CODEWITHSUNDEEP
rgenetic-algorithmfeature-selectionr-caretsimulated-annealing
Rouzbeh
Rouzbeh

Reputation: 11

Caret Genetic Algorithms Feature Selection

I am trying to use Caret Feature Selection using Genetic Algorithms or Simulated Annealing and I am getting an identical error message in both cases.

I have tried the most basic form of the gafs and safs with very simple input data frame.

> library(caret)
> head(n)
id   group hs.grad  race gender age m.status   political n.kids income score  time1  time2  time3
1 ID.1 control      no white female  37 divorced       other      1  96000        0.71  99.02 101.72 100.07
2 ID.2 control     yes white   male  34 divorced independent      0  16000 -0.43  43.78  45.54  45.79
3 ID.3   treat     yes white female  39    never    democrat      2  13000  1.80 100.23 101.01 103.00
4 ID.4 control     yes white female  29  married independent      4  12000 -0.05  95.64  99.61  96.38
5 ID.5 control     yes white female  36  married    democrat      0   7000 -0.50  47.25  47.25  49.11
6 ID.6 control     yes asian   male  19    never  republican      0  18000  0.00  77.66  78.43  85.68
> obj <- gafs(x=n[,1:8],
+             y=n$time3,
+             iters = 10)
Error in gafs.default(x = n[, 1:8], y = n$time3, iters = 10) : 
promise already under evaluation: recursive default argument reference or  earlier problems?

I appreciate if someone can share experience if had encountered with similar problem (BTW, n has only 14 observations, although I have tried with many different data frames and got the same error message)

Thanks

Upvotes: 0

Views: 1395

Answers (1)

anon
anon

Reputation:

I think the problem is that you are not providing the necessary gafsControl parameter. See the gafs call in the documentation example:

## Not run: 
set.seed(1)
train_data <- twoClassSim(100, noiseVars = 10)
test_data  <- twoClassSim(10,  noiseVars = 10)

## A short example 
ctrl <- gafsControl(functions = rfGA, 
                    method = "cv",
                    number = 3)

rf_search <- gafs(x = train_data[, -ncol(train_data)],
                  y = train_data$Class,
                  iters = 3,
                  gafsControl = ctrl)

Upvotes: 3

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