[R] Fix multiclass demo (#1940)

* Fix multiclass custom objective demo

* Use option not to boost from average instead of setting init score explicitly

* Reference #1846 when turning off boost_from_average

* Add trailing whitespace

R-package/demo/multiclass_custom_objective.R

@@ -8,18 +8,21 @@ data(iris)
 # For instance: 0, 1, 2, 3, 4, 5...
 iris$Species <- as.numeric(as.factor(iris$Species)) - 1
 
-# We cut the data set into 80% train and 20% validation
+# Create imbalanced training data (20, 30, 40 examples for classes 0, 1, 2)
+train <- as.matrix(iris[c(1:20, 51:80, 101:140), ])
 # The 10 last samples of each class are for validation
-
-train <- as.matrix(iris[c(1:40, 51:90, 101:140), ])
 test <- as.matrix(iris[c(41:50, 91:100, 141:150), ])
+
 dtrain <- lgb.Dataset(data = train[, 1:4], label = train[, 5])
 dtest <- lgb.Dataset.create.valid(dtrain, data = test[, 1:4], label = test[, 5])
-valids <- list(test = dtest)
+valids <- list(train = dtrain, test = dtest)
 
 # Method 1 of training with built-in multiclass objective
+# Note: need to turn off boost from average to match custom objective
+# (https://github.com/Microsoft/LightGBM/issues/1846)
 model_builtin <- lgb.train(list(),
                            dtrain,
+                           boost_from_average = FALSE,
                            100,
                            valids,
                            min_data = 1,
@@ -29,7 +32,8 @@ model_builtin <- lgb.train(list(),
                            metric = "multi_logloss",
                            num_class = 3)
 
-preds_builtin <- predict(model_builtin, test[, 1:4], rawscore = TRUE)
+preds_builtin <- predict(model_builtin, test[, 1:4], rawscore = TRUE, reshape = TRUE)
+probs_builtin <- exp(preds_builtin) / rowSums(exp(preds_builtin))
 
 # Method 2 of training with custom objective function
 
@@ -64,7 +68,6 @@ custom_multiclass_metric = function(preds, dtrain) {
     return(list(name = "error",
                 value = -mean(log(prob[cbind(1:length(labels), labels + 1)])),
                 higher_better = FALSE))
-
 }
 
 model_custom <- lgb.train(list(),
@@ -78,8 +81,10 @@ model_custom <- lgb.train(list(),
                           eval = custom_multiclass_metric,
                           num_class = 3)
 
-preds_custom <- predict(model_custom, test[, 1:4], rawscore = TRUE)
+preds_custom <- predict(model_custom, test[, 1:4], rawscore = TRUE, reshape = TRUE)
+probs_custom <- exp(preds_custom) / rowSums(exp(preds_custom))
 
 # compare predictions
-identical(preds_builtin, preds_custom)
+stopifnot(identical(probs_builtin, probs_custom))
+stopifnot(identical(preds_builtin, preds_custom))