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# ## Lending Club example using cleaned up dataset & h2o.ensemble ###
library(h2o )
h2o.init(nthreads = - 1 , max_mem_size = " 8G"
loan_csv <- " https://raw.githubusercontent.com/h2oai/app-consumer-loan/master/data/loan.csv"
data <- h2o.importFile(loan_csv ) # 163994 x 15
data $ bad_loan <- as.factor(data $ bad_loan )
rand <- h2o.runif(data , seed = 1 )
train <- data [rand $ rnd < = 0.8 , ]
valid <- data [rand $ rnd > 0.8 , ]
y <- " bad_loan"
x <- setdiff(names(data ), c(y , " int_rate"
library(h2oEnsemble )
# Specify the base learner library & the metalearner
learner <- c(" h2o.glm.wrapper" " h2o.randomForest.wrapper"
" h2o.gbm.wrapper" " h2o.deeplearning.wrapper"
metalearner <- " h2o.deeplearning.wrapper"
family <- " binomial"
# Train the ensemble using 5-fold CV to generate level-one data
# More CV folds will take longer to train, but should increase performance
fit <- h2o.ensemble(x = x , y = y ,
training_frame = train ,
validation_frame = NULL ,
family = family ,
learner = learner ,
metalearner = metalearner ,
cvControl = list (V = 5 , shuffle = TRUE ))
# Generate predictions on the test set
pred <- predict.h2o.ensemble(fit , valid )
predictions <- as.data.frame(pred $ pred )[,3 ] # third column, p1 is P(Y==1)
labels <- as.data.frame(valid [,c(y )])[,1 ]
# Ensemble test AUC
cvAUC :: AUC(predictions = predictions , labels = labels )
# 0.6802715
# Base learner test AUC (for comparison)
learner <- names(fit $ basefits )
L <- length(learner )
auc <- sapply(seq(L ), function (l ) cvAUC :: AUC(predictions = as.data.frame(pred $ basepred )[,l ], labels = labels ))
data.frame (learner , auc )
# learner auc
# 1 h2o.glm.wrapper 0.6721662
# 2 h2o.randomForest.wrapper 0.6673966
# 3 h2o.gbm.wrapper 0.6737319
# 4 h2o.deeplearning.wrapper 0.6696115
# Now let's try again with a more extensive set of base learners
# Here is an example of how to generate a custom learner wrappers:
h2o.glm.1 <- function (... , alpha = 0.0 ) h2o.glm.wrapper(... , alpha = alpha )
h2o.glm.2 <- function (... , alpha = 0.5 ) h2o.glm.wrapper(... , alpha = alpha )
h2o.glm.3 <- function (... , alpha = 1.0 ) h2o.glm.wrapper(... , alpha = alpha )
h2o.randomForest.1 <- function (... , ntrees = 200 , nbins = 50 , seed = 1 ) h2o.randomForest.wrapper(... , ntrees = ntrees , nbins = nbins , seed = seed )
h2o.randomForest.2 <- function (... , ntrees = 200 , sample_rate = 0.75 , seed = 1 ) h2o.randomForest.wrapper(... , ntrees = ntrees , sample_rate = sample_rate , seed = seed )
h2o.randomForest.3 <- function (... , ntrees = 200 , sample_rate = 0.85 , seed = 1 ) h2o.randomForest.wrapper(... , ntrees = ntrees , sample_rate = sample_rate , seed = seed )
h2o.gbm.1 <- function (... , ntrees = 100 , nbins = 100 , seed = 1 ) h2o.gbm.wrapper(... , ntrees = ntrees , nbins = nbins , seed = seed )
h2o.gbm.2 <- function (... , ntrees = 200 , nbins = 50 , seed = 1 ) h2o.gbm.wrapper(... , ntrees = ntrees , nbins = nbins , seed = seed )
h2o.gbm.3 <- function (... , ntrees = 100 , max_depth = 10 , seed = 1 ) h2o.gbm.wrapper(... , ntrees = ntrees , max_depth = max_depth , seed = seed )
h2o.gbm.4 <- function (... , ntrees = 100 , col_sample_rate = 0.8 , seed = 1 ) h2o.gbm.wrapper(... , ntrees = ntrees , col_sample_rate = col_sample_rate , seed = seed )
h2o.gbm.5 <- function (... , ntrees = 200 , col_sample_rate = 0.8 , seed = 1 ) h2o.gbm.wrapper(... , ntrees = ntrees , col_sample_rate = col_sample_rate , seed = seed )
h2o.gbm.6 <- function (... , ntrees = 200 , col_sample_rate = 0.7 , seed = 1 ) h2o.gbm.wrapper(... , ntrees = ntrees , col_sample_rate = col_sample_rate , seed = seed )
h2o.deeplearning.1 <- function (... , hidden = c(500 ,500 ), activation = " Rectifier" seed = 1 ) h2o.deeplearning.wrapper(... , hidden = hidden , activation = activation , seed = seed )
h2o.deeplearning.2 <- function (... , hidden = c(200 ,200 ,200 ), activation = " Tanh" seed = 1 ) h2o.deeplearning.wrapper(... , hidden = hidden , activation = activation , seed = seed )
h2o.deeplearning.3 <- function (... , hidden = c(500 ,500 ), activation = " RectifierWithDropout" seed = 1 ) h2o.deeplearning.wrapper(... , hidden = hidden , activation = activation , seed = seed )
learner <- c(" h2o.glm.1" " h2o.glm.2" " h2o.glm.3"
" h2o.randomForest.1" " h2o.randomForest.2" " h2o.randomForest.3"
" h2o.gbm.1" " h2o.gbm.2" " h2o.gbm.3" " h2o.gbm.4" " h2o.gbm.5" " h2o.gbm.6"
" h2o.deeplearning.1" " h2o.deeplearning.2" " h2o.deeplearning.3"
metalearner <- " h2o.deeplearning.wrapper"
family <- " binomial"
# Train the ensemble using 5-fold CV to generate level-one data
# More CV folds will take longer to train, but should increase performance
fit <- h2o.ensemble(x = x , y = y ,
training_frame = train ,
validation_frame = NULL ,
family = family ,
learner = learner ,
metalearner = metalearner ,
cvControl = list (V = 5 , shuffle = TRUE ))
# Generate predictions on the test set
pred <- predict.h2o.ensemble(fit , valid )
predictions <- as.data.frame(pred $ pred )[,3 ] # third column, p1 is P(Y==1)
labels <- as.data.frame(valid [,c(y )])[,1 ]
# Ensemble test AUC
cvAUC :: AUC(predictions = predictions , labels = labels )
# 0.6832164
# Base learner test AUC (for comparison)
L <- length(learner )
auc <- sapply(seq(L ), function (l ) cvAUC :: AUC(predictions = as.data.frame(pred $ basepred )[,l ], labels = labels ))
data.frame (learner , auc )
# learner auc
# 1 h2o.glm.1 0.6742993
# 2 h2o.glm.2 0.6743306
# 3 h2o.glm.3 0.6743672
# 4 h2o.randomForest.1 0.6702020
# 5 h2o.randomForest.2 0.6698333
# 6 h2o.randomForest.3 0.6698533
# 7 h2o.gbm.1 0.6801405
# 8 h2o.gbm.2 0.6785209
# 9 h2o.gbm.3 0.6699785
# 10 h2o.gbm.4 0.6805663
# 11 h2o.gbm.5 0.6790248
# 12 h2o.gbm.6 0.6790248
# 13 h2o.deeplearning.1 0.6772474
# 14 h2o.deeplearning.2 0.6696069
# 15 h2o.deeplearning.3 0.6802065
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