/
CLOUD.r
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CLOUD.r
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library(dplyr)
library(readr)
library(mvtnorm) # For multivariate Gaussian distribution
library(moments) # For generating moments
library(keras)
library(tensorflow)
GenerateTrainingData <- function(m, Nk, singlecopy_filename, duplicate_filename, training_prefix) {
# Could make following into function parameters
propPvC = 0.5 # For subfunctionalization
logThetaMin = -4
logThetaMax = 4
logAlphaMin = 0
logAlphaMax = 3
logSigmaSqMin = -2
logSigmaSqMax = 3
scenarios <- c("cons", "neoparent", "neochild", "sub", "spec")
tissues <- seq(1, m)
training_data <- matrix(0, nrow = 5*Nk, ncol = 2 + 8*m) # m tissues with 3 expressionv alues and 5 parameters each, tPC, and class label
set_of_tPC <- as_tibble(read.table(duplicate_filename, header = TRUE)) %>%
mutate(tPC = TPC / TPCA) %>%
select(tPC)
set_of_tPC <- set_of_tPC$tPC
for(sindex in 1:length(scenarios)) {
scenario <- scenarios[sindex]
for(i in 1:Nk) {
tPCA <- 1
tPC <- set_of_tPC[sample(1:length(set_of_tPC), 1)]
alpha <- 10^runif(m, min = logAlphaMin, max = logAlphaMax)
sigmaSq <- 10^runif(m, min = logSigmaSqMin, max = logSigmaSqMax)
thetaP <- c()
thetaC <- c()
thetaA <- c()
if(scenario == "cons") { # CONSERVATION
thetaA <- runif(m, min = logThetaMin, max = logThetaMax)
thetaP <- thetaA
thetaC <- thetaA
}
else if(scenario == "neoparent") { # NEO(P)
thetaA <- runif(m, min = logThetaMin, max = logThetaMax)
thetaP <- runif(m, min = logThetaMin, max = logThetaMax)
thetaC <- thetaA
}
else if(scenario == "neochild") { # NEO(C)
thetaA <- runif(m, min = logThetaMin, max = logThetaMax)
thetaC <- runif(m, min = logThetaMin, max = logThetaMax)
thetaP <- thetaA
}
else if(scenario == "sub") { # SUBFUNCTIONALIZATION
thetaP <- runif(m, min = logThetaMin, max = logThetaMax)
thetaC <- runif(m, min = logThetaMin, max = logThetaMax)
thetaA <- propPvC * thetaP + (1 - propPvC) * thetaC
}
else if(scenario == "spec") { # SPECIALIZATION
thetaA <- runif(m, min = logThetaMin, max = logThetaMax)
thetaP <- runif(m, min = logThetaMin, max = logThetaMax)
thetaC <- runif(m, min = logThetaMin, max = logThetaMax)
}
expression_vec <- c()
mu <- rep(0, 3)
CovMat <- matrix(0, nrow = 3, ncol = 3)
for(j in 1:m) {
mu[1] = (1 - exp(-alpha[j] * tPC)) * thetaP[j] + exp(-alpha[j] * tPC) * thetaA[j]
mu[2] = (1 - exp(-alpha[j] * tPC)) * thetaC[j] + exp(-alpha[j] * tPC) * thetaA[j]
mu[3] = thetaA[j]
CovMat[1, 1] = sigmaSq[j] / (2 * alpha[j])
CovMat[2, 2] = CovMat[1, 1]
CovMat[3, 3] = CovMat[1, 1]
CovMat[1, 2] = exp(-2 * alpha[j] * tPC) * sigmaSq[j] / (2 * alpha[j])
CovMat[2, 1] = CovMat[1, 2]
CovMat[1, 3] = exp(-2 * alpha[j] * tPCA) * sigmaSq[j] / (2 * alpha[j])
CovMat[3, 1] = CovMat[1, 3]
CovMat[2, 3] = CovMat[1, 3]
CovMat[3, 2] = CovMat[1, 3]
expression_vec <- c(expression_vec, rmvnorm(1, mean = mu, sigma = CovMat))
}
rowIndex <- (sindex - 1)*Nk + i # get the row of the training dataset
training_data[rowIndex, 1] = sindex
training_data[rowIndex, 2] = tPC
for(j in 1:(3*m)) {
training_data[rowIndex, 2 + j] = expression_vec[j]
}
for(j in 1:m) {
training_data[rowIndex, 2 + 3*m + j] = thetaP[j]
}
for(j in 1:m) {
training_data[rowIndex, 2 + 4*m + j] = thetaC[j]
}
for(j in 1:m) {
training_data[rowIndex, 2 + 5*m + j] = thetaA[j]
}
for(j in 1:m) {
training_data[rowIndex, 2 + 6*m + j] = log10(alpha[j])
}
for(j in 1:m) {
training_data[rowIndex, 2 + 7*m + j] = log10(sigmaSq[j])
}
}
}
column_labels <- c("Class", "tPC")
for(j in 1:m) {
column_labels <- c(column_labels, paste("eP", j, sep = ""), paste("eC", j, sep = ""), paste("eA", j, sep = ""))
}
for(j in 1:m) {
column_labels <- c(column_labels, paste("ThetaP", j, sep = ""))
}
for(j in 1:m) {
column_labels <- c(column_labels, paste("ThetaC", j, sep = ""))
}
for(j in 1:m) {
column_labels <- c(column_labels, paste("ThetaA", j, sep = ""))
}
for(j in 1:m) {
column_labels <- c(column_labels, paste("LogAlpha", j, sep = ""))
}
for(j in 1:m) {
column_labels <- c(column_labels, paste("LogSigmaSq", j, sep = ""))
}
colnames(training_data) <- column_labels
write.table(training_data, file = paste(training_prefix, ".data", sep = ""), row.names = FALSE)
GenerateFeatures(m, singlecopy_filename, paste(training_prefix, ".data", sep = ""), paste(training_prefix, ".features", sep = ""))
GenerateClassifierResponse(paste(training_prefix, ".data", sep = ""), paste(training_prefix, ".classes", sep = ""))
GeneratePredictorResponse(paste(training_prefix, ".data", sep = ""), paste(training_prefix, ".responses", sep = ""))
}
GenerateFeatures <- function(m, singlecopy_filename, input_filename, feature_filename) {
minexp = 1e-4
errorexp = 1e-5
single <- read.table(singlecopy_filename, header = TRUE)
for(i in 1:nrow(single)) {
for(j in 1:ncol(single)) {
single[i,j] <- log10(single[i,j] + minexp + rnorm(1, 0, errorexp)) # Transform data to log scale, accounting for expression of 0
}
}
eS1S2dist <- sqrt( rowSums((single[,1:m] - single[,(m+1):(2*m)])^2) ) # Euclidean distance between single-copy expression profiles
maxS1S2dist <- max(abs(eS1S2dist))
eS1S2cor <- c()
for(i in 1:nrow(single)) {
eS1S2cor[i] = cor(as.numeric(single[i,1:m]), as.numeric(single[i,(m+1):(2*m)]), method = "pearson")
}
rm(single)
features <- as_tibble( as.matrix(read.table(input_filename, header = TRUE)) ) %>%
select(tPC, starts_with("eP"), starts_with("eC"), starts_with("eA"))
eSumPC <- select(features, starts_with("eP")) + select(features, starts_with("eC"))
colnames(eSumPC) <- paste("eSumPC", seq(1, m), sep = "")
features <- bind_cols(features, as_tibble(eSumPC))
rm(eSumPC)
eDistPvC <- sqrt( rowSums( ( select(features, starts_with("eP")) - select(features, starts_with("eC")) )^2 ) ) # Euclidean distance between P and C
eDistPvA <- sqrt( rowSums( ( select(features, starts_with("eP")) - select(features, starts_with("eA")) )^2 ) ) # Euclidean distance between P and A
eDistCvA <- sqrt( rowSums( ( select(features, starts_with("eC")) - select(features, starts_with("eA")) )^2 ) ) # Euclidean distance between C and A
eDistPCvA <- sqrt( rowSums( ( select(features, starts_with("eSumPC")) - select(features, starts_with("eA")) )^2 ) ) # Euclidean distance between PC and A
features <- mutate(features, DistPvC = eDistPvC, DistPvA = eDistPvA, DistCvA = eDistCvA, DistPCvA = eDistPCvA)
rm(eDistPvC)
rm(eDistPvA)
rm(eDistCvA)
rm(eDistPCvA)
features <- features %>%
mutate(PBS_P = (DistPvC + DistPvA - DistCvA)/2) %>%
mutate(PBS_C = (DistPvC + DistCvA - DistPvA)/2) %>%
mutate(PBS_At = (DistPvA + DistCvA - DistPvC)/2) %>%
rowwise() %>%
mutate(RankDistPvC = mean(eS1S2dist < DistPvC)) %>%
mutate(RankDistPvA = mean(eS1S2dist < DistPvA)) %>%
mutate(RankDistCvA = mean(eS1S2dist < DistCvA)) %>%
mutate(RankDistPCvA = mean(eS1S2dist < DistPCvA)) %>%
mutate(DistPvC_m1 = moment((eS1S2dist - DistPvC) / maxS1S2dist, order = 1, central = FALSE, absolute = FALSE)) %>%
mutate(DistPvA_m1 = moment((eS1S2dist - DistPvA) / maxS1S2dist, order = 1, central = FALSE, absolute = FALSE)) %>%
mutate(DistCvA_m1 = moment((eS1S2dist - DistCvA) / maxS1S2dist, order = 1, central = FALSE, absolute = FALSE)) %>%
mutate(DistPCvA_m1 = moment((eS1S2dist - DistPCvA) / maxS1S2dist, order = 1, central = FALSE, absolute = FALSE)) %>%
mutate(DistPvC_m2 = moment((eS1S2dist - DistPvC) / maxS1S2dist, order = 2, central = FALSE, absolute = FALSE)) %>%
mutate(DistPvA_m2 = moment((eS1S2dist - DistPvA) / maxS1S2dist, order = 2, central = FALSE, absolute = FALSE)) %>%
mutate(DistCvA_m2 = moment((eS1S2dist - DistCvA) / maxS1S2dist, order = 2, central = FALSE, absolute = FALSE)) %>%
mutate(DistPCvA_m2 = moment((eS1S2dist - DistPCvA) / maxS1S2dist, order = 2, central = FALSE, absolute = FALSE)) %>%
mutate(DistPvC_m3 = moment((eS1S2dist - DistPvC) / maxS1S2dist, order = 3, central = FALSE, absolute = FALSE)) %>%
mutate(DistPvA_m3 = moment((eS1S2dist - DistPvA) / maxS1S2dist, order = 3, central = FALSE, absolute = FALSE)) %>%
mutate(DistCvA_m3 = moment((eS1S2dist - DistCvA) / maxS1S2dist, order = 3, central = FALSE, absolute = FALSE)) %>%
mutate(DistPCvA_m3 = moment((eS1S2dist - DistPCvA) / maxS1S2dist, order = 3, central = FALSE, absolute = FALSE)) %>%
mutate(DistPvC_m4 = moment((eS1S2dist - DistPvC) / maxS1S2dist, order = 4, central = FALSE, absolute = FALSE)) %>%
mutate(DistPvA_m4 = moment((eS1S2dist - DistPvA) / maxS1S2dist, order = 4, central = FALSE, absolute = FALSE)) %>%
mutate(DistCvA_m4 = moment((eS1S2dist - DistCvA) / maxS1S2dist, order = 4, central = FALSE, absolute = FALSE)) %>%
mutate(DistPCvA_m4 = moment((eS1S2dist - DistPCvA) / maxS1S2dist, order = 4, central = FALSE, absolute = FALSE)) %>%
mutate(DistPvC_m5 = moment((eS1S2dist - DistPvC) / maxS1S2dist, order = 5, central = FALSE, absolute = FALSE)) %>%
mutate(DistPvA_m5 = moment((eS1S2dist - DistPvA) / maxS1S2dist, order = 5, central = FALSE, absolute = FALSE)) %>%
mutate(DistCvA_m5 = moment((eS1S2dist - DistCvA) / maxS1S2dist, order = 5, central = FALSE, absolute = FALSE)) %>%
mutate(DistPCvA_m5 = moment((eS1S2dist - DistPCvA) / maxS1S2dist, order = 5, central = FALSE, absolute = FALSE)) %>%
mutate(DistPvC_m6 = moment((eS1S2dist - DistPvC) / maxS1S2dist, order = 6, central = FALSE, absolute = FALSE)) %>%
mutate(DistPvA_m6 = moment((eS1S2dist - DistPvA) / maxS1S2dist, order = 6, central = FALSE, absolute = FALSE)) %>%
mutate(DistCvA_m6 = moment((eS1S2dist - DistCvA) / maxS1S2dist, order = 6, central = FALSE, absolute = FALSE)) %>%
mutate(DistPCvA_m6 = moment((eS1S2dist - DistPCvA) / maxS1S2dist, order = 6, central = FALSE, absolute = FALSE)) %>%
mutate(DistPvC_m7 = moment((eS1S2dist - DistPvC) / maxS1S2dist, order = 7, central = FALSE, absolute = FALSE)) %>%
mutate(DistPvA_m7 = moment((eS1S2dist - DistPvA) / maxS1S2dist, order = 7, central = FALSE, absolute = FALSE)) %>%
mutate(DistCvA_m7 = moment((eS1S2dist - DistCvA) / maxS1S2dist, order = 7, central = FALSE, absolute = FALSE)) %>%
mutate(DistPCvA_m7 = moment((eS1S2dist - DistPCvA) / maxS1S2dist, order = 7, central = FALSE, absolute = FALSE)) %>%
mutate(DistPvC_m8 = moment((eS1S2dist - DistPvC) / maxS1S2dist, order = 8, central = FALSE, absolute = FALSE)) %>%
mutate(DistPvA_m8 = moment((eS1S2dist - DistPvA) / maxS1S2dist, order = 8, central = FALSE, absolute = FALSE)) %>%
mutate(DistCvA_m8 = moment((eS1S2dist - DistCvA) / maxS1S2dist, order = 8, central = FALSE, absolute = FALSE)) %>%
mutate(DistPCvA_m8 = moment((eS1S2dist - DistPCvA) / maxS1S2dist, order = 8, central = FALSE, absolute = FALSE)) %>%
ungroup()
eCorPvC <- c()
eCorPvA <- c()
eCorCvA <- c()
eCorPCvA <- c()
# Pearson correlations
for(i in 1:nrow(features)) {
eCorPvC[i] <- cor( as.numeric(select(features[i,], starts_with("eP"))), as.numeric(select(features[i,], starts_with("eC"))), method = "pearson" )
eCorPvA[i] <- cor( as.numeric(select(features[i,], starts_with("eP"))), as.numeric(select(features[i,], starts_with("eA"))), method = "pearson" )
eCorCvA[i] <- cor( as.numeric(select(features[i,], starts_with("eC"))), as.numeric(select(features[i,], starts_with("eA"))), method = "pearson" )
eCorPCvA[i] <- cor( as.numeric(select(features[i,], starts_with("eSumPC"))), as.numeric(select(features[i,], starts_with("eA"))), method = "pearson" )
}
features <- mutate(features, CorPvC = eCorPvC, CorPvA = eCorPvA, CorCvA = eCorCvA, CorPCvA = eCorPCvA)
rm(eCorPvC)
rm(eCorPvA)
rm(eCorCvA)
rm(eCorPCvA)
features <- features %>%
rowwise() %>%
mutate(RankCorPvC = mean(eS1S2cor < CorPvC)) %>%
mutate(RankCorPvA = mean(eS1S2cor < CorPvA)) %>%
mutate(RankCorCvA = mean(eS1S2cor < CorCvA)) %>%
mutate(RankCorPCvA = mean(eS1S2cor < CorPCvA)) %>%
mutate(CorPvC_m1 = moment(eS1S2cor - CorPvC, order = 1, central = FALSE, absolute = FALSE)) %>%
mutate(CorPvA_m1 = moment(eS1S2cor - CorPvA, order = 1, central = FALSE, absolute = FALSE)) %>%
mutate(CorCvA_m1 = moment(eS1S2cor - CorCvA, order = 1, central = FALSE, absolute = FALSE)) %>%
mutate(CorPCvA_m1 = moment(eS1S2cor - CorPCvA, order = 1, central = FALSE, absolute = FALSE)) %>%
mutate(CorPvC_m2 = moment(eS1S2cor - CorPvC, order = 2, central = FALSE, absolute = FALSE)) %>%
mutate(CorPvA_m2 = moment(eS1S2cor - CorPvA, order = 2, central = FALSE, absolute = FALSE)) %>%
mutate(CorCvA_m2 = moment(eS1S2cor - CorCvA, order = 2, central = FALSE, absolute = FALSE)) %>%
mutate(CorPCvA_m2 = moment(eS1S2cor - CorPCvA, order = 2, central = FALSE, absolute = FALSE)) %>%
mutate(CorPvC_m3 = moment(eS1S2cor - CorPvC, order = 3, central = FALSE, absolute = FALSE)) %>%
mutate(CorPvA_m3 = moment(eS1S2cor - CorPvA, order = 3, central = FALSE, absolute = FALSE)) %>%
mutate(CorCvA_m3 = moment(eS1S2cor - CorCvA, order = 3, central = FALSE, absolute = FALSE)) %>%
mutate(CorPCvA_m3 = moment(eS1S2cor - CorPCvA, order = 3, central = FALSE, absolute = FALSE)) %>%
mutate(CorPvC_m4 = moment(eS1S2cor - CorPvC, order = 4, central = FALSE, absolute = FALSE)) %>%
mutate(CorPvA_m4 = moment(eS1S2cor - CorPvA, order = 4, central = FALSE, absolute = FALSE)) %>%
mutate(CorCvA_m4 = moment(eS1S2cor - CorCvA, order = 4, central = FALSE, absolute = FALSE)) %>%
mutate(CorPCvA_m4 = moment(eS1S2cor - CorPCvA, order = 4, central = FALSE, absolute = FALSE)) %>%
mutate(CorPvC_m5 = moment(eS1S2cor - CorPvC, order = 5, central = FALSE, absolute = FALSE)) %>%
mutate(CorPvA_m5 = moment(eS1S2cor - CorPvA, order = 5, central = FALSE, absolute = FALSE)) %>%
mutate(CorCvA_m5 = moment(eS1S2cor - CorCvA, order = 5, central = FALSE, absolute = FALSE)) %>%
mutate(CorPCvA_m5 = moment(eS1S2cor - CorPCvA, order = 5, central = FALSE, absolute = FALSE)) %>%
mutate(CorPvC_m6 = moment(eS1S2cor - CorPvC, order = 6, central = FALSE, absolute = FALSE)) %>%
mutate(CorPvA_m6 = moment(eS1S2cor - CorPvA, order = 6, central = FALSE, absolute = FALSE)) %>%
mutate(CorCvA_m6 = moment(eS1S2cor - CorCvA, order = 6, central = FALSE, absolute = FALSE)) %>%
mutate(CorPCvA_m6 = moment(eS1S2cor - CorPCvA, order = 6, central = FALSE, absolute = FALSE)) %>%
mutate(CorPvC_m7 = moment(eS1S2cor - CorPvC, order = 7, central = FALSE, absolute = FALSE)) %>%
mutate(CorPvA_m7 = moment(eS1S2cor - CorPvA, order = 7, central = FALSE, absolute = FALSE)) %>%
mutate(CorCvA_m7 = moment(eS1S2cor - CorCvA, order = 7, central = FALSE, absolute = FALSE)) %>%
mutate(CorPCvA_m7 = moment(eS1S2cor - CorPCvA, order = 7, central = FALSE, absolute = FALSE)) %>%
mutate(CorPvC_m8 = moment(eS1S2cor - CorPvC, order = 8, central = FALSE, absolute = FALSE)) %>%
mutate(CorPvA_m8 = moment(eS1S2cor - CorPvA, order = 8, central = FALSE, absolute = FALSE)) %>%
mutate(CorCvA_m8 = moment(eS1S2cor - CorCvA, order = 8, central = FALSE, absolute = FALSE)) %>%
mutate(COrPCvA_m8 = moment(eS1S2cor - CorPCvA, order = 8, central = FALSE, absolute = FALSE)) %>%
ungroup() %>%
as.matrix()
write.table(features, file = feature_filename, row.names = FALSE)
}
GenerateClassifierResponse <- function(input_filename, response_filename) {
training_data <- as_tibble( as.matrix(read.table(input_filename, header = TRUE)) ) %>%
select(Class) %>%
as.matrix()
response <- matrix(0, nrow = nrow(training_data), ncol = 5)
for(i in 1:nrow(training_data)) {
response[i, training_data[i,1]] = 1
}
colnames(response) <- c("cons", "neoparent", "neochild", "sub", "spec")
write.table(response, file = response_filename, row.names = FALSE)
}
GeneratePredictorResponse <- function(input_filename, response_filename) {
training_data <- as_tibble( as.matrix(read.table(input_filename, header = TRUE)) ) %>%
select(starts_with("theta"), starts_with("LogAlpha"), starts_with("LogSigma")) %>%
as.matrix()
write.table(training_data, file = response_filename, row.names = FALSE)
}
ClassifierCV <- function(m, batchsize, num_epochs, log_lambda_min, log_lambda_max, num_lambda, gamma_min, gamma_max, num_gamma, training_prefix) {
CV <- 5
lambdas <- 10^seq(log_lambda_min, log_lambda_max, length = num_lambda)
gammas <- seq(gamma_min, gamma_max, length = num_gamma)
X <- as.matrix(read.table(paste(training_prefix, ".features", sep = ""), header = TRUE))
Y <- as.matrix(read.table(paste(training_prefix, ".classes", sep = ""), header = TRUE))
# standardize the input for training
X_means <- colMeans(X)
X_sds <- apply(X, 2, sd)
for(j in 1:ncol(X)) {
X[,j] = (X[,j] - X_means[j]) / X_sds[j]
}
# CV-fold cross validation
val_loss <- array(0, dim = c(length(gammas), length(lambdas)))
# Randomly choose balanced training/validation sets per fold
foldid_cons <- sample(rep(seq(CV), length = nrow(X)/5))
foldid_neoparent <- sample(rep(seq(CV), length = nrow(X)/5))
foldid_neochild <- sample(rep(seq(CV), length = nrow(X)/5))
foldid_sub <- sample(rep(seq(CV), length = nrow(X)/5))
foldid_spec <- sample(rep(seq(CV), length = nrow(X)/5))
foldid <- c(foldid_cons, foldid_neoparent, foldid_neochild, foldid_sub, foldid_spec)
rm(foldid_cons)
rm(foldid_neoparent)
rm(foldid_neochild)
rm(foldid_sub)
rm(foldid_spec)
# Perform K-fold CV, where K = CV
for(curr_fold in 1:CV) {
Xval <- X[foldid == curr_fold, ]
Xtrain <- X[foldid != curr_fold, ]
Yval <- Y[foldid == curr_fold, ]
Ytrain <- Y[foldid != curr_fold, ]
# standardize the input for train and val based on train
temp_means <- colMeans(Xtrain)
temp_sds <- apply(Xtrain, 2, sd)
for(j in 1:ncol(Xtrain)) {
Xtrain[,j] = (Xtrain[,j] - temp_means[j]) / temp_sds[j]
Xval[,j] = (Xval[,j] - temp_means[j]) / temp_sds[j]
}
for(i in 1:length(gammas)) {
for(j in 1:length(lambdas)) {
model <- keras_model_sequential()
model %>%
layer_dense(units = 256,
activation = 'relu',
kernel_regularizer = regularizer_l1_l2(l1 = gammas[i] * lambdas[j], l2 = (1 - gammas[i]) * lambdas[j]),
input_shape = c(ncol(Xtrain))) %>%
layer_dense(units = 128,
activation = 'relu',
kernel_regularizer = regularizer_l1_l2(l1 = gammas[i] * lambdas[j], l2 = (1 - gammas[i]) * lambdas[j])) %>%
layer_dense(units = 5,
activation = 'softmax',
kernel_regularizer = regularizer_l1_l2(l1 = gammas[i] * lambdas[j], l2 = (1 - gammas[i]) * lambdas[j]))
model %>%
compile(loss = 'categorical_crossentropy',
optimizer = optimizer_adam(),
metrics = c('categorical_crossentropy'))
history <- model %>%
fit(Xtrain, Ytrain,
epochs = num_epochs,
batch_size = batchsize,
validation_data = list(Xval, Yval),
verbose = 0) # verbose = 0 ensures it is silent
val_loss[i,j] <- val_loss[i,j] + as.data.frame(history) %>%
filter(data == "validation", metric == "categorical_crossentropy") %>%
select(value) %>%
min()
}
}
rm(Xval)
rm(Xtrain)
rm(Yval)
rm(Ytrain)
}
val_loss <- val_loss / CV
gamma_opt = gammas[ which(val_loss == min(val_loss), arr.ind = TRUE)[1] ]
lambda_opt = lambdas[ which(val_loss == min(val_loss), arr.ind = TRUE)[2] ]
cv_results <- matrix(0, 1, 3)
cv_results[,1] = min(val_loss)
cv_results[,2] = gamma_opt
cv_results[,3] = lambda_opt
colnames(cv_results) <- c("Loss", "Gamma", "Lambda")
write.table(cv_results, file = paste(training_prefix, ".classifier_cv", sep = ""), row.names = FALSE)
ClassifierFit(m, batchsize, num_epochs, training_prefix)
}
PredictorCV <- function(m, batchsize, num_epochs, log_lambda_min, log_lambda_max, num_lambda, gamma_min, gamma_max, num_gamma, training_prefix) {
CV <- 5
lambdas <- 10^seq(log_lambda_min, log_lambda_max, length = num_lambda)
gammas <- seq(gamma_min, gamma_max, length = num_gamma)
X <- as.matrix(read.table(paste(training_prefix, ".features", sep = ""), header = TRUE))
Y <- as.matrix(read.table(paste(training_prefix, ".responses", sep = ""), header = TRUE))
# standardize the input for training
X_means <- colMeans(X)
X_sds <- apply(X, 2, sd)
Y_means <- colMeans(Y)
Y_sds <- apply(Y, 2, sd)
for(j in 1:ncol(X)) {
X[,j] = (X[,j] - X_means[j]) / X_sds[j]
}
for(j in 1:ncol(Y)) {
Y[,j] = (Y[,j] - Y_means[j]) / Y_sds[j]
}
# CV-fold cross validation
val_loss <- array(0, dim = c(length(gammas), length(lambdas)))
# Randomly choose balanced training/validation sets per fold
foldid_cons <- sample(rep(seq(CV), length = nrow(X)/5))
foldid_neoparent <- sample(rep(seq(CV), length = nrow(X)/5))
foldid_neochild <- sample(rep(seq(CV), length = nrow(X)/5))
foldid_sub <- sample(rep(seq(CV), length = nrow(X)/5))
foldid_spec <- sample(rep(seq(CV), length = nrow(X)/5))
foldid <- c(foldid_cons, foldid_neoparent, foldid_neochild, foldid_sub, foldid_spec)
rm(foldid_cons)
rm(foldid_neoparent)
rm(foldid_neochild)
rm(foldid_sub)
rm(foldid_spec)
# Perform K-fold CV, where K = CV
for(curr_fold in 1:CV) {
Xval <- X[foldid == curr_fold, ]
Xtrain <- X[foldid != curr_fold, ]
Yval <- Y[foldid == curr_fold, ]
Ytrain <- Y[foldid != curr_fold, ]
# standardize the input for train and val based on train
temp_Xmeans <- colMeans(Xtrain)
temp_Xsds <- apply(Xtrain, 2, sd)
temp_Ymeans <- colMeans(Ytrain)
temp_Ysds <- apply(Ytrain, 2, sd)
for(j in 1:ncol(Xtrain)) {
Xtrain[,j] = (Xtrain[,j] - temp_Xmeans[j]) / temp_Xsds[j]
Xval[,j] = (Xval[,j] - temp_Xmeans[j]) / temp_Xsds[j]
}
for(j in 1:ncol(Ytrain)) {
Ytrain[,j] = (Ytrain[,j] - temp_Ymeans[j]) / temp_Ysds[j]
Yval[,j] = (Yval[,j] - temp_Ymeans[j]) / temp_Ysds[j]
}
for(i in 1:length(gammas)) {
for(j in 1:length(lambdas)) {
model <- keras_model_sequential()
model %>%
layer_dense(units = 256,
activation = 'relu',
kernel_regularizer = regularizer_l1_l2(l1 = gammas[i] * lambdas[j], l2 = (1 - gammas[i]) * lambdas[j]),
input_shape = c(ncol(Xtrain))) %>%
layer_dense(units = 128,
activation = 'relu',
kernel_regularizer = regularizer_l1_l2(l1 = gammas[i] * lambdas[j], l2 = (1 - gammas[i]) * lambdas[j])) %>%
layer_dense(units = ncol(Ytrain),
activation = 'linear',
kernel_regularizer = regularizer_l1_l2(l1 = gammas[i] * lambdas[j], l2 = (1 - gammas[i]) * lambdas[j]))
model %>%
compile(loss = 'mean_squared_error',
optimizer = optimizer_adam(),
metrics = c('mean_squared_error'))
history <- model %>%
fit(Xtrain, Ytrain,
epochs = num_epochs,
batch_size = batchsize,
validation_data = list(Xval, Yval),
verbose = 0) # verbose = 0 ensures it is silent
val_loss[i,j] <- val_loss[i,j] + as.data.frame(history) %>%
filter(data == "validation", metric == "mean_squared_error") %>%
select(value) %>%
min()
}
}
rm(Xval)
rm(Xtrain)
rm(Yval)
rm(Ytrain)
}
val_loss <- val_loss / CV
gamma_opt = gammas[ which(val_loss == min(val_loss), arr.ind = TRUE)[1] ]
lambda_opt = lambdas[ which(val_loss == min(val_loss), arr.ind = TRUE)[2] ]
cv_results <- matrix(0, 1, 3)
cv_results[,1] = min(val_loss)
cv_results[,2] = gamma_opt
cv_results[,3] = lambda_opt
colnames(cv_results) <- c("Loss", "Gamma", "Lambda")
write.table(cv_results, file = paste(training_prefix, ".predictor_cv", sep = ""), row.names = FALSE)
PredictorFit(m, batchsize, num_epochs, training_prefix)
}
ClassifierFit <- function(m, batchsize, num_epochs, training_prefix) {
cv_results <- read.table(paste(training_prefix, ".classifier_cv", sep = ""), header = TRUE)
gamma_opt <- cv_results$Gamma
lambda_opt <- cv_results$Lambda
X <- as.matrix(read.table(paste(training_prefix, ".features", sep = ""), header = TRUE))
Y <- as.matrix(read.table(paste(training_prefix, ".classes", sep = ""), header = TRUE))
# standardize the input for training
X_means <- colMeans(X)
X_sds <- apply(X, 2, sd)
for(j in 1:ncol(X)) {
X[,j] = (X[,j] - X_means[j]) / X_sds[j]
}
std_params <- data.frame("Xmeans" = X_means, "Xsds" = X_sds)
write.table(std_params, paste(training_prefix, ".X_stdparams", sep = ""), row.names = FALSE)
rm(std_params)
model <- keras_model_sequential()
model %>%
layer_dense(units = 256,
activation = 'relu',
kernel_regularizer = regularizer_l1_l2(l1 = gamma_opt * lambda_opt, l2 = (1 - gamma_opt) * lambda_opt),
input_shape = c(ncol(X))) %>%
layer_dense(units = 128,
activation = 'relu',
kernel_regularizer = regularizer_l1_l2(l1 = gamma_opt * lambda_opt, l2 = (1 - gamma_opt) * lambda_opt)) %>%
layer_dense(units = 5,
activation = 'softmax',
kernel_regularizer = regularizer_l1_l2(l1 = gamma_opt * lambda_opt, l2 = (1 - gamma_opt) * lambda_opt))
model %>%
compile(loss = 'categorical_crossentropy',
optimizer = optimizer_adam(),
metrics = c('categorical_crossentropy', 'accuracy'))
history <- model %>%
fit(X, Y,
epochs = num_epochs,
batch_size = batchsize,
verbose = 0) # verbose = 0 ensures it is silent
model %>% save_model_hdf5(paste(training_prefix, ".classifier.hdf5", sep = ""))
}
PredictorFit <- function(m, batchsize, num_epochs, training_prefix) {
cv_results <- read.table(paste(training_prefix, ".predictor_cv", sep = ""), header = TRUE)
gamma_opt <- cv_results$Gamma
lambda_opt <- cv_results$Lambda
X <- as.matrix(read.table(paste(training_prefix, ".features", sep = ""), header = TRUE))
Y <- as.matrix(read.table(paste(training_prefix, ".responses", sep = ""), header = TRUE))
# standardize the input and output for training
X_means <- colMeans(X)
X_sds <- apply(X, 2, sd)
Y_means <- colMeans(Y)
Y_sds <- apply(Y, 2, sd)
for(j in 1:ncol(X)) {
X[,j] = (X[,j] - X_means[j]) / X_sds[j]
}
for(j in 1:ncol(Y)) {
Y[,j] = (Y[,j] - Y_means[j]) / Y_sds[j]
}
std_params <- data.frame("Xmeans" = X_means, "Xsds" = X_sds)
write.table(std_params, paste(training_prefix, ".X_stdparams", sep = ""), row.names = FALSE)
std_params <- data.frame("Ymeans" = Y_means, "Ysds" = Y_sds)
write.table(std_params, paste(training_prefix, ".Y_stdparams", sep = ""), row.names = FALSE)
rm(std_params)
model <- keras_model_sequential()
model %>%
layer_dense(units = 256,
activation = 'relu',
kernel_regularizer = regularizer_l1_l2(l1 = gamma_opt * lambda_opt, l2 = (1 - gamma_opt) * lambda_opt),
input_shape = c(ncol(X))) %>%
layer_dense(units = 128,
activation = 'relu',
kernel_regularizer = regularizer_l1_l2(l1 = gamma_opt * lambda_opt, l2 = (1 - gamma_opt) * lambda_opt)) %>%
layer_dense(units = ncol(Y),
activation = 'linear',
kernel_regularizer = regularizer_l1_l2(l1 = gamma_opt * lambda_opt, l2 = (1 - gamma_opt) * lambda_opt))
model %>%
compile(loss = 'mean_squared_error',
optimizer = optimizer_adam(),
metrics = 'mean_squared_error')
history <- model %>%
fit(X, Y,
epochs = num_epochs,
batch_size = batchsize,
verbose = 0) # verbose = 0 ensures it is silent
model %>% save_model_hdf5(paste(training_prefix, ".predictor.hdf5", sep = ""))
}
CLOUDClassify <- function(training_prefix, testing_prefix) {
X <- as.matrix(read.table(paste(testing_prefix, ".features", sep = ""), header = TRUE))
# standardize the input for testing
std_params <- read.table(paste(training_prefix, ".X_stdparams", sep = ""), header = TRUE)
X_means <- c(std_params$Xmeans)
X_sds <- c(std_params$Xsds)
for(j in 1:ncol(X)) {
X[,j] = (X[,j] - X_means[j]) / X_sds[j]
}
rm(std_params)
model <- load_model_hdf5(paste(training_prefix, ".classifier.hdf5", sep = ""))
Yest_num <- model %>% predict_classes(X)
Yest <- data.frame("Class" = ifelse(Yest_num == 0, "cons",
ifelse(Yest_num == 1, "neoparent",
ifelse(Yest_num == 2, "neochild",
ifelse(Yest_num == 3, "sub", "spec")))))
write.table(Yest, paste(testing_prefix, ".classifications", sep = ""), row.names = FALSE)
}
CLOUDPredict <- function(training_prefix, testing_prefix) {
X <- as.matrix(read.table(paste(testing_prefix, ".features", sep = ""), header = TRUE))
# standardize the input for testing
std_params <- read.table(paste(training_prefix, ".X_stdparams", sep = ""), header = TRUE)
X_means <- c(std_params$Xmeans)
X_sds <- c(std_params$Xsds)
for(j in 1:ncol(X)) {
X[,j] = (X[,j] - X_means[j]) / X_sds[j]
}
rm(std_params)
model <- load_model_hdf5(paste(training_prefix, ".predictor.hdf5", sep = ""))
std_params <- read.table(paste(training_prefix, ".Y_stdparams", sep = ""), header = TRUE)
Y_means <- c(std_params$Ymeans)
Y_sds <- c(std_params$Ysds)
rm(std_params)
Yest_std <- model %>% predict(X)
Yest <- Yest_std
# un-standardize the responses
for(j in 1:ncol(Yest)) {
Yest[, j] = Yest_std[,j] * Y_sds[j] + Y_means[j]
}
Y <- as.matrix(read.table(paste(training_prefix, ".responses", sep = ""), header = TRUE))
colnames(Yest) <- colnames(Y)
rm(Y)
write.table(Yest, paste(testing_prefix, ".predictions", sep = ""), row.names = FALSE)
}