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        "author": "Will McGinnis",
        "author_email": "will@pedalwrencher.com",
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        "description": "Categorical Encoding Methods\n============================\n\n[![Travis Status](https://travis-ci.org/scikit-learn-contrib/categorical-encoding.svg?branch=master)](https://travis-ci.org/scikit-learn-contrib/categorical-encoding)\n[![Coveralls Status](https://coveralls.io/repos/scikit-learn-contrib/categorical-encoding/badge.svg?branch=master&service=github)](https://coveralls.io/r/scikit-learn-contrib/categorical-encoding)\n[![CircleCI Status](https://circleci.com/gh/scikit-learn-contrib/categorical-encoding.svg?style=shield&circle-token=:circle-token)](https://circleci.com/gh/scikit-learn-contrib/categorical-encoding/tree/master)\n[![DOI](https://zenodo.org/badge/47077067.svg)](https://zenodo.org/badge/latestdoi/47077067)\n\nA set of scikit-learn-style transformers for encoding categorical \nvariables into numeric by means of different techniques.\n\nImportant Links\n---------------\n\nDocumentation: [http://contrib.scikit-learn.org/categorical-encoding/](http://contrib.scikit-learn.org/categorical-encoding/)\n\nEncoding Methods\n----------------\n\n * Backward Difference Contrast [2][3]\n * BaseN [6]\n * Binary [5]\n * Count [10]\n * Hashing [1]\n * Helmert Contrast [2][3]\n * James-Stein Estimator [9]\n * LeaveOneOut [4]\n * M-estimator [7]\n * Ordinal [2][3]\n * One-Hot [2][3]\n * Polynomial Contrast [2][3]\n * Sum Contrast [2][3]\n * Target Encoding [7]\n * Weight of Evidence [8]\n\nInstallation\n------------\n\nThe package requires: `numpy`, `statsmodels`, and `scipy`.\n\nTo install the package, execute:\n\n```shell\n$ python setup.py install\n```\n\nor \n\n```shell\npip install category_encoders\n```\n\nor\n\n```shell\nconda install -c conda-forge category_encoders\n```\n\nTo install the development version, you may use:\n\n```shell\npip install --upgrade git+https://github.com/scikit-learn-contrib/categorical-encoding\n```\n\nUsage\n-----\n\nAll of the encoders are fully compatible sklearn transformers, so they can be used in pipelines or in your existing scripts. Supported input formats include numpy arrays and pandas dataframes. If the cols parameter isn't passed, all columns with object or pandas categorical data type will be encoded. Please see the docs for transformer-specific configuration options.\n\nExamples\n--------\nThere are two types of encoders: unsupervised and supervised. An unsupervised example:\n```python\nfrom category_encoders import *\nimport pandas as pd\nfrom sklearn.datasets import load_boston\n\n# prepare some data\nbunch = load_boston()\ny = bunch.target\nX = pd.DataFrame(bunch.data, columns=bunch.feature_names)\n\n# use binary encoding to encode two categorical features\nenc = BinaryEncoder(cols=['CHAS', 'RAD']).fit(X)\n\n# transform the dataset\nnumeric_dataset = enc.transform(X)\n```\n\nAnd a supervised example:\n```python\nfrom category_encoders import *\nimport pandas as pd\nfrom sklearn.datasets import load_boston\n\n# prepare some data\nbunch = load_boston()\ny_train = bunch.target[0:250]\ny_test = bunch.target[250:506]\nX_train = pd.DataFrame(bunch.data[0:250], columns=bunch.feature_names)\nX_test = pd.DataFrame(bunch.data[250:506], columns=bunch.feature_names)\n\n# use target encoding to encode two categorical features\nenc = TargetEncoder(cols=['CHAS', 'RAD']).fit(X_train, y_train)\n\n# transform the datasets\ntraining_numeric_dataset = enc.transform(X_train, y_train)\ntesting_numeric_dataset = enc.transform(X_test)\n```\n\nAdditional examples and benchmarks can be found in the `examples` directory.\n\nContributing\n------------\n\nCategory encoders is under active development, if you'd like to be involved, we'd love to have you. Check out the CONTRIBUTING.md file\nor open an issue on the github project to get started.\n\nReferences\n----------\n\n 1. Kilian Weinberger; Anirban Dasgupta; John Langford; Alex Smola; Josh Attenberg (2009). Feature Hashing for Large Scale Multitask Learning. Proc. ICML.\n 2. Contrast Coding Systems for categorical variables.  UCLA: Statistical Consulting Group. From https://stats.idre.ucla.edu/r/library/r-library-contrast-coding-systems-for-categorical-variables/.\n 3. Gregory Carey (2003). Coding Categorical Variables. From http://psych.colorado.edu/~carey/Courses/PSYC5741/handouts/Coding%20Categorical%20Variables%202006-03-03.pdf\n 4. Strategies to encode categorical variables with many categories. From https://www.kaggle.com/c/caterpillar-tube-pricing/discussion/15748#143154.\n 5. Beyond One-Hot: an exploration of categorical variables. From http://www.willmcginnis.com/2015/11/29/beyond-one-hot-an-exploration-of-categorical-variables/\n 6. BaseN Encoding and Grid Search in categorical variables. From http://www.willmcginnis.com/2016/12/18/basen-encoding-grid-search-category_encoders/\n 7. Daniele Miccii-Barreca (2001). A Preprocessing Scheme for High-Cardinality Categorical Attributes in Classification and Prediction Problems. SIGKDD Explor. Newsl. 3, 1. From http://dx.doi.org/10.1145/507533.507538\n 8. Weight of Evidence (WOE) and Information Value Explained. From https://www.listendata.com/2015/03/weight-of-evidence-woe-and-information.html\n 9. Empirical Bayes for multiple sample sizes. From http://chris-said.io/2017/05/03/empirical-bayes-for-multiple-sample-sizes/\n 10. Simple Count or Frequency Encoding. https://www.datacamp.com/community/tutorials/encoding-methodologies\n\n\n",
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