comparison sampling/strategies.R @ 0:205974c9568c tip

Initial commit. Predictions not included for lack of space.

+## Generic sampling function
+get_samples <- function(num_iter = 1, mode = 'cv', 
+                        N = 0, num_folds = 4, 
+                        stratified = F, keep_prop = F,
+                        db = "../db/gtzan.db", classes = NULL){
+  
+  if (mode == 'cv') 
+    get_cv_samples(
+      num_iter = num_iter, num_folds = num_folds,
+      stratified = stratified, db = db, classes = classes)
+  else 
+    get_bs_samples(
+      N = N, num_iter = num_iter, stratified = stratified, 
+      keep_prop = keep_prop, db = db, classes = classes)
+  
+}
+
+# CROSS-VALIDATION
+
+## Cross-Validation sampling
+get_cv_samples <- function(num_iter = 1, num_folds = 4, stratified = F, 
+  db = "../db/gtzan.db", classes = NULL){
+  
+  train <- data.frame()
+  test <- data.frame()
+  
+  for (i in 1:num_iter){
+    folds <- 
+      get_folds(num_folds = num_folds, stratified = stratified,
+                db = db, classes = classes)
+    
+    for (j in 1:num_folds){
+    
+      train <- rbind(train, 
+                     data.frame(ex_id = folds[[j]]$train,
+                              iter = i, fold = j,
+                              mode = 'cv',
+                              stratified = stratified))
+      test <- rbind(test,
+                    data.frame(ex_id = folds[[j]]$test,
+                             iter = i, fold = j,
+                             mode = 'cv',
+                             stratified = stratified))
+    }
+    
+  }
+ 
+  return(list(train = train, test = test))
+   
+}
+
+
+# Random assignment into folds
+.get_assignment <- function(N, num_folds){
+  
+  sample(
+    as.numeric(
+      sapply(1:num_folds, 
+             function(x) rep(x, N/num_folds))))
+  
+}
+
+# Obtain folds for Cross-Validation
+get_folds <- function(num_folds = 4, stratified = F, 
+                      db = "../db/gtzan.db", classes = NULL){
+  
+  res <- get_excerpts_classes(db = db, classes) %>%
+    arrange(class_id, ex_id_class)
+  
+  if(stratified){
+    assignment <- numeric(0)
+    for(i in unique(res$class_id)){
+      N_class <- 
+        filter(res, class_id == i) %>%
+        nrow()
+      assignment <- 
+        c(assignment, .get_assignment(N_class, num_folds))
+    }
+  }
+  else 
+    assignment <- .get_assignment(nrow(res), num_folds)
+  
+  folds <- vector("list", num_folds)
+  aux <- res$ex_id
+  
+  for (i in 1:num_folds){
+    folds[[i]][['test']] <- 
+      aux[which(assignment == i)]
+    folds[[i]][['train']] <- 
+      aux[which(assignment != i)]
+  }
+  
+  folds
+  
+}
+
+
+# BOOTSTRAP
+
+## Boostrap sampling
+get_bs_samples <- function(N, num_iter, stratified = F, keep_prop = F,
+               db = "../db/gtzan.db", classes = NULL,
+               hold_out_ex_ids = NULL){
+  
+  res <- get_excerpts_classes(db, classes)
+  
+  if (N == 0 | is.null(N)) 
+    N <- nrow(res)
+  
+  train <- data.frame()
+  test <- data.frame()
+  
+  for (i in 1:num_iter){
+    train_test <- 
+      get_bs_sample(N, stratified, keep_prop, db, classes, res,
+                    hold_out_ex_ids)
+    train <- rbind(train, 
+                   data.frame(ex_id = train_test$train,
+                              iter = i,
+                              mode = 'bs',
+                              stratified = stratified,
+                              keep_prop = keep_prop))
+    test <- rbind(test,
+                  data.frame(ex_id = train_test$test,
+                             iter = i,
+                             mode = 'bs',
+                             stratified = stratified,
+                             keep_prop = keep_prop))
+  }
+  
+  return(list(train = train, test = test))
+  
+}
+
+## Correct proportions for test set stratification
+.correct_prop <- function(samples, reference_samples, 
+                         db = "../db/gtzan.db"){
+  
+  ref <- as.factor(
+    get_classes(reference_samples, unique_classes = F, db = db))
+  classes <- factor(
+    get_classes(samples, unique_classes = F, db = db),
+    levels = levels(ref))
+  
+  ref_counts <- table(ref)
+  ref_prop <- ref_counts / sum(ref_counts)
+  
+  prior_counts <- table(classes)
+  post_counts <- round(ref_prop * min(table(classes) / ref_prop))
+  
+  post_samples <- numeric(0)
+  for (class in levels(classes)){
+    post_samples <- 
+      c(post_samples,
+        sample(samples[which(classes == class)], 
+               size = post_counts[[class]]))
+  }
+  
+  post_samples
+  
+}
+
+## Bootstrap single train/test pair
+get_bs_sample <- function(N, stratified = F, keep_prop = F,
+                          db = "../db/gtzan.db", classes = NULL,
+                          df = NULL,
+                          hold_out_ex_ids = NULL
+                          ){
+  
+  if(is.null(df))
+    df <- get_excerpts_classes(db, classes)
+
+  n_ex_class <- table(df$class_id)
+  print(n_ex_class)
+    
+  if(!is.null(hold_out_ex_ids))
+    df <- df %>% filter(! (ex_id %in% hold_out_ex_ids))
+  
+  train <- numeric(0)
+  
+  if(stratified){
+    for(j in unique(df$class_id)){
+      ex_class <- filter(df, class_id == j) %>%
+        select(ex_id) %>%
+        unlist() %>%
+        unname()
+      train <- c(train, 
+                 sample(ex_class, 
+                        size = n_ex_class[which(names(n_ex_class) == j)], 
+                        replace = T))
+    }
+  }
+  else{
+    train <- sample(df$ex_id, size = N, replace = T)
+  }
+  
+  test <- sort(c(df$ex_id[!(df$ex_id %in% train)], hold_out_ex_ids))
+  if(keep_prop){
+    test <- .correct_prop(test, reference = train)
+  }
+  
+  return(list(train = train, test = test))
+  
+}