{ "info": { "author": "Civis Analytics", "author_email": "opensource@civisanalytics.com", "bugtrack_url": null, "classifiers": [], "description": "civisml-extensions\n==================\n\n.. image:: https://www.travis-ci.org/civisanalytics/civisml-extensions.svg?branch=master\n :target: https://www.travis-ci.org/civisanalytics/civisml-extensions\n\nscikit-learn-compatible estimators from Civis Analytics\n\nInstallation\n------------\n\nInstallation with ``pip`` is recommended::\n\n $ pip install civisml-extensions\n\nFor development, a few additional dependencies are needed::\n\n $ pip install -r dev-requirements.txt\n\nContents and Usage\n------------------\n\nThis package contains `scikit-learn`_-compatible estimators for stacking (\n``StackedClassifier``, ``StackedRegressor``), non-negative linear regression (\n``NonNegativeLinearRegression``), preprocessing pandas_ ``DataFrames`` (\n``DataFrameETL``), and using Hyperband_ for cross-validating hyperparameters (\n``HyperbandSearchCV``).\n\nUsage of these estimators follows the standard sklearn conventions. Here is an\nexample of using the ``StackedClassifier``::\n\n >>> from sklearn.linear_model import LogisticRegression\n >>> from sklearn.ensemble import RandomForestClassifier\n >>> from civismlext.stacking import StackedClassifier\n >>> # Note that the final estimator 'metalr' is the meta-estimator\n >>> estlist = [('rf', RandomForestClassifier()),\n >>> ('lr', LogisticRegression()),\n >>> ('metalr', LogisticRegression())]\n >>> mysm = StackedClassifier(estlist)\n >>> # Set some parameters, if you didn't set them at instantiation\n >>> mysm.set_params(rf__random_state=7, lr__random_state=8,\n >>> metalr__random_state=9, metalr__C=10**7)\n >>> # Fit\n >>> mysm.fit(Xtrain, ytrain)\n >>> # Predict!\n >>> ypred = mysm.predict_proba(Xtest)\n\nYou can learn more about stacking and see an example use of the ``StackedRegressor`` and ``NonNegativeLinearRegression`` estimators in `a talk presented at PyData NYC`_ in November, 2017.\n\nSee the doc strings of the various estimators for more information.\n\nContributing\n------------\n\nSee ``CONTIBUTING.md`` for information about contributing to this project.\n\nLicense\n-------\n\nBSD-3\n\nSee ``LICENSE.md`` for details.\n\n.. _scikit-learn: http://scikit-learn.org/\n.. _pandas: http://pandas.pydata.org/\n.. _Hyperband: https://arxiv.org/abs/1603.06560\n.. _a talk presented at PyData NYC: https://www.youtube.com/watch?v=3gpf1lGwecA", "description_content_type": "", "docs_url": null, "download_url": "", "downloads": { "last_day": -1, "last_month": -1, "last_week": -1 }, "home_page": "https://www.civisanalytics.com", "keywords": "", "license": "BSD-3", "maintainer": "", "maintainer_email": "", "name": "civisml-extensions", "package_url": "https://pypi.org/project/civisml-extensions/", "platform": "", "project_url": "https://pypi.org/project/civisml-extensions/", "project_urls": { "Homepage": "https://www.civisanalytics.com" }, "release_url": "https://pypi.org/project/civisml-extensions/0.1.10/", "requires_dist": null, 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