Merge pull request #662 from pommedeterresautee/master
Improve feature importance on GLM model
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@ -82,7 +82,9 @@ xgb.importance <- function(feature_names = NULL, model = NULL, data = NULL, labe
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}
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linearDump <- function(feature_names, text){
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which(text == "weight:") %>% {a =. + 1; text[a:length(text)]} %>% as.numeric %>% data.table(Feature = feature_names, Weight = .)
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weights <- which(text == "weight:") %>% {a =. + 1; text[a:length(text)]} %>% as.numeric
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if(is.null(feature_names)) feature_names <- seq(to = length(weights))
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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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@ -15,7 +15,7 @@ df[,ID := NULL]
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sparse_matrix <- sparse.model.matrix(Improved~.-1, data = df)
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output_vector <- df[,Y := 0][Improved == "Marked",Y := 1][,Y]
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bst <- xgboost(data = sparse_matrix, label = output_vector, max.depth = 9,
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eta = 1, nthread = 2, nround = 10,objective = "binary:logistic")
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eta = 1, nthread = 2, nround = 10, objective = "binary:logistic")
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feature.names <- agaricus.train$data@Dimnames[[2]]
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@ -40,6 +40,15 @@ test_that("xgb.importance works with and without feature names", {
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xgb.importance(model = bst)
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})
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test_that("xgb.importance works with GLM model", {
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bst.GLM <- xgboost(data = sparse_matrix, label = output_vector,
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eta = 1, nthread = 2, nround = 10, objective = "binary:logistic", booster = "gblinear")
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importance.GLM <- xgb.importance(feature_names = sparse_matrix@Dimnames[[2]], model = bst.GLM)
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expect_equal(dim(importance.GLM), c(10, 2))
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expect_equal(colnames(importance.GLM), c("Feature", "Weight"))
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xgb.importance(model = bst.GLM)
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})
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test_that("xgb.plot.tree works with and without feature names", {
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xgb.plot.tree(feature_names = feature.names, model = bst)
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xgb.plot.tree(model = bst)
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