MLearn {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, ... )
    makeLearnerSchema(packname, mlfunname, converter, predicter)

### 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
`...` | 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 `obj` to predict `newdata`.

### 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

  diagDA; 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

adaI

  ada

BgbmI

  gbm, forcing the Bernoulli loss function.

blackboostI

  blackboost – you MUST supply a family parameter relevant for mboost package procedures

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

  [1/2020: rda dropped from CRAN, install from a CRAN archive to use] – you must supply the alpha and delta parameters to use this. Typically cross-validation is used to select these. See `rdacvI` below.

rdacvI

  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

hclustI(distMethod, agglomMethod)

  hclust – you must explicitly specify distance and agglomeration procedure.

kmeansI(centers, algorithm)

  kmeans – you must explicitly specify centers and algorithm name.

If the `parallel` package is attached, cross-validation will be distributed to cores using `mclapply`.

### Value

Instances of classifierOutput or clusteringOutput

### Author(s)

Vince Carey <stvjc@channing.harvard.edu>

### See Also

Try `example(hclustWidget, ask=FALSE)` for an interactive approach to cluster analysis tuning.

### Examples

    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

    if (requireNamespace("rda", quietly=TRUE)) {
     library("rda")
     r1 = MLearn(mol.biol~., vall2, MLInterfaces:::rdacvI, 1:40)
     confuMat(r1)
     RObject(r1)
     MLInterfaces:::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])

[Package MLInterfaces version 1.66.5 Index]