require(xgboost)
require(methods)

train <- read.csv('data/train.csv', header = TRUE, stringsAsFactors = FALSE)
test <- read.csv('data/test.csv', header = TRUE, stringsAsFactors = FALSE)
train <- train[, -1]
test <- test[, -1]

y <- train[, ncol(train)]
y <- gsub('Class_', '', y, fixed = TRUE)
y <- as.integer(y) - 1  # xgboost take features in [0,numOfClass)

x <- rbind(train[, -ncol(train)], test)
x <- as.matrix(x)
x <- matrix(as.numeric(x), nrow(x), ncol(x))
trind <- seq_along(y)
teind <- (nrow(train) + 1):nrow(x)

# Set necessary parameter
param <- list("objective" = "multi:softprob",
              "eval_metric" = "mlogloss",
              "num_class" = 9,
              "nthread" = 8)

# Run Cross Validation
cv.nrounds <- 50
bst.cv <- xgb.cv(
    param = param
    , data = x[trind, ]
    , label = y
    , nfold = 3
    , nrounds = cv.nrounds
)

# Train the model
nrounds <- 50
bst <- xgboost(param = param, data = x[trind, ], label = y, nrounds = nrounds)

# Make prediction
pred <- predict(bst, x[teind, ])
pred <- matrix(pred, 9, length(pred) / 9)
pred <- t(pred)

# Output submission
pred <- format(pred, digits = 2, scientific = FALSE) # shrink the size of submission
pred <- data.frame(seq_len(nrow(pred)), pred)
names(pred) <- c('id', paste0('Class_', 1:9))
write.csv(pred, file = 'submission.csv', quote = FALSE, row.names = FALSE)