Metadata-Version: 2.4
Name: seqme
Version: 0.1.1
Summary: Metrics for evaluating biological sequence generation models.
Project-URL: Documentation, https://seqme.readthedocs.io
Project-URL: Source, https://github.com/szczurek-lab/seqme
Project-URL: Home-page, https://github.com/szczurek-lab/seqme
Author: Rasmus Møller-Larsen, Adam Izdebski, Jan Olszewski
Maintainer: Rasmus Møller-Larsen
License: BSD 3-Clause License
        
        Copyright (c) 2025, Szczurek lab
        
        Redistribution and use in source and binary forms, with or without
        modification, are permitted provided that the following conditions are met:
        
        1. Redistributions of source code must retain the above copyright notice, this
           list of conditions and the following disclaimer.
        
        2. Redistributions in binary form must reproduce the above copyright notice,
           this list of conditions and the following disclaimer in the documentation
           and/or other materials provided with the distribution.
        
        3. Neither the name of the copyright holder nor the names of its
           contributors may be used to endorse or promote products derived from
           this software without specific prior written permission.
        
        THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
        AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
        IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
        DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
        FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
        DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
        SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
        CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
        OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
        OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
License-File: LICENSE
Classifier: Development Status :: 4 - Beta
Classifier: Environment :: Console
Classifier: Framework :: Jupyter
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: BSD License
Classifier: Natural Language :: English
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Programming Language :: Python :: 3.13
Classifier: Programming Language :: Python :: 3.14
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Scientific/Engineering :: Bio-Informatics
Classifier: Topic :: Scientific/Engineering :: Visualization
Requires-Python: >=3.10
Requires-Dist: matplotlib>=3.6
Requires-Dist: moocore
Requires-Dist: numpy
Requires-Dist: pandas>=1.5
Requires-Dist: pylatex
Requires-Dist: pylev
Requires-Dist: scikit-learn
Requires-Dist: scipy
Requires-Dist: torch
Requires-Dist: tqdm
Requires-Dist: umap-learn
Provides-Extra: aadescriptors
Requires-Dist: modlamp; extra == 'aadescriptors'
Provides-Extra: dev
Requires-Dist: ipykernel; extra == 'dev'
Requires-Dist: ipywidgets; extra == 'dev'
Requires-Dist: mypy; extra == 'dev'
Requires-Dist: nbstripout; extra == 'dev'
Requires-Dist: pre-commit>=3.0.0; extra == 'dev'
Requires-Dist: pytest; extra == 'dev'
Requires-Dist: ruff; extra == 'dev'
Provides-Extra: dnabert2
Requires-Dist: einops; extra == 'dnabert2'
Requires-Dist: transformers; extra == 'dnabert2'
Provides-Extra: doc
Requires-Dist: ipykernel; extra == 'doc'
Requires-Dist: ipywidgets; extra == 'doc'
Requires-Dist: myst-nb; extra == 'doc'
Requires-Dist: myst-parser; extra == 'doc'
Requires-Dist: nbsphinx; extra == 'doc'
Requires-Dist: scanpydoc; extra == 'doc'
Requires-Dist: seaborn; extra == 'doc'
Requires-Dist: sphinx; extra == 'doc'
Requires-Dist: sphinx-autodoc-typehints; extra == 'doc'
Requires-Dist: sphinx-book-theme; extra == 'doc'
Requires-Dist: sphinx-copybutton; extra == 'doc'
Requires-Dist: sphinx-gallery; extra == 'doc'
Provides-Extra: esm2
Requires-Dist: transformers; extra == 'esm2'
Provides-Extra: prostt5
Requires-Dist: sentencepiece; extra == 'prostt5'
Requires-Dist: transformers; extra == 'prostt5'
Provides-Extra: rna-fm
Requires-Dist: rna-fm; extra == 'rna-fm'
Description-Content-Type: text/markdown

<p align="left">
    <img src="https://raw.githubusercontent.com/szczurek-lab/seqme/main/docs/_static/logo_title.svg" alt="seqme logo" width="30%">
</p>

[![PyPI](https://img.shields.io/pypi/v/seqme.svg)](https://pypi.org/project/seqme/)
[![Python 3.10+](https://img.shields.io/badge/python-3.10+-blue.svg)](https://www.python.org/downloads/)
[![License](https://img.shields.io/github/license/szczurek-lab/seqme?v=2)](https://opensource.org/license/bsd-3-clause)
[![Read the Docs](https://img.shields.io/readthedocs/seqme)](https://seqme.readthedocs.io/)

**seqme** is a modular and highly extendable python library containing model-agnostic metrics for evaluating biological sequences.

## Installation

You need to have Python 3.10 or newer installed on your system. To install the base package do:

```bash
$ pip install seqme
```

To also install domain-specific models, add extras specifiers.
Read the [docs](https://seqme.readthedocs.io/en/latest/api/models_index.html) for more information on the supported models.

## Quick start

Install seqme and the protein language model, ESM-2.

```bash
$ pip install seqme[esm2]
```

Run in a Jupyter notebook:

```python
import seqme as sm

sequences = {
    "Random": ["MKQW", "RKSPL"],
    "UniProt": ["KKWQ", "RKSPL", "RASD"],
    "HydrAMP": ["MMRK", "RKSPL", "RRLSK", "RRLSK"],
}

cache = sm.ModelCache(
    models={"esm2": sm.models.Esm2(
        model_name="facebook/esm2_t6_8M_UR50D", batch_size=256, device="cpu")
    }
)

metrics = [
    sm.metrics.Uniqueness(),
    sm.metrics.Novelty(reference=sequences["UniProt"], reference_name="UniProt"),
    sm.metrics.FBD(reference=sequences["Random"], embedder=cache.model("esm2")),
]

df = sm.compute_metrics(sequences, metrics)
sm.show_table(df) # Note: Will only display the table in a notebook.
```

Read the [docs](https://seqme.readthedocs.io/en/latest/tutorials/index.html) for more tutorials and examples.

## Citation

Preprint is coming soon.
