MLearn_new {MLInterfaces}R Documentation

revised MLearn interface for machine learning

Description

revised MLearn interface for machine learning, emphasizing a schematic description of external learning functions like knn, lda, nnet, etc.

Usage

MLearn( formula, data, method, trainInd, mlSpecials, ... )
makeLearnerSchema(packname, mlfunname, converter)

Arguments

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.

Details

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).

randomForestI
randomForest. Note, that to obtain the default performance of randomForestB, you need to set mtry and sampsize parameters to sqrt(number of features) and table([training set response factor]) respectively, as these were not taken to be the function's defaults. Note you can use xvalSpec("NOTEST") as trainInd, to use all the samples; the RObject() result will print the misclassification matrix estimate along with OOB error rate estimate.
knnI(k=1,l=0)
knn; special support bridge required, defined in MLint
knn.cvI(k=1,l=0)
knn.cv; special support bridge required, defined in MLint. This option uses the embedded leave-one-out cross-validation of 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.
dldaI
stat.diag.da; special support bridge required, defined in MLint
nnetI
nnet
rpartI
rpart
ldaI
lda
svmI
svm
qdaI
qda
logisticI(threshold)
glm – with binomial family, expecting a dichotomous factor as response variable, not bulletproofed against other responses yet. If response probability estimate exceeds threshold, predict 1, else 0
ada
ada
lvqI
lvqtest after building codebook with lvqinit and updating with olvq1. You will need to write your own detailed schema if you want to tweak tuning parameters.
naiveBayesI
naiveBayes
baggingI
bagging
sldaI
slda
rdaI
rda – you must supply the alpha and delta parameters to use this. Typically cross-validation is used to select these. See rdacvI below.
rdacvI
rda.cv. This interface is complicated. The typical use includes cross-validation internal to the rda.cv function. That process searches a tuning parameter space and delivers an ordering on parameters. The interface selects the parameters by looking at all parameter configurations achieving the smallest min+1SE cv.error estimate, and taking the one among them that employed the -most- features (agnosticism). A final run of rda is then conducted with the tuning parameters set at that 'optimal' choice. The bridge code can be modified to facilitate alternative choices of the parameters in use. 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.
ksvmI
ksvm

Value

Instances of classifierOutput or clusteringOutput

Author(s)

Vince Carey <stvjc@channing.harvard.edu>

Examples

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


[Package MLInterfaces version 1.14.1 Index]