid-logloss:0.476524
Multiple eval metrics have been passed: 'valid-logloss' will be used for early stopping.
Will train until valid-logloss hasn't improved in 30 rounds.
Stopping. Best iteration:
[339] train-logloss:0.112297 valid-logloss:0.178553
{'colsample_bytree': 0.5222127042770424, 'eval_metric': 'logloss', 'max_delta_step': 7.0, 'nthread': 16, 'min_child_weight': 4.0, 'subsample': 0.4746548994750394, 'eta': 0.1, 'base_score': 0.2, 'objective': 'binary:logistic', 'alpha': 10, 'max_depth': 10, 'gamma': 0.1, 'lambda': 1e-05}
[0] train-logloss:0.451785 valid-logloss:0.452561
Multiple eval metrics have been passed: 'valid-logloss' will be used for early stopping.
Will train until valid-logloss hasn't improved in 30 rounds.
Stopping. Best iteration:
[309] train-logloss:0.094752 valid-logloss:0.176082
{'colsample_bytree': 0.4464093794343689, 'eval_metric': 'logloss', 'max_delta_step': 8.0, 'nthread': 16, 'min_child_weight': 10.0, 'subsample': 0.5349401848000775, 'eta': 0.1, 'base_score': 0.2, 'objective': 'binary:logistic', 'alpha': 1, 'max_depth': 7, 'gamma': 0.2, 'lambda': 100}
[0] train-logloss:0.457837 valid-logloss:0.458271
Multiple eval metrics have been passed: 'valid-logloss' will be used for early stopping.
Will train until valid-logloss hasn't improved in 30 rounds.
[500] train-logloss:0.138412 valid-logloss:0.176231
Stopping. Best iteration:
[610] train-logloss:0.131238 valid-logloss:0.175559
{'colsample_bytree': 0.7056575192744945, 'eval_metric': 'logloss', 'max_delta_step': 3.0, 'nthread': 16, 'min_child_weight': 7.0, 'subsample': 0.5797789060181122, 'eta': 0.1, 'base_score': 0.2, 'objective': 'binary:logistic', 'alpha': 0, 'max_depth': 11, 'gamma': 0.1, 'lambda': 0.1}
[0] train-logloss:0.46501 valid-logloss:0.465849
Multiple eval metrics have been passed: 'valid-logloss' will be used for early stopping.
Will train until valid-logloss hasn't improved in 30 rounds.
---------------------------------------------------------------------------
KeyboardInterrupt Traceback (most recent call last)
<ipython-input-8-b88d419a1f67> in <module>()
47 algo=tpe.suggest,
48 max_evals=100,
---> 49 trials=trials)
50
51 print best
/usr/local/lib/python2.7/dist-packages/hyperopt/fmin.pyc in fmin(fn, space, algo, max_evals, trials, rstate, allow_trials_fmin, pass_expr_memo_ctrl, catch_eval_exceptions, verbose, return_argmin)
305 verbose=verbose,
306 catch_eval_exceptions=catch_eval_exceptions,
--> 307 return_argmin=return_argmin,
308 )
309
/usr/local/lib/python2.7/dist-packages/hyperopt/base.pyc in fmin(self, fn, space, algo, max_evals, rstate, verbose, pass_expr_memo_ctrl, catch_eval_exceptions, return_argmin)
633 pass_expr_memo_ctrl=pass_expr_memo_ctrl,
634 catch_eval_exceptions=catch_eval_exceptions,
--> 635 return_argmin=return_argmin)
636
637
/usr/local/lib/python2.7/dist-packages/hyperopt/fmin.pyc in fmin(fn, space, algo, max_evals, trials, rstate, allow_trials_fmin, pass_expr_memo_ctrl, catch_eval_exceptions, verbose, return_argmin)
318 verbose=verbose)
319 rval.catch_eval_exceptions = catch_eval_exceptions
--> 320 rval.exhaust()
321 if return_argmin:
322 return trials.argmin
/usr/local/lib/python2.7/dist-packages/hyperopt/fmin.pyc in exhaust(self)
197 def exhaust(self):
198 n_done = len(self.trials)
--> 199 self.run(self.max_evals - n_done, block_until_done=self.async)
200 self.trials.refresh()
201 return self
/usr/local/lib/python2.7/dist-packages/hyperopt/fmin.pyc in run(self, N, block_until_done)
171 else:
172 # -- loop over trials and do the jobs directly
--> 173 self.serial_evaluate()
174
175 if stopped:
/usr/local/lib/python2.7/dist-packages/hyperopt/fmin.pyc in serial_evaluate(self, N)
90 ctrl = base.Ctrl(self.trials, current_trial=trial)
91 try:
---> 92 result = self.domain.evaluate(spec, ctrl)
93 except Exception as e:
94 logger.info('job exception: %s' % str(e))
/usr/local/lib/python2.7/dist-packages/hyperopt/base.pyc in evaluate(self, config, ctrl, attach_attachments)
838 memo=memo,
839 print_node_on_error=self.rec_eval_print_node_on_error)
--> 840 rval = self.fn(pyll_rval)
841
842 if isinstance(rval, (float, int, np.number)):
<ipython-input-8-b88d419a1f67> in objective(space)
24
25 watchlist = [(d_train, 'train'), (d_valid, 'valid')]
---> 26 bst = xgb.train(params, d_train, 2000, watchlist, early_stopping_rounds=30, verbose_eval=500)
27 logloss = log_loss(y_test, bst.predict(d_valid))
28
/usr/local/lib/python2.7/dist-packages/xgboost/training.pyc in train(params, dtrain, num_boost_round, evals, obj, feval, maximize, early_stopping_rounds, evals_result, verbose_eval, learning_rates, xgb_model, callbacks)
203 evals=evals,
204 obj=obj, feval=feval,
--> 205 xgb_model=xgb_model, callbacks=callbacks)
206
207
/usr/local/lib/python2.7/dist-packages/xgboost/training.pyc in _train_internal(params, dtrain, num_boost_round, evals, obj, feval, xgb_model, callbacks)
74 # Skip the first update if it is a recovery step.
75 if version % 2 == 0:
---> 76 bst.update(dtrain, i, obj)
77 bst.save_rabit_checkpoint()
78 version += 1
/usr/local/lib/python2.7/dist-packages/xgboost/core.pyc in update(self, dtrain, iteration, fobj)
804
805 if fobj is None:
--> 806 _check_call(_LIB.XGBoosterUpdateOneIter(self.handle, iteration, dtrain.handle))
807 else:
808 pred = self.predict(dtrain)
KeyboardInterrupt: