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Merge pull request #664 from pommedeterresautee/master

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Support GLM in importance plot + increase tests #Rstat
This commit is contained in:
Michaël Benesty 2015-12-02 11:10:00 +01:00
commit 88e7c6012b
3 changed files with 39 additions and 26 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
#'
#' 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 %>%
#' @param importance_matrix a \code{data.table} returned by the \code{xgb.importance} function.
@ -10,7 +10,7 @@
#'
#' @details
#' 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.
#'
#' @examples
@ -40,21 +40,29 @@ xgb.plot.importance <-
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
importance_matrix <-
importance_matrix[, .(Gain = sum(Gain)), by = Feature]
importance_matrix[, .(Gain.or.Weight = sum(get(y.axe.name))), by = Feature]
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]
plot <-
ggplot2::ggplot(
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()
) + 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()
)
@ -66,6 +74,6 @@ xgb.plot.importance <-
# They are mainly column names inferred by Data.table...
globalVariables(
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.
}
\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{
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.
}
\examples{

39
R-package/tests/testthat/test_helpers.R
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@ -14,50 +14,55 @@ df[,AgeCat := as.factor(ifelse(Age > 30, "Old", "Young"))]
df[,ID := NULL]
sparse_matrix <- sparse.model.matrix(Improved~.-1, data = df)
output_vector <- df[,Y := 0][Improved == "Marked",Y := 1][,Y]
bst <- xgboost(data = sparse_matrix, label = output_vector, max.depth = 9,
eta = 1, nthread = 2, nround = 10, objective = "binary:logistic")
bst.Tree <- xgboost(data = sparse_matrix, label = output_vector, max.depth = 9,
eta = 1, nthread = 2, nround = 10, objective = "binary:logistic", booster = "gbtree")
bst.GLM <- xgboost(data = sparse_matrix, label = output_vector,
eta = 1, nthread = 2, nround = 10, objective = "binary:logistic", booster = "gblinear")
feature.names <- agaricus.train$data@Dimnames[[2]]
test_that("xgb.dump works", {
capture.output(print(xgb.dump(bst)))
expect_true(xgb.dump(bst, 'xgb.model.dump', with.stats = T))
capture.output(print(xgb.dump(bst.Tree)))
capture.output(print(xgb.dump(bst.GLM)))
expect_true(xgb.dump(bst.Tree, 'xgb.model.dump', with.stats = T))
})
test_that("xgb.model.dt.tree works with and without feature names", {
names.dt.trees <- c("ID", "Feature", "Split", "Yes", "No", "Missing", "Quality", "Cover",
"Tree", "Yes.Feature", "Yes.Cover", "Yes.Quality", "No.Feature", "No.Cover", "No.Quality")
dt.tree <- xgb.model.dt.tree(feature_names = feature.names, model = bst)
dt.tree <- xgb.model.dt.tree(feature_names = feature.names, model = bst.Tree)
expect_equal(names.dt.trees, names(dt.tree))
expect_equal(dim(dt.tree), c(162, 15))
xgb.model.dt.tree(model = bst)
xgb.model.dt.tree(model = bst.Tree)
})
test_that("xgb.importance works with and without feature names", {
importance <- xgb.importance(feature_names = sparse_matrix@Dimnames[[2]], model = bst)
expect_equal(dim(importance), c(7, 4))
expect_equal(colnames(importance), c("Feature", "Gain", "Cover", "Frequency"))
xgb.importance(model = bst)
importance.Tree <- xgb.importance(feature_names = sparse_matrix@Dimnames[[2]], model = bst.Tree)
expect_equal(dim(importance.Tree), c(7, 4))
expect_equal(colnames(importance.Tree), c("Feature", "Gain", "Cover", "Frequency"))
xgb.importance(model = bst.Tree)
xgb.plot.importance(importance_matrix = importance.Tree)
})
test_that("xgb.importance works with GLM model", {
bst.GLM <- xgboost(data = sparse_matrix, label = output_vector,
eta = 1, nthread = 2, nround = 10, objective = "binary:logistic", booster = "gblinear")
importance.GLM <- xgb.importance(feature_names = sparse_matrix@Dimnames[[2]], model = bst.GLM)
expect_equal(dim(importance.GLM), c(10, 2))
expect_equal(colnames(importance.GLM), c("Feature", "Weight"))
xgb.importance(model = bst.GLM)
xgb.plot.importance(importance.GLM)
})
test_that("xgb.plot.tree works with and without feature names", {
xgb.plot.tree(feature_names = feature.names, model = bst)
xgb.plot.tree(model = bst)
xgb.plot.tree(feature_names = feature.names, model = bst.Tree)
xgb.plot.tree(model = bst.Tree)
})
test_that("xgb.plot.multi.trees works with and without feature names", {
xgb.plot.multi.trees(model = bst, feature_names = feature.names, features.keep = 3)
xgb.plot.multi.trees(model = bst, features.keep = 3)
xgb.plot.multi.trees(model = bst.Tree, feature_names = feature.names, features.keep = 3)
xgb.plot.multi.trees(model = bst.Tree, features.keep = 3)
})
test_that("xgb.plot.deepness works", {
xgb.plot.deepness(model = bst)
xgb.plot.deepness(model = bst.Tree)
})