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import platform
import unittest
import numpy as np
import pandas as pd
import sympy
from .. import PySRRegressor, sympy2torch
# Need to initialize Julia before importing torch...
def _import_torch():
if platform.system() == "Darwin":
# Import PyJulia, then Torch
from ..julia_helpers import init_julia
init_julia()
import torch
else:
# Import Torch, then PyJulia
# https://github.com/pytorch/pytorch/issues/78829
import torch
return torch
class TestTorch(unittest.TestCase):
def setUp(self):
np.random.seed(0)
def test_sympy2torch(self):
torch = _import_torch()
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_pandas(self):
torch = _import_torch()
X = pd.DataFrame(np.random.randn(100, 10))
y = np.ones(X.shape[0])
model = PySRRegressor(
progress=False,
max_evals=10000,
model_selection="accuracy",
extra_sympy_mappings={},
output_torch_format=True,
)
model.fit(X, y)
equations = pd.DataFrame(
{
"Equation": ["1.0", "cos(x1)", "square(cos(x1))"],
"Loss": [1.0, 0.1, 1e-5],
"Complexity": [1, 2, 3],
}
)
equations["Complexity Loss Equation".split(" ")].to_csv(
"equation_file.csv.bkup"
)
model.refresh(checkpoint_file="equation_file.csv")
tformat = model.pytorch()
self.assertEqual(str(tformat), "_SingleSymPyModule(expression=cos(x1)**2)")
np.testing.assert_almost_equal(
tformat(torch.tensor(X.values)).detach().numpy(),
np.square(np.cos(X.values[:, 1])), # Selection 1st feature
decimal=3,
)
def test_pipeline(self):
torch = _import_torch()
X = np.random.randn(100, 10)
y = np.ones(X.shape[0])
model = PySRRegressor(
progress=False,
max_evals=10000,
model_selection="accuracy",
output_torch_format=True,
)
model.fit(X, y)
equations = pd.DataFrame(
{
"Equation": ["1.0", "cos(x1)", "square(cos(x1))"],
"Loss": [1.0, 0.1, 1e-5],
"Complexity": [1, 2, 3],
}
)
equations["Complexity Loss Equation".split(" ")].to_csv(
"equation_file.csv.bkup"
)
model.refresh(checkpoint_file="equation_file.csv")
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])), # 2nd feature
decimal=3,
)
def test_mod_mapping(self):
torch = _import_torch()
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=3
)
def test_custom_operator(self):
torch = _import_torch()
X = np.random.randn(100, 3)
y = np.ones(X.shape[0])
model = PySRRegressor(
progress=False,
max_evals=10000,
model_selection="accuracy",
output_torch_format=True,
)
model.fit(X, y)
equations = pd.DataFrame(
{
"Equation": ["1.0", "mycustomoperator(x1)"],
"Loss": [1.0, 0.1],
"Complexity": [1, 2],
}
)
equations["Complexity Loss Equation".split(" ")].to_csv(
"equation_file_custom_operator.csv.bkup"
)
model.set_params(
equation_file="equation_file_custom_operator.csv",
extra_sympy_mappings={"mycustomoperator": sympy.sin},
extra_torch_mappings={"mycustomoperator": torch.sin},
)
model.refresh(checkpoint_file="equation_file_custom_operator.csv")
self.assertEqual(str(model.sympy()), "sin(x1)")
# Will automatically use the set global state from get_hof.
tformat = model.pytorch()
self.assertEqual(str(tformat), "_SingleSymPyModule(expression=sin(x1))")
np.testing.assert_almost_equal(
tformat(torch.tensor(X)).detach().numpy(),
np.sin(X[:, 1]),
decimal=3,
)
def test_feature_selection_custom_operators(self):
torch = _import_torch()
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
y = X["k15"] ** 2 + 2 * cos_approx(X["k20"])
model = PySRRegressor(
progress=False,
unary_operators=["cos_approx(x) = 1 - x^2 / 2 + x^4 / 24 + x^6 / 720"],
select_k_features=3,
maxsize=10,
early_stop_condition=1e-5,
extra_sympy_mappings={"cos_approx": cos_approx},
extra_torch_mappings={"cos_approx": cos_approx},
random_state=0,
deterministic=True,
procs=0,
multithreading=False,
)
np.random.seed(0)
model.fit(X.values, y.values)
torch_module = model.pytorch()
np_output = model.predict(X.values)
torch_output = torch_module(torch.tensor(X.values)).detach().numpy()
np.testing.assert_almost_equal(y.values, np_output, decimal=3)
np.testing.assert_almost_equal(y.values, torch_output, decimal=3)
def runtests():
"""Run all tests in test_torch.py."""
loader = unittest.TestLoader()
suite = unittest.TestSuite()
suite.addTests(loader.loadTestsFromTestCase(TestTorch))
runner = unittest.TextTestRunner()
return runner.run(suite)
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