49 lines
1.3 KiB
R
49 lines
1.3 KiB
R
require(xgboost)
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require(methods)
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train <- read.csv('data/train.csv', header = TRUE, stringsAsFactors = FALSE)
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test <- read.csv('data/test.csv', header = TRUE, stringsAsFactors = FALSE)
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train <- train[, -1]
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test <- test[, -1]
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y <- train[, ncol(train)]
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y <- gsub('Class_', '', y)
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y <- as.integer(y) - 1 # xgboost take features in [0,numOfClass)
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x <- rbind(train[, -ncol(train)], test)
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x <- as.matrix(x)
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x <- matrix(as.numeric(x), nrow(x), ncol(x))
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trind <- 1:length(y)
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teind <- (nrow(train) + 1):nrow(x)
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# Set necessary parameter
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param <- list("objective" = "multi:softprob",
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"eval_metric" = "mlogloss",
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"num_class" = 9,
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"nthread" = 8)
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# Run Cross Validation
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cv.nrounds <- 50
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bst.cv <- xgb.cv(
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param = param
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, data = x[trind, ]
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, label = y
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, nfold = 3
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, nrounds = cv.nrounds
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)
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# Train the model
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nrounds <- 50
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bst <- xgboost(param = param, data = x[trind, ], label = y, nrounds = nrounds)
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# Make prediction
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pred <- predict(bst, x[teind, ])
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pred <- matrix(pred, 9, length(pred) / 9)
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pred <- t(pred)
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# Output submission
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pred <- format(pred, digits = 2, scientific = FALSE) # shrink the size of submission
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pred <- data.frame(1:nrow(pred), pred)
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names(pred) <- c('id', paste0('Class_', 1:9))
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write.csv(pred, file = 'submission.csv', quote = FALSE, row.names = FALSE)
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