MLearn {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, ... ) makeLearnerSchema(packname, mlfunname, converter, predicter)
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 |
... |
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. |
predicter |
function – with parameters (obj, newdata, ...) that
tells how to use the material in |
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).
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.
knn; special support bridge required, defined in MLint
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.
diagDA; special support bridge required, defined in MLint
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
gbm, forcing the Bernoulli loss function.
blackboost – you MUST supply a family parameter relevant for mboost package procedures
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.
rda – you must supply the alpha and delta parameters to
use this. Typically cross-validation is used to select these. See rdacvI
below.
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.
hclust – you must explicitly specify distance and agglomeration procedure.
kmeans – you must explicitly specify centers and algorithm name.
If the parallel
package is attached, cross-validation will
be distributed to cores using mclapply
.
Instances of classifierOutput or clusteringOutput
Vince Carey <stvjc@channing.harvard.edu>
Try example(hclustWidget, ask=FALSE)
for an interactive
approach to cluster analysis tuning.
library("MASS") 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, trace=FALSE ) 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) # # new mboost interface -- you MUST supply family for nonGaussian response # require(party) # trafo ... killing cmd check blb.1 = MLearn(sp~CW+RW+FL, data=crabs, blackboostI, kp, family=mboost::Binomial() ) confuMat(blb.1) # # ExpressionSet illustration # data(sample.ExpressionSet) # needed to increase training set size to avoid a new randomForest condition # on empty class set.seed(1234) X = MLearn(type~., sample.ExpressionSet[100:250,], randomForestI, 1:19, 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, trace=FALSE ) 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, trace=FALSE ) 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, trace=FALSE ) 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, trace=FALSE ) 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, trace=FALSE ) 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 # illustrate clustering support cl1 = MLearn(~CW+RW+CL+FL+BD, data=crabs, hclustI(distFun=dist, cutParm=list(k=4))) plot(cl1) cl1a = MLearn(~CW+RW+CL+FL+BD, data=crabs, hclustI(distFun=dist, cutParm=list(k=4)), method="complete") plot(cl1a) cl2 = MLearn(~CW+RW+CL+FL+BD, data=crabs, kmeansI, centers=5, algorithm="Hartigan-Wong") plot(cl2, crabs[,-c(1:3)]) c3 = MLearn(~CL+CW+RW, crabs, pamI(dist), k=5) c3 plot(c3, data=crabs[,c("CL", "CW", "RW")]) # new interfaces to PLS thanks to Laurent Gatto set.seed(1234) kp = sample(1:200, size=120) plsda.1 = MLearn(sp~CW+RW, data=crabs, plsdaI, kp, probMethod="Bayes") plsda.1 confuMat(plsda.1) confuMat(plsda.1,t=.65) ## requires at least 0.65 post error prob to assign species plsda.2 = MLearn(type~., data=sample.ExpressionSet[100:250,], plsdaI, 1:16) plsda.2 confuMat(plsda.2) confuMat(plsda.2,t=.65) ## requires at least 0.65 post error prob to assign outcome ## examples for predict clout <- MLearn(type~., sample.ExpressionSet[100:250,], svmI , 1:16) predict(clout, sample.ExpressionSet[100:250,17:26])