MLearn_new {MLInterfaces} | R Documentation |
revised MLearn interface for machine learning, emphasizing a schematic description of external learning functions like knn, lda, nnet, etc.
MLearn( formula, data, method, trainInd, mlSpecials, ... ) makeLearnerSchema(packname, mlfunname, converter)
formula |
standard model formula |
data |
data.frame or ExpressionSet instance |
method |
instance of learnerSchema |
trainInd |
obligatory numeric vector of indices of data to be used for training; all other data are used for testing, or instance of the xvalSpec class |
mlSpecials |
see help(MLearn-OLD) for this parameter; learnerSchema design obviates need for this parameter, which is retained only for back-compatibility. |
... |
additional named arguments passed to external learning function |
packname |
character – name of package harboring a learner function |
mlfunname |
character – name of function to use |
converter |
function – with parameters (obj, data, trainInd) that tells how to convert the material in obj [produced by [packname::mlfunname] ] into a classifierOutput instance. |
The purpose of the MLearn methods is to provide a uniform calling sequence
to diverse machine learning algorithms. In R package, machine learning functions
can have parameters (x, y, ...)
or (formula, data, ...)
or some
other sequence, and these functions can return lists or vectors or other
sorts of things. With MLearn, we
always have calling sequence MLearn(formula, data, method, trainInd, ...)
,
and data
can be a data.frame
or ExpressionSet
. MLearn
will always return an S4 instance of classifierObject
or clusteringObject
.
At this time (1.13.x), NA values in predictors trigger an error.
To obtain documentation on the older (pre bioc 2.1) version of the MLearn method, please use help(MLearn-OLD).
knn.cv
, and thereby
achieves high performance. You can have more general cross-validation
using knnI
with an xvalSpec
, but it will be slower.
When using this learner schema, you should use the
numerical trainInd
setting with 1:N
where
N
is the number of samples.rdacvI
below.plotXvalRDA
is an interface to the plot method for objects of class rdacv defined in
package rda. You can use xvalSpec("NOTEST") with this procedure to
use all the samples to build the discriminator.Instances of classifierOutput or clusteringOutput
Vince Carey <stvjc@channing.harvard.edu>
data(crabs) set.seed(1234) kp = sample(1:200, size=120) rf1 = MLearn(sp~CW+RW, data=crabs, randomForestI, kp, ntree=600 ) rf1 nn1 = MLearn(sp~CW+RW, data=crabs, nnetI, kp, size=3, decay=.01 ) nn1 RObject(nn1) knn1 = MLearn(sp~CW+RW, data=crabs, knnI(k=3,l=2), kp) knn1 names(RObject(knn1)) dlda1 = MLearn(sp~CW+RW, data=crabs, dldaI, kp ) dlda1 names(RObject(dlda1)) lda1 = MLearn(sp~CW+RW, data=crabs, ldaI, kp ) lda1 names(RObject(lda1)) slda1 = MLearn(sp~CW+RW, data=crabs, sldaI, kp ) slda1 names(RObject(slda1)) svm1 = MLearn(sp~CW+RW, data=crabs, svmI, kp ) svm1 names(RObject(svm1)) ldapp1 = MLearn(sp~CW+RW, data=crabs, ldaI.predParms(method="debiased"), kp ) ldapp1 names(RObject(ldapp1)) qda1 = MLearn(sp~CW+RW, data=crabs, qdaI, kp ) qda1 names(RObject(qda1)) logi = MLearn(sp~CW+RW, data=crabs, glmI.logistic(threshold=0.5), kp, family=binomial ) # need family logi names(RObject(logi)) rp2 = MLearn(sp~CW+RW, data=crabs, rpartI, kp) rp2 ## recode data for RAB #nsp = ifelse(crabs$sp=="O", -1, 1) #nsp = factor(nsp) #ncrabs = cbind(nsp,crabs) #rab1 = MLearn(nsp~CW+RW, data=ncrabs, RABI, kp, maxiter=10) #rab1 # # new approach to adaboost # ada1 = MLearn(sp ~ CW+RW, data = crabs, method = adaI, trainInd = kp, type = "discrete", iter = 200) ada1 confuMat(ada1) # lvq.1 = MLearn(sp~CW+RW, data=crabs, lvqI, kp ) lvq.1 nb.1 = MLearn(sp~CW+RW, data=crabs, naiveBayesI, kp ) confuMat(nb.1) bb.1 = MLearn(sp~CW+RW, data=crabs, baggingI, kp ) confuMat(bb.1) # # ExpressionSet illustration # data(sample.ExpressionSet) X = MLearn(type~., sample.ExpressionSet[100:250,], randomForestI, 1:16, importance=TRUE ) library(randomForest) library(hgu95av2.db) opar = par(no.readonly=TRUE) par(las=2) plot(getVarImp(X), n=10, plat="hgu95av2", toktype="SYMBOL") par(opar) # # demonstrate cross validation # nn1cv = MLearn(sp~CW+RW, data=crabs[c(1:20,101:120),], nnetI, xvalSpec("LOO"), size=3, decay=.01 ) confuMat(nn1cv) nn2cv = MLearn(sp~CW+RW, data=crabs[c(1:20,101:120),], nnetI, xvalSpec("LOG",5, balKfold.xvspec(5)), size=3, decay=.01 ) confuMat(nn2cv) nn3cv = MLearn(sp~CW+RW+CL+BD+FL, data=crabs[c(1:20,101:120),], nnetI, xvalSpec("LOG",5, balKfold.xvspec(5), fsFun=fs.absT(2)), size=3, decay=.01 ) confuMat(nn3cv) nn4cv = MLearn(sp~.-index-sex, data=crabs[c(1:20,101:120),], nnetI, xvalSpec("LOG",5, balKfold.xvspec(5), fsFun=fs.absT(2)), size=3, decay=.01 ) confuMat(nn4cv) # # try with expression data # library(golubEsets) data(Golub_Train) litg = Golub_Train[ 100:150, ] g1 = MLearn(ALL.AML~. , litg, nnetI, xvalSpec("LOG",5, balKfold.xvspec(5), fsFun=fs.probT(.75)), size=3, decay=.01 ) confuMat(g1) # # illustrate rda.cv interface from package rda (requiring local bridge) # library(ALL) data(ALL) # # restrict to BCR/ABL or NEG # bio <- which( ALL$mol.biol %in% c("BCR/ABL", "NEG")) # # restrict to B-cell # isb <- grep("^B", as.character(ALL$BT)) kp <- intersect(bio,isb) all2 <- ALL[,kp] mads = apply(exprs(all2),1,mad) kp = which(mads>1) # get around 250 genes vall2 = all2[kp, ] vall2$mol.biol = factor(vall2$mol.biol) # drop unused levels r1 = MLearn(mol.biol~., vall2, rdacvI, 1:40) confuMat(r1) RObject(r1) plotXvalRDA(r1) # special interface to plots of parameter space