{"id":28185412,"url":"https://github.com/gperdrizet/ensembleset","last_synced_at":"2026-03-07T03:03:39.895Z","repository":{"id":291110874,"uuid":"976601373","full_name":"gperdrizet/ensembleset","owner":"gperdrizet","description":"Ensemble dataset generator for tabular data prediction and modeling projects.","archived":false,"fork":false,"pushed_at":"2025-12-02T17:21:23.000Z","size":18332,"stargazers_count":1,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2026-01-12T18:51:06.458Z","etag":null,"topics":["classification","ensemble","feature-engineering","machine-learning","regression","scikit-learn"],"latest_commit_sha":null,"homepage":"https://gperdrizet.github.io/ensembleset","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"gpl-3.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/gperdrizet.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,"zenodo":null}},"created_at":"2025-05-02T12:03:19.000Z","updated_at":"2025-12-02T17:19:23.000Z","dependencies_parsed_at":"2025-05-13T16:29:42.351Z","dependency_job_id":"0c89f5d9-d7c7-44bb-9cfc-8b3a2b442995","html_url":"https://github.com/gperdrizet/ensembleset","commit_stats":null,"previous_names":["gperdrizet/datasets","gperdrizet/engineered_datasets","gperdrizet/engineered-datasets","gperdrizet/ensembleset"],"tags_count":24,"template":false,"template_full_name":null,"purl":"pkg:github/gperdrizet/ensembleset","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/gperdrizet%2Fensembleset","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/gperdrizet%2Fensembleset/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/gperdrizet%2Fensembleset/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/gperdrizet%2Fensembleset/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/gperdrizet","download_url":"https://codeload.github.com/gperdrizet/ensembleset/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/gperdrizet%2Fensembleset/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":30206339,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-03-06T19:07:06.838Z","status":"online","status_checked_at":"2026-03-07T02:00:06.765Z","response_time":53,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"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":["classification","ensemble","feature-engineering","machine-learning","regression","scikit-learn"],"created_at":"2025-05-16T06:12:02.332Z","updated_at":"2026-03-07T03:03:39.886Z","avatar_url":"https://github.com/gperdrizet.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# EnsembleSet\n\n[![Publish to PyPI](https://github.com/gperdrizet/ensembleset/actions/workflows/publish-to-pypi.yml/badge.svg)](https://github.com/gperdrizet/ensembleset/actions/workflows/publish-to-pypi.yml) [![Publish to TestPyPI](https://github.com/gperdrizet/ensembleset/actions/workflows/publish-to-testpypi.yml/badge.svg)](https://github.com/gperdrizet/ensembleset/actions/workflows/publish-to-testpypi.yml) [![PR Validation](https://github.com/gperdrizet/ensembleset/actions/workflows/pr-validation.yml/badge.svg)](https://github.com/gperdrizet/ensembleset/actions/workflows/pr-validation.yml) [![pages-build-deployment](https://github.com/gperdrizet/ensembleset/actions/workflows/pages/pages-build-deployment/badge.svg)](https://github.com/gperdrizet/ensembleset/actions/workflows/pages/pages-build-deployment) [![Documentation](https://img.shields.io/badge/docs-GitHub%20Pages-blue)](https://gperdrizet.github.io/ensembleset/)\n\nEnsembleSet generates dataset ensembles by applying a randomized sequence of feature engineering methods to a randomized subset of input features.\n\n## 1. Installation\n\nInstall the pre-release alpha from PyPI with:\n\n```bash\npip install ensembleset\n```\n\n## 2. Usage\n\nSee the [example usage notebook](https://github.com/gperdrizet/ensembleset/blob/main/examples/regression_calorie_burn.ipynb).\n\nInitialize an EnsembleSet class instance, passing in the label name and training DataFrame. Optionally, include a test DataFrame and/or list of any string features and the path where you want EnsembleSet to put data. Then call the `make_datasets()` to generate an EnsembleSet, specifying:\n\n1. The number of individual datasets to generate.\n2. The fraction of features to randomly select for each feature engineering step.\n3. The number of feature engineering steps to run.\n\n```python\nimport ensembleset.dataset as ds\n\ndata_ensemble=ds.DataSet(\n    label='label_column_name',                       # Required\n    train_data=train_df,                             # Required\n    test_data=test_df,                               # Optional, defaults to None\n    string_features=['string_feature_column_names'], # Optional, defaults to None\n    data_directory='path/to/ensembleset/data'        # Optional, defaults to ./data\n)\n\ndata_ensemble.make_datasets(\n    n_datasets=10,         # Required\n    fraction_features=0.1, # Required\n    n_steps=5              # Required\n)\n```\n\nThe above call to `make_datasets()` will generate 10 different datasets using a random sequence of 5 feature engineering techniques applied to a randomly selected 10% of features. The feature selection is re-calculated after each feature engineering step. Each feature engineering step is applied to the test set if one is provided with a minimum of data leakage (e.g. gaussian KDE is calculated from training data only and then applied to training and testing data).\n\nBy default, generated datasets will be saved to HDF5 in `data/dataset.h5` using the following structure:\n\n```text\ndataset.h5\n├──train\n│   ├── labels\n|   ├── 1\n|   ├── .\n|   ├── .\n|   ├── .\n|   └── n\n│\n└──test\n    ├── labels\n    ├── 1\n    ├── .\n    ├── .\n    ├── .\n    └── n\n```\n\n## 3. Feature engineering\n\nThe currently implemented pool of feature engineering methods are:\n\n1. **One-hot encoding** for string features\n2. **Ordinal encoding** for string features\n3. **Log features** with bases 2, e or 10\n4. **Ratio features**\n5. **Exponential features** with base 2 or e\n6. **Sum features** with 2, 3, or 4\n7. **Difference features** with 2, 3 or 4 subtrahends\n8. **Polynomial features** with degree 2 or 3\n9. **Spline features** with degree 2, 3 or 4\n10. **Quantized features** with using randomly selected k-bins\n11. **Smoothed features** with gaussian kernel density estimation\n\nMajor feature engineering parameters are also randomly selected for each step.\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fgperdrizet%2Fensembleset","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fgperdrizet%2Fensembleset","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fgperdrizet%2Fensembleset/lists"}