{ "info": { "author": "Evan Harris", "author_email": "emitchellh@gmail.com", "bugtrack_url": null, "classifiers": [ "Development Status :: 4 - Beta", "Intended Audience :: Developers", "License :: OSI Approved :: MIT License", "Programming Language :: Python", "Topic :: Scientific/Engineering :: Artificial Intelligence" ], "description": "scikit-ext\n==========\n\n|PyPI| |Status| |License| |Travis|\n\n.. |PyPI| image:: https://img.shields.io/pypi/v/scikit-ext.svg\n :target: https://pypi.org/project/scikit-ext/\n\n.. |Status| image:: https://img.shields.io/pypi/status/scikit-ext.svg\n :target: https://pypi.org/project/scikit-ext/\n\n.. |License| image:: https://img.shields.io/pypi/l/scikit-ext.svg\n :target: https://github.com/denver1117/scikit-ext/blob/master/LICENSE\n\n.. |Travis| image:: https://travis-ci.org/denver1117/scikit-ext.svg?branch=master\n :target: https://travis-ci.org/denver1117/scikit-ext\n\nAbout\n~~~~~\n\nThe ``scikit_ext`` package contains various scikit-learn extensions,\nbuilt entirely on top of ``sklearn`` base classes. The package is\nseparated into two modules:\n`estimators `__\nand\n`scorers `__.\nFull documentation can be found\n`here `__.\n\nInstallation\n~~~~~~~~~~~~\n\n`Package Index on PyPI `__ To\ninstall:\n\n::\n\n pip install scikit-ext\n\nEstimators\n~~~~~~~~~~\n\n- ``MultiGridSearchCV``: Extension to native sklearn ``GridSearchCV``\n for multiple estimators and param\\_grids. Accepts a list of\n estimators and param\\_grids, iterating through each fitting a\n ``GridSearchCV`` model for each estimator/param\\_grid. Chooses the\n best fitted ``GridSearchCV`` model. Inherits sklearn's\n ``BaseSearchCV`` class, so attributes and methods are all similar to\n ``GridSearchCV``.\n- ``PrunedPipeline``: Extension to native sklearn ``Pipeline`` intended\n for text learning pipelines with a vectorization step and a feature\n selection step. Instead of remembering all vectorizer vocabulary\n elements and selecting appropriate features at prediction time, the\n extension prunes the vocabulary after fitting to only include\n elements who will ultimately survive the feature selection filter\n applied later in the pipeline. This reduces memory and improves\n prediction latency. Predictions will be identical to those made with\n a trained ``Pipeline`` model. Inherits sklearn's ``Pipeline`` class,\n so attributes and methods are all similar to ``Pipeline``.\n- ``ZoomGridSearchCV``: Extension to native sklearn ``GridSearchCV``.\n Fits multiple ``GridSearchCV`` models, updating the ``param_grid``\n after each iteration. The update looks at successful parameter values\n for each grid key. A new list of values is created which expands the\n resolution of the search values centered around the best performing\n value of the previous fit. This allows the standard grid search\n process to start with a small number of distant values for each\n parameter, and zoom in as the better performing corner of the\n hyperparameter search space becomes clear.\n- ``IterRandomEstimator``: Meta-Estimator intended primarily for\n unsupervised estimators whose fitted model can be heavily dependent\n on an arbitrary random initialization state. It is\n best used for problems where a ``fit_predict`` method is intended, so\n the only data used for prediction will be the same data on which the\n model was fitted.\n- ``OptimizedEnsemble``: An optimized ensemble class. Will find the\n optimal ``n_estimators`` parameter for the given ensemble estimator,\n according to the specified input parameters.\n- ``OneVsRestAdjClassifier``: One-Vs-Rest multiclass strategy. The\n adjusted version is a custom extension which overwrites the inherited\n ``predict_proba`` method with a more flexible method allowing custom\n normalization for the predicted probabilities. Any norm argument that\n can be passed directly to ``sklearn.preprocessing.normalize`` is\n allowed. Additionally, norm=None will skip the normalization step\n alltogeter. To mimick the inherited ``OneVsRestClassfier`` behavior,\n set norm='l2'. All other methods are inherited from\n ``OneVsRestClassifier``.\n\nScorers\n~~~~~~~\n\n- ``TimeScorer``: Score using estimated prediction latency of\n estimator.\n- ``MemoryScorer``: Score using estimated memory of pickled estimator\n object.\n- ``CombinedScorer``: Score combining multiple scorers by averaging\n their scores.\n- ``cluster_distribution_score``: Scoring function which scores the\n resulting cluster distribution accross classes. A more even\n distribution indicates a higher score.\n\nAuthors\n~~~~~~~\n\nEvan Harris\n\nLicense\n~~~~~~~\n\nThis project is licensed under the MIT License - see the LICENSE file\nfor details", "description_content_type": "", "docs_url": null, "download_url": "https://pypi.org/project/scikit-ext/#files", "downloads": { "last_day": -1, "last_month": -1, "last_week": -1 }, "home_page": "https://github.com/denver1117/scikit-ext", "keywords": "", "license": "MIT", "maintainer": "", "maintainer_email": "", "name": "scikit-ext", "package_url": "https://pypi.org/project/scikit-ext/", "platform": "", "project_url": "https://pypi.org/project/scikit-ext/", "project_urls": { "Download": "https://pypi.org/project/scikit-ext/#files", "Homepage": "https://github.com/denver1117/scikit-ext" }, "release_url": "https://pypi.org/project/scikit-ext/0.1.15/", "requires_dist": null, "requires_python": ">=3.5", "summary": "Various scikit-learn extensions", "version": "0.1.15" }, "last_serial": 5761836, 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