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add support of GLM model in importance plot function

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pommedeterresautee 2015-12-02 10:39:57 +01:00
parent a1c0ee0e66
commit d04f7005de
2 changed files with 17 additions and 9 deletions
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22
R-package/R/xgb.plot.importance.R
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@ -1,6 +1,6 @@
#' Plot feature importance bar graph #' Plot feature importance bar graph
#' #'
#' Read a data.table containing feature importance details and plot it. #' Read a data.table containing feature importance details and plot it (for both GLM and Trees).
#' #'
#' @importFrom magrittr %>% #' @importFrom magrittr %>%
#' @param importance_matrix a \code{data.table} returned by the \code{xgb.importance} function. #' @param importance_matrix a \code{data.table} returned by the \code{xgb.importance} function.
@ -10,7 +10,7 @@
#' #'
#' @details #' @details
#' The purpose of this function is to easily represent the importance of each feature of a model. #' The purpose of this function is to easily represent the importance of each feature of a model.
#' The function return a ggplot graph, therefore each of its characteristic can be overriden (to customize it). #' The function returns a ggplot graph, therefore each of its characteristic can be overriden (to customize it).
#' In particular you may want to override the title of the graph. To do so, add \code{+ ggtitle("A GRAPH NAME")} next to the value returned by this function. #' In particular you may want to override the title of the graph. To do so, add \code{+ ggtitle("A GRAPH NAME")} next to the value returned by this function.
#' #'
#' @examples #' @examples
@ -40,21 +40,29 @@ xgb.plot.importance <-
stop("Ckmeans.1d.dp package is required for plotting the importance", call. = FALSE) stop("Ckmeans.1d.dp package is required for plotting the importance", call. = FALSE)
} }
if(isTRUE(all.equal(colnames(importance_matrix), c("Feature", "Gain", "Cover", "Frequency")))){
y.axe.name <- "Gain"
} else if(isTRUE(all.equal(colnames(importance_matrix), c("Feature", "Weight")))){
y.axe.name <- "Weight"
} else {
stop("Importance matrix is not correct (column names issue)")
}
# To avoid issues in clustering when co-occurences are used # To avoid issues in clustering when co-occurences are used
importance_matrix <- importance_matrix <-
importance_matrix[, .(Gain = sum(Gain)), by = Feature] importance_matrix[, .(Gain.or.Weight = sum(get(y.axe.name))), by = Feature]
clusters <- clusters <-
suppressWarnings(Ckmeans.1d.dp::Ckmeans.1d.dp(importance_matrix[,Gain], numberOfClusters)) suppressWarnings(Ckmeans.1d.dp::Ckmeans.1d.dp(importance_matrix[,Gain.or.Weight], numberOfClusters))
importance_matrix[,"Cluster":= clusters$cluster %>% as.character] importance_matrix[,"Cluster":= clusters$cluster %>% as.character]
plot <- plot <-
ggplot2::ggplot( ggplot2::ggplot(
importance_matrix, ggplot2::aes( importance_matrix, ggplot2::aes(
x = stats::reorder(Feature, Gain), y = Gain, width = 0.05 x = stats::reorder(Feature, Gain.or.Weight), y = Gain.or.Weight, width = 0.05
), environment = environment() ), environment = environment()
) + ggplot2::geom_bar(ggplot2::aes(fill = Cluster), stat = "identity", position = ) + ggplot2::geom_bar(ggplot2::aes(fill = Cluster), stat = "identity", position =
"identity") + ggplot2::coord_flip() + ggplot2::xlab("Features") + ggplot2::ylab("Gain") + ggplot2::ggtitle("Feature importance") + ggplot2::theme( "identity") + ggplot2::coord_flip() + ggplot2::xlab("Features") + ggplot2::ylab(y.axe.name) + ggplot2::ggtitle("Feature importance") + ggplot2::theme(
plot.title = ggplot2::element_text(lineheight = .9, face = "bold"), panel.grid.major.y = ggplot2::element_blank() plot.title = ggplot2::element_text(lineheight = .9, face = "bold"), panel.grid.major.y = ggplot2::element_blank()
) )
@ -66,6 +74,6 @@ xgb.plot.importance <-
# They are mainly column names inferred by Data.table... # They are mainly column names inferred by Data.table...
globalVariables( globalVariables(
c( c(
"Feature", "Gain", "Cluster", "ggplot", "aes", "geom_bar", "coord_flip", "xlab", "ylab", "ggtitle", "theme", "element_blank", "element_text" "Feature", "Gain.or.Weight", "Cluster", "ggplot", "aes", "geom_bar", "coord_flip", "xlab", "ylab", "ggtitle", "theme", "element_blank", "element_text", "Gain.or.Weight"
) )
) )

4
R-package/man/xgb.plot.importance.Rd
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@ -15,11 +15,11 @@ xgb.plot.importance(importance_matrix = NULL, numberOfClusters = c(1:10))
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. 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.
} }
\description{ \description{
Read a data.table containing feature importance details and plot it. Read a data.table containing feature importance details and plot it (for both GLM and Trees).
} }
\details{ \details{
The purpose of this function is to easily represent the importance of each feature of a model. The purpose of this function is to easily represent the importance of each feature of a model.
The function return a ggplot graph, therefore each of its characteristic can be overriden (to customize it). The function returns a ggplot graph, therefore each of its characteristic can be overriden (to customize it).
In particular you may want to override the title of the graph. To do so, add \code{+ ggtitle("A GRAPH NAME")} next to the value returned by this function. In particular you may want to override the title of the graph. To do so, add \code{+ ggtitle("A GRAPH NAME")} next to the value returned by this function.
} }
\examples{ \examples{