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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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Flow, DL: Need better fail message if "Autoencoder" and "use_all_factor_levels" are both selected #13715

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

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

Testing dependencies for DL and got this error when I tried to select "Autoencoder" and "Use_all_factor_levels" simultaneously:

importFiles [ "/Users/jessicalanford/Desktop/repo archive/h2o_3_19/smalldata/Abalone.gz" ]

buildModel 'deeplearning', {"destination_key":"deeplearning-b587c33e-5ed0-4a56-9117-cca32a85bc09","dropNA20Cols":false,"n_folds":0,"activation":"Rectifier","hidden":[200,200],"epochs":10,"variable_importances":false,"replicate_training_data":true,"balance_classes":false,"checkpoint":"","use_all_factor_levels":true,"train_samples_per_iteration":-2,"adaptive_rate":true,"rho":0.99,"epsilon":1e-8,"input_dropout_ratio":0,"hidden_dropout_ratios":[],"l1":0,"l2":0,"score_interval":5,"score_training_samples":10000,"score_validation_samples":0,"autoencoder":false,"class_sampling_factors":[],"max_after_balance_size":5,"keep_cross_validation_splits":false,"override_with_best_model":true,"target_ratio_comm_to_comp":0.02,"seed":-2363143933064064000,"rate":0.005,"rate_annealing":0.000001,"rate_decay":1,"momentum_start":0,"momentum_ramp":1000000,"momentum_stable":0,"nesterov_accelerated_gradient":true,"max_w2":"Infinity","initial_weight_distribution":"UniformAdaptive","initial_weight_scale":1,"loss":"Automatic","score_duty_cycle":0.1,"classification_stop":0,"regression_stop":0.000001,"max_hit_ratio_k":10,"score_validation_sampling":"Uniform","diagnostics":true,"fast_mode":true,"ignore_const_cols":true,"force_load_balance":true,"single_node_mode":false,"shuffle_training_data":false,"missing_values_handling":"MeanImputation","quiet_mode":false,"max_confusion_matrix_size":20,"sparse":false,"col_major":false,"average_activation":0,"sparsity_beta":0,"max_categorical_features":2147483647,"reproducible":false}

Error evaluating cell Error calling POST /3/ModelBuilders.json/deeplearning with opts {"destination_key":"deeplearning-b587c33e-5ed0-4a56-9117-cca32a85bc09","dropNA20Cols":false,"n_folds":0,"activation":"Rectifier","hidden":"[200,200]","epochs":10,"variable_importances":false,"replicate_training_data":true,"balance_classes":false,"checkpoint":"","use_all_factor_levels":true,"train_samples_per_iteration":-2,"adaptive_rate":true,"rho":0.99,"epsilon":1e-8,"input_dropout_ratio":0,"hidden_dropout_ratios":null,"l1":0,"l2":0,"score_interval":5,"score_training_samples":10000,"score_validation_samples":0,"autoencoder":false,"class_sampling_factors":null,"max_after_balance_size":5,"keep_cross_validation_splits":false,"override_with_best_model":true,"target_ratio_comm_to_comp":0.02,"seed":-2363143933064064000,"rate":0.005,"rate_annealing":0.000001,"rate_decay":1,"momentum_start":0,"momentum_ramp":1000000,"momentum_stable":0,"nesterov_accelerated_gradient":true,"max_w2":"Infinity","initial_weight_distribution":"UniformAdaptive","initial_weight_scale":1,"loss":"Automatic","score_duty_cycle":0.1,"classification_stop":0,"regression_stop":0.000001,"max_hit_ratio_k":10,"score_validation_sampling":"Uniform","diagnostics":true,"fast_mode":true,"ignore_const_cols":true,"force_load_balance":true,"single_node_mode":false,"shuffle_training_data":false,"missing_values_handling":"MeanImputation","quiet_mode":false,"max_confusion_matrix_size":20,"sparse":false,"col_major":false,"average_activation":0,"sparsity_beta":0,"max_categorical_features":2147483647,"reproducible":false}

TOGGLE STACK TRACE Caught exception: java.lang.NullPointerException from: hex.deeplearning.DeepLearning.trainModel(DeepLearning.java:54) (java.lang.NullPointerException) hex.deeplearning.DeepLearning.trainModel(DeepLearning.java:54) water.api.ModelBuilderHandler.do_train(ModelBuilderHandler.java:27) hex.api.DeepLearningBuilderHandler.train(DeepLearningBuilderHandler.java:12) sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method) sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:57) sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43) java.lang.reflect.Method.invoke(Method.java:606) water.api.Handler.handle(Handler.java:57) water.api.RequestServer.handle(RequestServer.java:664) water.api.RequestServer.serve(RequestServer.java:602) water.NanoHTTPD$HTTPSession.run(NanoHTTPD.java:434) java.lang.Thread.run(Thread.java:745)

Pretty sure failure is due to that combination, based on DeepLearningModel.java: dl.hide("_use_all_factor_levels", "use_all_factor_levels is unsupported in combination with autoencoder.") But we may want a better failure message.

Flow version info: H2O BUILD GIT BRANCH master H2O BUILD GIT HASH 60d3f8e6d80eadeaf86e913d507adb925334622c H2O BUILD GIT DESCRIBE jenkins-master-1122-7-g60d3f8e-dirty H2O BUILD PROJECT VERSION 0.3.0.99999 H2O BUILT BY jessicalanford H2O BUILT ON 2015-04-03 11:28:53 FLOW VERSION 0.2.79

DinukaH2O commented 1 year ago

JIRA Issue Migration Info

Jira Issue: PUBDEV-724 Assignee: Arno Candel Reporter: J State: Resolved Fix Version: N/A Attachments: N/A Development PRs: N/A