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SubstanceClassClassifier_classifier.R
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SubstanceClassClassifier_classifier.R
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######################################################################################
## classifiers
#calc_auprc <- function(model, data){
# index_class2 <- data$Class == "m"
# index_class1 <- data$Class == "p"
# predictions <- predict(model, data, type = "prob")
# pr.curve(predictions$Class2[index_class2],
# predictions$Class2[index_class1],
# curve = TRUE)
#}
if(FALSE){
auprcSummary <- function(data, lev = NULL, model = NULL){
index_class1 <- data$obs == "p"
index_class2 <- data$obs == "m"
the_curve <- pr.curve(scores.class0 = data$p[index_class2], scores.class1 = data$p[index_class1], curve = FALSE)
out <- the_curve$auc.integral
names(out) <- "AUPRC"
out
}
twoClassSummary2 <- function(data, lev = NULL, model = NULL)
{
lvls <- levels(data$obs)
if (length(lvls) > 2)
stop(paste("Your outcome has", length(lvls), "levels. The twoClassSummary() function isn't appropriate."))
requireNamespaceQuietStop("ModelMetrics")
if (!all(levels(data[, "pred"]) == lvls))
stop("levels of observed and predicted data do not match")
rocAUC <- ModelMetrics::auc(ifelse(data$obs == lev[2], 0,
1), data[, lvls[1]])
out <- c(rocAUC, sensitivity(data[, "pred"], data[, "obs"],
lev[1]), specificity(data[, "pred"], data[, "obs"], lev[2]))
names(out) <- c("ROC", "Sens", "Spec")
out
}
}
caret_classifier <- list(
train = function(matrix_train, classes_pm_train, modelName, classWeights){
if(FALSE){
######################################################
## add class to matrix and use (inefficient->overhead) formula + data parameters of train
## add class factor column
matrix_train_caret <- matrix_train
rownames(matrix_train_caret) <- seq_len(nrow(matrix_train_caret))
matrix_train_caret <- cbind(as.data.frame(as.matrix(matrix_train_caret)), rep(x = 0, times = nrow(matrix_train_caret)))
colnames(matrix_train_caret)[[ncol(matrix_train_caret)]] <- "class"
posRows <- classes_pm_train=="+"
matrix_train_caret[ posRows, "class"] <- "p"
matrix_train_caret[!posRows, "class"] <- "m"
matrix_train_caret[, "class"] <- as.factor(matrix_train_caret[, "class"])
## rename mass column names
colnamesHere <- colnames(matrix_train_caret)
neutralLosses <- grepl(x = colnamesHere, pattern = "^\\-\\d+(\\.\\d+)?$")
fragments <- grepl(x = colnamesHere, pattern = "^\\d+(\\.\\d+)?$")
colnamesHere[neutralLosses] <- gsub(x = colnamesHere[neutralLosses], pattern = "^-", replacement = "m")
colnamesHere[fragments] <- paste("p", colnamesHere[fragments], sep = "")
colnames(matrix_train_caret) <- colnamesHere
}
######################################################
## treat data and outcome independently and use x + y parameters of train for the sake of efficiency
## to matrix
matrix_train_caret2 <- as.matrix(matrix_train)
rownames(matrix_train_caret2) <- seq_len(nrow(matrix_train_caret2))
classes_pm_train_caret2 <- classes_pm_train
## add class column
posRows <- classes_pm_train_caret2=="+"
classes_pm_train_caret2[ posRows] <- "p"
classes_pm_train_caret2[!posRows] <- "m"
class <- as.factor(classes_pm_train_caret2)
## make valid column names
colnamesHere <- colnames(matrix_train_caret2)
neutralLosses <- grepl(x = colnamesHere, pattern = "^\\-\\d+(\\.\\d+)?$")
fragments <- grepl(x = colnamesHere, pattern = "^\\d+(\\.\\d+)?$")
colnamesHere[neutralLosses] <- gsub(x = colnamesHere[neutralLosses], pattern = "^-", replacement = "m")
colnamesHere[fragments] <- paste("p", colnamesHere[fragments], sep = "")
colnames(matrix_train_caret2) <- colnamesHere
################################################################
## train control and class weights
useRoc <- FALSE
ctrl <- caret::trainControl(
#method = "none",
## cross validation
method = "repeatedcv",
number = 5,
repeats = 1,
## tuning
search = "random",
## misc
classProbs = TRUE,
summaryFunction = ifelse(test = useRoc, yes = caret::twoClassSummary, no = caret::prSummary),
#summaryFunction = auprcSummary,
#summaryFunction = caret::prSummary,
#summaryFunction = caret::twoClassSummary,
verboseIter = FALSE,
allowParallel = FALSE
)
#modelProps <- caret::getModelInfo()[[modelName]]
if(classWeights){
#weight_p <- (1 / table(class)["p"]) * 0.5
#weight_m <- (1 / table(class)["m"]) * 0.5
weight_p <- table(class)["m"]
weight_m <- table(class)["p"]
class_weights <- ifelse(test = class == "p", yes = weight_p, no = weight_m)
} else {
class_weights <- NULL
}
################################################################
## do
#set.seed(1)
#startTime <<- Sys.time()
log <- capture.output({
classifier <- caret::train(
#class ~ .,
#data = matrix_train_caret,
x = matrix_train_caret2,
y = class,
weights = class_weights,
method = modelName,
tuneLength = 10,
trControl = ctrl,
metric = ifelse(test = useRoc, yes = "ROC", no = "AUC"),
#preProc = c("center", "scale")
#tuneGrid = expand.grid(k = c(5, 11, 21, 25))
preProc = NULL
)
})
#endTime <- Sys.time()
#rocs <- classifier$results$ROC
## Variable Importance
#varImp(object=classifier)
#importanceDf <- varImp(object=classifier)[["importance"]]
return(classifier)
},
classify = function(classifier, matrix_test){
matrix_test_caret <- matrix_test
matrix_test_caret <- as.data.frame(as.matrix(matrix_test_caret))
## rename mass column names
colnamesHere <- colnames(matrix_test_caret)
neutralLosses <- grepl(x = colnamesHere, pattern = "^\\-\\d+(\\.\\d+)?$")
fragments <- grepl(x = colnamesHere, pattern = "^\\d+(\\.\\d+)?$")
colnamesHere[neutralLosses] <- gsub(x = colnamesHere[neutralLosses], pattern = "^-", replacement = "m")
colnamesHere[fragments] <- paste("p", colnamesHere[fragments], sep = "")
colnames(matrix_test_caret) <- colnamesHere
## https://www.analyticsvidhya.com/blog/2016/12/practical-guide-to-implement-machine-learning-with-caret-package-in-r-with-practice-problem/
#predictions<-predict.train(object=model_gbm,testSet[,predictors],type="raw")
#plsClasses <- predict(classifier, newdata = matrix_test)
log <- capture.output({
plsProbs <- predict(classifier, newdata = matrix_test_caret, type = "prob")
})
scores <- plsProbs$p #pred$predict[, "+", 1]
return(scores)
}
)
caret_classifier_stacked <- list(
train = function(matrix_train, classes_pm_train, modelNames){
## TODO class weights
######################################################
## treat data and outcome independently and use x + y parameters of train for the sake of efficiency
matrix_train_caret2 <- as.matrix(matrix_train)
rownames(matrix_train_caret2) <- seq_len(nrow(matrix_train_caret2))
classes_pm_train_caret2 <- classes_pm_train
posRows <- classes_pm_train_caret2=="+"
classes_pm_train_caret2[ posRows] <- "p"
classes_pm_train_caret2[!posRows] <- "m"
class <- as.factor(classes_pm_train_caret2)
colnamesHere <- colnames(matrix_train_caret2)
neutralLosses <- grepl(x = colnamesHere, pattern = "^\\-\\d+(\\.\\d+)?$")
fragments <- grepl(x = colnamesHere, pattern = "^\\d+(\\.\\d+)?$")
colnamesHere[neutralLosses] <- gsub(x = colnamesHere[neutralLosses], pattern = "^-", replacement = "m")
colnamesHere[fragments] <- paste("p", colnamesHere[fragments], sep = "")
colnames(matrix_train_caret2) <- colnamesHere
################################################################
## do
#ctrl <- trainControl(
# method = "repeatedcv",
# number = 5,
# repeats = 1,
# classProbs = TRUE,
# summaryFunction = twoClassSummary,
# verboseIter = FALSE,
# allowParallel = TRUE
#)
useRoc <- FALSE
ctrl <- caret::trainControl(
#method = "none",
method = "repeatedcv",
number = 5,
repeats = 1,
classProbs = TRUE,
summaryFunction = ifelse(test = useRoc, yes = caret::twoClassSummary, no = caret::prSummary),
#summaryFunction = auprcSummary,
#summaryFunction = caret::prSummary,
#summaryFunction = caret::twoClassSummary,
verboseIter = FALSE,
allowParallel = FALSE,
savePredictions="final",
#index=createResample(matrix_train_caret$class, 5)
index=createResample(class, 5)
)
#caret::getModelInfo()[[modelName]]
if(FALSE){
fastModels2 <- c(
"slda",
#"lda2",
"lda",
"pam",
#"simpls",
"pls",
#"kernelpls",
#"widekernelpls",
"binda",
#"gamLoess",
"OneR",
"rpart2",
"svmLinear",
"rpart1SE",
"glm",
"gam",
"C5.0Tree",
"C5.0Rules"
)
}
#modelNames <- paste(fastModels2, collapse = "|")
modelNameVector <- strsplit(x = modelNames, split = "\\|")[[1]]
#set.seed(1)
#startTime <<- Sys.time()
#sink("/dev/null")
log <- capture.output({
model_list <- caretList(
x = matrix_train_caret2,
y = class,
#tuneLength = 10,
trControl = ctrl,
metric = ifelse(test = useRoc, yes = "ROC", no = "AUC"),
#preProc = c("center", "scale")
preProc = NULL,
methodList=modelNameVector
)
})
#sink()
## ROC per model
#unlist(lapply(X = model_list, FUN = function(x){max(x$results$ROC)}))
if(FALSE){
for(modelName in modelNameVector){
print(modelName)
tryCatch(expr = {
ctrl <- caret::trainControl(
#method = "none",
method = "repeatedcv",
number = 5,
repeats = 1,
classProbs = TRUE,
summaryFunction = caret::twoClassSummary,
verboseIter = FALSE,
allowParallel = FALSE
)
#caret::getModelInfo()[[modelName]]
#set.seed(1)
#startTime <<- Sys.time()
classifier <- caret::train(
#class ~ .,
#data = matrix_train_caret,
x = matrix_train_caret2,
y = class,
method = modelName,
tuneLength = 10,
trControl = ctrl,
metric = "ROC",
#preProc = c("center", "scale")
preProc = NULL
)
print(classifier$results$ROC)
},error = function(e){
print(paste("error", e))
},warning = function(w){
print(paste("warning", w))
}
)
}
}
#endTime <- Sys.time()
#rocs <- classifier$results$ROC
## check prediction correlations
#tmp <- modelCor(resamples(model_list))
#tmp[tmp < 0.97 & tmp > -0.97] <- 0
#tmp
if(FALSE){
## ensembl
greedy_ensemble <- caretEnsemble(
all.models = model_list,
metric = ifelse(test = useRoc, yes = "ROC", no = "AUC"),
trControl = trainControl(
number=5,
summaryFunction=twoClassSummary,
classProbs=TRUE
))
#summary(greedy_ensemble)
}
## stacking
#sink("/dev/null")
log <- capture.output({
stacked_ensemble <- caretStack(
model_list,
#method="glm",
method="gbm",
#method="rf",
metric = ifelse(test = useRoc, yes = "ROC", no = "AUC"),
trControl=trainControl(
method="boot",
number=5,
savePredictions="final",
classProbs=TRUE,
summaryFunction=twoClassSummary
)
)
})
#sink()
## Variable Importance
#varImp(object=model_gbm)
return(stacked_ensemble)
},
classify = function(classifier, matrix_test){
matrix_test_caret <- matrix_test
matrix_test_caret <- as.data.frame(as.matrix(matrix_test_caret))
## rename mass column names
colnamesHere <- colnames(matrix_test_caret)
neutralLosses <- grepl(x = colnamesHere, pattern = "^\\-\\d+(\\.\\d+)?$")
fragments <- grepl(x = colnamesHere, pattern = "^\\d+(\\.\\d+)?$")
colnamesHere[neutralLosses] <- gsub(x = colnamesHere[neutralLosses], pattern = "^-", replacement = "m")
colnamesHere[fragments] <- paste("p", colnamesHere[fragments], sep = "")
colnames(matrix_test_caret) <- colnamesHere
## https://www.analyticsvidhya.com/blog/2016/12/practical-guide-to-implement-machine-learning-with-caret-package-in-r-with-practice-problem/
#predictions<-predict.train(object=model_gbm,testSet[,predictors],type="raw")
#p <- as.data.frame(predict(model_list, newdata=matrix_test_caret))
#print(p)
#plsClasses <- predict(classifier, newdata = matrix_test)
#sink("/dev/null")
log <- capture.output({
plsProbs <- predict(classifier, newdata = matrix_test_caret, type = "prob")
})
#sink()
scores <- plsProbs #pred$predict[, "+", 1]
return(scores)
}
)
splsda_classifier <- list(
train = function(matrix_train, classes_pm_train){
if(FALSE){
posRows <- classes_pm_train=="+"
colSumPos <- Matrix::colSums(x = matrix_train[ posRows, ])
#colSumNeg <- Matrix::colSums(x = matrix_train[!posRows, ])
colFrequencyPos <- colSumPos / sum( posRows)
selectedColumns <- colFrequencyPos >= 0.05
matrix_train2 <- matrix_train
matrix_train2[, !selectedColumns] <- 0
}
rownames(matrix_train) <- seq_len(nrow(matrix_train))
#caret_splsda = mixOmics::splsda(X = dataFrame2, Y = groupLabels, ncomp = numberOfComponents, scale = FALSE)
classifier <- mixOmics::splsda(X = matrix_train, Y = classes_pm_train, scale = F, ncomp = 1)
return(classifier)
},
classify = function(classifier, matrix_test){
#pred <- predict(classifier, matrix_test, probability=TRUE)
#scores <- attr(pred, "probabilities")
#scores <- scores[, "+"]
colnames(matrix_test) <- as.character(colnames(matrix_test))
rownames(matrix_test) <- seq_len(nrow(matrix_test))
pred <- predict(classifier, matrix_test, dist = "max.dist")
scores <- pred$predict[, "+", 1]
return(scores)
}
)
svm_classifier <- list(
train = function(matrix_train, classes_pm_train){
posRows <- classes_pm_train=="+"
colSumPos <- Matrix::colSums(x = matrix_train[ posRows, ])
#colSumNeg <- Matrix::colSums(x = matrix_train[!posRows, ])
colFrequencyPos <- colSumPos / sum( posRows)
selectedColumns <- colFrequencyPos >= 0.05
matrix_train2 <- matrix_train
matrix_train2[, !selectedColumns] <- 0
#matrix_train3 <- cbind(matrix_train2, rep(x = 0, times = nrow(matrix_train2)))
#colnames(matrix_train3)[[ncol(matrix_train3)]] <- "class"
#matrix_train3[posRows, "class"] <- 1
#classifier <- svm(matrix_train2, factor(classes_pm_train), kernel='linear')
#parametersHere <- tune(svm, train.x=matrix_train2, train.y=factor(classes_pm_train), kernel="radial", ranges=list(cost=10^(-1:2), gamma=c(.05, .1, .25,.5,1,2)))
classifier <- svm(
x = matrix_train2, y = factor(classes_pm_train), probability=TRUE,
kernel="radial"#, cost=parametersHere$best.parameters$cost, gamma=parametersHere$best.parameters$gamma
)
#trctrl <- trainControl(method = "repeatedcv", number = 10, repeats = 3)
#svm_Linear <- caret::train(class ~., data = matrix_train3, method = "svmLinear", trControl=trctrl, preProcess = c("center", "scale"), tuneLength = 10)
#svm_model <- svm(class ~ ., data=matrix_train3)
#svm.model <- svm(matrix_train3[,"class"] ~., data=matrix_train3, kernel="linear")
return(classifier)
},
classify = function(classifier, matrix_test){
pred <- predict(classifier, matrix_test, probability=TRUE)
scores <- attr(pred, "probabilities")
scores <- scores[, "+"]
return(scores)
}
)
lda_classifier <- list(
train = function(matrix_train, classes_pm_train){
if(FALSE){
posRows <- classes_pm_train=="+"
colSumPos <- Matrix::colSums(x = matrix_train[ posRows, ])
#colSumNeg <- Matrix::colSums(x = matrix_train[!posRows, ])
colFrequencyPos <- colSumPos / sum( posRows)
selectedColumns <- colFrequencyPos >= 0.05
matrix_train2 <- matrix_train[, selectedColumns]
#matrix_train2[, !selectedColumns] <- 0
}
#matrix_train3 <- cbind(matrix_train2, rep(x = 0, times = nrow(matrix_train2)))
#colnames(matrix_train3)[[ncol(matrix_train3)]] <- "class"
#matrix_train3[posRows, "class"] <- 1
## train and predict
## Error variable 105 appears to be constant within groups
## Error in La.svd(x, nu, nv) : error code 1 from Lapack routine 'dgesdd'
#classifier <- lda(x = matrix_train, grouping=classes_pm_train, method = "t")
#classifier <- lda(x = matrix_train, grouping=classes_pm_train)
scalingFactor <- 1
repeatComputation <- TRUE
while(repeatComputation){
classifier <- tryCatch(
{
result <- lda(x = matrix_train * scalingFactor, grouping=classes_pm_train)
repeatComputation <- FALSE
return(result)
}, error = function(e) {
if(e == "Error in La.svd(x, nu, nv): error code 1 from Lapack routine 'dgesdd'\n"){
print(paste(e, scalingFactor))
scalingFactor <<- scalingFactor + 1
}
}
)
}
#model <- lda(x = scale(dist(matrix_train)), grouping=classes_pm_train)
#model2 <- lda(formula=classes_pm_train~., data=as.data.frame(as.matrix(matrix_train)))
return(classifier)
},
classify = function(classifier, matrix_test){
#matrix_test2 <- matrix_test[, selectedColumns]
prediction <- predict(object=classifier, newdata=matrix_test)
#predicted_classes_pm <- as.character(prediction$class)
#scores_pos <- prediction$posterior[, "+"]
#scores_neg <- prediction$posterior[, "-"]
#if(!ratio){
# scores <- scores_pos
#} else {
# scores <- scores_pos / scores_neg
#}
scores <- prediction$posterior[, "+"]
return(scores)
}
)
colSumsRatio_classifier <- list(
train = function(matrix_train, classes_pm_train){
posRows <- classes_pm_train=="+"
colSumPos <- Matrix::colSums(x = matrix_train[ posRows, ])
colSumNeg <- Matrix::colSums(x = matrix_train[!posRows, ])
pseudoCount <- 1
colSums_PosNeg <- log2( ((colSumPos+pseudoCount) / sum( posRows)) / ((colSumNeg+pseudoCount) / sum(!posRows)) )
#colSums_PosNeg <- ((colSumPos+pseudoCount) / sum( posRows)) / ((colSumNeg+pseudoCount) / sum(!posRows)) -1
colSums_PosNeg[colSums_PosNeg < 0] <- 0 ## only pos fragments
#colSums_PosNeg[abs(colnames(matrix_train)) < 1] <- 0 ## no precursor-NLs
#names(colSums_PosNeg) <- colnames(matrix_train)
## storage
colSums_PosNeg <- unname(colSums_PosNeg)
return(colSums_PosNeg)
},
classify = function(classifier, matrix_test){
## convert matrix format
dgTMatrix <- as(matrix_test, "dgTMatrix")
matrixRows <- dgTMatrix@i
matrixCols <- dgTMatrix@j
matrixVals <- dgTMatrix@x
stMatrix_test <- simple_triplet_matrix(
i = matrixRows + 1,
j = matrixCols + 1,
v = matrixVals,
nrow = nrow(matrix_test),
ncol = ncol(matrix_test)
)
## score test matrix row by row
scores <- rowapply_simple_triplet_matrix(x = stMatrix_test, FUN = function(x){
sum(x * classifier)
})
return(scores)
}
)
colSumsPos_classifier <- list(
train = function(matrix_train, classes_pm_train){
posRows <- classes_pm_train=="+"
colSumPos <- Matrix::colSums(x = matrix_train[ posRows, ])
colSumNeg <- Matrix::colSums(x = matrix_train[!posRows, ])
colSums_PosNeg <- (colSumPos / sum( posRows)) - (colSumNeg / sum(!posRows))
colSums_PosNeg[colSums_PosNeg < 0] <- 0 ## only pos fragments
#colSums_PosNeg[abs(colnames(matrix_train)) < 1] <- 0 ## no precursor-NLs
#names(colSums_PosNeg) <- colnames(matrix_train)
## storage
colSums_PosNeg <- unname(colSums_PosNeg)
return(colSums_PosNeg)
},
classify = function(classifier, matrix_test){
## convert matrix format
dgTMatrix <- as(matrix_test, "dgTMatrix")
matrixRows <- dgTMatrix@i
matrixCols <- dgTMatrix@j
matrixVals <- dgTMatrix@x
stMatrix_test <- simple_triplet_matrix(
i = matrixRows + 1,
j = matrixCols + 1,
v = matrixVals,
nrow = nrow(matrix_test),
ncol = ncol(matrix_test)
)
## score test matrix row by row
scores <- rowapply_simple_triplet_matrix(x = stMatrix_test, FUN = function(x){
sum(x * classifier)
})
return(scores)
}
)
colSumsPosOnly_classifier <- list(
train = function(matrix_train, classes_pm_train){
posRows <- classes_pm_train=="+"
colSumPos <- Matrix::colSums(x = matrix_train[ posRows, ])
#colSumNeg <- Matrix::colSums(x = matrix_train[!posRows, ])
colSums_PosNeg <- (colSumPos / sum( posRows))
#colSums_PosNeg <- (colSumPos / sum( posRows)) - (colSumNeg / sum(!posRows))
#colSums_PosNeg[colSums_PosNeg < 0] <- 0 ## only pos fragments
#colSums_PosNeg[abs(colnames(matrix_train)) < 1] <- 0 ## no precursor-NLs
#names(colSums_PosNeg) <- colnames(matrix_train)
## storage
colSums_PosNeg <- unname(colSums_PosNeg)
return(colSums_PosNeg)
},
classify = function(classifier, matrix_test){
## convert matrix format
dgTMatrix <- as(matrix_test, "dgTMatrix")
matrixRows <- dgTMatrix@i
matrixCols <- dgTMatrix@j
matrixVals <- dgTMatrix@x
stMatrix_test <- simple_triplet_matrix(
i = matrixRows + 1,
j = matrixCols + 1,
v = matrixVals,
nrow = nrow(matrix_test),
ncol = ncol(matrix_test)
)
## score test matrix row by row
scores <- rowapply_simple_triplet_matrix(x = stMatrix_test, FUN = function(x){
sum(x * classifier)
})
return(scores)
}
)
## \text{score}(s) = \sum_{f \in s} P(f|s\text{ is in class}) - P(f|s\text{ is in not class})
## P(f|s\text{ is in class}) = \displaystyle \frac{\displaystyle\sum_{s\in S_+} \delta(f \in s)}{|S_+|}
## P(f|s\text{ is not in class}) = \displaystyle \frac{\displaystyle\sum_{s\in S_-} \delta(f \in s)}{|S_-|}
colSums_classifier <- list(
train = function(matrix_train, classes_pm_train){
posRows <- classes_pm_train=="+"
colSumPos <- Matrix::colSums(x = matrix_train[ posRows, ])
colSumNeg <- Matrix::colSums(x = matrix_train[!posRows, ])
colSums_PosNeg <- (colSumPos / sum( posRows)) - (colSumNeg / sum(!posRows))
#colSums_PosNeg[abs(colnames(matrix_train)) < 1] <- 0 ## no precursor-NLs
#names(colSums_PosNeg) <- colnames(matrix_train)
## storage
colSums_PosNeg <- unname(colSums_PosNeg)
return(colSums_PosNeg)
},
classify = function(classifier, matrix_test){
## convert matrix format
dgTMatrix <- as(matrix_test, "dgTMatrix")
matrixRows <- dgTMatrix@i
matrixCols <- dgTMatrix@j
matrixVals <- dgTMatrix@x
stMatrix_test <- simple_triplet_matrix(
i = matrixRows + 1,
j = matrixCols + 1,
v = matrixVals,
nrow = nrow(matrix_test),
ncol = ncol(matrix_test)
)
## score test matrix row by row
scores <- rowapply_simple_triplet_matrix(x = stMatrix_test, FUN = function(x){
sum(x * classifier)
})
return(scores)
}
)
colSumsLog_classifier <- list(
train = function(matrix_train, classes_pm_train){
posRows <- classes_pm_train=="+"
colSumPos <- Matrix::colSums(x = matrix_train[ posRows, ]) + max(.Machine$double.neg.eps, .Machine$double.eps)#1/sum( posRows)#1E-6#1
colSumNeg <- Matrix::colSums(x = matrix_train[!posRows, ]) + max(.Machine$double.neg.eps, .Machine$double.eps)#1/sum(!posRows)#1E-6#1
colSums_PosNeg <- log(colSumPos / sum( posRows)) - log(colSumNeg / sum(!posRows))
#colSums_PosNeg[abs(colnames(matrix_train)) < 1] <- 0 ## no precursor-NLs
#names(colSums_PosNeg) <- colnames(matrix_train)
return(colSums_PosNeg)
},
classify = function(classifier, matrix_test){
## convert matrix format
dgTMatrix <- as(matrix_test, "dgTMatrix")
matrixRows <- dgTMatrix@i
matrixCols <- dgTMatrix@j
matrixVals <- dgTMatrix@x
stMatrix_test <- simple_triplet_matrix(
i = matrixRows + 1,
j = matrixCols + 1,
v = matrixVals,
nrow = nrow(matrix_test),
ncol = ncol(matrix_test)
)
## score test matrix row by row
scores <- rowapply_simple_triplet_matrix(x = stMatrix_test, FUN = function(x){
sum(x * classifier)
})
return(scores)
}
)
tensorflow_classifier <- list(
train = function(matrix_train, classes_pm_train, topology){
####################################################
## parameters
printProgress <- FALSE
## model parameters
numberOfMS2PeakGroups <- ncol(matrix_train)
#numLayers = 0
#numNeurons_l = NULL
#dropout_l = NULL
numLayers = topology$numLayers
numNeurons_l = topology$numNeurons_l
dropout_l = topology$dropout_l
numberOfInputNeurons = numberOfMS2PeakGroups
numberOfOutputNeurons = 2
## train parameters
meanCenter <- FALSE
maximumNumberOfTrainingIterationsWithoutImprovement <- 10
maximumNumberOfTrainingIterations <- 1000
learningRate <- 0.2
numberOfSpectra_train <- length(classes_pm_train)
numberOfMS2PeakGroupsHere <- ncol(matrix_train)
matrix_train <- as.matrix(matrix_train)
if(meanCenter)
matrix_train <- as.matrix(apply(X = matrix_train, MARGIN = 2, FUN = function(x){
x - mean(x = x)
}))
####################################################
## class weights
negToPosRatio <- sum(classes_pm_train=="-") / sum(classes_pm_train=="+")
resultMultiplier <- vector(mode = "numeric", length = numberOfSpectra_train)
resultMultiplier[classes_pm_train=="+"] <- negToPosRatio
resultMultiplier[classes_pm_train=="-"] <- 1.
## classes with weigths each of 1
classes_pm_train2 <- integer(length = numberOfSpectra_train)
classes_pm_train2[classes_pm_train == "+"] <- 1L
classes_pm_train2[classes_pm_train == "-"] <- 0L
classes_pm_train2 <- matrix(data = c(classes_pm_train2, 1L - classes_pm_train2), nrow = numberOfSpectra_train)
####################################################
## Create the model
#variableScope <- tf$VariableScope(reuse = TRUE)
variableScope <- tf$VariableScope(reuse = FALSE)
tf$reset_default_graph()
modelList <- createTFModel(numLayers = numLayers, numNeurons_l = numNeurons_l, dropout_l = dropout_l, numberOfInputNeurons = numberOfInputNeurons, numberOfOutputNeurons = numberOfOutputNeurons, variableScope=variableScope)
dropout <- modelList$dropout
x <- modelList$x
y <- modelList$y
y_ <- modelList$y_
dropoutTensorNames <- modelList$dropoutTensorNames
multiLayerPerceptronStructure <- modelList$multiLayerPerceptronStructure
##########################
## define accuracy and auc
correct_prediction <- tf$equal(tf$argmax(y, 1L), tf$argmax(y_, 1L), name = "correct_prediction")
accuracy <- tf$reduce_mean(tf$cast(correct_prediction, tf$float32), name = "accuracy")
auc <- tf$metrics$auc(labels = y_, predictions = y, name = "AUC")[[2]]
##########################
## Define loss and optimizer
resultMult <- tf$constant(value = c(negToPosRatio, 1), dtype = tf$float32, name = "many_to_one_multiplier")
cross_entropy <- tf$reduce_mean(-tf$reduce_sum(tf$multiply(tf$multiply(y_, log(y)), resultMult), reduction_indices=1L), name = "weighted_cross_entropy")
multiLayerPerceptronStructure[length(multiLayerPerceptronStructure)+1] <- paste("loss_function", "weighted_cross_entropy", sep = "=")
##########################
# regularization: This is a good beta value to start with
if(FALSE){
regularizer_beta = 0.01
regularizer = tf$nn$l2_loss(tf$concat(values = list(inputToHidden_weights,tf$transpose(hiddenToOutput_weights)), axis = 0L))
cross_entropy = tf$reduce_mean(cross_entropy + regularizer_beta * regularizer)
}
#train_step <- tf$train$AdamOptimizer(learning_rate=learningRate, name = "AdamOptimizer")$minimize(loss = cross_entropy)
train_step <- tf$train$GradientDescentOptimizer(learning_rate = learningRate, name = "GradientDescentOptimizer")$minimize(loss = cross_entropy)
multiLayerPerceptronStructure[length(multiLayerPerceptronStructure)+1] <- paste("optimizer", "GradientDescentOptimizer", sep = "=")
multiLayerPerceptronStructure[length(multiLayerPerceptronStructure)+1] <- paste("learning_rate", learningRate, sep = "=")
####################################################
## Create session and initialize variables
#if(!exists(x = "sess")) sess <- tf$InteractiveSession()
#if(!exists(x = "sess")) sess <- tf$Session()
sess <- tf$Session()
sess$run(tf$global_variables_initializer())
sess$run(tf$local_variables_initializer())
bestAccuracy <- -1
iterationOfBestAccuracy <- -1
startTime <- Sys.time()
accuracies <- numeric()
dropoutTensorNames <- paste(dropoutTensorNames, 0, sep = ":")
#list1 <- list(x = matrix_train)
#list2 <- list(x = matrix_train, y_ = classes_pm_train2)
list1 <- list()
list2 <- list()
list1[[x$name ]] = matrix_train
list2[[x$name ]] = matrix_train
list2[[y_$name]] = classes_pm_train2
if(dropout){
list3 <- list2
list1[dropoutTensorNames] <- 1.0
list2[dropoutTensorNames] <- 1.0
list3[dropoutTensorNames] <- 0.5
##feed_dict_train <- dict(x = matrix_train, y_ = classes_pm_train2, "hidden_layer0_dropout_tensor:0" = tf$constant(value = 1.0, dtype = tf$float32, shape = shape(1), name = "dropout_tmp"))
##feed_dict_train <- dict(x = matrix_train, y_ = classes_pm_train2, "hidden_layer0_dropout_tensor:0" = 1.0)
#feed_dict_train <- dict(x = matrix_train, y_ = classes_pm_train2, keep_prob = 1.0)
#feed_dict_train2 <- dict(x = matrix_train, keep_prob = 1.0)
#feed_dict_train3 <- dict(x = matrix_train, y_ = classes_pm_train2, keep_prob = 0.5)
feed_dict_train <- dict(list2)
feed_dict_train2 <- dict(list1)
feed_dict_train3 <- dict(list3)
} else {
feed_dict_train <- dict(list2)
feed_dict_train2 <- dict(list1)
feed_dict_train3 <- feed_dict_train
}
####################################################
## train
for (i in 1:maximumNumberOfTrainingIterations) {
train_accuracy <- accuracy$eval(feed_dict = feed_dict_train, session = sess)
train_cross_entropy <- cross_entropy$eval(feed_dict = feed_dict_train, session = sess)
train_auc <- auc$eval(feed_dict = feed_dict_train, session = sess)
accuracies[[length(accuracies) + 1]] <- train_accuracy
if(train_accuracy > bestAccuracy){
bestAccuracy <- train_accuracy
iterationOfBestAccuracy <- i
} else {
accuracyVariance <- var(tail(accuracies, 10))
#if(is.na(accuracyVariance)) accuracyVariance <- 1.
if(i - iterationOfBestAccuracy > maximumNumberOfTrainingIterationsWithoutImprovement & accuracyVariance < 1e-6)
break;
}
if(printProgress) cat(paste(
classIdx, "/", numberOfClasses,
"class", class,
"; step", i,
"; accuracy tr", round(train_accuracy, digits = 4),
"; cross entropy", round(train_cross_entropy, digits = 4),
"; auc", round(train_auc, digits = 4),
ifelse(iterationOfBestAccuracy == i, " Increase", ""),
"\n"
))
train_step$run(feed_dict = feed_dict_train, session = sess)
}
endTime <- Sys.time()
time <- difftime(endTime, startTime, units = "secs")[[1]]
multiLayerPerceptronStructure[length(multiLayerPerceptronStructure)+1] <- paste("number_of_training_steps", i, sep = "=")
multiLayerPerceptronStructure[length(multiLayerPerceptronStructure)+1] <- paste("training_accuracy", bestAccuracy, sep = "=")
multiLayerPerceptronStructure[length(multiLayerPerceptronStructure)+1] <- paste("training_time(s)", time, sep = "=")
## close session
#sess$close()
modelList$multiLayerPerceptronStructure <- multiLayerPerceptronStructure
resultList <- list(
session = sess,
modelList = modelList
)
return(resultList)
},
classify = function(classifier, matrix_test){
list <- classifier
sess <- list$session
dropout <- list$modelList$dropout
y <- list$modelList$y
x <- list$modelList$x
dropoutTensorNames <- list$modelList$dropoutTensorNames
numberOfSpectra_test <- nrow(matrix_test)
numberOfMS2PeakGroupsHere <- ncol(matrix_test)
meanCenter <- FALSE
matrix_test <- as.matrix(matrix_test)
if(meanCenter)
matrix_test <- as.matrix(apply(X = matrix_test, MARGIN = 2, FUN = function(x){
x - mean(x = x)
}))
dropoutTensorNames <- paste(dropoutTensorNames, 0, sep = ":")
listTmp <- list()
listTmp[[x$name]] = matrix_test
if(dropout){
listTmp[dropoutTensorNames] <- 1.0
feed_dict_test2 <- dict(listTmp)
} else {
feed_dict_test2 <- dict(listTmp)
}
####################################################
## metrics
#correct_predictions <- correct_prediction$eval(feed_dict = feed_dict_test, session = sess)
probabilities <- y$eval(feed_dict = feed_dict_test2, session = sess)
#predictions_test <- tf$argmax(input = y, axis = 1L, name = "predictions_test")$eval(feed_dict = feed_dict_test2, session = sess)#, keep_prob = 1.0), session = sess)
#predictions_test <- as.integer(predictions_test)
predicted_scores <- probabilities[, 1]
#predicted_scores <- probabilities[, 1] - probabilities[, 2]
#predicted_scores <- probabilities[, 1] / probabilities[, 2]
sess$close()
return(predicted_scores)
}
)
######################################################################################
## classifiers_old
predict_LDA <- function(matrix_train, classes_pm_train, matrix_test, classes_pm_test, ratio){
## train and predict
model <- lda(x = matrix_train, grouping=classes_pm_train)
#model <- lda(x = scale(dist(matrix_train)), grouping=classes_pm_train)
#model2 <- lda(formula=classes_pm_train~., data=as.data.frame(as.matrix(matrix_train)))
prediction <- predict(object=model, newdata=matrix_test)
predicted_classes_pm <- as.character(prediction$class)
scores_pos <- prediction$posterior[, "+"]
scores_neg <- prediction$posterior[, "-"]
if(!ratio){
scores <- scores_pos
} else {
scores <- scores_pos / scores_neg
}
return(scores)
#return(predicted_classes_pm)
}
predict_Correlation <- function(matrix_train, classes_pm_train, matrix_test, classes_pm_test, ratio, corMethod = c("pearson", "kendall", "spearman"), linkage = c("single", "average", "centroid")){
if(corMethod == "kendall")
stop("Operation not supported")
correlations <- cor(x = t(as.matrix(matrix_test)), y = t(as.matrix(matrix_train)), method = corMethod)
if(FALSE){
## classes
switch(linkage,
"single"={
## single linkage
predicted_classes_pm <- unlist(apply(X = correlations, MARGIN = 1, FUN = function(x){
if(all(is.na(x)))
return("-")
else{
maxIdx <- which.max(x)
class <- classes_pm_train[maxIdx]
return(class)
}
}))
},
"average"={
## average linkage
predicted_classes_pm <- unlist(apply(X = correlations, MARGIN = 1, FUN = function(x){
if(all(is.na(x)))
return("-")
else{
plus <- x[classes_pm_train=="+"]
minus <- x[classes_pm_train=="-"]
if(mean(plus, na.rm = TRUE) > mean(minus, na.rm = TRUE))
#if(max(plus) > max(minus))
#if(mean(plus) > mean(minus))
return("+")
else
return("-")
}