Comments (11)
guitargeek
commented on August 22, 2024
Hello @inkzk!
Which XGBoost version are you using? Depending on the version, you might have to change the base response that is assumed implicitly by XGBoost depending on it's version (that's why FastForest might make the wrong guess):
https://github.com/guitargeek/XGBoost-FastForest/blob/master/include/fastforest.h#L62
Does that solve your problem? If yes, where should I have in your opinion documented this so the question would not have come up?
Thanks!
Jonas
from xgboost-fastforest.
inkzk
commented on August 22, 2024
Hello @inkzk!
Which XGBoost version are you using? Depending on the version, you might have to change the base response that is assumed implicitly by XGBoost depending on it's version (that's why FastForest might make the wrong guess):
https://github.com/guitargeek/XGBoost-FastForest/blob/master/include/fastforest.h#L62
Does that solve your problem? If yes, where should I have in your opinion documented this so the question would not have come up?
Thanks! Jonas
@guitargeek Thanks for ur reply. xgboost=2.0.3, and I dumpped it with xgbc.get_booster().dump_model('file_name.txt')
from xgboost-fastforest.
guitargeek
commented on August 22, 2024
Ok! Does it work if you call the FastForest with baseResponse=0.5?
from xgboost-fastforest.
inkzk
commented on August 22, 2024
Ok! Does it work if you call the FastForest with
baseResponse=0.5?
@guitargeek Still mismatch. below is my boost tree txt, could u please help verify that?
booster[0]:
0:[f0<0.464745909] yes=1,no=2,missing=2
1:[f1<0.688540101] yes=3,no=4,missing=4
3:leaf=-0.343334645
4:leaf=-0.0685302913
2:[f5<0.925550044] yes=5,no=6,missing=6
5:[f4<0.363732725] yes=7,no=8,missing=8
7:[f1<0.46713987] yes=11,no=12,missing=12
11:[f6<0.29543516] yes=15,no=16,missing=16
15:leaf=-0.11647056
16:leaf=0.578968108
12:[f7<0.630501032] yes=17,no=18,missing=18
17:[f1<0.550881445] yes=23,no=24,missing=24
23:leaf=0.490999013
24:leaf=1.44462729
18:leaf=0.287130892
8:[f0<0.957570314] yes=13,no=14,missing=14
13:[f4<0.428245842] yes=19,no=20,missing=20
19:[f0<0.540145934] yes=25,no=26,missing=26
25:leaf=-0.249268338
26:leaf=0.506970406
20:[f6<0.446160495] yes=27,no=28,missing=28
27:leaf=-0.32993868
28:leaf=0.101085864
14:[f4<0.40901643] yes=21,no=22,missing=22
21:leaf=0.1081504
22:leaf=0.976575971
6:[f5<0.963987529] yes=9,no=10,missing=10
9:leaf=-0.036273066
10:leaf=-0.33453387
booster[1]:
0:[f0<0.464745909] yes=1,no=2,missing=2
1:[f1<0.477993608] yes=3,no=4,missing=4
3:leaf=-0.331933796
4:[f2<0.698272228] yes=7,no=8,missing=8
7:leaf=0.271315485
8:leaf=-0.287922323
2:[f5<0.963987529] yes=5,no=6,missing=6
5:[f4<0.549994588] yes=9,no=10,missing=10
9:[f1<0.608612716] yes=11,no=12,missing=12
11:[f4<0.371538043] yes=15,no=16,missing=16
15:[f6<0.339155525] yes=19,no=20,missing=20
19:leaf=0.0493167825
20:leaf=0.690361202
16:[f3<0.322769463] yes=21,no=22,missing=22
21:leaf=0.139887109
22:leaf=-0.230679497
12:[f2<0.99801147] yes=17,no=18,missing=18
17:[f7<0.627484322] yes=23,no=24,missing=24
23:leaf=0.689768255
24:leaf=0.238464281
18:[f1<0.747833788] yes=25,no=26,missing=26
25:leaf=-0.061183922
26:leaf=0.425076067
10:[f5<0.684047699] yes=13,no=14,missing=14
13:leaf=-0.312829792
14:leaf=-0.0313760452
6:leaf=-0.317647189
booster[2]:
0:[f0<0.464745909] yes=1,no=2,missing=2
1:[f1<0.477993608] yes=3,no=4,missing=4
3:leaf=-0.321564645
4:[f4<0.338961661] yes=7,no=8,missing=8
7:leaf=0.275449038
8:leaf=-0.273527741
2:[f5<0.925550044] yes=5,no=6,missing=6
5:[f4<0.549994588] yes=9,no=10,missing=10
9:[f1<0.637513697] yes=13,no=14,missing=14
13:[f2<0.903187752] yes=17,no=18,missing=18
17:[f1<0.4708969] yes=21,no=22,missing=22
21:leaf=-0.0330066793
22:leaf=0.37014392
18:[f4<0.304078698] yes=23,no=24,missing=24
23:leaf=0.106471747
24:leaf=-0.294448912
14:[f2<0.9998734] yes=19,no=20,missing=20
19:[f4<0.485309005] yes=25,no=26,missing=26
25:leaf=0.449053407
26:leaf=0.0443474092
20:[f2<0.999961495] yes=27,no=28,missing=28
27:leaf=-0.0995458886
28:leaf=0.247802898
10:[f5<0.684047699] yes=15,no=16,missing=16
15:leaf=-0.295679003
16:leaf=-0.0476977304
6:[f4<0.566825867] yes=11,no=12,missing=12
11:leaf=-0.306028247
12:leaf=-0.0505427159
booster[3]:
0:[f0<0.464745909] yes=1,no=2,missing=2
1:[f1<0.477993608] yes=3,no=4,missing=4
3:leaf=-0.313885659
4:[f2<0.67366159] yes=7,no=8,missing=8
7:leaf=0.259458095
8:leaf=-0.260770559
2:[f5<0.963987529] yes=5,no=6,missing=6
5:[f4<0.576931775] yes=9,no=10,missing=10
9:[f6<0.446160495] yes=11,no=12,missing=12
11:[f1<0.706568837] yes=15,no=16,missing=16
15:[f2<0.825392783] yes=19,no=20,missing=20
19:leaf=0.12347234
20:leaf=-0.213362724
16:[f3<0.289854676] yes=21,no=22,missing=22
21:leaf=0.132648513
22:leaf=0.351812243
12:[f1<0.439495146] yes=17,no=18,missing=18
17:leaf=-0.19111
18:[f6<0.596998692] yes=23,no=24,missing=24
23:leaf=0.521652699
24:leaf=0.0923499465
10:[f3<0.528982401] yes=13,no=14,missing=14
13:leaf=-0.0462808385
14:leaf=-0.282250613
6:leaf=-0.292778313
booster[4]:
0:[f0<0.464745909] yes=1,no=2,missing=2
1:[f1<0.477993608] yes=3,no=4,missing=4
3:leaf=-0.307864249
4:[f2<0.603130698] yes=7,no=8,missing=8
7:leaf=0.258893311
8:leaf=-0.249272823
2:[f5<0.963987529] yes=5,no=6,missing=6
5:[f3<0.373620301] yes=9,no=10,missing=10
9:[f6<0.34447065] yes=11,no=12,missing=12
11:[f4<0.319153905] yes=15,no=16,missing=16
15:[f6<0.161429822] yes=21,no=22,missing=22
21:leaf=-0.0873716623
22:leaf=0.332722068
16:[f1<0.645725667] yes=23,no=24,missing=24
23:leaf=-0.173120648
24:leaf=0.112454645
12:[f3<0.280642003] yes=17,no=18,missing=18
17:leaf=-0.00607322948
18:[f4<0.402831346] yes=25,no=26,missing=26
25:leaf=0.504794717
26:leaf=0.11193727
10:[f1<0.481658846] yes=13,no=14,missing=14
13:leaf=-0.312682331
14:[f6<0.392236084] yes=19,no=20,missing=20
19:[f2<0.825392783] yes=27,no=28,missing=28
27:leaf=0.133586153
28:leaf=-0.233711541
20:[f4<0.576931775] yes=29,no=30,missing=30
29:leaf=0.284917057
30:leaf=-0.0806663111
6:leaf=-0.281438321
booster[5]:
0:[f0<0.422372401] yes=1,no=2,missing=2
1:[f1<0.577572346] yes=3,no=4,missing=4
3:leaf=-0.304021776
4:leaf=-0.0361476243
2:[f6<0.705945671] yes=5,no=6,missing=6
5:[f3<0.534836769] yes=7,no=8,missing=8
7:[f1<0.372503698] yes=9,no=10,missing=10
9:[f7<0.464734882] yes=13,no=14,missing=14
13:leaf=-0.0432436578
14:leaf=-0.2421837
10:[f2<0.614885151] yes=15,no=16,missing=16
15:[f4<0.298274696] yes=19,no=20,missing=20
19:leaf=0.459163845
20:leaf=0.0409142524
16:[f1<0.637513697] yes=21,no=22,missing=22
21:leaf=-0.00313727115
22:leaf=0.172455892
8:[f5<0.74511838] yes=11,no=12,missing=12
11:[f6<0.427681804] yes=17,no=18,missing=18
17:leaf=-0.272538722
18:leaf=-0.0781817734
12:leaf=0.0316307619
6:leaf=-0.278460264
booster[6]:
0:[f0<0.422372401] yes=1,no=2,missing=2
1:[f1<0.577572346] yes=3,no=4,missing=4
3:leaf=-0.299147278
4:leaf=-0.0309733022
2:[f5<0.963987529] yes=5,no=6,missing=6
5:[f3<0.534836769] yes=7,no=8,missing=8
7:[f1<0.462907314] yes=9,no=10,missing=10
9:[f3<0.369019806] yes=13,no=14,missing=14
13:[f5<0.325174898] yes=19,no=20,missing=20
19:leaf=-0.176679865
20:leaf=0.200613305
14:leaf=-0.274248511
10:[f6<0.34447065] yes=15,no=16,missing=16
15:[f5<0.308020294] yes=21,no=22,missing=22
21:leaf=0.282350868
22:leaf=-0.0354972519
16:[f7<0.615278244] yes=23,no=24,missing=24
23:leaf=0.254819155
24:leaf=-0.0218059495
8:[f5<0.592235029] yes=11,no=12,missing=12
11:leaf=-0.259386897
12:[f0<0.612285852] yes=17,no=18,missing=18
17:leaf=0.0781116933
18:leaf=-0.0929537565
6:leaf=-0.261656344
booster[7]:
0:[f0<0.422372401] yes=1,no=2,missing=2
1:[f1<0.577572346] yes=3,no=4,missing=4
3:leaf=-0.294328511
4:leaf=-0.0265360698
2:[f6<0.705945671] yes=5,no=6,missing=6
5:[f3<0.675634325] yes=7,no=8,missing=8
7:[f6<0.455611378] yes=9,no=10,missing=10
9:[f4<0.441228211] yes=13,no=14,missing=14
13:[f3<0.365643382] yes=17,no=18,missing=18
17:leaf=0.114852354
18:leaf=-0.0613230355
14:[f0<0.993976593] yes=19,no=20,missing=20
19:leaf=-0.280128807
20:leaf=0.148033738
10:[f2<0.9998734] yes=15,no=16,missing=16
15:[f1<0.477993608] yes=21,no=22,missing=22
21:leaf=-0.0729891807
22:leaf=0.355017543
16:leaf=-0.0998855606
8:[f2<0.807594597] yes=11,no=12,missing=12
11:leaf=0.00971503649
12:leaf=-0.24911049
6:leaf=-0.258191258
booster[8]:
0:[f1<0.372503698] yes=1,no=2,missing=2
1:[f5<0.419235408] yes=3,no=4,missing=4
3:leaf=-0.291896462
4:leaf=-0.0631172657
2:[f2<0.903187752] yes=5,no=6,missing=6
5:[f4<0.40580824] yes=7,no=8,missing=8
7:[f3<0.369019806] yes=11,no=12,missing=12
11:[f2<0.788840115] yes=19,no=20,missing=20
19:[f6<0.226668999] yes=31,no=32,missing=32
31:leaf=0.0228453334
32:leaf=0.366166145
20:[f1<0.533979893] yes=33,no=34,missing=34
33:leaf=-0.110894263
34:leaf=0.230455503
12:[f7<0.724237621] yes=21,no=22,missing=22
21:[f7<0.597474337] yes=35,no=36,missing=36
35:leaf=0.022377545
36:leaf=-0.253474891
22:leaf=0.282094359
8:[f5<0.526986599] yes=13,no=14,missing=14
13:leaf=-0.257934153
14:[f1<0.49794367] yes=23,no=24,missing=24
23:leaf=-0.169750243
24:leaf=0.260894358
6:[f0<0.908376873] yes=9,no=10,missing=10
9:[f2<0.969576836] yes=15,no=16,missing=16
15:[f1<0.698552966] yes=25,no=26,missing=26
25:[f7<0.520251691] yes=37,no=38,missing=38
37:leaf=-0.22391817
38:leaf=-0.0584116913
26:[f7<0.606402755] yes=39,no=40,missing=40
39:leaf=0.209930107
40:leaf=-0.00777901663
16:[f5<0.511613727] yes=27,no=28,missing=28
27:leaf=-0.321986586
28:leaf=-0.120414227
10:[f5<0.963987529] yes=17,no=18,missing=18
17:[f2<0.99801147] yes=29,no=30,missing=30
29:leaf=0.25882706
30:[f1<0.747833788] yes=41,no=42,missing=42
41:leaf=-0.152321249
42:leaf=0.0989169925
18:leaf=-0.215295702
booster[9]:
0:[f1<0.372503698] yes=1,no=2,missing=2
1:[f5<0.419235408] yes=3,no=4,missing=4
3:leaf=-0.285553634
4:leaf=-0.054535538
2:[f2<0.903187752] yes=5,no=6,missing=6
5:[f1<0.602751374] yes=7,no=8,missing=8
7:[f3<0.404040575] yes=11,no=12,missing=12
11:[f4<0.40580824] yes=19,no=20,missing=20
19:[f7<0.597474337] yes=25,no=26,missing=26
25:leaf=0.206440032
26:leaf=-0.0488133095
20:leaf=-0.20935376
12:[f1<0.49421373] yes=21,no=22,missing=22
21:leaf=-0.23904404
22:[f7<0.534033895] yes=27,no=28,missing=28
27:leaf=-0.142795011
28:leaf=0.103450797
8:[f7<0.485662758] yes=13,no=14,missing=14
13:leaf=0.078453891
14:leaf=0.287345886
6:[f1<0.637513697] yes=9,no=10,missing=10
9:[f4<0.304078698] yes=15,no=16,missing=16
15:leaf=-0.0134606585
16:leaf=-0.26535356
10:[f5<0.963987529] yes=17,no=18,missing=18
17:[f5<0.428650796] yes=23,no=24,missing=24
23:[f6<0.261970997] yes=29,no=30,missing=30
29:leaf=-0.17456913
30:leaf=0.0537675507
24:[f7<0.714149952] yes=31,no=32,missing=32
31:leaf=0.20446153
32:leaf=-0.113113686
18:leaf=-0.210487455
booster[10]:
0:[f1<0.372503698] yes=1,no=2,missing=2
1:[f5<0.419235408] yes=3,no=4,missing=4
3:leaf=-0.278420001
4:leaf=-0.0471023694
2:[f4<0.226391301] yes=5,no=6,missing=6
5:[f7<0.508040965] yes=7,no=8,missing=8
7:leaf=0.000757406524
8:leaf=0.319387585
6:[f4<0.576931775] yes=9,no=10,missing=10
9:[f6<0.705945671] yes=11,no=12,missing=12
11:[f6<0.339155525] yes=13,no=14,missing=14
13:[f1<0.550881445] yes=15,no=16,missing=16
15:leaf=-0.187881082
16:leaf=0.023978075
14:[f7<0.597474337] yes=17,no=18,missing=18
17:leaf=0.129458472
18:leaf=-0.0704982281
12:leaf=-0.22258167
10:leaf=-0.203735173
booster[11]:
0:[f0<0.422372401] yes=1,no=2,missing=2
1:[f1<0.49794367] yes=3,no=4,missing=4
3:leaf=-0.270729005
4:leaf=-0.0416518636
2:[f4<0.280614644] yes=5,no=6,missing=6
5:[f0<0.633971274] yes=7,no=8,missing=8
7:[f6<0.161429822] yes=11,no=12,missing=12
11:leaf=-0.0264336802
12:leaf=0.2811459
8:leaf=-0.0918180496
6:[f0<0.908376873] yes=9,no=10,missing=10
9:[f2<0.969576836] yes=13,no=14,missing=14
13:[f5<0.385503411] yes=17,no=18,missing=18
17:[f5<0.291640192] yes=25,no=26,missing=26
25:leaf=0.0533528067
26:leaf=-0.200874999
18:[f7<0.538562596] yes=27,no=28,missing=28
27:leaf=-0.0923695266
28:leaf=0.165786266
14:[f5<0.511613727] yes=19,no=20,missing=20
19:leaf=-0.299741477
20:leaf=-0.075053677
10:[f2<0.999821126] yes=15,no=16,missing=16
15:[f5<0.74511838] yes=21,no=22,missing=22
21:[f0<0.97059834] yes=29,no=30,missing=30
29:leaf=0.0192103647
30:leaf=0.23894991
22:leaf=-0.0374556482
16:[f4<0.462543845] yes=23,no=24,missing=24
23:leaf=-0.212168157
24:leaf=0.0329220667
booster[12]:
0:[f2<0.494544029] yes=1,no=2,missing=2
1:leaf=-0.262809455
2:[f4<0.327130586] yes=3,no=4,missing=4
3:[f2<0.848099828] yes=5,no=6,missing=6
5:[f1<0.405181319] yes=9,no=10,missing=10
9:leaf=-0.0547375083
10:[f3<0.400596529] yes=17,no=18,missing=18
17:[f7<0.471964955] yes=29,no=30,missing=30
29:leaf=0.0823116228
30:leaf=0.317718297
18:leaf=0.0225857422
6:[f7<0.60982132] yes=11,no=12,missing=12
11:[f1<0.533979893] yes=19,no=20,missing=20
19:leaf=-0.076042816
20:[f6<0.265769839] yes=31,no=32,missing=32
31:leaf=0.0277713481
32:leaf=0.193506137
12:leaf=-0.178375423
4:[f1<0.61749512] yes=7,no=8,missing=8
7:[f3<0.322769463] yes=13,no=14,missing=14
13:[f4<0.402831346] yes=21,no=22,missing=22
21:leaf=0.180170134
22:leaf=-0.114291616
14:[f4<0.363732725] yes=23,no=24,missing=24
23:[f6<0.269844413] yes=33,no=34,missing=34
33:leaf=-0.111085869
34:leaf=0.0691967607
24:[f2<0.67366159] yes=35,no=36,missing=36
35:leaf=-0.0159534644
36:leaf=-0.269385934
8:[f2<0.99801147] yes=15,no=16,missing=16
15:[f0<0.908376873] yes=25,no=26,missing=26
25:[f2<0.969576836] yes=37,no=38,missing=38
37:leaf=0.14410919
38:leaf=-0.114789091
26:leaf=0.249786764
16:[f3<0.2983073] yes=27,no=28,missing=28
27:[f7<0.528133452] yes=39,no=40,missing=40
39:leaf=-0.313391507
40:leaf=-0.00153360132
28:[f7<0.522722125] yes=41,no=42,missing=42
41:leaf=0.156105861
42:leaf=-0.119221091
booster[13]:
0:[f2<0.494544029] yes=1,no=2,missing=2
1:leaf=-0.251960576
2:[f6<0.705945671] yes=3,no=4,missing=4
3:[f3<0.675634325] yes=5,no=6,missing=6
5:[f1<0.637513697] yes=7,no=8,missing=8
7:[f4<0.40580824] yes=11,no=12,missing=12
11:[f6<0.34447065] yes=15,no=16,missing=16
15:leaf=-0.0412482992
16:leaf=0.148420811
12:[f6<0.455611378] yes=17,no=18,missing=18
17:leaf=-0.23686485
18:leaf=-0.0280051958
8:[f2<0.99801147] yes=13,no=14,missing=14
13:[f7<0.627484322] yes=19,no=20,missing=20
19:leaf=0.259605259
20:leaf=0.0205010045
14:[f3<0.294231057] yes=21,no=22,missing=22
21:leaf=-0.135383695
22:leaf=0.0869348422
6:[f2<0.807594597] yes=9,no=10,missing=10
9:leaf=-0.0315424502
10:leaf=-0.208580092
4:leaf=-0.209767893
booster[14]:
0:[f1<0.325174659] yes=1,no=2,missing=2
1:leaf=-0.241520524
2:[f4<0.327130586] yes=3,no=4,missing=4
3:[f3<0.437603265] yes=5,no=6,missing=6
5:[f2<0.883008003] yes=9,no=10,missing=10
9:[f1<0.49794367] yes=15,no=16,missing=16
15:[f6<0.200197995] yes=27,no=28,missing=28
27:leaf=-0.165324524
28:leaf=0.179644644
16:leaf=0.308889866
10:[f3<0.201739192] yes=17,no=18,missing=18
17:leaf=0.114547119
18:[f5<0.413971484] yes=29,no=30,missing=30
29:leaf=-0.209084675
30:leaf=0.0405442677
6:leaf=-0.156731039
4:[f0<0.795038283] yes=7,no=8,missing=8
7:[f4<0.401736468] yes=11,no=12,missing=12
11:[f2<0.788840115] yes=19,no=20,missing=20
19:[f4<0.356014639] yes=31,no=32,missing=32
31:leaf=-0.054115057
32:leaf=0.129571512
20:[f0<0.633971274] yes=33,no=34,missing=34
33:leaf=-0.213016257
34:leaf=0.0229289196
12:[f5<0.642284513] yes=21,no=22,missing=22
21:leaf=-0.22311987
22:leaf=-0.016692128
8:[f2<0.999821126] yes=13,no=14,missing=14
13:[f4<0.373637646] yes=23,no=24,missing=24
23:leaf=-0.0862897709
24:[f4<0.446527421] yes=35,no=36,missing=36
35:leaf=0.177606404
36:leaf=-0.0188792907
14:[f2<0.999969244] yes=25,no=26,missing=26
25:leaf=-0.200768694
26:leaf=0.0429412611
booster[15]:
0:[f0<0.422372401] yes=1,no=2,missing=2
1:[f3<0.208352372] yes=3,no=4,missing=4
3:leaf=-0.0619732291
4:leaf=-0.229850158
2:[f6<0.705945671] yes=5,no=6,missing=6
5:[f4<0.29255116] yes=7,no=8,missing=8
7:[f6<0.165122598] yes=9,no=10,missing=10
9:leaf=-0.0872740075
10:[f5<0.428650796] yes=13,no=14,missing=14
13:leaf=0.228381619
14:leaf=0.00277113914
8:[f1<0.46713987] yes=11,no=12,missing=12
11:[f3<0.369019806] yes=15,no=16,missing=16
15:[f1<0.409300059] yes=19,no=20,missing=20
19:leaf=0.0222900845
20:leaf=-0.0629349649
16:leaf=-0.202644885
12:[f6<0.34447065] yes=17,no=18,missing=18
17:[f6<0.317662865] yes=21,no=22,missing=22
21:leaf=0.010082203
22:leaf=-0.196786657
18:[f2<0.997871041] yes=23,no=24,missing=24
23:leaf=0.136315882
24:leaf=-0.0428045839
6:leaf=-0.19570154
booster[16]:
0:[f2<0.494544029] yes=1,no=2,missing=2
1:leaf=-0.219898552
2:[f6<0.705945671] yes=3,no=4,missing=4
3:[f3<0.675634325] yes=5,no=6,missing=6
5:[f1<0.571113765] yes=7,no=8,missing=8
7:[f2<0.690423727] yes=11,no=12,missing=12
11:[f1<0.4708969] yes=15,no=16,missing=16
15:leaf=-0.0377374217
16:leaf=0.17511788
12:[f6<0.34447065] yes=17,no=18,missing=18
17:leaf=-0.175342739
18:leaf=0.00834679697
8:[f0<0.600520432] yes=13,no=14,missing=14
13:leaf=0.278150171
14:[f4<0.4001638] yes=19,no=20,missing=20
19:leaf=-0.0722937435
20:leaf=0.0902283937
6:[f7<0.551590323] yes=9,no=10,missing=10
9:leaf=-0.194653437
10:leaf=-0.0113157313
4:leaf=-0.181837022
booster[17]:
0:[f1<0.325174659] yes=1,no=2,missing=2
1:leaf=-0.20747526
2:[f4<0.383024812] yes=3,no=4,missing=4
3:[f7<0.448171556] yes=5,no=6,missing=6
5:leaf=-0.109604843
6:[f3<0.437603265] yes=9,no=10,missing=10
9:[f2<0.977534413] yes=15,no=16,missing=16
15:[f6<0.151116922] yes=19,no=20,missing=20
19:leaf=-0.0337385722
20:leaf=0.159264073
16:[f6<0.286427647] yes=21,no=22,missing=22
21:leaf=0.102646679
22:leaf=-0.202553332
10:leaf=-0.0927646011
4:[f5<0.426278144] yes=7,no=8,missing=8
7:[f3<0.269215077] yes=11,no=12,missing=12
11:leaf=-0.00906175561
12:leaf=-0.222224444
8:[f4<0.392702997] yes=13,no=14,missing=14
13:leaf=-0.128316253
14:[f5<0.500192344] yes=17,no=18,missing=18
17:[f0<0.957570314] yes=23,no=24,missing=24
23:leaf=0.0148146516
24:leaf=0.237045392
18:[f3<0.512349129] yes=25,no=26,missing=26
25:leaf=-0.111909963
26:leaf=0.0763963088
booster[18]:
0:[f1<0.46713987] yes=1,no=2,missing=2
1:[f0<0.540145934] yes=3,no=4,missing=4
3:leaf=-0.19723703
4:[f2<0.737321436] yes=7,no=8,missing=8
7:leaf=0.132516995
8:leaf=-0.0996495336
2:[f2<0.848099828] yes=5,no=6,missing=6
5:[f0<0.612285852] yes=9,no=10,missing=10
9:[f1<0.543526292] yes=13,no=14,missing=14
13:[f2<0.645647883] yes=21,no=22,missing=22
21:leaf=0.141625986
22:leaf=-0.149567693
14:leaf=0.265230954
10:[f1<0.539158821] yes=15,no=16,missing=16
15:leaf=0.105466016
16:[f2<0.720247805] yes=23,no=24,missing=24
23:leaf=-0.216264173
24:leaf=0.0121331559
6:[f1<0.673115373] yes=11,no=12,missing=12
11:[f0<0.948889911] yes=17,no=18,missing=18
17:[f6<0.34447065] yes=25,no=26,missing=26
25:leaf=-0.226914674
26:leaf=-0.0403409936
18:leaf=0.0620711297
12:[f2<0.99801147] yes=19,no=20,missing=20
19:[f5<0.39393115] yes=27,no=28,missing=28
27:leaf=-0.00607117312
28:leaf=0.166878998
20:[f3<0.2983073] yes=29,no=30,missing=30
29:[f4<0.327130586] yes=31,no=32,missing=32
31:leaf=-0.0180181284
32:leaf=-0.194830343
30:[f3<0.326372564] yes=33,no=34,missing=34
33:leaf=0.197316736
34:leaf=-0.0569760092
booster[19]:
0:[f0<0.422372401] yes=1,no=2,missing=2
1:leaf=-0.164744183
2:[f3<0.373620301] yes=3,no=4,missing=4
3:[f2<0.977534413] yes=5,no=6,missing=6
5:[f0<0.570021033] yes=9,no=10,missing=10
9:[f3<0.306287706] yes=15,no=16,missing=16
15:leaf=0.121178553
16:leaf=-0.119341843
10:[f4<0.402831346] yes=17,no=18,missing=18
17:[f6<0.29543516] yes=25,no=26,missing=26
25:leaf=0.0329033472
26:leaf=0.20990245
18:leaf=-0.0107636759
6:[f0<0.963036537] yes=11,no=12,missing=12
11:[f1<0.688540101] yes=19,no=20,missing=20
19:leaf=-0.176529199
20:leaf=-0.0322653912
12:[f6<0.232030913] yes=21,no=22,missing=22
21:leaf=-0.146197587
22:[f7<0.528133452] yes=27,no=28,missing=28
27:leaf=0.0144899823
28:leaf=0.170358211
4:[f1<0.477993608] yes=7,no=8,missing=8
7:leaf=-0.196281388
8:[f0<0.557111859] yes=13,no=14,missing=14
13:leaf=0.175396875
14:[f0<0.894690931] yes=23,no=24,missing=24
23:[f6<0.383571625] yes=29,no=30,missing=30
29:leaf=-0.199342474
30:leaf=-0.0318984278
24:[f7<0.538562596] yes=31,no=32,missing=32
31:leaf=0.143692642
32:leaf=-0.101043411
from xgboost-fastforest.
guitargeek
commented on August 22, 2024
Thanks! Can you please also attach the output of:
xgbc.get_booster().save_model('model.json')Maybe attach the file instead of copy-pasting the output, because this quite a long one.
Also, what are the feature values at the point where you get the mismatch?
from xgboost-fastforest.
inkzk
commented on August 22, 2024
c.get_booster().save_model('model.json')
@guitargeek
Sorry for my network regulation, no attachment file could be uploaded...
Let feature be X = [[0.5]*8]
Output of python is [[0.5282326 , 0.47176743]], while fastforest got 0.90 after sigmoid with baseResponse=0.5
from xgboost-fastforest.
guitargeek
commented on August 22, 2024
@guitargeek Sorry for my network regulation, no attachment file could be uploaded...
Then maybe you could just copy-paste the content of a JSON file to a post here, just like you did with the txt dump?
If I don't have access to the original xgboost model, it's very hard to debug these kind of problems, even if I would like to 🙂
The problem with the txt files is that they can't be read back in to xgboost.
from xgboost-fastforest.
inkzk
commented on August 22, 2024
@guitargeek Nice idea! Json content as below:
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from xgboost-fastforest.
guitargeek
commented on August 22, 2024
Thanks for sharing! It has indeed something do do with the right value for the base_score, which is not dumped to the txt file that FastForest takes as an input.
I have added a section in the README that explains how to work around this problem:
https://github.com/guitargeek/XGBoost-FastForest?tab=readme-ov-file#models-with-non-zero-base-response
I hope this helps you! If you have further problems, feel free to re-open this issues or open a new one.
from xgboost-fastforest.
inkzk
commented on August 22, 2024
@guitargeek nice! NICE!
Small bug, base_score should acutally be base_response at third row in the README?
float base_score = /* the output of get_basescore from Python */;
float base_response = std::log(base_score / (1.0 - base_score));
float score = 1./(1. + std::exp(-fastForest(input.data(), base_score)));
from xgboost-fastforest.
guitargeek
commented on August 22, 2024
Thanks! I have updated that
from xgboost-fastforest.
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from xgboost-fastforest.