more code cleaning/polishing

bayes_opt/logger.py

@@ -8,7 +8,7 @@
 from colorama import Fore, just_fix_windows_console
 
 if TYPE_CHECKING:
-    from bayes_opt.parameter import ParameterConfig
+    from bayes_opt.parameter import ParamsType
 
 just_fix_windows_console()
 
@@ -28,9 +28,9 @@ class ScreenLogger:
 
     _default_cell_size = 9
     _default_precision = 4
-    _colour_new_max = Fore.MAGENTA
-    _colour_regular_message = Fore.RESET
-    _colour_reset = Fore.RESET
+    _color_new_max = Fore.MAGENTA
+    _color_regular_message = Fore.RESET
+    _color_reset = Fore.RESET
 
     def __init__(self, verbose: int = 2, is_constrained: bool = False) -> None:
         self._verbose = verbose
@@ -141,8 +141,8 @@ def _print_step(
         self,
         keys: list[str],
         result: dict[str, Any],
-        params_config: Mapping[str, ParameterConfig],
-        colour: str = _colour_regular_message,
+        params_config: Mapping[str, ParamsType],
+        color: str = _color_regular_message,
     ) -> str:
         """Print a step.
 
@@ -154,12 +154,12 @@ def _print_step(
         keys : list[str]
             The parameter keys.
 
-        params_config : Mapping[str, ParameterConfig]
+        params_config : Mapping[str, ParamsType]
             The configuration to map the key to the parameter for correct formatting.
 
-        colour : str, optional
+        color : str, optional
             Color to use for the output.
-            (Default value = _colour_regular_message, equivalent to Fore.RESET)
+            (Default value = _color_regular_message, equivalent to Fore.RESET)
 
         Returns
         -------
@@ -178,7 +178,7 @@ def _print_step(
             else params_config[key].to_string(val, self._default_cell_size)
             for key, val in params.items()
         ]
-        return "| " + " | ".join(colour + x + self._colour_reset for x in cells if x is not None) + " |"
+        return "| " + " | ".join(color + x + self._color_reset for x in cells if x is not None) + " |"
 
     def _print_header(self, keys: list[str]) -> str:
         """Print the header of the log.
@@ -258,7 +258,7 @@ def log_optimization_step(
         self,
         keys: list[str],
         result: dict[str, Any],
-        params_config: Mapping[str, ParameterConfig],
+        params_config: Mapping[str, ParamsType],
         current_max: dict[str, Any] | None,
     ) -> None:
         """Log an optimization step.
@@ -271,7 +271,7 @@ def log_optimization_step(
         result : dict[str, Any]
             The result dictionary for the most recent step.
 
-        params_config : Mapping[str, ParameterConfig]
+        params_config : Mapping[str, ParamsType]
             The configuration to map the key to the parameter for correct formatting.
 
         current_max : dict[str, Any] | None
@@ -283,8 +283,8 @@ def log_optimization_step(
             return
 
         if self._verbose == 2 or is_new_max:
-            colour = self._colour_new_max if is_new_max else self._colour_regular_message
-            line = self._print_step(keys, result, params_config, colour=colour) + "\n"
+            color = self._color_new_max if is_new_max else self._color_regular_message
+            line = self._print_step(keys, result, params_config, color=color) + "\n"
             if self._verbose:
                 print(line, end="")
 

tests/test_acquisition.py

@@ -406,130 +406,30 @@ def verify_optimizers_match(optimizer1, optimizer2):
     assert suggestion1 == suggestion2, f"\nSuggestion 1: {suggestion1}\nSuggestion 2: {suggestion2}"
 
 
-def test_integration_upper_confidence_bound(target_func_x_and_y, pbounds, tmp_path):
-    """Test save/load integration with UpperConfidenceBound acquisition."""
-    acquisition_function = UpperConfidenceBound(kappa=2.576)
-
-    # Create and run first optimizer
-    optimizer = BayesianOptimization(
-        f=target_func_x_and_y,
-        pbounds=pbounds,
-        acquisition_function=acquisition_function,
-        random_state=1,
-        verbose=0,
-    )
-    optimizer.maximize(init_points=2, n_iter=3)
-
-    # Save state
-    state_path = tmp_path / "ucb_state.json"
-    optimizer.save_state(state_path)
-
-    # Create new optimizer and load state
-    new_optimizer = BayesianOptimization(
-        f=target_func_x_and_y,
-        pbounds=pbounds,
-        acquisition_function=UpperConfidenceBound(kappa=2.576),
-        random_state=1,
-        verbose=0,
-    )
-    new_optimizer.load_state(state_path)
-
-    verify_optimizers_match(optimizer, new_optimizer)
-
-
-def test_integration_probability_improvement(target_func_x_and_y, pbounds, tmp_path):
-    """Test save/load integration with ProbabilityOfImprovement acquisition."""
-    acquisition_function = ProbabilityOfImprovement(xi=0.01)
-
-    optimizer = BayesianOptimization(
-        f=target_func_x_and_y,
-        pbounds=pbounds,
-        acquisition_function=acquisition_function,
-        random_state=1,
-        verbose=0,
-    )
-    optimizer.maximize(init_points=2, n_iter=3)
-
-    state_path = tmp_path / "pi_state.json"
-    optimizer.save_state(state_path)
-
-    new_optimizer = BayesianOptimization(
-        f=target_func_x_and_y,
-        pbounds=pbounds,
-        acquisition_function=ProbabilityOfImprovement(xi=0.01),
-        random_state=1,
-        verbose=0,
-    )
-    new_optimizer.load_state(state_path)
-
-    verify_optimizers_match(optimizer, new_optimizer)
-
-
-def test_integration_expected_improvement(target_func_x_and_y, pbounds, tmp_path):
-    """Test save/load integration with ExpectedImprovement acquisition."""
-    acquisition_function = ExpectedImprovement(xi=0.01)
-
-    optimizer = BayesianOptimization(
-        f=target_func_x_and_y,
-        pbounds=pbounds,
-        acquisition_function=acquisition_function,
-        random_state=1,
-        verbose=0,
-    )
-    optimizer.maximize(init_points=2, n_iter=3)
-
-    state_path = tmp_path / "ei_state.json"
-    optimizer.save_state(state_path)
-
-    new_optimizer = BayesianOptimization(
-        f=target_func_x_and_y,
-        pbounds=pbounds,
-        acquisition_function=ExpectedImprovement(xi=0.01),
-        random_state=1,
-        verbose=0,
-    )
-    new_optimizer.load_state(state_path)
-
-    verify_optimizers_match(optimizer, new_optimizer)
-
-
-def test_integration_constant_liar(target_func_x_and_y, pbounds, tmp_path):
-    """Test save/load integration with ConstantLiar acquisition."""
-    base_acq = UpperConfidenceBound(kappa=2.576)
-    acquisition_function = ConstantLiar(base_acquisition=base_acq)
-
-    optimizer = BayesianOptimization(
-        f=target_func_x_and_y,
-        pbounds=pbounds,
-        acquisition_function=acquisition_function,
-        random_state=1,
-        verbose=0,
-    )
-    optimizer.maximize(init_points=2, n_iter=3)
-
-    state_path = tmp_path / "cl_state.json"
-    optimizer.save_state(state_path)
-
-    new_optimizer = BayesianOptimization(
-        f=target_func_x_and_y,
-        pbounds=pbounds,
-        acquisition_function=ConstantLiar(base_acquisition=UpperConfidenceBound(kappa=2.576)),
-        random_state=1,
-        verbose=0,
-    )
-    new_optimizer.load_state(state_path)
-
-    verify_optimizers_match(optimizer, new_optimizer)
-
-
-def test_integration_gp_hedge(target_func_x_and_y, pbounds, tmp_path):
-    """Test save/load integration with GPHedge acquisition."""
-    base_acquisitions = [
-        UpperConfidenceBound(kappa=2.576),
-        ProbabilityOfImprovement(xi=0.01),
-        ExpectedImprovement(xi=0.01),
-    ]
-    acquisition_function = GPHedge(base_acquisitions=base_acquisitions)
+@pytest.mark.parametrize(
+    ("acquisition_fn_factory", "state_filename"),
+    [
+        (lambda: UpperConfidenceBound(kappa=2.576), "ucb_state.json"),
+        (lambda: ProbabilityOfImprovement(xi=0.01), "pi_state.json"),
+        (lambda: ExpectedImprovement(xi=0.01), "ei_state.json"),
+        (lambda: ConstantLiar(base_acquisition=UpperConfidenceBound(kappa=2.576)), "cl_state.json"),
+        (
+            lambda: GPHedge(
+                base_acquisitions=[
+                    UpperConfidenceBound(kappa=2.576),
+                    ProbabilityOfImprovement(xi=0.01),
+                    ExpectedImprovement(xi=0.01),
+                ]
+            ),
+            "gphedge_state.json",
+        ),
+    ],
+)
+def test_integration_acquisition_functions(
+    acquisition_fn_factory, state_filename, target_func_x_and_y, pbounds, tmp_path
+):
+    """Parametrized integration test for acquisition functions."""
+    acquisition_function = acquisition_fn_factory()
 
     optimizer = BayesianOptimization(
         f=target_func_x_and_y,
@@ -540,18 +440,13 @@ def test_integration_gp_hedge(target_func_x_and_y, pbounds, tmp_path):
     )
     optimizer.maximize(init_points=2, n_iter=3)
 
-    state_path = tmp_path / "gphedge_state.json"
+    state_path = tmp_path / state_filename
     optimizer.save_state(state_path)
 
-    new_base_acquisitions = [
-        UpperConfidenceBound(kappa=2.576),
-        ProbabilityOfImprovement(xi=0.01),
-        ExpectedImprovement(xi=0.01),
-    ]
     new_optimizer = BayesianOptimization(
         f=target_func_x_and_y,
         pbounds=pbounds,
-        acquisition_function=GPHedge(base_acquisitions=new_base_acquisitions),
+        acquisition_function=acquisition_fn_factory(),
         random_state=1,
         verbose=0,
     )

tests/test_logger.py

@@ -157,7 +157,7 @@ def test_step():
     # Test with custom color
     custom_color = Fore.RED
     step_str_colored = logger._print_step(
-        optimizer._space.keys, optimizer._space.res()[-1], optimizer._space.params_config, colour=custom_color
+        optimizer._space.keys, optimizer._space.res()[-1], optimizer._space.params_config, color=custom_color
     )
     assert custom_color in step_str_colored
 

tests/test_target_space.py

@@ -2,8 +2,8 @@
 
 import numpy as np
 import pytest
+from scipy.optimize import NonlinearConstraint
 
-from bayes_opt.constraint import ConstraintModel
 from bayes_opt.exception import NotUniqueError
 from bayes_opt.target_space import TargetSpace
 
@@ -99,7 +99,7 @@ def test_register():
 
 
 def test_register_with_constraint():
-    constraint = ConstraintModel(lambda x: x, -2, 2, transform=lambda x: x)
+    constraint = NonlinearConstraint(lambda x: x, -2, 2)
     space = TargetSpace(target_func, PBOUNDS, constraint=constraint)
 
     assert len(space) == 0
@@ -194,7 +194,7 @@ def test_y_max():
 
 def test_y_max_with_constraint():
     PBOUNDS = {"p1": (0, 10), "p2": (1, 100)}
-    constraint = ConstraintModel(lambda p1, p2: p1 - p2, -2, 2)
+    constraint = NonlinearConstraint(lambda p1, p2: p1 - p2, -2, 2)
     space = TargetSpace(target_func, PBOUNDS, constraint)
     assert space._target_max() is None
     space.probe(params={"p1": 1, "p2": 2})  # Feasible
@@ -228,7 +228,7 @@ def test_max():
 
 def test_max_with_constraint():
     PBOUNDS = {"p1": (0, 10), "p2": (1, 100)}
-    constraint = ConstraintModel(lambda p1, p2: p1 - p2, -2, 2)
+    constraint = NonlinearConstraint(lambda p1, p2: p1 - p2, -2, 2)
     space = TargetSpace(target_func, PBOUNDS, constraint=constraint)
 
     assert space.max() is None
@@ -241,7 +241,7 @@ def test_max_with_constraint():
 
 def test_max_with_constraint_identical_target_value():
     PBOUNDS = {"p1": (0, 10), "p2": (1, 100)}
-    constraint = ConstraintModel(lambda p1, p2: p1 - p2, -2, 2)
+    constraint = NonlinearConstraint(lambda p1, p2: p1 - p2, -2, 2)
     space = TargetSpace(target_func, PBOUNDS, constraint=constraint)
 
     assert space.max() is None