Replace deprecated configpsace methods

smac/acquisition/maximizer/local_search.py

@@ -153,7 +153,10 @@ def _get_initial_points(
         init_points = []
         n_init_points = n_points
         if len(previous_configs) < n_points:
-            sampled_points = self._configspace.sample_configuration(size=n_points - len(previous_configs))
+            if n_points - len(previous_configs) == 1:
+                sampled_points = [self._configspace.sample_configuration()]
+            else:
+                sampled_points = self._configspace.sample_configuration(size=n_points - len(previous_configs))
             n_init_points = len(previous_configs)
             if not isinstance(sampled_points, list):
                 sampled_points = [sampled_points]

smac/acquisition/maximizer/random_search.py

@@ -41,7 +41,7 @@ def _maximize(
         if n_points > 1:
             rand_configs = self._configspace.sample_configuration(size=n_points)
         else:
-            rand_configs = [self._configspace.sample_configuration(size=1)]
+            rand_configs = [self._configspace.sample_configuration()]
 
         if _sorted:
             for i in range(len(rand_configs)):

smac/facade/blackbox_facade.py

@@ -112,11 +112,11 @@ def get_kernel(scenario: Scenario) -> kernels.Kernel:
         cont_dims = np.where(np.array(types) == 0)[0]
         cat_dims = np.where(np.array(types) != 0)[0]
 
-        if (len(cont_dims) + len(cat_dims)) != len(scenario.configspace.get_hyperparameters()):
+        if (len(cont_dims) + len(cat_dims)) != len(list(scenario.configspace.values())):
             raise ValueError(
                 "The inferred number of continuous and categorical hyperparameters "
                 "must equal the total number of hyperparameters. Got "
-                f"{(len(cont_dims) + len(cat_dims))} != {len(scenario.configspace.get_hyperparameters())}."
+                f"{(len(cont_dims) + len(cat_dims))} != {len(list(scenario.configspace.values()))}."
             )
 
         # Constant Kernel

smac/initial_design/abstract_initial_design.py

@@ -77,7 +77,7 @@ def __init__(
 
         self._additional_configs = additional_configs
 
-        n_params = len(self._configspace.get_hyperparameters())
+        n_params = len(list(self._configspace.values()))
         if n_configs is not None:
             logger.info("Using `n_configs` and ignoring `n_configs_per_hyperparameter`.")
             self._n_configs = n_configs
@@ -174,10 +174,10 @@ def _transform_continuous_designs(
         configs : list[Configuration]
             Continuous transformed configs.
         """
-        params = configspace.get_hyperparameters()
+        params = list(configspace.values())
         for idx, param in enumerate(params):
             if isinstance(param, IntegerHyperparameter):
-                design[:, idx] = param._inverse_transform(param._transform(design[:, idx]))
+                design[:, idx] = param.to_vector(param.to_value(design[:, idx]))
             elif isinstance(param, NumericalHyperparameter):
                 continue
             elif isinstance(param, Constant):

smac/initial_design/factorial_design.py

@@ -22,7 +22,7 @@ class FactorialInitialDesign(AbstractInitialDesign):
     """Factorial initial design to select corner and middle configurations."""
 
     def _select_configurations(self) -> list[Configuration]:
-        params = self._configspace.get_hyperparameters()
+        params = list(self._configspace.values())
 
         values = []
         mid = []

smac/initial_design/latin_hypercube_design.py

@@ -16,7 +16,7 @@ class LatinHypercubeInitialDesign(AbstractInitialDesign):
     """
 
     def _select_configurations(self) -> list[Configuration]:
-        params = self._configspace.get_hyperparameters()
+        params = list(self._configspace.values())
 
         constants = 0
         for p in params:

smac/initial_design/random_design.py

@@ -12,9 +12,10 @@ class RandomInitialDesign(AbstractInitialDesign):
     """Initial design that evaluates random configurations."""
 
     def _select_configurations(self) -> list[Configuration]:
-        configs = self._configspace.sample_configuration(size=self._n_configs)
         if self._n_configs == 1:
-            configs = [configs]
+            configs = [self._configspace.sample_configuration()]
+        else:
+            configs = self._configspace.sample_configuration(size=self._n_configs)
         for config in configs:
             config.origin = "Initial Design: Random"
         return configs

smac/initial_design/sobol_design.py

@@ -22,14 +22,14 @@ class SobolInitialDesign(AbstractInitialDesign):
     def __init__(self, *args: Any, **kwargs: Any) -> None:
         super().__init__(*args, **kwargs)
 
-        if len(self._configspace.get_hyperparameters()) > 21201:
+        if len(list(self._configspace.values())) > 21201:
             raise ValueError(
                 "The default initial design Sobol sequence can only handle up to 21201 dimensions. "
                 "Please use a different initial design, such as the Latin Hypercube design."
             )
 
     def _select_configurations(self) -> list[Configuration]:
-        params = self._configspace.get_hyperparameters()
+        params = list(self._configspace.values())
 
         constants = 0
         for p in params:

smac/intensifier/successive_halving.py

@@ -210,7 +210,7 @@ def get_state(self) -> dict[str, Any]:  # noqa: D102
             for seed, configs in self._tracker[key]:
                 # We have to make key serializable
                 new_key = f"{key[0]},{key[1]}"
-                tracker[new_key].append((seed, [config.get_dictionary() for config in configs]))
+                tracker[new_key].append((seed, [dict(config) for config in configs]))
 
         return {"tracker": tracker}
 

smac/main/config_selector.py

@@ -177,7 +177,7 @@ def __iter__(self) -> Iterator[Configuration]:
                 # the configspace.
                 logger.debug("No data available to train the model. Sample a random configuration.")
 
-                config = self._scenario.configspace.sample_configuration(1)
+                config = self._scenario.configspace.sample_configuration()
                 self._call_callbacks_on_end(config)
                 yield config
                 self._call_callbacks_on_start()

smac/model/abstract_model.py

@@ -68,7 +68,7 @@ def __init__(
                         raise RuntimeError("Instances must have the same number of features.")
 
         self._n_features = n_features
-        self._n_hps = len(self._configspace.get_hyperparameters())
+        self._n_hps = len(list(self._configspace.values()))
 
         self._pca = PCA(n_components=self._pca_components)
         self._scaler = MinMaxScaler()

smac/model/gaussian_process/abstract_gaussian_process.py

@@ -171,7 +171,7 @@ def _get_all_priors(
 
     def _set_has_conditions(self) -> None:
         """Sets `has_conditions` on `current_param`."""
-        has_conditions = len(self._configspace.get_conditions()) > 0
+        has_conditions = len(self._configspace.conditions) > 0
         to_visit = []
         to_visit.append(self._kernel)
 

smac/model/random_forest/abstract_random_forest.py

@@ -28,9 +28,9 @@ def __init__(self, *args: Any, **kwargs: Any) -> None:
 
     def _impute_inactive(self, X: np.ndarray) -> np.ndarray:
         X = X.copy()
-        for idx, hp in enumerate(self._configspace.get_hyperparameters()):
+        for idx, hp in enumerate(list(self._configspace.values())):
             if idx not in self._conditional:
-                parents = self._configspace.get_parents_of(hp.name)
+                parents = self._configspace.parents_of[hp.name]
                 if len(parents) == 0:
                     self._conditional[idx] = False
                 else:

smac/runhistory/encoder/abstract_encoder.py

@@ -68,7 +68,7 @@ def __init__(
         self._instances = scenario.instances
         self._instance_features = scenario.instance_features
         self._n_features = scenario.count_instance_features()
-        self._n_params = len(scenario.configspace.get_hyperparameters())
+        self._n_params = len(list(scenario.configspace.values()))
 
         if self._instances is not None and self._n_features == 0:
             logger.warning(

smac/runhistory/runhistory.py

@@ -262,7 +262,7 @@ def add(
 
         # Construct keys and values for the data dictionary
         for key, value in (
-            ("config", config.get_dictionary()),
+            ("config", dict(config)),
             ("config_id", config_id),
             ("instance", instance),
             ("seed", seed),
@@ -783,7 +783,7 @@ def save(self, filename: str | Path = "runhistory.json") -> None:
         config_origins = {}
         for id_, config in self._ids_config.items():
             if id_ in config_ids_to_serialize:
-                configs[id_] = config.get_dictionary()
+                configs[id_] = dict(config)
 
             config_origins[id_] = config.origin
 

smac/runner/target_function_script_runner.py

@@ -132,7 +132,7 @@ def run(
         status = StatusType.SUCCESS
 
         # Add config arguments to the kwargs
-        for k, v in config.get_dictionary().items():
+        for k, v in dict(config).items():
             if k in kwargs:
                 raise RuntimeError(f"The key {k} is already in use. Please use a different one.")
             kwargs[k] = v

smac/utils/configspace.py

@@ -53,11 +53,11 @@ def get_types(
     The bounds for the instance features are *not* added in this function.
     """
     # Extract types vector for rf from config space and the bounds
-    types = [0] * len(configspace.get_hyperparameters())
+    types = [0] * len(list(configspace.values()))
     bounds = [(np.nan, np.nan)] * len(types)
 
-    for i, param in enumerate(configspace.get_hyperparameters()):
-        parents = configspace.get_parents_of(param.name)
+    for i, param in enumerate(list(configspace.values())):
+        parents = configspace.parents_of[param.name]
         if len(parents) == 0:
             can_be_inactive = False
         else:

tests/fixtures/config_selector.py

@@ -40,7 +40,7 @@ def __iter__(self):
             yield config
 
         while True:
-            config = self._scenario.configspace.sample_configuration(1)
+            config = self._scenario.configspace.sample_configuration()
             if config not in self._processed_configs:
                 self._processed_configs.append(config)
                 yield config

tests/test_acquisition/test_maximizers.py

@@ -205,8 +205,8 @@ def model(configspace: ConfigurationSpace):
     model = RandomForest(configspace)
 
     np.random.seed(0)
-    X = np.random.rand(100, len(configspace.get_hyperparameters()))
-    y = 1 - (np.sum(X, axis=1) / len(configspace.get_hyperparameters()))
+    X = np.random.rand(100, len(list(configspace.values())))
+    y = 1 - (np.sum(X, axis=1) / len(list(configspace.values())))
     model.train(X, y)
 
     return model

tests/test_initial_design/test_factorical_design.py

@@ -39,7 +39,7 @@ def get_ordinal_param(name: str):
         for i in range(n_dim):
             for j, get_param in enumerate(get_params):
                 param_name = f"x{i+1}_{j}"
-                cs.add_hyperparameter(get_param(param_name))
+                cs.add(get_param(param_name))
 
         design = FactorialInitialDesign(
             make_scenario(configspace=cs),

tests/test_initial_design/test_initial_design.py

@@ -45,7 +45,7 @@ def test_config_numbers(make_scenario, configspace_small):
     scenario = make_scenario(configspace_small)
     configs = configspace_small.sample_configuration(n_configs)
 
-    n_hps = len(configspace_small.get_hyperparameters())
+    n_hps = len(list(configspace_small.values()))
 
     dc = AbstractInitialDesign(
         scenario=scenario,

tests/test_intensifier/test_abstract_intensifier.py

@@ -19,7 +19,7 @@ def test_setting_runhistory(make_scenario, configspace_small, make_config_select
     intensifier.config_selector = make_config_selector(scenario, runhistory, n_initial_configs=1)
 
     config = configspace_small.get_default_configuration()
-    config2 = configspace_small.sample_configuration(1)
+    config2 = configspace_small.sample_configuration()
 
     # Add some entries to the runhistory
     runhistory.add(
@@ -55,7 +55,7 @@ def test_incumbent_selection_single_objective(make_scenario, configspace_small,
     intensifier.runhistory = runhistory
 
     config = configspace_small.get_default_configuration()
-    config2 = configspace_small.sample_configuration(1)
+    config2 = configspace_small.sample_configuration()
 
     runhistory.add(config=config, cost=50, time=0.0, instance=scenario.instances[0], seed=999)
     intensifier.update_incumbents(config)
@@ -88,7 +88,7 @@ def test_incumbent_selection_multi_objective(make_scenario, configspace_small, m
     intensifier.runhistory = runhistory
 
     config = configspace_small.get_default_configuration()
-    config2 = configspace_small.sample_configuration(1)
+    config2 = configspace_small.sample_configuration()
 
     runhistory.add(config=config, cost=[50, 10], time=0.0, instance=scenario.instances[0], seed=999)
     intensifier.update_incumbents(config)
@@ -182,8 +182,8 @@ def test_pareto_front1(make_scenario, configspace_small):
     runhistory = RunHistory()
     intensifier = AbstractIntensifier(scenario=scenario, max_config_calls=3, seed=0)
     intensifier.runhistory = runhistory
-    config1 = configspace_small.sample_configuration(1)
-    config2 = configspace_small.sample_configuration(1)
+    config1 = configspace_small.sample_configuration()
+    config2 = configspace_small.sample_configuration()
 
     runhistory.add(
         config=config1,
@@ -211,8 +211,8 @@ def test_pareto_front2(make_scenario, configspace_small):
     runhistory = RunHistory()
     intensifier = AbstractIntensifier(scenario=scenario, max_config_calls=3, seed=0)
     intensifier.runhistory = runhistory
-    config1 = configspace_small.sample_configuration(1)
-    config2 = configspace_small.sample_configuration(1)
+    config1 = configspace_small.sample_configuration()
+    config2 = configspace_small.sample_configuration()
 
     runhistory.add(
         config=config1,
@@ -240,9 +240,9 @@ def test_pareto_front3(make_scenario, configspace_small):
     runhistory = RunHistory()
     intensifier = AbstractIntensifier(scenario=scenario, max_config_calls=3, seed=0)
     intensifier.runhistory = runhistory
-    config1 = configspace_small.sample_configuration(1)
-    config2 = configspace_small.sample_configuration(1)
-    config3 = configspace_small.sample_configuration(1)
+    config1 = configspace_small.sample_configuration()
+    config2 = configspace_small.sample_configuration()
+    config3 = configspace_small.sample_configuration()
 
     runhistory.add(
         config=config1,

tests/test_intensifier/test_intensifier.py

@@ -192,7 +192,7 @@ def test_intensifier_with_filled_runhistory(make_scenario, configspace_small, ma
     intensifier.config_selector = make_config_selector(scenario, runhistory, n_initial_configs=1)
     intensifier.runhistory = runhistory
     config = configspace_small.get_default_configuration()
-    config2 = configspace_small.sample_configuration(1)
+    config2 = configspace_small.sample_configuration()
 
     # Add some entries to the runhistory
     runhistory.add(

tests/test_intensifier/test_successive_halving.py

@@ -131,7 +131,7 @@ def test_incumbents_any_budget(make_scenario, configspace_small, make_config_sel
     intensifier.config_selector = make_config_selector(scenario, runhistory, n_initial_configs=1)
     intensifier.runhistory = runhistory
     config = configspace_small.get_default_configuration()
-    config2 = configspace_small.sample_configuration(1)
+    config2 = configspace_small.sample_configuration()
 
     # Add some entries to the runhistory
     runhistory.add(config=config, cost=0.5, time=0.0, seed=8, budget=1, status=StatusType.SUCCESS)
@@ -176,7 +176,7 @@ def test_incumbents_highest_observed_budget(make_scenario, configspace_small, ma
     intensifier.runhistory = runhistory
     intensifier.__post_init__()
     config = configspace_small.get_default_configuration()
-    config2 = configspace_small.sample_configuration(1)
+    config2 = configspace_small.sample_configuration()
 
     # Add some entries to the runhistory
     runhistory.add(config=config, cost=0.5, time=0.0, seed=8, budget=1, status=StatusType.SUCCESS)
@@ -217,7 +217,7 @@ def test_incumbents_highest_budget(make_scenario, configspace_small, make_config
     intensifier.runhistory = runhistory
     intensifier.__post_init__()
     config = configspace_small.get_default_configuration()
-    config2 = configspace_small.sample_configuration(1)
+    config2 = configspace_small.sample_configuration()
 
     # Add some entries to the runhistory
     runhistory.add(config=config, cost=0.5, time=0.0, seed=8, budget=1, status=StatusType.SUCCESS)

tests/test_main/_test_boing.py

@@ -149,8 +149,8 @@ def test_do_switching(make_scenario):
 
 def test_subspace_extraction():
     cs = ConfigurationSpace(0)
-    cs.add_hyperparameter(UniformFloatHyperparameter("x0", 0.0, 1.0))
-    cs.add_hyperparameter(CategoricalHyperparameter("x1", [0, 1, 2, 3, 4, 5]))
+    cs.add(UniformFloatHyperparameter("x0", 0.0, 1.0))
+    cs.add(CategoricalHyperparameter("x1", [0, 1, 2, 3, 4, 5]))
 
     rf = RandomForest(
         cs,

tests/test_model/_test_gp_gpytorch.py

@@ -60,7 +60,7 @@ def get_gp(n_dimensions, rs, noise=None, normalize_y=True) -> GPyTorchGaussianPr
 
     configspace = ConfigurationSpace()
     for i in range(n_dimensions):
-        configspace.add_hyperparameter(UniformFloatHyperparameter("x%d" % i, 0, 1))
+        configspace.add(UniformFloatHyperparameter("x%d" % i, 0, 1))
 
     model = GPyTorchGaussianProcess(
         configspace=configspace,
@@ -122,9 +122,9 @@ def get_mixed_gp(cat_dims, cont_dims, rs, normalize_y=True):
 
     cs = ConfigurationSpace()
     for c in cont_dims:
-        cs.add_hyperparameter(UniformFloatHyperparameter("X%d" % c, 0, 1))
+        cs.add(UniformFloatHyperparameter("X%d" % c, 0, 1))
     for c in cat_dims:
-        cs.add_hyperparameter(CategoricalHyperparameter("X%d" % c, [0, 1, 2, 3]))
+        cs.add(CategoricalHyperparameter("X%d" % c, [0, 1, 2, 3]))
 
     model = GPyTorchGaussianProcess(
         configspace=cs,

tests/test_model/_test_lgpga.py

@@ -78,7 +78,7 @@ def setUp(self) -> None:
         self.gp_model, self.cs = generate_lgpga(self.kernel, n_dimensions=num_dims, rs=rs)
 
     def test_init(self):
-        np.testing.assert_equal(self.gp_model.cont_dims, np.arange(len(self.cs.get_hyperparameters())))
+        np.testing.assert_equal(self.gp_model.cont_dims, np.arange(len(list(self.cs.values()))))
         np.testing.assert_equal(self.gp_model.cat_dims, np.array([]))
 
     def test_update_attribute(self):