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2f38c9c
1
Parent(s):
253dd65
Move testing to unittest format
Browse files- test/test.py +35 -31
- test/test_jax.py +10 -8
test/test.py
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@@ -1,38 +1,42 @@
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import numpy as np
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from pysr import pysr
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import sympy
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X = np.random.randn(100, 5)
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y = X[:, 0]
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equations = pysr(X, y,
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print(equations)
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assert equations[0].iloc[-1]['MSE'] < 1e-4
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assert equations[1].iloc[-1]['MSE'] < 1e-4
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print("Test 3 - empty operator list, and single dimension input")
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equations = pysr(X, y,
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unary_operators=[], binary_operators=["plus"],
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**default_test_kwargs)
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print(equations)
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assert equations.iloc[-1]['MSE'] < 1e-4
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import unittest
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import numpy as np
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from pysr import pysr
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import sympy
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class TestPipeline(unittest.TestCase):
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def setUp(self):
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self.default_test_kwargs = dict(
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niterations=10,
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populations=4,
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user_input=False,
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annealing=True,
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useFrequency=False,
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)
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np.random.seed(0)
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self.X = np.random.randn(100, 5)
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def test_linear_relation(self):
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y = self.X[:, 0]
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equations = pysr(self.X, y, **self.default_test_kwargs)
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print(equations)
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self.assertLessEqual(equations.iloc[-1]['MSE'], 1e-4)
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def test_multioutput_custom_operator(self):
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y = self.X[:, [0, 1]]**2
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equations = pysr(self.X, y,
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unary_operators=["sq(x) = x^2"], binary_operators=["plus"],
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extra_sympy_mappings={'square': lambda x: x**2},
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**self.default_test_kwargs)
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print(equations)
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self.assertLessEqual(equations[0].iloc[-1]['MSE'], 1e-4)
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self.assertLessEqual(equations[1].iloc[-1]['MSE'], 1e-4)
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def test_empty_operators_single_input(self):
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X = np.random.randn(100, 1)
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y = X[:, 0] + 3.0
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equations = pysr(X, y,
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unary_operators=[], binary_operators=["plus"],
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**self.default_test_kwargs)
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print(equations)
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self.assertLessEqual(equations.iloc[-1]['MSE'], 1e-4)
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test/test_jax.py
CHANGED
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import numpy as np
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from pysr import pysr, sympy2jax
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from jax import numpy as jnp
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from jax import grad
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import sympy
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import unittest
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import numpy as np
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from pysr import pysr, sympy2jax
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from jax import numpy as jnp
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from jax import grad
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import sympy
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class TestJAX(unittest.TestCase):
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def test_sympy2jax(self):
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x, y, z = sympy.symbols('x y z')
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cosx = 1.0 * sympy.cos(x) + y
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key = random.PRNGKey(0)
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X = random.normal(key, (1000, 2))
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true = 1.0 * jnp.cos(X[:, 0]) + X[:, 1]
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f, params = sympy2jax(cosx, [x, y, z])
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self.assertTrue(jnp.all(jnp.isclose(f(X, params), true)).item())
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