MLearn_new           package:MLInterfaces           R Documentation

_r_e_v_i_s_e_d _M_L_e_a_r_n _i_n_t_e_r_f_a_c_e _f_o_r _m_a_c_h_i_n_e _l_e_a_r_n_i_n_g

_D_e_s_c_r_i_p_t_i_o_n:

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

_U_s_a_g_e:

     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)

_A_r_g_u_m_e_n_t_s:

 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.

_D_e_t_a_i_l_s:

     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).

     _r_a_n_d_o_m_F_o_r_e_s_t_I 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.

     _k_n_n_I(_k=_1,_l=_0) knn; special support bridge required, defined in
          MLint

     _d_l_d_a_I stat.diag.da; special support bridge required, defined in
          MLint

     _n_n_e_t_I nnet

     _r_p_a_r_t_I rpart

     _l_d_a_I lda

     _s_v_m_I svm

     _q_d_a_I qda

     _l_o_g_i_s_t_i_c_I(_t_h_r_e_s_h_o_l_d) 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

     _R_A_B_I RAB - an experimental implementation of real Adaboost of
          Friedman Hastie Tibshirani Ann Stat 2001

     _l_v_q_I 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.

     _n_a_i_v_e_B_a_y_e_s_I naiveBayes

     _b_a_g_g_i_n_g_I bagging

     _s_l_d_a_I slda

     _r_d_a_c_v_I 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.

_V_a_l_u_e:

     Instances of classifierOutput or clusteringOutput

_A_u_t_h_o_r(_s):

     Vince Carey <stvjc@channing.harvard.edu>

_E_x_a_m_p_l_e_s:

     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

