Metadata-Version: 1.1
Name: category-encoders
Version: 1.2.2
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/1.2.2
Description: Categorical Encoding Methods
        ============================
        
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        A set of scikit-learn-style transformers for encoding categorical 
        variables into numeric by means of different techniques.
        
        Important Links
        ---------------
        
        Documentation: [http://wdm0006.github.io/categorical_encoding/](http://wdm0006.github.io/categorical_encoding/)
        
        Encoding Methods
        ----------------
        
         * Ordinal [2][3]
         * One-Hot [2][3]
         * Binary
         * Helmert Contrast [2][3]
         * Sum Contrast [2][3]
         * Polynomial Contrast [2][3]
         * Backward Difference Contrast [2][3]
         * Hashing [1]
        
        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
        ```
            
        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=[...])
        
        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
        --------
        
        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)
        
        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 http://www.ats.ucla.edu/stat/r/library/contrast_coding.
         3. Gregory Carey (2003). Coding Categorical Variables, from http://psych.colorado.edu/~carey/Courses/PSYC5741/handouts/Coding%20Categorical%20Variables%202006-03-03.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
