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fastClassification
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@gtesei
gtesei committed Oct 18, 2015
1 parent c9bc9de commit d6cd521
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Showing 2 changed files with 13 additions and 13 deletions.
26 changes: 13 additions & 13 deletions R-package/R/fastClassification.R
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Original file line number Diff line number Diff line change
Expand Up @@ -113,33 +113,33 @@ ff.trainAndPredict.class = function(Ytrain ,

if (model.label == "glm") { ## logistic reg
l = getCaretFactors(y=Ytrain)
model <- train( x = Xtrain , y = l$y.cat , method = "glm", metric = getCaretMetric(metric=metric.label) , trControl = controlObject)
model <- caret::train( x = Xtrain , y = l$y.cat , method = "glm", metric = getCaretMetric(metric=metric.label) , trControl = controlObject)
pred.prob = predict(model , Xtest , type = "prob")[,l$fact.sign]
pred = predict(model,Xtest)
} else if (model.label == "lda") { ## lda
l = getCaretFactors(y=Ytrain)
model <- train( x = Xtrain , y = l$y.cat, method = "lda" , metric = getCaretMetric(metric=metric.label) , trControl = controlObject)
model <- caret::train( x = Xtrain , y = l$y.cat, method = "lda" , metric = getCaretMetric(metric=metric.label) , trControl = controlObject)
pred.prob = predict(model , Xtest , type = "prob")[,l$fact.sign]
pred = predict(model,Xtest)
} else if (model.label == "pls") { ## pls
l = getCaretFactors(y=Ytrain)
model <- train( x = Xtrain , y = l$y.cat,
model <- caret::train( x = Xtrain , y = l$y.cat,
method = "pls", tuneGrid = expand.grid(.ncomp = 1:10),
metric = getCaretMetric(metric=metric.label) , trControl = controlObject)
pred.prob = predict(model , Xtest , type = "prob")[,l$fact.sign]
pred = predict(model,Xtest)
} else if (model.label == "glmnet") { ## glmnet
l = getCaretFactors(y=Ytrain)
glmnGrid <- expand.grid(.alpha = c(0, .1, .2, .4, .6, .8, 1), .lambda = seq(.01, .2, length = 40))
model <- train( x = Xtrain , y = l$y.cat,
model <- caret::train( x = Xtrain , y = l$y.cat,
method = "glmnet", tuneGrid = glmnGrid,
metric = getCaretMetric(metric=metric.label), trControl = controlObject)
pred.prob = predict(model , Xtest , type = "prob")[,l$fact.sign]
pred = predict(model,Xtest)
} else if (model.label == "pam") { ## pam
l = getCaretFactors(y=Ytrain)
nscGrid <- data.frame(.threshold = 0:25)
model <- train( x = Xtrain , y = l$y.cat,
model <- caret::train( x = Xtrain , y = l$y.cat,
method = "pam", tuneGrid = nscGrid,
metric = getCaretMetric(metric=metric.label), trControl = controlObject)
pred.prob = predict(model , Xtest , type = "prob")[,l$fact.sign]
Expand All @@ -149,7 +149,7 @@ ff.trainAndPredict.class = function(Ytrain ,
nnetGrid <- expand.grid(.size = 1:10, .decay = c(0, .1, 1, 2))
maxSize <- max(nnetGrid$.size)
numWts <- 1*(maxSize * ( (dim(Xtrain)[2]) + 1) + maxSize + 1)
model <- train( x = Xtrain , y = l$y.cat,
model <- caret::train( x = Xtrain , y = l$y.cat,
method = "nnet", metric = getCaretMetric(metric=metric.label),
preProc = c( "spatialSign") ,
tuneGrid = nnetGrid , trace = FALSE , maxit = 2000 ,
Expand All @@ -159,7 +159,7 @@ ff.trainAndPredict.class = function(Ytrain ,
} else if (model.label == "svmRadial") { ## svm
l = getCaretFactors(y=Ytrain)
svmRGridReduced <- expand.grid(.sigma = kernlab::sigest(as.matrix(Xtrain)), .C = 2^(seq(-4, 4)))
model <- train( x = Xtrain , y = l$y.cat,
model <- caret::train( x = Xtrain , y = l$y.cat,
method = "svmRadial", tuneGrid = svmRGridReduced,
metric = getCaretMetric(metric=metric.label), fit = FALSE, trControl = controlObject)
pred.prob = predict(model , Xtest , type = "prob")[,l$fact.sign]
Expand Down Expand Up @@ -232,26 +232,26 @@ ff.trainAndPredict.class = function(Ytrain ,

} else if (model.label == "knn") { ## knn
l = getCaretFactors(y=Ytrain)
model <- train( x = Xtrain , y = l$y.cat,
model <- caret::train( x = Xtrain , y = l$y.cat,
method = "knn",
tuneGrid = data.frame(.k = c(4*(0:5)+1, 20*(1:5)+1, 50*(2:9)+1)),
metric = getCaretMetric(metric=metric.label), trControl = controlObject)
pred.prob = predict(model , Xtest , type = "prob")[,l$fact.sign]
pred = predict(model,Xtest)
} else if (model.label == "rpart") { ## class trees
l = getCaretFactors(y=Ytrain)
model <- train( x = Xtrain , y = l$y.cat,
model <- caret::train( x = Xtrain , y = l$y.cat,
method = "rpart", tuneLength = 30,
metric = getCaretMetric(metric=metric.label), trControl = controlObject)
pred.prob = predict(model , Xtest , type = "prob")[,l$fact.sign]
pred = predict(model,Xtest)
} else if (model.label == "C5.0") { ## boosted trees
l = getCaretFactors(y=Ytrain)
if (! best.tuning) {
model <- train( x = Xtrain , y = l$y.cat,
model <- caret::train( x = Xtrain , y = l$y.cat,
method = "C5.0", metric = getCaretMetric(metric=metric.label), trControl = controlObject)
} else {
model <- train( x = Xtrain , y = l$y.cat,
model <- caret::train( x = Xtrain , y = l$y.cat,
tuneGrid = expand.grid(.trials = c(1, (1:10)*10), .model = "tree", .winnow = c(TRUE, FALSE) ),
method = "C5.0", metric = getCaretMetric(metric=metric.label), trControl = controlObject)
}
Expand All @@ -260,13 +260,13 @@ ff.trainAndPredict.class = function(Ytrain ,
} else if (model.label == "bag") { ## bagging trees
l = getCaretFactors(y=Ytrain)
if (! best.tuning) {
model <- train( x = Xtrain , y = l$y.cat,
model <- caret::train( x = Xtrain , y = l$y.cat,
method = "bag", metric = getCaretMetric(metric=metric.label), trControl = controlObject, B = 50 ,
bagControl = bagControl(fit = plsBag$fit,
predict = plsBag$pred,
aggregate = plsBag$aggregate))
} else {
model <- train( x = Xtrain , y = l$y.cat,
model <- caret::train( x = Xtrain , y = l$y.cat,
method = "bag", metric = getCaretMetric(metric=metric.label) , trControl = controlObject,
tuneGrid = data.frame(vars = seq(1, 15, by = 2)),
bagControl = bagControl(fit = plsBag$fit,
Expand Down
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