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import unittest
import numpy as np
import pandas as pd
from pysr import sympy2torch, PySRRegressor
import torch
import sympy
class TestTorch(unittest.TestCase):
def setUp(self):
np.random.seed(0)
def test_sympy2torch(self):
x, y, z = sympy.symbols("x y z")
cosx = 1.0 * sympy.cos(x) + y
X = torch.tensor(np.random.randn(1000, 3))
true = 1.0 * torch.cos(X[:, 0]) + X[:, 1]
torch_module = sympy2torch(cosx, [x, y, z])
self.assertTrue(
np.all(np.isclose(torch_module(X).detach().numpy(), true.detach().numpy()))
)
def test_pipeline(self):
X = np.random.randn(100, 10)
equations = pd.DataFrame(
{
"Equation": ["1.0", "cos(x1)", "square(cos(x1))"],
"MSE": [1.0, 0.1, 1e-5],
"Complexity": [1, 2, 3],
}
)
equations["Complexity MSE Equation".split(" ")].to_csv(
"equation_file.csv.bkup", sep="|"
)
model = PySRRegressor(
model_selection="accuracy",
equation_file="equation_file.csv",
variables_names="x1 x2 x3".split(" "),
extra_sympy_mappings={},
output_torch_format=True,
multioutput=False,
nout=1,
selection=[1, 2, 3],
)
model.n_features = 2 # TODO: Why is this 2 and not 3?
model.using_pandas = False
model.refresh()
tformat = model.pytorch()
self.assertEqual(str(tformat), "_SingleSymPyModule(expression=cos(x1)**2)")
np.testing.assert_almost_equal(
tformat(torch.tensor(X)).detach().numpy(),
np.square(np.cos(X[:, 1])), # Selection 1st feature
decimal=4,
)
def test_mod_mapping(self):
x, y, z = sympy.symbols("x y z")
expression = x ** 2 + sympy.atanh(sympy.Mod(y + 1, 2) - 1) * 3.2 * z
module = sympy2torch(expression, [x, y, z])
X = torch.rand(100, 3).float() * 10
true_out = (
X[:, 0] ** 2 + torch.atanh(torch.fmod(X[:, 1] + 1, 2) - 1) * 3.2 * X[:, 2]
)
torch_out = module(X)
np.testing.assert_array_almost_equal(
true_out.detach(), torch_out.detach(), decimal=4
)
def test_custom_operator(self):
X = np.random.randn(100, 3)
equations = pd.DataFrame(
{
"Equation": ["1.0", "mycustomoperator(x0)"],
"MSE": [1.0, 0.1],
"Complexity": [1, 2],
}
)
equations["Complexity MSE Equation".split(" ")].to_csv(
"equation_file_custom_operator.csv.bkup", sep="|"
)
model = PySRRegressor(
model_selection="accuracy",
equation_file="equation_file_custom_operator.csv",
variables_names="x1 x2 x3".split(" "),
extra_sympy_mappings={"mycustomoperator": sympy.sin},
extra_torch_mappings={"mycustomoperator": torch.sin},
output_torch_format=True,
multioutput=False,
nout=1,
selection=[0, 1, 2],
)
model.n_features = 3
model.using_pandas = False
model.refresh()
# Will automatically use the set global state from get_hof.
tformat = model.pytorch()
self.assertEqual(str(tformat), "_SingleSymPyModule(expression=sin(x0))")
np.testing.assert_almost_equal(
tformat(torch.tensor(X)).detach().numpy(),
np.sin(X[:, 0]), # Selection 1st feature
decimal=4,
)
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