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#Setup | |
rm(list = ls(all = TRUE)) | |
gc(reset=TRUE) | |
set.seed(42) #From random.org | |
#Libraries | |
library(caret) | |
library(devtools) | |
install_github('caretEnsemble', 'zachmayer') #Install zach's caretEnsemble package | |
library(caretEnsemble) | |
#Data | |
library(mlbench) | |
data(BostonHousing2) | |
X <- model.matrix(cmedv~crim+zn+indus+chas+nox+rm+age+dis+ | |
rad+tax+ptratio+b+lstat+lat+lon, BostonHousing2)[,-1] | |
X <- data.frame(X) | |
Y <- BostonHousing2$cmedv | |
#Split train/test | |
train <- runif(nrow(X)) <= .66 | |
#Setup CV Folds | |
#returnData=FALSE saves some space | |
folds=5 | |
repeats=1 | |
myControl <- trainControl(method='cv', number=folds, repeats=repeats, returnResamp='none', | |
returnData=FALSE, savePredictions=TRUE, | |
verboseIter=TRUE, allowParallel=TRUE, | |
index=createMultiFolds(Y[train], k=folds, times=repeats)) | |
PP <- c('center', 'scale') | |
#Train some models | |
model1 <- train(X[train,], Y[train], method='gbm', trControl=myControl, | |
tuneGrid=expand.grid(.n.trees=500, .interaction.depth=15, .shrinkage = 0.01)) | |
model2 <- train(X[train,], Y[train], method='blackboost', trControl=myControl) | |
model3 <- train(X[train,], Y[train], method='parRF', trControl=myControl) | |
model4 <- train(X[train,], Y[train], method='mlpWeightDecay', trControl=myControl, trace=FALSE, preProcess=PP) | |
model5 <- train(X[train,], Y[train], method='ppr', trControl=myControl, preProcess=PP) | |
model6 <- train(X[train,], Y[train], method='earth', trControl=myControl, preProcess=PP) | |
model7 <- train(X[train,], Y[train], method='glm', trControl=myControl, preProcess=PP) | |
model8 <- train(X[train,], Y[train], method='svmRadial', trControl=myControl, preProcess=PP) | |
model9 <- train(X[train,], Y[train], method='gam', trControl=myControl, preProcess=PP) | |
model10 <- train(X[train,], Y[train], method='glmnet', trControl=myControl, preProcess=PP) | |
#Make a list of all the models | |
all.models <- list(model1, model2, model3, model4, model5, model6, model7, model8, model9, model10) | |
names(all.models) <- sapply(all.models, function(x) x$method) | |
sort(sapply(all.models, function(x) min(x$results$RMSE))) | |
#Make a greedy ensemble - currently can only use RMSE | |
greedy <- caretEnsemble(all.models, iter=1000L) | |
sort(greedy$weights, decreasing=TRUE) | |
greedy$error | |
#Make a linear regression ensemble | |
linear <- caretStack(all.models, method='glm', trControl=trainControl(method='cv')) | |
summary(linear$ens_model$finalModel) | |
linear$error | |
#Predict for test set: | |
preds <- data.frame(sapply(all.models, predict, newdata=X[!train,])) | |
preds$ENS_greedy <- predict(greedy, newdata=X[!train,]) | |
preds$ENS_linear <- predict(linear, newdata=X[!train,]) | |
sort(sqrt(colMeans((preds - Y[!train]) ^ 2))) |
Hi,
gbm needs to have the parameter .n.minobsinnode=10 or other values than 10
model1 <- train(X[train,], Y[train], method='gbm', trControl=myControl,tuneGrid=expand.grid(.n.trees=500, .n.minobsinnode=10, .interaction.depth=15, .shrinkage = 0.01 ))
otherwise the following error occurs.
Error in train.default(X[train, ], Y[train], method = "gbm", trControl = myControl, :
The tuning parameter grid should have columns n.trees, interaction.depth, shrinkage, n.minobsinnode
Also if no parallel backend is registered it will just run on one CPU or throw errors
Also bunch of dependencies are missing if installed in freshly installed R 3.2.2.
See also demo2.r
Cheers
Tobias
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according to the new released package you have to use caretList function to combine the models