MO-SMAC merge

README.md

@@ -12,10 +12,10 @@ SMAC offers a robust and flexible framework for Bayesian Optimization to support
 hyperparameter configurations for their (Machine Learning) algorithms, datasets and applications at hand. The main core 
 consists of Bayesian Optimization in combination with an aggressive racing mechanism to efficiently decide which of two configurations performs better.
 
-SMAC3 is written in Python3 and continuously tested with Python 3.8, 3.9, and 3.10. Its Random
+SMAC3 is written in Python3 and continuously tested with Python 3.8, 3.9, and 3.10 (and works with newer python versions). Its Random
 Forest is written in C++. In further texts, SMAC is representatively mentioned for SMAC3.
 
-> [Documentation](https://automl.github.io/SMAC3)
+> [Documentation](https://automl.github.io/SMAC3/latest/)
 
 > [Roadmap](https://github.com/orgs/automl/projects/5/views/2)
 
@@ -36,7 +36,7 @@ We hope you enjoy this new user experience as much as we do. 🚀
 
 ## Installation
 
-This instruction is for the installation on a Linux system, for Windows and Mac and further information see the [documentation](https://automl.github.io/SMAC3/main/1_installation.html).
+This instruction is for the installation on a Linux system, for Windows and Mac and further information see the [documentation](https://automl.github.io/SMAC3/latest/1_installation/).
 
 Create a new environment with python 3.10 and make sure swig is installed either on your system or
 inside the environment. We demonstrate the installation via anaconda in the following:
@@ -94,7 +94,7 @@ smac = HyperparameterOptimizationFacade(scenario, train)
 incumbent = smac.optimize()
 ```
 
-More examples can be found in the [documentation](https://automl.github.io/SMAC3/main/examples/).
+More examples can be found in the [documentation](https://automl.github.io/SMAC3/latest/examples/1%20Basics/1_quadratic_function/).
 
 ## Visualization via DeepCAVE
 
@@ -123,7 +123,7 @@ For all other inquiries, please write an email to smac[at]ai[dot]uni[dash]hannov
 ## Miscellaneous
 
 SMAC3 is developed by the [AutoML Groups of the Universities of Hannover and
-Freiburg](http://www.automl.org/).
+Freiburg](http://www.automl.org/). It is a featured optimizer on [AutoML Space](https://automl.space/automl-tools/).
 
 If you have found a bug, please report to [issues](https://github.com/automl/SMAC3/issues). Moreover, we are 
 appreciating any kind of help. Find our guidelines for contributing to this package 
@@ -144,4 +144,4 @@ If you use SMAC in one of your research projects, please cite our
 }
 ```
 
-Copyright (C) 2016-2022  [AutoML Group](http://www.automl.org).
+Copyright (c) 2025, [Leibniz University Hannover - Institute of AI](https://www.ai.uni-hannover.de/)

examples/2_multi_fidelity/1_mlp_epochs.py

@@ -80,7 +80,7 @@ def configspace(self) -> ConfigurationSpace:
 
         return cs
 
-    def train(self, config: Configuration, seed: int = 0, budget: int = 25) -> float:
+    def train(self, config: Configuration, seed: int = 0, instance: str = "0", budget: int = 25) -> dict[str, float]:
         # For deactivated parameters (by virtue of the conditions),
         # the configuration stores None-values.
         # This is not accepted by the MLP, so we replace them with placeholder values.
@@ -106,7 +106,7 @@ def train(self, config: Configuration, seed: int = 0, budget: int = 25) -> float
             cv = StratifiedKFold(n_splits=5, random_state=seed, shuffle=True)  # to make CV splits consistent
             score = cross_val_score(classifier, dataset.data, dataset.target, cv=cv, error_score="raise")
 
-        return 1 - np.mean(score)
+        return {"accuracy": 1 - np.mean(score)}
 
 
 def plot_trajectory(facades: list[AbstractFacade]) -> None:
@@ -147,9 +147,11 @@ def plot_trajectory(facades: list[AbstractFacade]) -> None:
             mlp.configspace,
             walltime_limit=60,  # After 60 seconds, we stop the hyperparameter optimization
             n_trials=500,  # Evaluate max 500 different trials
-            min_budget=1,  # Train the MLP using a hyperparameter configuration for at least 5 epochs
-            max_budget=25,  # Train the MLP using a hyperparameter configuration for at most 25 epochs
-            n_workers=8,
+            instances=[str(i) for i in range(10)],
+            objectives="accuracy",
+            # min_budget=1,  # Train the MLP using a hyperparameter configuration for at least 5 epochs
+            # max_budget=25,  # Train the MLP using a hyperparameter configuration for at most 25 epochs
+            n_workers=4,
         )
 
         # We want to run five random configurations before starting the optimization.

smac/acquisition/function/abstract_acquisition_function.py

@@ -50,7 +50,7 @@ def update(self, model: AbstractModel, **kwargs: Any) -> None:
 
         This method will be called after fitting the model, but before maximizing the acquisition
         function. As an examples, EI uses it to update the current fmin. The default implementation only updates the
-        attributes of the acqusition function which are already present.
+        attributes of the acquisition function which are already present.
 
         Calls `_update` to update the acquisition function attributes.
 
@@ -65,7 +65,7 @@ def update(self, model: AbstractModel, **kwargs: Any) -> None:
         self._update(**kwargs)
 
     def _update(self, **kwargs: Any) -> None:
-        """Update acsquisition function attributes
+        """Update acquisition function attributes
 
         Might be different for each child class.
         """