HROCH 1.2.1

Symbolic regression

HROCH

High-Performance python symbolic regression library based on parallel late acceptance hill-climbing

• Zero hyperparameter tunning.
• Accurate results in seconds or minutes, in contrast to slow GP-based methods.
• Small models size.
• Support mathematic equations and fuzzy logic operators.
• 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
• CLI

Supported instructions
math	add, sub, mul, div, 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

Dependencies

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

Installation

pip install HROCH

Usage

from HROCH import PHCRegressor

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

Changelog

v1.2

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

v1.1

• Improved late acceptance hillclimbing

v1.0

• First release

SRBench