java.lang.AssertionError: invalid gradient/direction, got positive differential 0.0010490659578066802

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  • Running multinomial (with lambda = 0, family='multinomial', rest default) on the given dataset result in following error 09-19 04:36:13.387 172.17.2.154:50000 3547 #70588-12 INFO: POST /3/ModelBuilders/glm, parms: {missing_values_handling=MeanImputation, lambda=0, family=multinomial, training_frame=py_10_sid_bb70, response_column=C6} 09-19 04:36:13.391 172.17.2.154:50000 3547 FJ-1-41 INFO: Building H2O GLM model with these parameters: 09-19 04:36:13.391 172.17.2.154:50000 3547 FJ-1-41 INFO: {"_train":{"name":"py_10_sid_bb70","type":"Key"},"_valid":null,"_nfolds":0,"_keep_cross_validation_predictions":false,"_keep_cross_validation_fold_assignment":false,"_parallelize_cross_validation":true,"_auto_rebalance":true,"_seed":-1,"_fold_assignment":"AUTO","_categorical_encoding":"AUTO","_distribution":"AUTO","_tweedie_power":1.5,"_quantile_alpha":0.5,"_huber_alpha":0.9,"_ignored_columns":null,"_ignore_const_cols":true,"_weights_column":null,"_offset_column":null,"_fold_column":null,"_is_cv_model":false,"_score_each_iteration":false,"_max_runtime_secs":0.0,"_stopping_rounds":3,"_stopping_metric":"deviance","_stopping_tolerance":1.0E-4,"_response_column":"C6","_balance_classes":false,"_max_after_balance_size":5.0,"_class_sampling_factors":null,"_max_confusion_matrix_size":20,"_checkpoint":null,"_pretrained_autoencoder":null,"_standardize":true,"_family":"multinomial","_link":"family_default","_solver":"AUTO","_tweedie_variance_power":0.0,"_tweedie_link_power":1.0,"_alpha":null,"_lambda":[0.0],"_missing_values_handling":"MeanImputation","_prior":-1.0,"_lambda_search":false,"_nlambdas":-1,"_non_negative":false,"_exactLambdas":false,"_lambda_min_ratio":-1.0,"_use_all_factor_levels":false,"_max_iterations":-1,"_intercept":true,"_beta_epsilon":1.0E-4,"_objective_epsilon":-1.0,"_gradient_epsilon":-1.0,"_obj_reg":-1.0,"_compute_p_values":false,"_remove_collinear_columns":false,"_interactions":null,"_early_stopping":true,"_beta_constraints":null,"_max_active_predictors":-1,"_stdOverride":false} 09-19 04:36:13.395 172.17.2.154:50000 3547 FJ-1-41 INFO: GLM[dest=GLM_model_python_1474284809696_2955, iter=0 lmb=.0E0 obj=1.3183 imp=.1E1 bdf=.22E1] picked solver IRLSM made vecs [[95, {/172.17.2.154:50000:0:}], [95, {/172.17.2.154:50000:0:}]] 09-19 04:36:13.396 172.17.2.154:50000 3547 FJ-1-41 INFO: GLM[dest=GLM_model_python_1474284809696_2955, iter=0 lmb=.0E0 obj=1.3183 imp=.1E1 bdf=.22E1] Class 0 got 5 active columns out of 5 total 09-19 04:36:13.396 172.17.2.154:50000 3547 FJ-1-41 INFO: GLM[dest=GLM_model_python_1474284809696_2955, iter=0 lmb=.0E0 obj=1.3183 imp=.1E1 bdf=.22E1] Class 1 got 5 active columns out of 5 total 09-19 04:36:13.396 172.17.2.154:50000 3547 FJ-1-41 INFO: GLM[dest=GLM_model_python_1474284809696_2955, iter=0 lmb=.0E0 obj=1.3183 imp=.1E1 bdf=.22E1] Class 2 got 5 active columns out of 5 total 09-19 04:36:13.396 172.17.2.154:50000 3547 FJ-1-41 INFO: GLM[dest=GLM_model_python_1474284809696_2955, iter=0 lmb=.0E0 obj=1.3183 imp=.1E1 bdf=.22E1] Class 3 got 5 active columns out of 5 total 09-19 04:36:13.399 172.17.2.154:50000 3547 FJ-1-41 INFO: GLM[dest=GLM_model_python_1474284809696_2955, iter=0 lmb=.0E0 obj=1.3183 imp=.1E1 bdf=.22E1] computed in 2+1+0+0=3ms, step = 1.4165876969584055 09-19 04:36:13.402 172.17.2.154:50000 3547 FJ-1-41 INFO: GLM[dest=GLM_model_python_1474284809696_2955, iter=0 lmb=.0E0 obj=1.3183 imp=.1E1 bdf=.22E1] computed in 2+1+0+0=3ms, step = 0.733585713016148 09-19 04:36:13.417 172.17.2.154:50000 3547 FJ-1-41 INFO: GLM[dest=GLM_model_python_1474284809696_2955, iter=0 lmb=.0E0 obj=1.3183 imp=.1E1 bdf=.22E1] computed in 8+6+1+0=15ms, step = 1.3305058187791514 09-19 04:36:13.429 172.17.2.154:50000 3547 FJ-1-41 INFO: GLM[dest=GLM_model_python_1474284809696_2955, iter=0 lmb=.0E0 obj=1.3183 imp=.1E1 bdf=.22E1] computed in 2+9+0+1=12ms, step = 0.3680862155100146 09-19 04:36:13.432 172.17.2.154:50000 3547 FJ-1-41 INFO: GLM[dest=GLM_model_python_1474284809696_2955, iter=1 lmb=.0E0 obj=0.7632 imp=.42E0 bdf=.16E1] computed in 1+1+0+1=3ms, step = 1.5871265752052253 09-19 04:36:13.439 172.17.2.154:50000 3547 FJ-1-41 INFO: GLM[dest=GLM_model_python_1474284809696_2955, iter=1 lmb=.0E0 obj=0.7632 imp=.42E0 bdf=.16E1] computed in 1+5+0+1=7ms, step = 1.0 09-19 04:36:13.442 172.17.2.154:50000 3547 FJ-1-41 ERRR: java.lang.AssertionError: invalid gradient/direction, got positive differential 0.0010490659578066802 09-19 04:36:13.442 172.17.2.154:50000 3547 FJ-1-41 ERRR: at hex.optimization.OptimizationUtils$MoreThuente.evaluate(OptimizationUtils.java:334) 09-19 04:36:13.442 172.17.2.154:50000 3547 FJ-1-41 ERRR: at hex.glm.GLM$GLMDriver.fitIRLSM_multinomial(GLM.java:606) 09-19 04:36:13.442 172.17.2.154:50000 3547 FJ-1-41 ERRR: at hex.glm.GLM$GLMDriver.fitModel(GLM.java:907) 09-19 04:36:13.442 172.17.2.154:50000 3547 FJ-1-41 ERRR: at hex.glm.GLM$GLMDriver.computeSubmodel(GLM.java:980) 09-19 04:36:13.442 172.17.2.154:50000 3547 FJ-1-41 ERRR: at hex.glm.GLM$GLMDriver.computeImpl(GLM.java:1042) 09-19 04:36:13.442 172.17.2.154:50000 3547 FJ-1-41 ERRR: at hex.ModelBuilder$Driver.compute2(ModelBuilder.java:169) 09-19 04:36:13.442 172.17.2.154:50000 3547 FJ-1-41 ERRR: at hex.glm.GLM$GLMDriver.compute2(GLM.java:515) 09-19 04:36:13.442 172.17.2.154:50000 3547 FJ-1-41 ERRR: at water.H2O$H2OCountedCompleter.compute(H2O.java:1198) 09-19 04:36:13.442 172.17.2.154:50000 3547 FJ-1-41 ERRR: at jsr166y.CountedCompleter.exec(CountedCompleter.java:468) 09-19 04:36:13.442 172.17.2.154:50000 3547 FJ-1-41 ERRR: at jsr166y.ForkJoinTask.doExec(ForkJoinTask.java:263) 09-19 04:36:13.442 172.17.2.154:50000 3547 FJ-1-41 ERRR: at jsr166y.ForkJoinPool$WorkQueue.runTask(ForkJoinPool.java:974) 09-19 04:36:13.442 172.17.2.154:50000 3547 FJ-1-41 ERRR: at jsr166y.ForkJoinPool.runWorker(ForkJoinPool.java:1477) 09-19 04:36:13.442 172.17.2.154:50000 3547 FJ-1-41 ERRR: at jsr166y.ForkJoinWorkerThread.run(ForkJoinWorkerThread.java:104) 09-19 04:36:13.618 172.17.2.154:50000 3547 #70588-15 INFO: DELETE /4/sessions/_sid_bb70, parms: {}
    via by Tomas Nykodym,
  • on - H2O Build git hash b097777d1ce872722cdcd48264e6be45ee462230 H2O Built on 2016-12-01 14:50:11 alpha =0; nfold=5; meanimputaion; seed = 4962489898628097000 {code:java} [1] "dataset-71" [1] "/home/nidhi/auto_sklearn_csv/hypothyroid.arff.txt" |======================================================================| 100% | | 0% java.lang.AssertionError: invalid gradient/direction, got positive differential 0.028143596300444426 java.lang.AssertionError: invalid gradient/direction, got positive differential 0.028143596300444426 at hex.optimization.OptimizationUtils$MoreThuente.evaluate(OptimizationUtils.java:334) at hex.glm.GLM$GLMDriver.fitIRLSM(GLM.java:677) at hex.glm.GLM$GLMDriver.fitModel(GLM.java:933) at hex.glm.GLM$GLMDriver.computeSubmodel(GLM.java:1002) at hex.glm.GLM$GLMDriver.computeImpl(GLM.java:1071) at hex.ModelBuilder$Driver.compute2(ModelBuilder.java:169) at hex.glm.GLM$GLMDriver.compute2(GLM.java:535) at water.H2O$H2OCountedCompleter.compute(H2O.java:1214) at jsr166y.CountedCompleter.exec(CountedCompleter.java:468) at jsr166y.ForkJoinTask.doExec(ForkJoinTask.java:263) at jsr166y.ForkJoinPool$WorkQueue.runTask(ForkJoinPool.java:974) at jsr166y.ForkJoinPool.runWorker(ForkJoinPool.java:1477) at jsr166y.ForkJoinWorkerThread.run(ForkJoinWorkerThread.java:104) Error: java.lang.AssertionError: invalid gradient/direction, got positive differential 0.028143596300444426 {code} {code:java} data = h2o.importFile("/home/nidhi/hypothyroid.arff.txt",destination_frame = "data",header = T) response = "binaryClass" response_indx = which(names(data)==response) data[,response_indx] = as.factor(data[,response_indx]) data = h2o.assign(data,key = "data") if (length(h2o.levels(data[,response_indx]))>2){ family ="multinomial" }else{ family = "binomial" } id_col_idx = which(names(data) %in% c("ID","Id")) if(length(id_col_idx)>0){ #remove id column data = h2o.assign(data[,-id_col_idx],key = "data") } myY = response myX = setdiff(names(data),response) griables = list(alpha = c(0,.2,.4,.6,.8,1),missing_values_handling = c("MeanImputation", "Skip")) gg = h2o.grid(algorithm = "glm",grid_id = "aa", x=myX, y=myY, training_frame=data, hyper_params= griables,family = family,nfolds=5,lambda_search=TRUE, search_criteria =list(strategy = "RandomDiscrete",stopping_metric = "AUTO", stopping_tolerance = 0.0001, stopping_rounds = 3)) {code}
    via by Nidhi Mehta,
    • java.lang.AssertionError: invalid gradient/direction, got positive differential 0.0010490659578066802 at hex.optimization.OptimizationUtils$MoreThuente.evaluate(OptimizationUtils.java:334) at hex.glm.GLM$GLMDriver.fitIRLSM_multinomial(GLM.java:606) at hex.glm.GLM$GLMDriver.fitModel(GLM.java:907) at hex.glm.GLM$GLMDriver.computeSubmodel(GLM.java:980) at hex.glm.GLM$GLMDriver.computeImpl(GLM.java:1042) at hex.ModelBuilder$Driver.compute2(ModelBuilder.java:169) at hex.glm.GLM$GLMDriver.compute2(GLM.java:515) at water.H2O$H2OCountedCompleter.compute(H2O.java:1198) at jsr166y.CountedCompleter.exec(CountedCompleter.java:468) 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)
    No Bugmate found.