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import unittest
from functools import partial
import numpy as np
import pandas as pd
import sympy
from pysr import PySRRegressor, sympy2jax
class TestJAX(unittest.TestCase):
def setUp(self):
np.random.seed(0)
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]
f, params = sympy2jax(cosx, [x, y, z])
self.assertTrue(jnp.all(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])
model = PySRRegressor(
progress=False,
max_evals=10000,
output_jax_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")
jformat = model.jax()
np.testing.assert_almost_equal(
np.array(jformat["callable"](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)
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")
jformat = model.jax()
np.testing.assert_almost_equal(
np.array(jformat["callable"](jnp.array(X), jformat["parameters"])),
np.square(np.cos(X[:, 1])), # Select feature 1
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)})
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(
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_jax_mappings={
"cos_approx": "(lambda x: 1 - x**2 / 2 + x**4 / 24 + x**6 / 720)"
},
random_state=0,
deterministic=True,
procs=0,
multithreading=False,
)
np.random.seed(0)
model.fit(X.values, y.values)
f, parameters = model.jax().values()
np_prediction = model.predict
jax_prediction = partial(f, parameters=parameters)
np_output = np_prediction(X.values)
jax_output = jax_prediction(X.values)
np.testing.assert_almost_equal(y.values, np_output, decimal=3)
np.testing.assert_almost_equal(y.values, jax_output, decimal=3)
def runtests(just_tests=False):
"""Run all tests in test_jax.py."""
tests = [TestJAX]
if just_tests:
return tests
loader = unittest.TestLoader()
suite = unittest.TestSuite()
for test in tests:
suite.addTests(loader.loadTestsFromTestCase(test))
runner = unittest.TextTestRunner()
return runner.run(suite)
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