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fix example

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pommedeterresautee 2015-11-30 16:33:33 +01:00
parent 425a5dd094
commit b67902ebdd
4 changed files with 12 additions and 14 deletions
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6
R-package/R/xgb.importance.R
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@ -52,11 +52,11 @@
#' bst <- xgboost(data = agaricus.train$data, label = agaricus.train$label, max.depth = 2,
#' eta = 1, nthread = 2, nround = 2,objective = "binary:logistic")
#'
#' # train$data@@Dimnames[[2]] represents the column names of the sparse matrix.
#' xgb.importance(train$data@@Dimnames[[2]], model = bst)
#' # agaricus.train$data@@Dimnames[[2]] represents the column names of the sparse matrix.
#' xgb.importance(agaricus.train$data@@Dimnames[[2]], model = bst)
#'
#' # Same thing with co-occurence computation this time
#' xgb.importance(train$data@@Dimnames[[2]], model = bst, data = train$data, label = train$label)
#' xgb.importance(agaricus.train$data@@Dimnames[[2]], model = bst, data = agaricus.train$data, label = agaricus.train$label)
#'
#' @export
xgb.importance <- function(feature_names = NULL, model = NULL, data = NULL, label = NULL, target = function(x) ( (x + label) == 2)){

7
R-package/R/xgb.plot.importance.R
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@ -19,13 +19,12 @@
#' #Both dataset are list with two items, a sparse matrix and labels
#' #(labels = outcome column which will be learned).
#' #Each column of the sparse Matrix is a feature in one hot encoding format.
#' train <- agaricus.train
#'
#' bst <- xgboost(data = train$data, label = train$label, max.depth = 2,
#' bst <- xgboost(data = agaricus.train$data, label = agaricus.train$label, max.depth = 2,
#' eta = 1, nthread = 2, nround = 2,objective = "binary:logistic")
#'
#' #train$data@@Dimnames[[2]] represents the column names of the sparse matrix.
#' importance_matrix <- xgb.importance(train$data@@Dimnames[[2]], model = bst)
#' #agaricus.train$data@@Dimnames[[2]] represents the column names of the sparse matrix.
#' importance_matrix <- xgb.importance(agaricus.train$data@@Dimnames[[2]], model = bst)
#' xgb.plot.importance(importance_matrix)
#'
#' @export

6
R-package/man/xgb.importance.Rd
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@ -58,11 +58,11 @@ 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")
# train$data@Dimnames[[2]] represents the column names of the sparse matrix.
xgb.importance(train$data@Dimnames[[2]], model = bst)
# agaricus.train$data@Dimnames[[2]] represents the column names of the sparse matrix.
xgb.importance(agaricus.train$data@Dimnames[[2]], model = bst)
# Same thing with co-occurence computation this time
xgb.importance(train$data@Dimnames[[2]], model = bst, data = train$data, label = train$label)
xgb.importance(agaricus.train$data@Dimnames[[2]], model = bst, data = agaricus.train$data, label = agaricus.train$label)
}

7
R-package/man/xgb.plot.importance.Rd
View File

@ -28,13 +28,12 @@ data(agaricus.train, package='xgboost')
#Both dataset are list with two items, a sparse matrix and labels
#(labels = outcome column which will be learned).
#Each column of the sparse Matrix is a feature in one hot encoding format.
train <- agaricus.train
bst <- xgboost(data = train$data, label = train$label, max.depth = 2,
bst <- xgboost(data = agaricus.train$data, label = agaricus.train$label, max.depth = 2,
eta = 1, nthread = 2, nround = 2,objective = "binary:logistic")
#train$data@Dimnames[[2]] represents the column names of the sparse matrix.
importance_matrix <- xgb.importance(train$data@Dimnames[[2]], model = bst)
#agaricus.train$data@Dimnames[[2]] represents the column names of the sparse matrix.
importance_matrix <- xgb.importance(agaricus.train$data@Dimnames[[2]], model = bst)
xgb.plot.importance(importance_matrix)
}