Add permutations and use new paradigms

Signed-off-by: Liang Zhang <psychelzh@outlook.com>

_scripts/predict_phenotypes.R

@@ -7,71 +7,104 @@ tar_option_set(
 )
 setup_parallel_plan()
 
+config_cpm <- prepare_config_cpm(
+  config == "gsr",
+  run == "full",
+  thresh_method == "alpha",
+  thresh_level == 0.01
+)
 cpm_branches <- tarchetypes::tar_map(
   config_cpm,
-  names = !starts_with("file"),
-  tarchetypes::tar_rep(
+  names = !c(file_fc, fd),
+  tar_target(
     cpm_result,
     apply(
-      qs::qread(file_scores_factor)[subjs_to_keep, ], 2,
-      \(scores) {
-        cpmr::cpm(
-          qs::qread(file_fc)[subjs_to_keep, ],
-          scores,
-          confounds = qs::qread(file_confounds)[subjs_to_keep, ],
-          thresh_method = thresh_method,
-          thresh_level = thresh_level,
-          kfolds = 10
-        )
-      }
+      scores, 2,
+      perform_cpm,
+      fc = qs::qread(file_fc)[subjs_keep_neural, ],
+      confounds = match_confounds(users_confounds, fd),
+      bias_correct = FALSE,
+      thresh_method = thresh_method,
+      thresh_level = thresh_level
     ),
-    batches = 4,
-    reps = 5,
-    iteration = "list",
     retrieval = "worker",
     storage = "worker"
   ),
-  tarchetypes::tar_rep2(
+  tar_target(
     cpm_performance,
     lapply_tar_batches(
       cpm_result,
       extract_cpm_performance
     ) |>
       list_rbind(names_to = "latent"),
-    cpm_result,
     retrieval = "worker",
     storage = "worker"
   )
 )
-list(
-  tar_target(
-    file_scores_factor,
-    path_obj_from_proj("scores_bf_full", "confirm_factors"),
-    format = "file_fast"
+
+cpm_branches_perms <- tarchetypes::tar_map(
+  dplyr::filter(
+    config_cpm,
+    task == "am",
+    thresh_method == "alpha",
+    thresh_level == 0.01
   ),
+  names = !c(file_fc, fd),
+  tarchetypes::tar_rep(
+    cpm_result_perm,
+    apply(
+      scores, 2,
+      perform_cpm_perm,
+      fc = qs::qread(file_fc)[subjs_keep_neural, ],
+      confounds = match_confounds(users_confounds, fd),
+      bias_correct = FALSE,
+      thresh_method = thresh_method,
+      thresh_level = thresh_level
+    ),
+    batches = 100,
+    reps = 10,
+    iteration = "list",
+    retrieval = "worker",
+    storage = "worker"
+  ),
+  tarchetypes::tar_rep2(
+    cpm_performance_perm,
+    lapply_tar_batches(
+      cpm_result_perm,
+      extract_cpm_performance
+    ) |>
+      list_rbind_tar_batches(names_to = "index"),
+    cpm_result_perm,
+    retrieval = "worker",
+    storage = "worker"
+  )
+)
+
+list(
   tarchetypes::tar_file_read(
-    subjs_keep_neural,
-    path_obj_from_proj("subjs_keep_neural", "prepare_neural"),
+    scores,
+    path_obj_from_proj("scores", "cognition_structure"),
     read = qs::qread(!!.x)
   ),
-  tar_target(
-    subjs_to_keep,
-    # intersect() does not work for integer64
-    # https://github.com/truecluster/bit64/issues/29
-    intersect(
-      as.character(subjs_keep_neural),
-      rownames(qs::qread(file_scores_factor))
-    )
-  ),
-  tar_prep_files_cpm(),
+  tar_prepare_cpm(),
   cpm_branches,
   tarchetypes::tar_combine(
     cpm_performance,
     cpm_branches$cpm_performance,
     command = bind_rows_meta(
       !!!.x,
-      .prefix = "cpm_performance",
-      .type = "cpm"
+      .names = c(names(config_fc), names(hypers_cpm)),
+      .prefix = "cpm_performance"
+    )
+  ),
+  cpm_branches_perms,
+  tarchetypes::tar_combine(
+    cpm_performance_perm,
+    cpm_branches_perms$cpm_performance_perm,
+    command = bind_rows_meta(
+      !!!.x,
+      .names = c(names(config_fc), names(hypers_cpm)),
+      .prefix = "cpm_performance_perm"
     )
   )
 )