https://github.com/smtorg/smt-design-space-ext

SMT design space extension for hierarchical variable handling
https://github.com/smtorg/smt-design-space-ext

Last synced: 5 months ago
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SMT design space extension for hierarchical variable handling

• Host: GitHub
• URL: https://github.com/smtorg/smt-design-space-ext
• Owner: SMTorg
• License: bsd-3-clause
• Created: 2024-09-10T13:38:31.000Z (almost 2 years ago)
• Default Branch: main
• Last Pushed: 2025-11-24T05:13:33.000Z (8 months ago)
• Last Synced: 2025-12-09T12:15:54.039Z (8 months ago)
• Language: Jupyter Notebook
• Homepage:
• Size: 118 KB
• Stars: 2
• Watchers: 4
• Forks: 2
• Open Issues: 5
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          

# SMT Design Space Extension 🌳

**Enhancing SMT with Hierarchical & Conditional Design Spaces**

This package is an extension to the SMT (Surrogate Modeling Toolbox), offering a powerful framework to define and manage **hierarchical**, **mixed-type**, and **conditionally active** variables in design spaces.

---

## 🔍 What It Does

- **Hierarchical variables**: Support for nested conditional variables (e.g., a rotor configuration branch that only activates when `use_rotor = yes`).

- **Mixed types**: Handles continuous, integer or categorical variables uniformly.

- **Conditional activation**: Meta-variables powerfully control lower-level variable activation based on context.

- **Graph-based design space representation**: Clean and intuitive implementation of complex, branching designs.

- **Extensible subtress**: Easily add new types or layers of conditional logic.

---

## 📖 Why It Matters

In many engineering and simulation contexts, the design parameters form a structured, dependent hierarchy—optimizing them without a formal framework can lead to errors or inefficient models. This extension provides:

- A **formal representation** of hierarchical dependencies.

- Seamless integration with surrogate modeling tools and optimization routines.

- A foundation for improved experimental design and architectural exploration.

---

## 🔗 Reference

This implementation is based on:

> **Hierarchical Modeling and Architecture Optimization: Review and Unified Framework**  

> P. Saves, E. Hallé‑Hannan, J. Bussemaker, Y. Diouane, N. Bartoli (June 2025). arXiv:2506.22621 ([arXiv](https://arxiv.org/abs/2506.22621))

The paper provides a comprehensive survey and introduces a unified graph-based model that serves as the theoretical foundation for this extension.

---

## 📦 Installation

```bash

pip install smt-design-space-ext

```

https://pypi.org/project/smt-design-space-ext/

Requirements. See [Requirements](requirements.txt) for details.

---

## 🚀 Quick Start Example

```python

from smt_design_space_ext import (

    HAS_CONFIG_SPACE,

    HAS_ADSG,

    AdsgDesignSpaceImpl,

    ConfigSpaceDesignSpaceImpl,

    BaseDesignSpace,

    CategoricalVariable,

    FloatVariable,

    IntegerVariable,

    OrdinalVariable,

)

 ds = ConfigSpaceDesignSpaceImpl(

            [

                CategoricalVariable(["A", "B", "C"]),  # x0

                CategoricalVariable(["E", "F"]),  # x1

                IntegerVariable(0, 1),  # x2

                FloatVariable(0.1, 1),  # x3

            ],

            random_state=42,

        )

        ds.declare_decreed_var(

            decreed_var=3, meta_var=0, meta_value="A"

        )  # Activate x3 if x0 == A

```

This dynamically builds a tree-like structure of variables, enabling clear and constrained space exploration.

---

## 🧩 Integration

- Integrates readily with SMT’s Kriging modules.

- Compatible with Bayesian or gradient‑based optimizers.

- Prepare space definitions for use with surrogate modeling pipelines.

---

## 📚 Additional Resources

- The **SMT repository** contains tutorials and example notebooks to demonstrate usage.

- Issue tracker and usage discussions can be found under `SMTorg/smt-design-space-ext`.

- Author metadata and license summary confirm this is BSD‑licensed.

---

## ✅ Citation

If you use this package in research, please cite:

```

P. Saves, E. Hallé‑Hannan, J. Bussemaker, Y. Diouane, N. Bartoli,

“Hierarchical Modeling and Architecture Optimization: Review and Unified Framework,”

arXiv:2506.22621, June 2025.

```

---

## 📜 License

Distributed under the **BSD-3-Clause** license. See [LICENSE](LICENSE) for details.