{"id":19631737,"url":"https://github.com/vanderschaarlab/invase","last_synced_at":"2025-04-28T06:33:28.605Z","repository":{"id":114909442,"uuid":"530705705","full_name":"vanderschaarlab/INVASE","owner":"vanderschaarlab","description":"INVASE: Instance-wise Variable Selection . 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(https://openreview.net/forum?id=BJg_roAcK7)\n\n## :rocket: Installation\n\nThe library can be installed from PyPI using\n```bash\n$ pip install invase\n```\nor from source, using\n```bash\n$ pip install .\n```\n## :boom: Sample Usage\n```python\nimport pandas as pd\n\nfrom sklearn.datasets import load_iris\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.linear_model import LogisticRegression\n\nfrom invase import INVASE\n\nX, y = load_iris(return_X_y=True, as_frame = True)\n\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)\n\n## Load the model\nmodel = LogisticRegression()\n\nmodel.fit(X_train, y_train)\n\n## Load INVASE\nexplainer = INVASE(\n    model, \n    X_train, \n    y_train, \n    n_epoch=1000, \n    prefit = True, # the model is already trained\n)\n\n## Explain\nexplainer.explain(X_test.head(5))\n```\n\n\n## :hammer: Tests\n\nInstall the testing dependencies using\n```bash\npip install .[testing]\n```\nThe tests can be executed using\n```bash\npytest -vsx\n```\n\n## Citing\nIf you use this code, please cite the associated paper:\n```\n@inproceedings{\n    yoon2018invase,\n    title={{INVASE}: Instance-wise Variable Selection using Neural Networks},\n    author={Jinsung Yoon and James Jordon and Mihaela van der Schaar},\n    booktitle={International Conference on Learning Representations},\n    year={2019},\n    url={https://openreview.net/forum?id=BJg_roAcK7},\n}\n```\n","funding_links":[],"categories":[],"sub_categories":[],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fvanderschaarlab%2Finvase","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fvanderschaarlab%2Finvase","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fvanderschaarlab%2Finvase/lists"}