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Merge branch 'development' into v2.4.0
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.github/workflows/citation.yml

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runs-on: ubuntu-latest
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steps:
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- name: Check out a copy of the repository
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uses: actions/checkout@v5
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uses: actions/checkout@v6
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- name: Check whether the citation metadata from CITATION.cff is valid
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uses: citation-file-format/cffconvert-github-action@2.0.0

.github/workflows/dist.yml

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- name: Setup Python
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.github/workflows/docs.yml

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build:
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- uses: actions/checkout@v6
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- uses: actions/setup-python@v6
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python-version: "3.10"

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

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.github/workflows/pre-commit.yml

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.github/workflows/pytest.yml

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- name: Checkout
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.gitignore

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# Pyre type checker
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.pyre/
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# SMAC Logs
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*smac3-output_*
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*smac3_output*
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# Dask Logs
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tmp/smac_dask_slurm
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# macOS files
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.DS_Store
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.vscode
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projects
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_api
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_api
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branin.pkl

CHANGELOG.md

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- Ask and tell without initial design and warmstarting
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- Random forest replacement (sklearn instead of pyrfr)
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- Deterministic configuration of SMAC
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- Add a description of parallelization in the documentation (#1226)
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## Examples
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- Add target function with additional arguments (#1134)
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- Adapt parallelization example (#1214)
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## Bugfixes
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- Ask and tell without initial design may no longer return a config from the initial design - if it is not "removed".
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# Reproducibility
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Reproducibility can only be ensured if one worker is used and no time (wallclock or CPU time) is involved.
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Reproducibility can only be ensured if one worker is used and no time (wallclock or CPU time) is involved.
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!!! warning
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SMBO.reset() will not seed smac with the original seed. If you want to have a full reset, please set the seed again after calling reset.

docs/advanced_usage/1_components.md

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## [Initial Design][smac.initial_design.abstract_initial_design]
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The surrogate model needs data to be trained. Therefore, the initial design is used to generate the initial data points.
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We provide random, latin hypercube, sobol, factorial and default initial designs. The default initial design uses
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the default configuration from the configuration space and with the factorial initial design, we generate corner
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points of the configuration space. The sobol sequences are an example of quasi-random low-discrepancy sequences and
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the latin hypercube design is a statistical method for generating a near-random sample of parameter values from
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We provide [random][smac.initial_design.random_design], [latin hypercube][smac.initial_design.latin_hypercube_design], [sobol][smac.initial_design.sobol_design], [factorial][smac.initial_design.factorial_design] and [default-based][smac.initial_design.default_design] initial designs. The [default-based][smac.initial_design.default_design] initial design uses
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the default configuration from the configuration space and with the [factorial initial design][smac.initial_design.factorial_design], we generate corner
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points of the configuration space. The [sobol initial design][smac.initial_design.sobol_design] uses quasi-random low-discrepancy sequences and
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the [latin hypercube initial design][smac.initial_design.latin_hypercube_design] is a statistical method for generating a near-random sample of parameter values from
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a multidimensional distribution.
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The initial design configurations are yielded by the config selector first. Moreover, the config selector keeps
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!!! warning
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Distributions provided in the initial design are ignored.
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[](){#random-design}
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## [Random Design][smac.initial_design.random_design]
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