DOC: Update docs / examples

Author: kernc

sambo/_smbo.py

@@ -293,7 +293,7 @@ def ask(
 
         acq_func : Callable, default ACQ_FUNCS['UCB']
             Acquisition function used to guide the selection of candidate solutions.
-            By default, upper confidence bound (i.e. `mean + kappa * std` where `mean`
+            By default, upper confidence bound (i.e. `mean - kappa * std` where `mean`
             and `std` are surrogate models' predicted results).
 
             .. tip::
@@ -305,7 +305,7 @@ def ask(
 
         kappa : float or list[float], default 0
             The upper/lower-confidence-bound parameter, used by `acq_func`, that
-            balances exploration vs exploitation.
+            balances exploration (<0) vs exploitation (>0).
 
             Can also be an array of values to use sequentially for `n_cadidates`.
 

sambo/_test.py

@@ -386,7 +386,7 @@ def test_make_doc_plots(self):
                 name = plot_func.__name__.removeprefix("plot_")
                 with self.subTest(plot=name):
                     try:
-                        fig = plot_func(*zip(BUILTIN_METHODS, results),
+                        fig = plot_func(*zip((f'method={m!r}' for m in BUILTIN_METHODS), results),
                                         **KWARGS.get(plot_func, {}))
                     except TypeError:
                         fig = plot_func(results[0], **KWARGS.get(plot_func, {}))  # FIXME: plot (1, 3) subplots
@@ -419,7 +419,7 @@ def evaluate(x):
 
             result = optimizer.run()
             results.append(result)
-        named_results = [(f'estimator={e!r}', r) for e, r in zip(BUILTIN_ESTIMATORS, results)]
+        named_results = [(f"method='smbo', estimator={e!r}", r) for e, r in zip(BUILTIN_ESTIMATORS, results)]
         fig = plot_convergence(*named_results, true_minimum=0)
         www_dir = Path(__file__).parent.parent / 'www'
         www_dir.mkdir(exist_ok=True)