basic_walkthrough.R

require(xgboost)
require(methods)

# we load in the agaricus dataset
# In this example, we are aiming to predict whether a mushroom is edible
data(agaricus.train, package = 'xgboost')
data(agaricus.test, package = 'xgboost')
train <- agaricus.train
test <- agaricus.test
# the loaded data is stored in sparseMatrix, and label is a numeric vector in {0,1}
class(train$label)
class(train$data)

#-------------Basic Training using XGBoost-----------------
# this is the basic usage of xgboost you can put matrix in data field
# note: we are putting in sparse matrix here, xgboost naturally handles sparse input
# use sparse matrix when your feature is sparse(e.g. when you are using one-hot encoding vector)
print("Training xgboost with sparseMatrix")
bst <- xgboost(data = train$data, label = train$label, max_depth = 2, eta = 1, nrounds = 2,
               nthread = 2, objective = "binary:logistic")
# alternatively, you can put in dense matrix, i.e. basic R-matrix
print("Training xgboost with Matrix")
bst <- xgboost(data = as.matrix(train$data), label = train$label, max_depth = 2, eta = 1, nrounds = 2,
               nthread = 2, objective = "binary:logistic")

# you can also put in xgb.DMatrix object, which stores label, data and other meta datas needed for advanced features
print("Training xgboost with xgb.DMatrix")
dtrain <- xgb.DMatrix(data = train$data, label = train$label)
bst <- xgboost(data = dtrain, max_depth = 2, eta = 1, nrounds = 2, nthread = 2,
               objective = "binary:logistic")

# Verbose = 0,1,2
print("Train xgboost with verbose 0, no message")
bst <- xgboost(data = dtrain, max_depth = 2, eta = 1, nrounds = 2,
               nthread = 2, objective = "binary:logistic", verbose = 0)
print("Train xgboost with verbose 1, print evaluation metric")
bst <- xgboost(data = dtrain, max_depth = 2, eta = 1, nrounds = 2,
               nthread = 2, objective = "binary:logistic", verbose = 1)
print("Train xgboost with verbose 2, also print information about tree")
bst <- xgboost(data = dtrain, max_depth = 2, eta = 1, nrounds = 2,
               nthread = 2, objective = "binary:logistic", verbose = 2)

# you can also specify data as file path to a LIBSVM format input
# since we do not have this file with us, the following line is just for illustration
# bst <- xgboost(data = 'agaricus.train.svm', max_depth = 2, eta = 1, nrounds = 2,objective = "binary:logistic")

#--------------------basic prediction using xgboost--------------
# you can do prediction using the following line
# you can put in Matrix, sparseMatrix, or xgb.DMatrix
pred <- predict(bst, test$data)
err <- mean(as.numeric(pred > 0.5) != test$label)
print(paste("test-error=", err))

#-------------------save and load models-------------------------
# save model to binary local file
xgb.save(bst, "xgboost.model")
# load binary model to R
# Function doesn't take 'nthreads', but can be set like this:
RhpcBLASctl::omp_set_num_threads(1)
bst2 <- xgb.load("xgboost.model")
pred2 <- predict(bst2, test$data)
# pred2 should be identical to pred
print(paste("sum(abs(pred2-pred))=", sum(abs(pred2 - pred))))

# save model to R's raw vector
raw <- xgb.save.raw(bst)
# load binary model to R
bst3 <- xgb.load.raw(raw)
pred3 <- predict(bst3, test$data)
# pred3 should be identical to pred
print(paste("sum(abs(pred3-pred))=", sum(abs(pred3 - pred))))

#----------------Advanced features --------------
# to use advanced features, we need to put data in xgb.DMatrix
dtrain <- xgb.DMatrix(data = train$data, label = train$label)
dtest <- xgb.DMatrix(data = test$data, label = test$label)
#---------------Using an evaluation set----------------
# 'evals' is a list of xgb.DMatrix, each of them is tagged with name
evals <- list(train = dtrain, test = dtest)
# to train with an evaluation set, use xgb.train, which contains more advanced features
# 'evals' argument allows us to monitor the evaluation result on all data in the list
print("Train xgboost using xgb.train with evaluation data")
bst <- xgb.train(data = dtrain, max_depth = 2, eta = 1, nrounds = 2, evals = evals,
                 nthread = 2, objective = "binary:logistic")
# we can change evaluation metrics, or use multiple evaluation metrics
print("train xgboost using xgb.train with evaluation data, watch logloss and error")
bst <- xgb.train(data = dtrain, max_depth = 2, eta = 1, nrounds = 2, evals = evals,
                 eval_metric = "error", eval_metric = "logloss",
                 nthread = 2, objective = "binary:logistic")

# xgb.DMatrix can also be saved using xgb.DMatrix.save
xgb.DMatrix.save(dtrain, "dtrain.buffer")
# to load it in, simply call xgb.DMatrix
dtrain2 <- xgb.DMatrix("dtrain.buffer")
bst <- xgb.train(data = dtrain2, max_depth = 2, eta = 1, nrounds = 2, evals = evals,
                 nthread = 2, objective = "binary:logistic")
# information can be extracted from xgb.DMatrix using getinfo
label <- getinfo(dtest, "label")
pred <- predict(bst, dtest)
err <- as.numeric(sum(as.integer(pred > 0.5) != label)) / length(label)
print(paste("test-error=", err))

# You can dump the tree you learned using xgb.dump into a text file
dump_path <- file.path(tempdir(), 'dump.raw.txt')
xgb.dump(bst, dump_path, with_stats = TRUE)

# Finally, you can check which features are the most important.
print("Most important features (look at column Gain):")
imp_matrix <- xgb.importance(feature_names = colnames(train$data), model = bst)
print(imp_matrix)

# Feature importance bar plot by gain
print("Feature importance Plot : ")
print(xgb.plot.importance(importance_matrix = imp_matrix))