subset no longer in-place

R/moc_algo.R

@@ -1,33 +1,32 @@
-moc_algo = function(predictor, x_interest, pred_column, target, param_set, lower, upper, sdevs_num_feats, 
+moc_algo = function(predictor, x_interest, pred_column, target, param_set, lower, upper, sdevs_num_feats,
   epsilon,  fixed_features, max_changed, mu, termination_crit, n_generations, p_rec, p_rec_gen,
-  p_mut, p_mut_gen, p_mut_use_orig, k, weights, init_strategy, distance_function, cond_sampler = NULL, 
+  p_mut, p_mut_gen, p_mut_use_orig, k, weights, init_strategy, distance_function, cond_sampler = NULL,
   ref_point, quiet = TRUE) {
-  
+
   codomain = ps(
     dist_target = p_dbl(tags = "minimize"),
     dist_x_interest = p_dbl(tags = "minimize"),
     no_changed = p_int(tags = "minimize"),
     dist_train = p_dbl(tags = "minimize")
   )
-  
+
   fitness_function = make_fitness_function(
     predictor, x_interest, pred_column, target, weights, k, fixed_features, param_set, distance_function
   )
-  
+
   flex_cols = setdiff(names(x_interest), fixed_features)
   if (!is.null(sdevs_num_feats)) {
     sdevs_flex_num_feats = sdevs_num_feats[names(sdevs_num_feats) %in% flex_cols]
   }
-
-  param_set_flex = param_set$clone()
-  param_set_flex$subset(flex_cols)
-
+
+  param_set_flex = param_set$clone()$subset(flex_cols)
+
   objective = bbotk::ObjectiveRFunDt$new(
-    fun = fitness_function, 
-    domain = param_set_flex, 
+    fun = fitness_function,
+    domain = param_set_flex,
     codomain = codomain
   )
-  
+
   if (n_generations > 0L) {
     if (termination_crit == "gens") {
       terminator = bbotk::trm("gens", generations = n_generations)
@@ -40,89 +39,89 @@ moc_algo = function(predictor, x_interest, pred_column, target, param_set, lower
             },
             include_previous_generations = TRUE,
             min_delta = 0.00,
-            patience = n_generations), 
+            patience = n_generations),
           bbotk::trm("gens", generations = 500)
         )
       )
     }
   } else {
     terminator = bbotk::trm("none")
   }
-  
+
   oi = bbotk::OptimInstanceMultiCrit$new(
-    objective, 
+    objective,
     terminator = terminator
   )
-  
+
   if (n_generations > 0L) {
     # Mutator
     if (is.null(cond_sampler)) {
       op_m = make_moc_mutator(
-        ps = param_set_flex, 
-        x_interest = x_interest, 
-        max_changed = max_changed, 
-        sdevs = sdevs_flex_num_feats, 
+        ps = param_set_flex,
+        x_interest = x_interest,
+        max_changed = max_changed,
+        sdevs = sdevs_flex_num_feats,
         p_mut = p_mut,
-        p_mut_gen = p_mut_gen, 
+        p_mut_gen = p_mut_gen,
         p_mut_use_orig = p_mut_use_orig
       )
     } else {
       op_m = make_moc_conditional_mutator(
-        ps = param_set_flex, 
+        ps = param_set_flex,
         x_interest = x_interest,
-        max_changed = max_changed, 
+        max_changed = max_changed,
         p_mut = p_mut,
-        p_mut_gen = p_mut_gen, 
+        p_mut_gen = p_mut_gen,
         p_mut_use_orig = p_mut_use_orig,
         cond_sampler = cond_sampler
       )
     }
-    
+
     # Recombinator
     op_r = make_moc_recombinator(
-      ps = param_set_flex, 
-      x_interest = x_interest, 
-      max_changed = max_changed, 
+      ps = param_set_flex,
+      x_interest = x_interest,
+      max_changed = max_changed,
       p_rec = p_rec,
       p_rec_gen = p_rec_gen
     )
-    
+
     # Selectors
     # TODO: Replace this by tournament selection
     selobj1 = scl("one", objective = 1L)
     op_parent = sel("best", selobj1)
-    
+
     sel_nondom_penalized = ScalorNondomPenalized$new(epsilon)
-    op_survival = sel("best", sel_nondom_penalized)   
-    
+    op_survival = sel("best", sel_nondom_penalized)
+
     mies_prime_operators(
-      search_space = oi$search_space, 
-      mutators = list(op_m), 
+      search_space = oi$search_space,
+      mutators = list(op_m),
       recombinators = list(op_r),
       selectors = list(op_parent, op_survival)
-    )  
+    )
   }
-  
+
   pop_initializer = make_moc_pop_initializer(
-    ps = param_set_flex, 
-    x_interest = x_interest, 
-    max_changed = max_changed, 
-    init_strategy = init_strategy, 
-    flex_cols = flex_cols, 
-    sdevs = sdevs_flex_num_feats, 
-    lower = lower, 
-    upper = upper, 
+    ps = param_set_flex,
+    x_interest = x_interest,
+    max_changed = max_changed,
+    init_strategy = init_strategy,
+    flex_cols = flex_cols,
+    sdevs = sdevs_flex_num_feats,
+    lower = lower,
+    upper = upper,
     predictor = predictor,
     fitness_function = fitness_function,
     mu = mu
   )
-  
+
   if (quiet) {
     quiet(mies_init_population(inst = oi, mu = mu, initializer = pop_initializer))
   } else {
     mies_init_population(inst = oi, mu = mu, initializer = pop_initializer)
   }
-  
+
   if (n_generations > 0L) {
     tryCatch({
       repeat {
@@ -132,7 +131,7 @@ moc_algo = function(predictor, x_interest, pred_column, target, param_set, lower
           warning = function(w){
             if(grepl("no columns to delete or assign RHS to", w$message)){
               invokeRestart("muffleWarning")
-            } 
+            }
           })
         if (quiet) {
           quiet(mies_evaluate_offspring(oi, offspring))
@@ -145,20 +144,20 @@ moc_algo = function(predictor, x_interest, pred_column, target, param_set, lower
     })
   }
   bbotk::assign_result_default(oi)
-  
+
   # Re-attach fixed features
   if (!is.null(fixed_features)) {
     oi$result[, (fixed_features) := x_interest[, fixed_features, with = FALSE]]
   }
-  
+
   # Transform factor column w.r.t to original data
   factor_cols = names(which(sapply(predictor$data$X, is.factor)))
   for (factor_col in factor_cols) {
     fact_col_pred = predictor$data$X[[factor_col]]
     value =  factor(oi$result[[factor_col]], levels = levels(fact_col_pred), ordered = is.ordered(fact_col_pred))
     oi$result[, (factor_col) := value]
   }
-  
+
   int_cols = names(which(sapply(predictor$data$X, is.integer)))
   if (length(int_cols) > 0L) {
     oi$result[, (int_cols) := lapply(.SD, as.integer), .SDcols = int_cols]