Skip to main content

Ruled based feature engineering for regression

Project description

https://travis-ci.org/Ohjeah/symfeat.svg?branch=master https://badge.fury.io/py/symfeat.svg https://img.shields.io/pypi/pyversions/symfeat.svg https://zenodo.org/badge/79949716.svg

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

Features

  • Builds a features based on all valid rule specified combinations
  • Discards non-finite transformations
  • Remove equivalent based on expressions or numeric values

Installation

pip install symfeat

Usage

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

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Files for symfeat, version 0.4.5
Filename, size File type Python version Upload date Hashes
Filename, size symfeat-0.4.5.tar.gz (3.5 kB) File type Source Python version None Upload date Hashes View
Filename, size symfeat-0.4.5-py3-none-any.whl (5.2 kB) File type Wheel Python version 3.5 Upload date Hashes View

Supported by

AWS AWS Cloud computing Datadog Datadog Monitoring Facebook / Instagram Facebook / Instagram PSF Sponsor Fastly Fastly CDN Google Google Object Storage and Download Analytics Huawei Huawei PSF Sponsor Microsoft Microsoft PSF Sponsor NVIDIA NVIDIA PSF Sponsor Pingdom Pingdom Monitoring Salesforce Salesforce PSF Sponsor Sentry Sentry Error logging StatusPage StatusPage Status page