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Merge pull request #670 from pommedeterresautee/master

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Add code im demo to use the pred leaf in R
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Michaël Benesty 2015-12-04 16:35:43 +01:00
commit 375192efa1
1 changed files with 38 additions and 7 deletions
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R-package/demo/predict_leaf_indices.R
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@ -1,4 +1,9 @@
require(xgboost) require(xgboost)
require(data.table)
require(Matrix)
set.seed(1982)
# load in the agaricus dataset # load in the agaricus dataset
data(agaricus.train, package='xgboost') data(agaricus.train, package='xgboost')
data(agaricus.test, package='xgboost') data(agaricus.test, package='xgboost')
@ -6,16 +11,42 @@ dtrain <- xgb.DMatrix(data = agaricus.train$data, label = agaricus.train$label)
dtest <- xgb.DMatrix(data = agaricus.test$data, label = agaricus.test$label) dtest <- xgb.DMatrix(data = agaricus.test$data, label = agaricus.test$label)
param <- list(max.depth=2, eta=1, silent=1, objective='binary:logistic') param <- list(max.depth=2, eta=1, silent=1, objective='binary:logistic')
watchlist <- list(eval = dtest, train = dtrain) nround = 4
nround = 5
# training the model for two rounds # training the model for two rounds
bst = xgb.train(params = param, data = dtrain, nrounds = nround, nthread = 2, watchlist = watchlist) bst = xgb.train(params = param, data = dtrain, nrounds = nround, nthread = 2)
cat('start testing prediction from first n trees\n')
# Model accuracy without new features
accuracy.before <- sum((predict(bst, agaricus.test$data) >= 0.5) == agaricus.test$label) / length(agaricus.test$label)
### predict using first 2 tree
pred_with_leaf = predict(bst, dtest, ntreelimit = 2, predleaf = TRUE)
head(pred_with_leaf)
# by default, we predict using all the trees # by default, we predict using all the trees
pred_with_leaf = predict(bst, dtest, predleaf = TRUE) pred_with_leaf = predict(bst, dtest, predleaf = TRUE)
head(pred_with_leaf) head(pred_with_leaf)
create.new.tree.features <- function(model, original.features){
pred_with_leaf = predict(model, original.features, predleaf = TRUE)
cols <- list()
for(i in 1:length(trees)){
# max is not the real max but it s not important for the purpose of adding features
max <- max(pred_with_leaf[,i])
cols[[i]] <- factor(x = pred_with_leaf[,i], level = seq(to = max))
}
cBind(original.features, sparse.model.matrix( ~ ., as.data.frame(cols)))
}
# Convert previous features to one hot encoding
new.features.train <- create.new.tree.features(bst, agaricus.train$data)
new.features.test <- create.new.tree.features(bst, agaricus.test$data)
# learning with new features
new.dtrain <- xgb.DMatrix(data = new.features.train, label = agaricus.train$label)
new.dtest <- xgb.DMatrix(data = new.features.test, label = agaricus.test$label)
watchlist <- list(train = new.dtrain)
bst <- xgb.train(params = param, data = new.dtrain, nrounds = nround, nthread = 2)
# Model accuracy with new features
accuracy.after <- sum((predict(bst, new.dtest) >= 0.5) == agaricus.test$label) / length(agaricus.test$label)
# Here the accuracy was already good and is now perfect.
print(paste("The accuracy was", accuracy.before, "before adding leaf features and it is now", accuracy.after, "!"))