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KernelAggClassifier.R
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KernelAggClassifier.R
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#### --------------------------------------------------------------------------- ####
#### -------- A kernel-based combined classification rule - Mojirsheibani (2000) ####
#### --------------------------------------------------------------------------- ####
#### Check if package "pacman" is already installed
lookup_packages <- installed.packages()[,1]
if(!("fontawesome" %in% lookup_packages))
install.packages("fontawesome")
#### To be installed or loaded
lookup_packages <- installed.packages()[,1]
if(!("pacman" %in% lookup_packages))
install.packages("pacman")
# To be installed or loaded
pacman::p_load(magrittr)
pacman::p_load(tidyverse)
## package for "generateMachines"
pacman::p_load(tree)
pacman::p_load(nnet)
pacman::p_load(e1071)
pacman::p_load(randomForest)
pacman::p_load(FNN)
pacman::p_load(xgboost)
pacman::p_load(adabag)
pacman::p_load(keras)
pacman::p_load(pracma)
pacman::p_load(latex2exp)
pacman::p_load(plotly)
rm(lookup_packages)
# ------------------------------------------------------------------------------------------------ #
# Function: `setBasicParameter`
# -----------------------------
setBasicParameter <- function(k = 10,
ntree = 300,
mtry = NULL,
ker_svm = "radial",
deg_svm = 3,
mfinal_boost = 50,
boostrap = TRUE,
eta_xgb = 1,
nrounds_xgb = 100,
early_stop_xgb = NULL,
max_depth_xgb = 3,
param_xgb = NULL){
return(list(
k = k,
ntree = ntree,
mtry = mtry,
ker_svm = ker_svm,
deg_svm = deg_svm,
mfinal_boost = mfinal_boost,
boostrap = boostrap,
eta_xgb = eta_xgb,
nrounds_xgb = nrounds_xgb,
early_stop_xgb = early_stop_xgb,
max_depth_xgb = max_depth_xgb,
param_xgb = param_xgb)
)
}
# ------------------------------------------------------------------------------------------------ #
# Function: `generateMachines`
# ----------------------------
### Note: You may need to modify the function accordingly if you want to build different types of basic machines.
generateMachines <- function(train_input,
train_response,
scale_input = FALSE,
machines = NULL,
splits = 0.5,
basicMachineParam = setBasicParameter(),
silent = FALSE){
k <- basicMachineParam$k
ntree <- basicMachineParam$ntree
mtry <- basicMachineParam$mtry
ker_svm <- basicMachineParam$ker_svm
deg_svm <- basicMachineParam$deg_svm
mfinal_boost = basicMachineParam$mfinal_boost
boostrap = basicMachineParam$boostrap
eta_xgb <- basicMachineParam$eta_xgb
nrounds_xgb <- basicMachineParam$nrounds_xgb
early_stop_xgb <- basicMachineParam$early_stop_xgb
max_depth_xgb <- basicMachineParam$max_depth_xgb
param_xgb <- basicMachineParam$param_xgb
class_xgb <- unique(train_response)
numberOfClasses <- length(class_xgb)
if(is.null(param_xgb)){
param_xgb <- list("objective" = "multi:softmax",
"eval_metric" = "mlogloss",
"num_class" = numberOfClasses+1)
}
# Packages
pacman::p_load(nnet)
pacman::p_load(e1071)
pacman::p_load(tree)
pacman::p_load(randomForest)
pacman::p_load(FNN)
pacman::p_load(xgboost)
pacman::p_load(maboost)
# Preparing data
input_names <- colnames(train_input)
input_size <- dim(train_input)
df_input <- train_input_scale <- train_input
if(scale_input){
maxs <- map_dbl(.x = df_input, .f = max)
mins <- map_dbl(.x = df_input, .f = min)
train_input_scale <- scale(train_input,
center = mins,
scale = maxs - mins)
}
if(is.matrix(train_input_scale)){
df_input <- as_tibble(train_input_scale)
matrix_input <- train_input_scale
} else{
df_input <- train_input_scale
matrix_input <- as.matrix(train_input_scale)
}
# Machines
svm_machine <- function(x, pa = NULL){
mod <- svm(x = df_train_x1,
y = train_y1,
kernel = ker_svm,
degree = deg_svm,
type = "C-classification")
res <- predict(mod,
newdata = x)
return(list(pred = res,
model = mod))
}
tree_machine <- function(x, pa = NULL) {
mod <- tree(as.formula(paste("train_y1~",
paste(input_names,
sep = "",
collapse = "+"),
collapse = "",
sep = "")),
data = df_train_x1)
res <- predict(mod, x, type = 'class')
return(list(pred = res,
model = mod))
}
knn_machine <- function(x, k0) {
mod <- knn(train = matrix_train_x1,
test = x,
cl = train_y1,
k = k0)
return(list(pred = mod,
model = k0))
}
RF_machine <- function(x, ntree0) {
if(is.null(mtry)){
mod <- randomForest(x = df_train_x1,
y = train_y1,
ntree = ntree0)
}else{
mod <- randomForest(x = df_train_x1,
y = train_y1,
ntree = ntree0,
mtry = mtry)
}
res <- as.vector(predict(mod, x))
return(list(pred = res,
model = mod))
}
xgb_machine <- function(x, nrounds_xgb0){
mod <- xgboost(data = matrix_train_x1,
label = train_y1,
params = param_xgb,
eta = eta_xgb,
early_stopping_rounds = early_stop_xgb,
max_depth = max_depth_xgb,
verbose = 0,
nrounds = nrounds_xgb0)
res <- class_xgb[predict(mod, x)]
return(list(pred = res,
model = mod))
}
ada_machine <- function(x, mfinal0){
data_tem <- cbind(df_train_x1, "target" = train_y1)
mod_ <- boosting(target ~ .,
data = data_tem,
mfinal = mfinal0,
boos = boostrap)
res <- predict.boosting(mod_,
newdata = as.data.frame(x))
return(list(pred = res$class,
model = mod_))
}
logit_machine <- function(x, pa = NULL){
mod <- multinom(as.formula(paste("train_y1~",
paste(input_names,
sep = "",
collapse = "+"),
collapse = "",
sep = "")),
data = df_train_x1,
trace = FALSE)
res <- predict(mod,
newdata = x)
return(list(pred = res,
model = mod))
}
# All machines
all_machines <- list(knn = knn_machine,
tree = tree_machine,
rf = RF_machine,
logit = logit_machine,
svm = svm_machine,
adaboost = ada_machine,
xgb = xgb_machine)
# All parameters
all_parameters <- list(knn = k,
tree = 1,
rf = ntree,
logit = NA,
svm = deg_svm,
adaboost = mfinal_boost,
xgb = nrounds_xgb)
lookup_machines <- c("knn", "tree", "rf", "logit", "svm", "xgb", "adaboost")
if(is.null(machines)){
mach <- lookup_machines
}else{
mach <- map_chr(.x = machines,
.f = ~ match.arg(.x, lookup_machines))
}
# Extracting data
M <- length(mach)
size_D1 <- floor(splits*input_size[1])
id_D1 <- logical(input_size[1])
id_D1[sample(input_size[1], size_D1)] <- TRUE
df_train_x1 <- df_input[id_D1,]
matrix_train_x1 <- matrix_input[id_D1,]
train_y1 <- train_response[id_D1]
df_train_x2 <- df_input[!id_D1,]
matrix_train_x2 <- matrix_input[!id_D1,]
# Function to extract df and model from 'map' function
extr_df <- function(x, nam, id){
return(tibble("{nam}_{id}" := as.vector(pred_m[[x]]$pred)))
}
extr_mod <- function(x, id){
return(pred_m[[x]]$model)
}
pred_D2 <- c()
all_mod <- c()
if(!silent){
cat("\n* Building basic machines ...\n")
cat("\t~ Progress:")
}
for(m in 1:M){
if(mach[m] %in% c("knn", "xgb")){
x0_test <- matrix_train_x2
} else {
x0_test <- df_train_x2
}
if(is.null(all_parameters[[mach[m]]])){
para_ <- 1
}else{
para_ <- all_parameters[[mach[m]]]
}
pred_m <- map(para_,
.f = ~ all_machines[[mach[m]]](x0_test, .x))
tem0 <- imap_dfc(.x = 1:length(para_),
.f = ~ extr_df(x = .x, nam = mach[m], id = para_[.x]))
tem1 <- map(.x = 1:length(para_),
.f = extr_mod)
names(tem0) <- names(tem1) <- paste0(mach[m], 1:length(para_))
pred_D2 <- bind_cols(pred_D2, as_tibble(tem0))
all_mod[[mach[m]]] <- tem1
if(!silent){
cat(" ... ", round(m/M, 2)*100L,"%", sep = "")
}
}
if(scale_input){
return(list(predict2 = pred_D2,
models = all_mod,
id2 = !id_D1,
train_data = list(train_input = train_input_scale,
train_response = train_response,
classes = class_xgb),
scale_max = maxs,
scale_min = mins))
} else{
return(list(predict2 = pred_D2,
models = all_mod,
id2 = !id_D1,
train_data = list(train_input = train_input_scale,
train_response = train_response,
classes = class_xgb)))
}
}
# ------------------------------------------------------------------------------------------------ #
# Function: `setGridParameter`
# ----------------------------
setGridParameter <- function(min_val = 1e-5,
max_val = 0.5,
n_val = 300,
parameters = NULL,
print_result = TRUE,
figure = TRUE){
return(list(min_val = min_val,
max_val = max_val,
n_val = n_val,
parameters = parameters,
print_result = print_result,
figure = figure))
}
# ------------------------------------------------------------------------------------------------ #
# Function: `gridOptimizer`
# -------------------------
gridOptimizer <- function(obj_func,
setParameter = setGridParameter(),
naive = FALSE,
silent = FALSE,
ker = NULL){
t0 <- Sys.time()
if(!naive){
if(is.null(setParameter$parameters)){
param <- seq(setParameter$min_val,
setParameter$max_val,
length.out = setParameter$n_val)
} else{
param <- setParameter$parameters
}
risk <- param %>%
map_dbl(.f = obj_func)
id_opt <- which.min(risk)
opt_ep <- param[id_opt]
opt_risk <- risk[id_opt]
if(setParameter$print_result & !silent){
cat("\n* Grid search for",ker,"kernel...\n ~ observed parameter :", opt_ep)
}
if(setParameter$figure){
tibble(x = param,
y = risk) %>%
ggplot(aes(x = x, y = y)) +
geom_line(color = "skyblue", linewidth = 0.75) +
geom_point(aes(x = opt_ep, y = opt_risk), color = "red") +
geom_vline(xintercept = opt_ep, color = "red", linetype = "longdash") +
labs(title = "Error as function of parameter",
x = "Parameter",
y = "Error") -> p
print(p)
}
} else{
opt_ep = NA
opt_risk = NA
risk = NA
}
t1 <- Sys.time()
return(list(
opt_param = opt_ep,
opt_error = opt_risk,
all_risk = risk,
run.time = difftime(t1,
t0,
units = "secs")[[1]])
)
}
# ------------------------------------------------------------------------------------------------ #
# Function: `dist_matrix`
# -----------------------
dist_matrix <- function(basicMachines,
n_cv = 5,
id_shuffle = NULL){
n <- nrow(basicMachines$predict2)
n_each_fold <- floor(n/n_cv)
# shuffled indices
if(is.null(id_shuffle)){
shuffle <- 1:(n_cv-1) %>%
rep(n_each_fold) %>%
c(., rep(n_cv, n - n_each_fold * (n_cv - 1))) %>%
sample()
}else{
shuffle <- id_shuffle
}
# the prediction matrix D_l
df_ <- as.matrix(basicMachines$predict2)
pair_dist <- function(M, N){
res_ <- 1:nrow(N) %>%
map_dfc(.f = (\(id) tibble('{{id}}' := rowSums(sweep(M, 2, N[id,], FUN = "!=")))))
return(res_)
}
L <- 1:n_cv %>%
map(.f = ~ pair_dist(df_[shuffle != .x,],
df_[shuffle == .x,]))
return(list(dist = L,
id_shuffle = shuffle,
n_cv = n_cv))
}
# ------------------------------------------------------------------------------------------------ #
# Fitting parameter
# -----------------
fit_parameter <- function(train_design,
train_response,
scale_input = FALSE,
build_machine = TRUE,
machines = NULL,
splits = 0.5,
n_cv = 5,
inv_sigma = sqrt(.5),
alp = 2,
kernels = "gaussian",
setMachineParam = setBasicParameter(),
setGridParam = setGridParameter(),
silent = FALSE){
if(is.list(train_response)){
train_response = train_response[[1]]
}
kernels_lookup <- c("gaussian", "epanechnikov", "biweight", "triweight", "triangular", "naive")
kernel_real <- kernels %>%
map_chr(.f = ~ match.arg(.x,
kernels_lookup))
if(build_machine){
mach2 <- generateMachines(train_input = train_design,
train_response = train_response,
scale_input = scale_input,
machines = machines,
splits = splits,
basicMachineParam = setMachineParam,
silent = silent)
} else{
mach2 <- list(predict2 = train_design,
models = colnames(train_design),
id2 = rep(TRUE, nrow(train_design)),
train_data = list(train_response = train_response,
classes = unique(train_response)))
}
# distance matrix to compute loss function
n_ker <- length(kernels)
id_shuf <- NULL
dist_all <- dist_matrix(basicMachines = mach2,
n_cv = n_cv,
id_shuffle = id_shuf)
# Kernel functions
# ================
# Gaussian kernel
gaussian_kernel <- function(.ep = .05,
.dist_matrix,
.train_response2,
.inv_sigma = sqrt(.5),
.alpha = 2){
kern_fun <- function(x, id, D){
tem0 <- as.matrix(exp(-(x*D)^(.alpha/2)*.inv_sigma^.alpha))
y_hat <- map_dfc(.x = 1:ncol(tem0),
.f = (\(x_) tibble("{x_}" := tapply(tem0[, x_],
INDEX = .train_response2[.dist_matrix$id_shuffle != id],
FUN = sum)))) %>%
map_chr(.f = (\(x) names(which.max(x))))
return(mean(y_hat != .train_response2[.dist_matrix$id_shuffle == id]))
}
temp <- map2(.x = 1:.dist_matrix$n_cv,
.y = .dist_matrix$dist,
.f = ~ kern_fun(x = .ep,
id = .x,
D = .y))
return(Reduce("+", temp)/.dist_matrix$n_cv)
}
# Epanechnikov
epanechnikov_kernel <- function(.ep = .05,
.dist_matrix,
.train_response2){
kern_fun <- function(x, id, D){
tem0 <- as.matrix(1- x*D)
tem0[tem0 < 0] = 0
y_hat <- map_dfc(.x = 1:ncol(tem0),
.f = (\(x_) tibble("{x_}" := tapply(tem0[, x_],
INDEX = .train_response2[.dist_matrix$id_shuffle != id],
FUN = sum)))) %>%
map_chr(.f = (\(x) names(which.max(x))))
return(mean(y_hat != .train_response2[.dist_matrix$id_shuffle == id]))
}
temp <- map2(.x = 1:.dist_matrix$n_cv,
.y = .dist_matrix$dist,
.f = ~ kern_fun(x = .ep,
id = .x,
D = .y))
return(Reduce("+", temp)/.dist_matrix$n_cv)
}
# Biweight
biweight_kernel <- function(.ep = .05,
.dist_matrix,
.train_response2){
kern_fun <- function(x, id, D){
tem0 <- as.matrix(1- x*D)
tem0[tem0 < 0] = 0
y_hat <- map_dfc(.x = 1:ncol(tem0),
.f = (\(x_) tibble("{x_}" := tapply(tem0[, x_],
INDEX = .train_response2[.dist_matrix$id_shuffle != id],
FUN = sum)))) %>%
map_chr(.f = (\(x) names(which.max(x))))
return(mean(y_hat != .train_response2[.dist_matrix$id_shuffle == id]))
}
temp <- map2(.x = 1:.dist_matrix$n_cv,
.y = .dist_matrix$dist,
.f = ~ kern_fun(x = .ep,
id = .x,
D = .y))
return(Reduce("+", temp)/.dist_matrix$n_cv)
}
# Triweight
triweight_kernel <- function(.ep = .05,
.dist_matrix,
.train_response2){
kern_fun <- function(x, id, D){
tem0 <- as.matrix(1- x*D)
tem0[tem0 < 0] = 0
y_hat <- map_dfc(.x = 1:ncol(tem0),
.f = (\(x_) tibble("{x_}" := tapply(tem0[, x_],
INDEX = .train_response2[.dist_matrix$id_shuffle != id],
FUN = sum)))) %>%
map_chr(.f = (\(x) names(which.max(x))))
return(mean(y_hat != .train_response2[.dist_matrix$id_shuffle == id]))
}
temp <- map2(.x = 1:.dist_matrix$n_cv,
.y = .dist_matrix$dist,
.f = ~ kern_fun(x = .ep,
id = .x,
D = .y))
return(Reduce("+", temp)/.dist_matrix$n_cv)
}
# Triangular
triangular_kernel <- function(.ep = .05,
.dist_matrix,
.train_response2){
kern_fun <- function(x, id, D){
tem0 <- as.matrix(1- x*D)
tem0[tem0 < 0] <- 0
y_hat <- map_dfc(.x = 1:ncol(tem0),
.f = (\(x_) tibble("{x_}" := tapply(tem0[, x_],
INDEX = .train_response2[.dist_matrix$id_shuffle != id],
FUN = sum)))) %>%
map_chr(.f = (\(x) names(which.max(x))))
return(mean(y_hat != .train_response2[.dist_matrix$id_shuffle == id]))
}
temp <- map2(.x = 1:.dist_matrix$n_cv,
.y = .dist_matrix$dist,
.f = ~ kern_fun(x = .ep,
id = .x,
D = .y))
return(Reduce("+", temp)/.dist_matrix$n_cv)
}
# error function
error_cv <- function(x,
.dist_matrix = NULL,
.kernel_func = NULL,
.train_response2 = NULL){
res <- .kernel_func(.ep = x,
.dist_matrix = .dist_matrix,
.train_response2 = .train_response2)
return(res/n_cv)
}
# list of kernel functions
list_funs <- list(gaussian = gaussian_kernel,
epanechnikov = epanechnikov_kernel,
biweight = biweight_kernel,
triweight = triweight_kernel,
triangular = triangular_kernel,
naive = epanechnikov_kernel)
# error for all kernel functions
error_func <- kernel_real %>%
map(.f = ~ (\(x) error_cv(x,
.dist_matrix = dist_all,
.kernel_func = list_funs[[.x]],
.train_response2 = train_response[mach2$id2])))
names(error_func) <- kernel_real
# Optimization
parameters <- map(.x = kernel_real,
.f = ~ gridOptimizer(obj_fun = error_func[[.x]],
setParameter = setGridParam,
naive = .x == "naive",
silent = silent,
ker = .x))
names(parameters) <- kernel_real
return(list(opt_parameters = parameters,
add_parameters = list(scale_input = scale_input,
inv_sigma = inv_sigma,
alp = alp),
basic_machines = mach2))
}
# ------------------------------------------------------------------------------------------------ #
# Prediction
# ----------
kernel_pred <- function(.h,
.y2,
.distance,
.kern = "gaussian",
.inv_sig = sqrt(.5),
.alp = 2){
dis <- as.matrix(.distance)
# Kernel functions
# ================
gaussian_kernel <- function(.ep,
.inv_sigma = .inv_sig,
.alpha = .alp){
tem0 <- exp(- (.ep*dis)^(.alpha/2)*.inv_sig^.alpha)
y_hat <- map_dfc(.x = 1:ncol(tem0),
.f = (\(x_) tibble("{x_}" := tapply(tem0[, x_],
INDEX = .y2,
FUN = sum)))) %>%
map_chr(.f = (\(x) names(which.max(x))))
return(as.vector(y_hat))
}
# Epanechnikov
epanechnikov_kernel <- function(.ep){
tem0 <- 1- .ep*dis
tem0[tem0 < 0] = 0
y_hat <- map_dfc(.x = 1:ncol(tem0),
.f = (\(x_) tibble("{x_}" := tapply(tem0[, x_],
INDEX = .y2,
FUN = sum)))) %>%
map_chr(.f = (\(x) names(which.max(x))))
return(as.vector(y_hat))
}
# Biweight
biweight_kernel <- function(.ep){
tem0 <- 1- .ep*dis
tem0[tem0 < 0] = 0
y_hat <- map_dfc(.x = 1:ncol(tem0),
.f = (\(x_) tibble("{x_}" := tapply(tem0[, x_],
INDEX = .y2,
FUN = sum)))) %>%
map_chr(.f = (\(x) names(which.max(x))))
return(as.vector(y_hat))
}
# Triweight
triweight_kernel <- function(.ep){
tem0 <- 1- .ep*dis
tem0[tem0 < 0] = 0
y_hat <- map_dfc(.x = 1:ncol(tem0),
.f = (\(x_) tibble("{x_}" := tapply(tem0[, x_],
INDEX = .y2,
FUN = sum)))) %>%
map_chr(.f = (\(x) names(which.max(x))))
return(as.vector(y_hat))
}
# Triangular
triangular_kernel <- function(.ep){
tem0 <- 1- .ep*dis
tem0[tem0 < 0] <- 0
y_hat <- map_dfc(.x = 1:ncol(tem0),
.f = (\(x_) tibble("{x_}" := tapply(tem0[, x_],
INDEX = .y2,
FUN = sum)))) %>%
map_chr(.f = (\(x) names(which.max(x))))
return(as.vector(y_hat))
}
# Naive
naive_kernel <- function(.ep = NULL){
y_hat <- map_dfc(.x = 1:ncol(dis),
.f = (\(x_) tibble("{x_}" := tapply(dis[, x_] == 0,
INDEX = .y2,
FUN = sum)))) %>%
map_chr(.f = (\(x) names(which.max(x))))
return(as.vector(y_hat))
}
# Prediction
kernel_list <- list(gaussian = gaussian_kernel,
epanechnikov = epanechnikov_kernel,
biweight = biweight_kernel,
triweight = triweight_kernel,
triangular = triangular_kernel,
naive = naive_kernel)
res <- tibble(as.vector(kernel_list[[.kern]](.ep = .h)))
names(res) <- .kern
return(res)
}
# ------------------------------------------------------------------------------------------------ #
# Functions: `predict_agg`
# ------------------------
### Prediction
predict_agg <- function(fitted_models,
new_data,
test_response = NULL,
naive = FALSE){
opt_param <- fitted_models$opt_parameters
add_param <- fitted_models$add_parameters
basic_mach <- fitted_models$basic_machines
kern0 <- names(opt_param)
new_data_ <- new_data
# if basic machines are built
if(is.list(basic_mach$models)){
mat_input <- as.matrix(basic_mach$train_data$train_input)
if(add_param$scale_input){
new_data_ <- scale(new_data,
center = basic_mach$scale_min,
scale = basic_mach$scale_max - basic_mach$scale_min)
}
if(is.matrix(new_data_)){
mat_test <- new_data_
df_test <- as_tibble(new_data_)
} else {
mat_test <- as.matrix(new_data_)
df_test <- new_data_
}
# Prediction test by basic machines
built_models <- basic_mach$models
pred_test <- function(meth){
if(meth == "knn"){
pre <- 1:length(built_models[[meth]]) %>%
map_dfc(.f = (\(k) tibble('{{k}}' := FNN::knn(train = mat_input[!basic_mach$id2,],
test = mat_test,
cl = basic_mach$train_data$train_response[!basic_mach$id2],
k = built_models[[meth]][[k]]))))
}
if(meth == "xgb"){
pre <- 1:length(built_models[[meth]]) %>%
map_dfc(.f = (\(k) tibble('{{k}}' := as.vector(basic_mach$train_data$classes[predict(built_models[[meth]][[k]], mat_test)]))))
}
if(meth == "adaboost"){
pre <- 1:length(built_models[[meth]]) %>%
map_dfc(.f = (\(k) tibble('{{k}}' := as.vector(predict.boosting(built_models[[meth]][[k]], as.data.frame(df_test))$class))))
}
if(!(meth %in% c("xgb", "knn", "adaboost"))){
pre <- 1:length(built_models[[meth]]) %>%
map_dfc(.f = (\(k) tibble('{{k}}' := as.vector(predict(built_models[[meth]][[k]], df_test, type = 'class')))))
}
colnames(pre) <- names(built_models[[meth]])
return(pre)
}
pred_test_all <- names(built_models) %>%
map_dfc(.f = pred_test)
} else{
pred_test_all <- new_data_
}
pred_test0 <- pred_test_all
# Prediction train2
pred_train_all <- basic_mach$predict2
colnames(pred_test_all) <- colnames(pred_train_all)
d_train <- dim(pred_train_all)
d_test <- dim(pred_test_all)
pred_test_mat <- as.matrix(pred_test_all)
pred_train_mat <- as.matrix(pred_train_all)
# Distance matrix
dists <- 1:d_test[1] %>%
map_dfc(.f = (\(id) tibble('{{id}}' := rowSums(sweep(pred_train_mat, 2, pred_test_mat[id,], FUN = "!=")))))
prediction <- 1:length(kern0) %>%
map_dfc(.f = ~ kernel_pred(.h = opt_param[[kern0[.x]]]$opt_param,
.y2 = basic_mach$train_data$train_response[basic_mach$id2],
.distance = dists,
.kern = kern0[.x],
.inv_sig = add_param$inv_sigma,
.alp = add_param$alp))
if(is.null(test_response)){
return(list(fitted_aggregate = prediction,
fitted_machine = pred_test_all))
} else{
error <- cbind(pred_test_all, prediction) %>%
dplyr::mutate(y_test = test_response) %>%
dplyr::summarise_all(.funs = ~ (. != y_test)) %>%
dplyr::select(-y_test) %>%
dplyr::summarise_all(.funs = ~ mean(.))
return(list(fitted_aggregate = prediction,
fitted_machine = pred_test0,
mis_error = error,
accuracy = 1 - error))
}
}
# ------------------------------------------------------------------------------------------------ #
# Function: `KernelAggClassifier`
# -----------------------
KernelAggClassifier <- function(train_design,
train_response,
test_design,
test_response = NULL,
scale_input = FALSE,
build_machine = TRUE,
machines = NULL,
splits = 0.5,
n_cv = 5,
inv_sigma = sqrt(.5),
alp = 2,
kernels = "gaussian",
setMachineParam = setBasicParameter(),
setGridParam = setGridParameter(),
silent = FALSE){
# build machines + tune parameter
fit_mod <- fit_parameter(train_design = train_design,
train_response = train_response,
scale_input = scale_input,
build_machine = build_machine,
machines = machines,
splits = splits,
n_cv = n_cv,
inv_sigma = inv_sigma,
alp = alp,
kernels = kernels,
setMachineParam = setMachineParam,
setGridParam = setGridParam,
silent = silent)
# prediction
if(is.list(test_response)){
test_response <- test_response[[1]]
}
pred <- predict_agg(fitted_models = fit_mod,
new_data = test_design,
test_response = test_response)
return(list(fitted_aggregate = pred$fitted_aggregate,
fitted_machine = pred$fitted_machine,
pred_train2 = fit_mod$basic_machines$predict2,
opt_parameter = fit_mod$opt_parameters,
mis_class = pred$mis_error,
accuracy = pred$accuracy,
kernels = kernels,
ind_D2 = fit_mod$basic_machines$id2))
}
# ------------------------------------------------------------------------------------------------ #