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h2oai / h2o-3

H2O is an Open Source, Distributed, Fast & Scalable Machine Learning Platform: Deep Learning, Gradient Boosting (GBM) & XGBoost, Random Forest, Generalized Linear Modeling (GLM with Elastic Net), K-Means, PCA, Generalized Additive Models (GAM), RuleFit, Support Vector Machine (SVM), Stacked Ensembles, Automatic Machine Learning (AutoML), etc.
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GBM:Got exception 'class java.lang.AssertionError', with msg 'Something is wrong with GBM trees since returned prediction is Infinity #13713

Closed exalate-issue-sync[bot] closed 1 year ago

exalate-issue-sync[bot] commented 1 year ago

H2O BUILD GIT HASH d213bdf11a97b71cf913194f2231d75146d71e7e On a two node cluster(might happen on one)- Fails in approx ~10 min parseFiles paths: ["/home/nidhi/h2o-dev/bigdata/laptop/usecases/cup98LRN_z.csv"] destination_key: "cup98LRN_z.hex" parse_type: "CSV" separator: 44 number_columns: 482 single_quotes: false column_names: 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delete_on_done: true check_header: 1 chunk_size: 524288

parseFiles paths: ["/home/nidhi/h2o-dev/bigdata/laptop/usecases/cup98VAL_z.csv"] destination_key: "cup98VAL_z.hex" parse_type: "CSV" separator: 44 number_columns: 482 single_quotes: false column_names: 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delete_on_done: true check_header: 1 chunk_size: 524288

buildModel 'gbm', {"destination_key":"gbm-cf2b12f0-9f6f-48a4-acac-6f42052c5932","training_frame":"cup98LRN_z.hex","validation_frame":"cup98VAL_z.hex","dropNA20Cols":false,"response_column":"OSOURCE","ntrees":50,"max_depth":5,"min_rows":10,"nbins":20,"learn_rate":0.1,"loss":"multinomial","balance_classes":false,"class_sampling_factors":[],"max_after_balance_size":5,"seed":0} Got exception 'class java.lang.AssertionError', with msg 'Something is wrong with GBM trees since returned prediction is [0.0, 1.1743252500891685, -0.28454260528087616, 3.0264896601438522, -0.300055168569088, 0.7435242235660553, -0.28547532111406326, -0.2964564338326454, -0.24810117110610008, 0.2528955861926079, -0.131042274646461, -0.2998165115714073, -0.1127264704555273, -0.30626535415649414, -0.299826443195343, -0.30122558027505875, -0.2818661779165268, -0.13545069843530655, -0.1152833104133606, -0.29983747005462646, 0.304083202034235, 0.3090212866663933, -0.30271925777196884, -0.28841541707515717, 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hex.genmodel.GenModel.GBM_rescale(GenModel.java:183) at hex.tree.gbm.GBMModel.score0(GBMModel.java:59) at hex.tree.gbm.GBMModel.score0(GBMModel.java:37) at hex.tree.Score.map(Score.java:48) at water.MRTask.compute2(MRTask.java:415) at water.MRTask.compute2(MRTask.java:374) at water.MRTask.compute2(MRTask.java:374) at water.MRTask.compute2(MRTask.java:374) at water.H2O$H2OCountedCompleter.compute(H2O.java:678) at jsr166y.CountedCompleter.exec(CountedCompleter.java:429) at jsr166y.ForkJoinTask.doExec(ForkJoinTask.java:263) at jsr166y.ForkJoinPool$WorkQueue.runTask(ForkJoinPool.java:974) at jsr166y.ForkJoinPool.runWorker(ForkJoinPool.java:1477) at jsr166y.ForkJoinWorkerThread.run(ForkJoinWorkerThread.java:104)

exalate-issue-sync[bot] commented 1 year ago

Nidhi Mehta commented: On H2O BUILD GIT HASH 4c93ddfd92801fdef60961d44ccb7cf512f37a90 On two nodes Looks like some chunk is missing- parseFiles paths: ["/home/nidhi/h2o-dev/bigdata/laptop/usecases/cup98LRN_z.csv"] destination_key: "cup98LRN_z.hex" parse_type: "CSV" separator: 44 number_columns: 482 single_quotes: false column_names: 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delete_on_done: true check_header: 1 chunk_size: 524288

buildModel 'gbm', {"destination_key":"gbm-f9f73a56-687e-4681-8720-d4a179aa2f3f","training_frame":"cup98LRN_z.hex","ignored_columns":["","RECPGVG"],"dropNA20Cols":false,"response_column":"OSOURCE","ntrees":50,"max_depth":5,"min_rows":10,"nbins":20,"learn_rate":0.1,"loss":"multinomial","balance_classes":false,"class_sampling_factors":[],"max_after_balance_size":5,"seed":0}

java.lang.AssertionError at hex.ModelMetricsMultinomial$MetricBuilderMultinomial.perRow(ModelMetricsMultinomial.java:99) at hex.tree.Score.map(Score.java:53) at water.MRTask.compute2(MRTask.java:415) at water.MRTask.compute2(MRTask.java:374) at water.MRTask.compute2(MRTask.java:374) at water.MRTask.compute2(MRTask.java:374) at water.MRTask.compute2(MRTask.java:374) at water.MRTask.compute2(MRTask.java:374) at water.H2O$H2OCountedCompleter.compute(H2O.java:680) at jsr166y.CountedCompleter.exec(CountedCompleter.java:429) at jsr166y.ForkJoinTask.doExec(ForkJoinTask.java:263) at jsr166y.ForkJoinPool$WorkQueue.runTask(ForkJoinPool.java:974) at jsr166y.ForkJoinPool.runWorker(ForkJoinPool.java:1477) at jsr166y.ForkJoinWorkerThread.run(ForkJoinWorkerThread.java:104) 04-06 22:26:19.216 172.16.2.184:54545 16644 FJ-1-39 ERRR: Error: Missing chunk 119 for $04ff9e090000ffffffff$nfs://home/nidhi/h2o-dev/bigdata/laptop/usecases/cup98LRN_z.csv onExCompletion for hex.tree.Score@711ae512 onExCompletion for hex.tree.Score@711ae512

exalate-issue-sync[bot] commented 1 year ago

Arno Candel commented: Reproduced on 1 node (mr-0xd1), infinity introduced in GBM_rescale.

Got exception 'class java.lang.AssertionError', with msg 'Something is wrong with GBM trees since returned prediction is [0.0, 1.197975866496563, -0.28458334505558014, -0.033680135384202003, -0.30006178468465805, 0.9007728397846222, -0.29234615713357925, -0.2962179332971573, -0.2998272702097893, 0.22422577813267708, 0.001958012580871582, -0.29984042048454285, ... 5, 0.7864273823797703, -0.25147680193185806, 0.5636077672243118, -0.2998337969183922, -0.13751921942457557, Infinity, -0.15872759371995926, ... .4530247710645199, -0.23025134950876236, -0.29983846843242645] at hex.genmodel.GenModel.GBM_rescale(GenModel.java:183) at hex.tree.gbm.GBMModel.score0(GBMModel.java:59) at hex.tree.gbm.GBMModel.score0(GBMModel.java:37) at hex.tree.Score.map(Score.java:48) at water.MRTask.compute2(MRTask.java:415) at water.MRTask.compute2(MRTask.java:374) at water.MRTask.compute2(MRTask.java:374) at water.MRTask.compute2(MRTask.java:374) at water.H2O$H2OCountedCompleter.compute(H2O.java:680) at jsr166y.CountedCompleter.exec(CountedCompleter.java:429) at jsr166y.ForkJoinTask.doExec(ForkJoinTask.java:263) at jsr166y.ForkJoinPool$WorkQueue.runTask(ForkJoinPool.java:974) at jsr166y.ForkJoinPool.runWorker(ForkJoinPool.java:1477) at jsr166y.ForkJoinWorkerThread.run(ForkJoinWorkerThread.java:104)

exalate-issue-sync[bot] commented 1 year ago

Arno Candel commented: double gp._gss[k][i] = 1e-44 => double g = 1/1e-44 = 1e44 => (float)g -> Inf

The code checks if _gss == 0, but it should check whether Math.abs(_gss) < 1e-15 or something like that.

exalate-issue-sync[bot] commented 1 year ago

Arno Candel commented: It's running now - but adding trees makes results worse. Note: the previous Infinities creep in at tree 3.

04-07 19:48:37.144 172.16.2.181:53322 19275 # Session INFO: Method: POST , URI: /3/ModelBuilders.json/gbm, route: /3/ModelBuilders/gbm, parms: {max_after_balance_size=5, validation_frame=cup98VAL_z.hex, response_column=OSOURCE, dropNA20Cols=false, max_depth=5, balance_classes=false, nbins=20, seed=0, training_frame=cup98LRN_z.hex, learn_rate=0.1, destination_key=gbm-cf2b12f0-9f6f-48a4-acac-6f42052c5932, ntrees=50, class_sampling_factors=, min_rows=10, loss=multinomial} 04-07 19:48:37.234 172.16.2.181:53322 19275 FJ-0-23 INFO: {"_destination_key":{"name":"gbm-cf2b12f0-9f6f-48a4-acac-6f42052c5932","type":"Key"},"_train":{"name":"cup98LRN_z.hex","type":"Key"},"_valid":{"name":"cup98VAL_z.hex","type":"Key"},"_ignored_columns":null,"_dropNA20Cols":false,"_dropConsCols":true,"_score_each_iteration":false,"_max_confusion_matrix_size":20,"_response_column":"OSOURCE","_balance_classes":false,"_max_after_balance_size":5.0,"_class_sampling_factors":null,"_max_hit_ratio_k":10,"_ntrees":50,"_max_depth":5,"_min_rows":10,"_nbins":20,"_score_each_iteration":false,"_seed":0,"_checkpoint":false,"_loss":"multinomial","_learn_rate":0.1} 04-07 19:48:39.325 172.16.2.181:53322 19275 FJ-0-23 INFO: training r2 is 0.9999843, mse is 0.9977691, logloss is 6.7979403, with 0x896 trees (average of 1.0 nodes) 04-07 19:52:30.072 172.16.2.181:53322 19275 FJ-0-23 INFO: training r2 is 0.99998784, mse is 0.77241397, logloss is 8.735325, with 1x896 trees (average of 17.206472 nodes) 04-07 19:57:44.225 172.16.2.181:53322 19275 FJ-0-23 INFO: training r2 is 0.9999866, mse is 0.8553659, logloss is 16.175417, with 2x896 trees (average of 20.957031 nodes) 04-07 20:03:10.394 172.16.2.181:53322 19275 FJ-0-23 INFO: training r2 is 0.9999864, mse is 0.86475646, logloss is 18.824417, with 3x896 trees (average of 22.789806 nodes) 04-07 20:08:35.045 172.16.2.181:53322 19275 FJ-0-23 INFO: training r2 is 0.9999863, mse is 0.87356645, logloss is 20.472101, with 4x896 trees (average of 23.498325 nodes) 04-07 20:14:02.392 172.16.2.181:53322 19275 FJ-0-23 INFO: training r2 is 0.9999861, mse is 0.8833435, logloss is 22.243744, with 5x896 trees (average of 23.71518 nodes) 04-07 20:19:33.029 172.16.2.181:53322 19275 FJ-0-23 INFO: training r2 is 0.9999858, mse is 0.90249455, logloss is 24.287045, with 6x896 trees (average of 23.636347 nodes) 04-07 20:25:09.486 172.16.2.181:53322 19275 FJ-0-23 INFO: training r2 is 0.9999855, mse is 0.9239879, logloss is 27.141312, with 7x896 trees (average of 23.464605 nodes) 04-07 20:35:02.465 172.16.2.181:53322 19275 FJ-0-23 INFO: training r2 is 0.9999844, mse is 0.99311554, logloss is 34.272266, with 9x896 trees (average of 22.69494 nodes) 04-07 20:48:41.757 172.16.2.181:53322 19275 FJ-0-23 INFO: training r2 is 0.9999844, mse is 0.99312603, logloss is 34.27263, with 12x896 trees (average of 21.666666 nodes) 04-07 21:02:43.993 172.16.2.181:53322 19275 FJ-0-23 INFO: training r2 is 0.9999844, mse is 0.99312603, logloss is 34.27263, with 15x896 trees (average of 20.88125 nodes) 04-07 21:21:03.117 172.16.2.181:53322 19275 FJ-0-23 INFO: training r2 is 0.9999844, mse is 0.99312603, logloss is 34.27263, with 19x896 trees (average of 20.177631 nodes) 04-07 21:44:02.031 172.16.2.181:53322 19275 FJ-0-23 INFO: training r2 is 0.9999844, mse is 0.99312603, logloss is 34.27263, with 24x896 trees (average of 19.605003 nodes) 04-07 22:12:19.811 172.16.2.181:53322 19275 FJ-0-23 INFO: training r2 is 0.9999844, mse is 0.99312603, logloss is 34.27263, with 30x896 trees (average of 19.15346 nodes) 04-07 22:44:53.729 172.16.2.181:53322 19275 FJ-0-23 INFO: training r2 is 0.9999844, mse is 0.99312603, logloss is 34.27263, with 37x896 trees (average of 18.803541 nodes) 04-07 23:22:21.470 172.16.2.181:53322 19275 FJ-0-23 INFO: training r2 is 0.9999844, mse is 0.99312603, logloss is 34.27263, with 45x896 trees (average of 18.527058 nodes) 04-07 23:47:42.851 172.16.2.181:53322 19275 FJ-0-23 INFO: training r2 is 0.9999844, mse is 0.99312603, logloss is 34.27263, with 50x896 trees (average of 18.39614 nodes)

exalate-issue-sync[bot] commented 1 year ago

Arno Candel commented: Fixed by Cliff's overflow changes.

DinukaH2O commented 1 year ago

JIRA Issue Migration Info

Jira Issue: PUBDEV-722 Assignee: Arno Candel Reporter: Nidhi Mehta State: Resolved Fix Version: N/A Attachments: N/A Development PRs: N/A