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
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and provide commented, minimal, self-contained, reproducible code.
