I am new to R, so I am sure I am making a simple mistake. I am including complete information in hopes someone can help me. Basically my data in R looks good, I write it to a file, and every value is off by 1. Here is my flow:> str(prediction)Factor w/ 10 levels "0","1","2","3",..: 3 1 10 10 4 8 1 4 1 4 ... - attr(*, "names")= chr [1:28000] "1" "2" "3" "4" ...> print(prediction)1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 2 0 9 9 3 7 0 3 0 3 5 7 4 0 4 3 3 1 9 0 9 1 1 ok, so it shows my values are 2, 0, 9, 9, 3 etc # I write my file out write(prediction, file="prediction.csv") # look at the first 10 values $ head -10 prediction.csv 3 1 10 10 4 8 1 4 1 4 6 8 5 1 5 4 4 2 10 1 10 2 2 6 8 5 3 8 5 8 8 6 5 3 7 3 6 6 2 7 8 8 5 10 9 8 9 3 7 8 The complete work of what I did was as follows: # First I load in a dataset, label the first column as a factor> dataset <- read.csv('train.csv',head=TRUE) > dataset$label <- as.factor(dataset$label)# it has 42000 obs. 785 variables> str(dataset)'data.frame': 42000 obs. of 785 variables: $ label : Factor w/ 10 levels "0","1","2","3",..: 2 1 2 5 1 1 8 4 6 4 ... $ pixel0 : int 0 0 0 0 0 0 0 0 0 0 ... $ pixel1 : int 0 0 0 0 0 0 0 0 0 0 ... $ pixel2 : int 0 0 0 0 0 0 0 0 0 0 ... [list output truncated] # I make a sampling testset and trainset> index <- 1:nrow(dataset) > testindex <- sample(index, trunc(length(index)*30/100)) > testset <- dataset[testindex,] > trainset <- dataset[-testindex,]# build model, predict, view> model <- svm(label~., data = trainset, type="C-classification", kernel="radial", gamma=0.0000001, cost=16) > prediction <- predict(model, testset) > tab <- table(pred = prediction, true = testset[,1])true pred 0 1 2 3 4 5 6 7 8 9 0 1210 0 3 1 0 5 7 2 5 8 1 0 1415 2 0 2 1 0 7 5 0 2 0 2 1127 12 3 0 2 7 2 0 3 0 0 7 1296 0 10 0 2 15 6 4 1 1 8 2 1201 2 4 3 5 16 5 3 1 0 13 0 1100 3 1 2 3 6 3 0 3 0 5 9 1263 0 1 0 7 0 2 9 6 6 1 0 1296 1 13 8 3 5 7 11 1 2 0 2 1190 4 9 1 1 2 3 17 2 0 4 4 1190 Ok everything looks great up to this point..........so I try to apply my model to a "real" testset, which is the same format as my previous dataset, except it does not have the label/factor column, so its 28000 obs 784 variables:> testset <- read.csv('test.csv',head=TRUE) > str(testset)'data.frame': 28000 obs. of 784 variables: $ pixel0 : int 0 0 0 0 0 0 0 0 0 0 ... $ pixel1 : int 0 0 0 0 0 0 0 0 0 0 ... $ pixel2 : int 0 0 0 0 0 0 0 0 0 0 ... [list output truncated]> prediction <- predict(model, testset) > summary(prediction)0 1 2 3 4 5 6 7 8 9 2780 3204 2824 2767 2771 2516 2744 2898 2736 2760> print(prediction)1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 2 0 9 9 3 7 0 3 0 3 5 7 4 0 4 3 3 1 9 0 9 1 1 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 5 7 4 2 7 4 7 7 5 4 2 6 2 5 5 1 6 7 7 4 9 8 7 [list output truncated]> write(prediction, file="prediction.csv")$ head -10 prediction.csv 3 1 10 10 4 8 1 4 1 4 6 8 5 1 5 4 4 2 10 1 10 2 2 6 8 5 3 8 5 8 8 6 5 3 7 3 6 6 2 7 8 8 5 10 9 8 9 3 7 8 I am obviously making a mistake. Everything is off by a value of 1. Can someone tell me what I am doing wrong? Brian [[alternative HTML version deleted]]
A followup to my own post, I believe I figured this out, but if I should be doing something different please correct:> prediction.out <- levels(prediction)[prediction] > write(prediction.out, file="prediction.csv")This gives me my correctly adjusted values Brian On Nov 20, 2012, at 2:30 PM, Brian Feeny wrote:> I am new to R, so I am sure I am making a simple mistake. I am including complete information in hopes > someone can help me. > > Basically my data in R looks good, I write it to a file, and every value is off by 1. > > Here is my flow: > >> str(prediction) > Factor w/ 10 levels "0","1","2","3",..: 3 1 10 10 4 8 1 4 1 4 ... > - attr(*, "names")= chr [1:28000] "1" "2" "3" "4" ... >> print(prediction) > 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 > 2 0 9 9 3 7 0 3 0 3 5 7 4 0 4 3 3 1 9 0 9 1 1 > > ok, so it shows my values are 2, 0, 9, 9, 3 etc > > # I write my file out > write(prediction, file="prediction.csv") > > # look at the first 10 values > $ head -10 prediction.csv > 3 1 10 10 4 > 8 1 4 1 4 > 6 8 5 1 5 > 4 4 2 10 1 > 10 2 2 6 8 > 5 3 8 5 8 > 8 6 5 3 7 > 3 6 6 2 7 > 8 8 5 10 9 > 8 9 3 7 8 > > The complete work of what I did was as follows: > > # First I load in a dataset, label the first column as a factor >> dataset <- read.csv('train.csv',head=TRUE) >> dataset$label <- as.factor(dataset$label) > > # it has 42000 obs. 785 variables >> str(dataset) > 'data.frame': 42000 obs. of 785 variables: > $ label : Factor w/ 10 levels "0","1","2","3",..: 2 1 2 5 1 1 8 4 6 4 ... > $ pixel0 : int 0 0 0 0 0 0 0 0 0 0 ... > $ pixel1 : int 0 0 0 0 0 0 0 0 0 0 ... > $ pixel2 : int 0 0 0 0 0 0 0 0 0 0 ... > [list output truncated] > > # I make a sampling testset and trainset >> index <- 1:nrow(dataset) >> testindex <- sample(index, trunc(length(index)*30/100)) >> testset <- dataset[testindex,] >> trainset <- dataset[-testindex,] > > # build model, predict, view >> model <- svm(label~., data = trainset, type="C-classification", kernel="radial", gamma=0.0000001, cost=16) >> prediction <- predict(model, testset) >> tab <- table(pred = prediction, true = testset[,1]) > true > pred 0 1 2 3 4 5 6 7 8 9 > 0 1210 0 3 1 0 5 7 2 5 8 > 1 0 1415 2 0 2 1 0 7 5 0 > 2 0 2 1127 12 3 0 2 7 2 0 > 3 0 0 7 1296 0 10 0 2 15 6 > 4 1 1 8 2 1201 2 4 3 5 16 > 5 3 1 0 13 0 1100 3 1 2 3 > 6 3 0 3 0 5 9 1263 0 1 0 > 7 0 2 9 6 6 1 0 1296 1 13 > 8 3 5 7 11 1 2 0 2 1190 4 > 9 1 1 2 3 17 2 0 4 4 1190 > > > Ok everything looks great up to this point..........so I try to apply my model to a "real" testset, which is the same format as my previous > dataset, except it does not have the label/factor column, so its 28000 obs 784 variables: > >> testset <- read.csv('test.csv',head=TRUE) >> str(testset) > 'data.frame': 28000 obs. of 784 variables: > $ pixel0 : int 0 0 0 0 0 0 0 0 0 0 ... > $ pixel1 : int 0 0 0 0 0 0 0 0 0 0 ... > $ pixel2 : int 0 0 0 0 0 0 0 0 0 0 ... > [list output truncated] > >> prediction <- predict(model, testset) >> summary(prediction) > 0 1 2 3 4 5 6 7 8 9 > 2780 3204 2824 2767 2771 2516 2744 2898 2736 2760 >> print(prediction) > 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 > 2 0 9 9 3 7 0 3 0 3 5 7 4 0 4 3 3 1 9 0 9 1 1 > 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 > 5 7 4 2 7 4 7 7 5 4 2 6 2 5 5 1 6 7 7 4 9 8 7 > [list output truncated] > >> write(prediction, file="prediction.csv") > $ head -10 prediction.csv > 3 1 10 10 4 > 8 1 4 1 4 > 6 8 5 1 5 > 4 4 2 10 1 > 10 2 2 6 8 > 5 3 8 5 8 > 8 6 5 3 7 > 3 6 6 2 7 > 8 8 5 10 9 > 8 9 3 7 8 > > > I am obviously making a mistake. Everything is off by a value of 1. > > > Can someone tell me what I am doing wrong? > > Brian > > > > [[alternative HTML version deleted]] > > ______________________________________________ > R-help at r-project.org mailing list > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide http://www.R-project.org/posting-guide.html > and provide commented, minimal, self-contained, reproducible code.
On 20/11/2012 2:30 PM, Brian Feeny wrote:> I am new to R, so I am sure I am making a simple mistake. I am including complete information in hopes > someone can help me. > > Basically my data in R looks good, I write it to a file, and every value is off by 1. > > Here is my flow: > > > str(prediction) > Factor w/ 10 levels "0","1","2","3",..: 3 1 10 10 4 8 1 4 1 4 ... > - attr(*, "names")= chr [1:28000] "1" "2" "3" "4" ...You have a factor, not numerical data. Apparently write() is writing out the factor values (index into the levels) rather than their string representation. (I've never used write(). Normally would use cat() or write.csv() or something related to write data to a file for reading outside of R. ) write.csv() will write out the strings, by default in quotes, but there are lots of arguments to control the formatting. Duncan Murdoch> > print(prediction) > 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 > 2 0 9 9 3 7 0 3 0 3 5 7 4 0 4 3 3 1 9 0 9 1 1 > > ok, so it shows my values are 2, 0, 9, 9, 3 etc > > # I write my file out > write(prediction, file="prediction.csv") > > # look at the first 10 values > $ head -10 prediction.csv > 3 1 10 10 4 > 8 1 4 1 4 > 6 8 5 1 5 > 4 4 2 10 1 > 10 2 2 6 8 > 5 3 8 5 8 > 8 6 5 3 7 > 3 6 6 2 7 > 8 8 5 10 9 > 8 9 3 7 8 > > The complete work of what I did was as follows: > > # First I load in a dataset, label the first column as a factor > > dataset <- read.csv('train.csv',head=TRUE) > > dataset$label <- as.factor(dataset$label) > > # it has 42000 obs. 785 variables > > str(dataset) > 'data.frame': 42000 obs. of 785 variables: > $ label : Factor w/ 10 levels "0","1","2","3",..: 2 1 2 5 1 1 8 4 6 4 ... > $ pixel0 : int 0 0 0 0 0 0 0 0 0 0 ... > $ pixel1 : int 0 0 0 0 0 0 0 0 0 0 ... > $ pixel2 : int 0 0 0 0 0 0 0 0 0 0 ... > [list output truncated] > > # I make a sampling testset and trainset > > index <- 1:nrow(dataset) > > testindex <- sample(index, trunc(length(index)*30/100)) > > testset <- dataset[testindex,] > > trainset <- dataset[-testindex,] > > # build model, predict, view > > model <- svm(label~., data = trainset, type="C-classification", kernel="radial", gamma=0.0000001, cost=16) > > prediction <- predict(model, testset) > > tab <- table(pred = prediction, true = testset[,1]) > true > pred 0 1 2 3 4 5 6 7 8 9 > 0 1210 0 3 1 0 5 7 2 5 8 > 1 0 1415 2 0 2 1 0 7 5 0 > 2 0 2 1127 12 3 0 2 7 2 0 > 3 0 0 7 1296 0 10 0 2 15 6 > 4 1 1 8 2 1201 2 4 3 5 16 > 5 3 1 0 13 0 1100 3 1 2 3 > 6 3 0 3 0 5 9 1263 0 1 0 > 7 0 2 9 6 6 1 0 1296 1 13 > 8 3 5 7 11 1 2 0 2 1190 4 > 9 1 1 2 3 17 2 0 4 4 1190 > > > Ok everything looks great up to this point..........so I try to apply my model to a "real" testset, which is the same format as my previous > dataset, except it does not have the label/factor column, so its 28000 obs 784 variables: > > > testset <- read.csv('test.csv',head=TRUE) > > str(testset) > 'data.frame': 28000 obs. of 784 variables: > $ pixel0 : int 0 0 0 0 0 0 0 0 0 0 ... > $ pixel1 : int 0 0 0 0 0 0 0 0 0 0 ... > $ pixel2 : int 0 0 0 0 0 0 0 0 0 0 ... > [list output truncated] > > > prediction <- predict(model, testset) > > summary(prediction) > 0 1 2 3 4 5 6 7 8 9 > 2780 3204 2824 2767 2771 2516 2744 2898 2736 2760 > > print(prediction) > 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 > 2 0 9 9 3 7 0 3 0 3 5 7 4 0 4 3 3 1 9 0 9 1 1 > 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 > 5 7 4 2 7 4 7 7 5 4 2 6 2 5 5 1 6 7 7 4 9 8 7 > [list output truncated] > > > write(prediction, file="prediction.csv") > $ head -10 prediction.csv > 3 1 10 10 4 > 8 1 4 1 4 > 6 8 5 1 5 > 4 4 2 10 1 > 10 2 2 6 8 > 5 3 8 5 8 > 8 6 5 3 7 > 3 6 6 2 7 > 8 8 5 10 9 > 8 9 3 7 8 > > > I am obviously making a mistake. Everything is off by a value of 1. > > > Can someone tell me what I am doing wrong? > > Brian > > > > [[alternative HTML version deleted]] > > ______________________________________________ > R-help at r-project.org mailing list > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide http://www.R-project.org/posting-guide.html > and provide commented, minimal, self-contained, reproducible code.
Hello, You are seeing the levels of a factor but saving its values. Internally, factors are coded as consecutive integers starting at 1, and that's what is saved to file using write.table. To have the levels "0", "1", etc and not the corresponding values 1, 2, etc, try levels(prediction)[prediction] or as.integer(levels(prediction)[prediction]) Hope this helps, Rui Barradas Em 20-11-2012 19:30, Brian Feeny escreveu:> I am new to R, so I am sure I am making a simple mistake. I am including complete information in hopes > someone can help me. > > Basically my data in R looks good, I write it to a file, and every value is off by 1. > > Here is my flow: > >> str(prediction) > Factor w/ 10 levels "0","1","2","3",..: 3 1 10 10 4 8 1 4 1 4 ... > - attr(*, "names")= chr [1:28000] "1" "2" "3" "4" ... >> print(prediction) > 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 > 2 0 9 9 3 7 0 3 0 3 5 7 4 0 4 3 3 1 9 0 9 1 1 > > ok, so it shows my values are 2, 0, 9, 9, 3 etc > > # I write my file out > write(prediction, file="prediction.csv") > > # look at the first 10 values > $ head -10 prediction.csv > 3 1 10 10 4 > 8 1 4 1 4 > 6 8 5 1 5 > 4 4 2 10 1 > 10 2 2 6 8 > 5 3 8 5 8 > 8 6 5 3 7 > 3 6 6 2 7 > 8 8 5 10 9 > 8 9 3 7 8 > > The complete work of what I did was as follows: > > # First I load in a dataset, label the first column as a factor >> dataset <- read.csv('train.csv',head=TRUE) >> dataset$label <- as.factor(dataset$label) > # it has 42000 obs. 785 variables >> str(dataset) > 'data.frame': 42000 obs. of 785 variables: > $ label : Factor w/ 10 levels "0","1","2","3",..: 2 1 2 5 1 1 8 4 6 4 ... > $ pixel0 : int 0 0 0 0 0 0 0 0 0 0 ... > $ pixel1 : int 0 0 0 0 0 0 0 0 0 0 ... > $ pixel2 : int 0 0 0 0 0 0 0 0 0 0 ... > [list output truncated] > > # I make a sampling testset and trainset >> index <- 1:nrow(dataset) >> testindex <- sample(index, trunc(length(index)*30/100)) >> testset <- dataset[testindex,] >> trainset <- dataset[-testindex,] > # build model, predict, view >> model <- svm(label~., data = trainset, type="C-classification", kernel="radial", gamma=0.0000001, cost=16) >> prediction <- predict(model, testset) >> tab <- table(pred = prediction, true = testset[,1]) > true > pred 0 1 2 3 4 5 6 7 8 9 > 0 1210 0 3 1 0 5 7 2 5 8 > 1 0 1415 2 0 2 1 0 7 5 0 > 2 0 2 1127 12 3 0 2 7 2 0 > 3 0 0 7 1296 0 10 0 2 15 6 > 4 1 1 8 2 1201 2 4 3 5 16 > 5 3 1 0 13 0 1100 3 1 2 3 > 6 3 0 3 0 5 9 1263 0 1 0 > 7 0 2 9 6 6 1 0 1296 1 13 > 8 3 5 7 11 1 2 0 2 1190 4 > 9 1 1 2 3 17 2 0 4 4 1190 > > > Ok everything looks great up to this point..........so I try to apply my model to a "real" testset, which is the same format as my previous > dataset, except it does not have the label/factor column, so its 28000 obs 784 variables: > >> testset <- read.csv('test.csv',head=TRUE) >> str(testset) > 'data.frame': 28000 obs. of 784 variables: > $ pixel0 : int 0 0 0 0 0 0 0 0 0 0 ... > $ pixel1 : int 0 0 0 0 0 0 0 0 0 0 ... > $ pixel2 : int 0 0 0 0 0 0 0 0 0 0 ... > [list output truncated] > >> prediction <- predict(model, testset) >> summary(prediction) > 0 1 2 3 4 5 6 7 8 9 > 2780 3204 2824 2767 2771 2516 2744 2898 2736 2760 >> print(prediction) > 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 > 2 0 9 9 3 7 0 3 0 3 5 7 4 0 4 3 3 1 9 0 9 1 1 > 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 > 5 7 4 2 7 4 7 7 5 4 2 6 2 5 5 1 6 7 7 4 9 8 7 > [list output truncated] > >> write(prediction, file="prediction.csv") > $ head -10 prediction.csv > 3 1 10 10 4 > 8 1 4 1 4 > 6 8 5 1 5 > 4 4 2 10 1 > 10 2 2 6 8 > 5 3 8 5 8 > 8 6 5 3 7 > 3 6 6 2 7 > 8 8 5 10 9 > 8 9 3 7 8 > > > I am obviously making a mistake. Everything is off by a value of 1. > > > Can someone tell me what I am doing wrong? > > Brian > > > > [[alternative HTML version deleted]] > > ______________________________________________ > R-help at r-project.org mailing list > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide http://www.R-project.org/posting-guide.html > and provide commented, minimal, self-contained, reproducible code.