Metadata-Version: 1.1
Name: symfeat
Version: 0.3.0
Summary: Ruled based feature engineering for regression
Home-page: https://github.com/ohjeah/symfeat
Author: Markus Quade
Author-email: info@markusqua.de
License: MIT
Description: 
        symfeat
        =======
        
        .. image:: https://travis-ci.org/Ohjeah/symfeat.svg?branch=master
            :target: https://travis-ci.org/Ohjeah/symfeat
        .. image:: https://badge.fury.io/py/symfeat.svg
            :target: https://badge.fury.io/py/symfeat
        .. image:: https://zenodo.org/badge/79949716.svg
            :target: https://zenodo.org/badge/latestdoi/79949716
        
        **symfeat** is a rule based feature engineering library to be used as a
        preprocessor for regression tasks.
        
        It is based on:
        
            Mcconaghy, T. (2011). FFX: Fast, Scalable, Deterministic Symbolic Regression Technology. Genetic Programming Theory and Practice IX, 235-260. `DOI: 10.1007/978-1-4614-1770-5\_13 <http://dx.doi.org/10.1007/978-1-4614-1770-5_13>`_
        
        
        Features
        --------
        - Builds a features based on all valid rule specified combinations
        - Discards non-finite transformations
        - Optional: remove equivalent expressions
        
        
        Installation
        ------------
        
        ``pip install symfeat``
        
        
        Usage
        -----
        
        .. code-block:: python
        
            import numpy as np
            import symfeat as sf
        
            operators = {"sin": np.sin}
            exponents = [1, 2, -1, -2]
        
            x = np.random.normal(size=10).reshape(-1, 1)
        
            sym = sf.SymbolicFeatures(exponents=exponents, operators=operators)
            features = sym.fit_transform(x)
            names = sym.names
        
Platform: UNKNOWN
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
