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Martin Bubel
parent
ef7d2f299c
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393f9938ea
1 changed files with 66 additions and 30 deletions
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@ -3,11 +3,22 @@ import scipy
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from scipy.special import cbrt
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from GPy.models import GradientChecker
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import random
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_lim_val = np.finfo(np.float64).max
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_lim_val_exp = np.log(_lim_val)
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_lim_val_square = np.sqrt(_lim_val)
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_lim_val_cube = cbrt(_lim_val)
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from GPy.likelihoods.link_functions import Identity, Probit, Cloglog, Log, Log_ex_1, Reciprocal, Heaviside, ScaledProbit
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from GPy.likelihoods.link_functions import (
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Identity,
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Probit,
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Cloglog,
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Log,
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Log_ex_1,
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Reciprocal,
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Heaviside,
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ScaledProbit,
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)
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class LinkFunctionTests(np.testing.TestCase):
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def setUp(self):
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@ -21,48 +32,70 @@ class LinkFunctionTests(np.testing.TestCase):
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def check_gradient(self, link_func, lim_of_inf, test_lim=False):
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grad = GradientChecker(link_func.transf, link_func.dtransf_df, x0=self.mid_f)
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self.assertTrue(grad.checkgrad(verbose=True))
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grad2 = GradientChecker(link_func.dtransf_df, link_func.d2transf_df2, x0=self.mid_f)
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grad2 = GradientChecker(
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link_func.dtransf_df, link_func.d2transf_df2, x0=self.mid_f
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)
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self.assertTrue(grad2.checkgrad(verbose=True))
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grad3 = GradientChecker(link_func.d2transf_df2, link_func.d3transf_df3, x0=self.mid_f)
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grad3 = GradientChecker(
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link_func.d2transf_df2, link_func.d3transf_df3, x0=self.mid_f
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)
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self.assertTrue(grad3.checkgrad(verbose=True))
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grad = GradientChecker(link_func.transf, link_func.dtransf_df, x0=self.small_f)
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self.assertTrue(grad.checkgrad(verbose=True))
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grad2 = GradientChecker(link_func.dtransf_df, link_func.d2transf_df2, x0=self.small_f)
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grad2 = GradientChecker(
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link_func.dtransf_df, link_func.d2transf_df2, x0=self.small_f
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)
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self.assertTrue(grad2.checkgrad(verbose=True))
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grad3 = GradientChecker(link_func.d2transf_df2, link_func.d3transf_df3, x0=self.small_f)
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grad3 = GradientChecker(
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link_func.d2transf_df2, link_func.d3transf_df3, x0=self.small_f
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)
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self.assertTrue(grad3.checkgrad(verbose=True))
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grad = GradientChecker(link_func.transf, link_func.dtransf_df, x0=self.zero_f)
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self.assertTrue(grad.checkgrad(verbose=True))
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grad2 = GradientChecker(link_func.dtransf_df, link_func.d2transf_df2, x0=self.zero_f)
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grad2 = GradientChecker(
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link_func.dtransf_df, link_func.d2transf_df2, x0=self.zero_f
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)
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self.assertTrue(grad2.checkgrad(verbose=True))
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grad3 = GradientChecker(link_func.d2transf_df2, link_func.d3transf_df3, x0=self.zero_f)
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grad3 = GradientChecker(
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link_func.d2transf_df2, link_func.d3transf_df3, x0=self.zero_f
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)
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self.assertTrue(grad3.checkgrad(verbose=True))
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#Do a limit test if the large f value is too large
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large_f = np.clip(self.large_f, -np.inf, lim_of_inf-1e-3)
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# Do a limit test if the large f value is too large
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large_f = np.clip(self.large_f, -np.inf, lim_of_inf - 1e-3)
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grad = GradientChecker(link_func.transf, link_func.dtransf_df, x0=large_f)
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self.assertTrue(grad.checkgrad(verbose=True))
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grad2 = GradientChecker(link_func.dtransf_df, link_func.d2transf_df2, x0=large_f)
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grad2 = GradientChecker(
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link_func.dtransf_df, link_func.d2transf_df2, x0=large_f
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)
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self.assertTrue(grad2.checkgrad(verbose=True))
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grad3 = GradientChecker(link_func.d2transf_df2, link_func.d3transf_df3, x0=large_f)
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grad3 = GradientChecker(
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link_func.d2transf_df2, link_func.d3transf_df3, x0=large_f
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)
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self.assertTrue(grad3.checkgrad(verbose=True))
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if test_lim:
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print("Testing limits")
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#Remove some otherwise we are too close to the limit for gradcheck to work effectively
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# Remove some otherwise we are too close to the limit for gradcheck to work effectively
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lim_of_inf = lim_of_inf - 1e-4
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grad = GradientChecker(link_func.transf, link_func.dtransf_df, x0=lim_of_inf)
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grad = GradientChecker(
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link_func.transf, link_func.dtransf_df, x0=lim_of_inf
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)
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self.assertTrue(grad.checkgrad(verbose=True))
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grad2 = GradientChecker(link_func.dtransf_df, link_func.d2transf_df2, x0=lim_of_inf)
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grad2 = GradientChecker(
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link_func.dtransf_df, link_func.d2transf_df2, x0=lim_of_inf
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)
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self.assertTrue(grad2.checkgrad(verbose=True))
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grad3 = GradientChecker(link_func.d2transf_df2, link_func.d3transf_df3, x0=lim_of_inf)
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grad3 = GradientChecker(
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link_func.d2transf_df2, link_func.d3transf_df3, x0=lim_of_inf
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)
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self.assertTrue(grad3.checkgrad(verbose=True))
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def check_overflow(self, link_func, lim_of_inf):
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#Check that it does something sensible beyond this limit,
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#note this is not checking the value is correct, just that it isn't nan
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# Check that it does something sensible beyond this limit,
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# note this is not checking the value is correct, just that it isn't nan
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beyond_lim_of_inf = lim_of_inf + 100.0
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self.assertFalse(np.isinf(link_func.transf(beyond_lim_of_inf)))
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self.assertFalse(np.isinf(link_func.dtransf_df(beyond_lim_of_inf)))
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@ -78,14 +111,14 @@ class LinkFunctionTests(np.testing.TestCase):
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np.testing.assert_almost_equal(np.exp(self.mid_f), link.transf(self.mid_f))
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assert np.isinf(np.exp(np.log(self.f_upper_lim)))
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#Check the clipping works
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# Check the clipping works
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np.testing.assert_almost_equal(link.transf(self.f_lower_lim), 0, decimal=5)
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self.assertTrue(np.isfinite(link.transf(self.f_upper_lim)))
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self.check_overflow(link, lim_of_inf)
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#Check that it would otherwise fail
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# Check that it would otherwise fail
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beyond_lim_of_inf = lim_of_inf + 10.0
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old_err_state = np.seterr(over='ignore')
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old_err_state = np.seterr(over="ignore")
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self.assertTrue(np.isinf(np.exp(beyond_lim_of_inf)))
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np.seterr(**old_err_state)
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@ -93,21 +126,24 @@ class LinkFunctionTests(np.testing.TestCase):
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link = Log_ex_1()
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lim_of_inf = _lim_val_exp
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np.testing.assert_almost_equal(scipy.special.log1p(np.exp(self.mid_f)), link.transf(self.mid_f))
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np.testing.assert_almost_equal(
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scipy.special.log1p(np.exp(self.mid_f)), link.transf(self.mid_f)
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)
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assert np.isinf(scipy.special.log1p(np.exp(np.log(self.f_upper_lim))))
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#Check the clipping works
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# Check the clipping works
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np.testing.assert_almost_equal(link.transf(self.f_lower_lim), 0, decimal=5)
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#Need to look at most significant figures here rather than the decimals
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np.testing.assert_approx_equal(link.transf(self.f_upper_lim), scipy.special.log1p(_lim_val), significant=5)
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# Need to look at most significant figures here rather than the decimals
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np.testing.assert_approx_equal(
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link.transf(self.f_upper_lim), scipy.special.log1p(_lim_val), significant=5
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)
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self.check_overflow(link, lim_of_inf)
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#Check that it would otherwise fail
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# Check that it would otherwise fail
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beyond_lim_of_inf = lim_of_inf + 10.0
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old_err_state = np.seterr(over='ignore')
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old_err_state = np.seterr(over="ignore")
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self.assertTrue(np.isinf(scipy.special.log1p(np.exp(beyond_lim_of_inf))))
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np.seterr(**old_err_state)
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def test_log_gradients(self):
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# transf dtransf_df d2transf_df2 d3transf_df3
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link = Log()
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@ -117,14 +153,14 @@ class LinkFunctionTests(np.testing.TestCase):
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def test_identity_gradients(self):
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link = Identity()
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lim_of_inf = _lim_val
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#FIXME: Should be able to think of a way to test the limits of this
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# FIXME: Should be able to think of a way to test the limits of this
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self.check_gradient(link, lim_of_inf, test_lim=False)
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def test_probit_gradients(self):
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link = Probit()
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lim_of_inf = _lim_val
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self.check_gradient(link, lim_of_inf, test_lim=True)
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def test_scaledprobit_gradients(self):
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link = ScaledProbit(nu=random.random())
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lim_of_inf = _lim_val
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@ -144,5 +180,5 @@ class LinkFunctionTests(np.testing.TestCase):
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def test_reciprocal_gradients(self):
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link = Reciprocal()
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lim_of_inf = _lim_val
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#Does not work with much smaller values, and values closer to zero than 1e-5
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# Does not work with much smaller values, and values closer to zero than 1e-5
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self.check_gradient(link, lim_of_inf, test_lim=True)
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