{ "info": { "author": "Martin Skarzynski", "author_email": "marskar@gmail.com", "bugtrack_url": null, "classifiers": [ "License :: OSI Approved :: MIT License", "Operating System :: OS Independent", "Programming Language :: Python :: 3" ], "description": "# The `skippy` python package\n\nSkip the boilerplate of scikit-learn machine learning examples.\n\n## Installation\n```bash\npip install skippy\n```\n\n## Usage\n\nIn a shell environment, you can run `skippy`\nwith no arguments to perform a Logistic Regression\non the `digits` dataset.\n\nThis will produce a 10 x 10 confusion matrix\nwith the Accuracy Score at the top.\n\nYou can also pass arguments to skippy at the command line.\n\nFor example, \n```bash\nskippy -data diabetes -type linear_model -name Lasso\n# Or\nskippy -d diabetes -t linear_model -n Lasso\n```\nwill run a linear regression with lasso regularization (L1)\non the `diabetes` dataset.\n\nThe `data` argument can be any of\nthe following built-in scikit-learn datasets:\n- Regression\n - `boston`\n - `diabetes`\n- Classification\n - `digits`\n - `iris`\n - `wine`\n - `breast_cancer`\n\nThe `type` and `name` arguments are\nreferring to the model type and name from scikit-learn.\nThe `type` is the submodule, e.g. \n- `linear_model`\n- `naive_bayes`\n- `ensemble`\n- `svm`\n\nwhile the `name` is the what is actually imported, e.g.\n- `LinearRegression`\n- `GaussianNB`\n- `RandomForestRegressor`\n- `SVC`\n\nSimplify code to a single function call per step:\n```python\nfrom sklearn.metrics import confusion_matrix, accuracy_score\nimport skippy as skp\n\ndata = skp.get_data('digits')\nx_train, x_test, y_train, y_test = skp.split_data(data)\n\nmodel = skp.get_model(model_type='ensemble',\n model_name='RandomForestClassifier')\n\nfit = model.fit(x_train, y_train)\nskp.pickle_model(filename='digits_rf.pickle', model=fit)\npredictions = fit.predict(x_test)\n\nconfmat = confusion_matrix(y_true=y_test, y_pred=predictions)\naccuracy = accuracy_score(y_true=y_test, y_pred=predictions)\n\nskp.confusion_matrix_plot(cm=confmat,\n acc=accuracy,\n filename='digits_rf.png')\n```\n\nOr run a whole pipeline with one function:\n\n```python\nimport skippy as skp\n\nskp.classification(dataset='digits',\n model_type='ensemble',\n model_name='RandomForestClassifier',\n pickle_name='digits_rf.pickle',\n plot_name='digits_rf.png')\n```\n\nFor inspiration, look at the example pipelines in the\n[pipelines folder](https://github.com/marskar/skippy/tree/master/pipelines)\nof the\n[skippy repo](https://github.com/marskar/skippy).\n\n\n", "description_content_type": "text/markdown", "docs_url": null, "download_url": "", "downloads": { "last_day": -1, "last_month": -1, "last_week": -1 }, "home_page": "https://github.com/marskar/skippy", "keywords": "", "license": "", "maintainer": "", "maintainer_email": "", "name": "skippy", "package_url": "https://pypi.org/project/skippy/", "platform": "", "project_url": 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