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An open source python library for non-linear piecewise symbolic regression based on Genetic Programming

Project description

PS-Tree Documentation Status

An open source python library for non-linear piecewise symbolic regression based on Genetic Programming


Piece-wise non-linear regression is a long-standing problem in the machine learning domain that has long plagued machine learning researchers. It is extremely difficult for users to determine the correct partition scheme and non-linear model when there is no prior information. To address this issue, we proposed piece-wise non-linear regression tree (PS-Tree), an automated piece-wise non-linear regression method based on decision tree and genetic programming techniques. Based on such an algorithm framework, our method can produce an explainable model with high accuracy in a short period of time.


pip install -U pstree


  • A fully automated piece-wise non-linear regression tool

  • A fast genetic programming based symbolic regression tool


An example of usage:

X, y = load_diabetes(return_X_y=True)
x_train, x_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)
r = PSTreeRegressor(regr_class=GPRegressor, tree_class=DecisionTreeRegressor,
                    height_limit=6, n_pop=25, n_gen=100,
                    basic_primitive='optimal', size_objective=True), y_train)
print(r2_score(y_test, r.predict(x_test)))

Experimental results on SRBench:


    title={PS-Tree: A piecewise symbolic regression tree},
    author={Zhang, Hengzhe and Zhou, Aimin and Qian, Hong and Zhang, Hu},
    journal={Swarm and Evolutionary Computation},


This package was created with Cookiecutter and the audreyr/cookiecutter-pypackage project template.


0.1.0 (2021-06-28)

  • First release on PyPI.

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