[R] parameter style consistency
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@@ -2,12 +2,9 @@
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#'
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#' May improve the learning by adding new features to the training data based on the decision trees from a previously learned model.
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#'
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#' @importFrom magrittr %>%
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#' @importFrom Matrix cBind
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#' @importFrom Matrix sparse.model.matrix
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#'
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#' @param model decision tree boosting model learned on the original data
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#' @param training.data original data (usually provided as a \code{dgCMatrix} matrix)
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#' @param data original data (usually provided as a \code{dgCMatrix} matrix)
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#' @param ... currently not used
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#'
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#' @return \code{dgCMatrix} matrix including both the original data and the new features.
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#'
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@@ -54,7 +51,7 @@
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#' dtrain <- xgb.DMatrix(data = agaricus.train$data, label = agaricus.train$label)
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#' dtest <- xgb.DMatrix(data = agaricus.test$data, label = agaricus.test$label)
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#'
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#' param <- list(max.depth=2, eta=1, silent=1, objective='binary:logistic')
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#' param <- list(max_depth=2, eta=1, silent=1, objective='binary:logistic')
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#' nround = 4
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#'
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#' bst = xgb.train(params = param, data = dtrain, nrounds = nround, nthread = 2)
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@@ -79,13 +76,14 @@
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#' cat(paste("The accuracy was", accuracy.before, "before adding leaf features and it is now", accuracy.after, "!\n"))
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#'
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#' @export
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xgb.create.features <- function(model, training.data){
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pred_with_leaf = predict(model, training.data, predleaf = TRUE)
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xgb.create.features <- function(model, data, ...){
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check.deprecation(...)
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pred_with_leaf = predict(model, data, predleaf = TRUE)
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cols <- list()
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for(i in 1:length(trees)){
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# max is not the real max but it s not important for the purpose of adding features
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leaf.id <- sort(unique(pred_with_leaf[,i]))
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cols[[i]] <- factor(x = pred_with_leaf[,i], level = leaf.id)
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leaf_id <- sort(unique(pred_with_leaf[,i]))
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cols[[i]] <- factor(x = pred_with_leaf[,i], level = leaf_id)
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}
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cBind(training.data, sparse.model.matrix( ~ . -1, as.data.frame(cols)))
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}
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cBind(data, sparse.model.matrix( ~ . -1, as.data.frame(cols)))
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}
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