move fib and prune_results into utilities

thursday/go_benchmark_it.py

@@ -2,7 +2,6 @@ from .go_benchmark_lists import *
 from .go_benchmarks import problems_2d, problems_3d, problems_4d
 from .parties import parties
 from .utilities import OWrap, COWrap, m1, m33, m36, prog
-from .utilities import perform_another_experimental_scoring_method
 import numpy as np
 
 all_problems = {
@@ -155,12 +154,11 @@ for problem_list in GO_BENCHMARKS.values():
 
 
 def main(argv, display=True):
+    from .utilities import fib
+    from .utilities import prune_results, perform_another_experimental_scoring_method
     from tqdm import tqdm
     import sys
 
-    def fib(n):
-        return pow(2 << n, n + 1, (4 << 2 * n) - (2 << n) - 1) % (2 << n)
-
     def optimizer_filter(name):
         # worst scoring optimizers: (awards=(5, 3, 2, 1)) (obj=146, opt=389) (dims=2, evals=80)
         #   evosax_pbt                       with (score:   0, price: 593) -593
@@ -262,36 +260,6 @@ def main(argv, display=True):
 
         return s
 
-    def prune_results(results, multiple):
-        # if there are more than `multiple` results for one optimizer+objective pair,
-        # then trim the bottom and top until there are only `multiple` left.
-        new_results = {}
-        for obj_name, obj_res in results.items():
-            new_res = {}
-            for fopt, opt_name, extra in sorted(obj_res):
-                l = new_res.setdefault(opt_name, [[], []])
-                l[0].append(fopt)
-                l[1].append(extra)
-            slices = {}
-            for opt_name, res in new_res.items():
-                # in the event that an odd number of results needs to be trimmed,
-                # prefer trimming from the bottom (i.e. worse solutions get removed first).
-                fopts, extras = res
-                down = (len(fopts) - multiple) // 2
-                up = len(fopts) - (len(fopts) - multiple + 1) // 2
-                slices[opt_name] = slice(down, up)
-            for opt_name, res in new_res.items():
-                fopts, extras = res
-                s = slices[opt_name]
-                fopts, extras = fopts[s], extras[s]
-                if not no_summary:
-                    assert len(fopts) == multiple, (len(fopts), multiple)
-                if len(fopts) == multiple:
-                    for fopt, extra in zip(fopts, extras):
-                        result = (fopt, opt_name, extra)
-                        new_results.setdefault(obj_name, []).append(result)
-        return results
-
     reset = "\033[m"
 
     quieter = True
@@ -388,7 +356,7 @@ def main(argv, display=True):
                 run += 1
 
     all_results = results
-    results = prune_results(results, multiple)
+    results = prune_results(results, multiple, _check=not no_summary)
 
     scores, prices = {}, {}
     all_opt_names = set()

thursday/utilities/math.py

@@ -21,3 +21,7 @@ def phi(d):
     for i in range(30 if d == 1 else max(10, 28 - d)):
         x = pow(1 + x, 1 / (d + 1))
     return x
+
+
+def fib(n):
+    return pow(2 << n, n + 1, (4 << 2 * n) - (2 << n) - 1) % (2 << n)

thursday/utilities/scoring.py

@@ -1,3 +1,34 @@
+def prune_results(results, multiple, _check=False):
+    # if there are more than `multiple` results for one optimizer+objective pair,
+    # then trim the bottom and top until there are only `multiple` left.
+    new_results = {}
+    for obj_name, obj_res in results.items():
+        new_res = {}
+        for fopt, opt_name, extra in sorted(obj_res):
+            l = new_res.setdefault(opt_name, [[], []])
+            l[0].append(fopt)
+            l[1].append(extra)
+        slices = {}
+        for opt_name, res in new_res.items():
+            # in the event that an odd number of results needs to be trimmed,
+            # prefer trimming from the bottom (i.e. worse solutions get removed first).
+            fopts, extras = res
+            down = (len(fopts) - multiple) // 2
+            up = len(fopts) - (len(fopts) - multiple + 1) // 2
+            slices[opt_name] = slice(down, up)
+        for opt_name, res in new_res.items():
+            fopts, extras = res
+            s = slices[opt_name]
+            fopts, extras = fopts[s], extras[s]
+            if _check:
+                assert len(fopts) == multiple, (len(fopts), multiple)
+            if len(fopts) == multiple:
+                for fopt, extra in zip(fopts, extras):
+                    result = (fopt, opt_name, extra)
+                    new_results.setdefault(obj_name, []).append(result)
+    return results
+
+
 def perform_another_experimental_scoring_method(results):
     if len(results) and len(something := next(iter(results.values()))[0]) == 3:
         history_length = len(something[2])