5879acde9a
* [CI] Move lint to a separate script * [CI] Improved lintr launcher * Add lintr as a separate action * Add custom parsing logic to print out logs * Fix lintr issues in demos * Run R demos * Fix CRAN checks * Install XGBoost into R env before running lintr * Install devtools (needed to run demos)
52 lines
1.8 KiB
R
52 lines
1.8 KiB
R
require(xgboost)
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# load in the agaricus dataset
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data(agaricus.train, package = 'xgboost')
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data(agaricus.test, package = 'xgboost')
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dtrain <- xgb.DMatrix(agaricus.train$data, label = agaricus.train$label)
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dtest <- xgb.DMatrix(agaricus.test$data, label = agaricus.test$label)
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nrounds <- 2
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param <- list(max_depth = 2, eta = 1, nthread = 2, objective = 'binary:logistic')
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cat('running cross validation\n')
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# do cross validation, this will print result out as
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# [iteration] metric_name:mean_value+std_value
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# std_value is standard deviation of the metric
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xgb.cv(param, dtrain, nrounds, nfold = 5, metrics = {'error'})
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cat('running cross validation, disable standard deviation display\n')
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# do cross validation, this will print result out as
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# [iteration] metric_name:mean_value+std_value
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# std_value is standard deviation of the metric
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xgb.cv(param, dtrain, nrounds, nfold = 5,
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metrics = 'error', showsd = FALSE)
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###
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# you can also do cross validation with cutomized loss function
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# See custom_objective.R
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##
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print ('running cross validation, with cutomsized loss function')
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logregobj <- function(preds, dtrain) {
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labels <- getinfo(dtrain, "label")
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preds <- 1 / (1 + exp(-preds))
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grad <- preds - labels
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hess <- preds * (1 - preds)
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return(list(grad = grad, hess = hess))
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}
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evalerror <- function(preds, dtrain) {
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labels <- getinfo(dtrain, "label")
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err <- as.numeric(sum(labels != (preds > 0))) / length(labels)
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return(list(metric = "error", value = err))
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}
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param <- list(max_depth = 2, eta = 1,
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objective = logregobj, eval_metric = evalerror)
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# train with customized objective
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xgb.cv(params = param, data = dtrain, nrounds = nrounds, nfold = 5)
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# do cross validation with prediction values for each fold
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res <- xgb.cv(params = param, data = dtrain, nrounds = nrounds, nfold = 5, prediction = TRUE)
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res$evaluation_log
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length(res$pred)
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