Merge pull request #191 from MilesCranmer/backend-update

Update backend: `x^y` instead of `abs(x)^y`, and high-precision constants

pysr/sr.py

@@ -41,13 +41,14 @@ already_ran = False
 sympy_mappings = {
     "div": lambda x, y: x / y,
     "mult": lambda x, y: x * y,
+    "sqrt": lambda x: sympy.sqrt(x),
     "sqrt_abs": lambda x: sympy.sqrt(abs(x)),
     "square": lambda x: x**2,
     "cube": lambda x: x**3,
     "plus": lambda x, y: x + y,
     "sub": lambda x, y: x - y,
     "neg": lambda x: -x,
-    "pow": lambda x, y: sympy.Function("unimplemented_pow")(x, y),
+    "pow": lambda x, y: x**y,
     "pow_abs": lambda x, y: abs(x) ** y,
     "cos": sympy.cos,
     "sin": sympy.sin,
@@ -59,7 +60,7 @@ sympy_mappings = {
     "acos": sympy.acos,
     "asin": sympy.asin,
     "atan": sympy.atan,
-    "acosh": lambda x: sympy.acosh(abs(x) + 1),
+    "acosh": lambda x: sympy.acosh(x),
     "acosh_abs": lambda x: sympy.acosh(abs(x) + 1),
     "asinh": sympy.asinh,
     "atanh": lambda x: sympy.atanh(sympy.Mod(x + 1, 2) - 1),
@@ -68,6 +69,10 @@ sympy_mappings = {
     "mod": sympy.Mod,
     "erf": sympy.erf,
     "erfc": sympy.erfc,
+    "log": lambda x: sympy.log(x),
+    "log10": lambda x: sympy.log(x, 10),
+    "log2": lambda x: sympy.log(x, 2),
+    "log1p": lambda x: sympy.log(x + 1),
     "log_abs": lambda x: sympy.log(abs(x)),
     "log10_abs": lambda x: sympy.log(abs(x), 10),
     "log2_abs": lambda x: sympy.log(abs(x), 2),

pysr/version.py

@@ -1,2 +1,2 @@
-__version__ = "0.10.4-1"
-__symbolic_regression_jl_version__ = "0.10.1"
+__version__ = "0.11.0"
+__symbolic_regression_jl_version__ = "0.12.0"

test/test.py

@@ -82,6 +82,22 @@ class TestPipeline(unittest.TestCase):
         print(model.equations_)
         self.assertLessEqual(model.equations_.iloc[-1]["loss"], 1e-4)
 
+    def test_high_precision_search(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",
+            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(
@@ -182,6 +198,12 @@ class TestPipeline(unittest.TestCase):
             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)