| Name | dgeb JSON |
| Version |
0.2.0
JSON |
| download |
| home_page | None |
| Summary | Diverse Genomic Embedding Benchmark |
| upload_time | 2024-09-03 18:20:37 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | None |
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| keywords |
scientific software
genomic embeddings
machine learning
benchmark
|
| VCS |
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---
title: DGEB
app_file : leaderboard/app.py
sdk: docker
sdk_version: 4.36.1
---
<h1 align="center">Diverse Genomic Embedding Benchmark</h1>
<p align="center">
<a href="https://github.com/tattabio/dgeb/releases">
<img alt="GitHub release" src="https://img.shields.io/github/v/release/tattabio/dgeb.svg">
</a>
<a href="https://www.biorxiv.org/content/10.1101/2024.07.10.602933v1">
<img alt="bioRxiv URL" src="https://img.shields.io/badge/bioRxiv-602933v1-b31b1b.svg">
</a>
<a href="https://github.com/tattabio/dgeb/blob/main/LICENSE">
<img alt="License" src="https://img.shields.io/github/license/tattabio/dgeb.svg">
</a>
<a href="https://pepy.tech/project/dgeb">
<img alt="Downloads" src="https://static.pepy.tech/personalized-badge/dgeb?period=total&units=international_system&left_color=grey&right_color=orange&left_text=Downloads">
</a>
</p>
<h4 align="center">
<p>
<a href="#installation">Installation</a> |
<a href="#usage">Usage</a> |
<a href="https://huggingface.co/spaces/tattabio/DGEB">Leaderboard</a> |
<a href="#citing">Citing</a>
<p>
</h4>
<h3 align="center">
<a href="https://huggingface.co/spaces/dgeb"><img style="float: middle; padding: 10px 10px 10px 10px;" width="100" height="100" src="./docs/images/tatta_logo.png" /></a>
</h3>
DGEB is a benchmark for evaluating biological sequence models on functional and evolutionary information.
DGEB is designed to evaluate model embeddings using:
- Diverse sequences accross the tree of life.
- Diverse tasks that capture different aspects of biological function.
- Both amino acid and nucleotide sequences.
The current version of DGEB consists of 18 datasets covering all three domains of life (Bacteria, Archaea and Eukarya). DGEB evaluates embeddings using six different embedding tasks: Classification, BiGene mining, Evolutionary Distance Similarity (EDS), Pair Classification, Clustering, and Retrieval.
We welcome contributions of new tasks and datasets.
## Installation
Install DGEB using pip.
```bash
pip install dgeb
```
## Usage
- Launch evaluation using the python script (see [cli.py](https://github.com/tattabio/dgeb/blob/main/dgeb/cli.py)):
```bash
dgeb --model facebook/esm2_t6_8M_UR50D
```
- To see all supported models and tasks:
```bash
dgeb --help
```
- Using the python API:
```py
import dgeb
model = dgeb.get_model("facebook/esm2_t6_8M_UR50D")
tasks = dgeb.get_tasks_by_modality(dgeb.Modality.PROTEIN)
evaluation = dgeb.DGEB(tasks=tasks)
# Writes results to `output_folder`, and returns a list of TaskResult.
# You can disable writing to json by setting `output_folder=None`.
results = evaluation.run(model, output_folder="results")
```
### Using a custom model
Custom models should be wrapped with the `dgeb.models.BioSeqTransformer` abstract class, and specify the modality, number of layers, and embedding dimension. See [models.py](https://github.com/tattabio/dgeb/blob/main/dgeb/models.py) for additional examples on custom model loading and inference.
```python
import dgeb
from dgeb.models import BioSeqTransformer
from dgeb.tasks.tasks import Modality
class MyModel(BioSeqTransformer):
@property
def modality(self) -> Modality:
return Modality.PROTEIN
@property
def num_layers(self) -> int:
return self.config.num_hidden_layers
@property
def embed_dim(self) -> int:
return self.config.hidden_size
model = MyModel(model_name='path_to/huggingface_model')
tasks = dgeb.get_tasks_by_modality(model.modality)
evaluation = dgeb.DGEB(tasks=tasks)
evaluation.run(model)
```
### Evaluating on a custom dataset
**We strongly encourage users to contribute their custom datasets to DGEB. Please open a PR adding your dataset so that the community can benefit!**
To evaluate on a custom dataset, first upload your dataset to the [Huggingface Hub](https://huggingface.co/docs/hub/en/datasets-adding). Then define a `Task` subclass with `TaskMetadata` that points to your huggingface dataset. For example, a classification task on a custom dataset can be defined as follows:
```python
import dgeb
from dgeb.models import BioSeqTransformer
from dgeb.tasks import Dataset, Task, TaskMetadata, TaskResult
from dgeb.tasks.classification_tasks import run_classification_task
class MyCustomTask(Task):
metadata = TaskMetadata(
id="my_custom_classification",
display_name="...",
description="...",
type="classification",
modality=Modality.PROTEIN,
datasets=[
Dataset(
path="path_to/huggingface_dataset",
revision="...",
)
],
primary_metric_id="f1",
)
def run(self, model: BioSeqTransformer) -> TaskResult:
return run_classification_task(model, self.metadata)
model = dgeb.get_model("facebook/esm2_t6_8M_UR50D")
evaluation = dgeb.DGEB(tasks=[MyCustomTask])
evaluation.run(model)
```
## Leaderboard
To add your submission to the DGEB leaderboard, proceed through the following instructions.
1. Fork the DGEB repository by following GitHub's instruction [Forking Workflow](https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/proposing-changes-to-your-work-with-pull-requests/creating-a-pull-request-from-a-fork).
2. Add your submission .json file to the leaderboard/submissions/<HF_MODEL_NAME>/ directory.
```bash
mv /path/to/<SUBMISSION_FILE>.json /path/to/DGEB/leaderboard/submissions/<HF_MODEL_NAME>/
```
4. Update your fork with the new submission:
```bash
git add leaderboard/submissions/<HF_MODEL_NAME>/<SUBMISSION_FILE>.json
git commit -m "Add submission for <HF_MODEL_NAME>"
git push
```
5. Open a pull request to the main branch of the repository via the Github interface.
6. Once the PR is review and merged, your submission will be added to the leaderboard!
## Acknowledgements
DGEB follows the design of text embedding bechmark [MTEB](https://github.com/embeddings-benchmark/mteb) developed by Huggingface 🤗. The evaluation code is adapted from the MTEB codebase.
## Citing
DGEB was introduced in "[Diverse Genomic Embedding Benchmark for Functional Evaluation Across the Tree of Life](https://www.biorxiv.org/content/10.1101/2024.07.10.602933v1)", feel free to cite:
```
@article{WestRoberts2024,
title = {Diverse Genomic Embedding Benchmark for functional evaluation across the tree of life},
url = {http://dx.doi.org/10.1101/2024.07.10.602933},
DOI = {10.1101/2024.07.10.602933},
publisher = {Cold Spring Harbor Laboratory},
author = {West-Roberts, Jacob and Kravitz, Joshua and Jha, Nishant and Cornman, Andre and Hwang, Yunha},
year = {2024},
month = jul
}
```
Raw data
{
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"keywords": "scientific software, genomic embeddings, machine learning, benchmark",
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"download_url": "https://files.pythonhosted.org/packages/d6/df/684e6c09b30131c792d03d3c14453b99c23b0db70cb48d030f9950dde9c3/dgeb-0.2.0.tar.gz",
"platform": null,
"description": "---\ntitle: DGEB\napp_file : leaderboard/app.py\nsdk: docker\nsdk_version: 4.36.1\n---\n<h1 align=\"center\">Diverse Genomic Embedding Benchmark</h1>\n\n<p align=\"center\">\n <a href=\"https://github.com/tattabio/dgeb/releases\">\n <img alt=\"GitHub release\" src=\"https://img.shields.io/github/v/release/tattabio/dgeb.svg\">\n </a>\n <a href=\"https://www.biorxiv.org/content/10.1101/2024.07.10.602933v1\">\n <img alt=\"bioRxiv URL\" src=\"https://img.shields.io/badge/bioRxiv-602933v1-b31b1b.svg\">\n </a>\n <a href=\"https://github.com/tattabio/dgeb/blob/main/LICENSE\">\n <img alt=\"License\" src=\"https://img.shields.io/github/license/tattabio/dgeb.svg\">\n </a>\n <a href=\"https://pepy.tech/project/dgeb\">\n <img alt=\"Downloads\" src=\"https://static.pepy.tech/personalized-badge/dgeb?period=total&units=international_system&left_color=grey&right_color=orange&left_text=Downloads\">\n </a>\n</p>\n\n<h4 align=\"center\">\n <p>\n <a href=\"#installation\">Installation</a> |\n <a href=\"#usage\">Usage</a> |\n <a href=\"https://huggingface.co/spaces/tattabio/DGEB\">Leaderboard</a> |\n <a href=\"#citing\">Citing</a>\n <p>\n</h4>\n\n<h3 align=\"center\">\n <a href=\"https://huggingface.co/spaces/dgeb\"><img style=\"float: middle; padding: 10px 10px 10px 10px;\" width=\"100\" height=\"100\" src=\"./docs/images/tatta_logo.png\" /></a>\n</h3>\n\nDGEB is a benchmark for evaluating biological sequence models on functional and evolutionary information.\n\nDGEB is designed to evaluate model embeddings using:\n\n- Diverse sequences accross the tree of life.\n- Diverse tasks that capture different aspects of biological function.\n- Both amino acid and nucleotide sequences.\n\nThe current version of DGEB consists of 18 datasets covering all three domains of life (Bacteria, Archaea and Eukarya). DGEB evaluates embeddings using six different embedding tasks: Classification, BiGene mining, Evolutionary Distance Similarity (EDS), Pair Classification, Clustering, and Retrieval.\n\nWe welcome contributions of new tasks and datasets.\n\n## Installation\n\nInstall DGEB using pip.\n\n```bash\npip install dgeb\n```\n\n## Usage\n\n- Launch evaluation using the python script (see [cli.py](https://github.com/tattabio/dgeb/blob/main/dgeb/cli.py)):\n\n```bash\ndgeb --model facebook/esm2_t6_8M_UR50D\n```\n\n- To see all supported models and tasks:\n\n```bash\ndgeb --help\n```\n\n- Using the python API:\n\n```py\nimport dgeb\n\nmodel = dgeb.get_model(\"facebook/esm2_t6_8M_UR50D\")\ntasks = dgeb.get_tasks_by_modality(dgeb.Modality.PROTEIN)\nevaluation = dgeb.DGEB(tasks=tasks)\n# Writes results to `output_folder`, and returns a list of TaskResult.\n# You can disable writing to json by setting `output_folder=None`.\nresults = evaluation.run(model, output_folder=\"results\")\n```\n\n### Using a custom model\n\nCustom models should be wrapped with the `dgeb.models.BioSeqTransformer` abstract class, and specify the modality, number of layers, and embedding dimension. See [models.py](https://github.com/tattabio/dgeb/blob/main/dgeb/models.py) for additional examples on custom model loading and inference.\n\n```python\nimport dgeb\nfrom dgeb.models import BioSeqTransformer\nfrom dgeb.tasks.tasks import Modality\n\nclass MyModel(BioSeqTransformer):\n\n @property\n def modality(self) -> Modality:\n return Modality.PROTEIN\n\n @property\n def num_layers(self) -> int:\n return self.config.num_hidden_layers\n\n @property\n def embed_dim(self) -> int:\n return self.config.hidden_size\n\n\nmodel = MyModel(model_name='path_to/huggingface_model')\ntasks = dgeb.get_tasks_by_modality(model.modality)\nevaluation = dgeb.DGEB(tasks=tasks)\nevaluation.run(model)\n```\n\n### Evaluating on a custom dataset\n\n**We strongly encourage users to contribute their custom datasets to DGEB. Please open a PR adding your dataset so that the community can benefit!**\n\nTo evaluate on a custom dataset, first upload your dataset to the [Huggingface Hub](https://huggingface.co/docs/hub/en/datasets-adding). Then define a `Task` subclass with `TaskMetadata` that points to your huggingface dataset. For example, a classification task on a custom dataset can be defined as follows:\n\n```python\nimport dgeb\nfrom dgeb.models import BioSeqTransformer\nfrom dgeb.tasks import Dataset, Task, TaskMetadata, TaskResult\nfrom dgeb.tasks.classification_tasks import run_classification_task\n\nclass MyCustomTask(Task):\n metadata = TaskMetadata(\n id=\"my_custom_classification\",\n display_name=\"...\",\n description=\"...\",\n type=\"classification\",\n modality=Modality.PROTEIN,\n datasets=[\n Dataset(\n path=\"path_to/huggingface_dataset\",\n revision=\"...\",\n )\n ],\n primary_metric_id=\"f1\",\n )\n\n def run(self, model: BioSeqTransformer) -> TaskResult:\n return run_classification_task(model, self.metadata)\n\nmodel = dgeb.get_model(\"facebook/esm2_t6_8M_UR50D\")\nevaluation = dgeb.DGEB(tasks=[MyCustomTask])\nevaluation.run(model)\n```\n\n## Leaderboard\n\nTo add your submission to the DGEB leaderboard, proceed through the following instructions.\n\n1. Fork the DGEB repository by following GitHub's instruction [Forking Workflow](https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/proposing-changes-to-your-work-with-pull-requests/creating-a-pull-request-from-a-fork).\n\n2. Add your submission .json file to the leaderboard/submissions/<HF_MODEL_NAME>/ directory. \n\n```bash\nmv /path/to/<SUBMISSION_FILE>.json /path/to/DGEB/leaderboard/submissions/<HF_MODEL_NAME>/\n```\n\n4. Update your fork with the new submission:\n\n```bash\ngit add leaderboard/submissions/<HF_MODEL_NAME>/<SUBMISSION_FILE>.json\ngit commit -m \"Add submission for <HF_MODEL_NAME>\"\ngit push\n```\n\n5. Open a pull request to the main branch of the repository via the Github interface.\n\n6. Once the PR is review and merged, your submission will be added to the leaderboard!\n\n\n## Acknowledgements\n\nDGEB follows the design of text embedding bechmark [MTEB](https://github.com/embeddings-benchmark/mteb) developed by Huggingface \ud83e\udd17. The evaluation code is adapted from the MTEB codebase.\n\n## Citing\n\nDGEB was introduced in \"[Diverse Genomic Embedding Benchmark for Functional Evaluation Across the Tree of Life](https://www.biorxiv.org/content/10.1101/2024.07.10.602933v1)\", feel free to cite:\n\n```\n@article{WestRoberts2024,\n title = {Diverse Genomic Embedding Benchmark for functional evaluation across the tree of life},\n url = {http://dx.doi.org/10.1101/2024.07.10.602933},\n DOI = {10.1101/2024.07.10.602933},\n publisher = {Cold Spring Harbor Laboratory},\n author = {West-Roberts, Jacob and Kravitz, Joshua and Jha, Nishant and Cornman, Andre and Hwang, Yunha},\n year = {2024},\n month = jul \n}\n```\n",
"bugtrack_url": null,
"license": "Apache License Version 2.0, January 2004 http://www.apache.org/licenses/ TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 1. Definitions. \"License\" shall mean the terms and conditions for use, reproduction, and distribution as defined by Sections 1 through 9 of this document. \"Licensor\" shall mean the copyright owner or entity authorized by the copyright owner that is granting the License. \"Legal Entity\" shall mean the union of the acting entity and all other entities that control, are controlled by, or are under common control with that entity. For the purposes of this definition, \"control\" means (i) the power, direct or indirect, to cause the direction or management of such entity, whether by contract or otherwise, or (ii) ownership of fifty percent (50%) or more of the outstanding shares, or (iii) beneficial ownership of such entity. \"You\" (or \"Your\") shall mean an individual or Legal Entity exercising permissions granted by this License. \"Source\" form shall mean the preferred form for making modifications, including but not limited to software source code, documentation source, and configuration files. \"Object\" form shall mean any form resulting from mechanical transformation or translation of a Source form, including but not limited to compiled object code, generated documentation, and conversions to other media types. \"Work\" shall mean the work of authorship, whether in Source or Object form, made available under the License, as indicated by a copyright notice that is included in or attached to the work (an example is provided in the Appendix below). \"Derivative Works\" shall mean any work, whether in Source or Object form, that is based on (or derived from) the Work and for which the editorial revisions, annotations, elaborations, or other modifications represent, as a whole, an original work of authorship. For the purposes of this License, Derivative Works shall not include works that remain separable from, or merely link (or bind by name) to the interfaces of, the Work and Derivative Works thereof. \"Contribution\" shall mean any work of authorship, including the original version of the Work and any modifications or additions to that Work or Derivative Works thereof, that is intentionally submitted to Licensor for inclusion in the Work by the copyright owner or by an individual or Legal Entity authorized to submit on behalf of the copyright owner. For the purposes of this definition, \"submitted\" means any form of electronic, verbal, or written communication sent to the Licensor or its representatives, including but not limited to communication on electronic mailing lists, source code control systems, and issue tracking systems that are managed by, or on behalf of, the Licensor for the purpose of discussing and improving the Work, but excluding communication that is conspicuously marked or otherwise designated in writing by the copyright owner as \"Not a Contribution.\" \"Contributor\" shall mean Licensor and any individual or Legal Entity on behalf of whom a Contribution has been received by Licensor and subsequently incorporated within the Work. 2. Grant of Copyright License. Subject to the terms and conditions of this License, each Contributor hereby grants to You a perpetual, worldwide, non-exclusive, no-charge, royalty-free, irrevocable copyright license to reproduce, prepare Derivative Works of, publicly display, publicly perform, sublicense, and distribute the Work and such Derivative Works in Source or Object form. 3. Grant of Patent License. Subject to the terms and conditions of this License, each Contributor hereby grants to You a perpetual, worldwide, non-exclusive, no-charge, royalty-free, irrevocable (except as stated in this section) patent license to make, have made, use, offer to sell, sell, import, and otherwise transfer the Work, where such license applies only to those patent claims licensable by such Contributor that are necessarily infringed by their Contribution(s) alone or by combination of their Contribution(s) with the Work to which such Contribution(s) was submitted. 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