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migrate rv_transformation_tests to pytest

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This commit is contained in:
Martin Bubel 2023-10-10 19:53:26 +02:00
parent 5fde9d2edd
commit ca2092f12e
1 changed files with 10 additions and 44 deletions
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54
GPy/testing/rv_transformation_tests.py
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@ -3,7 +3,7 @@
Test if hyperparameters in models are properly transformed.
"""
import pytest
import numpy as np
import scipy.stats as st
import GPy
@ -23,7 +23,7 @@ class TestModel(GPy.core.Model):
return 0.0
class RVTransformationTestCase(unittest.TestCase):
class TestRVTransformation:
def _test_trans(self, trans):
m = TestModel()
prior = GPy.priors.LogGaussian(0.5, 0.1)
@ -53,9 +53,7 @@ class RVTransformationTestCase(unittest.TestCase):
# plt.show(block=True)
# END OF PLOT
# The following test cannot be very accurate
self.assertTrue(
np.linalg.norm(pdf_phi - kde(phi)) / np.linalg.norm(kde(phi)) <= 1e-1
)
assert np.linalg.norm(pdf_phi - kde(phi)) / np.linalg.norm(kde(phi)) <= 1e-1
def _test_grad(self, trans):
np.random.seed(1234)
@ -65,54 +63,22 @@ class RVTransformationTestCase(unittest.TestCase):
m.theta.constrain(trans)
m.randomize()
print(m)
self.assertTrue(m.checkgrad(1))
assert m.checkgrad(1)
def test_Logexp(self):
self._test_trans(GPy.constraints.Logexp())
@unittest.skip("Gradient not checking right, @jameshensman what is going on here?")
@pytest.mark.skip(
"Gradient not checking right, @jameshensman what is going on here?"
)
def test_Logexp_grad(self):
self._test_grad(GPy.constraints.Logexp())
def test_Exponent(self):
self._test_trans(GPy.constraints.Exponent())
@unittest.skip("Gradient not checking right, @jameshensman what is going on here?")
@pytest.mark.skip(
"Gradient not checking right, @jameshensman what is going on here?"
)
def test_Exponent_grad(self):
self._test_grad(GPy.constraints.Exponent())
if __name__ == "__main__":
unittest.main()
quit()
m = TestModel()
prior = GPy.priors.LogGaussian(0.0, 0.9)
m.theta.set_prior(prior)
# The following should return the PDF in terms of the transformed quantities
p_phi = lambda phi: np.exp(-m._objective_grads(phi)[0])
# Let's look at the transformation phi = log(exp(theta - 1))
trans = GPy.constraints.Exponent()
m.theta.constrain(trans)
# Plot the transformed probability density
phi = np.linspace(-8, 8, 100)
fig, ax = plt.subplots()
# Let's draw some samples of theta and transform them so that we see
# which one is right
theta_s = prior.rvs(10000)
# Transform it to the new variables
phi_s = trans.finv(theta_s)
# And draw their histogram
ax.hist(phi_s, normed=True, bins=100, alpha=0.25, label="Empirical")
# This is to be compared to the PDF of the model expressed in terms of these new
# variables
ax.plot(phi, [p_phi(p) for p in phi], label="Transformed PDF", linewidth=2)
ax.set_xlim(-3, 10)
ax.set_xlabel(r"transformed $\theta$", fontsize=16)
ax.set_ylabel("PDF", fontsize=16)
plt.legend(loc="best")
# Now let's test the gradients
m.checkgrad(verbose=True)
# And show the plot
plt.show(block=True)