Metadata-Version: 2.4
Name: torch_measure
Version: 0.1.0
Summary: PyTorch-native measurement science toolkit: IRT, CAT, psychometric metrics, and factor models for AI evaluation
Author: AIMS Foundations
License-Expression: MIT
Project-URL: Homepage, https://github.com/aims-foundations/torch_measure
Project-URL: Documentation, https://torch-measure.readthedocs.io
Project-URL: Repository, https://github.com/aims-foundations/torch_measure
Project-URL: Issues, https://github.com/aims-foundations/torch_measure/issues
Keywords: pytorch,irt,item-response-theory,psychometrics,measurement,evaluation,adaptive-testing,benchmarks
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Science/Research
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Scientific/Engineering :: Mathematics
Requires-Python: >=3.10
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: torch>=2.0
Requires-Dist: numpy>=1.24
Requires-Dist: scipy>=1.10
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Requires-Dist: matplotlib>=3.7
Requires-Dist: seaborn>=0.12
Requires-Dist: tueplots>=0.0.14
Requires-Dist: tabpfn<3,>=2.2
Provides-Extra: dev
Requires-Dist: torch_measure[docs,test]; extra == "dev"
Requires-Dist: pre-commit; extra == "dev"
Requires-Dist: ruff; extra == "dev"
Provides-Extra: test
Requires-Dist: pytest>=7.0; extra == "test"
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Provides-Extra: docs
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Provides-Extra: all
Requires-Dist: torch_measure[dev]; extra == "all"
Dynamic: license-file

# torch_measure

[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
[![Python 3.10+](https://img.shields.io/badge/python-3.10+-blue.svg)](https://www.python.org/downloads/)
[![PyTorch](https://img.shields.io/badge/PyTorch-2.0+-ee4c2c.svg)](https://pytorch.org/)
[![Discord](https://img.shields.io/badge/Discord-join%20chat-5865F2.svg)](https://discord.gg/F6xbEwvvhb)

**PyTorch-native toolkit for predictive evaluation of AI systems.**

Benchmark scores increasingly gate deployment decisions but rarely predict how a model will behave in production. `torch_measure` treats evaluation itself as a predictive modeling problem: latent-variable models infer a system's capability directly from sparse benchmark observations and predict its performance on unseen tasks. Built on PyTorch, with GPU-accelerated IRT, factor models, amortized inference, adaptive testing, and tabular baselines.

## Installation

With **pip**:

```bash
pip install torch_measure
```

With **[uv](https://docs.astral.sh/uv/)** (faster; drop-in replacement for pip):

```bash
uv pip install torch_measure        # into the active environment
uv add torch_measure                # into a uv-managed project
```

## Contributing

We welcome contributions! Please see our [contributing guidelines](CONTRIBUTING.md) for details, or drop by our [Discord](https://discord.gg/F6xbEwvvhb) to chat.
