Symbolic regression and classification library

High-Performance python symbolic regression library based on parallel local search

• Zero hyperparameter tunning.
• Accurate results in seconds or minutes, in contrast to slow GP-based methods.
• Small models size.
• Support for regression, classification and fuzzy math.
• Support 32 and 64 bit floating point arithmetic.
• Work with unprotected version of math operators (log, sqrt, division)
• Speedup search by using feature importances computed from bbox model

Supported instructions
math	add, sub, mul, div, pdiv, inv, minv, sq2, pow, exp, log, sqrt, cbrt, aq
goniometric	sin, cos, tan, asin, acos, atan, sinh, cosh, tanh
other	nop, max, min, abs, floor, ceil, lt, gt, lte, gte
fuzzy	f_and, f_or, f_xor, f_impl, f_not, f_nand, f_nor, f_nxor, f_nimpl

Sources

C++20 source code available in separate repo sr_core

Dependencies

• AVX2 instructions set(all modern CPU support this)
• numpy
• sklearn

Installation

pip install HROCH

Usage

Symbolic_Regression_Demo.ipynb

Documentation

from HROCH import SymbolicRegressor

reg = SymbolicRegressor(num_threads=8, time_limit=60.0, problem='math', precision='f64')
reg.fit(X_train, y_train)
yp = reg.predict(X_test)

Changelog

v1.4

• Sklearn compatibility
• Classificators:
  • NonlinearLogisticRegressor for a binary classification
  • SymbolicClassifier for multiclass classification
  • FuzzyRegressor for a special binary classification
• Xi corelation used for filter unrelated features

Older versions

v1.3

• Public c++ sources
• Commanline interface changed to cpython
• Support for classification score logloss and accuracy
• Support for final transformations:
  • ordinal regression
  • logistic function
  • clipping
• Acess to equations from all paralel hillclimbers
• User defined constants

v1.2

• Features probability as input parameter
• Custom instructions set
• Parallel hilclimbing parameters

v1.1

• Improved late acceptance hillclimbing

v1.0

• First release

SRBench

full results