README.md

In PeerChristensen/autoMLviz: Plotting model performances from H2OAutoML objects

autoMLviz: Functions for plotting H2O AutoML model performance

What it does

The package currently contains functions for plotting ROC curves, AUC bar charts, as well as gains, lift and response charts for communicating classification model performance.

Installation

install.packages("devtools")
library(devtools)

install_github("PeerCHristensen/autoMLviz")

Automated machine learning with H2O

We’ll load some preprocessed sample data and create train, validation and test sets with the caret package before creating an H2OAutoML object, which will contain the performance metrics from multiple models.

library(tidyverse)
library(caret)
library(h2o)

h2o.init()

##  Connection successful!
## 
## R is connected to the H2O cluster: 
##     H2O cluster uptime:         3 minutes 12 seconds 
##     H2O cluster timezone:       Europe/Copenhagen 
##     H2O data parsing timezone:  UTC 
##     H2O cluster version:        3.22.1.1 
##     H2O cluster version age:    5 months and 4 days !!! 
##     H2O cluster name:           H2O_started_from_R_peerchristensen_flk366 
##     H2O cluster total nodes:    1 
##     H2O cluster total memory:   3.54 GB 
##     H2O cluster total cores:    8 
##     H2O cluster allowed cores:  8 
##     H2O cluster healthy:        TRUE 
##     H2O Connection ip:          localhost 
##     H2O Connection port:        54321 
##     H2O Connection proxy:       NA 
##     H2O Internal Security:      FALSE 
##     H2O API Extensions:         XGBoost, Algos, AutoML, Core V3, Core V4 
##     R Version:                  R version 3.6.0 (2019-04-26)

h2o.no_progress()

df <- read_csv("https://raw.github.com/peerchristensen/churn_prediction/master/telecom_churn_prep.csv") %>%
  mutate_if(is.character,factor)

index <- createDataPartition(df$Churn, p = 0.7, list = FALSE)

train_data     <- df[index, ]
val_test_data  <- df[-index, ]

index2 <- createDataPartition(val_test_data$Churn, p = 0.5, list = FALSE)

valid_data <- val_test_data[-index2, ]
test_data  <- val_test_data[index2, ]

train_hf <- as.h2o(train_data)
valid_hf <- as.h2o(valid_data)
test_hf  <- as.h2o(test_data)

outcome    <- "Churn"
predictors <- setdiff(names(train_hf), outcome)

autoML <- h2o.automl(x = predictors, 
                     y = outcome,
                     training_frame    = train_hf,
                     validation_frame  = valid_hf,
                     leaderboard_frame = test_hf,
                     balance_classes   = TRUE,
                     max_runtime_secs  = 100)

Plotting ROC curves

First we’ll evaluate the performance of the trained models with ROC curves plotted using ggplot2. By default, roc_curves will compare all models in the AutoML leaderboard. By setting best = TRUE, only the best model will be shown. Setting save_png = TRUE will save the plot as a picture. The function also returns the relevant data, in case you want to customise the plot. Note that the test_data argument must be specified. Furthermore, you can control how many models you would like to include in your plots by setting the n_modelsargument. The default number of models to return is five.

library(autoMLviz)

roc_curves(autoML, test_data = test_hf)

## # A tibble: 1,960 x 7
##    model_id     algorithm      tpr     fpr model_rank model_id1   model_id2
##    <chr>        <chr>        <dbl>   <dbl>      <int> <chr>       <chr>    
##  1 1: StackedE… stackeden… 0       0                1 1: Stacked… _NULL    
##  2 1: StackedE… stackeden… 0       0                1 1: Stacked… _NULL    
##  3 1: StackedE… stackeden… 0.00357 0                1 1: Stacked… _NULL    
##  4 1: StackedE… stackeden… 0.00357 0.00129          1 1: Stacked… _NULL    
##  5 1: StackedE… stackeden… 0.0107  0.00129          1 1: Stacked… _NULL    
##  6 1: StackedE… stackeden… 0.0107  0.00388          1 1: Stacked… _NULL    
##  7 1: StackedE… stackeden… 0.0143  0.00388          1 1: Stacked… _NULL    
##  8 1: StackedE… stackeden… 0.0214  0.00388          1 1: Stacked… _NULL    
##  9 1: StackedE… stackeden… 0.0321  0.00388          1 1: Stacked… _NULL    
## 10 1: StackedE… stackeden… 0.0357  0.00646          1 1: Stacked… _NULL    
## # … with 1,950 more rows

Bar charts comparing area under the curve (AUC)

auc_bars(autoML, test_data = test_hf)

Gains, Lift and Response charts

Two functions producing gains, lift and response charts are included. Whereas lift4gains() shows the performance of the best model, lift4gains2() can be used to compare all models in the AutoML leaderboard.

To get a reference line in the response plot, you need to pass the proportion of target class observations to the response_refargument.

lift4gains(autoML, response_ref = .265)

Note that AutoML objects may return different numbers of quantiles for some models causing some of the lines to appear incomplete.

As gains, lift and response charts can sometimes be difficult to understand, the explain argument can be set to TRUE, which adds subtitles to each plot explaining how they should be interpreted.

lift4gains2(autoML, response_ref = .265, explain = TRUE)

Variable importance

With autoMLviz, you can also plot variable importannce for the best model. varImp_plot() calls the standard h2o plotting function, whereas varImp_ggplot() creates the plot using ggplot and a theme that is consistent with the above figures. If the best model is an ensemble model, both functions will create two plots. One showing the model importance, and the second showing variable importance for the most important model within the ensemble.

varImp_plot(autoML)

## [1] "Ensemble model: Plotting Model importance and Variable importances of model with highest importance"

varImp_ggplot(autoML)

## [1] "Ensemble model: Plotting Model importance and Variable importances of model with highest importance"

PeerChristensen/autoMLviz documentation built on Sept. 16, 2019, 1:19 p.m.