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migrate rv_transformation_tests to pytest

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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. Test if hyperparameters in models are properly transformed.
""" """
import pytest
import numpy as np import numpy as np
import scipy.stats as st import scipy.stats as st
import GPy import GPy
@ -23,7 +23,7 @@ class TestModel(GPy.core.Model):
return 0.0 return 0.0
class RVTransformationTestCase(unittest.TestCase): class TestRVTransformation:
def _test_trans(self, trans): def _test_trans(self, trans):
m = TestModel() m = TestModel()
prior = GPy.priors.LogGaussian(0.5, 0.1) prior = GPy.priors.LogGaussian(0.5, 0.1)
@ -53,9 +53,7 @@ class RVTransformationTestCase(unittest.TestCase):
# plt.show(block=True) # plt.show(block=True)
# END OF PLOT # END OF PLOT
# The following test cannot be very accurate # The following test cannot be very accurate
self.assertTrue( assert np.linalg.norm(pdf_phi - kde(phi)) / np.linalg.norm(kde(phi)) <= 1e-1
np.linalg.norm(pdf_phi - kde(phi)) / np.linalg.norm(kde(phi)) <= 1e-1
)
def _test_grad(self, trans): def _test_grad(self, trans):
np.random.seed(1234) np.random.seed(1234)
@ -65,54 +63,22 @@ class RVTransformationTestCase(unittest.TestCase):
m.theta.constrain(trans) m.theta.constrain(trans)
m.randomize() m.randomize()
print(m) print(m)
self.assertTrue(m.checkgrad(1)) assert m.checkgrad(1)
def test_Logexp(self): def test_Logexp(self):
self._test_trans(GPy.constraints.Logexp()) 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): def test_Logexp_grad(self):
self._test_grad(GPy.constraints.Logexp()) self._test_grad(GPy.constraints.Logexp())
def test_Exponent(self): def test_Exponent(self):
self._test_trans(GPy.constraints.Exponent()) 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): def test_Exponent_grad(self):
self._test_grad(GPy.constraints.Exponent()) 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)