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[R-package] Fix demos not using lgb.Dataset.create.valid #1993

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Feb 4, 2019
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[R-package] Fix demos not using lgb.Dataset.create.valid #1993

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2 changes: 1 addition & 1 deletion R-package/demo/boost_from_prediction.R
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Expand Up @@ -5,7 +5,7 @@ require(methods)
data(agaricus.train, package = "lightgbm")
data(agaricus.test, package = "lightgbm")
dtrain <- lgb.Dataset(agaricus.train$data, label = agaricus.train$label)
dtest <- lgb.Dataset(agaricus.test$data, label = agaricus.test$label)
dtest <- lgb.Dataset.create.valid(dtrain, data = agaricus.test$data, label = agaricus.test$label)

valids <- list(eval = dtest, train = dtrain)
#--------------------Advanced features ---------------------------
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5 changes: 3 additions & 2 deletions R-package/demo/categorical_features_rules.R
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Expand Up @@ -70,8 +70,9 @@ my_data_test <- as.matrix(bank_test[, 1:16, with = FALSE])
# The categorical features can be passed to lgb.train to not copy and paste a lot
dtrain <- lgb.Dataset(data = my_data_train,
label = bank_train$y)
dtest <- lgb.Dataset(data = my_data_test,
label = bank_test$y)
dtest <- lgb.Dataset.create.valid(dtrain,
data = my_data_test,
label = bank_test$y)

# We can now train a model
model <- lgb.train(list(objective = "binary",
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2 changes: 1 addition & 1 deletion R-package/demo/cross_validation.R
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Expand Up @@ -3,7 +3,7 @@ require(lightgbm)
data(agaricus.train, package = "lightgbm")
data(agaricus.test, package = "lightgbm")
dtrain <- lgb.Dataset(agaricus.train$data, label = agaricus.train$label)
dtest <- lgb.Dataset(agaricus.test$data, label = agaricus.test$label)
dtest <- lgb.Dataset.create.valid(dtrain, data = agaricus.test$data, label = agaricus.test$label)

nrounds <- 2
param <- list(num_leaves = 4,
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2 changes: 1 addition & 1 deletion R-package/demo/early_stopping.R
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Expand Up @@ -6,7 +6,7 @@ data(agaricus.train, package = "lightgbm")
data(agaricus.test, package = "lightgbm")

dtrain <- lgb.Dataset(agaricus.train$data, label = agaricus.train$label)
dtest <- lgb.Dataset(agaricus.test$data, label = agaricus.test$label)
dtest <- lgb.Dataset.create.valid(dtrain, data = agaricus.test$data, label = agaricus.test$label)

# Note: for customized objective function, we leave objective as default
# Note: what we are getting is margin value in prediction
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