{"categories":[{"label":"Software Development","url":"https://skillfed.io/packages/category/software-development/12"},{"label":"Scientific/Engineering","url":"https://skillfed.io/packages/category/scientific-engineering/5"},{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/7"}],"enrichment":{"capability":"ConfigSpace defines and manipulates configuration spaces for hyperparameter optimization and algorithm configuration, supporting continuous, discrete, and categorical parameters with sampling and constraint handling.","skillfed_tags":["hyperparameter-optimization","automl","algorithm-configuration"],"use_cases":["Define hyperparameter spaces for machine learning model tuning in AutoML pipelines","Sample random or structured configurations for algorithm benchmarking and empirical evaluation","Represent parameter constraints and dependencies in black-box optimization frameworks like SMAC","Build configuration search spaces for neural architecture search or algorithm selection tasks"],"what_it_does":"ConfigSpace is a Python library for defining and working with configuration spaces\u2014structured representations of algorithm parameters and hyperparameters. It provides a domain-specific language to declare parameters as continuous ranges, discrete integers, or categorical choices, and then sample valid configurations from that space. The library is commonly used in automated machine learning and hyperparameter optimization workflows, where you need to systematically explore parameter combinations.\n\nThe package depends on numpy, scipy, and pyparsing for numerical operations and parsing, plus typing_extensions and more_itertools for compatibility and iteration utilities. It supports Python 3.9 and later, is actively maintained, and carries no known security vulnerabilities. The library is permissive-licensed under BSD 3-clause, with a subset of code (the nx subpackage) also under BSD from NetworkX.","worth_installing":"Yes. ConfigSpace is a stable, actively maintained library with low install friction, no security issues, and a permissive license. It is purpose-built for hyperparameter optimization and algorithm configuration workflows. Install it if you are working on AutoML, hyperparameter tuning, or algorithm configuration tasks and need a structured way to define and sample parameter spaces."},"id":"configspace","links":{"html":"https://skillfed.io/packages/configspace","md":"https://skillfed.io/packages/configspace.md","pypi":"https://pypi.org/project/configspace/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2025-12-19","license_spdx":null,"license_treatment":"permissive","name":"ConfigSpace","python_support":"supports_current","summary":"Creation and manipulation of parameter configuration spaces for automated algorithm configuration and hyperparameter tuning. "},"popularity":{"monthly_downloads":241655,"position":8872,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"1.2.2"}