Add multiclass example in R (train & prediction) (#290)

* Add multiclass example in R

* Update subsequent files

R-package/demo/00Index

@@ -2,3 +2,4 @@ basic_walkthrough               Basic feature walkthrough
 boost_from_prediction           Boosting from existing prediction
 early_stopping                  Early Stop in training
 cross_validation                Cross Validation
+multiclass                      Multiclass training/prediction

R-package/demo/README.md

@@ -4,3 +4,4 @@ LightGBM R examples
 * [Boosting from existing prediction](boost_from_prediction.R)
 * [Early Stopping](early_stopping.R)
 * [Cross Validation](cross_validation.R)
+* [Multiclass Training/Prediction](multiclass.R)

R-package/demo/multiclass.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
+params <- list(objective="multiclass", metric="multi_error", num_class=3)
+model <- lgb.train(params, dtrain, 100, valids, min_data=1, learning_rate=1, early_stopping_rounds=10)
+
+# we can predict on test data, outputs a 90-length vector
+# order: obs1 class1, obs1 class2, obs1 class3, obs2 class1, obs2 class2, obs2 class3...
+my_preds <- predict(model, test[, 1:4])
+
+# method 2 of training, identical
+model <- lgb.train(list(), dtrain, 100, valids, min_data=1, learning_rate=1, early_stopping_rounds=10, objective="multiclass", metric="multi_error", num_class=3)
+
+# we can predict on test data, identical
+my_preds <- predict(model, test[, 1:4])
+
+# a (30x3) matrix with the predictions, use parameter reshape
+# class1 class2 class3
+#   obs1   obs1   obs1
+#   obs2   obs2   obs2
+#   ....   ....   ....
+my_preds <- predict(model, test[, 1:4], reshape=TRUE)
+
+# we can also get the predicted scores before the Sigmoid/Softmax application
+my_preds <- predict(model, test[, 1:4], rawscore=TRUE)
+
+# raw score predictions as matrix instead of vector
+my_preds <- predict(model, test[, 1:4], rawscore=TRUE, reshape=TRUE)
+
+# we can also get the leaf index
+my_preds <- predict(model, test[, 1:4], predleaf=TRUE)
+
+# preddict leaf index as matrix instead of vector
+my_preds <- predict(model, test[, 1:4], predleaf=TRUE, reshape=TRUE)