import inspect 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 import warnings class TestPipeline(unittest.TestCase): def setUp(self): # Using inspect, # get default niterations from PySRRegressor, and double them: default_niterations = ( inspect.signature(PySRRegressor.__init__).parameters["niterations"].default ) default_populations = ( inspect.signature(PySRRegressor.__init__).parameters["populations"].default ) self.default_test_kwargs = dict( model_selection="accuracy", niterations=default_niterations * 2, populations=default_populations * 2, ) self.rstate = np.random.RandomState(0) self.X = self.rstate.randn(100, 5) def test_linear_relation(self): y = self.X[:, 0] model = PySRRegressor(**self.default_test_kwargs) model.fit(self.X, y) 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_custom_complexity(self): y = self.X[:, [0, 1]] ** 2 model = PySRRegressor( unary_operators=["square_op(x) = x^2"], extra_sympy_mappings={"square_op": lambda x: x**2}, complexity_of_operators={"square_op": 2, "plus": 1}, binary_operators=["plus"], verbosity=0, **self.default_test_kwargs, procs=0, # Test custom operators with constraints: nested_constraints={"square_op": {"square_op": 3}}, constraints={"square_op": 10}, ) model.fit(self.X, y) equations = model.equations print(equations) self.assertIn("square_op", model.equations[0].iloc[-1]["equation"]) self.assertLessEqual(equations[0].iloc[-1]["loss"], 1e-4) self.assertLessEqual(equations[1].iloc[-1]["loss"], 1e-4) test_y1 = model.predict(self.X) test_y2 = model.predict(self.X, index=[-1, -1]) mse1 = np.average((test_y1 - y) ** 2) mse2 = np.average((test_y2 - y) ** 2) self.assertLessEqual(mse1, 1e-4) self.assertLessEqual(mse2, 1e-4) bad_y = model.predict(self.X, index=[0, 0]) bad_mse = np.average((bad_y - y) ** 2) self.assertGreater(bad_mse, 1e-4) def test_multioutput_weighted_with_callable_temp_equation(self): X = self.X.copy() y = X[:, [0, 1]] ** 2 w = self.rstate.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(X.copy(), y, weights=w) # These tests are flaky, so don't fail test: try: np.testing.assert_almost_equal( model.predict(X.copy())[:, 0], X[:, 0] ** 2, decimal=4 ) except AssertionError: print("Error in test_multioutput_weighted_with_callable_temp_equation") print("Model equations: ", model.sympy()[0]) print("True equation: x0^2") try: np.testing.assert_almost_equal( model.predict(X.copy())[:, 1], X[:, 1] ** 2, decimal=4 ) except AssertionError: print("Error in test_multioutput_weighted_with_callable_temp_equation") print("Model equations: ", model.sympy()[1]) print("True equation: x1^2") def test_empty_operators_single_input_multirun(self): X = self.rstate.randn(100, 1) y = X[:, 0] + 3.0 regressor = PySRRegressor( 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__()) def test_noisy(self): y = self.X[:, [0, 1]] ** 2 + self.rstate.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_with_nested_constraints(self): X = pd.DataFrame( { "T": self.rstate.randn(500), "x": self.rstate.randn(500), "unused_feature": self.rstate.randn(500), } ) true_fn = lambda x: np.array(x["T"] + x["x"] ** 2 + 1.323837) y = true_fn(X) noise = self.rstate.randn(500) * 0.01 y = y + noise # We also test y as a pandas array: y = pd.Series(y) # Resampled array is a different order of features: Xresampled = pd.DataFrame( { "unused_feature": self.rstate.randn(100), "x": self.rstate.randn(100), "T": self.rstate.randn(100), } ) model = PySRRegressor( unary_operators=[], binary_operators=["+", "*", "/", "-"], **self.default_test_kwargs, Xresampled=Xresampled, denoise=True, nested_constraints={"/": {"+": 1, "-": 1}, "+": {"*": 4}}, ) 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"] X2 = pd.DataFrame( { "T": self.rstate.randn(100), "unused_feature": self.rstate.randn(100), "x": self.rstate.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) def test_high_dim_selection_early_stop(self): X = pd.DataFrame({f"k{i}": self.rstate.randn(10000) for i in range(10)}) Xresampled = pd.DataFrame({f"k{i}": self.rstate.randn(100) for i in range(10)}) y = X["k7"] ** 2 + np.cos(X["k9"]) * 3 model = PySRRegressor( unary_operators=["cos"], select_k_features=3, early_stop_condition=1e-4, # Stop once most accurate equation is <1e-4 MSE Xresampled=Xresampled, maxsize=12, **self.default_test_kwargs, ) model.fit(X, y) model.set_params(model_selection="accuracy") model.predict(X) self.assertLess(np.average((model.predict(X) - y) ** 2), 1e-4) 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", variable_names="x0 x1".split(" "), extra_sympy_mappings={}, output_jax_format=False, model_selection="accuracy", ) self.model.n_features = 2 self.model.refresh() self.equations = self.model.equations self.rstate = np.random.RandomState(0) def test_best(self): self.assertEqual(self.model.sympy(), sympy.cos(sympy.Symbol("x0")) ** 2) def test_index_selection(self): self.assertEqual(self.model.sympy(-1), sympy.cos(sympy.Symbol("x0")) ** 2) self.assertEqual(self.model.sympy(2), sympy.cos(sympy.Symbol("x0")) ** 2) self.assertEqual(self.model.sympy(1), sympy.cos(sympy.Symbol("x0"))) self.assertEqual(self.model.sympy(0), 1.0) def test_best_tex(self): self.assertEqual(self.model.latex(), "\\cos^{2}{\\left(x_{0} \\right)}") def test_best_lambda(self): X = self.rstate.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): self.rstate = np.random.RandomState(0) def test_feature_selection(self): X = self.rstate.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 = self.rstate.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) ) class TestMiscellaneous(unittest.TestCase): """Test miscellaneous functions.""" def test_deprecation(self): """Ensure that deprecation works as expected. This should give a warning, and sets the correct value. """ with self.assertWarns(UserWarning): model = PySRRegressor(fractionReplaced=0.2) # This is a deprecated parameter, so we should get a warning. # The correct value should be set: self.assertEqual(model.params["fraction_replaced"], 0.2) def test_size_warning(self): """Ensure that a warning is given for a large input size.""" model = PySRRegressor() X = np.random.randn(10001, 2) y = np.random.randn(10001) with warnings.catch_warnings(): warnings.simplefilter("error") with self.assertRaises(Exception) as context: model.fit(X, y) self.assertIn("more than 10,000", str(context.exception)) def test_feature_warning(self): """Ensure that a warning is given for large number of features.""" model = PySRRegressor() X = np.random.randn(100, 10) y = np.random.randn(100) with warnings.catch_warnings(): warnings.simplefilter("error") with self.assertRaises(Exception) as context: model.fit(X, y) self.assertIn("with 10 features or more", str(context.exception))