BREAKING: Disable automatic sympy simplification

Dockerfile

@@ -1,8 +1,8 @@
 # This builds a dockerfile containing a working copy of PySR
 # with all pre-requisites installed.
 
-ARG JLVERSION=1.9.4
-ARG PYVERSION=3.11.6
+ARG JLVERSION=1.10.4
+ARG PYVERSION=3.12.2
 ARG BASE_IMAGE=bullseye
 
 FROM julia:${JLVERSION}-${BASE_IMAGE} AS jl

pysr/export_jax.py

@@ -55,7 +55,9 @@ def sympy2jaxtext(expr, parameters, symbols_in, extra_jax_mappings=None):
     if issubclass(expr.func, sympy.Float):
         parameters.append(float(expr))
         return f"parameters[{len(parameters) - 1}]"
-    elif issubclass(expr.func, sympy.Rational):
+    elif issubclass(expr.func, sympy.Rational) or issubclass(
+        expr.func, sympy.NumberSymbol
+    ):
         return f"{float(expr)}"
     elif issubclass(expr.func, sympy.Integer):
         return f"{int(expr)}"

pysr/export_sympy.py

@@ -87,7 +87,12 @@ def pysr2sympy(
         **sympy_mappings,
     }
 
-    return sympify(equation, locals=local_sympy_mappings)
+    try:
+        return sympify(equation, locals=local_sympy_mappings, evaluate=False)
+    except TypeError as e:
+        if "got an unexpected keyword argument 'evaluate'" in str(e):
+            return sympify(equation, locals=local_sympy_mappings)
+        raise TypeError(f"Error processing equation '{equation}'") from e
 
 
 def assert_valid_sympy_symbol(var_name: str) -> None:

pysr/export_torch.py

@@ -116,6 +116,11 @@ def __init__(self, *, expr, _memodict, _func_lookup, **kwargs):
                     self._value = int(expr)
                     self._torch_func = lambda: self._value
                     self._args = ()
+                elif issubclass(expr.func, sympy.NumberSymbol):
+                    # Can get here from exp(1) or exact pi
+                    self._value = float(expr)
+                    self._torch_func = lambda: self._value
+                    self._args = ()
                 elif issubclass(expr.func, sympy.Symbol):
                     self._name = expr.name
                     self._torch_func = lambda value: value

pysr/test/test.py

@@ -674,7 +674,7 @@ def test_pickle_with_temp_equation_file(self):
             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)
+        np.testing.assert_array_almost_equal(y_predictions, y_predictions2)
 
     def test_scikit_learn_compatibility(self):
         """Test PySRRegressor compatibility with scikit-learn."""
@@ -1039,7 +1039,7 @@ def test_multi_output(self):
         middle_part_2 = r"""
             $y_{1} = x_{1}$ & $1$ & $1.32$ & $0.0$ \\
             $y_{1} = \cos{\left(x_{1} \right)}$ & $2$ & $0.0520$ & $3.23$ \\
-            $y_{1} = x_{0}^{2} x_{1}$ & $5$ & $2.00 \cdot 10^{-15}$ & $10.3$ \\
+            $y_{1} = x_{0} x_{0} x_{1}$ & $5$ & $2.00 \cdot 10^{-15}$ & $10.3$ \\
         """
         true_latex_table_str = "\n\n".join(
             self.create_true_latex(part, include_score=True)
@@ -1092,7 +1092,7 @@ def test_latex_break_long_equation(self):
         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} & $30$ & $1.12 \cdot 10^{-15}$ & $1.09$ \\
+        \begin{minipage}{0.8\linewidth} \vspace{-1em} \begin{dmath*} y = x_{0} x_{0} x_{0} + x_{0} x_{0} x_{0} x_{0} x_{0} + 3.20 x_{0} - 1.20 x_{1} + x_{1} x_{1} 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} x_{0} x_{0} + 3.20 x_{0} - 1.20 x_{1} + x_{1} x_{1} 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

pysr/test/test_jax.py

@@ -5,27 +5,29 @@
 import pandas as pd
 import sympy
 
+import pysr
 from pysr import PySRRegressor, sympy2jax
 
 
 class TestJAX(unittest.TestCase):
     def setUp(self):
         np.random.seed(0)
+        from jax import numpy as jnp
+
+        self.jnp = jnp
 
     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]
+        true = 1.0 * self.jnp.cos(X[:, 0]) + X[:, 1]
         f, params = sympy2jax(cosx, [x, y, z])
-        self.assertTrue(jnp.all(jnp.isclose(f(X, params), true)).item())
+        self.assertTrue(self.jnp.all(self.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])
@@ -52,14 +54,12 @@ def test_pipeline_pandas(self):
         jformat = model.jax()
 
         np.testing.assert_almost_equal(
-            np.array(jformat["callable"](jnp.array(X), jformat["parameters"])),
+            np.array(jformat["callable"](self.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)
@@ -81,11 +81,39 @@ def test_pipeline(self):
         jformat = model.jax()
 
         np.testing.assert_almost_equal(
-            np.array(jformat["callable"](jnp.array(X), jformat["parameters"])),
+            np.array(jformat["callable"](self.jnp.array(X), jformat["parameters"])),
             np.square(np.cos(X[:, 1])),  # Select feature 1
             decimal=3,
         )
 
+    def test_avoid_simplification(self):
+        ex = pysr.export_sympy.pysr2sympy(
+            "square(exp(sign(0.44796443))) + 1.5 * x1",
+            feature_names_in=["x1"],
+            extra_sympy_mappings={"square": lambda x: x**2},
+        )
+        f, params = pysr.export_jax.sympy2jax(ex, [sympy.symbols("x1")])
+        key = np.random.RandomState(0)
+        X = key.randn(10, 1)
+        np.testing.assert_almost_equal(
+            np.array(f(self.jnp.array(X), params)),
+            np.square(np.exp(np.sign(0.44796443))) + 1.5 * X[:, 0],
+            decimal=3,
+        )
+
+    def test_issue_656(self):
+        import sympy
+
+        E_plus_x1 = sympy.exp(1) + sympy.symbols("x1")
+        f, params = pysr.export_jax.sympy2jax(E_plus_x1, [sympy.symbols("x1")])
+        key = np.random.RandomState(0)
+        X = key.randn(10, 1)
+        np.testing.assert_almost_equal(
+            np.array(f(self.jnp.array(X), params)),
+            np.exp(1) + X[:, 0],
+            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)})

pysr/test/test_torch.py

@@ -4,6 +4,7 @@
 import pandas as pd
 import sympy
 
+import pysr
 from pysr import PySRRegressor, sympy2torch
 
 
@@ -153,10 +154,43 @@ def test_custom_operator(self):
             decimal=3,
         )
 
+    def test_avoid_simplification(self):
+        # SymPy should not simplify without permission
+        torch = self.torch
+        ex = pysr.export_sympy.pysr2sympy(
+            "square(exp(sign(0.44796443))) + 1.5 * x1",
+            # ^ Normally this would become exp1 and require
+            #   its own mapping
+            feature_names_in=["x1"],
+            extra_sympy_mappings={"square": lambda x: x**2},
+        )
+        m = pysr.export_torch.sympy2torch(ex, ["x1"])
+        rng = np.random.RandomState(0)
+        X = rng.randn(10, 1)
+        np.testing.assert_almost_equal(
+            m(torch.tensor(X)).detach().numpy(),
+            np.square(np.exp(np.sign(0.44796443))) + 1.5 * X[:, 0],
+            decimal=3,
+        )
+
+    def test_issue_656(self):
+        # Should correctly map numeric symbols to floats
+        E_plus_x1 = sympy.exp(1) + sympy.symbols("x1")
+        m = pysr.export_torch.sympy2torch(E_plus_x1, ["x1"])
+        X = np.random.randn(10, 1)
+        np.testing.assert_almost_equal(
+            m(self.torch.tensor(X)).detach().numpy(),
+            np.exp(1) + X[:, 0],
+            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)})
-        cos_approx = lambda x: 1 - (x**2) / 2 + (x**4) / 24 + (x**6) / 720
+
+        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(