[R] Fix CRAN test notes. (#8428)
- Limit the number of used CPU cores in examples. - Add a note for the constraint. - Bring back the cleanup script.
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@@ -15,9 +15,11 @@ selected per iteration.}
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}
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\value{
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Results are stored in the \code{coefs} element of the closure.
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The \code{\link{xgb.gblinear.history}} convenience function provides an easy way to access it.
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The \code{\link{xgb.gblinear.history}} convenience function provides an easy
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way to access it.
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With \code{xgb.train}, it is either a dense of a sparse matrix.
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While with \code{xgb.cv}, it is a list (an element per each fold) of such matrices.
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While with \code{xgb.cv}, it is a list (an element per each fold) of such
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matrices.
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}
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\description{
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Callback closure for collecting the model coefficients history of a gblinear booster
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@@ -38,7 +40,7 @@ Callback function expects the following values to be set in its calling frame:
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# without considering the 2nd order interactions:
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x <- model.matrix(Species ~ .^2, iris)[,-1]
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colnames(x)
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dtrain <- xgb.DMatrix(scale(x), label = 1*(iris$Species == "versicolor"))
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dtrain <- xgb.DMatrix(scale(x), label = 1*(iris$Species == "versicolor"), nthread = 2)
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param <- list(booster = "gblinear", objective = "reg:logistic", eval_metric = "auc",
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lambda = 0.0003, alpha = 0.0003, nthread = 2)
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# For 'shotgun', which is a default linear updater, using high eta values may result in
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@@ -63,14 +65,14 @@ matplot(xgb.gblinear.history(bst), type = 'l')
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# For xgb.cv:
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bst <- xgb.cv(param, dtrain, nfold = 5, nrounds = 100, eta = 0.8,
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callbacks = list(cb.gblinear.history()))
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callbacks = list(cb.gblinear.history()))
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# coefficients in the CV fold #3
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matplot(xgb.gblinear.history(bst)[[3]], type = 'l')
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#### Multiclass classification:
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#
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dtrain <- xgb.DMatrix(scale(x), label = as.numeric(iris$Species) - 1)
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dtrain <- xgb.DMatrix(scale(x), label = as.numeric(iris$Species) - 1, nthread = 2)
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param <- list(booster = "gblinear", objective = "multi:softprob", num_class = 3,
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lambda = 0.0003, alpha = 0.0003, nthread = 2)
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# For the default linear updater 'shotgun' it sometimes is helpful
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