v2.4.0

.github/workflows/citation.yml

@@ -11,7 +11,7 @@ jobs:
     runs-on: ubuntu-latest
     steps:
       - name: Check out a copy of the repository
-        uses: actions/checkout@v4
+        uses: actions/checkout@v5
 
       - name: Check whether the citation metadata from CITATION.cff is valid
         uses: citation-file-format/[email protected]

.github/workflows/dist.yml

@@ -28,10 +28,10 @@ jobs:
 
     steps:
     - name: Checkout
-      uses: actions/checkout@v4
+      uses: actions/checkout@v5
 
     - name: Setup Python
-      uses: actions/setup-python@v5
+      uses: actions/setup-python@v6
       with:
         python-version: "3.10"
 

.github/workflows/docs.yml

@@ -17,8 +17,8 @@ jobs:
   build:
     runs-on: ubuntu-latest
     steps:
-      - uses: actions/checkout@v4
-      - uses: actions/setup-python@v5
+      - uses: actions/checkout@v5
+      - uses: actions/setup-python@v6
         with:
           python-version: "3.10"
       - name: "Install dependancies"

.github/workflows/pre-commit-update.yml

@@ -11,9 +11,9 @@ jobs:
   auto-update:
     runs-on: ubuntu-latest
     steps:
-      - uses: actions/checkout@v4
+      - uses: actions/checkout@v5
 
-      - uses: actions/setup-python@v5
+      - uses: actions/setup-python@v6
 
       - uses: browniebroke/pre-commit-autoupdate-action@main
 

.github/workflows/pre-commit.yml

@@ -27,12 +27,12 @@ jobs:
     runs-on: ubuntu-latest
 
     steps:
-    - uses: actions/checkout@v4
+    - uses: actions/checkout@v5
       with:
         submodules: recursive
 
     - name: Setup Python 3.10
-      uses: actions/setup-python@v5
+      uses: actions/setup-python@v6
       with:
         python-version: "3.10"
 

.github/workflows/pytest.yml

@@ -49,15 +49,15 @@ jobs:
     strategy:
       fail-fast: false
       matrix:
-        python-version: ["3.8", "3.9", "3.10"]
+        python-version: ["3.9", "3.10", "3.11"]
         os: ["ubuntu-latest"]
 
     steps:
     - name: Checkout
-      uses: actions/checkout@v4
+      uses: actions/checkout@v5
 
     - name: Setup Python
-      uses: actions/setup-python@v5
+      uses: actions/setup-python@v6
       with:
         python-version: ${{ matrix.python-version }}
 
@@ -103,12 +103,12 @@ jobs:
     strategy:
       fail-fast: false
       matrix:
-        python-version: ["3.8", "3.9", "3.10"]
+        python-version: ["3.9", "3.10", "3.11"]
         os: ["ubuntu-latest"]
 
     steps:
     - name: Checkout
-      uses: actions/checkout@v4
+      uses: actions/checkout@v5
 
     - name: Conda install
       uses: conda-incubator/setup-miniconda@v3
@@ -143,15 +143,15 @@ jobs:
     strategy:
       fail-fast: false
       matrix:
-        python-version: ["3.8", "3.9", "3.10"]
+        python-version: ["3.9", "3.10", "3.11"]
         os: ["ubuntu-latest"]
 
     steps:
     - name: Checkout
-      uses: actions/checkout@v4
+      uses: actions/checkout@v5
 
     - name: Setup Python
-      uses: actions/setup-python@v5
+      uses: actions/setup-python@v6
       with:
         python-version: ${{ matrix.python-version }}
 

.github/workflows/recent_reminder.yml

@@ -32,7 +32,7 @@ jobs:
         echo "$(<recent_issues.md) <br />" >> mail.html
     - name: Send mail
       id: mail
-      uses: dawidd6/action-send-mail@v4
+      uses: dawidd6/action-send-mail@v6
       with:
         server_address: ${{secrets.MAIL_SERVER_ADDRESS}}
         server_port: ${{secrets.MAIL_SERVER_PORT}}

CHANGELOG.md

@@ -1,5 +1,23 @@
-# 2.3.1
+# 2.4.0
+## Improvements
+- Replace random forest from pyrfr with random forest from sklearn (#1246)
+- Submit trials to runners in SMBO instead of running configs directly (#937)
+- `target_function` becomes optional in Facade when using ask and tell exclusively (#946)
+
+## Documentation
+- Ask and tell without initial design and warmstarting
+
+## Examples
+- Add target function with additional arguments (#1134)
+
+## Bugfixes 
+- Ask and tell without initial design may no longer return a config from the initial design - if it is not "removed".
+- Addressing situations where the acquisition function suggests configurations that have already been sampled in prior iterations (#1216)
 
+## Misc
+- Rename retries to max_new_config_tries in ConfigSelector (#1207)
+
+# 2.3.1
 ## Misc
 - New SMAC logo
 - Fix doc link in README

CITATION.cff

@@ -9,7 +9,7 @@ date-released: "2016-08-17"
 url: "https://automl.github.io/SMAC3/master/index.html"
 repository-code: "https://github.com/automl/SMAC3"
 
-version: "2.3.1"
+version: "2.4.0"
 
 type: "software"
 keywords:

README.md

@@ -47,11 +47,6 @@ conda create -n SMAC python=3.10
 conda activate SMAC
 ```
 
-Install swig:
-```
-conda install gxx_linux-64 gcc_linux-64 swig
-```
-
 Install SMAC via PyPI:
 ```
 pip install smac
@@ -63,6 +58,20 @@ git clone https://github.com/automl/SMAC3.git && cd SMAC3
 make install-dev
 ```
 
+## Running SMAC with pyrfr
+starting from 2.4.0, SMAC uses random forest from [sklearn](https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestRegressor.html)
+instead of random forest from [pyrfr](https://pypi.org/project/pyrfr/) as the default surrogate model for HPO tasks.
+However, you could still use the old pyrfr surrogate model by calling `smac.facade.old.HyperparameterOptimizationRFRFacade`
+and `smac.facade.old.MultiFidelityRFRFacade`
+
+To work with pyrfr, you need to first install gcc, gxx, and swig:
+```
+conda install gxx_linux-64 gcc_linux-64 swig
+```
+then install smac with the pyrfr option:
+```
+pip install smac[pyrfr]
+```
 
 ## Minimal Example
 

docs/3_getting_started.md

@@ -2,7 +2,7 @@
 # Getting Started
 
 SMAC needs four core components (configuration space, target function, scenario and a facade) to run an
-optimization process, all of which are explained on this page.
+optimization process called Sequential Model Based Optimization (SMBO), all of which are explained on this page.
 
 They interact in the following way:
 
@@ -93,8 +93,8 @@ scenario = Scenario(
 !!! warn
     By default Facades will try to warmstart on preexisting logs. This behavior can be specified using the `overwrite` parameter.
 
-A [facade][smac.facade.abstract_facade] is the entry point to SMAC, which constructs a default optimization 
-pipeline for you. SMAC offers various facades, which satisfy many common use cases and are crucial to
+A [facade][smac.facade.abstract_facade] is the entry point to SMAC, which constructs a Sequential Model Based Optimization (`SMBO`) object for you.
+SMAC offers various facades, which satisfy many common use cases and are crucial to
 achieving peak performance. The idea behind the facades is to provide a simple interface to all of SMAC's components,
 which is easy to use and understand and without the need of deep diving into the material. However, experts are
 invited to change the components to their specific hyperparameter optimization needs. The following
@@ -139,4 +139,15 @@ smac = MFFacade(scenario=scenario, target_function=train)
 smac = ACFacade(scenario=scenario, target_function=train)
 smac = RFacade(scenario=scenario, target_function=train)
 smac = HBFacade(scenario=scenario, target_function=train)
-```
+```
+
+## SMBO
+Each `Facade` creates a [Sequential Model Based Optimization (SMBO)][smac.main.smbo] object for you. In addition to starting the optimization with
+```python
+incumbent = smbo.optimize()
+```
+it supports access to e.g. the runhistory with
+````
+runhistory = smbo.runhistory()
+````
+For more information check out the [API][smac.main.smbo].

docs/advanced_usage/5_ask_and_tell.md

@@ -15,5 +15,7 @@ and report the results of the trial.
     different budgets, they, obviously, can not be considered. However, all user-provided configurations will flow 
     into the intensification process.
 
+Notice: if you are exclusively using the ask-and-tell interface and do not use `smac.optimize()`, then smac no longer
+is responsible for the evaluation of the trials and therefore the Facade no longer will require a specified `target_algorithm` argument.
 
 Please have a look at our [ask-and-tell example](../examples/1%20Basics/3_ask_and_tell.md).

docs/devnotes/release_flow.md

@@ -0,0 +1,64 @@
+# How to Create a New Release
+Export version numer, e.g.
+```bash
+export VERSION="2.4.0"
+```
+If you do not use `uv`, remove `uv` from the commands.
+
+1. Refresh main.
+    ```bash
+    git checkout main
+    git pull
+    ```
+
+1. Checkout development branch:
+    ```bash
+    git checkout development
+    git pull
+    ```
+
+1. Run `make tests` to ensure everything works. If tests run through, proceed.
+    ```bash
+    make tests
+    ```
+
+1. Create new branch from development with name e.g. `v${VERSION}`:
+    ```bash
+    git branch v${VERSION}
+    git checkout v${VERSION}
+    ```
+
+1. Merge main into branch
+    ```bash
+    git merge main
+    ```
+
+1. Check `CHANGELOG.md` whether the version number is correct and the order is fine.
+1. Replace version numbers everywhere: in `CITATION.cff`, `__init__.py`.
+1. Create a PR to merge branch `v${VERSION}` into `main`. As description you can use the changelog notes.
+1. Test installation with a fresh environment, see `test_package.sh`.
+1. Merge PR if tests are fine and installation is fine.
+1. Create release, add notes from changelog.
+1. Update doc link.
+1. Deploy github pages (replace version in the following command):
+    ```bash
+    mike deploy "v${VERSION}" latest -u -p --title "v${VERSION} (latest)"
+    ```
+
+1. Upload to testpypi:
+    ```bash
+    python -m twine upload --repository testpypi dist/*
+    ```
+
+1. Test from testpypi:
+    ```bash
+    uv pip uninstall smac
+    uv pip install --index-url https://test.pypi.org/simple/ --extra-index-url https://pypi.org/simple/ smac==${VERSION}
+    python -c 'import smac'
+    ```
+    If this is fine, proceed.
+
+1. Upload to pypi: 
+    ```bash
+    python -m twine upload dist/*
+    ```

docs/devnotes/test_package.sh

@@ -0,0 +1,9 @@
+export SMACVERSION="2.4.0"
+make clean 
+make build
+pip install uv
+rm -r smac_test || true
+uv venv --python=3.12 smac_test
+source smac_test/bin/activate
+uv pip install dist/smac-$SMACVERSION.tar.gz
+python -c 'import smac'

docs/index.md

@@ -9,6 +9,20 @@ SMAC is a tool for algorithm configuration to optimize the parameters of arbitra
 
 SMAC3 is written in Python3 and continuously tested with Python 3.8, 3.9, and 3.10. Its Random Forest is written in C++. In the following, SMAC is representatively mentioned for SMAC3.
 
+## Features
+
+* Open source + active maintenance
+* Rich search space with floats, ordinals, categoricals and conditions
+* Ask-and-Tell Interface
+* Continue and Warmstart Optimization
+* Intensification mechanism to efficiently compare configurations
+* User priors
+* Parallelization, local and on a cluster with Dask
+* Multi-fidelity optimization, e.g. when we can evaluate our function with different resolutions
+* Multi-objective optimization with ParEGO
+* Optimization across many tasks (aka algorithm configuration)
+* Function to optimize can either be pythonic or called via a script
+* Easily extensible with callbacks
 
 ## Cite Us
 If you use SMAC, please cite our [JMLR paper](https://jmlr.org/papers/v23/21-0888.html):

examples/1_basics/2_svm_cv.py

@@ -53,8 +53,8 @@ def configspace(self) -> ConfigurationSpace:
     def train(self, config: Configuration, seed: int = 0) -> float:
         """Creates a SVM based on a configuration and evaluates it on the
         iris-dataset using cross-validation."""
-        config_dict = config.get_dictionary()
-        if "gamma" in config:
+        config_dict = dict(config)
+        if "gamma" in config_dict:
             config_dict["gamma"] = config_dict["gamma_value"] if config_dict["gamma"] == "value" else "auto"
             config_dict.pop("gamma_value", None)
 

examples/1_basics/3_ask_and_tell.py

@@ -2,6 +2,10 @@
 # Flags: doc-Runnable
 
 This examples show how to use the Ask-and-Tell interface.
+
+Notice, that the ask-and-tell interface will still use the initial design specified in the facade.
+Should you wish to add your own evaluated configurations instead or deactivate the initial
+design all together, please refer to the warmstarting example in conjunction with this one.
 """
 
 from ConfigSpace import Configuration, ConfigurationSpace, Float
@@ -52,7 +56,7 @@ def train(self, config: Configuration, seed: int = 0) -> float:
     # Now we use SMAC to find the best hyperparameters
     smac = HyperparameterOptimizationFacade(
         scenario,
-        model.train,
+        target_function=model.train,
         intensifier=intensifier,
         overwrite=True,
     )
@@ -68,7 +72,14 @@ def train(self, config: Configuration, seed: int = 0) -> float:
         smac.tell(info, value)
 
     # After calling ask+tell, we can still optimize
-    # Note: SMAC will optimize the next 90 trials because 10 trials already have been evaluated
+    # Note: SMAC will optimize the next 90 trials because 10 trials already have been evaluated.
+    # If we however choose not to call optimize; e.g. because we want to manage heavy
+    # computation of model.train completely outside smac, but still use it to suggest new
+    # configurations, then n_trials will only be relevant for the initial design in combination
+    # with initial design max_ratio! In fact in an only ask+tell case, we could even set
+    # target_function=None in the constructor, because smac wouldn't even need to know
+    # what the target function is. But that will prevent us from calling optimize and validate later
+    # on.
     incumbent = smac.optimize()
 
     # Get cost of default configuration