COREMat Framework
COREMat (COmputable REgression for Materials) is a Python framework for automated machine learning on tabular materials-science datasets — alloy research, materials-property prediction, and similar regression workloads. It automates the full workflow behind a single configuration: data ingestion, preprocessing, nested-CV training with hyperparameter optimisation, iterative feature selection, SHAP-based explainability, OOF/Hold-out evaluation, and interactive HTML reporting. The model registry ships 70+ algorithm variants (gradient boosting, kernel methods, linear models, neural nets, foundation models) behind a unified ModelSpec interface.
What You Get
Every trained model gets a self-contained, interactive HTML report — open it in a browser, no server or notebook required. Excerpt of what's inside:
| Section | What it shows |
|---|---|
| Comparison Leaderboard | All models in a run ranked by weighted Skill Score (OOF, switchable to Hold-out Test) |
| Actual vs. Predicted | Square scatter with color-by-feature and linear/log axis toggle |
| Residuals & Error Distribution | Residuals-vs-Predicted scatter, ECDF/histogram error analysis |
| Round Trajectory | Feature-selection score and feature count per round |
| SHAP / Feature Importance | Per-round SHAP summary plots and contribution bars |
| Model Parameters | Curated hyperparameters and preprocessing policy |
Every report is built post-hoc from persisted per-round predictions — re-opening a report never re-runs a model.
Installation
Requirements: Python >= 3.12, < 3.13
From PyPI (recommended — available from the first public release):
pip install coremat
# or with uv:
uv pip install coremat
From the Uni Rostock GitLab Registry (alternative, requires GitLab account):
uv pip install git+https://gitlab.uni-rostock.de/cs1302/coremat.git@main
Developers (editable install):
git clone https://gitlab.uni-rostock.de/cs1302/coremat.git
cd coremat
uv venv && uv sync
source .venv/bin/activate # Linux/macOS
.venv\Scripts\activate # Windows
TabPFN Authentication
TabPFN models require a free PriorLabs account. Set up once after installation:
coremat auth tabpfn
Getting Started
The documentation (mkdocs) is the single source of truth for usage — it covers the full quick-start walkthrough, sample data generation, and the resulting artifact layout. Start at docs/index.md or the Minimal Run guide.
Documentation
| Document | Description |
|---|---|
| docs/index.md | Quick-start walkthrough and installation |
| docs/cli_guide.md | Full CLI reference — all commands, options, and examples |
| docs/api.md | Public API reference — exported symbols and usage |
| docs/reference/models/index.md | All 70+ registered models with preprocessing policies |
| docs/architecture.md | Layer model, module responsibilities, dependency graph |
| docs/development.md | Dev setup, reproducibility guide, contribution notes |
| docs/metrics.md | Metric definitions and evaluation methodology |
| docs/verbose_levels.md | Logging verbosity levels and output examples |
Building the docs
uv add mkdocs mkdocs-material
uv run -m mkdocs serve # local preview at http://127.0.0.1:8000, live-reloads on edit
uv run -m mkdocs build --strict --site-dir /tmp/site # artifact build; --strict turns broken links/nav into errors
The pages CI job runs the same --strict build as a documentation lint — GitLab
Pages is not hosted on this instance, so nothing is deployed automatically.
Publishing the built site/ output anywhere remains a manual step until a
hosting target is chosen (see the CI/CD section in docs/development.md).
Citing COREMat
If you use COREMat in your research, please cite it. Machine-readable citation
metadata is provided in CITATION.cff at the root of this
repository.
Acknowledgements
This project was developed using an agentic AI-assisted workflow with Claude Code by Anthropic.
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