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, ... )
    xvalSpec( type, niter=0, partitionFunc= function(data, classLab, iternum ) {
                                    (1:nrow(data))[-iternum] },
                             fsFun = function(formula, data) formula )
    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
`type` | "LOO" to specify leave-one-out cross-validation; any other token implies use of a `partitionFunc`
`niter` | numeric, specifying number of cross-validation iterations, i.e., the number of partitions to be formed; ignored if `type` is "LOO".
`partitionFunc` | function, with parameters data (bound to data.frame), clab (bound to character string), iternum (bound to numeric index into sequence of 1:`niter`). This function's job is to provide the indices of training cases for each cross-validation step. An example is `balKfold.xvspec`, which computes a series of indices that are approximately balanced with respect to frequency of outcome types.
`fsFun` | function, with parameters formula, data. The function must return a formula suitable for defining a model on the basis of the main input data. A candidate fsFun is given in examples below.
`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

This implementation attempts to reduce complexity of the basic MLInterfaces engine. The primary MLearn method, which includes "learnerSchema" in its signature, is very concise. Details of massaging inputs and outputs are left to a learnerSchema class instance. The MLint_devel vignette describes the schema formulation. learnerSchema instances are provided for following methods; the naming convention is that the method basename is prepended to `I'.

Note that some schema instances are presented as functions. The parameters must be set to use these models.

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.
knnI(k=1,l=0)
  knn; special support bridge required, defined in MLint
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
RABI
  RAB – an experimental implementation of real Adaboost of Friedman Hastie Tibshirani Ann Stat 2001
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
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.

### 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
    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)
    varImpPlot(RObject(X))
    #
    # 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)
    #
    # illustrate feature selection -- following function keeps features that
    # discriminate in the top 25 percent of all features according to rowttests
    #
    fsFun.rowtQ3 = function(formula, data) {
     # facilitation of a rowttests with a formula/data.frame takes a little work
     mf = model.frame(formula, data)
     mm = model.matrix(formula, data)
     respind = attr( terms(formula, data=data), "response" )
     x = mm
     if ("(Intercept)" %in% colnames(x)) x = x[,-which(colnames(x) == "(Intercept)")]
     y = mf[, respind]
     respname = names(mf)[respind]
     nuy = length(unique(y))
     if (nuy > 2) warning("number of unique values of response exceeds 2")
     #dm = t(data.matrix(x))
     #dm = matrix(as.double(dm), nr=nrow(dm)) # rowttests seems fussy
     ans = abs( rowttests(t(x), factor(y), tstatOnly=TRUE)[[1]] )
     names(ans) = colnames(x)
     ans = names( ans[ which(ans > quantile(ans, .75) ) ] )
     btick = function(x) paste("`", x, "`", sep="")  # support for nonsyntactic varnames
     as.formula( paste(respname, paste(btick(ans), collapse="+"), sep="~"))
    }

    nn3cv = MLearn(sp~CW+RW+CL+BD+FL, data=crabs[c(1:20,101:120),], nnetI,
       xvalSpec("LOG",5, balKfold.xvspec(5), fsFun=fsFun.rowtQ3), 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=fsFun.rowtQ3), 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=fsFun.rowtQ3), 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 1.12.0 Index]