How to directly plot ROC of h2o model object in R

Question

My apologies if I\'m missing something obvious. I\'ve been thoroughly enjoying working with h2o in the last few days using R interface. I would like to evaluate my model, sa

Answer 1

A naive solution is to use plot() generic function to plot a H2OMetrics object:

logit_fit <- h2o.glm(colnames(training)[-1],'y',training_frame =
    training.hex,validation_frame=validation.hex,family = 'binomial')
plot(h2o.performance(logit_fit),valid=T),type='roc')

This will give us a plot:

But it is hard to customize, especially to change the line type, since the type parameter is already taken as 'roc'. Also I have not found a way to plot multiple models' ROC curves together on one plot. I have come up with a method to extract true positive rate and false positive rate from the H2OMetrics object and use ggplot2 to plot the ROC curves on one plot by myself. Here is the example code(uses a lot of tidyverse syntax):

# for example I have 4 H2OModels
list(logit_fit,dt_fit,rf_fit,xgb_fit) %>% 
  # map a function to each element in the list
  map(function(x) x %>% h2o.performance(valid=T) %>% 
        # from all these 'paths' in the object
        .@metrics %>% .$thresholds_and_metric_scores %>% 
        # extracting true positive rate and false positive rate
        .[c('tpr','fpr')] %>% 
        # add (0,0) and (1,1) for the start and end point of ROC curve
        add_row(tpr=0,fpr=0,.before=T) %>% 
        add_row(tpr=0,fpr=0,.before=F)) %>% 
  # add a column of model name for future grouping in ggplot2
  map2(c('Logistic Regression','Decision Tree','Random Forest','Gradient Boosting'),
        function(x,y) x %>% add_column(model=y)) %>% 
  # reduce four data.frame to one
  reduce(rbind) %>% 
  # plot fpr and tpr, map model to color as grouping
  ggplot(aes(fpr,tpr,col=model))+
  geom_line()+
  geom_segment(aes(x=0,y=0,xend = 1, yend = 1),linetype = 2,col='grey')+
  xlab('False Positive Rate')+
  ylab('True Positive Rate')+
  ggtitle('ROC Curve for Four Models')

Then the ROC curve is: