pysr/test/test.py

import inspect
import os
import pickle as pkl
import tempfile
import traceback
import unittest
import warnings
from pathlib import Path

import numpy as np
import pandas as pd
import sympy
from sklearn.utils.estimator_checks import check_estimator

from .. import PySRRegressor, julia_helpers
from ..export_latex import sympy2latex
from ..feature_selection import _handle_feature_selection, run_feature_selection
from ..sr import _check_assertions, _process_constraints, idx_model_selection
from ..utils import _csv_filename_to_pkl_filename

DEFAULT_PARAMS = inspect.signature(PySRRegressor.__init__).parameters
DEFAULT_NITERATIONS = DEFAULT_PARAMS["niterations"].default
DEFAULT_POPULATIONS = DEFAULT_PARAMS["populations"].default
DEFAULT_NCYCLES = DEFAULT_PARAMS["ncyclesperiteration"].default


class TestPipeline(unittest.TestCase):
    def setUp(self):
        # Using inspect,
        # get default niterations from PySRRegressor, and double them:
        self.default_test_kwargs = dict(
            progress=False,
            model_selection="accuracy",
            niterations=DEFAULT_NITERATIONS * 2,
            populations=DEFAULT_POPULATIONS * 2,
            temp_equation_file=True,
        )
        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,
            early_stop_condition="stop_if(loss, complexity) = loss < 1e-4 && complexity == 1",
        )
        model.fit(self.X, y)
        print(model.equations_)
        self.assertLessEqual(model.get_best()["loss"], 1e-4)

    def test_linear_relation_named(self):
        y = self.X[:, 0]
        model = PySRRegressor(
            **self.default_test_kwargs,
            early_stop_condition="stop_if(loss, complexity) = loss < 1e-4 && complexity == 1",
        )
        model.fit(self.X, y, variable_names=["c1", "c2", "c3", "c4", "c5"])
        self.assertIn("c1", model.equations_.iloc[-1]["equation"])

    def test_linear_relation_weighted(self):
        y = self.X[:, 0]
        weights = np.ones_like(y)
        model = PySRRegressor(
            **self.default_test_kwargs,
            early_stop_condition="stop_if(loss, complexity) = loss < 1e-4 && complexity == 1",
        )
        model.fit(self.X, y, weights=weights)
        print(model.equations_)
        self.assertLessEqual(model.get_best()["loss"], 1e-4)

    def test_multiprocessing_turbo_custom_objective(self):
        rstate = np.random.RandomState(0)
        y = self.X[:, 0]
        y += rstate.randn(*y.shape) * 1e-4
        model = PySRRegressor(
            **self.default_test_kwargs,
            # Turbo needs to work with unsafe operators:
            unary_operators=["sqrt"],
            procs=2,
            multithreading=False,
            turbo=True,
            early_stop_condition="stop_if(loss, complexity) = loss < 1e-10 && complexity == 1",
            full_objective="""
            function my_objective(tree::Node{T}, dataset::Dataset{T}, options::Options) where T
                prediction, flag = eval_tree_array(tree, dataset.X, options)
                !flag && return T(Inf)
                abs3(x) = abs(x) ^ 3
                return sum(abs3, prediction .- dataset.y) / length(prediction)
            end
            """,
        )
        model.fit(self.X, y)
        print(model.equations_)
        best_loss = model.equations_.iloc[-1]["loss"]
        self.assertLessEqual(best_loss, 1e-10)
        self.assertGreaterEqual(best_loss, 0.0)

    def test_high_precision_search_custom_loss(self):
        y = 1.23456789 * self.X[:, 0]
        model = PySRRegressor(
            **self.default_test_kwargs,
            early_stop_condition="stop_if(loss, complexity) = loss < 1e-4 && complexity == 3",
            loss="my_loss(prediction, target) = (prediction - target)^2",
            precision=64,
            parsimony=0.01,
            warm_start=True,
        )
        model.fit(self.X, y)
        from pysr.sr import Main

        # We should have that the model state is now a Float64 hof:
        Main.test_state = model.raw_julia_state_
        self.assertTrue(Main.eval("typeof(test_state[2]).parameters[1] == Float64"))

    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 turbo:
            turbo=True,
            # Test custom operators with constraints:
            nested_constraints={"square_op": {"square_op": 3}},
            constraints={"square_op": 10},
            early_stop_condition="stop_if(loss, complexity) = loss < 1e-4 && complexity == 3",
        )
        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,
            delete_tempfiles=False,
            early_stop_condition="stop_if(loss, complexity) = loss < 1e-4 && complexity == 2",
        )
        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=3
            )
        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=3
            )
        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_complex_equations_anonymous_stop(self):
        X = self.rstate.randn(100, 3) + 1j * self.rstate.randn(100, 3)
        y = (2 + 1j) * np.cos(X[:, 0] * (0.5 - 0.3j))
        model = PySRRegressor(
            binary_operators=["+", "-", "*"],
            unary_operators=["cos"],
            **self.default_test_kwargs,
            early_stop_condition="(loss, complexity) -> loss <= 1e-4 && complexity <= 6",
        )
        model.fit(X, y)
        test_y = model.predict(X)
        self.assertTrue(np.issubdtype(test_y.dtype, np.complexfloating))
        self.assertLessEqual(np.average(np.abs(test_y - y) ** 2), 1e-4)

    def test_empty_operators_single_input_warm_start(self):
        X = self.rstate.randn(100, 1)
        y = X[:, 0] + 3.0
        regressor = PySRRegressor(
            unary_operators=[],
            binary_operators=["plus"],
            **self.default_test_kwargs,
            early_stop_condition="stop_if(loss, complexity) = loss < 1e-4 && complexity == 3",
        )
        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=1,
            ncyclesperiteration=2,
            warm_start=True,
            early_stop_condition=None,
        )
        # Check that the the julia state is saved:
        from pysr.sr import Main

        # We should have that the model state is now a Float32 hof:
        Main.test_state = regressor.raw_julia_state_
        self.assertTrue(Main.eval("typeof(test_state[2]).parameters[1] == Float32"))
        # This should exit almost immediately, and use the old equations
        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_warm_start_set_at_init(self):
        # Smoke test for bug where warm_start=True is set at init
        y = self.X[:, 0]
        regressor = PySRRegressor(warm_start=True, max_evals=10)
        regressor.fit(self.X, y)

    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,
            early_stop_condition="stop_if(loss, complexity) = loss < 0.05 && complexity == 2",
        )
        # We expect in this case that the "best"
        # equation should be the right one:
        model.set_params(model_selection="best")
        # Also try without a temp equation file:
        model.set_params(temp_equation_file=False)
        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,
            denoise=True,
            nested_constraints={"/": {"+": 1, "-": 1}, "+": {"*": 4}},
            early_stop_condition="stop_if(loss, complexity) = loss < 1e-3 && complexity == 7",
        )
        model.fit(X, y, Xresampled=Xresampled)
        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
            maxsize=12,
            **self.default_test_kwargs,
        )
        model.set_params(model_selection="accuracy")
        model.fit(X, y, Xresampled=Xresampled)
        self.assertLess(np.average((model.predict(X) - y) ** 2), 1e-4)
        # Again, but with numpy arrays:
        model.fit(X.values, y.values, Xresampled=Xresampled.values)
        self.assertLess(np.average((model.predict(X.values) - y.values) ** 2), 1e-4)

    def test_load_model(self):
        """See if we can load a ran model from the equation file."""
        csv_file_data = """
        Complexity,Loss,Equation
        1,0.19951081,"1.9762075"
        3,0.12717344,"(f0 + 1.4724599)"
        4,0.104823045,"pow_abs(2.2683423, cos(f3))\""""
        # Strip the indents:
        csv_file_data = "\n".join([l.strip() for l in csv_file_data.split("\n")])

        for from_backup in [False, True]:
            rand_dir = Path(tempfile.mkdtemp())
            equation_filename = str(rand_dir / "equation.csv")
            with open(equation_filename + (".bkup" if from_backup else ""), "w") as f:
                f.write(csv_file_data)
            model = PySRRegressor.from_file(
                equation_filename,
                n_features_in=5,
                feature_names_in=["f0", "f1", "f2", "f3", "f4"],
                binary_operators=["+", "*", "/", "-", "^"],
                unary_operators=["cos"],
            )
            X = self.rstate.rand(100, 5)
            y_truth = 2.2683423 ** np.cos(X[:, 3])
            y_test = model.predict(X, 2)

            np.testing.assert_allclose(y_truth, y_test)

    def test_load_model_simple(self):
        # Test that we can simply load a model from its equation file.
        y = self.X[:, [0, 1]] ** 2
        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,
            early_stop_condition="stop_if(loss, complexity) = loss < 0.05 && complexity == 2",
        )
        rand_dir = Path(tempfile.mkdtemp())
        equation_file = rand_dir / "equations.csv"
        model.set_params(temp_equation_file=False)
        model.set_params(equation_file=equation_file)
        model.fit(self.X, y)

        # lambda functions are removed from the pickling, so we need
        # to pass it during the loading:
        model2 = PySRRegressor.from_file(
            model.equation_file_, extra_sympy_mappings={"sq": lambda x: x**2}
        )

        np.testing.assert_allclose(model.predict(self.X), model2.predict(self.X))

        # Try again, but using only the pickle file:
        for file_to_delete in [str(equation_file), str(equation_file) + ".bkup"]:
            if os.path.exists(file_to_delete):
                os.remove(file_to_delete)

        pickle_file = rand_dir / "equations.pkl"
        model3 = PySRRegressor.from_file(
            model.equation_file_, extra_sympy_mappings={"sq": lambda x: x**2}
        )
        np.testing.assert_allclose(model.predict(self.X), model3.predict(self.X))


def manually_create_model(equations, feature_names=None):
    if feature_names is None:
        feature_names = ["x0", "x1"]

    model = PySRRegressor(
        progress=False,
        niterations=1,
        extra_sympy_mappings={},
        output_jax_format=False,
        model_selection="accuracy",
        equation_file="equation_file.csv",
    )

    # Set up internal parameters as if it had been fitted:
    if isinstance(equations, list):
        # Multi-output.
        model.equation_file_ = "equation_file.csv"
        model.nout_ = len(equations)
        model.selection_mask_ = None
        model.feature_names_in_ = np.array(feature_names, dtype=object)
        for i in range(model.nout_):
            equations[i]["complexity loss equation".split(" ")].to_csv(
                f"equation_file.csv.out{i+1}.bkup"
            )
    else:
        model.equation_file_ = "equation_file.csv"
        model.nout_ = 1
        model.selection_mask_ = None
        model.feature_names_in_ = np.array(feature_names, dtype=object)
        equations["complexity loss equation".split(" ")].to_csv(
            "equation_file.csv.bkup"
        )

    model.refresh()

    return model


class TestBest(unittest.TestCase):
    def setUp(self):
        self.rstate = np.random.RandomState(0)
        self.X = self.rstate.randn(10, 2)
        self.y = np.cos(self.X[:, 0]) ** 2
        equations = pd.DataFrame(
            {
                "equation": ["1.0", "cos(x0)", "square(cos(x0))"],
                "loss": [1.0, 0.1, 1e-5],
                "complexity": [1, 2, 3],
            }
        )
        self.model = manually_create_model(equations)
        self.equations_ = self.model.equations_

    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.X
        y = self.y
        for f in [self.model.predict, self.equations_.iloc[-1]["lambda_format"]]:
            np.testing.assert_almost_equal(f(X), y, decimal=3)

    def test_all_selection_strategies(self):
        equations = pd.DataFrame(
            dict(
                loss=[1.0, 0.1, 0.01, 0.001 * 1.4, 0.001],
                score=[0.5, 1.0, 0.5, 0.5, 0.3],
            )
        )
        idx_accuracy = idx_model_selection(equations, "accuracy")
        self.assertEqual(idx_accuracy, 4)
        idx_best = idx_model_selection(equations, "best")
        self.assertEqual(idx_best, 3)
        idx_score = idx_model_selection(equations, "score")
        self.assertEqual(idx_score, 1)


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_csv_to_pkl_conversion(self):
        """Test that csv filename to pkl filename works as expected."""
        tmpdir = Path(tempfile.mkdtemp())
        equation_file = tmpdir / "equations.389479384.28378374.csv"
        expected_pkl_file = tmpdir / "equations.389479384.28378374.pkl"

        # First, test inputting the paths:
        test_pkl_file = _csv_filename_to_pkl_filename(equation_file)
        self.assertEqual(test_pkl_file, str(expected_pkl_file))

        # Next, test inputting the strings.
        test_pkl_file = _csv_filename_to_pkl_filename(str(equation_file))
        self.assertEqual(test_pkl_file, str(expected_pkl_file))

    def test_deprecation(self):
        """Ensure that deprecation works as expected.

        This should give a warning, and sets the correct value.
        """
        with self.assertWarns(FutureWarning):
            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.fraction_replaced, 0.2)

    def test_power_law_warning(self):
        """Ensure that a warning is given for a power law operator."""
        with self.assertWarns(UserWarning):
            _process_constraints(["^"], [], {})

    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))

    def test_deterministic_warnings(self):
        """Ensure that warnings are given for determinism"""
        model = PySRRegressor(random_state=0)
        X = np.random.randn(100, 2)
        y = np.random.randn(100)
        with warnings.catch_warnings():
            warnings.simplefilter("error")
            with self.assertRaises(Exception) as context:
                model.fit(X, y)
            self.assertIn("`deterministic`", str(context.exception))

    def test_deterministic_errors(self):
        """Setting deterministic without random_state should error"""
        model = PySRRegressor(deterministic=True)
        X = np.random.randn(100, 2)
        y = np.random.randn(100)
        with self.assertRaises(ValueError):
            model.fit(X, y)

    def test_changed_options_warning(self):
        """Check that a warning is given if Julia options are changed."""
        if julia_helpers.julia_kwargs_at_initialization is None:
            julia_helpers.init_julia(julia_kwargs={"threads": 2, "optimize": 3})

        cur_init = julia_helpers.julia_kwargs_at_initialization

        threads_to_change = cur_init["threads"] + 1
        with warnings.catch_warnings():
            warnings.simplefilter("error")
            with self.assertRaises(Exception) as context:
                julia_helpers.init_julia(
                    julia_kwargs={"threads": threads_to_change, "optimize": 3}
                )
            self.assertIn("Julia has already started", str(context.exception))
            self.assertIn("threads", str(context.exception))

    def test_extra_sympy_mappings_undefined(self):
        """extra_sympy_mappings=None errors for custom operators"""
        model = PySRRegressor(unary_operators=["square2(x) = x^2"])
        X = np.random.randn(100, 2)
        y = np.random.randn(100)
        with self.assertRaises(ValueError):
            model.fit(X, y)

    def test_sympy_function_fails_as_variable(self):
        model = PySRRegressor()
        X = np.random.randn(100, 2)
        y = np.random.randn(100)
        with self.assertRaises(ValueError) as cm:
            model.fit(X, y, variable_names=["x1", "N"])
        self.assertIn("Variable name", str(cm.exception))

    def test_bad_variable_names_fail(self):
        model = PySRRegressor()
        X = np.random.randn(100, 1)
        y = np.random.randn(100)

        with self.assertRaises(ValueError) as cm:
            model.fit(X, y, variable_names=["Tr(Tij)"])
        self.assertIn("Invalid variable name", str(cm.exception))

        with self.assertRaises(ValueError) as cm:
            model.fit(X, y, variable_names=["f{c}"])
        self.assertIn("Invalid variable name", str(cm.exception))

    def test_pickle_with_temp_equation_file(self):
        """If we have a temporary equation file, unpickle the estimator."""
        model = PySRRegressor(
            populations=int(1 + DEFAULT_POPULATIONS / 5),
            temp_equation_file=True,
            procs=0,
            multithreading=False,
        )
        nout = 3
        X = np.random.randn(100, 2)
        y = np.random.randn(100, nout)
        model.fit(X, y)
        contents = model.equation_file_contents_.copy()

        y_predictions = model.predict(X)

        equation_file_base = model.equation_file_
        for i in range(1, nout + 1):
            assert not os.path.exists(str(equation_file_base) + f".out{i}.bkup")

        with tempfile.NamedTemporaryFile() as pickle_file:
            pkl.dump(model, pickle_file)
            pickle_file.seek(0)
            model2 = pkl.load(pickle_file)

        contents2 = model2.equation_file_contents_
        cols_to_check = ["equation", "loss", "complexity"]
        for frame1, frame2 in zip(contents, contents2):
            pd.testing.assert_frame_equal(frame1[cols_to_check], frame2[cols_to_check])

        y_predictions2 = model2.predict(X)
        np.testing.assert_array_equal(y_predictions, y_predictions2)

    def test_scikit_learn_compatibility(self):
        """Test PySRRegressor compatibility with scikit-learn."""
        model = PySRRegressor(
            niterations=int(1 + DEFAULT_NITERATIONS / 10),
            populations=int(1 + DEFAULT_POPULATIONS / 3),
            ncyclesperiteration=int(2 + DEFAULT_NCYCLES / 10),
            verbosity=0,
            progress=False,
            random_state=0,
            deterministic=True,  # Deterministic as tests require this.
            procs=0,
            multithreading=False,
            warm_start=False,
            temp_equation_file=True,
        )  # Return early.

        check_generator = check_estimator(model, generate_only=True)
        exception_messages = []
        for _, check in check_generator:
            if check.func.__name__ == "check_complex_data":
                # We can use complex data, so avoid this check.
                continue
            try:
                with warnings.catch_warnings():
                    warnings.simplefilter("ignore")
                    check(model)
                print("Passed", check.func.__name__)
            except Exception:
                error_message = str(traceback.format_exc())
                exception_messages.append(
                    f"{check.func.__name__}:\n" + error_message + "\n"
                )
                print("Failed", check.func.__name__, "with:")
                # Add a leading tab to error message, which
                # might be multi-line:
                print("\n".join([(" " * 4) + row for row in error_message.split("\n")]))
        # If any checks failed don't let the test pass.
        self.assertEqual(len(exception_messages), 0)

    def test_param_groupings(self):
        """Test that param_groupings are complete"""
        param_groupings_file = Path(__file__).parent.parent / "param_groupings.yml"
        # Read the file, discarding lines ending in ":",
        # and removing leading "\s*-\s*":
        params = []
        with open(param_groupings_file, "r") as f:
            for line in f.readlines():
                if line.strip().endswith(":"):
                    continue
                if line.strip().startswith("-"):
                    params.append(line.strip()[1:].strip())

        regressor_params = [
            p for p in DEFAULT_PARAMS.keys() if p not in ["self", "kwargs"]
        ]

        # Check the sets are equal:
        self.assertSetEqual(set(params), set(regressor_params))


TRUE_PREAMBLE = "\n".join(
    [
        r"\usepackage{breqn}",
        r"\usepackage{booktabs}",
        "",
        "...",
        "",
    ]
)


class TestLaTeXTable(unittest.TestCase):
    def setUp(self):
        equations = pd.DataFrame(
            dict(
                equation=["x0", "cos(x0)", "x0 + x1 - cos(x1 * x0)"],
                loss=[1.052, 0.02315, 1.12347e-15],
                complexity=[1, 2, 8],
            )
        )
        self.model = manually_create_model(equations)
        self.maxDiff = None

    def create_true_latex(self, middle_part, include_score=False):
        if include_score:
            true_latex_table_str = r"""
                \begin{table}[h]
                \begin{center}
                \begin{tabular}{@{}cccc@{}}
                \toprule
                Equation & Complexity & Loss & Score \\
                \midrule"""
        else:
            true_latex_table_str = r"""
                \begin{table}[h]
                \begin{center}
                \begin{tabular}{@{}ccc@{}}
                \toprule
                Equation & Complexity & Loss \\
                \midrule"""
        true_latex_table_str += middle_part
        true_latex_table_str += r"""\bottomrule
            \end{tabular}
            \end{center}
            \end{table}
        """
        # First, remove empty lines:
        true_latex_table_str = "\n".join(
            [line.strip() for line in true_latex_table_str.split("\n") if len(line) > 0]
        )
        return true_latex_table_str.strip()

    def test_simple_table(self):
        latex_table_str = self.model.latex_table(
            columns=["equation", "complexity", "loss"]
        )
        middle_part = r"""
            $y = x_{0}$ & $1$ & $1.05$ \\
            $y = \cos{\left(x_{0} \right)}$ & $2$ & $0.0232$ \\
            $y = x_{0} + x_{1} - \cos{\left(x_{0} x_{1} \right)}$ & $8$ & $1.12 \cdot 10^{-15}$ \\
        """
        true_latex_table_str = (
            TRUE_PREAMBLE + "\n" + self.create_true_latex(middle_part)
        )
        self.assertEqual(latex_table_str, true_latex_table_str)

    def test_other_precision(self):
        latex_table_str = self.model.latex_table(
            precision=5, columns=["equation", "complexity", "loss"]
        )
        middle_part = r"""
            $y = x_{0}$ & $1$ & $1.0520$ \\
            $y = \cos{\left(x_{0} \right)}$ & $2$ & $0.023150$ \\
            $y = x_{0} + x_{1} - \cos{\left(x_{0} x_{1} \right)}$ & $8$ & $1.1235 \cdot 10^{-15}$ \\
        """
        true_latex_table_str = (
            TRUE_PREAMBLE + "\n" + self.create_true_latex(middle_part)
        )
        self.assertEqual(latex_table_str, true_latex_table_str)

    def test_include_score(self):
        latex_table_str = self.model.latex_table()
        middle_part = r"""
            $y = x_{0}$ & $1$ & $1.05$ & $0.0$ \\
            $y = \cos{\left(x_{0} \right)}$ & $2$ & $0.0232$ & $3.82$ \\
            $y = x_{0} + x_{1} - \cos{\left(x_{0} x_{1} \right)}$ & $8$ & $1.12 \cdot 10^{-15}$ & $5.11$ \\
        """
        true_latex_table_str = (
            TRUE_PREAMBLE
            + "\n"
            + self.create_true_latex(middle_part, include_score=True)
        )
        self.assertEqual(latex_table_str, true_latex_table_str)

    def test_last_equation(self):
        latex_table_str = self.model.latex_table(
            indices=[2], columns=["equation", "complexity", "loss"]
        )
        middle_part = r"""
            $y = x_{0} + x_{1} - \cos{\left(x_{0} x_{1} \right)}$ & $8$ & $1.12 \cdot 10^{-15}$ \\
        """
        true_latex_table_str = (
            TRUE_PREAMBLE + "\n" + self.create_true_latex(middle_part)
        )
        self.assertEqual(latex_table_str, true_latex_table_str)

    def test_multi_output(self):
        equations1 = pd.DataFrame(
            dict(
                equation=["x0", "cos(x0)", "x0 + x1 - cos(x1 * x0)"],
                loss=[1.052, 0.02315, 1.12347e-15],
                complexity=[1, 2, 8],
            )
        )
        equations2 = pd.DataFrame(
            dict(
                equation=["x1", "cos(x1)", "x0 * x0 * x1"],
                loss=[1.32, 0.052, 2e-15],
                complexity=[1, 2, 5],
            )
        )
        equations = [equations1, equations2]
        model = manually_create_model(equations)
        middle_part_1 = r"""
            $y_{0} = x_{0}$ & $1$ & $1.05$ & $0.0$ \\
            $y_{0} = \cos{\left(x_{0} \right)}$ & $2$ & $0.0232$ & $3.82$ \\
            $y_{0} = x_{0} + x_{1} - \cos{\left(x_{0} x_{1} \right)}$ & $8$ & $1.12 \cdot 10^{-15}$ & $5.11$ \\
        """
        middle_part_2 = r"""
            $y_{1} = x_{1}$ & $1$ & $1.32$ & $0.0$ \\
            $y_{1} = \cos{\left(x_{1} \right)}$ & $2$ & $0.0520$ & $3.23$ \\
            $y_{1} = x_{0}^{2} x_{1}$ & $5$ & $2.00 \cdot 10^{-15}$ & $10.3$ \\
        """
        true_latex_table_str = "\n\n".join(
            self.create_true_latex(part, include_score=True)
            for part in [middle_part_1, middle_part_2]
        )
        true_latex_table_str = TRUE_PREAMBLE + "\n" + true_latex_table_str
        latex_table_str = model.latex_table()

        self.assertEqual(latex_table_str, true_latex_table_str)

    def test_latex_float_precision(self):
        """Test that we can print latex expressions with custom precision"""
        expr = sympy.Float(4583.4485748, dps=50)
        self.assertEqual(sympy2latex(expr, prec=6), r"4583.45")
        self.assertEqual(sympy2latex(expr, prec=5), r"4583.4")
        self.assertEqual(sympy2latex(expr, prec=4), r"4583.")
        self.assertEqual(sympy2latex(expr, prec=3), r"4.58 \cdot 10^{3}")
        self.assertEqual(sympy2latex(expr, prec=2), r"4.6 \cdot 10^{3}")

        # Multiple numbers:
        x = sympy.Symbol("x")
        expr = x * 3232.324857384 - 1.4857485e-10
        self.assertEqual(
            sympy2latex(expr, prec=2), r"3.2 \cdot 10^{3} x - 1.5 \cdot 10^{-10}"
        )
        self.assertEqual(
            sympy2latex(expr, prec=3), r"3.23 \cdot 10^{3} x - 1.49 \cdot 10^{-10}"
        )
        self.assertEqual(
            sympy2latex(expr, prec=8), r"3232.3249 x - 1.4857485 \cdot 10^{-10}"
        )

    def test_latex_break_long_equation(self):
        """Test that we can break a long equation inside the table"""
        long_equation = """
        - cos(x1 * x0) + 3.2 * x0 - 1.2 * x1 + x1 * x1 * x1 + x0 * x0 * x0
        + 5.2 * sin(0.3256 * sin(x2) - 2.6 * x0) + x0 * x0 * x0 * x0 * x0
        + cos(cos(x1 * x0) + 3.2 * x0 - 1.2 * x1 + x1 * x1 * x1 + x0 * x0 * x0)
        """
        long_equation = "".join(long_equation.split("\n")).strip()
        equations = pd.DataFrame(
            dict(
                equation=["x0", "cos(x0)", long_equation],
                loss=[1.052, 0.02315, 1.12347e-15],
                complexity=[1, 2, 30],
            )
        )
        model = manually_create_model(equations)
        latex_table_str = model.latex_table()
        middle_part = r"""
        $y = x_{0}$ & $1$ & $1.05$ & $0.0$ \\
        $y = \cos{\left(x_{0} \right)}$ & $2$ & $0.0232$ & $3.82$ \\
        \begin{minipage}{0.8\linewidth} \vspace{-1em} \begin{dmath*} y = x_{0}^{5} + x_{0}^{3} + 3.20 x_{0} + x_{1}^{3} - 1.20 x_{1} - 5.20 \sin{\left(2.60 x_{0} - 0.326 \sin{\left(x_{2} \right)} \right)} - \cos{\left(x_{0} x_{1} \right)} + \cos{\left(x_{0}^{3} + 3.20 x_{0} + x_{1}^{3} - 1.20 x_{1} + \cos{\left(x_{0} x_{1} \right)} \right)} \end{dmath*} \end{minipage} & $30$ & $1.12 \cdot 10^{-15}$ & $1.09$ \\
        """
        true_latex_table_str = (
            TRUE_PREAMBLE
            + "\n"
            + self.create_true_latex(middle_part, include_score=True)
        )
        self.assertEqual(latex_table_str, true_latex_table_str)


class TestDimensionalConstraints(unittest.TestCase):
    def setUp(self):
        self.default_test_kwargs = dict(
            progress=False,
            model_selection="accuracy",
            niterations=DEFAULT_NITERATIONS * 2,
            populations=DEFAULT_POPULATIONS * 2,
            temp_equation_file=True,
        )
        self.rstate = np.random.RandomState(0)
        self.X = self.rstate.randn(100, 5)

    def test_dimensional_constraints(self):
        y = np.cos(self.X[:, [0, 1]])
        model = PySRRegressor(
            binary_operators=[
                "my_add(x, y) = x + y",
                "my_sub(x, y) = x - y",
                "my_mul(x, y) = x * y",
            ],
            unary_operators=["my_cos(x) = cos(x)"],
            **self.default_test_kwargs,
            early_stop_condition=1e-8,
            select_k_features=3,
            extra_sympy_mappings={
                "my_cos": sympy.cos,
                "my_add": lambda x, y: x + y,
                "my_sub": lambda x, y: x - y,
                "my_mul": lambda x, y: x * y,
            },
        )
        model.fit(self.X, y, X_units=["m", "m", "m", "m", "m"], y_units=["m", "m"])

        # The best expression should have complexity larger than just 2:
        for i in range(2):
            self.assertGreater(model.get_best()[i]["complexity"], 2)
            self.assertLess(model.get_best()[i]["loss"], 1e-6)
            self.assertGreater(
                model.equations_[i].query("complexity <= 2").loss.min(), 1e-6
            )

    def test_unit_checks(self):
        """This just checks the number of units passed"""
        use_custom_variable_names = False
        variable_names = None
        weights = None
        args = (use_custom_variable_names, variable_names, weights)
        valid_units = [
            (np.ones((10, 2)), np.ones(10), ["m/s", "s"], "m"),
            (np.ones((10, 1)), np.ones(10), ["m/s"], None),
            (np.ones((10, 1)), np.ones(10), None, "m/s"),
            (np.ones((10, 1)), np.ones(10), None, ["m/s"]),
            (np.ones((10, 1)), np.ones((10, 1)), None, ["m/s"]),
            (np.ones((10, 1)), np.ones((10, 2)), None, ["m/s", ""]),
        ]
        for X, y, X_units, y_units in valid_units:
            _check_assertions(
                X,
                *args,
                y,
                X_units,
                y_units,
            )
        invalid_units = [
            (np.ones((10, 2)), np.ones(10), ["m/s", "s", "s^2"], None),
            (np.ones((10, 2)), np.ones(10), ["m/s", "s", "s^2"], "m"),
            (np.ones((10, 2)), np.ones((10, 2)), ["m/s", "s"], ["m"]),
            (np.ones((10, 1)), np.ones((10, 1)), "m/s", ["m"]),
        ]
        for X, y, X_units, y_units in invalid_units:
            with self.assertRaises(ValueError):
                _check_assertions(
                    X,
                    *args,
                    y,
                    X_units,
                    y_units,
                )

    def test_unit_propagation(self):
        """Check that units are propagated correctly.

        This also tests that variables have the correct names.
        """
        X = np.ones((100, 3))
        y = np.ones((100, 1))
        temp_dir = Path(tempfile.mkdtemp())
        equation_file = str(temp_dir / "equation_file.csv")
        model = PySRRegressor(
            binary_operators=["+", "*"],
            early_stop_condition="(l, c) -> l < 1e-6 && c == 3",
            progress=False,
            model_selection="accuracy",
            niterations=DEFAULT_NITERATIONS * 2,
            populations=DEFAULT_POPULATIONS * 2,
            complexity_of_constants=10,
            weight_mutate_constant=0.0,
            should_optimize_constants=False,
            multithreading=False,
            deterministic=True,
            procs=0,
            random_state=0,
            equation_file=equation_file,
            warm_start=True,
        )
        model.fit(
            X,
            y,
            X_units=["m", "s", "A"],
            y_units=["m*A"],
        )
        best = model.get_best()
        self.assertIn("x0", best["equation"])
        self.assertNotIn("x1", best["equation"])
        self.assertIn("x2", best["equation"])
        self.assertEqual(best["complexity"], 3)
        self.assertEqual(model.equations_.iloc[0].complexity, 1)
        self.assertGreater(model.equations_.iloc[0].loss, 1e-6)

        # With pkl file:
        pkl_file = str(temp_dir / "equation_file.pkl")
        model2 = PySRRegressor.from_file(pkl_file)
        best2 = model2.get_best()
        self.assertIn("x0", best2["equation"])

        # From csv file alone (we need to delete pkl file:)
        # First, we delete the pkl file:
        os.remove(pkl_file)
        model3 = PySRRegressor.from_file(
            equation_file, binary_operators=["+", "*"], n_features_in=X.shape[1]
        )
        best3 = model3.get_best()
        self.assertIn("x0", best3["equation"])

        # Try warm start, but with no units provided (should
        # be a different dataset, and thus different result):
        model.fit(X, y)
        model.early_stop_condition = "(l, c) -> l < 1e-6 && c == 1"
        self.assertEqual(model.equations_.iloc[0].complexity, 1)
        self.assertLess(model.equations_.iloc[0].loss, 1e-6)


# TODO: Determine desired behavior if second .fit() call does not have units


def runtests():
    """Run all tests in test.py."""
    suite = unittest.TestSuite()
    loader = unittest.TestLoader()
    test_cases = [
        TestPipeline,
        TestBest,
        TestFeatureSelection,
        TestMiscellaneous,
        TestLaTeXTable,
        TestDimensionalConstraints,
    ]
    for test_case in test_cases:
        tests = loader.loadTestsFromTestCase(test_case)
        suite.addTests(tests)
    runner = unittest.TextTestRunner()
    return runner.run(suite)