79 lines
3.4 KiB
Python
79 lines
3.4 KiB
Python
def prune_results(results, multiple, _check=False):
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# if there are more than `multiple` results for one optimizer+objective pair,
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# then trim the bottom and top until there are only `multiple` left.
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new_results = {}
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for obj_name, obj_res in results.items():
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new_res = {}
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for fopt, opt_name, extra in sorted(obj_res):
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l = new_res.setdefault(opt_name, [[], []])
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l[0].append(fopt)
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l[1].append(extra)
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slices = {}
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for opt_name, res in new_res.items():
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# in the event that an odd number of results needs to be trimmed,
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# prefer trimming from the bottom (i.e. worse solutions get removed first).
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fopts, extras = res
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down = (len(fopts) - multiple) // 2
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up = len(fopts) - (len(fopts) - multiple + 1) // 2
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slices[opt_name] = slice(down, up)
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for opt_name, res in new_res.items():
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fopts, extras = res
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s = slices[opt_name]
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fopts, extras = fopts[s], extras[s]
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if _check:
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assert len(fopts) == multiple, (len(fopts), multiple)
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if len(fopts) == multiple:
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for fopt, extra in zip(fopts, extras):
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result = (fopt, opt_name, extra)
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new_results.setdefault(obj_name, []).append(result)
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return results
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def perform_another_experimental_scoring_method(results):
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if len(results) and len(something := next(iter(results.values()))[0]) == 3:
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history_length = len(something[2])
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each = {}
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# for i in (history_length - 1,):
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for i in range(history_length):
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# for k, v in results.items(): for vi in v: assert len(vi) == 3, vi
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l = {k: [(res[2][i], res[1]) for res in v] for k, v in results.items()}
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for k, v in perform_another_experimental_scoring_method(l).items():
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each.setdefault(k, []).append(v)
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return {k: sum(v) / len(v) for k, v in each.items()}
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new_results = {}
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all_opt_names = set()
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for obj_name, obj_res in results.items():
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all_res = {}
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for fopt, opt_name in obj_res:
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all_res.setdefault(fopt, []).append(opt_name)
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all_opt_names.add(opt_name)
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new_results[obj_name] = dict(sorted(all_res.items()))
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limited_by_floating_point_precision = 53
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best_ranks_and_counts = {}
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for outer_rank in range(1, limited_by_floating_point_precision + 1):
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for obj_name, all_res in new_results.items():
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for fopt, opt_names in all_res.items():
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dirty = False
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for opt_name in set(opt_names):
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if opt_name in best_ranks_and_counts:
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rank, count = best_ranks_and_counts[opt_name]
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if rank == outer_rank:
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best_ranks_and_counts[opt_name] = (rank, count + 1)
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dirty = True
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else:
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best_ranks_and_counts[opt_name] = (outer_rank, 1)
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dirty = True
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if dirty:
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break
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scores = {k: 0.0 for k in all_opt_names}
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for opt_name, (rank, count) in best_ranks_and_counts.items():
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points = 2 ** (1 - rank)
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count = min(count, limited_by_floating_point_precision)
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scores[opt_name] = score = sum(points / 2**i for i in range(count))
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return scores
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