Metadata-Version: 1.1
Name: category-encoders
Version: 1.2.8
Summary: A collection sklearn transformers to encode categorical variables as numeric
Home-page: https://github.com/wdm0006/categorical_encoding
Author: Will McGinnis
Author-email: will@pedalwrencher.com
License: BSD
Download-URL: https://github.com/wdm0006/categorical_encoding/tarball/1.2.8
Description: Categorical Encoding Methods
        ============================
        
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        [![DOI](https://zenodo.org/badge/47077067.svg)](https://zenodo.org/badge/latestdoi/47077067)
        
        A set of scikit-learn-style transformers for encoding categorical 
        variables into numeric by means of different techniques.
        
        Important Links
        ---------------
        
        Documentation: [http://contrib.scikit-learn.org/categorical-encoding/](http://contrib.scikit-learn.org/categorical-encoding/)
        
        Encoding Methods
        ----------------
        
         * Ordinal [2][3]
         * One-Hot [2][3]
         * Binary [5]
         * Helmert Contrast [2][3]
         * Sum Contrast [2][3]
         * Polynomial Contrast [2][3]
         * Backward Difference Contrast [2][3]
         * Hashing [1]
         * BaseN [6]
         * LeaveOneOut [4]
         * Target Encoding [7]
        
        Usage
        -----
        
        The package by itself comes with a single module and an estimator. Before
        installing the module you will need `numpy`, `statsmodels`, and `scipy`.
        
        To install the module execute:
        
        ```shell
        $ python setup.py install
        ```
        
        or 
        
        ```
        pip install category_encoders
        ```
        
        or
        
        ```
        conda install -c conda-forge category_encoders
        ```
            
        To use:
        
            import category_encoders as ce
            
            encoder = ce.BackwardDifferenceEncoder(cols=[...])
            encoder = ce.BinaryEncoder(cols=[...])
            encoder = ce.HashingEncoder(cols=[...])
            encoder = ce.HelmertEncoder(cols=[...])
            encoder = ce.OneHotEncoder(cols=[...])
            encoder = ce.OrdinalEncoder(cols=[...])
            encoder = ce.SumEncoder(cols=[...])
            encoder = ce.PolynomialEncoder(cols=[...])
            encoder = ce.BaseNEncoder(cols=[...])
            encoder = ce.TargetEncoder(cols=[...])
            encoder = ce.LeaveOneOutEncoder(cols=[...])
        
        All of these are fully compatible sklearn transformers, so they can be used in pipelines or in your existing scripts. If 
        the cols parameter isn't passed, every non-numeric column will be encoded. Please see the 
        docs for transformer-specific configuration options.
        
        Examples
        --------
        
            from category_encoders import *
            import pandas as pd
            from sklearn.datasets import load_boston
        
            # prepare some data
            bunch = load_boston()
            y = bunch.target
            X = pd.DataFrame(bunch.data, columns=bunch.feature_names)
        
            # use binary encoding to encode two categorical features
            enc = BinaryEncoder(cols=['CHAS', 'RAD']).fit(X, y)
        
            # transform the dataset
            numeric_dataset = enc.transform(X)
        
        In the examples directory, there is an example script used to benchmark
        different encoding techniques on various datasets.
        
        The datasets used in the examples are car, mushroom, and splice datasets 
        from the UCI dataset repository, found here:
        
        [datasets](https://archive.ics.uci.edu/ml/datasets)
        
        Contributing
        ------------
        
        Category encoders is under active development, if you'd like to be involved, we'd love to have you. Check out the CONTRIBUTING.md file
        or open an issue on the github project to get started.
        
        License
        -------
        
        BSD 3-Clause
        
        References:
        -----------
        
         1. Kilian Weinberger; Anirban Dasgupta; John Langford; Alex Smola; Josh Attenberg (2009). Feature Hashing for Large Scale Multitask Learning. Proc. ICML.
         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/.
         3. Gregory Carey (2003). Coding Categorical Variables, from http://psych.colorado.edu/~carey/Courses/PSYC5741/handouts/Coding%20Categorical%20Variables%202006-03-03.pdf
         4. Strategies to encode categorical variables with many categories. from https://www.kaggle.com/c/caterpillar-tube-pricing/discussion/15748#143154.
         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/
         6. BaseN Encoding and Grid Search in categorical variables. from http://www.willmcginnis.com/2016/12/18/basen-encoding-grid-search-category_encoders/
         7. A Preprocessing Scheme for High-Cardinality Categorical Attributes in Classification and Prediction Problems. from https://kaggle2.blob.core.windows.net/forum-message-attachments/225952/7441/high%20cardinality%20categoricals.pdf
        
Keywords: python data science machine learning pandas sklearn
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: Programming Language :: Python :: 3
