LightGBMError: Tree model should contain num_cat field (version incompatibility issue)

Created on 25 Jul 2018  路  15Comments  路  Source: microsoft/LightGBM

Hello all,

Some of my models have been created and saved in LightGBM version 2.0.1.
When model.predict(x) called on these models, recent LightGBM returns the error:
Tree model should contain num_cat field

I suspect something changed in model format since a while. Please advise how to convert old models to new format.

Thank you.

picture alexander-rakhlin

All 15 comments

you can add 'num_cat=0' for all trees, in the model file.

guolinke picture guolinke on 28 Jul 2018
👍1

Thanks. How do I do that from Python if the model was saved with pickle?

alexander-rakhlin picture alexander-rakhlin on 28 Jul 2018

you can rollback to version v2.0.1, and then load that model from pickle, and save to text format.
Then do that to the model file.

guolinke picture guolinke on 28 Jul 2018
👍1

After conversion and upgrade to v2.1.2 the model returns uniform scores for all classes, i.e. 0.25 for each of 4 classes. Any ideas?
The model looks like this:

num_class=4
num_tree_per_iteration=4
label_index=0
max_feature_idx=2047
objective=multiclass num_class:4
feature_names=Column_0 Column_1 Column_2 Column_3 Column_4 Column_5 Column_6 Column_7 Column_8 Column_9 Column_10 Column_11 Column_12 Column_13 Column_14 Column_15 Column_16 Column_17 Column_18 Column_19 Column_20 Column_21 Column_22 Column_23 Column_24 Column_25 Column_26 Column_27 Column_28 Column_29 Column_30 Column_31 Column_32 Column_33 Column_34 Column_35 Column_36 Column_37 Column_38 Column_39 Column_40 Column_41 Column_42 Column_43 Column_44 Column_45 Column_46 Column_47 Column_48 Column_49 Column_50 Column_51 Column_52 Column_53 Column_54 Column_55 Column_56 Column_57 Column_58 Column_59 Column_60 Column_61 Column_62 Column_63 Column_64 Column_65 Column_66 Column_67 Column_68 Column_69 Column_70 Column_71 Column_72 Column_73 Column_74 Column_75 Column_76 Column_77 Column_78 Column_79 Column_80 Column_81 Column_82 Column_83 Column_84 Column_85 Column_86 Column_87 Column_88 Column_89 Column_90 Column_91 Column_92 Column_93 Column_94 Column_95 Column_96 Column_97 Column_98 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[0:1.3737916946411133] [0:3.1390199661254883] [0.0044594528153538704:8.9926576614379883] [0.0025931776035577059:3.4927804470062256] [0:2.9638352394104004] [0:7.9598274230957031] [0:1.183591365814209] [0:1.1301107406616211] [0:1.9598908424377441] [0:2.3340961933135986] [0:3.4236340522766113] [0:5.3241724967956543] [0:2.735527515411377] [0.0013439330505207181:6.233705997467041] [0:1.1630735397338867] [0:1.0356857776641846] [0:2.1838939189910889] [0:0.52652788162231445] [0:3.3310906887054443] [0:2.2150685787200928] [0:1.9164528846740723] [0:6.8901591300964355] [0:1.8903694152832031] [0:1.3230743408203125] [0:6.3476963043212891] [0:4.6878032684326172] [0:1.0424474477767944] [0:4.064659595489502] [0:1.0754835605621338] [0:2.1412618160247803] [0:1.5694135427474976] [0:1.2069436311721802] [0:1.5682833194732666] [0:0.56230896711349487] [0:2.7724609375] [0:2.9056100845336914] [0:1.1418460607528687]

Tree=0
num_leaves=74
num_cat=0
split_feature=180 338 1032 183 206 1722 1062 1271 829 248 1300 561 1552 70 150 1024 1146 867 1000 1151 202 1700 356 131 1195 834 203 395 681 469 595 710 24 1073 1073 94 707 1766 1731 1236 672 1722 142 517 1024 119 296 1247 1574 672 1061 511 1636 1488 1221 293 192 599 1151 860 1069 1218 150 761 21 1745 14 168 2 15 2 2 2
split_gain=1376.8169851787911 975.76219423693396 520.77578839611033 463.73920280747416 410.34760268548263 342.89074541008995 200.32273864112884 175.05499940900609 134.30715918153641 127.52346688107025 147.81784426861773 124.21705861705863 122.59810505529833 114.96840820381806 111.85760122230711 101.86284258697711 89.816027816623816 87.171011994541416 85.222996350577432 82.616848500280753 76.284527376392219 75.576971659412578 70.585989183880429 64.492919254658375 60.669044793448727 55.670440149313379 54.881896782155323 52.706513409961687 49.528497931136897 66.845291005291003 46.803418803418793 44.657318604576972 43.525672039902247 42.79480153976715 41.960038059140764 40.40751321172543 38.089433806846557 38.420374807502185 36.684496559385479 41.088174982911831 36.430383387973279 33.849227519918514 27.451116415188295 26.781329478381878 26.288888888888863 26.220015278838808 25.591799905261077 22.733061224489791 21.830362204166931 21.101932367149761 19.970527556734453 15.891796368054344 15.175956220414271 13.20000000000001 12.023075225505295 25.245977011494251 11.388235294117644 10.708960129006755 10.150094012949125 9.85048024033172 7.8208213257488524 7.4198867553958365 6.8923076923076909 4.5193339145776577 2.4675324675324681 1.1323943661971825 0.84179104477611943 0.48229665071770711 0.12435465768800213 0.11726907630522021 0.10769230769230376 1.7763568394002505e-15 1.7763568394002505e-15
threshold=0.36076921224594116 2.1428085565567017 4.526303768157959 0.79669705033302307 0.77312049269676208 0.89103138446807861 3.0529745817184448 0.20185420662164688 0.13457623869180679 5.3402459621429443 0.21597809344530106 1.6891941428184509 0.39382743835449219 4.9487364292144775 3.1328575611114502 1.3607125282287598 0.2201567143201828 2.5193591117858887 1.1103225350379944 3.6878747940063477 0.55549493432044983 0.23555675148963928 0.48125565052032471 0.65925049781799316 2.2045632600784302 0.48898401856422424 1.6074733138084412 0.14752992242574692 0.3324766606092453 0.75516042113304138 0.13848630338907242 1.4449621438980103 2.1951134204864502 2.0236219167709351 1.6797621846199036 0.6275966465473175 1.497036874294281 0.50274577736854553 0.78874728083610535 0.52911895513534546 0.73879677057266235 0.64129522442817688 0.73085817694664001 0.13564730435609818 0.4654892235994339 0.17862743884325027 0.46663710474967957 4.454169750213623 0.64950579404830933 1.4369332790374756 0.021060480736196041 3.3863111734390259 0.51881024241447449 0.25334088504314423 0.51565498113632202 0.61418318748474121 0.70173460245132446 1.4708291292190552 4.5418639183044434 1.4677932262420654 0.083240889012813568 0.048502186313271523 1.7766684889793396 0.046540072187781334 1.3055705428123474 0.7172120213508606 0.80997291207313538 0.20751399546861649 0.053980724886059761 0.073280580341815948 0.057893969118595123 0.036369768902659416 0.30344806611537933
decision_type=0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
default_value=0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1.7976931348623157e+308 0 1.7976931348623157e+308 0 0 0 0 0 0 0 0 0 0 0 1.7976931348623157e+308 0 0 0 0 0 0 0 0 0 0 0 1.7976931348623157e+308 0 1.7976931348623157e+308 0 0 0 0 0 0 0 0 0 0 1.7976931348623157e+308 0 0 0 0 0 0 0 0
left_child=2 3 9 5 12 7 11 62 19 33 35 14 21 23 51 31 28 52 22 36 -20 24 -9 60 49 32 42 44 34 -30 -16 47 58 38 -10 43 37 -5 48 -40 -14 50 -18 59 -23 -45 61 -12 57 -3 -17 69 65 -19 63 -56 -28 -1 -13 -11 -6 -7 71 -42 72 -8 -21 -63 -62 -4 -32 -2 -15
right_child=1 4 6 8 13 46 17 18 16 10 15 25 40 64 30 41 26 53 20 66 -22 27 -24 -25 -26 -27 56 -29 29 -31 70 -33 -34 -35 -36 -37 -38 -39 39 -41 54 -43 -44 45 -46 -47 -48 -49 -50 -51 -52 -53 -54 -55 55 -57 -58 -59 -60 -61 68 67 -64 -65 -66 -67 -68 -69 -70 -71 -72 -73 -74
leaf_parent=57 71 49 69 37 60 61 65 22 34 59 47 58 40 72 30 50 42 53 20 66 20 44 22 23 24 25 56 27 29 29 70 31 32 33 34 35 36 37 39 39 63 41 42 45 44 45 46 47 48 49 50 51 52 53 55 55 56 57 58 59 68 67 62 63 64 65 66 67 68 69 70 71 72
leaf_value=-0.063081114333421215 -0.066666666666666652 0.11884057971014493 -0.053333333333333337 -0.022222222222222223 0.10400000000000001 0.040000000000000008 -0.066666666666666666 0.18633461047254152 -0.036124794745484405 -0.061716171617161725 -0.056000000000000008 0.1993091537132988 0.11111111111111112 -0.066666666666666652 -0.055555555555555559 0.022222222222222223 0.17762237762237765 0.20000000000000001 -0.023030303030303033 -0.026666666666666658 0.1773049645390071 -0.013333333333333334 0.052121212121212124 -0.066666666666666666 0.1923809523809524 -0.029629629629629631 -0.066666666666666652 -0.066666666666666666 -0.026666666666666668 0.15174603174603174 0.18666666666666668 0.17419354838709677 -0.053333333333333337 0.13118279569892474 0.17866666666666667 0.13978494623655915 -0.066666666666666666 0.17082066869300913 0.17192982456140352 -0.066666666666666652 0.013333333333333334 -0.0039215686274509803 -0.011111111111111112 -0.035294117647058816 0.18888888888888888 0.15151515151515152 0.12222222222222223 0.12380952380952381 0.087179487179487189 -0.042705314009661842 0.19080459770114944 0.085714285714285715 0.072463768115942032 0.03999999999999998 -0.066666666666666652 0.16000000000000003 0.080000000000000016 0.038095238095238099 0.080000000000000016 0.044444444444444446 0.18666666666666668 -0.066666666666666666 0.040000000000000008 -0.06467760649759656 -0 -0.026666666666666668 -0.066666666666666666 -0.040000000000000008 0.20000000000000001 -0.066666666666666666 0.20000000000000001 -0.066666666666666666 -0.066666666666666666
leaf_count=7586 59 23 20 30 25 20 335 2088 812 808 175 386 33 32 24 21 143 44 110 20 94 20 110 28 35 36 48 34 120 105 20 31 20 31 25 31 22 329 38 39 20 34 24 51 120 33 24 21 26 345 174 21 23 20 38 20 20 28 20 24 20 189 20 2011 20 20 47 20 277 146 84 25 25
internal_value=0 0.52651813018785498 -0.387399549983928 1.0130254588513914 -0.1744508423970996 1.4297450767186179 0.61525840853158331 1.6126629422718808 0.35415472779369628 -0.52096310057188655 -0.067949631741506297 1.1145431145431146 -0.40978140051871065 1.3130366900858703 0.32380952380952382 0.69590643274853803 0.087895142636854273 -0.26244343891402716 1.7024701637524284 1.125 0.69281045751633985 0.25765453495089541 1.7961783439490446 1.7104761904761905 -0.13068651778329199 1.6536796536796536 1.0014184397163119 1.157088122605364 -0.11424984306340238 0.56592592592592594 1.5 -0.079295154185022032 1.8184663536776213 -0.60299432111512652 -0.29709279171644765 -0.43576205561422032 1.4190726159230096 1.5468895078922935 -0.61068636344002414 0.51082251082251084 -0.59126610116242539 1.4643377001455604 1.5049900199600799 -0.49781659388646288 1.6000000000000001 0.38095238095238093 -0.38208168642951251 -0.36734693877551022 -0.62198952879581149 -0.32608695652173914 1.7264957264957266 -0.48128342245989303 -0.56084656084656082 1.5 -0.61815860858464977 0.11494252873563218 -0.23529411764705882 -0.62709044742141673 1.9343185550082103 -0.58653846153846156 1.9171842650103519 -0.55021834061135366 -0.46153846153846156 -0.63909404234367306 -0.4935064935064935 -0.64413145539906103 -0.54726368159203975 -0.64114832535885169 1.9910213243546577 -0.6506024096385542 1.9743589743589745 -0.66666666666666663 -0.66666666666666663
internal_count=18000 7630 10370 4504 3126 2759 1219 2506 1745 9151 1403 777 2699 427 315 456 1297 442 2402 448 204 577 2198 350 403 462 235 174 1062 225 128 227 426 7748 837 947 381 359 7717 77 2122 229 167 916 140 84 253 196 7640 368 195 187 378 64 2089 58 68 7614 406 832 322 229 104 2031 77 355 67 209 297 166 104 84 57
shrinkage=0.1
has_categorical=0
....
....
Column_1912=1
Column_1913=1
Column_1914=1
Column_1918=1
Column_1919=1
Column_1484=1
Column_1449=1
Column_945=1
Column_1055=1
Column_1928=1
Column_1931=1
Column_1933=1

pandas_categorical:null
alexander-rakhlin picture alexander-rakhlin on 28 Jul 2018

you can try to replace the decision_type=0 0 0 ... to decision_type=2 2 2 ...

guolinke picture guolinke on 29 Jul 2018
👍1

Still the same 0.25 for all classes.

The output of model.dump():

{'name': 'tree', 'version': 'v2', 'num_class': 4, 'num_tree_per_iteration': 4, 'label_index': 0, 'max_feature_idx': 2047, 'feature_names': ['Column_0', 'Column_1', 'Column_2', 'Column_3', 'Column_4', 'Column_5', 'Column_6', 'Column_7', 'Column_8', 'Column_9', 'Column_10', 'Column_11', 'Column_12', 'Column_13', 'Column_14', 'Column_15', 'Column_16', 'Column_17', 'Column_18', 'Column_19', 'Column_20', 'Column_21', 'Column_22', 'Column_23', 'Column_24', 'Column_25', 'Column_26', 'Column_27', 'Column_28', 'Column_29', 'Column_30', 'Column_31', 'Column_32', 'Column_33', 'Column_34', 'Column_35', 'Column_36', 'Column_37', 'Column_38', 'Column_39', 'Column_40', 'Column_41', 'Column_42', 'Column_43', 'Column_44', 'Column_45', 'Column_46', 'Column_47', 'Column_48', 'Column_49', 'Column_50', 'Column_51', 'Column_52', 'Column_53', 'Column_54', 'Column_55', 'Column_56', 'Column_57', 'Column_58', 'Column_59', 'Column_60', 'Column_61', 'Column_62', 'Column_63', 'Column_64', 'Column_65', 'Column_66', 'Column_67', 'Column_68', 'Column_69', 'Column_70', 'Column_71', 'Column_72', 'Column_73', 'Column_74', 'Column_75', 'Column_76', 'Column_77', 'Column_78', 'Column_79', 'Column_80', 'Column_81', 'Column_82', 'Column_83', 'Column_84', 'Column_85', 'Column_86', 'Column_87', 'Column_88', 'Column_89', 'Column_90', 'Column_91', 'Column_92', 'Column_93', 'Column_94', 'Column_95', 'Column_96', 'Column_97', 'Column_98', 'Column_99', 'Column_100', 'Column_101', 'Column_102', 'Column_103', 'Column_104', 'Column_105', 'Column_106', 'Column_107', 'Column_108', 'Column_109', 'Column_110', 'Column_111', 'Column_112', 'Column_113', 'Column_114', 'Column_115', 'Column_116', 'Column_117', 'Column_118', 'Column_119', 'Column_120', 'Column_121', 'Column_122', 'Column_123', 'Column_124', 'Column_125', 'Column_126', 'Column_127', 'Column_128', 'Column_129', 'Column_130', 'Column_131', 'Column_132', 'Column_133', 'Column_134', 'Column_135', 'Column_136', 'Column_137', 'Column_138', 'Column_139', 'Column_140', 'Column_141', 'Column_142', 'Column_143', 'Column_144', 'Column_145', 'Column_146', 'Column_147', 'Column_148', 'Column_149', 'Column_150', 'Column_151', 'Column_152', 'Column_153', 'Column_154', 'Column_155', 'Column_156', 'Column_157', 'Column_158', 'Column_159', 'Column_160', 'Column_161', 'Column_162', 'Column_163', 'Column_164', 'Column_165', 'Column_166', 'Column_167', 'Column_168', 'Column_169', 'Column_170', 'Column_171', 'Column_172', 'Column_173', 'Column_174', 'Column_175', 'Column_176', 'Column_177', 'Column_178', 'Column_179', 'Column_180', 'Column_181', 'Column_182', 'Column_183', 'Column_184', 'Column_185', 'Column_186', 'Column_187', 'Column_188', 'Column_189', 'Column_190', 'Column_191', 'Column_192', 'Column_193', 'Column_194', 'Column_195', 'Column_196', 'Column_197', 'Column_198', 'Column_199', 'Column_200', 'Column_201', 'Column_202', 'Column_203', 'Column_204', 'Column_205', 'Column_206', 'Column_207', 'Column_208', 'Column_209', 'Column_210', 'Column_211', 'Column_212', 'Column_213', 'Column_214', 'Column_215', 'Column_216', 'Column_217', 'Column_218', 'Column_219', 'Column_220', 'Column_221', 'Column_222', 'Column_223', 'Column_224', 'Column_225', 'Column_226', 'Column_227', 'Column_228', 'Column_229', 'Column_230', 'Column_231', 'Column_232', 'Column_233', 'Column_234', 'Column_235', 'Column_236', 'Column_237', 'Column_238', 'Column_239', 'Column_240', 'Column_241', 'Column_242', 'Column_243', 'Column_244', 'Column_245', 'Column_246', 'Column_247', 'Column_248', 'Column_249', 'Column_250', 'Column_251', 'Column_252', 'Column_253', 'Column_254', 'Column_255', 'Column_256', 'Column_257', 'Column_258', 'Column_259', 'Column_260', 'Column_261', 'Column_262', 'Column_263', 'Column_264', 'Column_265', 'Column_266', 'Column_267', 'Column_268', 'Column_269', 'Column_270', 'Column_271', 'Column_272', 'Column_273', 'Column_274', 'Column_275', 'Column_276', 'Column_277', 'Column_278', 'Column_279', 'Column_280', 'Column_281', 'Column_282', 'Column_283', 'Column_284', 'Column_285', 'Column_286', 'Column_287', 'Column_288', 'Column_289', 'Column_290', 'Column_291', 'Column_292', 'Column_293', 'Column_294', 'Column_295', 'Column_296', 'Column_297', 'Column_298', 'Column_299', 'Column_300', 'Column_301', 'Column_302', 'Column_303', 'Column_304', 'Column_305', 'Column_306', 'Column_307', 'Column_308', 'Column_309', 'Column_310', 'Column_311', 'Column_312', 'Column_313', 'Column_314', 'Column_315', 'Column_316', 'Column_317', 'Column_318', 'Column_319', 'Column_320', 'Column_321', 'Column_322', 'Column_323', 'Column_324', 'Column_325', 'Column_326', 'Column_327', 'Column_328', 'Column_329', 'Column_330', 'Column_331', 'Column_332', 'Column_333', 'Column_334', 'Column_335', 'Column_336', 'Column_337', 'Column_338', 'Column_339', 'Column_340', 'Column_341', 'Column_342', 'Column_343', 'Column_344', 'Column_345', 'Column_346', 'Column_347', 'Column_348', 'Column_349', 'Column_350', 'Column_351', 'Column_352', 'Column_353', 'Column_354', 'Column_355', 'Column_356', 'Column_357', 'Column_358', 'Column_359', 'Column_360', 'Column_361', 'Column_362', 'Column_363', 'Column_364', 'Column_365', 'Column_366', 'Column_367', 'Column_368', 'Column_369', 'Column_370', 'Column_371', 'Column_372', 'Column_373', 'Column_374', 'Column_375', 'Column_376', 'Column_377', 'Column_378', 'Column_379', 'Column_380', 'Column_381', 'Column_382', 'Column_383', 'Column_384', 'Column_385', 'Column_386', 'Column_387', 'Column_388', 'Column_389', 'Column_390', 'Column_391', 'Column_392', 'Column_393', 'Column_394', 'Column_395', 'Column_396', 'Column_397', 'Column_398', 'Column_399', 'Column_400', 'Column_401', 'Column_402', 'Column_403', 'Column_404', 'Column_405', 'Column_406', 'Column_407', 'Column_408', 'Column_409', 'Column_410', 'Column_411', 'Column_412', 'Column_413', 'Column_414', 'Column_415', 'Column_416', 'Column_417', 'Column_418', 'Column_419', 'Column_420', 'Column_421', 'Column_422', 'Column_423', 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{'split_index': 68, 'split_feature': 2, 'split_gain': 0.12435466051101685, 'threshold': 0.05398072488605976, 'decision_type': '<=', 'default_left': True, 'missing_type': 'None', 'internal_value': 1.9910213243546577, 'internal_count': 297, 'left_child': {'leaf_index': 61, 'leaf_value': 0.18666666666666668, 'leaf_count': 20}, 'right_child': {'leaf_index': 69, 'leaf_value': 0.2, 'leaf_count': 277}}}, 'right_child': {'leaf_index': 24, 'leaf_value': -0.06666666666666667, 'leaf_count': 28}}, 'right_child': {'split_index': 64, 'split_feature': 21, 'split_gain': 2.4675323963165283, 'threshold': 1.3055705428123474, 'decision_type': '<=', 'default_left': True, 'missing_type': 'None', 'internal_value': -0.4935064935064935, 'internal_count': 77, 'left_child': {'split_index': 72, 'split_feature': 2, 'split_gain': 1.7763568394002505e-15, 'threshold': 0.30344806611537933, 'decision_type': '<=', 'default_left': True, 'missing_type': 'None', 'internal_value': -0.6666666666666665, 'internal_count': 57, 'left_child': {'leaf_index': 14, 'leaf_value': -0.06666666666666665, 'leaf_count': 32}, 'right_child': {'leaf_index': 73, 'leaf_value': -0.06666666666666667, 'leaf_count': 25}}, 'right_child': {'leaf_index': 65, 'leaf_value': 0, 'leaf_count': 20}}}}}}}]}

And if model.predict(x_test, raw_score=True) returns all zeros

alexander-rakhlin picture alexander-rakhlin on 29 Jul 2018

Is tree_sizes= mandatory in model file? If yes, how to add it?

alexander-rakhlin picture alexander-rakhlin on 29 Jul 2018

tree_sizes is not required.
Sorry, I just find there are some 1.7976931348623157e+308 in your default_value.
So here is the solution.
set decision_type=6, for the corresponding defualt_value=0.
for the rest(with default_value=1.7976931348623157e+308), set the decision_type=4.

guolinke picture guolinke on 29 Jul 2018

Still the same 0.25 for all classes. The converted model.txt:

num_class=4
num_tree_per_iteration=4
label_index=0
max_feature_idx=1407
objective=multiclass num_class:4
feature_names=Column_0 Column_1 Column_2 Column_3 Column_4 Column_5 Column_6 Column_7 Column_8 Column_9 Column_10 Column_11 Column_12 Column_13 Column_14 Column_15 Column_16 Column_17 Column_18 Column_19 Column_20 Column_21 Column_22 Column_23 Column_24 Column_25 Column_26 Column_27 Column_28 Column_29 Column_30 Column_31 Column_32 Column_33 Column_34 Column_35 Column_36 Column_37 Column_38 Column_39 Column_40 Column_41 Column_42 Column_43 Column_44 Column_45 Column_46 Column_47 Column_48 Column_49 Column_50 Column_51 Column_52 Column_53 Column_54 Column_55 Column_56 Column_57 Column_58 Column_59 Column_60 Column_61 Column_62 Column_63 Column_64 Column_65 Column_66 Column_67 Column_68 Column_69 Column_70 Column_71 Column_72 Column_73 Column_74 Column_75 Column_76 Column_77 Column_78 Column_79 Column_80 Column_81 Column_82 Column_83 Column_84 Column_85 Column_86 Column_87 Column_88 Column_89 Column_90 Column_91 Column_92 Column_93 Column_94 Column_95 Column_96 Column_97 Column_98 Column_99 Column_100 Column_101 Column_102 Column_103 Column_104 Column_105 Column_106 Column_107 Column_108 Column_109 Column_110 Column_111 Column_112 Column_113 Column_114 Column_115 Column_116 Column_117 Column_118 Column_119 Column_120 Column_121 Column_122 Column_123 Column_124 Column_125 Column_126 Column_127 Column_128 Column_129 Column_130 Column_131 Column_132 Column_133 Column_134 Column_135 Column_136 Column_137 Column_138 Column_139 Column_140 Column_141 Column_142 Column_143 Column_144 Column_145 Column_146 Column_147 Column_148 Column_149 Column_150 Column_151 Column_152 Column_153 Column_154 Column_155 Column_156 Column_157 Column_158 Column_159 Column_160 Column_161 Column_162 Column_163 Column_164 Column_165 Column_166 Column_167 Column_168 Column_169 Column_170 Column_171 Column_172 Column_173 Column_174 Column_175 Column_176 Column_177 Column_178 Column_179 Column_180 Column_181 Column_182 Column_183 Column_184 Column_185 Column_186 Column_187 Column_188 Column_189 Column_190 Column_191 Column_192 Column_193 Column_194 Column_195 Column_196 Column_197 Column_198 Column_199 Column_200 Column_201 Column_202 Column_203 Column_204 Column_205 Column_206 Column_207 Column_208 Column_209 Column_210 Column_211 Column_212 Column_213 Column_214 Column_215 Column_216 Column_217 Column_218 Column_219 Column_220 Column_221 Column_222 Column_223 Column_224 Column_225 Column_226 Column_227 Column_228 Column_229 Column_230 Column_231 Column_232 Column_233 Column_234 Column_235 Column_236 Column_237 Column_238 Column_239 Column_240 Column_241 Column_242 Column_243 Column_244 Column_245 Column_246 Column_247 Column_248 Column_249 Column_250 Column_251 Column_252 Column_253 Column_254 Column_255 Column_256 Column_257 Column_258 Column_259 Column_260 Column_261 Column_262 Column_263 Column_264 Column_265 Column_266 Column_267 Column_268 Column_269 Column_270 Column_271 Column_272 Column_273 Column_274 Column_275 Column_276 Column_277 Column_278 Column_279 Column_280 Column_281 Column_282 Column_283 Column_284 Column_285 Column_286 Column_287 Column_288 Column_289 Column_290 Column_291 Column_292 Column_293 Column_294 Column_295 Column_296 Column_297 Column_298 Column_299 Column_300 Column_301 Column_302 Column_303 Column_304 Column_305 Column_306 Column_307 Column_308 Column_309 Column_310 Column_311 Column_312 Column_313 Column_314 Column_315 Column_316 Column_317 Column_318 Column_319 Column_320 Column_321 Column_322 Column_323 Column_324 Column_325 Column_326 Column_327 Column_328 Column_329 Column_330 Column_331 Column_332 Column_333 Column_334 Column_335 Column_336 Column_337 Column_338 Column_339 Column_340 Column_341 Column_342 Column_343 Column_344 Column_345 Column_346 Column_347 Column_348 Column_349 Column_350 Column_351 Column_352 Column_353 Column_354 Column_355 Column_356 Column_357 Column_358 Column_359 Column_360 Column_361 Column_362 Column_363 Column_364 Column_365 Column_366 Column_367 Column_368 Column_369 Column_370 Column_371 Column_372 Column_373 Column_374 Column_375 Column_376 Column_377 Column_378 Column_379 Column_380 Column_381 Column_382 Column_383 Column_384 Column_385 Column_386 Column_387 Column_388 Column_389 Column_390 Column_391 Column_392 Column_393 Column_394 Column_395 Column_396 Column_397 Column_398 Column_399 Column_400 Column_401 Column_402 Column_403 Column_404 Column_405 Column_406 Column_407 Column_408 Column_409 Column_410 Column_411 Column_412 Column_413 Column_414 Column_415 Column_416 Column_417 Column_418 Column_419 Column_420 Column_421 Column_422 Column_423 Column_424 Column_425 Column_426 Column_427 Column_428 Column_429 Column_430 Column_431 Column_432 Column_433 Column_434 Column_435 Column_436 Column_437 Column_438 Column_439 Column_440 Column_441 Column_442 Column_443 Column_444 Column_445 Column_446 Column_447 Column_448 Column_449 Column_450 Column_451 Column_452 Column_453 Column_454 Column_455 Column_456 Column_457 Column_458 Column_459 Column_460 Column_461 Column_462 Column_463 Column_464 Column_465 Column_466 Column_467 Column_468 Column_469 Column_470 Column_471 Column_472 Column_473 Column_474 Column_475 Column_476 Column_477 Column_478 Column_479 Column_480 Column_481 Column_482 Column_483 Column_484 Column_485 Column_486 Column_487 Column_488 Column_489 Column_490 Column_491 Column_492 Column_493 Column_494 Column_495 Column_496 Column_497 Column_498 Column_499 Column_500 Column_501 Column_502 Column_503 Column_504 Column_505 Column_506 Column_507 Column_508 Column_509 Column_510 Column_511 Column_512 Column_513 Column_514 Column_515 Column_516 Column_517 Column_518 Column_519 Column_520 Column_521 Column_522 Column_523 Column_524 Column_525 Column_526 Column_527 Column_528 Column_529 Column_530 Column_531 Column_532 Column_533 Column_534 Column_535 Column_536 Column_537 Column_538 Column_539 Column_540 Column_541 Column_542 Column_543 Column_544 Column_545 Column_546 Column_547 Column_548 Column_549 Column_550 Column_551 Column_552 Column_553 Column_554 Column_555 Column_556 Column_557 Column_558 Column_559 Column_560 Column_561 Column_562 Column_563 Column_564 Column_565 Column_566 Column_567 Column_568 Column_569 Column_570 Column_571 Column_572 Column_573 Column_574 Column_575 Column_576 Column_577 Column_578 Column_579 Column_580 Column_581 Column_582 Column_583 Column_584 Column_585 Column_586 Column_587 Column_588 Column_589 Column_590 Column_591 Column_592 Column_593 Column_594 Column_595 Column_596 Column_597 Column_598 Column_599 Column_600 Column_601 Column_602 Column_603 Column_604 Column_605 Column_606 Column_607 Column_608 Column_609 Column_610 Column_611 Column_612 Column_613 Column_614 Column_615 Column_616 Column_617 Column_618 Column_619 Column_620 Column_621 Column_622 Column_623 Column_624 Column_625 Column_626 Column_627 Column_628 Column_629 Column_630 Column_631 Column_632 Column_633 Column_634 Column_635 Column_636 Column_637 Column_638 Column_639 Column_640 Column_641 Column_642 Column_643 Column_644 Column_645 Column_646 Column_647 Column_648 Column_649 Column_650 Column_651 Column_652 Column_653 Column_654 Column_655 Column_656 Column_657 Column_658 Column_659 Column_660 Column_661 Column_662 Column_663 Column_664 Column_665 Column_666 Column_667 Column_668 Column_669 Column_670 Column_671 Column_672 Column_673 Column_674 Column_675 Column_676 Column_677 Column_678 Column_679 Column_680 Column_681 Column_682 Column_683 Column_684 Column_685 Column_686 Column_687 Column_688 Column_689 Column_690 Column_691 Column_692 Column_693 Column_694 Column_695 Column_696 Column_697 Column_698 Column_699 Column_700 Column_701 Column_702 Column_703 Column_704 Column_705 Column_706 Column_707 Column_708 Column_709 Column_710 Column_711 Column_712 Column_713 Column_714 Column_715 Column_716 Column_717 Column_718 Column_719 Column_720 Column_721 Column_722 Column_723 Column_724 Column_725 Column_726 Column_727 Column_728 Column_729 Column_730 Column_731 Column_732 Column_733 Column_734 Column_735 Column_736 Column_737 Column_738 Column_739 Column_740 Column_741 Column_742 Column_743 Column_744 Column_745 Column_746 Column_747 Column_748 Column_749 Column_750 Column_751 Column_752 Column_753 Column_754 Column_755 Column_756 Column_757 Column_758 Column_759 Column_760 Column_761 Column_762 Column_763 Column_764 Column_765 Column_766 Column_767 Column_768 Column_769 Column_770 Column_771 Column_772 Column_773 Column_774 Column_775 Column_776 Column_777 Column_778 Column_779 Column_780 Column_781 Column_782 Column_783 Column_784 Column_785 Column_786 Column_787 Column_788 Column_789 Column_790 Column_791 Column_792 Column_793 Column_794 Column_795 Column_796 Column_797 Column_798 Column_799 Column_800 Column_801 Column_802 Column_803 Column_804 Column_805 Column_806 Column_807 Column_808 Column_809 Column_810 Column_811 Column_812 Column_813 Column_814 Column_815 Column_816 Column_817 Column_818 Column_819 Column_820 Column_821 Column_822 Column_823 Column_824 Column_825 Column_826 Column_827 Column_828 Column_829 Column_830 Column_831 Column_832 Column_833 Column_834 Column_835 Column_836 Column_837 Column_838 Column_839 Column_840 Column_841 Column_842 Column_843 Column_844 Column_845 Column_846 Column_847 Column_848 Column_849 Column_850 Column_851 Column_852 Column_853 Column_854 Column_855 Column_856 Column_857 Column_858 Column_859 Column_860 Column_861 Column_862 Column_863 Column_864 Column_865 Column_866 Column_867 Column_868 Column_869 Column_870 Column_871 Column_872 Column_873 Column_874 Column_875 Column_876 Column_877 Column_878 Column_879 Column_880 Column_881 Column_882 Column_883 Column_884 Column_885 Column_886 Column_887 Column_888 Column_889 Column_890 Column_891 Column_892 Column_893 Column_894 Column_895 Column_896 Column_897 Column_898 Column_899 Column_900 Column_901 Column_902 Column_903 Column_904 Column_905 Column_906 Column_907 Column_908 Column_909 Column_910 Column_911 Column_912 Column_913 Column_914 Column_915 Column_916 Column_917 Column_918 Column_919 Column_920 Column_921 Column_922 Column_923 Column_924 Column_925 Column_926 Column_927 Column_928 Column_929 Column_930 Column_931 Column_932 Column_933 Column_934 Column_935 Column_936 Column_937 Column_938 Column_939 Column_940 Column_941 Column_942 Column_943 Column_944 Column_945 Column_946 Column_947 Column_948 Column_949 Column_950 Column_951 Column_952 Column_953 Column_954 Column_955 Column_956 Column_957 Column_958 Column_959 Column_960 Column_961 Column_962 Column_963 Column_964 Column_965 Column_966 Column_967 Column_968 Column_969 Column_970 Column_971 Column_972 Column_973 Column_974 Column_975 Column_976 Column_977 Column_978 Column_979 Column_980 Column_981 Column_982 Column_983 Column_984 Column_985 Column_986 Column_987 Column_988 Column_989 Column_990 Column_991 Column_992 Column_993 Column_994 Column_995 Column_996 Column_997 Column_998 Column_999 Column_1000 Column_1001 Column_1002 Column_1003 Column_1004 Column_1005 Column_1006 Column_1007 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Tree=0
num_leaves=65
num_cat=0
split_feature=763 990 1126 492 357 385 1105 793 633 511 433 1296 377 699 27 990 1050 1159 129 415 1257 813 508 402 289 222 1038 1017 1162 961 1255 1149 409 576 695 489 1269 592 334 1133 668 381 861 1105 748 115 692 977 365 5 857 934 338 225 47 748 142 32 1 14 3 1 1 1
split_gain=1617.130005524881 1007.7213094080598 389.65180683284939 370.06873349258763 283.2920264832585 267.15424915841425 243.65950664995268 247.07671199484616 233.25172867459651 190.33428912741522 168.20347299541982 141.97629393160119 139.33709417396057 137.83231850117102 117.31345379304764 111.2822202210794 100.92192416153168 99.153429383755338 132.56660168940871 90.384769108145235 86.845484438790663 103.93394495412844 80.860786536668911 76.428129454172677 73.529191532658388 72.642274282705984 71.878642966421125 70.860849671517144 69.603769497002574 58.728648710140831 56.352331313498155 55.091817169743535 43.906114230057895 41.530529842984606 36.46016330322027 31.807968632473546 26.665531125916573 23.365607799923964 22.993884714131809 22.989213122040443 22.591087344028523 22.376862050328214 22.203508771929826 19.278450611636728 15.998988088963845 15.32121212121212 14.064957264957258 7.7953348148244004 7.5272727272727309 6.3000000000000043 6.1483062808750901 5.7845004668534052 5.3234567901234584 3.4478873239436751 1.9148936170212769 1.9087719298245602 0.94736842105263008 0.9241379310344815 0.3063829787234047 0.12692307692307736 0.12026143790850341 3.5527136788005009e-15 3.5527136788005009e-15 1.7763568394002505e-15
threshold=4.8415703773498535 3.9234237670898438 0.2705068439245224 19.993982315063477 139.86444091796875 6.6659915447235107 3.0153566598892212 2.8842093944549561 36.423601150512695 12.072065830230713 35.550239562988281 1.5874586701393127 281.29995727539063 20.965291976928711 121.3217887878418 0.77985566854476929 5.3709728717803955 12.852740287780762 268.94046020507813 11.496284008026123 0.032490929588675499 18.516993522644043 33.68028450012207 6.1808881759643555 221.11510467529297 58.549776077270508 0.37267850339412689 10.986806869506836 0.1237301379442215 1.6055150032043457 3.4713791608810425 8.2174587249755859 25.09398078918457 23.892687797546387 32.96122932434082 42.969579696655273 5.5049962997436523 56.716226577758789 276.46452331542969 0.058191884309053421 20.505768775939941 112.88064575195313 27.19935417175293 2.8997538089752197 31.904057502746582 192.85713958740234 28.505621910095215 0.52725687623023987 35.850376129150391 258.33889770507813 5.4118728637695313 6.8564553260803223 125.09873199462891 218.57395935058594 91.934139251708984 30.511537551879883 228.69864654541016 36.864768981933594 215.68643188476563 200.73116302490234 236.3155517578125 360.30947875976563 351.86962890625 175.27864837646484
decision_type=6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 4 4 6 6 6 6 6 6 6 6 4 6 4 6 6 6
default_value=0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1.7976931348623157e+308 1.7976931348623157e+308 0 0 0 0 0 0 0 0 1.7976931348623157e+308 0 1.7976931348623157e+308 0 0 0
left_child=3 2 5 14 12 15 7 9 10 51 20 13 19 -12 48 45 29 27 52 38 21 -4 40 49 54 -21 56 33 32 36 -26 -22 53 -16 -35 37 -7 -25 47 -31 -11 -24 61 50 -13 -2 -8 -5 -1 -9 -6 60 -19 -10 -17 62 -18 -27 -28 -52 -3 -14 -30 -47
right_child=1 6 8 4 43 16 46 23 28 22 11 44 42 -15 17 24 26 18 -20 25 31 -23 41 35 30 57 58 -29 55 39 -32 -33 -34 34 -36 -37 -38 -39 -40 -41 -42 -43 -44 -45 -46 63 -48 -49 -50 -51 59 -53 -54 -55 -56 -57 -58 -59 -60 -61 -62 -63 -64 -65
leaf_parent=48 45 60 21 47 50 36 46 49 53 40 13 44 61 13 33 54 56 52 18 25 31 21 41 37 30 57 58 27 62 39 30 31 32 34 34 35 36 37 38 39 40 41 42 43 44 63 46 47 48 49 59 51 52 53 54 55 56 57 58 59 60 61 62 63
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internal_count=18000 7705 4711 10295 1144 1534 2994 2838 3177 617 2883 711 687 532 9151 581 953 9085 228 554 2172 218 393 2221 482 131 142 8857 294 811 435 1954 104 8819 8785 2173 743 2153 423 68 90 303 133 457 179 99 156 403 66 48 436 224 108 71 47 190 95 87 47 416 204 113 170 79
shrinkage=0.1
has_categorical=0
....
alexander-rakhlin picture alexander-rakhlin on 29 Jul 2018

For a reason I do not understand the model loaded from the .txt _like_ above when saved with model.save_model only saves the 1-st tree, and last decision_type is set to 0

version=v2
num_class=4
num_tree_per_iteration=4
label_index=0
max_feature_idx=2047
objective=multiclass num_class:4
feature_names=Column_0 Column_1 Column_2 Column_3 Column_4 Column_5 Column_6 Column_7 Column_8 Column_9 Column_10 Column_11 Column_12 Column_13 Column_14 Column_15 Column_16 Column_17 Column_18 Column_19 Column_20 Column_21 Column_22 Column_23 Column_24 Column_25 Column_26 Column_27 Column_28 Column_29 Column_30 Column_31 Column_32 Column_33 Column_34 Column_35 Column_36 Column_37 Column_38 Column_39 Column_40 Column_41 Column_42 Column_43 Column_44 Column_45 Column_46 Column_47 Column_48 Column_49 Column_50 Column_51 Column_52 Column_53 Column_54 Column_55 Column_56 Column_57 Column_58 Column_59 Column_60 Column_61 Column_62 Column_63 Column_64 Column_65 Column_66 Column_67 Column_68 Column_69 Column_70 Column_71 Column_72 Column_73 Column_74 Column_75 Column_76 Column_77 Column_78 Column_79 Column_80 Column_81 Column_82 Column_83 Column_84 Column_85 Column_86 Column_87 Column_88 Column_89 Column_90 Column_91 Column_92 Column_93 Column_94 Column_95 Column_96 Column_97 Column_98 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[0:4.2033648490905762] [0:3.0232326984405518] [0.11111298948526382:10.271963119506836] [0:1.0266063213348389] [0:3.3957314491271973] [0:7.6245489120483398] [0:0.42483469843864441] [0.026938809081912041:4.1115336418151855] [0:2.7686505317687988] [0.00013881320774089545:3.0060570240020752] [0:2.1653385162353516] [0:3.1469073295593262] [0:2.4063475131988525] [0:1.7622331380844116] [0:3.3983762264251709] [0:1.6230373382568359] [0:6.9851617813110352] [0:1.0923985242843628] [0:0.53366512060165405] [0.093261756002902985:12.163115501403809] [0:1.7040406465530396] [0:5.8118987083435059] [0:4.0857133865356445] [0:2.5496256351470947] [0:0.84308624267578125] [0.0055449306964874268:10.750954627990723] [0:1.673378586769104] [0:2.9062526226043701] [0:2.3651139736175537] [0:0.95738238096237183] [0.066855818033218384:9.1489152908325195] [0:3.0864262580871582] [0:2.1591777801513672] [0:1.7101122140884399] [0:3.3817996978759766] [0.035615049302577972:9.1501703262329102] [0:3.8396656513214111] [0:4.5803470611572266] [0:0.78888672590255737] [0:1.1664044857025146] [0:2.8759109973907471] [0:2.7060713768005371] [0:3.1148085594177246] [0:0.62189853191375732] [0.006897724699229002:4.5133986473083496] [0:1.968402624130249] [0.040521193295717239:12.047640800476074] [0:4.6604752540588379] [0:2.2475345134735107] [0:2.5181968212127686] [0:4.1732320785522461] [0:4.9712300300598145] [0:4.4416885375976563] [0:2.2176868915557861] [0:2.0473017692565918] [0.00098283553961664438:4.6014561653137207] [0:2.4215865135192871] [0:4.0873246192932129] [0:2.4942564964294434] [0:2.8475208282470703] [0:1.9758327007293701] [0:3.1547231674194336] [0:6.1673831939697266] [0:2.6135215759277344] [0:1.7169837951660156] [0:1.4263004064559937] [0.0010227733291685581:4.4909491539001465] [0:3.1853153705596924] [0.0046870163641870022:4.5943498611450195] [0.0023569243494421244:4.1385583877563477] [0:5.3252925872802734] [0:1.2854405641555786] [0:2.7457892894744873] [0:1.8347859382629395] [0:7.1192336082458496] [0:1.3737916946411133] [0:3.1390199661254883] [0.0044594528153538704:8.9926576614379883] [0.0025931776035577059:3.4927804470062256] [0:2.9638352394104004] [0:7.9598274230957031] [0:1.183591365814209] [0:1.1301107406616211] [0:1.9598908424377441] [0:2.3340961933135986] [0:3.4236340522766113] [0:5.3241724967956543] [0:2.735527515411377] [0.0013439330505207181:6.233705997467041] [0:1.1630735397338867] [0:1.0356857776641846] [0:2.1838939189910889] [0:0.52652788162231445] [0:3.3310906887054443] [0:2.2150685787200928] [0:1.9164528846740723] [0:6.8901591300964355] [0:1.8903694152832031] [0:1.3230743408203125] [0:6.3476963043212891] [0:4.6878032684326172] [0:1.0424474477767944] [0:4.064659595489502] [0:1.0754835605621338] [0:2.1412618160247803] [0:1.5694135427474976] [0:1.2069436311721802] [0:1.5682833194732666] [0:0.56230896711349487] [0:2.7724609375] [0:2.9056100845336914] [0:1.1418460607528687]
tree_sizes=6291

Tree=0
num_leaves=80
num_cat=0
split_feature=180 338 1032 1159 1236 672 1552 464 466 811 697 1728 1436 877 1383 635 911 930 594 787 756 1384 410 312 79 1383 1631 1086 583 1302 760 1013 1811 1546 294 594 300 851 1681 564 604 113 1607 1856 241 11 241 443 697 1234 1814 710 113 957 455 1551 707 1362 635 1047 241 236 1383 455 420 1186 68 63 1829 350 1198 312 24 472 127 19 11 6 2
split_gain=1376.82 975.762 520.776 380.539 381.706 329.233 224.973 212.168 209.708 165.418 144.209 121.338 118.618 116.109 125.664 102.105 96.9141 83.0528 82.8817 80.9109 69.7556 67.6635 65.4879 68.7619 60.5335 60.1039 57.8682 56.8649 56.1802 51.2605 50.1843 48.4224 48.2625 45.7177 44.6224 39.5765 37.464 36.5951 35.7591 40.7076 31.6776 31.5103 30.5981 33.1276 30.5741 29.7726 27.6982 24.5903 24.5853 24.5244 22.4977 22.4 21.4851 19.907 18.8222 17.8336 17.0857 13.1355 12.6345 10.9575 10.2288 10.0813 8.63742 8.29036 23.1563 7.51111 4.97778 4.2386 4.11816 3.00995 3.00654 2.98321 2.27513 1.11079 1.09565 0.825 0.678261 0.396581 0.0910053
threshold=0.36076921224594116 2.1428085565567017 4.526303768157959 0.23990211635828018 0.74720394611358643 1.1642255783081055 0.35967206954956055 0.5178910493850708 0.5162988007068634 1.4895598292350769 2.0218998193740845 0.6400221586227417 0.13682897388935089 1.9306356906890869 0.24758397787809372 0.60376745462417603 0.084242943674325943 0.60231894254684448 0.16290144622325897 0.73875963687896729 1.9975257515907288 0.58454716205596924 0.47012971341609955 1.5284878611564636 2.3031270503997803 0.12701031565666199 0.093157116323709488 3.1677341461181641 0.55776450037956238 0.39228400588035583 0.35080406069755554 1.5199816226959229 1.4663049578666687 0.22858067601919174 0.040948301553726196 0.26969297230243683 1.1022397875785828 0.28282344341278076 1.5913393497467041 0.47143881022930145 0.75797367095947266 0.41627170145511627 0.58537647128105164 3.8767472505569458 1.2798981666564941 0.86473128199577332 0.66840490698814392 0.19812782853841782 3.178227424621582 1.2383372187614441 0.88156482577323914 0.71325203776359558 0.67254495620727539 0.31697523593902588 1.5307341814041138 0.57676514983177185 0.20821515470743179 0.4330575168132782 3.3769177198410034 0.16330605745315552 2.0735733509063721 0.13611551374197006 0.39593656361103058 1.6623767614364624 2.4304845333099365 0.18246364593505859 0.19169319421052933 1.1539228558540344 7.7092001438140869 0.29899589717388153 0.027027345262467861 1.8834301233291626 0.1622558981180191 0.50266563892364502 1.7527245879173279 0.072241675108671188 0.097270939499139786 0.030189191922545433 0.16975507885217667
decision_type=6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 4 6 6 6 6 6 6 6 6 6 6 4 6 6 6 4 4 6 6 6 6 6 4 4 6 4 6 6 4 0
left_child=2 3 13 8 11 17 10 9 15 22 59 24 27 21 29 42 40 49 37 33 32 45 55 72 34 50 -20 52 46 51 65 -9 -12 48 -5 -29 -21 -17 57 -40 -11 -32 43 -2 62 58 -16 -35 -10 75 -23 -15 -6 74 69 73 -8 -27 60 -7 -1 -18 -30 68 -65 -13 -31 -22 -58 -14 -42 -61 -24 -4 -19 -3 -48 -38 -25
right_child=1 5 7 4 12 6 56 31 19 16 20 30 54 14 28 18 61 53 26 36 67 25 23 78 -26 38 -28 35 44 66 41 -33 -34 47 -36 -37 77 -39 39 -41 70 -43 -44 -45 -46 -47 76 -49 -50 -51 -52 -53 -54 -55 -56 -57 63 -59 -60 71 -62 -63 -64 64 -66 -67 -68 -69 -70 -71 -72 -73 -74 -75 -76 -77 -78 -79 -80
leaf_value=-0.06360436466033087 -0.061826981246218993 -0.066666666666666666 -0.066042154566744726 0.001904761904761905 0.13043478260869565 0.05185185185185185 0.085714285714285715 0.20000000000000001 -0.046666666666666662 -0.0086956521739130436 -0.012500000000000001 0.19555555555555557 -0.065671641791044774 0.013333333333333334 -0.011764705882352939 -0.055555555555555559 -0.066666666666666666 0.20000000000000001 0.17629629629629631 0.15000000000000002 0.20000000000000001 0.20000000000000001 -0.066666666666666666 0.20000000000000001 -0.057142857142857141 -0.055128205128205134 -0.02207650273224044 0.19411764705882353 -0.007058823529411765 -0.066666666666666666 0.17866666666666667 0.0035087719298245615 0.14796747967479676 0.022222222222222223 0.18794107571085988 -0.033333333333333333 -0.03999999999999998 0.15897435897435896 0.12888888888888891 -0.06133333333333333 0.13333333333333333 -0.020046620046620049 0.12820512820512822 0.11724137931034483 0.11724137931034483 0.090196078431372562 0.19999999999999996 0.19347604485219164 0.16521739130434784 0.1492063492063492 0.0028985507246376799 0.20000000000000001 -0.053968253968253971 0.026666666666666668 0.081481481481481488 0.093333333333333338 -0.064970645792563606 0.032679738562091505 0.066666666666666666 -0.065306122448979584 0.053333333333333337 0.066666666666666638 -0.063749088256746897 -0.066666666666666652 0.13650793650793652 0.07999999999999996 0.03999999999999998 0.12 0.0060606060606060615 -0 0.20000000000000001 -0.0074074074074074077 -0 -0.026666666666666668 0.16000000000000003 -0.026666666666666644 0.16000000000000003 -0.066666666666666666 0.18666666666666659
leaf_count=7576 551 44 427 35 23 27 21 279 40 23 128 60 268 20 34 24 62 210 45 32 151 47 43 43 28 416 305 136 85 28 25 38 82 24 1946 24 20 182 75 50 20 143 26 29 29 34 26 327 23 21 23 120 63 20 27 20 2044 51 20 196 20 20 457 52 21 20 20 20 22 20 184 27 20 20 20 20 20 58 20
internal_value=0 0.526518 -0.3874 1.01303 1.38018 -0.174451 -0.358056 0.615258 0.399367 0.211382 0.499736 1.67981 0.174688 -0.520963 0.0580056 0.0126219 1.18015 1.35522 0.517986 1.257 1.13211 -0.579403 -0.293423 0.751323 1.81284 -0.0785498 0.0342857 1.02439 -0.281618 1.23404 0.602151 1.76446 0.501587 1.58776 1.84654 1.6 0.0121212 1.33981 -0.247748 0.528 1.72981 0.0952381 -0.451045 -0.528736 -0.461179 -0.622745 1 1.81766 0.306878 -0.0392157 1.35238 1.73333 -0.0465116 1.82933 -0.488889 -0.575303 -0.608642 -0.455389 -0.629552 -0.464 -0.632965 -0.341463 -0.548585 -0.623033 -0.0821918 1.66667 -0.222222 1.90643 -0.642143 -0.611111 1.93464 -0.58296 -0.455026 -0.642804 1.96522 -0.541667 1.82609 -0.598291 1.95767
internal_count=18000 7630 10370 4504 2818 3126 2791 1219 1686 902 631 2257 561 9151 839 1162 309 335 556 524 381 8312 593 126 2009 662 350 246 651 188 248 317 210 414 1981 160 110 206 592 125 227 168 606 580 571 7650 80 351 63 85 70 140 86 250 315 467 2160 467 7616 250 7596 82 542 2139 73 80 48 171 2066 288 204 223 63 447 230 64 46 78 63
shrinkage=0.1



feature importances:
Column_241=3
Column_1383=3
Column_113=2
Column_635=2
Column_11=2
Column_312=2
Column_594=2
Column_455=2
Column_697=2
Column_19=1
Column_24=1
Column_63=1
Column_68=1
Column_79=1
Column_2=1
Column_127=1
Column_180=1
Column_236=1
Column_294=1
Column_300=1
Column_338=1
Column_350=1
Column_410=1
Column_420=1
Column_443=1
Column_464=1
Column_466=1
Column_472=1
Column_564=1
Column_583=1
Column_604=1
Column_672=1
Column_707=1
Column_710=1
Column_756=1
Column_760=1
Column_787=1
Column_811=1
Column_851=1
Column_877=1
Column_911=1
Column_930=1
Column_957=1
Column_1013=1
Column_1032=1
Column_1047=1
Column_1086=1
Column_1159=1
Column_1186=1
Column_1198=1
Column_1234=1
Column_1236=1
Column_1302=1
Column_1362=1
Column_6=1
Column_1384=1
Column_1436=1
Column_1546=1
Column_1551=1
Column_1552=1
Column_1607=1
Column_1631=1
Column_1681=1
Column_1728=1
Column_1811=1
Column_1814=1
Column_1829=1
Column_1856=1

pandas_categorical:null
alexander-rakhlin picture alexander-rakhlin on 29 Jul 2018

To make the story short, here is one of the models I am trying to import (partially converted)
lgbm-Inception-0.5-650-f9-s4.txt

alexander-rakhlin picture alexander-rakhlin on 29 Jul 2018

@alexander-rakhlin can you also provide a sample of testing data ?

guolinke picture guolinke on 30 Jul 2018

@guolinke here it is:
xy.zip

alexander-rakhlin picture alexander-rakhlin on 30 Jul 2018

@alexander-rakhlin please remove the has_categorical=0

guolinke picture guolinke on 31 Jul 2018
👍1

@guolinke thank you, this worked!

alexander-rakhlin picture alexander-rakhlin on 31 Jul 2018
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