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| import unittest | |
| from functools import partial | |
| import numpy as np | |
| import pandas as pd | |
| import sympy | |
| from pysr import PySRRegressor, sympy2jax | |
| class TestJAX(unittest.TestCase): | |
| def setUp(self): | |
| np.random.seed(0) | |
| def test_sympy2jax(self): | |
| from jax import numpy as jnp | |
| from jax import random | |
| 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_pandas(self): | |
| from jax import numpy as jnp | |
| X = pd.DataFrame(np.random.randn(100, 10)) | |
| y = np.ones(X.shape[0]) | |
| model = PySRRegressor( | |
| progress=False, | |
| max_evals=10000, | |
| output_jax_format=True, | |
| ) | |
| model.fit(X, y) | |
| equations = pd.DataFrame( | |
| { | |
| "Equation": ["1.0", "cos(x1)", "square(cos(x1))"], | |
| "Loss": [1.0, 0.1, 1e-5], | |
| "Complexity": [1, 2, 3], | |
| } | |
| ) | |
| equations["Complexity Loss Equation".split(" ")].to_csv( | |
| "equation_file.csv.bkup" | |
| ) | |
| model.refresh(checkpoint_file="equation_file.csv") | |
| jformat = model.jax() | |
| np.testing.assert_almost_equal( | |
| np.array(jformat["callable"](jnp.array(X), jformat["parameters"])), | |
| np.square(np.cos(X.values[:, 1])), # Select feature 1 | |
| decimal=3, | |
| ) | |
| def test_pipeline(self): | |
| from jax import numpy as jnp | |
| X = np.random.randn(100, 10) | |
| y = np.ones(X.shape[0]) | |
| model = PySRRegressor(progress=False, max_evals=10000, output_jax_format=True) | |
| model.fit(X, y) | |
| equations = pd.DataFrame( | |
| { | |
| "Equation": ["1.0", "cos(x1)", "square(cos(x1))"], | |
| "Loss": [1.0, 0.1, 1e-5], | |
| "Complexity": [1, 2, 3], | |
| } | |
| ) | |
| equations["Complexity Loss Equation".split(" ")].to_csv( | |
| "equation_file.csv.bkup" | |
| ) | |
| model.refresh(checkpoint_file="equation_file.csv") | |
| 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=3, | |
| ) | |
| def test_feature_selection_custom_operators(self): | |
| rstate = np.random.RandomState(0) | |
| X = pd.DataFrame({f"k{i}": rstate.randn(2000) for i in range(10, 21)}) | |
| def cos_approx(x): | |
| return 1 - (x**2) / 2 + (x**4) / 24 + (x**6) / 720 | |
| y = X["k15"] ** 2 + 2 * cos_approx(X["k20"]) | |
| model = PySRRegressor( | |
| progress=False, | |
| unary_operators=["cos_approx(x) = 1 - x^2 / 2 + x^4 / 24 + x^6 / 720"], | |
| select_k_features=3, | |
| maxsize=10, | |
| early_stop_condition=1e-5, | |
| extra_sympy_mappings={"cos_approx": cos_approx}, | |
| extra_jax_mappings={ | |
| "cos_approx": "(lambda x: 1 - x**2 / 2 + x**4 / 24 + x**6 / 720)" | |
| }, | |
| random_state=0, | |
| deterministic=True, | |
| procs=0, | |
| multithreading=False, | |
| ) | |
| np.random.seed(0) | |
| model.fit(X.values, y.values) | |
| f, parameters = model.jax().values() | |
| np_prediction = model.predict | |
| jax_prediction = partial(f, parameters=parameters) | |
| np_output = np_prediction(X.values) | |
| jax_output = jax_prediction(X.values) | |
| np.testing.assert_almost_equal(y.values, np_output, decimal=3) | |
| np.testing.assert_almost_equal(y.values, jax_output, decimal=3) | |
| def runtests(just_tests=False): | |
| """Run all tests in test_jax.py.""" | |
| tests = [TestJAX] | |
| if just_tests: | |
| return tests | |
| loader = unittest.TestLoader() | |
| suite = unittest.TestSuite() | |
| for test in tests: | |
| suite.addTests(loader.loadTestsFromTestCase(test)) | |
| runner = unittest.TextTestRunner() | |
| return runner.run(suite) | |