add automatically-generated lists of optimizers

notwacube.py

@@ -7,6 +7,82 @@ BASELINE_OPTIMIZERS = [
     quasirandom_cube,
 ]
 
+
+def collect_everything():
+    G = globals().values()
+    λ = lambda G: [
+        g for g in G if callable(g) and getattr(g, "__name__", "").endswith("_cube")
+    ]
+    _seen = {}
+    for thing in sum((λ(g) for g in G if type(g) is list), λ(G)):
+        key = thing.__name__
+        if key in _seen and _seen[key] is not thing:
+            pass  # print(f"Ignored: seen twice: {key}", file=sys.stderr)
+        _seen[key] = thing
+    # return list(_seen.values())
+    return [v for k, v in sorted(_seen.items(), key=lambda t: t[0])]
+
+
+FUCKING_EVERYTHING = collect_everything()
+
+try:
+    from previous import PREVIOUSLY_POSITIVE, PREVIOUSLY_NEGATIVE
+except ModuleNotFoundError:
+    PREVIOUSLY_POSITIVE, PREVIOUSLY_NEGATIVE = [], []
+else:
+    g = {v.__name__: v for v in FUCKING_EVERYTHING}  # globals()
+
+    def refind(stuff):
+        _temp = []
+        for opt in stuff:
+            _old_len = len(_temp)
+            if opt in g:
+                _temp.append(g[opt])
+            elif opt.startswith("ng_") or opt.startswith("ngx_"):
+                if opt.startswith("ng_"):
+                    ngx = "ngx_" + opt.removeprefix("ng_")
+                else:
+                    ngx = "ng_" + opt.removeprefix("ngx_")  # whatever
+                if ngx in g:
+                    _temp.append(g[ngx])
+                else:
+                    for ng_opt in NEVERGRAD2_OPTIMIZERS:
+                        if ng_opt.__name__ == opt or ng_opt.__name__ == ngx:
+                            _temp.append(ng_opt)
+            if len(_temp) == _old_len:
+                # FIXME: this text is not necessarily for PREVIOUSLY_POSITIVE!
+                print(
+                    f"Ignored: failed to find {opt} for PREVIOUSLY_POSITIVE",
+                    file=sys.stderr,
+                )
+        return _temp
+
+    PREVIOUSLY_POSITIVE = refind(PREVIOUSLY_POSITIVE)
+    PREVIOUSLY_NEGATIVE = refind(PREVIOUSLY_NEGATIVE)
+    del g
+
+    for opt in (another_random_cube, quasirandom_cube):
+        if opt not in PREVIOUSLY_POSITIVE:
+            PREVIOUSLY_POSITIVE.append(opt)
+    for opt in (another_random_cube, quasirandom_cube):
+        if opt not in PREVIOUSLY_NEGATIVE:
+            PREVIOUSLY_NEGATIVE.append(opt)
+
+try:
+    from elo_blacklist import blacklisted
+except ModuleNotFoundError:
+    WHITELISTED_OPTIMIZERS = FUCKING_EVERYTHING.copy()
+else:
+    WHITELISTED_OPTIMIZERS = [
+        opt
+        for opt in FUCKING_EVERYTHING
+        if opt.__name__.removesuffix("_cube") not in blacklisted
+    ]
+
 book_of_optimizers = dict(
     baseline=BASELINE_OPTIMIZERS,
+    everything=FUCKING_EVERYTHING,
+    negative=PREVIOUSLY_NEGATIVE,
+    positive=PREVIOUSLY_POSITIVE,
+    whitelisted=WHITELISTED_OPTIMIZERS,
 )