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setup.py
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setup.py
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from setuptools import setup
ldesc = """
# HROCH
**High-Performance c++ 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|
## Dependencies
- AVX2 instructions set(all modern CPU support this)
- numpy
- sklearn
- scipy
## Installation
```sh
pip install HROCH
```
## Usage
[Symbolic_Regression_Demo.ipynb](examples/Symbolic_Regression_Demo.ipynb) [](https://colab.research.google.com/github/janoPig/HROCH/blob/main/examples/Symbolic_Regression_Demo.ipynb)
[Documentation](https://janopig.github.io/HROCH/HROCH.html)
```python
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
### 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
"""
setup(
name='HROCH',
version='1.4.12',
description='Symbolic regression and classification',
long_description=ldesc,
long_description_content_type="text/markdown",
author='Jano',
author_email='hroch.regression@gmail.com',
url='https://github.com/janoPig/HROCH/',
project_urls={
'Documentation': 'https://janopig.github.io/HROCH/HROCH.html',
'Source': 'https://github.com/janoPig/HROCH',
'Tracker': 'https://github.com/janoPig/HROCH/issues',
},
keywords=['scikit-learn', 'symbolic-regression', 'classification', 'fuzzy-logic', 'interpretable-ml', 'machine-learning'],
classifiers=['Operating System :: Microsoft :: Windows',
'Operating System :: POSIX :: Linux',
'Topic :: Scientific/Engineering :: Artificial Intelligence',
'License :: OSI Approved :: MIT License',
'Programming Language :: Python',
'Programming Language :: Python :: 3',
'Programming Language :: Python :: 3.8',
'Programming Language :: Python :: 3.9',
'Programming Language :: Python :: 3.10',
'Programming Language :: Python :: 3.11',
'Programming Language :: Python :: 3.12'],
packages=['HROCH'],
license='MIT',
include_package_data=True,
install_requires=['numpy', 'scikit-learn', 'scipy'],
)