6f16f0ef58
* Fix various typos * Add override to functions that are overridden gcc gives warnings about functions that are being overridden by not being marked as oveirridden. This fixes it. * Use bst_float consistently Use bst_float for all the variables that involve weight, leaf value, gradient, hessian, gain, loss_chg, predictions, base_margin, feature values. In some cases, when due to additions and so on the value can take a larger value, double is used. This ensures that type conversions are minimal and reduces loss of precision.
44 lines
1.2 KiB
R
44 lines
1.2 KiB
R
require(xgboost)
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require(methods)
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train = read.csv('data/train.csv',header=TRUE,stringsAsFactors = F)
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test = read.csv('data/test.csv',header=TRUE,stringsAsFactors = F)
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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.nround = 50
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bst.cv = xgb.cv(param=param, data = x[trind,], label = y,
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nfold = 3, nrounds=cv.nround)
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# Train the model
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nround = 50
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bst = xgboost(param=param, data = x[trind,], label = y, nrounds=nround)
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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=F) # 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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