WIP: Fixes combinatorial. Triangle values

utils/config_fabric.py

@@ -85,6 +85,7 @@ def create_objectives_grid(df, objectives, n_para_obj=2, method="combinatorial")
             sel_features = df.index.to_list()
             parameters_o = "objectives, "
             parameters = get_ranges_from_stats(df, sorted(objectives))
+            objectives = sorted(sel_features)
             tasks = f"list(itertools.product({parameters}))[0]"
 
         elif method=="range-from-csv":
@@ -126,12 +127,28 @@ def set_generator_experiments(generator_params):
 
     def handle_combinatorial(sel_features, stats, tuple_values):
         triangular_option = double_switch("Square", "Triangular")
-        all_combinations = [combinations(sel_features, r) for r in range(1, len(sel_features) + 1)]
-        all_combinations = [comb for sublist in all_combinations for comb in sublist]
-
-        for comb in all_combinations:
-            sel_stats = stats.loc[list(comb)]
-            experiments = create_objectives_grid(sel_stats, tuple_values, n_para_obj=len(tuple_values), method="combinatorial")
+        if triangular_option:
+            experiments = []
+            elements = sel_features
+            # List to store all combinations
+            all_combinations = []
+
+            # Generate combinations of length 1, 2, and 3
+            for r in range(1, len(elements) + 1):
+                # Generate combinations of length r
+                combinations_r = list(combinations(elements, r))
+                # Extend the list of all combinations
+                all_combinations.extend(combinations_r)
+            st.write(all_combinations)
+            all_combinations = [combinations(sel_features, r) for r in range(1, len(sel_features) + 1)]
+            all_combinations = [comb for sublist in all_combinations for comb in sublist]
+
+            # Print or use the result as needed
+            for comb in all_combinations:
+                sel_stats = stats.loc[list(comb)]
+                experiments += create_objectives_grid(sel_stats, tuple_values, n_para_obj=len(tuple_values), method="combinatorial")
+        else:
+            experiments = create_objectives_grid(stats, tuple_values, n_para_obj=len(tuple_values))
         return experiments
 
     def handle_grid_option(grid_option, df, sel_features):
@@ -139,7 +156,7 @@ def set_generator_experiments(generator_params):
             combinatorial = double_switch("Range", "Combinatorial")
             if combinatorial:
                 add_quantile = st.slider('Add %-quantile', min_value=0.0, max_value=100.0, value=50.0, step=5.0)
-                stats = df.describe().transpose()
+                stats = df.describe().transpose().sort_index()
                 stats[f"{int(add_quantile)}%"] = df.quantile(q=add_quantile / 100)
                 st.write(stats)
                 tuple_values = st.multiselect("Tuples including", list(stats.columns)[3:], default=['min', 'max'])