symfeat 0.4.5

Ruled based feature engineering for regression

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