43 lines
1.4 KiB
R
43 lines
1.4 KiB
R
library(stringr)
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library(data.table)
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data(agaricus.train, package='xgboost')
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#Both dataset are list with two items, a sparse matrix and labels
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#(labels = outcome column which will be learned).
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#Each column of the sparse Matrix is a feature in one hot encoding format.
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train <- agaricus.train
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bst <- xgboost(data = train$data, label = train$label, max.depth = 5,
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eta = 1, nthread = 2, nround = 2,objective = "binary:logistic")
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#agaricus.test$data@Dimnames[[2]] represents the column names of the sparse matrix.
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tree.matrix <- xgb.model.dt.tree(agaricus.train$data@Dimnames[[2]], model = bst)
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# first number of the path represents the tree, then the following numbers are related to the path to follow
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# root init
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root.nodes <- tree.matrix[str_detect(ID, "\\d+-0"), ID]
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tree.matrix[ID == root.nodes, Abs.Position:=root.nodes]
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precedent.nodes <- root.nodes
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while(tree.matrix[,sum(is.na(Abs.Position))] > 0) {
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yes.row.nodes <- tree.matrix[Abs.Position %in% precedent.nodes & !is.na(Yes)]
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no.row.nodes <- tree.matrix[Abs.Position %in% precedent.nodes & !is.na(No)]
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yes.nodes.abs.pos <- yes.row.nodes[, Abs.Position] %>% paste0("-0")
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no.nodes.abs.pos <- no.row.nodes[, Abs.Position] %>% paste0("-1")
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tree.matrix[ID == yes.row.nodes[, Yes], Abs.Position := yes.nodes.abs.pos]
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tree.matrix[ID == no.row.nodes[, No], Abs.Position := no.nodes.abs.pos]
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precedent.nodes <- c(yes.nodes.abs.pos, no.nodes.abs.pos)
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}
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tree.matrix
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