Fitting models with the'h2o' engine
As an example, we will fit a random forest model to theconcrete data. This will be a regression model with theoutcome being the compressive strength of concrete mixtures.
library(tidymodels)library(agua)library(ggplot2)tidymodels_prefer()theme_set(theme_bw())# start h2o serverh2o_start()data(concrete,package ="modeldata")concrete<- concrete%>%group_by(across(-compressive_strength))%>%summarize(compressive_strength =mean(compressive_strength),.groups ="drop")concrete#> # A tibble: 992 × 9#> cement blast_furn…¹ fly_ash water super…² coars…³ fine_…⁴ age compr…⁵#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <int> <dbl>#> 1 102 153 0 192 0 887 942 3 4.57#> 2 102 153 0 192 0 887 942 7 7.68#> 3 102 153 0 192 0 887 942 28 17.3#> 4 102 153 0 192 0 887 942 90 25.5#> 5 108. 162. 0 204. 0 938. 849 3 2.33#> 6 108. 162. 0 204. 0 938. 849 7 7.72#> 7 108. 162. 0 204. 0 938. 849 28 20.6#> 8 108. 162. 0 204. 0 938. 849 90 29.2#> 9 116 173 0 192 0 910. 892. 3 6.28#> 10 116 173 0 192 0 910. 892. 7 10.1#> # … with 982 more rows, and abbreviated variable names#> # ¹blast_furnace_slag, ²superplasticizer, ³coarse_aggregate,#> # ⁴fine_aggregate, ⁵compressive_strengthNote that we need to callh2o_start() orh2o::h2o.init() to start the h2o instance. The h2o serverhandles computations related to estimation and prediction, and passesthe results back to R. agua takes care of data conversion and errorhandling, it also tries to store as least objects on the server aspossible. The h2o server will automatically terminate once R session isclosed. You can useh2o::h2o.removeAll() to remove allserver-side objects andh2o::h2o.shutdown() to manuallystop the server.
The rest of the syntax of model fitting and prediction are identicalto the usage of any other engine in tidymodels.
set.seed(1501)concrete_split<-initial_split(concrete,strata = compressive_strength)concrete_train<-training(concrete_split)concrete_test<-testing(concrete_split)rf_spec<-rand_forest(mtry =3,trees =500)%>%set_engine("h2o",histogram_type ="Random")%>%set_mode("regression")normalized_rec<-recipe(compressive_strength~ .,data = concrete_train)%>%step_normalize(all_predictors())rf_wflow<-workflow()%>%add_model(rf_spec)%>%add_recipe(normalized_rec)rf_fit<-fit(rf_wflow,data = concrete_train)rf_fit#> ══ Workflow [trained] ════════════════════════════════════════════════════#> Preprocessor: Recipe#> Model: rand_forest()#>#> ── Preprocessor ──────────────────────────────────────────────────────────#> 1 Recipe Step#>#> • step_normalize()#>#> ── Model ─────────────────────────────────────────────────────────────────#> Model Details:#> ==============#>#> H2ORegressionModel: drf#> Model ID: DRF_model_R_1665503649643_6#> Model Summary:#> number_of_trees number_of_internal_trees model_size_in_bytes min_depth#> 1 500 500 2652880 15#> max_depth mean_depth min_leaves max_leaves mean_leaves#> 1 20 17.97600 375 450 417.48000#>#>#> H2ORegressionMetrics: drf#> ** Reported on training data. **#> ** Metrics reported on Out-Of-Bag training samples **#>#> MSE: 26.5#> RMSE: 5.15#> MAE: 3.7#> RMSLE: 0.169#> Mean Residual Deviance : 26.5predict(rf_fit,new_data = concrete_test)#> # A tibble: 249 × 1#> .pred#> <dbl>#> 1 6.42#> 2 9.54#> 3 9.20#> 4 25.5#> 5 6.60#> 6 28.6#> 7 10.0#> 8 31.9#> 9 12.1#> 10 11.4#> # … with 239 more rowsHere, we specify the engine argumenthistogram_type = "Random" to use the extremely randomizedtrees (XRT) algorithm. For all available engine arguments, consult theengine specific help page for “h2o” of that model. For instance, the h2olink in the help page ofrand_forest() shows that it usesh2o::h2o.randomForest(), whose arguments can be passed inas engine arguments inset_engine().
You can also usefit_resamples() with h2o models.
concrete_folds<-vfold_cv(concrete_train,strata = compressive_strength)fit_resamples(rf_wflow,resamples = concrete_folds)#> # Resampling results#> # 10-fold cross-validation using stratification#> # A tibble: 10 × 4#> splits id .metrics .notes#> <list> <chr> <list> <list>#> 1 <split [667/76]> Fold01 <tibble [2 × 4]> <tibble [0 × 3]>#> 2 <split [667/76]> Fold02 <tibble [2 × 4]> <tibble [0 × 3]>#> 3 <split [667/76]> Fold03 <tibble [2 × 4]> <tibble [0 × 3]>#> 4 <split [667/76]> Fold04 <tibble [2 × 4]> <tibble [0 × 3]>#> 5 <split [667/76]> Fold05 <tibble [2 × 4]> <tibble [0 × 3]>#> 6 <split [668/75]> Fold06 <tibble [2 × 4]> <tibble [0 × 3]>#> 7 <split [671/72]> Fold07 <tibble [2 × 4]> <tibble [0 × 3]>#> 8 <split [671/72]> Fold08 <tibble [2 × 4]> <tibble [0 × 3]>#> 9 <split [671/72]> Fold09 <tibble [2 × 4]> <tibble [0 × 3]>#> 10 <split [671/72]> Fold10 <tibble [2 × 4]> <tibble [0 × 3]>Variable importance scores can be visualized by the vip package.