{"id":25200746,"url":"https://github.com/jiajiexiao/seqinfer","last_synced_at":"2026-02-10T18:35:25.622Z","repository":{"id":182693571,"uuid":"665816167","full_name":"jiajiexiao/SeqInfer","owner":"jiajiexiao","description":"A Python library to infer from sequences","archived":false,"fork":false,"pushed_at":"2024-10-27T04:38:09.000Z","size":141,"stargazers_count":1,"open_issues_count":1,"forks_count":0,"subscribers_count":1,"default_branch":"master","last_synced_at":"2025-05-12T13:15:41.990Z","etag":null,"topics":["deep-learning","featurization","generative-modeling","sequence-learning"],"latest_commit_sha":null,"homepage":"https://pypi.org/project/seqinfer/","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/jiajiexiao.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2023-07-13T04:24:57.000Z","updated_at":"2024-10-27T04:38:12.000Z","dependencies_parsed_at":"2024-10-21T20:36:43.622Z","dependency_job_id":"6c88a114-6f34-40f9-98eb-f60d7e9fd0bd","html_url":"https://github.com/jiajiexiao/SeqInfer","commit_stats":null,"previous_names":["jiajiexiao/seqlearn","jiajiexiao/seqinfer"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/jiajiexiao%2FSeqInfer","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/jiajiexiao%2FSeqInfer/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/jiajiexiao%2FSeqInfer/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/jiajiexiao%2FSeqInfer/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/jiajiexiao","download_url":"https://codeload.github.com/jiajiexiao/SeqInfer/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":253745196,"owners_count":21957319,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["deep-learning","featurization","generative-modeling","sequence-learning"],"created_at":"2025-02-10T04:33:48.641Z","updated_at":"2026-02-10T18:35:25.576Z","avatar_url":"https://github.com/jiajiexiao.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# SeqInfer\n\n\u003c!-- [![codecov](https://codecov.io/gh/username/repository/branchname/graph/badge.svg)](https://codecov.io/gh/username/repository)--\u003e\n\nSeqInfer is a Python package to infer from sequence, enabling outcome prediction, sequence\ngeneration, and meaningful representation discovery, etc for sequence-like data. \n\nInitially focused on biological sequences such as DNA, RNA, and protein sequences, it aims to provide\nessential tools and algorithms for handling sequence data. However, the package is designed to be\neasily expandable to accommodate other types of sequences, such as SMILE strings or time series.\nRelevant helper modules may be added in the future development. \n\n**This library was renamed to SeqInfer from SeqLearn to avoid potential conflicts and confusion given that SeqLearn has been used by other people's repo.\n\n## Table of Contents\n\n-   [Installation](README.md#installation)\n-   [Usage](README.md#usage)\n-   [Project Structure](README.md#project-structure)\n-   [Examples](README.md#examples)\n-   [Contributing](README.md#contributing)\n-   [License](README.md#license)\n\n## Installation\nYou can install `SeqInfer` using pip:\n\n\n`pip install seqinfer` \n\nHowever, it is currently recommended to install it directly from git repo given the the pip release may\nnot be up to date in the early stage of development of SeqInfer.\n\n`pip install git+https://github.com/jiajiexiao/seqinfer.git`\n\n\n\n## Usage\n\nTo use SeqInfer, simply import the desired modules from the `seq` and `infer` sub-packages.\n\nFor example, you can prepare the data as below: \n```python\nfrom seqinfer.seq.datasets import SeqFromFileDataset\nfrom seqinfer.seq.transforms import Compose, KmerTokenizer, OneHotEncoder, ToTensor\nfrom seqinfer.seq.vocabularies import unambiguous_dna_vocabulary_dict\n\nseq_dataset = SeqFromFileDataset(\n    seq_file=\"examples/toys/CCA-TXXAGG-AG-TGG-TC-A-T/pos.fasta\",\n    seq_file_fmt=\"fasta\",\n    transform_sequences=Compose(\n        [\n            KmerTokenizer(\n                k=1,\n                stride=1,\n                vocab_dict=unambiguous_dna_vocabulary_dict,\n                num_output_tokens=3,\n                special_tokens=None,\n            ),\n            OneHotEncoder(vocab_size=len(unambiguous_dna_vocabulary_dict)),\n            ToTensor(),\n        ]\n    ),\n)\n```\n\n\n\n## Project Structure\n\nThe SeqInfer package is organized into two major parts:\n\n1.  `seq`: Contains modules to define and manage the data/dataset of sequences and provides various\n    related transformation operations.\n2.  `infer`: Contains modules for different learners (learning algorithms) to conduct learning\n    tasks such as classification, regression, self-supervised representation learning, sequence\n    generation, etc.\n\n## Examples\n\nThe `examples` folder contains illustrative examples demonstrating the usage of SeqInfer for various\ntasks, including classification, regression, multitask learning, etc. Each example includes a README\nto guide you through the usage and expected results.\n\n## Contributing\n\nWe welcome contributions to improve and extend SeqInfer. If you would like to contribute, please\nfollow our [contribution guidelines](CONTRIBUTING.md) (To be added).\n\n## License\n\nThis project is licensed under the MIT License - see the [LICENSE](/LICENSE) file for details.\n\n----------\n\nWe hope you find SeqInfer useful for your sequence learning tasks! If you encounter any issues or\nhave suggestions for improvement, please feel free to open an issue or submit a pull request. Happy\ncoding!","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fjiajiexiao%2Fseqinfer","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fjiajiexiao%2Fseqinfer","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fjiajiexiao%2Fseqinfer/lists"}