[R] parameter style consistency
This commit is contained in:
Vadim Khotilovich
parent
56bd442b31
commit
e9eb34fabc
@ -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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cBind(data, sparse.model.matrix( ~ . -1, as.data.frame(cols)))
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}
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@ -2,14 +2,6 @@
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#'
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#' Create a \code{data.table} of the most important features of a model.
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#'
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#' @importFrom data.table data.table
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#' @importFrom data.table setnames
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#' @importFrom data.table :=
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#' @importFrom magrittr %>%
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#' @importFrom Matrix colSums
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#' @importFrom Matrix cBind
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#' @importFrom Matrix sparseVector
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#'
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#' @param feature_names names of each feature as a \code{character} vector. Can be extracted from a sparse matrix (see example). If model dump already contains feature names, this argument should be \code{NULL}.
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#' @param model generated by the \code{xgb.train} function.
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#' @param data the dataset used for the training step. Will be used with \code{label} parameter for co-occurence computation. More information in \code{Detail} part. This parameter is optional.
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@ -46,14 +38,13 @@
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#' @examples
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#' data(agaricus.train, package='xgboost')
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#'
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#' bst <- xgboost(data = agaricus.train$data, label = agaricus.train$label, max.depth = 2,
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#' eta = 1, nthread = 2, nround = 2,objective = "binary:logistic")
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#' bst <- xgboost(data = agaricus.train$data, label = agaricus.train$label, max_depth = 2,
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#' eta = 1, nthread = 2, nrounds = 2,objective = "binary:logistic")
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#'
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#' # agaricus.train$data@@Dimnames[[2]] represents the column names of the sparse matrix.
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#' xgb.importance(agaricus.train$data@@Dimnames[[2]], model = bst)
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#' xgb.importance(colnames(agaricus.train$data), model = bst)
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#'
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#' # Same thing with co-occurence computation this time
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#' xgb.importance(agaricus.train$data@@Dimnames[[2]], model = bst, data = agaricus.train$data, label = agaricus.train$label)
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#' xgb.importance(colnames(agaricus.train$data), model = bst, data = agaricus.train$data, label = agaricus.train$label)
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#'
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#' @export
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xgb.importance <- function(feature_names = NULL, model = NULL, data = NULL, label = NULL, target = function(x) ( (x + label) == 2)){
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@ -84,7 +75,7 @@ xgb.importance <- function(feature_names = NULL, model = NULL, data = NULL, labe
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data.table(Feature = feature_names, Weight = weights)
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}
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model.text.dump <- xgb.dump(model = model, with.stats = T)
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model.text.dump <- xgb.dump(model = model, with_stats = T)
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if(model.text.dump[2] == "bias:"){
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result <- model.text.dump %>% linearDump(feature_names, .)
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@ -9,8 +9,8 @@
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#' data(agaricus.test, package='xgboost')
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#' train <- agaricus.train
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#' test <- agaricus.test
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#' bst <- xgboost(data = train$data, label = train$label, max.depth = 2,
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#' eta = 1, nthread = 2, nround = 2,objective = "binary:logistic")
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#' bst <- xgboost(data = train$data, label = train$label, max_depth = 2,
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#' eta = 1, nthread = 2, nrounds = 2,objective = "binary:logistic")
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#' xgb.save(bst, 'xgb.model')
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#' bst <- xgb.load('xgb.model')
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#' pred <- predict(bst, test$data)
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@ -26,6 +26,6 @@ xgb.load <- function(modelfile) {
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} else {
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bst <- xgb.handleToBooster(handle, NULL)
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}
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bst <- xgb.Booster.check(bst)
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bst <- xgb.Booster.check(bst, saveraw = TRUE)
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return(bst)
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}
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@ -2,16 +2,11 @@
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#'
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#' Parse a boosted tree model text dump into a \code{data.table} structure.
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#'
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#' @importFrom data.table data.table
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#' @importFrom data.table :=
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#' @importFrom magrittr %>%
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#' @importFrom stringr str_match
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#'
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#' @param feature_names character vector of feature names. If the model already
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#' contains feature names, this argument should be \code{NULL} (default value)
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#' @param model object of class \code{xgb.Booster}
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#' @param text \code{character} vector previously generated by the \code{xgb.dump}
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#' function (where parameter \code{with.stats = TRUE} should have been set).
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#' function (where parameter \code{with_stats = TRUE} should have been set).
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#' @param n_first_tree limit the parsing to the \code{n} first trees.
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#' If set to \code{NULL}, all trees of the model are parsed.
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#'
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@ -40,8 +35,8 @@
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#'
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#' data(agaricus.train, package='xgboost')
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#'
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#' bst <- xgboost(data = agaricus.train$data, label = agaricus.train$label, max.depth = 2,
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#' eta = 1, nthread = 2, nround = 2,objective = "binary:logistic")
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#' bst <- xgboost(data = agaricus.train$data, label = agaricus.train$label, max_depth = 2,
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#' eta = 1, nthread = 2, nrounds = 2,objective = "binary:logistic")
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#'
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#' (dt <- xgb.model.dt.tree(colnames(agaricus.train$data), bst))
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#'
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@ -71,12 +66,12 @@ xgb.model.dt.tree <- function(feature_names = NULL, model = NULL, text = NULL,
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}
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if(is.null(text)){
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text <- xgb.dump(model = model, with.stats = T)
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text <- xgb.dump(model = model, with_stats = T)
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}
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position <- which(!is.na(str_match(text, "booster")))
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addTreeId <- function(x, i) paste(i,x,sep = "-")
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add.tree.id <- function(x, i) paste(i, x, sep = "-")
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anynumber_regex <- "[-+]?[0-9]*\\.?[0-9]+([eE][-+]?[0-9]+)?"
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@ -88,7 +83,7 @@ xgb.model.dt.tree <- function(feature_names = NULL, model = NULL, text = NULL,
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td <- td[Tree <= n_first_tree & !grepl('^booster', t)]
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td[, Node := str_match(t, "(\\d+):")[,2] %>% as.numeric ]
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td[, ID := addTreeId(Node, Tree)]
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td[, ID := add.tree.id(Node, Tree)]
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td[, isLeaf := !is.na(str_match(t, "leaf"))]
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# parse branch lines
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@ -97,7 +92,7 @@ xgb.model.dt.tree <- function(feature_names = NULL, model = NULL, text = NULL,
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"gain=(", anynumber_regex, "),cover=(", anynumber_regex, ")")
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# skip some indices with spurious capture groups from anynumber_regex
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xtr <- str_match(t, rx)[, c(2,3,5,6,7,8,10)]
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xtr[, 3:5] <- addTreeId(xtr[, 3:5], Tree)
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xtr[, 3:5] <- add.tree.id(xtr[, 3:5], Tree)
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lapply(1:ncol(xtr), function(i) xtr[,i])
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}]
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# assign feature_names when available
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@ -2,7 +2,6 @@
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#'
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#' Plot multiple graph aligned by rows and columns.
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#'
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#' @importFrom data.table data.table
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#' @param cols number of columns
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#' @return NULL
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multiplot <- function(..., cols = 1) {
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@ -42,18 +41,18 @@ edge.parser <- function(element) {
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#' Extract path from root to leaf from data.table
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#' @param dt.tree data.table containing the nodes and edges of the trees
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get.paths.to.leaf <- function(dt.tree) {
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get.paths.to.leaf <- function(dt_tree) {
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dt.not.leaf.edges <-
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dt.tree[Feature != "Leaf",.(ID, Yes, Tree)] %>% list(dt.tree[Feature != "Leaf",.(ID, No, Tree)]) %>% rbindlist(use.names = F)
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dt_tree[Feature != "Leaf",.(ID, Yes, Tree)] %>% list(dt_tree[Feature != "Leaf",.(ID, No, Tree)]) %>% rbindlist(use.names = F)
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trees <- dt.tree[,unique(Tree)]
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trees <- dt_tree[,unique(Tree)]
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paths <- list()
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for (tree in trees) {
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graph <-
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igraph::graph_from_data_frame(dt.not.leaf.edges[Tree == tree])
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paths.tmp <-
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igraph::shortest_paths(graph, from = paste0(tree, "-0"), to = dt.tree[Tree == tree &
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igraph::shortest_paths(graph, from = paste0(tree, "-0"), to = dt_tree[Tree == tree &
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Feature == "Leaf", c(ID)])
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paths <- c(paths, paths.tmp$vpath)
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}
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@ -64,11 +63,6 @@ get.paths.to.leaf <- function(dt.tree) {
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#'
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#' Generate a graph to plot the distribution of deepness among trees.
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#'
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#' @importFrom data.table data.table
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#' @importFrom data.table rbindlist
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#' @importFrom data.table setnames
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#' @importFrom data.table :=
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#' @importFrom magrittr %>%
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#' @param model dump generated by the \code{xgb.train} function.
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#'
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#' @return Two graphs showing the distribution of the model deepness.
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@ -78,7 +72,7 @@ get.paths.to.leaf <- function(dt.tree) {
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#' by tree deepness level.
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#'
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#' The purpose of this function is to help the user to find the best trade-off to set
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#' the \code{max.depth} and \code{min_child_weight} parameters according to the bias / variance trade-off.
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#' the \code{max_depth} and \code{min_child_weight} parameters according to the bias / variance trade-off.
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#'
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#' See \link{xgb.train} for more information about these parameters.
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#'
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@ -94,8 +88,8 @@ get.paths.to.leaf <- function(dt.tree) {
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#' @examples
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#' data(agaricus.train, package='xgboost')
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#'
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#' bst <- xgboost(data = agaricus.train$data, label = agaricus.train$label, max.depth = 15,
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#' eta = 1, nthread = 2, nround = 30, objective = "binary:logistic",
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#' bst <- xgboost(data = agaricus.train$data, label = agaricus.train$label, max_depth = 15,
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#' eta = 1, nthread = 2, nrounds = 30, objective = "binary:logistic",
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#' min_child_weight = 50)
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#'
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#' xgb.plot.deepness(model = bst)
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@ -2,9 +2,9 @@
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#'
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#' Read a data.table containing feature importance details and plot it (for both GLM and Trees).
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#'
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#' @importFrom magrittr %>%
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#' @param importance_matrix a \code{data.table} returned by the \code{xgb.importance} function.
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#' @param numberOfClusters a \code{numeric} vector containing the min and the max range of the possible number of clusters of bars.
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#' @param n_clusters a \code{numeric} vector containing the min and the max range of the possible number of clusters of bars.
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#' @param ... currently not used
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#'
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#' @return A \code{ggplot2} bar graph representing each feature by a horizontal bar. Longer is the bar, more important is the feature. Features are classified by importance and clustered by importance. The group is represented through the color of the bar.
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#'
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@ -20,16 +20,16 @@
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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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#'
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#' bst <- xgboost(data = agaricus.train$data, label = agaricus.train$label, max.depth = 2,
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#' eta = 1, nthread = 2, nround = 2,objective = "binary:logistic")
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#' bst <- xgboost(data = agaricus.train$data, label = agaricus.train$label, max_depth = 2,
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#' eta = 1, nthread = 2, nrounds = 2, objective = "binary:logistic")
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#'
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#' #agaricus.train$data@@Dimnames[[2]] represents the column names of the sparse matrix.
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#' importance_matrix <- xgb.importance(agaricus.train$data@@Dimnames[[2]], model = bst)
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#' importance_matrix <- xgb.importance(colnames(agaricus.train$data), model = bst)
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#' xgb.plot.importance(importance_matrix)
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#'
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#' @export
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xgb.plot.importance <-
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function(importance_matrix = NULL, numberOfClusters = c(1:10)) {
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function(importance_matrix = NULL, n_clusters = c(1:10), ...) {
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check.deprecation(...)
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if (!"data.table" %in% class(importance_matrix)) {
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stop("importance_matrix: Should be a data.table.")
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}
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@ -53,7 +53,7 @@ xgb.plot.importance <-
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importance_matrix[, .(Gain.or.Weight = sum(get(y.axe.name))), by = Feature]
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clusters <-
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suppressWarnings(Ckmeans.1d.dp::Ckmeans.1d.dp(importance_matrix[,Gain.or.Weight], numberOfClusters))
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suppressWarnings(Ckmeans.1d.dp::Ckmeans.1d.dp(importance_matrix[,Gain.or.Weight], n_clusters))
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importance_matrix[,"Cluster":= clusters$cluster %>% as.character]
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plot <-
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@ -2,19 +2,12 @@
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#'
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#' Visualization of the ensemble of trees as a single collective unit.
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#'
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#' @importFrom data.table data.table
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#' @importFrom data.table rbindlist
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#' @importFrom data.table setnames
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#' @importFrom data.table :=
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#' @importFrom magrittr %>%
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#' @importFrom stringr str_detect
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#' @importFrom stringr str_extract
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#'
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#' @param model dump generated by the \code{xgb.train} function.
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#' @param feature_names names of each feature as a \code{character} vector. Can be extracted from a sparse matrix (see example). If model dump already contains feature names, this argument should be \code{NULL}.
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#' @param features.keep number of features to keep in each position of the multi trees.
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#' @param plot.width width in pixels of the graph to produce
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#' @param plot.height height in pixels of the graph to produce
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#' @param features_keep number of features to keep in each position of the multi trees.
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#' @param plot_width width in pixels of the graph to produce
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#' @param plot_height height in pixels of the graph to produce
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#' @param ... currently not used
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#'
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#' @return Two graphs showing the distribution of the model deepness.
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#'
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@ -34,7 +27,7 @@
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#' Moreover, the trees tend to reuse the same features.
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#'
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#' The function will project each tree on one, and keep for each position the
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#' \code{features.keep} first features (based on Gain per feature measure).
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#' \code{features_keep} first features (based on Gain per feature measure).
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#'
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#' This function is inspired by this blog post:
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#' \url{https://wellecks.wordpress.com/2015/02/21/peering-into-the-black-box-visualizing-lambdamart/}
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@ -42,15 +35,16 @@
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#' @examples
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#' data(agaricus.train, package='xgboost')
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#'
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#' bst <- xgboost(data = agaricus.train$data, label = agaricus.train$label, max.depth = 15,
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#' eta = 1, nthread = 2, nround = 30, objective = "binary:logistic",
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#' bst <- xgboost(data = agaricus.train$data, label = agaricus.train$label, max_depth = 15,
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#' eta = 1, nthread = 2, nrounds = 30, objective = "binary:logistic",
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#' min_child_weight = 50)
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#'
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#' p <- xgb.plot.multi.trees(model = bst, feature_names = agaricus.train$data@Dimnames[[2]], features.keep = 3)
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#' p <- xgb.plot.multi.trees(model = bst, feature_names = colnames(agaricus.train$data), features_keep = 3)
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#' print(p)
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#'
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#' @export
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xgb.plot.multi.trees <- function(model, feature_names = NULL, features.keep = 5, plot.width = NULL, plot.height = NULL){
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xgb.plot.multi.trees <- function(model, feature_names = NULL, features_keep = 5, plot_width = NULL, plot_height = NULL, ...){
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check.deprecation(...)
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tree.matrix <- xgb.model.dt.tree(feature_names = feature_names, model = model)
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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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@ -80,7 +74,7 @@ xgb.plot.multi.trees <- function(model, feature_names = NULL, features.keep = 5,
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tree.matrix[,`:=`(abs.node.position=remove.tree(abs.node.position), Yes=remove.tree(Yes), No=remove.tree(No))]
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nodes.dt <- tree.matrix[,.(Quality = sum(Quality)),by = .(abs.node.position, Feature)][,.(Text =paste0(Feature[1:min(length(Feature), features.keep)], " (", Quality[1:min(length(Quality), features.keep)], ")") %>% paste0(collapse = "\n")), by=abs.node.position]
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nodes.dt <- tree.matrix[,.(Quality = sum(Quality)),by = .(abs.node.position, Feature)][,.(Text =paste0(Feature[1:min(length(Feature), features_keep)], " (", Quality[1:min(length(Quality), features_keep)], ")") %>% paste0(collapse = "\n")), by=abs.node.position]
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edges.dt <- tree.matrix[Feature != "Leaf",.(abs.node.position, Yes)] %>% list(tree.matrix[Feature != "Leaf",.(abs.node.position, No)]) %>% rbindlist() %>% setnames(c("From", "To")) %>% .[,.N,.(From, To)] %>% .[,N:=NULL]
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nodes <- DiagrammeR::create_nodes(nodes = nodes.dt[,abs.node.position],
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@ -104,7 +98,7 @@ xgb.plot.multi.trees <- function(model, feature_names = NULL, features.keep = 5,
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edges_df = edges,
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graph_attrs = "rankdir = LR")
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DiagrammeR::render_graph(graph, width = plot.width, height = plot.height)
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DiagrammeR::render_graph(graph, width = plot_width, height = plot_height)
|
||||
}
|
||||
|
||||
globalVariables(
|
||||
|
||||
@ -2,14 +2,12 @@
|
||||
#'
|
||||
#' Read a tree model text dump and plot the model.
|
||||
#'
|
||||
#' @importFrom data.table data.table
|
||||
#' @importFrom data.table :=
|
||||
#' @importFrom magrittr %>%
|
||||
#' @param feature_names names of each feature as a \code{character} vector. Can be extracted from a sparse matrix (see example). If model dump already contains feature names, this argument should be \code{NULL}.
|
||||
#' @param model generated by the \code{xgb.train} function. Avoid the creation of a dump file.
|
||||
#' @param n_first_tree limit the plot to the n first trees. If \code{NULL}, all trees of the model are plotted. Performance can be low for huge models.
|
||||
#' @param plot.width the width of the diagram in pixels.
|
||||
#' @param plot.height the height of the diagram in pixels.
|
||||
#' @param plot_width the width of the diagram in pixels.
|
||||
#' @param plot_height the height of the diagram in pixels.
|
||||
#' @param ... currently not used.
|
||||
#'
|
||||
#' @return A \code{DiagrammeR} of the model.
|
||||
#'
|
||||
@ -28,15 +26,14 @@
|
||||
#' @examples
|
||||
#' data(agaricus.train, package='xgboost')
|
||||
#'
|
||||
#' bst <- xgboost(data = agaricus.train$data, label = agaricus.train$label, max.depth = 2,
|
||||
#' eta = 1, nthread = 2, nround = 2,objective = "binary:logistic")
|
||||
#' bst <- xgboost(data = agaricus.train$data, label = agaricus.train$label, max_depth = 2,
|
||||
#' eta = 1, nthread = 2, nrounds = 2,objective = "binary:logistic")
|
||||
#'
|
||||
#' # agaricus.train$data@@Dimnames[[2]] represents the column names of the sparse matrix.
|
||||
#' xgb.plot.tree(feature_names = agaricus.train$data@@Dimnames[[2]], model = bst)
|
||||
#' xgb.plot.tree(feature_names = colnames(agaricus.train$data), model = bst)
|
||||
#'
|
||||
#' @export
|
||||
xgb.plot.tree <- function(feature_names = NULL, model = NULL, n_first_tree = NULL, plot.width = NULL, plot.height = NULL){
|
||||
|
||||
xgb.plot.tree <- function(feature_names = NULL, model = NULL, n_first_tree = NULL, plot_width = NULL, plot_height = NULL, ...){
|
||||
check.deprecation(...)
|
||||
if (class(model) != "xgb.Booster") {
|
||||
stop("model: Has to be an object of class xgb.Booster model generaged by the xgb.train function.")
|
||||
}
|
||||
@ -75,7 +72,7 @@ xgb.plot.tree <- function(feature_names = NULL, model = NULL, n_first_tree = NUL
|
||||
edges_df = edges,
|
||||
graph_attrs = "rankdir = LR")
|
||||
|
||||
DiagrammeR::render_graph(graph, width = plot.width, height = plot.height)
|
||||
DiagrammeR::render_graph(graph, width = plot_width, height = plot_height)
|
||||
}
|
||||
|
||||
# Avoid error messages during CRAN check.
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user