Revert "Docs/mkdocs-migration-cleanup"

examples/4_advanced_optimizer/4_intensify_crossvalidation.py

@@ -2,7 +2,7 @@
 # Flags: doc-Runnable
 
 An example of optimizing a simple support vector machine on the digits dataset. In contrast to the
-[simple example](/examples/1%20Basics/2_svm_cv/), in which all cross-validation folds are executed
+[simple example](../../1_basics/2_svm_cv), in which all cross-validation folds are executed
 at once, we use the intensification mechanism described in the original 
 [SMAC paper](https://link.springer.com/chapter/10.1007/978-3-642-25566-3_40) as also demonstrated
 by [Auto-WEKA](https://dl.acm.org/doi/10.1145/2487575.2487629). This mechanism allows us to

mkdocs.yaml

@@ -153,7 +153,7 @@ plugins:
         python:
           paths: [smac]
           # Extra objects which allow for linking to external docs
-          inventories:
+          import:
             - 'https://docs.python.org/3/objects.inv'
             - 'https://numpy.org/doc/stable/objects.inv'
             - 'https://pandas.pydata.org/docs/objects.inv'
@@ -197,14 +197,12 @@ nav:
   - Installation: "1_installation.md"
   - Package Overview: "2_package_overview.md"
   - Getting Started: "3_getting_started.md"
-  - Minimal Example: "4_minimal_example.md"
   - Advanced Usage:
     - "advanced_usage/1_components.md"
     - "advanced_usage/2_multi_fidelity.md"
     - "advanced_usage/3_multi_objective.md"
     - "advanced_usage/4_instances.md"
     - "advanced_usage/5_ask_and_tell.md"
-    - "advanced_usage/5.1_warmstarting.md"
     - "advanced_usage/6_commandline.md"
     - "advanced_usage/7_stopping_criteria.md"
     - "advanced_usage/8_logging.md"
@@ -221,9 +219,6 @@ nav:
     - "6_references.md"
     - "7_glossary.md"
     - "8_faq.md"
-    - "9_license.md"
-    - "10_experimental.md"
-    - "images/README.md"
   # - Contributing:
   #   - "contributing/index.md"
   #   - "contributing/contributing-a-benchmark.md"

smac/acquisition/function/confidence_bound.py

@@ -21,7 +21,7 @@ class AbstractConfidenceBound(AbstractAcquisitionFunction):
 
     Example for LCB (UCB adds the variance term instead of subtracting it):
 
-    $LCB(X) = \mu(\mathbf{X}) - \sqrt{\beta_t}\sigma(\mathbf{X})$ [SKKS10](/6_references/#SKKS10).
+    $LCB(X) = \mu(\mathbf{X}) - \sqrt{\beta_t}\sigma(\mathbf{X})$ [SKKS10](../../../docs/6_references).
 
     with
 

smac/acquisition/function/probability_improvement.py

@@ -19,9 +19,10 @@
 class PI(AbstractAcquisitionFunction):
     r"""Probability of Improvement
 
-    :math:`P(f_{t+1}(\mathbf{X})\geq f(\mathbf{X^+}))` :math:`:=`
-    :math:`\Phi(\\frac{ \mu(\mathbf{X})-f(\mathbf{X^+}) }{ \sigma(\mathbf{X}) })`
-    with :math:`f(X^+)` as the incumbent and :math:`\Phi` the cdf of the standard normal.
+    $$
+    P(f_{t+1}(\mathbf{X})\geq f(\mathbf{X^+})) := \Phi\left(\frac{ \mu(\mathbf{X})-f(\mathbf{X^+}) }{ \sigma(\mathbf{X}) }\right)
+    $$
+    with $f(X^+)$ as the incumbent and $\Phi$ the cdf of the standard normal.
 
     Parameters
     ----------

smac/acquisition/function/thompson.py

@@ -23,6 +23,11 @@ class TS(AbstractAcquisitionFunction):
 
     $TS(X) \sim \mathcal{N}(\mu(\mathbf{X}),\sigma(\mathbf{X}))$
     Returns -TS(X) as the acquisition_function optimizer maximizes the acquisition value.
+
+    Parameters
+    ----------
+    xi : float, defaults to 0.0
+        TS does not require xi here, we only wants to make it consistent with other acquisition functions.
     """
 
     @property

smac/acquisition/maximizer/abstract_acquisition_maximizer.py

@@ -27,15 +27,10 @@ class AbstractAcquisitionMaximizer:
 
     Parameters
     ----------
-    configspace : ConfigurationSpace
-        Configuration space used for sampling.
-    acquisition_function : AbstractAcquisitionFunction | None, defaults to None
-        Acquisition function to maximize.
-    challengers : int, defaults to 5000
-        Number of configurations sampled during the optimization process. Details depend on the used maximizer.
-        Also, the number of configurations that is returned by calling `maximize`.
+    configspace : ConfigurationSpace acquisition_function : AbstractAcquisitionFunction
+    challengers : int, defaults to 5000 Number of configurations sampled during the optimization process,
+    details depend on the used maximizer. Also, the number of configurations that is returned by calling `maximize`.
     seed : int, defaults to 0
-        Random seed.
     """
 
     def __init__(

smac/acquisition/maximizer/differential_evolution.py

@@ -22,15 +22,13 @@ def check_kwarg(cls: type, kwarg_name: str) -> bool:
 
     Parameters
     ----------
-    cls : type
-        The class to inspect.
-    kwarg_name : str
-        The name of the keyword argument to check.
+        cls (type): The class to inspect.
+        kwarg_name (str): The name of the keyword argument to check.
 
     Returns
     -------
-    bool
-        True if the class's __init__ method accepts the keyword argument, otherwise False.
+        bool: True if the class's __init__ method accepts the keyword argument,
+              otherwise False.
     """
     # Get the signature of the class's __init__ method
     init_signature = inspect.signature(cls.__init__)  # type: ignore[misc]

smac/facade/abstract_facade.py

@@ -347,6 +347,9 @@ def validate(
         ----------
         config : Configuration
             Configuration to validate
+        instances : list[str] | None, defaults to None
+            Which instances to validate. If None, all instances specified in the scenario are used.
+            In case that the budget type is real-valued, this argument is ignored.
         seed : int | None, defaults to None
             If None, the seed from the scenario is used.
 

smac/facade/blackbox_facade.py

@@ -239,7 +239,7 @@ def get_initial_design(  # type: ignore
         scenario: Scenario,
         *,
         n_configs: int | None = None,
-        n_configs_per_hyperparameter: int = 8,
+        n_configs_per_hyperparamter: int = 8,
         max_ratio: float = 0.25,
         additional_configs: list[Configuration] = None,
     ) -> SobolInitialDesign:
@@ -257,15 +257,15 @@ def get_initial_design(  # type: ignore
         max_ratio: float, defaults to 0.25
             Use at most ``scenario.n_trials`` * ``max_ratio`` number of configurations in the initial design.
             Additional configurations are not affected by this parameter.
-        additional_configs: list[Configuration], defaults to None
+        additional_configs: list[Configuration], defaults to []
             Adds additional configurations to the initial design.
         """
         if additional_configs is None:
             additional_configs = []
         return SobolInitialDesign(
             scenario=scenario,
             n_configs=n_configs,
-            n_configs_per_hyperparameter=n_configs_per_hyperparameter,
+            n_configs_per_hyperparameter=n_configs_per_hyperparamter,
             max_ratio=max_ratio,
             additional_configs=additional_configs,
             seed=scenario.seed,

smac/facade/hyperparameter_optimization_facade.py

@@ -136,7 +136,7 @@ def get_initial_design(  # type: ignore
         scenario: Scenario,
         *,
         n_configs: int | None = None,
-        n_configs_per_hyperparameter: int = 10,
+        n_configs_per_hyperparamter: int = 10,
         max_ratio: float = 0.25,
         additional_configs: list[Configuration] | None = None,
     ) -> SobolInitialDesign:
@@ -154,13 +154,13 @@ def get_initial_design(  # type: ignore
         max_ratio: float, defaults to 0.25
             Use at most ``scenario.n_trials`` * ``max_ratio`` number of configurations in the initial design.
             Additional configurations are not affected by this parameter.
-        additional_configs: list[Configuration], defaults to None
+        additional_configs: list[Configuration], defaults to []
             Adds additional configurations to the initial design.
         """
         return SobolInitialDesign(
             scenario=scenario,
             n_configs=n_configs,
-            n_configs_per_hyperparameter=n_configs_per_hyperparameter,
+            n_configs_per_hyperparameter=n_configs_per_hyperparamter,
             max_ratio=max_ratio,
             additional_configs=additional_configs,
         )

smac/facade/multi_fidelity_facade.py

@@ -72,7 +72,7 @@ def get_initial_design(  # type: ignore
         scenario: Scenario,
         *,
         n_configs: int | None = None,
-        n_configs_per_hyperparameter: int = 10,
+        n_configs_per_hyperparamter: int = 10,
         max_ratio: float = 0.25,
         additional_configs: list[Configuration] = None,
     ) -> RandomInitialDesign:
@@ -90,15 +90,15 @@ def get_initial_design(  # type: ignore
         max_ratio: float, defaults to 0.25
             Use at most ``scenario.n_trials`` * ``max_ratio`` number of configurations in the initial design.
             Additional configurations are not affected by this parameter.
-        additional_configs: list[Configuration], defaults to None
+        additional_configs: list[Configuration], defaults to []
             Adds additional configurations to the initial design.
         """
         if additional_configs is None:
             additional_configs = []
         return RandomInitialDesign(
             scenario=scenario,
             n_configs=n_configs,
-            n_configs_per_hyperparameter=n_configs_per_hyperparameter,
+            n_configs_per_hyperparameter=n_configs_per_hyperparamter,
             max_ratio=max_ratio,
             additional_configs=additional_configs,
         )

smac/model/gaussian_process/gaussian_process.py

@@ -101,7 +101,7 @@ def _train(
         ----------
         X : np.ndarray [#samples, #hyperparameters + #features]
             Input data points.
-        y : np.ndarray [#samples, #objectives]
+        Y : np.ndarray [#samples, #objectives]
             The corresponding target values.
         optimize_hyperparameters: boolean
             If set to true, the hyperparameters are optimized, otherwise the default hyperparameters of the kernel are
@@ -276,9 +276,9 @@ def sample_functions(self, X_test: np.ndarray, n_funcs: int = 1) -> np.ndarray:
 
         Parameters
         ----------
-        X_test : np.ndarray [#samples, #hyperparameters + #features]
+        X : np.ndarray [#samples, #hyperparameters + #features]
             Input data points.
-        n_funcs : int
+        n_funcs: int
             Number of function values that are drawn at each test point.
 
         Returns

smac/model/random_forest/random_forest.py

@@ -113,11 +113,11 @@ def __init__(
         Parameters
         ----------
         n_estimators : int, default=100
-            The number of trees in the forest.
+        The number of trees in the forest.
 
-            .. versionchanged:: 0.22
-                The default value of ``n_estimators`` changed from 10 to 100
-                in 0.22.
+        .. versionchanged:: 0.22
+           The default value of ``n_estimators`` changed from 10 to 100
+           in 0.22.
 
         criterion : {"squared_error", "absolute_error", "friedman_mse", "poisson"}, \
                 default="squared_error"

smac/random_design/modulus_design.py

@@ -51,15 +51,15 @@ class DynamicModulusRandomDesign(AbstractRandomDesign):
     Parameters
     ----------
     start_modulus : float, defaults to 2.0
-          Initially, every modulus-th configuration will be at random.
+       Initially, every modulus-th configuration will be at random.
     modulus_increment : float, defaults to 0.3
-          Increase modulus by this amount in every iteration.
+       Increase modulus by this amount in every iteration.
     end_modulus : float, defaults to np.inf
-          The maximum modulus ever used. If the value is reached before the optimization
-          is over, it is not further increased. If it is not reached before the optimization is over,
-          there will be no adjustment to make sure that the `end_modulus` is reached.
+       The maximum modulus ever used. If the value is reached before the optimization
+       is over, it is not further increased. If it is not reached before the optimization is over,
+       there will be no adjustment to make sure that the `end_modulus` is reached.
     seed : int, defaults to 0
-          Integer used to initialize the random state. This class does not use the seed.
+        Integer used to initialize the random state. This class does not use the seed.
     """
 
     def __init__(

smac/runhistory/runhistory.py

@@ -307,10 +307,8 @@ def add_trial(self, info: TrialInfo, value: TrialValue) -> None:
 
         Parameters
         ----------
-        info : TrialInfo
+        trial : TrialInfo
             The ``TrialInfo`` object of the running trial.
-        value : TrialValue
-            The ``TrialValue`` object of the running trial.
         """
         self.add(
             config=info.config,