53 lines
2.1 KiB
R
53 lines
2.1 KiB
R
require(xgboost)
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require(methods)
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data(iris)
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# we use iris data as example dataset
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# iris is a dataset with 3 types of iris
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# we will show how to use xgboost to do binary classification here
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# so the class label will be whether the flower is of type setosa
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iris[,5] <- as.numeric(iris[,5]=='setosa')
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iris <- as.matrix(iris)
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set.seed(20)
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# random split train and test set
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test_ind <- sample(1:nrow(iris),50)
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train_ind <- setdiff(1:nrow(iris),test_ind)
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trainX = iris[train_ind,1:4]
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trainY = iris[train_ind,5]
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testX = iris[train_ind,1:4]
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testY = iris[test_ind,5]
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#-------------------------------------
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# this is the basic usage of xgboost
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# you can put matrix in data field
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bst <- xgboost(data = trainX, label = trainY, max_depth = 1, eta = 1, nround = 2,
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objective = "binary:logistic")
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# alternatively, you can put sparse matrix, this is helpful when your data is sparse
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# for example, when you use one-hot encoding for feature vectors
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sparseX <- as(trainX, "sparseMatrix")
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bst <- xgboost(data = sparseX, label = trainY, max_depth = 1, eta = 1, nround = 2,
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objective = "binary:logistic")
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# you can also specify data as file path to a LibSVM format input
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# since we do not have libsvm format file for iris, next line is only for illustration
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# bst <- xgboost(data = 'iris.svm', max_depth = 2, eta = 1, nround = 2, objective = "binary:logistic")
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dtrain <- xgb.DMatrix(iris[train_ind,1:4], label=iris[train_ind,5])
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dtest <- xgb.DMatrix(iris[test_ind,1:4], label=iris[test_ind,5])
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param <- list(max_depth=2,eta=1,silent=1,objective='binary:logistic')
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watchlist <- list(eval = dtest, train = dtrain)
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num_round <- 2
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bst <- xgb.train(param, dtrain, num_round, watchlist)
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preds <- predict(bst, dtest)
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labels <- getinfo(dtest,'label')
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cat('error=', mean(as.numeric(preds>0.5)!=labels),'\n')
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xgb.save(bst, 'xgb.model')
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xgb.dump(bst, 'dump.raw.txt')
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xgb.dump(bst, 'dump.nuce.txt','../data/featmap.txt')
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bst2 <- xgb.load('xgb.model')
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preds2 <- predict(bst2,dtest)
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stopifnot(sum((preds-preds2)^2)==0)
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############################ Test xgb.DMatrix with local file, sparse matrix and dense matrix in R.
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