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1-h2o.R
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1-h2o.R
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## h2o 3.10.0.8
library(h2o)
h2o.init(max_mem_size = "50g", nthreads = -1)
dx_train <- h2o.importFile("train-10m.csv")
dx_valid <- h2o.importFile("valid.csv")
dx_test <- h2o.importFile("test.csv")
## to have same normalization as for the other DL libs that don't auto normalize
dx_train$DepTime <- dx_train$DepTime/2500
dx_valid$DepTime <- dx_valid$DepTime/2500
dx_test$DepTime <- dx_test$DepTime/2500
dx_train$Distance <- log10(dx_train$Distance)/4
dx_valid$Distance <- log10(dx_valid$Distance)/4
dx_test$Distance <- log10(dx_test$Distance)/4
Xnames <- names(dx_train)[which(names(dx_train)!="dep_delayed_15min")]
system.time({
md <- h2o.deeplearning(x = Xnames, y = "dep_delayed_15min", training_frame = dx_train, validation_frame = dx_valid,
## DEFAULT: activation = "Rectifier", hidden = c(200,200),
epochs = 100, stopping_rounds = 10, stopping_metric = "AUC", stopping_tolerance = 0)
})
h2o.performance(md, dx_test)@metrics$AUC
## print epochs: 1: best AUC (on validation) 2: early stopping
d_scoring <- md@model$scoring_history
d_scoring[d_scoring$validation_auc==max(d_scoring$validation_auc, na.rm=TRUE),]$epochs[2:3]
# user system elapsed
# 2.891 0.247 275.616
#> h2o.performance(md, dx_test)@metrics$AUC
#[1] 0.7307861
#> ## print epochs: 1: best AUC (on validation) 2: early stopping
#[1] 0.1698963 1.8196927
system.time({
md <- h2o.deeplearning(x = Xnames, y = "dep_delayed_15min", training_frame = dx_train, validation_frame = dx_valid,
activation = "Rectifier", hidden = c(50,50,50,50), input_dropout_ratio = 0.2,
epochs = 100, stopping_rounds = 10, stopping_metric = "AUC", stopping_tolerance = 0)
})
h2o.performance(md, dx_test)@metrics$AUC
## print epochs: 1: best AUC (on validation) 2: early stopping
d_scoring <- md@model$scoring_history
d_scoring[d_scoring$validation_auc==max(d_scoring$validation_auc, na.rm=TRUE),]$epochs[2:3]
# user system elapsed
# 1.701 0.191 144.301
#> h2o.performance(md, dx_test)@metrics$AUC
#[1] 0.7321902
#> ## print epochs: 1: best AUC (on validation) 2: early stopping
#[1] 1.549485 2.719795
system.time({
md <- h2o.deeplearning(x = Xnames, y = "dep_delayed_15min", training_frame = dx_train, validation_frame = dx_valid,
activation = "Rectifier", hidden = c(50,50,50,50),
epochs = 100, stopping_rounds = 10, stopping_metric = "AUC", stopping_tolerance = 0)
})
h2o.performance(md, dx_test)@metrics$AUC
## print epochs: 1: best AUC (on validation) 2: early stopping
d_scoring <- md@model$scoring_history
d_scoring[d_scoring$validation_auc==max(d_scoring$validation_auc, na.rm=TRUE),]$epochs[2:3]
# user system elapsed
# 1.393 0.203 108.415
#> h2o.performance(md, dx_test)@metrics$AUC
#[1] 0.7315689
#> ## print epochs: 1: best AUC (on validation) 2: early stopping
#[1] 0.1498604 1.9090842
system.time({
md <- h2o.deeplearning(x = Xnames, y = "dep_delayed_15min", training_frame = dx_train, validation_frame = dx_valid,
activation = "Rectifier", hidden = c(20,20),
epochs = 100, stopping_rounds = 10, stopping_metric = "AUC", stopping_tolerance = 0)
})
h2o.performance(md, dx_test)@metrics$AUC
## print epochs: 1: best AUC (on validation) 2: early stopping
d_scoring <- md@model$scoring_history
d_scoring[d_scoring$validation_auc==max(d_scoring$validation_auc, na.rm=TRUE),]$epochs[2:3]
# user system elapsed
# 1.406 0.174 104.139
#> h2o.performance(md, dx_test)@metrics$AUC
#[1] 0.7306146
#> ## print epochs: 1: best AUC (on validation) 2: early stopping
#[1] 1.870624 4.640307
system.time({
md <- h2o.deeplearning(x = Xnames, y = "dep_delayed_15min", training_frame = dx_train, validation_frame = dx_valid,
activation = "Rectifier", hidden = c(20),
epochs = 100, stopping_rounds = 10, stopping_metric = "AUC", stopping_tolerance = 0)
})
h2o.performance(md, dx_test)@metrics$AUC
## print epochs: 1: best AUC (on validation) 2: early stopping
d_scoring <- md@model$scoring_history
d_scoring[d_scoring$validation_auc==max(d_scoring$validation_auc, na.rm=TRUE),]$epochs[2:3]
# user system elapsed
# 1.482 0.097 124.429
#> h2o.performance(md, dx_test)@metrics$AUC
#[1] 0.7315768
#> ## print epochs: 1: best AUC (on validation) 2: early stopping
#[1] 6.399249 6.669262
system.time({
md <- h2o.deeplearning(x = Xnames, y = "dep_delayed_15min", training_frame = dx_train, validation_frame = dx_valid,
activation = "Rectifier", hidden = c(10),
epochs = 100, stopping_rounds = 10, stopping_metric = "AUC", stopping_tolerance = 0)
})
h2o.performance(md, dx_test)@metrics$AUC
## print epochs: 1: best AUC (on validation) 2: early stopping
d_scoring <- md@model$scoring_history
d_scoring[d_scoring$validation_auc==max(d_scoring$validation_auc, na.rm=TRUE),]$epochs[2:3]
# user system elapsed
# 1.685 0.117 152.939
#> h2o.performance(md, dx_test)@metrics$AUC
#[1] 0.7325564
#> ## print epochs: 1: best AUC (on validation) 2: early stopping
#[1] 3.220322 11.859830
system.time({
md <- h2o.deeplearning(x = Xnames, y = "dep_delayed_15min", training_frame = dx_train, validation_frame = dx_valid,
activation = "Rectifier", hidden = c(5),
epochs = 100, stopping_rounds = 10, stopping_metric = "AUC", stopping_tolerance = 0)
})
h2o.performance(md, dx_test)@metrics$AUC
## print epochs: 1: best AUC (on validation) 2: early stopping
d_scoring <- md@model$scoring_history
d_scoring[d_scoring$validation_auc==max(d_scoring$validation_auc, na.rm=TRUE),]$epochs[2:3]
# user system elapsed
# 1.345 0.173 107.650
#> h2o.performance(md, dx_test)@metrics$AUC
#[1] 0.7294642
#> ## print epochs: 1: best AUC (on validation) 2: early stopping
#[1] 1.679884 9.310295
system.time({
md <- h2o.deeplearning(x = Xnames, y = "dep_delayed_15min", training_frame = dx_train, validation_frame = dx_valid,
activation = "Rectifier", hidden = c(1),
epochs = 100, stopping_rounds = 10, stopping_metric = "AUC", stopping_tolerance = 0)
})
h2o.performance(md, dx_test)@metrics$AUC
## print epochs: 1: best AUC (on validation) 2: early stopping
d_scoring <- md@model$scoring_history
d_scoring[d_scoring$validation_auc==max(d_scoring$validation_auc, na.rm=TRUE),]$epochs[2:3]
# user system elapsed
# 1.435 0.225 121.663
#> h2o.performance(md, dx_test)@metrics$AUC
#[1] 0.7121256
#> ## print epochs: 1: best AUC (on validation) 2: early stopping
#[1] 3.579505 13.149986
system.time({
md <- h2o.deeplearning(x = Xnames, y = "dep_delayed_15min", training_frame = dx_train, validation_frame = dx_valid,
activation = "Rectifier", hidden = c(200,200), l1 = 1e-5, l2 = 1e-5,
epochs = 100, stopping_rounds = 10, stopping_metric = "AUC", stopping_tolerance = 0)
})
h2o.performance(md, dx_test)@metrics$AUC
## print epochs: 1: best AUC (on validation) 2: early stopping
d_scoring <- md@model$scoring_history
d_scoring[d_scoring$validation_auc==max(d_scoring$validation_auc, na.rm=TRUE),]$epochs[2:3]
# user system elapsed
# 2.740 0.314 267.694
#> h2o.performance(md, dx_test)@metrics$AUC
#[1] 0.7312349
#> ## print epochs: 1: best AUC (on validation) 2: early stopping
#[1] 0.5702015 1.8402960
system.time({
md <- h2o.deeplearning(x = Xnames, y = "dep_delayed_15min", training_frame = dx_train, validation_frame = dx_valid,
activation = "RectifierWithDropout", hidden = c(200,200,200,200), hidden_dropout_ratios=c(0.2,0.1,0.1,0),
epochs = 100, stopping_rounds = 10, stopping_metric = "AUC", stopping_tolerance = 0)
})
h2o.performance(md, dx_test)@metrics$AUC
## print epochs: 1: best AUC (on validation) 2: early stopping
d_scoring <- md@model$scoring_history
d_scoring[d_scoring$validation_auc==max(d_scoring$validation_auc, na.rm=TRUE),]$epochs[2:3]
# user system elapsed
# 4.324 0.418 443.058
#> h2o.performance(md, dx_test)@metrics$AUC
#[1] 0.7332076
#> ## print epochs: 1: best AUC (on validation) 2: early stopping
#[1] 1.280414 2.000519
system.time({
md <- h2o.deeplearning(x = Xnames, y = "dep_delayed_15min", training_frame = dx_train, validation_frame = dx_valid,
activation = "Rectifier", hidden = c(200,200),
rho = 0.95, epsilon = 1e-06, ## default: rho = 0.99, epsilon = 1e-08
epochs = 100, stopping_rounds = 10, stopping_metric = "AUC", stopping_tolerance = 0)
})
h2o.performance(md, dx_test)@metrics$AUC
## print epochs: 1: best AUC (on validation) 2: early stopping
d_scoring <- md@model$scoring_history
d_scoring[d_scoring$validation_auc==max(d_scoring$validation_auc, na.rm=TRUE),]$epochs[2:3]
# user system elapsed
# 2.823 0.615 242.150
#> h2o.performance(md, dx_test)@metrics$AUC
#[1] 0.7110229
#> d_scoring <- md@model$scoring_history
#> d_scoring[d_scoring$validation_auc==max(d_scoring$validation_auc, na.rm=TRUE),]$epochs[2:3]
#[1] 0.3701672 1.7100978
system.time({
md <- h2o.deeplearning(x = Xnames, y = "dep_delayed_15min", training_frame = dx_train, validation_frame = dx_valid,
activation = "Rectifier", hidden = c(200,200),
rho = 0.999, epsilon = 1e-08, ## default: rho = 0.99, epsilon = 1e-08
epochs = 100, stopping_rounds = 10, stopping_metric = "AUC", stopping_tolerance = 0)
})
h2o.performance(md, dx_test)@metrics$AUC
## print epochs: 1: best AUC (on validation) 2: early stopping
d_scoring <- md@model$scoring_history
d_scoring[d_scoring$validation_auc==max(d_scoring$validation_auc, na.rm=TRUE),]$epochs[2:3]
# user system elapsed
# 2.989 0.362 272.354
#> h2o.performance(md, dx_test)@metrics$AUC
#[1] 0.7334915
#> d_scoring <- md@model$scoring_history
#> d_scoring[d_scoring$validation_auc==max(d_scoring$validation_auc, na.rm=TRUE),]$epochs[2:3]
#[1] 0.4300052 1.9097695
system.time({
md <- h2o.deeplearning(x = Xnames, y = "dep_delayed_15min", training_frame = dx_train, validation_frame = dx_valid,
activation = "Rectifier", hidden = c(200,200),
rho = 0.9999, epsilon = 1e-08, ## default: rho = 0.99, epsilon = 1e-08
epochs = 100, stopping_rounds = 10, stopping_metric = "AUC", stopping_tolerance = 0)
})
h2o.performance(md, dx_test)@metrics$AUC
## print epochs: 1: best AUC (on validation) 2: early stopping
d_scoring <- md@model$scoring_history
d_scoring[d_scoring$validation_auc==max(d_scoring$validation_auc, na.rm=TRUE),]$epochs[2:3]
# user system elapsed
# 2.778 0.228 264.193
#> h2o.performance(md, dx_test)@metrics$AUC
#[1] 0.7265394
#> d_scoring <- md@model$scoring_history
#> d_scoring[d_scoring$validation_auc==max(d_scoring$validation_auc, na.rm=TRUE),]$epochs[2:3]
#[1] 0.270231 1.960119
system.time({
md <- h2o.deeplearning(x = Xnames, y = "dep_delayed_15min", training_frame = dx_train, validation_frame = dx_valid,
activation = "Rectifier", hidden = c(200,200),
rho = 0.999, epsilon = 1e-06, ## default: rho = 0.99, epsilon = 1e-08
epochs = 100, stopping_rounds = 10, stopping_metric = "AUC", stopping_tolerance = 0)
})
h2o.performance(md, dx_test)@metrics$AUC
## print epochs: 1: best AUC (on validation) 2: early stopping
d_scoring <- md@model$scoring_history
d_scoring[d_scoring$validation_auc==max(d_scoring$validation_auc, na.rm=TRUE),]$epochs[2:3]
# user system elapsed
# 3.094 0.711 287.199
#> h2o.performance(md, dx_test)@metrics$AUC
#[1] 0.7219705
#> d_scoring <- md@model$scoring_history
#> d_scoring[d_scoring$validation_auc==max(d_scoring$validation_auc, na.rm=TRUE),]$epochs[2:3]
#[1] 0.6599353 2.3602646
system.time({
md <- h2o.deeplearning(x = Xnames, y = "dep_delayed_15min", training_frame = dx_train, validation_frame = dx_valid,
activation = "Rectifier", hidden = c(200,200),
rho = 0.999, epsilon = 1e-09, ## default: rho = 0.99, epsilon = 1e-08
epochs = 100, stopping_rounds = 10, stopping_metric = "AUC", stopping_tolerance = 0)
})
h2o.performance(md, dx_test)@metrics$AUC
## print epochs: 1: best AUC (on validation) 2: early stopping
d_scoring <- md@model$scoring_history
d_scoring[d_scoring$validation_auc==max(d_scoring$validation_auc, na.rm=TRUE),]$epochs[2:3]
# user system elapsed
# 2.826 0.505 261.192
#> h2o.performance(md, dx_test)@metrics$AUC
#[1] 0.7317512
#> d_scoring <- md@model$scoring_history
#> d_scoring[d_scoring$validation_auc==max(d_scoring$validation_auc, na.rm=TRUE),]$epochs[2:3]
#[1] 0.2597259 1.8000915
system.time({
md <- h2o.deeplearning(x = Xnames, y = "dep_delayed_15min", training_frame = dx_train, validation_frame = dx_valid,
activation = "Rectifier", hidden = c(200,200),
adaptive_rate = FALSE, ## default: rate = 0.005, rate_decay = 1, momentum_stable = 0,
epochs = 100, stopping_rounds = 10, stopping_metric = "AUC", stopping_tolerance = 0)
})
h2o.performance(md, dx_test)@metrics$AUC
## print epochs: 1: best AUC (on validation) 2: early stopping
d_scoring <- md@model$scoring_history
d_scoring[d_scoring$validation_auc==max(d_scoring$validation_auc, na.rm=TRUE),]$epochs[2:3]
# user system elapsed
# 3.335 0.238 340.291
#> h2o.performance(md, dx_test)@metrics$AUC
#[1] 0.7302248
#> d_scoring <- md@model$scoring_history
#> d_scoring[d_scoring$validation_auc==max(d_scoring$validation_auc, na.rm=TRUE),]$epochs[2:3]
#[1] 0.2198763 1.1098714
system.time({
md <- h2o.deeplearning(x = Xnames, y = "dep_delayed_15min", training_frame = dx_train, validation_frame = dx_valid,
activation = "Rectifier", hidden = c(200,200),
adaptive_rate = FALSE, rate = 0.001, momentum_start = 0.5, momentum_ramp = 1e5, momentum_stable = 0.99,
epochs = 100, stopping_rounds = 10, stopping_metric = "AUC", stopping_tolerance = 0)
})
h2o.performance(md, dx_test)@metrics$AUC
## print epochs: 1: best AUC (on validation) 2: early stopping
d_scoring <- md@model$scoring_history
d_scoring[d_scoring$validation_auc==max(d_scoring$validation_auc, na.rm=TRUE),]$epochs[2:3]
# user system elapsed
# 4.056 0.383 412.792
#> h2o.performance(md, dx_test)@metrics$AUC
#[1] 0.7327167
#> d_scoring <- md@model$scoring_history
#> d_scoring[d_scoring$validation_auc==max(d_scoring$validation_auc, na.rm=TRUE),]$epochs[2:3]
#[1] 0.2098676 0.7397312
system.time({
md <- h2o.deeplearning(x = Xnames, y = "dep_delayed_15min", training_frame = dx_train, validation_frame = dx_valid,
activation = "Rectifier", hidden = c(200,200),
adaptive_rate = FALSE, rate = 0.01, momentum_start = 0.5, momentum_ramp = 1e5, momentum_stable = 0.99,
epochs = 100, stopping_rounds = 10, stopping_metric = "AUC", stopping_tolerance = 0)
})
h2o.performance(md, dx_test)@metrics$AUC
## print epochs: 1: best AUC (on validation) 2: early stopping
d_scoring <- md@model$scoring_history
d_scoring[d_scoring$validation_auc==max(d_scoring$validation_auc, na.rm=TRUE),]$epochs[2:3]
# user system elapsed
# 2.844 0.222 276.620
#> h2o.performance(md, dx_test)@metrics$AUC
#[1] 0.7330604
#> d_scoring <- md@model$scoring_history
#> d_scoring[d_scoring$validation_auc==max(d_scoring$validation_auc, na.rm=TRUE),]$epochs[2:3]
#[1] 0.1199309 0.9497759
system.time({
md <- h2o.deeplearning(x = Xnames, y = "dep_delayed_15min", training_frame = dx_train, validation_frame = dx_valid,
activation = "Rectifier", hidden = c(200,200),
adaptive_rate = FALSE, rate = 0.01, rate_annealing = 1e-05,
momentum_start = 0.5, momentum_ramp = 1e5, momentum_stable = 0.99,
epochs = 100, stopping_rounds = 10, stopping_metric = "AUC", stopping_tolerance = 0)
})
h2o.performance(md, dx_test)@metrics$AUC
## print epochs: 1: best AUC (on validation) 2: early stopping
d_scoring <- md@model$scoring_history
d_scoring[d_scoring$validation_auc==max(d_scoring$validation_auc, na.rm=TRUE),]$epochs[2:3]
# user system elapsed
# 3.802 0.494 361.527
#> h2o.performance(md, dx_test)@metrics$AUC
#[1] 0.7352361
#> d_scoring <- md@model$scoring_history
#> d_scoring[d_scoring$validation_auc==max(d_scoring$validation_auc, na.rm=TRUE),]$epochs[2:3]
#[1] 0.3296777 1.0097397
system.time({
md <- h2o.deeplearning(x = Xnames, y = "dep_delayed_15min", training_frame = dx_train, validation_frame = dx_valid,
activation = "Rectifier", hidden = c(200,200),
adaptive_rate = FALSE, rate = 0.01, rate_annealing = 1e-04,
momentum_start = 0.5, momentum_ramp = 1e5, momentum_stable = 0.99,
epochs = 100, stopping_rounds = 10, stopping_metric = "AUC", stopping_tolerance = 0)
})
h2o.performance(md, dx_test)@metrics$AUC
## print epochs: 1: best AUC (on validation) 2: early stopping
d_scoring <- md@model$scoring_history
d_scoring[d_scoring$validation_auc==max(d_scoring$validation_auc, na.rm=TRUE),]$epochs[2:3]
# user system elapsed
# 37.083 6.723 3762.738
#> h2o.performance(md, dx_test)@metrics$AUC
#[1] 0.7275267
#> d_scoring[d_scoring$validation_auc==max(d_scoring$validation_auc, na.rm=TRUE),]$epochs[2:3]
#[1] 8.629915 8.780104
system.time({
md <- h2o.deeplearning(x = Xnames, y = "dep_delayed_15min", training_frame = dx_train, validation_frame = dx_valid,
activation = "Rectifier", hidden = c(200,200),
adaptive_rate = FALSE, rate = 0.01, rate_annealing = 1e-05,
momentum_start = 0.5, momentum_ramp = 1e5, momentum_stable = 0.9,
epochs = 100, stopping_rounds = 10, stopping_metric = "AUC", stopping_tolerance = 0)
})
h2o.performance(md, dx_test)@metrics$AUC
## print epochs: 1: best AUC (on validation) 2: early stopping
d_scoring <- md@model$scoring_history
d_scoring[d_scoring$validation_auc==max(d_scoring$validation_auc, na.rm=TRUE),]$epochs[2:3]
# user system elapsed
# 3.850 0.220 355.542
#> h2o.performance(md, dx_test)@metrics$AUC
#[1] 0.7348393
#> d_scoring <- md@model$scoring_history
#> d_scoring[d_scoring$validation_auc==max(d_scoring$validation_auc, na.rm=TRUE),]$epochs[2:3]
#[1] 0.4099075 0.9699861
system.time({
md <- h2o.deeplearning(x = Xnames, y = "dep_delayed_15min", training_frame = dx_train, validation_frame = dx_valid,
activation = "Rectifier", hidden = c(200,200),
adaptive_rate = FALSE, rate = 0.01, rate_annealing = 1e-05,
momentum_start = 0.5, momentum_ramp = 1e4, momentum_stable = 0.9,
epochs = 100, stopping_rounds = 10, stopping_metric = "AUC", stopping_tolerance = 0)
})
h2o.performance(md, dx_test)@metrics$AUC
## print epochs: 1: best AUC (on validation) 2: early stopping
d_scoring <- md@model$scoring_history
d_scoring[d_scoring$validation_auc==max(d_scoring$validation_auc, na.rm=TRUE),]$epochs[2:3]
# user system elapsed
# 3.560 0.191 337.696
#> h2o.performance(md, dx_test)@metrics$AUC
#[1] 0.7329273
#> d_scoring <- md@model$scoring_history
#> d_scoring[d_scoring$validation_auc==max(d_scoring$validation_auc, na.rm=TRUE),]$epochs[2:3]
#[1] 0.2500784 0.9199458