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| import unittest | |
| from unittest.mock import patch | |
| import numpy as np | |
| from pysr import PySRRegressor | |
| from pysr.sr import run_feature_selection, _handle_feature_selection | |
| import sympy | |
| from sympy import lambdify | |
| import pandas as pd | |
| class TestPipeline(unittest.TestCase): | |
| def setUp(self): | |
| self.default_test_kwargs = dict( | |
| niterations=10, | |
| populations=4, | |
| annealing=True, | |
| useFrequency=False, | |
| ) | |
| np.random.seed(0) | |
| self.X = np.random.randn(100, 5) | |
| def test_linear_relation(self): | |
| y = self.X[:, 0] | |
| model = PySRRegressor(**self.default_test_kwargs) | |
| model.fit(self.X, y) | |
| model.set_params(model_selection="accuracy") | |
| print(model.equations) | |
| self.assertLessEqual(model.get_best()["loss"], 1e-4) | |
| def test_multiprocessing(self): | |
| y = self.X[:, 0] | |
| model = PySRRegressor(**self.default_test_kwargs, procs=2, multithreading=False) | |
| model.fit(self.X, y) | |
| print(model.equations) | |
| self.assertLessEqual(model.equations.iloc[-1]["loss"], 1e-4) | |
| def test_multioutput_custom_operator_quiet(self): | |
| y = self.X[:, [0, 1]] ** 2 | |
| model = PySRRegressor( | |
| unary_operators=["sq(x) = x^2"], | |
| extra_sympy_mappings={"sq": lambda x: x ** 2}, | |
| binary_operators=["plus"], | |
| verbosity=0, | |
| **self.default_test_kwargs, | |
| procs=0, | |
| ) | |
| model.fit(self.X, y) | |
| equations = model.equations | |
| print(equations) | |
| self.assertLessEqual(equations[0].iloc[-1]["loss"], 1e-4) | |
| self.assertLessEqual(equations[1].iloc[-1]["loss"], 1e-4) | |
| def test_multioutput_weighted_with_callable_temp_equation(self): | |
| y = self.X[:, [0, 1]] ** 2 | |
| w = np.random.rand(*y.shape) | |
| w[w < 0.5] = 0.0 | |
| w[w >= 0.5] = 1.0 | |
| # Double equation when weights are 0: | |
| y = (2 - w) * y | |
| # Thus, pysr needs to use the weights to find the right equation! | |
| model = PySRRegressor( | |
| unary_operators=["sq(x) = x^2"], | |
| binary_operators=["plus"], | |
| extra_sympy_mappings={"sq": lambda x: x ** 2}, | |
| **self.default_test_kwargs, | |
| procs=0, | |
| temp_equation_file=True, | |
| delete_tempfiles=False, | |
| ) | |
| model.fit(self.X, y, weights=w) | |
| np.testing.assert_almost_equal( | |
| model.predict(self.X)[:, 0], self.X[:, 0] ** 2, decimal=4 | |
| ) | |
| np.testing.assert_almost_equal( | |
| model.predict(self.X)[:, 1], self.X[:, 1] ** 2, decimal=4 | |
| ) | |
| def test_empty_operators_single_input_multirun(self): | |
| X = np.random.randn(100, 1) | |
| y = X[:, 0] + 3.0 | |
| regressor = PySRRegressor( | |
| model_selection="accuracy", | |
| unary_operators=[], | |
| binary_operators=["plus"], | |
| **self.default_test_kwargs, | |
| ) | |
| self.assertTrue("None" in regressor.__repr__()) | |
| regressor.fit(X, y) | |
| self.assertTrue("None" not in regressor.__repr__()) | |
| self.assertTrue(">>>>" in regressor.__repr__()) | |
| self.assertLessEqual(regressor.equations.iloc[-1]["loss"], 1e-4) | |
| np.testing.assert_almost_equal(regressor.predict(X), y, decimal=1) | |
| # Test if repeated fit works: | |
| regressor.set_params(niterations=0) | |
| regressor.fit(X, y) | |
| self.assertLessEqual(regressor.equations.iloc[-1]["loss"], 1e-4) | |
| np.testing.assert_almost_equal(regressor.predict(X), y, decimal=1) | |
| # Tweak model selection: | |
| regressor.set_params(model_selection="best") | |
| self.assertEqual(regressor.get_params()["model_selection"], "best") | |
| self.assertTrue("None" not in regressor.__repr__()) | |
| self.assertTrue(">>>>" in regressor.__repr__()) | |
| # "best" model_selection should also give a decent loss: | |
| np.testing.assert_almost_equal(regressor.predict(X), y, decimal=1) | |
| def test_noisy(self): | |
| np.random.seed(1) | |
| y = self.X[:, [0, 1]] ** 2 + np.random.randn(self.X.shape[0], 1) * 0.05 | |
| model = PySRRegressor( | |
| # Test that passing a single operator works: | |
| unary_operators="sq(x) = x^2", | |
| binary_operators="plus", | |
| extra_sympy_mappings={"sq": lambda x: x ** 2}, | |
| **self.default_test_kwargs, | |
| procs=0, | |
| denoise=True, | |
| ) | |
| model.fit(self.X, y) | |
| self.assertLessEqual(model.get_best()[1]["loss"], 1e-2) | |
| self.assertLessEqual(model.get_best()[1]["loss"], 1e-2) | |
| def test_pandas_resample(self): | |
| np.random.seed(1) | |
| X = pd.DataFrame( | |
| { | |
| "T": np.random.randn(500), | |
| "x": np.random.randn(500), | |
| "unused_feature": np.random.randn(500), | |
| } | |
| ) | |
| true_fn = lambda x: np.array(x["T"] + x["x"] ** 2 + 1.323837) | |
| y = true_fn(X) | |
| noise = np.random.randn(500) * 0.01 | |
| y = y + noise | |
| # Resampled array is a different order of features: | |
| Xresampled = pd.DataFrame( | |
| { | |
| "unused_feature": np.random.randn(100), | |
| "x": np.random.randn(100), | |
| "T": np.random.randn(100), | |
| } | |
| ) | |
| model = PySRRegressor( | |
| unary_operators=[], | |
| binary_operators=["+", "*", "/", "-"], | |
| **self.default_test_kwargs, | |
| Xresampled=Xresampled, | |
| denoise=True, | |
| select_k_features=2, | |
| ) | |
| model.fit(X, y) | |
| self.assertNotIn("unused_feature", model.latex()) | |
| self.assertIn("T", model.latex()) | |
| self.assertIn("x", model.latex()) | |
| self.assertLessEqual(model.get_best()["loss"], 1e-1) | |
| fn = model.get_best()["lambda_format"] | |
| self.assertListEqual(list(sorted(fn._selection)), [0, 1]) | |
| X2 = pd.DataFrame( | |
| { | |
| "T": np.random.randn(100), | |
| "unused_feature": np.random.randn(100), | |
| "x": np.random.randn(100), | |
| } | |
| ) | |
| self.assertLess(np.average((fn(X2) - true_fn(X2)) ** 2), 1e-1) | |
| self.assertLess(np.average((model.predict(X2) - true_fn(X2)) ** 2), 1e-1) | |
| class TestBest(unittest.TestCase): | |
| def setUp(self): | |
| equations = pd.DataFrame( | |
| { | |
| "equation": ["1.0", "cos(x0)", "square(cos(x0))"], | |
| "loss": [1.0, 0.1, 1e-5], | |
| "complexity": [1, 2, 3], | |
| } | |
| ) | |
| equations["complexity loss equation".split(" ")].to_csv( | |
| "equation_file.csv.bkup", sep="|" | |
| ) | |
| self.model = PySRRegressor( | |
| equation_file="equation_file.csv", | |
| variables_names="x0 x1".split(" "), | |
| extra_sympy_mappings={}, | |
| output_jax_format=False, | |
| multioutput=False, | |
| nout=1, | |
| ) | |
| self.model.n_features = 2 | |
| self.model.refresh() | |
| self.equations = self.model.equations | |
| def test_best(self): | |
| self.assertEqual(self.model.sympy(), sympy.cos(sympy.Symbol("x0")) ** 2) | |
| def test_best_tex(self): | |
| self.assertEqual(self.model.latex(), "\\cos^{2}{\\left(x_{0} \\right)}") | |
| def test_best_lambda(self): | |
| X = np.random.randn(10, 2) | |
| y = np.cos(X[:, 0]) ** 2 | |
| for f in [self.model.predict, self.equations.iloc[-1]["lambda_format"]]: | |
| np.testing.assert_almost_equal(f(X), y, decimal=4) | |
| class TestFeatureSelection(unittest.TestCase): | |
| def setUp(self): | |
| np.random.seed(0) | |
| def test_feature_selection(self): | |
| X = np.random.randn(20000, 5) | |
| y = X[:, 2] ** 2 + X[:, 3] ** 2 | |
| selected = run_feature_selection(X, y, select_k_features=2) | |
| self.assertEqual(sorted(selected), [2, 3]) | |
| def test_feature_selection_handler(self): | |
| X = np.random.randn(20000, 5) | |
| y = X[:, 2] ** 2 + X[:, 3] ** 2 | |
| var_names = [f"x{i}" for i in range(5)] | |
| selected_X, selection = _handle_feature_selection( | |
| X, | |
| select_k_features=2, | |
| variable_names=var_names, | |
| y=y, | |
| ) | |
| self.assertTrue((2 in selection) and (3 in selection)) | |
| selected_var_names = [var_names[i] for i in selection] | |
| self.assertEqual(set(selected_var_names), set("x2 x3".split(" "))) | |
| np.testing.assert_array_equal( | |
| np.sort(selected_X, axis=1), np.sort(X[:, [2, 3]], axis=1) | |
| ) | |