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| import unittest | |
| import numpy as np | |
| import pandas as pd | |
| from pysr import sympy2torch, get_hof | |
| import torch | |
| import sympy | |
| class TestTorch(unittest.TestCase): | |
| def test_sympy2torch(self): | |
| x, y, z = sympy.symbols('x y z') | |
| cosx = 1.0 * sympy.cos(x) + y | |
| X = torch.randn((1000, 3)) | |
| true = 1.0 * torch.cos(X[:, 0]) + X[:, 1] | |
| torch_module = sympy2torch(cosx, [x, y, z]) | |
| self.assertTrue( | |
| np.all(np.isclose(torch_module(X).detach().numpy(), true.detach().numpy())) | |
| ) | |
| def test_pipeline(self): | |
| X = np.random.randn(100, 10) | |
| equations = pd.DataFrame({ | |
| 'Equation': ['1.0', 'cos(x0)', 'square(cos(x0))'], | |
| 'MSE': [1.0, 0.1, 1e-5], | |
| 'Complexity': [1, 2, 3] | |
| }) | |
| equations['Complexity MSE Equation'.split(' ')].to_csv( | |
| 'equation_file.csv.bkup', sep='|') | |
| equations = get_hof( | |
| 'equation_file.csv', n_features=2, variables_names='x1 x2 x3'.split(' '), | |
| extra_sympy_mappings={}, output_torch_format=True, | |
| multioutput=False, nout=1, selection=[1, 2, 3]) | |
| tformat = equations.iloc[-1].torch_format | |
| np.testing.assert_almost_equal( | |
| tformat(torch.tensor(X)).detach().numpy(), | |
| np.square(np.cos(X[:, 1])) #Selection 1st feature | |
| ) | |