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tel_ensemble_v2.R
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tel_ensemble_v2.R
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# One Y per level
train.n$Y_0 <- 0
train.n$Y_0[train.n$fault_severity == 0] <- 1
train.n$Y_1 <- 0
train.n$Y_1[train.n$fault_severity == 1] <- 1
train.n$Y_2 <- 0
train.n$Y_2[train.n$fault_severity == 2] <- 1
# Load the package, start H2O
require(h2oEnsemble)
trainHex <- as.h2o(train.n, destination_frame="train_tel.hex")
testHex <- as.h2o(test.n, destination_frame="test_tel.hex")
trainHex[,"fault_severity_factor"] <- as.factor(trainHex[,"fault_severity"])
trainHex[,"location"] <- as.factor(trainHex[,"location"])
trainHex[,"fit3_factor"] <- as.factor(trainHex[,"fit3.cluster"])
trainHex[,"fit20_factor"] <- as.factor(trainHex[,"fit20.cluster"])
testHex[,"location"] <- as.factor(testHex[,"location"])
testHex[,"fit2a_factor"] <- as.factor(testHex[,"fit2a.cluster"])
testHex[,"fit3a_factor"] <- as.factor(testHex[,"fit3a.cluster"])
testHex[,"fit5a_factor"] <- as.factor(testHex[,"fit5a.cluster"])
testHex[,"fit3_factor"] <- as.factor(testHex[,"fit3.cluster"])
testHex[,"fit20_factor"] <- as.factor(testHex[,"fit20.cluster"])
trainHex[,"Y_0"] <- as.factor(trainHex[,"Y_0"])
trainHex[,"Y_1"] <- as.factor(trainHex[,"Y_1"])
trainHex[,"Y_2"] <- as.factor(trainHex[,"Y_2"])
# Stage 1 Models
h2o.init(nthreads=-1)
learner <- c("h2o.glm.wrapper", "h2o.randomForest.wrapper",
"h2o.gbm.wrapper", "h2o.deeplearning.wrapper")
x_names <- colnames(testHex)
x_names <- x_names[!(x_names == "id")]
x_names <- x_names[!(x_names %in% a$variable[a$percentage < .001])]
fit.y0 <- h2o.ensemble(x = x_names,
y = "Y_0",
training_frame = trainHex,
learner = learner,
metalearner = "h2o.glm.wrapper",#metalearner <- "SL.nnls"
parallel = "multicore",
cvControl = list(V=5))
fit.y0
fit.y1 <- h2o.ensemble(x = x_names,
y = "Y_1",
training_frame = trainHex,
learner = learner,
metalearner = "h2o.glm.wrapper",#metalearner <- "SL.nnls"
parallel = "multicore",
cvControl = list(V=5))
fit.y1
fit.y2 <- h2o.ensemble(x = x_names,
y = "Y_2",
training_frame = trainHex,
learner = learner,
metalearner = "h2o.glm.wrapper",#metalearner <- "SL.nnls"
parallel = "multicore",
cvControl = list(V=5))
fit.y2
# Generate predictions on the training set
pred0 <- predict(fit.y0, trainHex)
pred1 <- predict(fit.y1, trainHex)
pred2 <- predict(fit.y2, trainHex)
labels0 <- as.data.frame(train.n$Y_0)[,1]
labels1 <- as.data.frame(train.n$Y_1)[,1]
labels2 <- as.data.frame(train.n$Y_2)[,1]
pred0.test <- predict(fit.y0, testHex)
pred1.test <- predict(fit.y1, testHex)
pred2.test <- predict(fit.y2, testHex)
# Ensemble test AUC
auc <- as.data.frame(pred0$pred)[,3]
AUC(predictions=as.numeric(auc), labels=labels0)
auc <- as.data.frame(pred1$pred)[,3]
AUC(predictions=as.numeric(auc), labels=labels1)
auc <- as.data.frame(pred2$pred)[,3]
AUC(predictions=as.numeric(auc), labels=labels2)
# 0.9529252 for Label 0
# 0.9530592 for Label 1
# 0.9877254 for Label 2
# Base learner test AUC (for comparison)
L <- length(learner)
sapply(seq(L), function(l) AUC(predictions = as.data.frame(pred0$basepred)[,l], labels = labels0))
sapply(seq(L), function(l) AUC(predictions = as.data.frame(pred1$basepred)[,l], labels = labels1))
sapply(seq(L), function(l) AUC(predictions = as.data.frame(pred2$basepred)[,l], labels = labels2))
# Label 0: [1] 0.9376163 0.9594962 0.9421740 0.9108918
# Label 1: [1] 0.9257349 0.9595132 0.9309237 0.9014940
# Label 2: [1] 0.9768262 0.9884636 0.9844859 0.9582718
# Note RF base did as well, or slightly better, than the GLM meta learner, however
# I am trying using the GLM metalearners based on the paper I just read
# Add predictions to data
train.predictions <- cbind(as.data.frame(pred0$pred[,3]),
as.data.frame(pred1$pred[,3]),
as.data.frame(pred2$pred[,3])
)
setnames(train.predictions, c("pr_label0_ensemble",
"pr_label1_ensemble",
"pr_label2_ensemble")
)
test.predictions <- cbind(as.data.frame(pred0.test$pred[,3]),
as.data.frame(pred1.test$pred[,3]),
as.data.frame(pred2.test$pred[,3])
)
setnames(test.predictions, c("pr_label0_ensemble",
"pr_label1_ensemble",
"pr_label2_ensemble")
)
train.n <- cbind(train.n, train.predictions)
test.n <- cbind(test.n, test.predictions)
saveRDS(train.n, "train.n.level1.RDS")
saveRDS(test.n, "test.n.level1.RDS")
# Stage 2 Model (same as in tel.R)
trainHex <- as.h2o(train.n, destination_frame="train_tel.hex")
testHex <- as.h2o(test.n, destination_frame="test_tel.hex")
trainHex[,"fault_severity_factor"] <- as.factor(trainHex[,"fault_severity"])
trainHex[,"location"] <- as.factor(trainHex[,"location"])
trainHex[,"fit2a_factor"] <- as.factor(trainHex[,"fit2a.cluster"])
trainHex[,"fit3a_factor"] <- as.factor(trainHex[,"fit3a.cluster"])
trainHex[,"fit5a_factor"] <- as.factor(trainHex[,"fit5a.cluster"])
trainHex[,"fit3_factor"] <- as.factor(trainHex[,"fit3.cluster"])
trainHex[,"fit20_factor"] <- as.factor(trainHex[,"fit20.cluster"])
testHex[,"location"] <- as.factor(testHex[,"location"])
testHex[,"fit2a_factor"] <- as.factor(testHex[,"fit2a.cluster"])
testHex[,"fit3a_factor"] <- as.factor(testHex[,"fit3a.cluster"])
testHex[,"fit5a_factor"] <- as.factor(testHex[,"fit5a.cluster"])
testHex[,"fit3_factor"] <- as.factor(testHex[,"fit3.cluster"])
testHex[,"fit20_factor"] <- as.factor(testHex[,"fit20.cluster"])
x_names <- colnames(testHex)
x_names <- x_names[!(x_names == "id")]
#x_names <- x_names[!(x_names %in% a$variable[a$percentage < .001])]
length(x_names)
glmHex <- h2o.glm( x = x_names,
y = "fault_severity_factor",
training_frame = trainHex,
model_id = "telstra_level2_glm.hex",
solver = "L_BFGS",
family = "multinomial",
ignore_const_cols = T,
nfold = 5,
standardize = T,
alpha = 0)
# ,
# lambda_search = T,
# nlambdas = 10
glmHex
h2o.mse(glmHex)
sqrt(h2o.mse(glmHex)) # Level 2 Ridge GlM had 5-fold CV RMSE of .4879109
# actual LB RMSE **0.74474**,
# level 1 logit LB RMSE .61095
h2o.varimp(glmHex)
#a <- as.data.frame(h2o.varimp(rfHex))
#saveRDS(a, "a.RDS")
rm(stage1.drf.pred)
stage1.drf.pred <- as.data.frame(h2o.predict(glmHex, testHex))
# Submission --------------------------------------------------------------
# For classification:
rm(sub)
stage1.drf.pred$predict <- NULL
sub <- cbind(sample$id, stage1.drf.pred)
setnames(sub, colnames(sample))
head(sub)
write.csv(sub, "tel_sub_level2_ridge.csv", row.names = F)
# For regression:
sub <- sample
sub$predict_1 <- 0
sub$predict_0[stage1.drf.pred < .5] <- 1
sub$predict_1[stage1.drf.pred < 1.5 & stage1.drf.pred > .5] <- 1
sub$predict_2[stage1.drf.pred > 1.5] <- 1
summary(sub)
summary(train$fault_severity)
write.csv(sub, "tel_sub.csv", row.names = F)
# Level 1 (not level 2) Submission
rm(sub)
sub <- cbind(sample$id, test.predictions)
setnames(sub, colnames(sample))
head(sub)
write.csv(sub, "tel_sub_level1_logit.csv", row.names = F)