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| import unittest | |
| import numpy as np | |
| from pysr import sympy2jax, PySRRegressor | |
| import pandas as pd | |
| from jax import numpy as jnp | |
| from jax import random | |
| from jax import grad | |
| import sympy | |
| class TestJAX(unittest.TestCase): | |
| def setUp(self): | |
| np.random.seed(0) | |
| def test_sympy2jax(self): | |
| x, y, z = sympy.symbols("x y z") | |
| cosx = 1.0 * sympy.cos(x) + y | |
| key = random.PRNGKey(0) | |
| X = random.normal(key, (1000, 2)) | |
| true = 1.0 * jnp.cos(X[:, 0]) + X[:, 1] | |
| f, params = sympy2jax(cosx, [x, y, z]) | |
| self.assertTrue(jnp.all(jnp.isclose(f(X, params), true)).item()) | |
| def test_pipeline(self): | |
| X = np.random.randn(100, 10) | |
| equations = pd.DataFrame( | |
| { | |
| "Equation": ["1.0", "cos(x1)", "square(cos(x1))"], | |
| "MSE": [1.0, 0.1, 1e-5], | |
| "Complexity": [1, 2, 3], | |
| } | |
| ) | |
| equations["Complexity MSE Equation".split(" ")].to_csv( | |
| "equation_file.csv.bkup", sep="|" | |
| ) | |
| model = PySRRegressor( | |
| equation_file="equation_file.csv", | |
| output_jax_format=True, | |
| variable_names="x1 x2 x3".split(" "), | |
| ) | |
| model.selection = [1, 2, 3] | |
| model.n_features = 3 | |
| model.using_pandas = False | |
| model.refresh() | |
| jformat = model.jax() | |
| np.testing.assert_almost_equal( | |
| np.array(jformat["callable"](jnp.array(X), jformat["parameters"])), | |
| np.square(np.cos(X[:, 1])), # Select feature 1 | |
| decimal=4, | |
| ) | |