Permit use of custom objective function in multiclass problem. (#1229)

R-package/demo/multiclass_custom_objective.R

+require(lightgbm)
+
+# We load the default iris dataset shipped with R
+data(iris)
+
+# We must convert factors to numeric
+# They must be starting from number 0 to use multiclass
+# 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
+# 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)
+
+# Method 1 of training with built-in multiclass objective
+model_builtin <- lgb.train(list(),
+                           dtrain,
+                           100,
+                           valids,
+                           min_data = 1,
+                           learning_rate = 1,
+                           early_stopping_rounds = 10,
+                           objective = "multiclass",
+                           metric = "multi_logloss",
+                           num_class = 3)
+
+preds_builtin <- predict(model_builtin, test[, 1:4], rawscore = TRUE)
+
+# Method 2 of training with custom objective function
+
+# User defined objective function, given prediction, return gradient and second order gradient
+custom_multiclass_obj = function(preds, dtrain) {
+    labels = getinfo(dtrain, "label")
+    
+    # preds is a matrix with rows corresponding to samples and colums corresponding to choices
+    preds = matrix(preds, nrow = length(labels))
+    
+    # to prevent overflow, normalize preds by row
+    preds = preds - apply(preds, 1, max)
+    prob = exp(preds) / rowSums(exp(preds))
+    
+    # compute gradient
+    grad = prob
+    grad[cbind(1:length(labels), labels + 1)] = grad[cbind(1:length(labels), labels + 1)] - 1
+    
+    # compute hessian (approximation)
+    hess = 2 * prob * (1 - prob)
+    
+    return(list(grad = grad, hess = hess))
+}
+
+# define custom metric 
+custom_multiclass_metric = function(preds, dtrain) {
+    labels = getinfo(dtrain, "label")
+    preds = matrix(preds, nrow = length(labels))
+    preds = preds - apply(preds, 1, max)
+    prob = exp(preds) / rowSums(exp(preds))
+    
+    return(list(name = "error",
+                value = -mean(log(prob[cbind(1:length(labels), labels + 1)])),
+                higher_better = FALSE))
+    
+}
+
+model_custom <- lgb.train(list(),
+                          dtrain,
+                          100,
+                          valids,
+                          min_data = 1,
+                          learning_rate = 1,
+                          early_stopping_rounds = 10,
+                          objective = custom_multiclass_obj,
+                          eval = custom_multiclass_metric,
+                          num_class = 3)
+
+preds_custom <- predict(model_custom, test[, 1:4], rawscore = TRUE)
+
+# compare predictions
+identical(preds_builtin, preds_custom)
+

src/io/config.cpp

@@ -194,9 +194,11 @@ void OverallConfig::Set(const std::unordered_map<std::string, std::string>& para
 
 void OverallConfig::CheckParamConflict() {
   // check if objective_type, metric_type, and num_class match
-  bool objective_type_multiclass = (objective_type == std::string("multiclass")
-                                    || objective_type == std::string("multiclassova"));
   int num_class_check = boosting_config.num_class;
+  bool objective_type_multiclass = (objective_type == std::string("multiclass")
+                                    || objective_type == std::string("multiclassova")
+                                    || (objective_type == std::string("none") && num_class_check > 1));
+  
   if (objective_type_multiclass) {
     if (num_class_check <= 1) {
       Log::Fatal("Number of classes should be specified and greater than 1 for multiclass training");