Cleaning of demo

R-package/demo/basic_walkthrough.R

@@ -102,9 +102,9 @@ xgb.dump(bst, "dump.raw.txt", with.stats = T)
 
 # Finally, you can check which features are the most important.
 print("Most important features (look at column Gain):")
-imp_matrix <- xgb.importance(feature_names = train$data@Dimnames[[2]], filename_dump = "dump.raw.txt")
+imp_matrix <- xgb.importance(feature_names = train$data@Dimnames[[2]], model = bst)
 print(imp_matrix)
 
 # Feature importance bar plot by gain
 print("Feature importance Plot : ")
-print(xgb.plot.importance(imp_matrix))
+print(xgb.plot.importance(importance_matrix = imp_matrix))

R-package/demo/boost_from_prediction.R

@@ -23,4 +23,4 @@ setinfo(dtrain, "base_margin", ptrain)
 setinfo(dtest, "base_margin", ptest)
 
 print('this is result of boost from initial prediction')
-bst <- xgb.train( param, dtrain, 1, watchlist )
+bst <- xgb.train(params = param, data = dtrain, nrounds = 1, watchlist = watchlist)

R-package/demo/create_sparse_matrix.R

@@ -67,10 +67,9 @@ output_vector = df[,Y:=0][Improved == "Marked",Y:=1][,Y]
 cat("Learning...\n")
 bst <- xgboost(data = sparse_matrix, label = output_vector, max.depth = 9,
                eta = 1, nthread = 2, nround = 10,objective = "binary:logistic")
-xgb.dump(bst, 'xgb.model.dump', with.stats = T)
 
 # sparse_matrix@Dimnames[[2]] represents the column names of the sparse matrix.
-importance <- xgb.importance(sparse_matrix@Dimnames[[2]], 'xgb.model.dump')
+importance <- xgb.importance(feature_names = sparse_matrix@Dimnames[[2]], model = bst)
 print(importance)
 # According to the matrix below, the most important feature in this dataset to predict if the treatment will work is the Age. The second most important feature is having received a placebo or not. The sex is third. Then we see our generated features (AgeDiscret). We can see that their contribution is very low (Gain column).
 

R-package/demo/cross_validation.R

@@ -43,9 +43,9 @@ evalerror <- function(preds, dtrain) {
 param <- list(max.depth=2,eta=1,silent=1,
               objective = logregobj, eval_metric = evalerror)
 # train with customized objective
-xgb.cv(param, dtrain, nround, nfold = 5)
+xgb.cv(params = param, data = dtrain, nrounds = nround, nfold = 5)
 
 # do cross validation with prediction values for each fold
-res <- xgb.cv(param, dtrain, nround, nfold=5, prediction = TRUE)
+res <- xgb.cv(params = param, data = dtrain, nrounds = nround, nfold = 5, prediction = TRUE)
 res$dt
 length(res$pred)

R-package/demo/predict_leaf_indices.R

@@ -2,15 +2,15 @@ require(xgboost)
 # load in the agaricus dataset
 data(agaricus.train, package='xgboost')
 data(agaricus.test, package='xgboost')
-dtrain <- xgb.DMatrix(agaricus.train$data, label = agaricus.train$label)
-dtest <- xgb.DMatrix(agaricus.test$data, label = agaricus.test$label)
+dtrain <- xgb.DMatrix(data = agaricus.train$data, label = agaricus.train$label)
+dtest <- xgb.DMatrix(data = agaricus.test$data, label = agaricus.test$label)
 
 param <- list(max.depth=2, eta=1, silent=1, objective='binary:logistic')
 watchlist <- list(eval = dtest, train = dtrain)
 nround = 5
 
 # training the model for two rounds
-bst = xgb.train(param, dtrain, nround, nthread = 2, watchlist)
+bst = xgb.train(params = param, data = dtrain, nrounds = nround, nthread = 2, watchlist = watchlist)
 cat('start testing prediction from first n trees\n')
 
 ### predict using first 2 tree