Alien-XGBoost

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xgboost/CONTRIBUTORS.md  view on Meta::CPAN

* Willing to spent time on maintaining and lead the project.

New committers will be proposed by current committer members, with support from more than two of current committers.

List of Contributors
--------------------
* [Full List of Contributors](https://github.com/dmlc/xgboost/graphs/contributors)
  - To contributors: please add your name to the list when you submit a patch to the project:)
* [Kailong Chen](https://github.com/kalenhaha)
  - Kailong is an early contributor of xgboost, he is creator of ranking objectives in xgboost.
* [Skipper Seabold](https://github.com/jseabold)
  - Skipper is the major contributor to the scikit-learn module of xgboost.
* [Zygmunt ZajÄ…c](https://github.com/zygmuntz)
  - Zygmunt is the master behind the early stopping feature frequently used by kagglers.
* [Ajinkya Kale](https://github.com/ajkl)
* [Boliang Chen](https://github.com/cblsjtu)
* [Yangqing Men](https://github.com/yanqingmen)
  - Yangqing is the creator of xgboost java package.
* [Engpeng Yao](https://github.com/yepyao)
* [Giulio](https://github.com/giuliohome)
  - Giulio is the creator of windows project of xgboost

xgboost/R-package/R/xgb.ggplot.R  view on Meta::CPAN

  importance_matrix[, Cluster := as.character(clusters$cluster)]

  plot <-
    ggplot2::ggplot(importance_matrix, 
                    ggplot2::aes(x = factor(Feature, levels = rev(Feature)), y = Importance, width = 0.05),
                    environment = environment()) + 
    ggplot2::geom_bar(ggplot2::aes(fill = Cluster), stat = "identity", position = "identity") + 
    ggplot2::coord_flip() + 
    ggplot2::xlab("Features") + 
    ggplot2::ggtitle("Feature importance") + 
    ggplot2::theme(plot.title = ggplot2::element_text(lineheight = .9, face = "bold"), 
                   panel.grid.major.y = ggplot2::element_blank())
  return(plot)
}


#' @rdname xgb.plot.deepness
#' @export
xgb.ggplot.deepness <- function(model = NULL, which = c("2x1", "max.depth", "med.depth", "med.weight")) {

  if (!requireNamespace("ggplot2", quietly = TRUE))

xgboost/R-package/R/xgb.ggplot.R  view on Meta::CPAN

  setkey(dt_summaries, 'Depth')

  if (which == "2x1") {
    p1 <-
      ggplot2::ggplot(dt_summaries) +
      ggplot2::geom_bar(ggplot2::aes(x = Depth, y = N), stat = "Identity") +
      ggplot2::xlab("") +
      ggplot2::ylab("Number of leafs") +
      ggplot2::ggtitle("Model complexity") +
      ggplot2::theme(
        plot.title = ggplot2::element_text(lineheight = 0.9, face = "bold"),
        panel.grid.major.y = ggplot2::element_blank(),
        axis.ticks = ggplot2::element_blank(),
        axis.text.x = ggplot2::element_blank()
      )
  
    p2 <- 
      ggplot2::ggplot(dt_summaries) +
      ggplot2::geom_bar(ggplot2::aes(x = Depth, y = Cover), stat = "Identity") + 
      ggplot2::xlab("Leaf depth") +
      ggplot2::ylab("Weighted cover")

xgboost/R-package/R/xgb.plot.tree.R  view on Meta::CPAN

#'        If it is square loss, this simply corresponds to the number of instances seen by a split
#'        or collected by a leaf during training.
#'        The deeper in the tree a node is, the lower this metric will be.
#'  \item \code{Gain} (for split nodes): the information gain metric of a split
#'        (corresponds to the importance of the node in the model).
#'  \item \code{Value} (for leafs): the margin value that the leaf may contribute to prediction.
#' } 
#' The tree root nodes also indicate the Tree index (0-based).
#' 
#' The "Yes" branches are marked by the "< split_value" label.
#' The branches that also used for missing values are marked as bold
#' (as in "carrying extra capacity").
#' 
#' This function uses \href{http://www.graphviz.org/}{GraphViz} as a backend of DiagrammeR.
#'  
#' @return
#' 
#' When \code{render = TRUE}:
#' returns a rendered graph object which is an \code{htmlwidget} of class \code{grViz}.
#' Similar to ggplot objects, it needs to be printed to see it when not running from command line.
#' 

xgboost/R-package/R/xgb.plot.tree.R  view on Meta::CPAN

    label     = dt$label,
    fillcolor = dt$filledcolor,
    shape     = dt$shape,
    data      = dt$Feature)
  
  edges <- DiagrammeR::create_edge_df(
    from  = match(dt[Feature != "Leaf", c(ID)] %>% rep(2), dt$ID),
    to    = match(dt[Feature != "Leaf", c(Yes, No)], dt$ID),
    label = dt[Feature != "Leaf", paste("<", Split)] %>%
            c(rep("", nrow(dt[Feature != "Leaf"]))),
    style = dt[Feature != "Leaf", ifelse(Missing == Yes, "bold", "solid")] %>%
            c(dt[Feature != "Leaf", ifelse(Missing == No, "bold", "solid")]),
    rel   = "leading_to")

  graph <- DiagrammeR::create_graph(
      nodes_df = nodes,
      edges_df = edges,
      attr_theme = NULL
      ) %>%
    DiagrammeR::add_global_graph_attrs(
      attr_type = "graph",
      attr  = c("layout", "rankdir"),

xgboost/R-package/man/xgb.plot.tree.Rd  view on Meta::CPAN

       If it is square loss, this simply corresponds to the number of instances seen by a split
       or collected by a leaf during training.
       The deeper in the tree a node is, the lower this metric will be.
 \item \code{Gain} (for split nodes): the information gain metric of a split
       (corresponds to the importance of the node in the model).
 \item \code{Value} (for leafs): the margin value that the leaf may contribute to prediction.
} 
The tree root nodes also indicate the Tree index (0-based).

The "Yes" branches are marked by the "< split_value" label.
The branches that also used for missing values are marked as bold
(as in "carrying extra capacity").

This function uses \href{http://www.graphviz.org/}{GraphViz} as a backend of DiagrammeR.
}
\examples{
data(agaricus.train, package='xgboost')

bst <- xgboost(data = agaricus.train$data, label = agaricus.train$label, max_depth = 3,
               eta = 1, nthread = 2, nrounds = 2,objective = "binary:logistic")
# plot all the trees

xgboost/R-package/vignettes/vignette.css  view on Meta::CPAN

	
table th {
	padding: 4px 8px 4px 8px;
  text-align: center;
  color: white;
	background: #606AAA;
	border-bottom: 1px solid #919699;
	border-right: 1px solid #919699;
}
table th p {
	font-weight: bold;
	margin-bottom: 0px; 
}
	
table td {
	padding: 8px;	
	vertical-align: top;
	border-bottom: 1px solid #919699;
	border-right: 1px solid #919699;
}